adalib/numpy-cond-gen-0 · Datasets at Hugging Face

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from hsi_toolkit.util import img_det from hsi_toolkit.dev.dim_reduction import mnf import numpy as np def mtmf_statistic(hsi_img,tgt_sig, mask = None): """ Mixture Tuned Matched Filter Infeasibility Statistic Inputs: hsi_image - n_row x n_col x n_band hyperspectral image tgt_sig - target signature (n_band x 1 - column vector) mask - binary image limiting detector operation to pixels where mask is true if not present or empty, no mask restrictions are used Outputs: mtmf_out - MTMF infeasibility statistic alpha - matched filter output 8/12/2012 - <NAME> - <EMAIL> 12/2018 - Python Implementation by Yutai Zhou """ if tgt_sig.ndim == 1: tgt_sig = tgt_sig[:, np.newaxis] mnf_img, n_dim, mnf_vecs, mnf_eigvals, mnf_mu = mnf(hsi_img,1); # tgt_sig = tgt_sig[:n_dim,0][:,np.newaxis] s = mnf_vecs @ (tgt_sig - mnf_mu) mtmf_out, kwargsout = img_det(mtmf_helper, mnf_img, s, mnf_eigvals = mnf_eigvals) return mtmf_out, kwargsout['alpha'] def mtmf_helper(hsi_data, tgt_sig, kwargs): mnf_eigvals = kwargs['mnf_eigvals'] n_band, n_pixel = hsi_data.shape z = hsi_data s = tgt_sig sts = s.T @ s alpha = np.zeros(n_pixel) mtmf_data = np.zeros(n_pixel) ev =	np.sqrt(mnf_eigvals)	numpy.sqrt
""" ****************************************************************************** 2D Robot Localization - Benchmark ****************************************************************************** Goals of this script: - implement different UKFs on the 2D robot localization example. - design the Extended Kalman Filter (EKF) and the Invariant Extended Kalman Filter (IEKF) :cite:`barrauInvariant2017`. - compare the different algorithms with Monte-Carlo simulations. We assume the reader is already familiar with the considered problem described in the tutorial. We previously designed an UKF with a standard uncertainty representation. An advantage of the versatility of the UKF is to speed up implementation, tests, and comparision of algorithms with different uncertainty representations. Indeed, for the given problem, three different UKFs emerge, defined respectively as: 1) The state is embedded in :math:`SO(2) \\times \mathbb{R}^2`, where: * the retraction :math:`\\varphi(.,.)` is the :math:`SO(2)` exponential for orientation and the vector addition for position. * the inverse retraction :math:`\\varphi^{-1}(.,.)` is the :math:`SO(2)` logarithm for orientation and the vector subtraction for position. 2) The state is embedded in :math:`SE(2)` with left multiplication, i.e. - the retraction :math:`\\varphi(.,.)` is the :math:`SE(2)` exponential, where the state multiplies on the left the uncertainty :math:`\\boldsymbol{\\xi}`. - the inverse retraction :math:`\\varphi^{-1}(.,.)` is the :math:`SE(2)` logarithm. - this left UKF on :math:`SE(2)` corresponds to the Invariant Extended Kalman Filter (IEKF) recommended in :cite:`barrauInvariant2017`. 3) The state is embedded in :math:`SE(2)` with right multiplication, i.e. - the retraction :math:`\\varphi(.,.)` is the :math:`SE(2)` exponential, where the state multiplies on the right the uncertainty :math:`\\boldsymbol{\\xi}`. - the inverse retraction :math:`\\varphi^{-1}(.,.)` is the :math:`SE(2)` logarithm. We tests the filters on simulation with strong initial heading error. """ ################################################################################ # Import # ============================================================================== from ukfm import SO2, UKF, EKF from ukfm import LOCALIZATION as MODEL import ukfm import numpy as np import matplotlib ukfm.utils.set_matplotlib_config() ################################################################################ # We compare the filters on a large number of Monte-Carlo runs. # Monte-Carlo runs N_mc = 100 ################################################################################ # Simulation Setting # ============================================================================== # We set the simulation as in :cite:`barrauInvariant2017`, section IV. The robot # drives along a 10 m diameter circle for 40 seconds with high rate odometer # measurements (100 Hz) and low rate GPS measurements (1 Hz). The vehicle gets # moderate angular velocity uncertainty and highly precise linear velocity. The # initial values of the heading error is very strong, 45° standard # deviation, while the initial position is known. # sequence time (s) T = 40 # odometry frequency (Hz) odo_freq = 100 # create the model model = MODEL(T, odo_freq) # odometry noise standard deviation odo_std = np.array([0.01, # speed (v/m) 0.01, # speed (v/m) 1 / 180 * np.pi]) # angular speed (rad/s) # GPS frequency (Hz) gps_freq = 1 # GPS noise standard deviation (m) gps_std = 1 # radius of the circle trajectory (m) radius = 5 # initial heading error standard deviation theta0_std = 45/180np.pi ################################################################################ # Filter Design # ============================================================================== # The UKFs are compared to an Extended Kalman FIlter (EKF) and an Invariant EKF # (IEKF). The EKF has the same uncertainty representation as the UKF with the # retraction on :math:`SO(2) \times \mathbb{R}^2`, whereas the IEKF has the same # uncertainty representation as the UKF with the left retraction on # :math:`SE(2)`. # propagation noise covariance matrix Q = np.diag(odo_std2) # measurement noise covariance matrix R = gps_std2np.eye(2) # initial covariance matrix P0 = np.zeros((3, 3)) # we take into account initial heading error P0[0, 0] = theta0_std ** 2 # sigma point parameter alpha = np.array([1e-3, 1e-3, 1e-3]) ################################################################################ # We set error variables before launching Monte-Carlo simulations. As we have # five similar methods, the code is redundant. ukf_err = np.zeros((N_mc, model.N, 3)) left_ukf_err = np.zeros_like(ukf_err) right_ukf_err = np.zeros_like(ukf_err) iekf_err = np.zeros_like(ukf_err) ekf_err = np.zeros_like(ukf_err) ################################################################################ # We record Normalized Estimation Error Squared (NEES) for consistency # evaluation (see Results). ukf_nees =	np.zeros((N_mc, model.N, 2))	numpy.zeros
# -- coding: utf-8 -- """ ############################################################################### # # autoPACK Authors: <NAME>, <NAME>, <NAME>, <NAME> # Based on COFFEE Script developed by <NAME> between 2005 and 2010 # with assistance from <NAME> in 2009 and periodic input # from Arthur Olson's Molecular Graphics Lab # # BaseGrid.py Authors: <NAME> & <NAME> with editing/enhancement from Ludovic Autin # # Translation to Python initiated March 1, 2010 by <NAME> with <NAME> # # Class restructuring and organization: Michel Sanner # # Copyright: <NAME> ©2010 # # This file "BaseGrid.py" is part of autoPACK, cellPACK. # # autoPACK is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # autoPACK is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with autoPACK (See "CopyingGNUGPL" in the installation. # If not, see <http://www.gnu.org/licenses/>. # # ############################################################################### @author: <NAME>, <NAME>, & <NAME> """ import logging import numpy from scipy import spatial import math from math import ceil, floor from random import randrange import cellpack.autopack as autopack from cellpack.autopack.ldSequence import cHaltonSequence3 from cellpack.mgl_tools.RAPID import RAPIDlib from cellpack.mgl_tools.bhtree import bhtreelib # Kevin Grid point class class gridPoint: def __init__(self, i, globalC, isPolyhedron): self.index = int(i) self.isOutside = None self.minDistance = ( 99999 # Only store a number here if within certain distance from polyhedron ) self.representsPolyhedron = isPolyhedron self.closeFaces = [] self.closestFaceIndex = 0 self.testedEndpoint = None self.allDistances = ( [] ) # Stores a tuple list of distances to all points. (point,distance) = (5,2.5) self.globalCoord = numpy.array( globalC ) # Stores the global coordinate associated with this point class BaseGrid: """ The Grid class ========================== This class handle the use of grid to control the packing. The grid keep information of 3d positions, distances, freePoints and inside/surface points from organelles. NOTE : thi class could be completely replace if openvdb is wrapped to python. """ @staticmethod def reorder_free_points(pt, freePoints, nbFreePoints): # TODO: move this to env class, ing shouldn't aware of the whole grid # Swap the newly inside point value with the value of the last free point # Point will no longer be considered "free" because it will be beyond the range of # nbFreePoints. The value of the point itself is the history of it's original index # so any future swaps will still result in the correct index being move into the range # of nbFreePoints nbFreePoints -= 1 vKill = freePoints[pt] vLastFree = freePoints[nbFreePoints] freePoints[vKill] = vLastFree freePoints[vLastFree] = vKill # Turn on these printlines if there is a problem with incorrect points showing in display points # self.log.debug("***********pt = masterGridPointValue = %d", pt) # self.log.debug("nbFreePointAfter = %d", nbFreePoints) # self.log.debug("vKill = %d", vKill) # self.log.debug("vLastFree = %d", vLastFree) # self.log.debug("freePoints[vKill] = %d", freePoints[vKill]) # self.log.debug("freePoints[vLastFree] = %d", freePoints[vLastFree]) # self.log.debug("pt = masterGridPointValue = %d", pt) # self.log.debug("freePoints[nbFreePoints-1] = %d", freePoints[nbFreePoints]) # self.log.debug("freePoints[pt] = %d", freePoints[pt]) # freePoints will now have all the available indices between 0 and nbFreePoints in # freePoints[nbFreePoints:] won't necessarily be the indices of inside points return freePoints, nbFreePoints @staticmethod def updateDistances( insidePoints, newDistPoints, freePoints, nbFreePoints, distance, ): # self.log.info( # "*********updating Distances %d %d", nbFreePoints, len(insidePoints) # ) # TODO: move this to env class, ing shouldn't aware of the whole grid # t1 = time() # distChanges = {} for pt, dist in list(insidePoints.items()): try: freePoints, nbFreePoints = BaseGrid.reorder_free_points( pt, freePoints, nbFreePoints ) except Exception: print(pt, "not in freeePoints*****************************") pass distance[pt] = dist # self.log.debug("update free points loop %d", time() - t1) # t2 = time() for pt, dist in list(newDistPoints.items()): if pt not in insidePoints: distance[pt] = dist # self.log.debug("update distance loop %d", time() - t2) return nbFreePoints def __init__( self, boundingBox=([0, 0, 0], [0.1, 0.1, 0.1]), space=1, setup=True, lookup=0 ): self.log = logging.getLogger("grid") self.log.propagate = False # a grid is attached to an environnement self.boundingBox = boundingBox # this list provides the id of the component this grid points belongs # to. The id is an integer where 0 is the Histological Volume, and +i is # the surface of compartment i and -i is the interior of compartment i # in the list self. compartments self.gridPtId = [] # will be a list of indices into 3D of compartment # of points that have not yet been used by the fill algorithm # entries are removed from this list as grid points are used up # during hte fill. This list is used to pick points randomly during # the fill self.freePoints = [] self.nbFreePoints = 0 # this list evolves in parallel with self.freePoints and provides # the distance to the closest surface (either an already placed # object (or an compartment surface NOT IMPLEMENTED) self.distToClosestSurf = [] self.distToClosestSurf_store = [] self.diag = self.getDiagonal() self.gridSpacing = space # 1.1547#cubic grid with a diagonal spacing equal to that smallest packing radius self.nbGridPoints = None self.nbSurfacePoints = 0 self.gridVolume = 0 # will be the total number of grid points # list of (x,y,z) for each grid point (x index moving fastest) self.masterGridPositions = [] self._x = None self._y = None self._z = None # this are specific for each compartment self.aInteriorGrids = [] self.aSurfaceGrids = [] # bhtree self.surfPtsBht = None self.ijkPtIndice = [] self.filename = None # used for storing before fill so no need rebuild self.result_filename = None # used after fill to store result self.tree = None self.tree_free = None self.encapsulatingGrid = 0 self.lookup = lookup self.center = None self.backup = None if setup: self.setup(boundingBox, space) # use np.roll to have periodic condition # what about collision ? def setup(self, boundingBox, space): # TODO : verify the gridSpacing calculation / setup after reading the recipe if space == 0: space = 20 self.gridSpacing = space # * 1.1547 self.boundingBox = boundingBox if self.lookup == 0: self.create3DPointLookupCover() elif self.lookup == 1: self.create3DPointLookup() elif self.lookup == 2: self.create3DPointLookup_loop() nx, ny, nz = self.nbGridPoints self.ijkPtIndice = self.cartesian([range(nx), range(ny), range(nz)]) self.getDiagonal() self.nbSurfacePoints = 0 self.log.info("SETUP BASE GRID %d %d", self.gridVolume, self.gridSpacing) self.gridPtId = numpy.zeros(self.gridVolume, "i") # [0]nbPoints # self.distToClosestSurf = [self.diag]self.gridVolume#surface point too? self.distToClosestSurf = ( numpy.ones(self.gridVolume) * self.diag ) # (self.distToClosestSurf) self.freePoints = list(range(self.gridVolume)) self.nbFreePoints = len(self.freePoints) self.log.info( "Lookup: %d, bounding box: %r, gridSpacing %r, length gridPtId %r", self.lookup, boundingBox, self.gridSpacing, len(self.gridPtId), ) self.setupBoundaryPeriodicity() return self.gridSpacing def reset( self, ): # reset the distToClosestSurf and the freePoints # boundingBox should be the same otherwise why keeping the grid # self.gridPtId = numpy.zeros(self.gridVolume,'i') # self.distToClosestSurf = numpy.ones(self.gridVolume)self.diag#(self.distToClosestSurf) self.log.info("reset Grid distance to closest surface and freePoints") self.distToClosestSurf = ( numpy.array(self.distToClosestSurf[:]) 0.0 ) + self.diag # self.distToClosestSurf[:] = self.diag # numpy.array([self.diag]len(self.distToClosestSurf))#surface point too? self.freePoints = list(range(len(self.freePoints))) self.nbFreePoints = len(self.freePoints) def removeFreePoint(self, pti): tmp = self.freePoints[self.nbFreePoints] # last one self.freePoints[self.nbFreePoints] = pti self.freePoints[pti] = tmp self.nbFreePoints -= 1 def getDiagonal(self, boundingBox=None): if boundingBox is None: boundingBox = self.boundingBox self.diag = numpy.sqrt( (numpy.array(boundingBox[0]) - numpy.array(boundingBox[1])) * 2 ).sum() return self.diag def create3DPointLookup_loop(self, boundingBox=None): """ Fill the orthogonal bounding box described by two global corners with an array of points spaces pGridSpacing apart.: """ if boundingBox is None: boundingBox = self.boundingBox xl, yl, zl = boundingBox[0] self.gridVolume, self.nbGridPoints = self.computeGridNumberOfPoint( boundingBox, self.gridSpacing ) nx, ny, nz = self.nbGridPoints pointArrayRaw = numpy.zeros((nx * ny * nz, 3), "f") self.ijkPtIndice = numpy.zeros((nx * ny * nz, 3), "i") # this is unused space = self.gridSpacing # Vector for lower left broken into real of only the z coord. i = 0 padding = space / 2.0 for zi in range(nz): for yi in range(ny): for xi in range(nx): x = xl + xi * space + padding y = yl + yi * space + padding z = zl + zi * space + padding pointArrayRaw[i] = (x, y, z) self.ijkPtIndice[i] = (xi, yi, zi) i += 1 self.log.info("grid spacing %d", space) self.masterGridPositions = pointArrayRaw def create3DPointLookup(self, boundingBox=None): """ Fill the orthogonal bounding box described by two global corners with an array of points spaces pGridSpacing apart. Optimized version using numpy broadcasting """ if boundingBox is None: boundingBox = self.boundingBox space = self.gridSpacing # we want the diagonal of the voxel, not the diagonal of the plane, so the second 1.1547 is was incorrect environmentBoxEqualFillBox = False self.log.info("Using create3DPointLookup") if environmentBoxEqualFillBox: # environment.environmentBoxEqualFillBox: self._x = x = numpy.arange( boundingBox[0][0], boundingBox[1][0], space ) # 1.1547) gridspacing is already multiplied by 1.1547 self._y = y = numpy.arange( boundingBox[0][1], boundingBox[1][1], space ) # 1.1547) self._z = z = numpy.arange( boundingBox[0][2], boundingBox[1][2], space ) # 1.1547) else: self._x = x = numpy.arange( boundingBox[0][0] - space, boundingBox[1][0] + space, space ) # 1.1547) gridspacing is already multiplied by 1.1547 self._y = y = numpy.arange( boundingBox[0][1] - space, boundingBox[1][1] + space, space ) # 1.1547) self._z = z = numpy.arange( boundingBox[0][2] - space, boundingBox[1][2] + space, space ) # 1.1547) nx = len( x ) # sizes must be +1 or the right, top, and back edges don't get any points using this numpy.arange method ny = len(y) nz = len(z) # Dec 5 2013, we need to confirm that the getPointsInBox function is also using +1, or potential neighbors will be missed # This used to be fine, but it may have changed? self.nbGridPoints = [nx, ny, nz] self.gridVolume = nx * ny * nz self.ijkPtIndice = numpy.ndindex(nx, ny, nz) # this is 60% faster than the for loop # self.masterGridPositions = numpy.array(list(numpy.broadcast(numpy.ix_(x, y, z)))) # self.masterGridPositions = numpy.vstack(numpy.meshgrid(x,y,z)).reshape(3,-1).T self.masterGridPositions = ( numpy.vstack(numpy.meshgrid(x, y, z, copy=False)).reshape(3, -1).T ) # this ay be faster but don't know the implication # from http://stackoverflow.com/questions/18253210/creating-a-numpy-array-of-3d-coordinates-from-three-1d-arrays def create3DPointLookupCover(self, boundingBox=None): """ Fill the orthogonal bounding box described by two global corners with an array of points spaces pGridSpacing apart. Optimized version using numpy broadcasting """ if boundingBox is None: boundingBox = self.boundingBox space = self.gridSpacing S = numpy.array(boundingBox[1]) - numpy.array(boundingBox[0]) NX, NY, NZ = numpy.around(S / (self.gridSpacing)) # / 1.1547)) if NX == 0: NX = 1 if NY == 0: NY = 1 if NZ == 0: NZ = 1 self.log.info("using create3DPointLookupCover") # we want the diagonal of the voxel, not the diagonal of the plane, so the second 1.1547 is was incorrect environmentBoxEqualFillBox = True # np.linspace(2.0, 3.0, num=5) if environmentBoxEqualFillBox: # environment.environmentBoxEqualFillBox: self._x = x = numpy.linspace( boundingBox[0][0], boundingBox[1][0], int(NX) ) # 1.1547) gridspacing is already multiplied by 1.1547 self._y = y = numpy.linspace( boundingBox[0][1], boundingBox[1][1], int(NY) ) # 1.1547) self._z = z = numpy.linspace( boundingBox[0][2], boundingBox[1][2], int(NZ) ) # 1.1547) else: self._x = x = numpy.arange( boundingBox[0][0], boundingBox[1][0] + space, space ) # 1.1547) gridspacing is already multiplied by 1.1547 self._y = y = numpy.arange( boundingBox[0][1], boundingBox[1][1] + space, space ) # 1.1547) self._z = z = numpy.arange( boundingBox[0][2], boundingBox[1][2] + space, space ) # 1.1547) xyz = numpy.meshgrid(x, y, z, copy=False) nx = len( x ) # sizes must be +1 or the right, top, and back edges don't get any points using this numpy.arange method ny = len(y) nz = len(z) self.gridSpacing = (x[1] - x[0]) 1.1547 # ? should I multiply here ? self.nbGridPoints = [nx, ny, nz] self.gridVolume = nx * ny * nz self.ijkPtIndice = numpy.ndindex(nx, ny, nz) self.masterGridPositions = numpy.vstack(xyz).reshape(3, -1).T # self.masterGridPositions = numpy.vstack(numpy.meshgrid(x,y,z,copy=False)).reshape(3,-1).T def getPointCompartmentId(self, point, ray=1): # check if point inside on of the compartments # surface point ? n_comp = len(self.histoVol.compartments) if n_comp: for i in range(n_comp): inside = self.checkPointInside_rapid( self.histoVol.compartments[i], point, self.histoVol.grid.diag, ray=ray, ) if inside: return -(i + 1) # comp=comp-1 # the point is not inside , is it on the surface ? ie distance to surface < X? for i in range(n_comp): distance, nb = self.histoVol.compartments[i].OGsrfPtsBht.query(point) if distance < 10.0: return i + 1 return 0 def getClosestGridPoint(self, pt3d): if self.tree is None: self.tree = spatial.cKDTree(self.masterGridPositions, leafsize=10) distance, nb = self.tree.query(pt3d) # len of ingr posed so far return distance, nb def getClosestFreeGridPoint( self, pt3d, compId=None, updateTree=True, ball=0.0, distance=0.0 ): free_indices = self.freePoints[: self.nbFreePoints] arr = numpy.array(self.masterGridPositions[free_indices]) indices = numpy.nonzero(numpy.equal(self.gridPtId[free_indices], compId)) distances = self.distToClosestSurf[free_indices] if not len(indices): return None tree_free = spatial.cKDTree(arr[indices], leafsize=10) arr = arr[indices] # arr of free indice in compartments res = tree_free.query_ball_point(pt3d, ball) # max distance if not len(res): return None all_distances = distances[res] all_pts = arr[res] ind = numpy.nonzero( numpy.logical_and( numpy.greater_equal(all_distances, distance), numpy.less(all_distances, distance * 1.5), ) )[0] if not len(ind): return None # should pick closest ? targetPoint = all_pts[ ind[randrange(len(ind))] ] # randomly pick free surface point at given distance return targetPoint free_indices = self.freePoints[: self.nbFreePoints] arr = numpy.array(self.masterGridPositions[free_indices]) if self.tree_free is None or updateTree: if compId is not None: arr = numpy.array(self.masterGridPositions[free_indices]) indices = numpy.nonzero( numpy.equal(self.gridPtId[free_indices], compId) ) self.tree_free = spatial.cKDTree(arr[indices], leafsize=10) arr = arr[indices] else: self.tree_free = spatial.cKDTree( self.masterGridPositions[: self.nbFreePoints], leafsize=10 ) if distance != 0.0: res = self.tree_free.query_ball_point(pt3d, distance) # return 0, res, arr else: res = self.tree_free.query(pt3d) # len of ingr posed so far return res, arr def cartesian(self, arrays, out=None): """ #http://stackoverflow.com/questions/1208118/using-numpy-to-build-an-array-of-all-combinations-of-two-arrays Generate a cartesian product of input arrays. Parameters ---------- arrays : list of array-like 1-D arrays to form the cartesian product of. out : ndarray Array to place the cartesian product in. Returns ------- out : ndarray 2-D array of shape (M, len(arrays)) containing cartesian products formed of input arrays. Examples -------- >>> cartesian(([1, 2, 3], [4, 5], [6, 7])) array([[1, 4, 6], [1, 4, 7], [1, 5, 6], [1, 5, 7], [2, 4, 6], [2, 4, 7], [2, 5, 6], [2, 5, 7], [3, 4, 6], [3, 4, 7], [3, 5, 6], [3, 5, 7]]) """ arrays = [numpy.asarray(x) for x in arrays] dtype = arrays[0].dtype n = numpy.prod([x.size for x in arrays]) if out is None: out = numpy.zeros([n, len(arrays)], dtype=dtype) m = int(n / arrays[0].size) out[:, 0] = numpy.repeat(arrays[0], m) if arrays[1:]: self.cartesian(arrays[1:], out=out[0:m, 1:]) for j in range(1, arrays[0].size): out[j * m : (j + 1) * m, 1:] = out[0:m, 1:] return out def getPointFrom3D(self, pt3d): """ get point number from 3d coordinates """ x, y, z = pt3d # Continuous 3D point to be discretized spacing1 = ( 1.0 / self.gridSpacing ) # Grid spacing = diagonal of the voxel determined by smallest packing radius ( NX, NY, NZ, ) = ( self.nbGridPoints ) # vector = [length, height, depth] of grid, units = gridPoints OX, OY, OZ = self.boundingBox[0] # origin of fill grid # Algebra gives nearest gridPoint ID to pt3D i = min(NX - 1, max(0, round((x - OX) * spacing1))) j = min(NY - 1, max(0, round((y - OY) * spacing1))) k = min(NZ - 1, max(0, round((z - OZ) * spacing1))) return int(k * NX * NY + j * NX + i) def getIJK(self, ptInd): """ get i,j,k (3d) indices from u (1d) only work for grid point, not compartments points """ if ptInd > self.nbGridPoints[0] * self.nbGridPoints[1] * self.nbGridPoints[2]: return [0, 0, 0] return self.ijkPtIndice[ptInd] def setupBoundaryPeriodicity(self): # we create a dictionary for the adjacent cell of the current grid. self.sizeXYZ = numpy.array(self.boundingBox[1]) - numpy.array( self.boundingBox[0] ) self.periodic_table = {} self.periodic_table["left"] = ( numpy.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) * self.sizeXYZ ) self.periodic_table["right"] = ( numpy.array([[-1, 0, 0], [0, -1, 0], [0, 0, -1]]) * self.sizeXYZ ) def getPositionPeridocity(self, pt3d, jitter, cutoff): tr = [] if autopack.biasedPeriodicity: biased = numpy.array(autopack.biasedPeriodicity) else: biased = numpy.array(jitter) if not autopack.testPeriodicity: return tr ox, oy, oz = self.boundingBox[0] ex, ey, ez = self.boundingBox[1] px, py, pz = pt3d p_xyz = [0, 0, 0] # can I use rapid and find the collision ? # distance plane X dist_origin_x = px - ox dist_edge_x = ex - px dx = 0 if dist_origin_x < dist_edge_x: dx = dist_origin_x # 1 p_xyz[0] = 1 else: dx = dist_edge_x # -1 p_xyz[0] = -1 if dx < cutoff and dx != 0: pass else: p_xyz[0] = 0 # distance plane Y doy = py - oy dey = ey - py dy = 0 if doy < dey: dy = doy # 1 p_xyz[1] = 1 else: dy = dey # -1 p_xyz[1] = -1 if dy < cutoff and dy != 0.0: pass else: p_xyz[1] = 0 # distance plane Z doz = pz - oz dez = ez - pz dz = 0 if doz < dez: dz = doz # 1 p_xyz[2] = 1 else: dz = dez # -1 p_xyz[2] = -1 if dz < cutoff and dz != 0: pass else: p_xyz[2] = 0 p_xyz = numpy.array(p_xyz) * biased # for 2D we need 3 corner tiles # for 3D we need 7 corner tiles corner = numpy.zeros((4, 3)) indices_non_zero = numpy.nonzero(p_xyz)[0] for i in indices_non_zero: # i is the axis that is close to the point tr.append(pt3d + (self.periodic_table["left"][i] * p_xyz[i])) # 0,1,2 corner[0] += self.periodic_table["left"][i] * p_xyz[i] # 1 # the corner are # X+Y+Z corner[0] # X+Y+0 corner[1] # X+0+Z corner[2] # 0+Y+Z corner[3] if len(indices_non_zero) == 2: # two axis cross-> three pos tr.append(pt3d + corner[0]) if len(indices_non_zero) == 3: # in a corner need total 7 pos, never happen in 2D corner[1] = ( self.periodic_table["left"][0] * p_xyz[0] + self.periodic_table["left"][1] * p_xyz[1] ) corner[2] = ( self.periodic_table["left"][0] * p_xyz[0] + self.periodic_table["left"][2] * p_xyz[2] ) corner[3] = ( self.periodic_table["left"][1] * p_xyz[1] + self.periodic_table["left"][2] * p_xyz[2] ) for i in range(4): # 4+1=5 tr.append(pt3d + corner[i]) return tr def checkPointInside(self, pt3d, dist=None, jitter=[1, 1, 1], bb=None): """ Check if the given 3d points is inside the grid """ if bb is None: bb = self.boundingBox origin = numpy.array(bb[0]) edge = numpy.array(bb[1]) for i in range(len(edge)): if edge[i] < self.gridSpacing: edge[i] = self.gridSpacing packing_location = numpy.array(pt3d) # jitter test1 = packing_location < origin test2 = packing_location > edge if True in test1 or True in test2: # outside return False else: if dist is not None: # distance to closest wall d1 = (packing_location - origin) jitter s1 = min(x for x in d1[d1 != 0] if x != 0) d2 = (edge - packing_location) * jitter s2 = min(x for x in d2[d2 != 0] if x != 0) if s1 <= dist or s2 <= dist: self.log.info("s1 s2 smaller than dist %d %d %d", s1, s2, dist) return False return True def getCenter(self): """ Get the center of the grid """ if self.center is None: self.center = [0.0, 0.0, 0.0] for i in range(3): self.center[i] = (self.boundingBox[0][i] + self.boundingBox[1][i]) / 2.0 return self.center def getRadius(self): """ Get the radius the grid """ d = numpy.array(self.boundingBox[0]) - numpy.array(self.boundingBox[1]) s = numpy.sum(d * d) return math.sqrt(s) def getPointsInSphere(self, pt, radius): if self.tree is None: self.tree = spatial.cKDTree(self.masterGridPositions, leafsize=10) # add surface points ptIndices = self.tree.query_ball_point(pt, radius) # , n_jobs=-1) return ptIndices def getPointsInCubeFillBB(self, bb, pt, radius, addSP=True, info=False): """ Return all grid points indices inside the given bounding box. NOTE : need to fix with grid build with numpy arrange """ spacing1 = 1.0 / self.gridSpacing NX, NY, NZ = self.nbGridPoints OX, OY, OZ = self.boundingBox[ 0 ] # origin of fill grid-> bottom lef corner not origin ox, oy, oz = bb[0] ex, ey, ez = bb[1] i0 = int(max(0, floor((ox - OX) * spacing1))) i1 = int(min(NX, int((ex - OX) * spacing1)) + 1) j0 = int(max(0, floor((oy - OY) * spacing1))) j1 = int(min(NY, int((ey - OY) * spacing1)) + 1) k0 = int(max(0, floor((oz - OZ) * spacing1))) k1 = int(min(NZ, int((ez - OZ) * spacing1)) + 1) i0 = int(min(NX - 1, max(0, round((ox - OX) * spacing1)))) j0 = int(min(NY - 1, max(0, round((oy - OY) * spacing1)))) k0 = int(min(NZ - 1, max(0, round((oz - OZ) * spacing1)))) i1 = int(min(NX, max(0, round((ex - OX) * spacing1)))) j1 = int(min(NY, max(0, round((ey - OY) * spacing1)))) k1 = int(min(NZ, max(0, round((ez - OZ) * spacing1)))) if NZ == 1: k0 = 0 k1 = 1 elif NY == 1: j0 = 0 j1 = 1 elif NX == 1: i0 = 0 i1 = 1 ptIndices = [] pid = numpy.mgrid[i0:i1, j0:j1, k0:k1] ijk = numpy.vstack(pid).reshape(3, -1).T # in case 2D, meaning one of the dimension is 1 if NZ == 1: ptIndices = [p[2] + p[1] + NX * p[0] for p in ijk] elif NY == 1: ptIndices = [p[2] + p[1] + NX * p[0] for p in ijk] elif NX == 1: ptIndices = [p[2] + NY * p[1] + p[0] for p in ijk] else: 0.02451198 # add surface points if addSP and self.nbSurfacePoints != 0: result = numpy.zeros((self.nbSurfacePoints,), "i") nb = self.surfPtsBht.closePoints(tuple(pt), radius, result) # nb = self.surfPtsBht.query(tuple(pt),k=self.nbSurfacePoints) ptIndices.extend( list(map(lambda x, length=self.gridVolume: x + length, result[:nb])) ) return ptIndices def test_points_in_bb(self, bb, pt): # given a bounding box, does the point is contains in it origin = numpy.array(bb[0]) E = numpy.array(bb[1]) P = numpy.array(pt) # jitter test1 = P < origin test2 = P > E inside = False if True in test1 or True in test2: # outside inside = False return inside def getPointsInCube(self, bb, pt, radius, addSP=True, info=False): """ Return all grid points indicesinside the given bounding box. """ spacing1 = 1.0 / self.gridSpacing NX, NY, NZ = self.nbGridPoints OX, OY, OZ = self.boundingBox[ 0 ] # origin of Pack grid-> bottom lef corner not origin ox, oy, oz = bb[0] ex, ey, ez = bb[1] i0 = int(max(0, floor((ox - OX) spacing1))) i1 = int(min(NX, int((ex - OX) * spacing1) + 1)) j0 = int(max(0, floor((oy - OY) * spacing1))) j1 = int(min(NY, int((ey - OY) * spacing1) + 1)) k0 = int(max(0, floor((oz - OZ) * spacing1))) k1 = int(min(NZ, int((ez - OZ) * spacing1) + 1)) zPlaneLength = NX * NY ptIndices = [] for z in range(int(k0), int(k1)): offz = z * zPlaneLength for y in range(int(j0), int(j1)): off = y * NX + offz for x in range(int(i0), int(i1)): ptIndices.append(x + off) # add surface points if addSP and self.nbSurfacePoints != 0: result = numpy.zeros((self.nbSurfacePoints,), "i") nb = self.surfPtsBht.closePoints(tuple(pt), radius, result) dimx, dimy, dimz = self.nbGridPoints ptIndices.extend( list(map(lambda x, length=self.gridVolume: x + length, result[:nb])) ) return ptIndices def computeGridNumberOfPoint(self, boundingBox, space): """ Return the grid size : total number of point and number of point per axes """ xl, yl, zl = boundingBox[0] xr, yr, zr = boundingBox[1] encapsulatingGrid = self.encapsulatingGrid # Graham Added on Oct17 to allow for truly 2D grid for test fills... may break everything! nx = int(ceil((xr - xl) / space)) + encapsulatingGrid ny = int(ceil((yr - yl) / space)) + encapsulatingGrid nz = int(ceil((zr - zl) / space)) + encapsulatingGrid # nx = nx if (nx == 1) else nx-1 # ny = ny if (ny == 1) else ny-1 # nz = nz if (nz == 1) else nz-1 return nx * ny * nz, (nx, ny, nz) def set_surfPtsBht(self, verts): self.surfPtsBht = None if verts is not None and len(verts): self.surfPtsBht = bhtreelib.BHtree(verts, None, 10) self.nbSurfacePoints = len(verts) def set_surfPtscht(self, verts): self.surfPtsBht = None if verts is not None and len(verts): self.surfPtsBht = spatial.cKDTree(verts, leafsize=10) self.nbSurfacePoints = len(verts) def computeExteriorVolume(self, compartments=None, space=None, fbox_bb=None): # compute exterior volume, totalVolume without compartments volume unitVol = self.gridSpacing ** 3 totalVolume = self.gridVolume * unitVol if fbox_bb is not None: V, nbG = self.computeGridNumberOfPoint(fbox_bb, space) totalVolume = V * unitVol if compartments is not None: for o in compartments: # totalVolume -= o.surfaceVolume totalVolume -= o.interiorVolume return totalVolume def computeVolume(self, space=None, fbox_bb=None): # compute exterior volume, totalVolume without compartments volume unitVol = self.gridSpacing ** 3 totalVolume = self.gridVolume * unitVol if fbox_bb is not None: V, nbG = self.computeGridNumberOfPoint(fbox_bb, space) totalVolume = V * unitVol return totalVolume def create_rapid_model(self): self.rapid_model = RAPIDlib.RAPID_model() # need triangle and vertices self.rapid_model.addTriangles( numpy.array(self.vertices, "f"), numpy.array(self.faces, "i") ) def get_rapid_model(self): if self.rapid_model is None: self.create_rapid_model() return self.rapid_model def one_rapid_ray(self, pt1, pt2, diag): # return number of triangle /triangle contact helper = autopack.helper rm = self.get_rapid_model() v1 = numpy.array(pt1) direction = helper.unit_vector(pt2 - pt1) * diag v2 = v1 + direction if sum(v1) == 0.0: v3 = v2 + numpy.array([0.0, 1.0, 0.0]) else: v3 = v2 + helper.unit_vector(numpy.cross(v1, v2)) f = [0, 1, 2] ray_model = RAPIDlib.RAPID_model() ray_model.addTriangles( numpy.array([v1, v2, v3], "f"), numpy.array( [f], "i", ), ) RAPIDlib.RAPID_Collide_scaled( numpy.identity(3), numpy.array([0.0, 0.0, 0.0], "f"), 1.0, rm,	numpy.identity(3)	numpy.identity
# Copyright (c) 2016,2017 MetPy Developers. # Distributed under the terms of the BSD 3-Clause License. # SPDX-License-Identifier: BSD-3-Clause """Contains a collection of generally useful calculation tools.""" import functools import warnings import numpy as np import numpy.ma as ma from scipy.spatial import cKDTree from . import height_to_pressure_std, pressure_to_height_std from ..package_tools import Exporter from ..units import check_units, units exporter = Exporter(globals()) @exporter.export def resample_nn_1d(a, centers): """Return one-dimensional nearest-neighbor indexes based on user-specified centers. Parameters ---------- a : array-like 1-dimensional array of numeric values from which to extract indexes of nearest-neighbors centers : array-like 1-dimensional array of numeric values representing a subset of values to approximate Returns ------- An array of indexes representing values closest to given array values """ ix = [] for center in centers: index = (np.abs(a - center)).argmin() if index not in ix: ix.append(index) return ix @exporter.export def nearest_intersection_idx(a, b): """Determine the index of the point just before two lines with common x values. Parameters ---------- a : array-like 1-dimensional array of y-values for line 1 b : array-like 1-dimensional array of y-values for line 2 Returns ------- An array of indexes representing the index of the values just before the intersection(s) of the two lines. """ # Difference in the two y-value sets difference = a - b # Determine the point just before the intersection of the lines # Will return multiple points for multiple intersections sign_change_idx, = np.nonzero(np.diff(np.sign(difference))) return sign_change_idx @exporter.export @units.wraps(('=A', '=B'), ('=A', '=B', '=B')) def find_intersections(x, a, b, direction='all'): """Calculate the best estimate of intersection. Calculates the best estimates of the intersection of two y-value data sets that share a common x-value set. Parameters ---------- x : array-like 1-dimensional array of numeric x-values a : array-like 1-dimensional array of y-values for line 1 b : array-like 1-dimensional array of y-values for line 2 direction : string, optional specifies direction of crossing. 'all', 'increasing' (a becoming greater than b), or 'decreasing' (b becoming greater than a). Defaults to 'all'. Returns ------- A tuple (x, y) of array-like with the x and y coordinates of the intersections of the lines. """ # Find the index of the points just before the intersection(s) nearest_idx = nearest_intersection_idx(a, b) next_idx = nearest_idx + 1 # Determine the sign of the change sign_change = np.sign(a[next_idx] - b[next_idx]) # x-values around each intersection _, x0 = _next_non_masked_element(x, nearest_idx) _, x1 = _next_non_masked_element(x, next_idx) # y-values around each intersection for the first line _, a0 = _next_non_masked_element(a, nearest_idx) _, a1 = _next_non_masked_element(a, next_idx) # y-values around each intersection for the second line _, b0 = _next_non_masked_element(b, nearest_idx) _, b1 = _next_non_masked_element(b, next_idx) # Calculate the x-intersection. This comes from finding the equations of the two lines, # one through (x0, a0) and (x1, a1) and the other through (x0, b0) and (x1, b1), # finding their intersection, and reducing with a bunch of algebra. delta_y0 = a0 - b0 delta_y1 = a1 - b1 intersect_x = (delta_y1 * x0 - delta_y0 * x1) / (delta_y1 - delta_y0) # Calculate the y-intersection of the lines. Just plug the x above into the equation # for the line through the a points. One could solve for y like x above, but this # causes weirder unit behavior and seems a little less good numerically. intersect_y = ((intersect_x - x0) / (x1 - x0)) * (a1 - a0) + a0 # If there's no intersections, return if len(intersect_x) == 0: return intersect_x, intersect_y # Check for duplicates duplicate_mask = (np.ediff1d(intersect_x, to_end=1) != 0) # Make a mask based on the direction of sign change desired if direction == 'increasing': mask = sign_change > 0 elif direction == 'decreasing': mask = sign_change < 0 elif direction == 'all': return intersect_x[duplicate_mask], intersect_y[duplicate_mask] else: raise ValueError('Unknown option for direction: {0}'.format(str(direction))) return intersect_x[mask & duplicate_mask], intersect_y[mask & duplicate_mask] @exporter.export def interpolate_nans(x, y, kind='linear'): """Interpolate NaN values in y. Interpolate NaN values in the y dimension. Works with unsorted x values. Parameters ---------- x : array-like 1-dimensional array of numeric x-values y : array-like 1-dimensional array of numeric y-values kind : string specifies the kind of interpolation x coordinate - 'linear' or 'log', optional. Defaults to 'linear'. Returns ------- An array of the y coordinate data with NaN values interpolated. """ x_sort_args = np.argsort(x) x = x[x_sort_args] y = y[x_sort_args] nans = np.isnan(y) if kind == 'linear': y[nans] = np.interp(x[nans], x[~nans], y[~nans]) elif kind == 'log': y[nans] = np.interp(np.log(x[nans]), np.log(x[~nans]), y[~nans]) else: raise ValueError('Unknown option for kind: {0}'.format(str(kind))) return y[x_sort_args] def _next_non_masked_element(a, idx): """Return the next non masked element of a masked array. If an array is masked, return the next non-masked element (if the given index is masked). If no other unmasked points are after the given masked point, returns none. Parameters ---------- a : array-like 1-dimensional array of numeric values idx : integer index of requested element Returns ------- Index of next non-masked element and next non-masked element """ try: next_idx = idx + a[idx:].mask.argmin() if ma.is_masked(a[next_idx]): return None, None else: return next_idx, a[next_idx] except (AttributeError, TypeError, IndexError): return idx, a[idx] def delete_masked_points(arrs): """Delete masked points from arrays. Takes arrays and removes masked points to help with calculations and plotting. Parameters ---------- arrs : one or more array-like source arrays Returns ------- arrs : one or more array-like arrays with masked elements removed """ if any(hasattr(a, 'mask') for a in arrs): keep = ~functools.reduce(np.logical_or, (np.ma.getmaskarray(a) for a in arrs)) return tuple(ma.asarray(a[keep]) for a in arrs) else: return arrs @exporter.export def reduce_point_density(points, radius, priority=None): r"""Return a mask to reduce the density of points in irregularly-spaced data. This function is used to down-sample a collection of scattered points (e.g. surface data), returning a mask that can be used to select the points from one or more arrays (e.g. arrays of temperature and dew point). The points selected can be controlled by providing an array of ``priority`` values (e.g. rainfall totals to ensure that stations with higher precipitation remain in the mask). Parameters ---------- points : (N, K) array-like N locations of the points in K dimensional space radius : float minimum radius allowed between points priority : (N, K) array-like, optional If given, this should have the same shape as ``points``; these values will be used to control selection priority for points. Returns ------- (N,) array-like of boolean values indicating whether points should be kept. This can be used directly to index numpy arrays to return only the desired points. Examples -------- >>> metpy.calc.reduce_point_density(np.array([1, 2, 3]), 1.) array([ True, False, True], dtype=bool) >>> metpy.calc.reduce_point_density(np.array([1, 2, 3]), 1., ... priority=np.array([0.1, 0.9, 0.3])) array([False, True, False], dtype=bool) """ # Handle 1D input if points.ndim < 2: points = points.reshape(-1, 1) # Make a kd-tree to speed searching of data. tree = cKDTree(points) # Need to use sorted indices rather than sorting the position # so that the keep mask matches original* order. if priority is not None: # Need to sort the locations in decreasing priority. sorted_indices = np.argsort(priority)[::-1] else: # Take advantage of iterator nature of range here to avoid making big lists sorted_indices = range(len(points)) # Keep all points initially keep = np.ones(len(points), dtype=np.bool) # Loop over all the potential points for ind in sorted_indices: # Only proceed if we haven't already excluded this point if keep[ind]: # Find the neighbors and eliminate them neighbors = tree.query_ball_point(points[ind], radius) keep[neighbors] = False # We just removed ourselves, so undo that keep[ind] = True return keep def _get_bound_pressure_height(pressure, bound, heights=None, interpolate=True): """Calculate the bounding pressure and height in a layer. Given pressure, optional heights, and a bound, return either the closest pressure/height or interpolated pressure/height. If no heights are provided, a standard atmosphere is assumed. Parameters ---------- pressure : `pint.Quantity` Atmospheric pressures bound : `pint.Quantity` Bound to retrieve (in pressure or height) heights : `pint.Quantity`, optional Atmospheric heights associated with the pressure levels. Defaults to using heights calculated from ``pressure`` assuming a standard atmosphere. interpolate : boolean, optional Interpolate the bound or return the nearest. Defaults to True. Returns ------- `pint.Quantity` The bound pressure and height. """ # Bound is given in pressure if bound.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}: # If the bound is in the pressure data, we know the pressure bound exactly if bound in pressure: bound_pressure = bound # If we have heights, we know the exact height value, otherwise return standard # atmosphere height for the pressure if heights is not None: bound_height = heights[pressure == bound_pressure] else: bound_height = pressure_to_height_std(bound_pressure) # If bound is not in the data, return the nearest or interpolated values else: if interpolate: bound_pressure = bound # Use the user specified bound if heights is not None: # Interpolate heights from the height data bound_height = log_interp(bound_pressure, pressure, heights) else: # If not heights given, use the standard atmosphere bound_height = pressure_to_height_std(bound_pressure) else: # No interpolation, find the closest values idx = (np.abs(pressure - bound)).argmin() bound_pressure = pressure[idx] if heights is not None: bound_height = heights[idx] else: bound_height = pressure_to_height_std(bound_pressure) # Bound is given in height elif bound.dimensionality == {'[length]': 1.0}: # If there is height data, see if we have the bound or need to interpolate/find nearest if heights is not None: if bound in heights: # Bound is in the height data bound_height = bound bound_pressure = pressure[heights == bound] else: # Bound is not in the data if interpolate: bound_height = bound # Need to cast back to the input type since interp (up to at least numpy # 1.13 always returns float64. This can cause upstream users problems, # resulting in something like np.append() to upcast. bound_pressure = np.interp(np.atleast_1d(bound), heights, pressure).astype(bound.dtype) * pressure.units else: idx = (np.abs(heights - bound)).argmin() bound_pressure = pressure[idx] bound_height = heights[idx] else: # Don't have heights, so assume a standard atmosphere bound_height = bound bound_pressure = height_to_pressure_std(bound) # If interpolation is on, this is all we need, if not, we need to go back and # find the pressure closest to this and refigure the bounds if not interpolate: idx = (np.abs(pressure - bound_pressure)).argmin() bound_pressure = pressure[idx] bound_height = pressure_to_height_std(bound_pressure) # Bound has invalid units else: raise ValueError('Bound must be specified in units of length or pressure.') # If the bound is out of the range of the data, we shouldn't extrapolate if (bound_pressure < np.min(pressure)) or (bound_pressure > np.max(pressure)): raise ValueError('Specified bound is outside pressure range.') if heights is not None: if (bound_height > np.max(heights)) or (bound_height < np.min(heights)): raise ValueError('Specified bound is outside height range.') return bound_pressure, bound_height @exporter.export @check_units('[length]') def get_layer_heights(heights, depth, args, kwargs): """Return an atmospheric layer from upper air data with the requested bottom and depth. This function will subset an upper air dataset to contain only the specified layer using the heights only. Parameters ---------- heights : array-like Atmospheric heights depth : `pint.Quantity` The thickness of the layer args : array-like Atmospheric variable(s) measured at the given pressures bottom : `pint.Quantity`, optional The bottom of the layer interpolate : bool, optional Interpolate the top and bottom points if they are not in the given data. Defaults to True. with_agl : bool, optional Returns the heights as above ground level by subtracting the minimum height in the provided heights. Defaults to False. Returns ------- `pint.Quantity, pint.Quantity` The height and data variables of the layer """ bottom = kwargs.pop('bottom', None) interpolate = kwargs.pop('interpolate', True) with_agl = kwargs.pop('with_agl', False) # Make sure pressure and datavars are the same length for datavar in args: if len(heights) != len(datavar): raise ValueError('Height and data variables must have the same length.') # If we want things in AGL, subtract the minimum height from all height values if with_agl: sfc_height = np.min(heights) heights -= sfc_height # If the bottom is not specified, make it the surface if bottom is None: bottom = heights[0] # Make heights and arguments base units heights = heights.to_base_units() bottom = bottom.to_base_units() # Calculate the top of the layer top = bottom + depth ret = [] # returned data variables in layer # Ensure heights are sorted in ascending order sort_inds = np.argsort(heights) heights = heights[sort_inds] # Mask based on top and bottom inds = (heights >= bottom) & (heights <= top) heights_interp = heights[inds] # Interpolate heights at bounds if necessary and sort if interpolate: # If we don't have the bottom or top requested, append them if top not in heights_interp: heights_interp = np.sort(np.append(heights_interp, top)) * heights.units if bottom not in heights_interp: heights_interp = np.sort(np.append(heights_interp, bottom)) * heights.units ret.append(heights_interp) for datavar in args: # Ensure that things are sorted in ascending order datavar = datavar[sort_inds] if interpolate: # Interpolate for the possibly missing bottom/top values datavar_interp = interp(heights_interp, heights, datavar) datavar = datavar_interp else: datavar = datavar[inds] ret.append(datavar) return ret @exporter.export @check_units('[pressure]') def get_layer(pressure, args, kwargs): r"""Return an atmospheric layer from upper air data with the requested bottom and depth. This function will subset an upper air dataset to contain only the specified layer. The bottom of the layer can be specified with a pressure or height above the surface pressure. The bottom defaults to the surface pressure. The depth of the layer can be specified in terms of pressure or height above the bottom of the layer. If the top and bottom of the layer are not in the data, they are interpolated by default. Parameters ---------- pressure : array-like Atmospheric pressure profile args : array-like Atmospheric variable(s) measured at the given pressures heights: array-like, optional Atmospheric heights corresponding to the given pressures. Defaults to using heights calculated from ``p`` assuming a standard atmosphere. bottom : `pint.Quantity`, optional The bottom of the layer as a pressure or height above the surface pressure. Defaults to the lowest pressure or height given. depth : `pint.Quantity`, optional The thickness of the layer as a pressure or height above the bottom of the layer. Defaults to 100 hPa. interpolate : bool, optional Interpolate the top and bottom points if they are not in the given data. Defaults to True. Returns ------- `pint.Quantity, pint.Quantity` The pressure and data variables of the layer """ # Pop off keyword arguments heights = kwargs.pop('heights', None) bottom = kwargs.pop('bottom', None) depth = kwargs.pop('depth', 100 * units.hPa) interpolate = kwargs.pop('interpolate', True) # If we get the depth kwarg, but it's None, set it to the default as well if depth is None: depth = 100 * units.hPa # Make sure pressure and datavars are the same length for datavar in args: if len(pressure) != len(datavar): raise ValueError('Pressure and data variables must have the same length.') # If the bottom is not specified, make it the surface pressure if bottom is None: bottom = pressure[0] bottom_pressure, bottom_height = _get_bound_pressure_height(pressure, bottom, heights=heights, interpolate=interpolate) # Calculate the top if whatever units depth is in if depth.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}: top = bottom_pressure - depth elif depth.dimensionality == {'[length]': 1}: top = bottom_height + depth else: raise ValueError('Depth must be specified in units of length or pressure') top_pressure, _ = _get_bound_pressure_height(pressure, top, heights=heights, interpolate=interpolate) ret = [] # returned data variables in layer # Ensure pressures are sorted in ascending order sort_inds = np.argsort(pressure) pressure = pressure[sort_inds] # Mask based on top and bottom pressure inds = (pressure <= bottom_pressure) & (pressure >= top_pressure) p_interp = pressure[inds] # Interpolate pressures at bounds if necessary and sort if interpolate: # If we don't have the bottom or top requested, append them if top_pressure not in p_interp: p_interp = np.sort(np.append(p_interp, top_pressure)) * pressure.units if bottom_pressure not in p_interp: p_interp = np.sort(np.append(p_interp, bottom_pressure)) * pressure.units ret.append(p_interp[::-1]) for datavar in args: # Ensure that things are sorted in ascending order datavar = datavar[sort_inds] if interpolate: # Interpolate for the possibly missing bottom/top values datavar_interp = log_interp(p_interp, pressure, datavar) datavar = datavar_interp else: datavar = datavar[inds] ret.append(datavar[::-1]) return ret @exporter.export @units.wraps(None, ('=A', '=A')) def interp(x, xp, args, *kwargs): r"""Interpolates data with any shape over a specified axis. Interpolation over a specified axis for arrays of any shape. Parameters ---------- x : array-like 1-D array of desired interpolated values. xp : array-like The x-coordinates of the data points. args : array-like The data to be interpolated. Can be multiple arguments, all must be the same shape as xp. axis : int, optional The axis to interpolate over. Defaults to 0. fill_value: float, optional Specify handling of interpolation points out of data bounds. If None, will return ValueError if points are out of bounds. Defaults to nan. Returns ------- array-like Interpolated values for each point with coordinates sorted in ascending order. Examples -------- >>> x = np.array([1., 2., 3., 4.]) >>> y = np.array([1., 2., 3., 4.]) >>> x_interp = np.array([2.5, 3.5]) >>> metpy.calc.interp(x_interp, x, y) array([ 2.5, 3.5]) Notes ----- xp and args must be the same shape. """ # Pull out keyword args fill_value = kwargs.pop('fill_value', np.nan) axis = kwargs.pop('axis', 0) # Make x an array x = np.asanyarray(x).reshape(-1) # Save number of dimensions in xp ndim = xp.ndim # Sort input data sort_args =	np.argsort(xp, axis=axis)	numpy.argsort
from __future__ import absolute_import from __future__ import print_function import pytest import os import numpy as np from numpy.testing import assert_allclose from keras import backend as K import keras from keras.models import Sequential from keras.layers import Dense, Activation from keras.utils import np_utils from keras.utils.test_utils import get_test_data from keras.models import model_from_json, model_from_yaml from keras import losses from keras.engine.training_utils import make_batches input_dim = 16 num_hidden = 8 num_classes = 4 batch_size = 32 epochs = 1 @pytest.fixture def in_tmpdir(tmpdir): """Runs a function in a temporary directory. Checks that the directory is empty afterwards. """ with tmpdir.as_cwd(): yield None assert not tmpdir.listdir() def test_sequential_pop(): model = Sequential() model.add(Dense(num_hidden, input_dim=input_dim)) model.add(Dense(num_classes)) model.compile(loss='mse', optimizer='sgd') x = np.random.random((batch_size, input_dim)) y =	np.random.random((batch_size, num_classes))	numpy.random.random
import numpy as np from gmmmc.gmm import GMM from gmmmc.proposals.proposals import Proposal import pdb import logging class GaussianStepMeansProposal(Proposal): """Gaussian Proposal distribution for means of a GMM""" def __init__(self, step_sizes=(0.001,)): """ Gaussian proposal distribution for the means. The multivariate Gaussian is centered at the means of the current state in the Markov Chain and has covariance given by step_sizes. Multiple step sizes can be specified. The proposal algorithm will take these steps in the sequence specified in step_sizes. Parameters ---------- step_sizes : 1-D array_like Iterable containing the sequence of step sizes (covariances of the Gaussian proposal distribution" """ super(GaussianStepMeansProposal, self).__init__() self.step_sizes = step_sizes self.count_accepted = np.zeros((len(step_sizes),)) self.count_illegal = np.zeros((len(step_sizes),)) self.count_proposed = np.zeros((len(step_sizes),)) def propose(self, X, gmm, target, n_jobs=1): """ Propose a new set of GMM means. Parameters ---------- X : 2-D array like of shape (n_samples, n_features) The observed data or evidence. gmm : GMM object The current state (set of gmm parameters) in the Markov Chain target : GMMPosteriorTarget object The target distribution to be found. n_jobs : int Number of cpu cores to use in the calculation of log probabilities. Returns ------- : GMM A new GMM object initialised with new mean parameters. """ new_means = np.array(gmm.means) beta = target.beta prior = target.prior steps = [np.random.multivariate_normal(np.zeros(gmm.n_features), step_size * np.eye(gmm.n_features), size=gmm.n_mixtures) for step_size in self.step_sizes] # calculation of prior probabilities of only the means, since only means will change log_priors = np.array([prior.means_prior.log_prob_single(gmm.means[mixture], mixture) for mixture in xrange(gmm.n_mixtures)]) log_prob_priors = np.sum(log_priors) previous_prob = beta * gmm.log_likelihood(X, n_jobs) + np.sum(log_priors) for i, step in enumerate(steps): for mixture in xrange(gmm.n_mixtures): self.count_proposed[i] += 1 # propose new means new_mixture_means = gmm.means[mixture] + step[mixture] # try out the new means proposed_means = np.array(new_means) proposed_means[mixture] = new_mixture_means proposed_gmm = GMM(proposed_means, np.array(gmm.covars), np.array(gmm.weights)) # calculate new prior new_log_prob_mixture = prior.means_prior.log_prob_single(new_mixture_means, mixture) new_log_prob_priors = log_prob_priors - log_priors[mixture] + new_log_prob_mixture # priors proposed_prob = beta * proposed_gmm.log_likelihood(X, n_jobs) + new_log_prob_priors # ratio ratio = proposed_prob - previous_prob if ratio > 0 or ratio > np.log(np.random.uniform()): # accept proposal new_means = proposed_means previous_prob = proposed_prob # update prior probability calculation log_prob_priors = new_log_prob_priors log_priors[mixture] = new_log_prob_mixture self.count_accepted[i] += 1 return GMM(new_means, np.array(gmm.covars), np.array(gmm.weights)) class GaussianStepCovarProposal(Proposal): def __init__(self, step_sizes=(0.001,)): """ Gaussian proposal function for the covariances of the GMM. Parameters ---------- step_sizes : array_like Array of covariance values for the Gaussian proposal. """ super(GaussianStepCovarProposal, self).__init__() self.step_sizes = step_sizes self.count_accepted = np.zeros((len(step_sizes),)) self.count_illegal = np.zeros((len(step_sizes),)) self.count_proposed = np.zeros((len(step_sizes),)) def propose(self, X, gmm, target, n_jobs=1): """ Propose a new set of GMM covariances (diagonal only). Parameters ---------- X : 2-D array like of shape (n_samples, n_features) The observed data or evidence. gmm : GMM object The current state (set of gmm parameters) in the Markov Chain target : GMMPosteriorTarget object The target distribution to be found. n_jobs : int Number of cpu cores to use in the calculation of log probabilities. Returns ------- : GMM A new GMM object initialised with new covariance parameters. """ new_covars = np.array(gmm.covars) beta = target.beta prior = target.prior previous_prob = beta * gmm.log_likelihood(X, n_jobs) + prior.log_prob(gmm) steps = [np.random.multivariate_normal(np.zeros(gmm.n_features), step_size * np.eye(gmm.n_features), size=gmm.n_mixtures) for step_size in self.step_sizes] log_priors = np.array([prior.means_prior.log_prob_single(gmm.means[mixture], mixture) for mixture in xrange(gmm.n_mixtures)]) log_prob_priors = np.sum(log_priors) for i, step in enumerate(steps): for mixture in xrange(gmm.n_mixtures): self.count_proposed[i] += 1 # propose new covars new_mixture_covars = gmm.covars[mixture] + step[mixture] if (new_mixture_covars > 0).all(): # check covariances are valid # try out the new covars proposed_covars = np.array(new_covars) proposed_covars[mixture] = new_mixture_covars proposed_gmm = GMM(np.array(gmm.means), proposed_covars, np.array(gmm.weights)) # calculate desired distribution new_log_prob_mixture = prior.covars_prior.log_prob_single(new_mixture_covars, mixture) new_log_prob_priors = log_prob_priors - log_priors[mixture] + new_log_prob_mixture proposed_prob = beta * proposed_gmm.log_likelihood(X, n_jobs) + new_log_prob_priors # ratio ratio = proposed_prob - previous_prob if ratio > 0 or ratio > np.log(np.random.uniform()): # accept proposal new_covars = proposed_covars previous_prob = proposed_prob log_prob_priors = new_log_prob_priors log_priors[mixture] = new_log_prob_mixture self.count_accepted[i] += 1 else: self.count_illegal[i] += 1 return GMM(np.array(gmm.means), np.array(new_covars), np.array(gmm.weights)) class GaussianStepWeightsProposal(Proposal): def __init__(self, n_mixtures, step_sizes=(0.001,), threshold=0.001): """ Gaussian proposal function for the weights of a GMM. Parameters ---------- n_mixtures step_sizes Notes ---------- The proposal function works by projecting the weight vector w onto the simplex defined by w_1 + w_2 + ..... w_n = 1 , 0<=w_i<=1. The change of basis matrix is found by finding n-1 vectors lying on the plane and using gramm schmidt to get an orthonormal basis. A Gaussian proposal function in (n-1)-d space is used to find the next point on the simplex. """ super(GaussianStepWeightsProposal, self).__init__() self.step_sizes = step_sizes self.n_mixtures = n_mixtures self.count_accepted = np.zeros((len(step_sizes),)) self.count_illegal = np.zeros((len(step_sizes),)) self.count_proposed = np.zeros((len(step_sizes),)) self.threshold = threshold if n_mixtures > 1: # get change of basis matrix mapping n dim coodinates to n-1 dim coordinates on simplex # x1 + x2 + x3 ..... =1 points = np.random.dirichlet([1 for i in xrange(n_mixtures)], size=n_mixtures - 1) points = points.T self.plane_origin = np.ones((n_mixtures)) / float(n_mixtures) # get vectors parallel to plane from its center (1/n,1/n,....) parallel = points - np.ones(points.shape) / float(n_mixtures) # do gramm schmidt to get mutually orthonormal vectors (basis) self.e, _ = np.linalg.qr(parallel) def transformSimplex(self, weights): """ Project weight vector onto the normal simplex. Parameters ---------- weights : array_like of shape (n_mixtures,) vector of weights for each gaussian component Returns ------- : array_like of shape (n_mixtures-1,) vector of weights projected onto the simplex plane """ # project onto the simplex return np.dot(self.e.T, weights - self.plane_origin) def invTransformSimplex(self, simplex_coords): """ Transforms a point on the simplex to the original vector space. Parameters ---------- simplex_coords : array_like of shape (n_mixtures - 1,) Coordinates of a weight vector on the simplex. Returns ------- : array_like of shape(n_mixtures,) vector of weights. """ return self.plane_origin + np.dot(self.e, simplex_coords) def propose(self, X, gmm, target, n_jobs=1): """ Propose a new set of weight vectors. Parameters ---------- X : 2-D array like of shape (n_samples, n_features) The observed data or evidence. gmm : GMM object The current state (set of gmm parameters) in the Markov Chain target : GMMPosteriorTarget object The target distribution to be found. n_jobs : int Number of cpu cores to use in the calculation of log probabilities. Returns ------- : GMM A new GMM object initialised with new covariance parameters. """ accepted = False cur_gmm = gmm if gmm.n_mixtures > 1: for i, step_size in enumerate(self.step_sizes): self.count_proposed[i] += 1 current_weights_transformed = self.transformSimplex(cur_gmm.weights) proposed_weights_transformed = np.random.multivariate_normal(current_weights_transformed, np.eye(self.n_mixtures - 1) * step_size) proposed_weights = self.invTransformSimplex(proposed_weights_transformed) if np.logical_and(0 <= proposed_weights, proposed_weights <= 1).all()\ and np.isclose(np.sum(proposed_weights), 1.0) and (proposed_weights>self.threshold).all(): previous_prob = target.log_prob(X, cur_gmm, n_jobs) proposed_gmm = GMM(np.array(cur_gmm.means), np.array(cur_gmm.covars), proposed_weights) proposed_prob = target.log_prob(X, proposed_gmm, n_jobs) ratio = proposed_prob - previous_prob if ratio > 0 or ratio > np.log(np.random.uniform()): # accept proposal self.count_accepted[i] += 1 accepted = True cur_gmm = proposed_gmm else: self.count_illegal[i] += 1 if accepted is True: return cur_gmm else: return GMM(np.array(gmm.means), np.array(gmm.covars), np.array(gmm.weights)) class GaussianTuningStepMeansProposal(Proposal): """Gaussian Proposal distribution for means of a GMM""" def __init__(self, step_sizes=(0.001,), limit=200): """ Gaussian proposal distribution for the means. The multivariate Gaussian is centered at the means of the current state in the Markov Chain and has covariance given by step_sizes. Multiple step sizes can be specified. The proposal algorithm will take these steps in the sequence specified in step_sizes. Parameters ---------- step_sizes : 1-D array_like Iterable containing the sequence of step sizes (covariances of the Gaussian proposal distribution" """ super(GaussianTuningStepMeansProposal, self).__init__() self.limit = limit self.count_steps = 0 self.count_acceptance_bucket = np.zeros((len(step_sizes),)) self.record = [] self.step_sizes = step_sizes self.count_accepted = np.zeros((len(step_sizes),)) self.count_illegal = np.zeros((len(step_sizes),)) self.count_proposed = np.zeros((len(step_sizes),)) def propose(self, X, gmm, target, n_jobs=1): """ Propose a new set of GMM means. Parameters ---------- X : 2-D array like of shape (n_samples, n_features) The observed data or evidence. gmm : GMM object The current state (set of gmm parameters) in the Markov Chain target : GMMPosteriorTarget object The target distribution to be found. n_jobs : int Number of cpu cores to use in the calculation of log probabilities. Returns ------- : GMM A new GMM object initialised with new mean parameters. """ new_means = np.array(gmm.means) beta = target.beta prior = target.prior steps = [np.random.multivariate_normal(np.zeros(gmm.n_features), step_size * np.eye(gmm.n_features), size=gmm.n_mixtures) for step_size in self.step_sizes] # calculation of prior probabilities of only the means, since only means will change log_priors = np.array([prior.means_prior.log_prob_single(gmm.means[mixture], mixture) for mixture in xrange(gmm.n_mixtures)]) log_prob_priors =	np.sum(log_priors)	numpy.sum
from private.symbol import TSymbol, NTSymbol, BondSymbol from private.utils import _node_match, _node_match_prod_rule, _edge_match, masked_softmax from private.hypergraph import Hypergraph, common_node_list from collections import Counter from copy import deepcopy from functools import partial from abc import ABCMeta, abstractmethod from networkx.algorithms.isomorphism import GraphMatcher from typing import List, Dict, Tuple import networkx as nx import numpy as np import torch import os import random import warnings DEBUG = False class GraphGrammarBase(metaclass=ABCMeta): @abstractmethod def learn(self): pass @abstractmethod def sample(self): pass class ProductionRule(object): """ A class of a production rule Attributes ---------- lhs : Hypergraph or None the left hand side of the production rule. if None, the rule is a starting rule. rhs : Hypergraph the right hand side of the production rule. """ def __init__(self, lhs, rhs): self.lhs = lhs self.rhs = rhs @property def is_start_rule(self) -> bool: return self.lhs.num_nodes == 0 @property def is_gluing(self) -> bool: if self.lhs: return len(self.lhs.get_all_NT_edges()) > 1 return False @property def is_ending(self) -> bool: return len(self.rhs.get_all_NT_edges()) == 0 @property def ext_node(self) -> Dict[int, str]: """ return a dict of external nodes """ if self.is_start_rule: return {} else: ext_node_dict = {} for each_node in self.lhs.nodes: ext_node_dict[self.lhs.node_attr(each_node)["ext_id"]] = each_node return ext_node_dict @property def lhs_nt_symbol(self) -> NTSymbol: if self.is_start_rule: return NTSymbol(degree=0, is_aromatic=False, bond_symbol_list=[]) else: # return self.lhs.edge_attr(list(self.lhs.edges)[0])['symbol'] return [self.lhs.edge_attr(edge)['symbol'] for edge in list(self.lhs.edges)] def rhs_adj_mat(self, node_edge_list): ''' return the adjacency matrix of rhs of the production rule ''' return nx.adjacency_matrix(self.rhs.hg, node_edge_list) def draw(self, file_path=None): return self.rhs.draw(file_path) def is_same(self, prod_rule, ignore_order=False): """ judge whether this production rule is the same as the input one, `prod_rule` Parameters ---------- prod_rule : ProductionRule production rule to be compared Returns ------- is_same : bool isomap : dict isomorphism of nodes and hyperedges. ex) {'bond_42': 'bond_37', 'bond_2': 'bond_1', 'e36': 'e11', 'e16': 'e12', 'e25': 'e18', 'bond_40': 'bond_38', 'e26': 'e21', 'bond_41': 'bond_39'}. key comes from `prod_rule`, value comes from `self`. """ if self.is_start_rule: if not prod_rule.is_start_rule: return False, {} else: if prod_rule.is_start_rule: return False, {} else: if prod_rule.lhs.num_nodes != self.lhs.num_nodes: return False, {} if prod_rule.rhs.num_nodes != self.rhs.num_nodes: return False, {} if prod_rule.rhs.num_edges != self.rhs.num_edges: return False, {} subhg_bond_symbol_counter \ = Counter([prod_rule.rhs.node_attr(each_node)['symbol'] \ for each_node in prod_rule.rhs.nodes]) each_bond_symbol_counter \ = Counter([self.rhs.node_attr(each_node)['symbol'] \ for each_node in self.rhs.nodes]) if subhg_bond_symbol_counter != each_bond_symbol_counter: return False, {} subhg_atom_symbol_counter \ = Counter([prod_rule.rhs.edge_attr(each_edge)['symbol'] \ for each_edge in prod_rule.rhs.edges]) each_atom_symbol_counter \ = Counter([self.rhs.edge_attr(each_edge)['symbol'] \ for each_edge in self.rhs.edges]) if subhg_atom_symbol_counter != each_atom_symbol_counter: return False, {} gm = GraphMatcher(prod_rule.rhs.hg, self.rhs.hg, partial(_node_match_prod_rule, ignore_order=ignore_order), partial(_edge_match, ignore_order=ignore_order)) try: return True, next(gm.isomorphisms_iter()) except StopIteration: return False, {} def graph_rule_applied_to(self, input_hg, selected_edge=None, selected_iso_mapping=None, vis=False): """ augment `hg` by replacing `edge` with `self.rhs`. Parameters ---------- hg : Hypergraph edge : str `edge` must belong to `hg` Returns ------- hg : Hypergraph resultant hypergraph nt_edge_list : list list of non-terminal edges """ hg = deepcopy(input_hg) nt_edge_dict = {} if self.is_start_rule: node_idx = hg.num_nodes # hg = Hypergraph() node_map_rhs = {} # node id in rhs -> node id in hg, where rhs is augmented. for num_idx, each_node in enumerate(self.rhs.nodes): hg.add_node(f"bond_{num_idx+node_idx}", attr_dict=self.rhs.node_attr(each_node)) node_map_rhs[each_node] = f"bond_{num_idx+node_idx}" for each_edge in self.rhs.edges: node_list = [] for each_node in self.rhs.nodes_in_edge(each_edge): node_list.append(node_map_rhs[each_node]) if isinstance(self.rhs.nodes_in_edge(each_edge), set): node_list = set(node_list) edge_id = hg.add_edge( node_list, attr_dict=self.rhs.edge_attr(each_edge)) if "nt_idx" in hg.edge_attr(edge_id): nt_edge_dict[hg.edge_attr(edge_id)["nt_idx"]] = edge_id nt_edge_list = [nt_edge_dict[key] for key in range(len(nt_edge_dict))] return hg, nt_edge_list, True else: hg_NT_edges = hg.get_all_NT_edges() lhs_NT_edges = self.lhs.get_all_NT_edges() match_flag = [] lhs_hg_matched_edge_dict = {} assert len(lhs_NT_edges) == 1 # print("NTs:") # print([edge.edges for edge in hg_NT_edges]) # print([edge.edge_attr(list(edge.edges)[0])['symbol'].symbol for edge in hg_NT_edges]) # eq = lhs_NT_edges[0] == hg_NT_edges[0] for lhs_edge in lhs_NT_edges: if lhs_edge in hg_NT_edges: match_flag.append(True) assert len(lhs_edge.edges) == 1 for hg_NT_edge in hg_NT_edges: if hg_NT_edge == lhs_edge: if list(lhs_edge.edges)[0] not in lhs_hg_matched_edge_dict.keys(): lhs_hg_matched_edge_dict[list(lhs_edge.edges)[0]] = [hg_NT_edge] else: lhs_hg_matched_edge_dict[list(lhs_edge.edges)[0]].append(hg_NT_edge) else: match_flag.append(False) if not all(match_flag): return hg, [], False # for edge in hg.edges: # print("edge: {} -> nodes: {}".format(edge, hg.nodes_in_edge(edge))) # order of nodes that belong to the non-terminal edge in hg nt_order_dict = {} # hg_node -> order ("bond_17" : 1) nt_order_dict_inv = {} # order -> hg_node to_rm_edges = [] assert len(lhs_NT_edges) == 1 for _i, edge_lhs in enumerate(lhs_NT_edges): edges_hg = lhs_hg_matched_edge_dict[list(edge_lhs.edges)[0]] edges_name_to_hg = {list(edge_hg.edges)[0]:edge_hg for edge_hg in edges_hg} edges_cand = edges_name_to_hg.keys() # filter out those removed edges in previous iterations edges = [_edge for _edge in edges_cand if _edge not in to_rm_edges] # if it is empty, meaning that there is only actually one NT in hg that matches multiple NTs in the lhs, the rule should be abandoned if len(edges) == 0: return hg, [], False # TODO add options edge = selected_edge if edge == None: edge = np.random.choice(edges, 1)[0] # print('selected edge:', edge) # edge_hg = edges_hg[edges_cand.index(edge)] edge_hg = edges_name_to_hg[edge] iso_mappings = edge_hg.find_isomorphism_mapping(edge_lhs, vis) # From edge_hg to edge_lhs # print("mapping:", iso_mapping) if selected_iso_mapping is None: iso_mapping =	np.random.choice(iso_mappings, 1)	numpy.random.choice
#!/usr/bin/env python # -- coding:utf-8 -- from collections import Iterable from enum import Enum import os import pkg_resources import sys import pandas as pd import numpy as np import scipy as sp from clonesig import mixin_init_parameters try: rows, columns = os.popen('stty size', 'r').read().split() pd.set_option('display.width', int(columns)) pd.set_option('display.max_columns', 200) except: print("running on server, otherwise please investigate") # this is the threshold to consider eigenvalues null to get an approximation # of the degree of freedom of the cosine distance matrix for signatures EV_DOF_THRESHOLD = 0.5 def _get_projected(L, R, x): x[x < L] = L[x < L] x[x > R] = R[x > R] return x def log_binomial_coeff(n, k): return (sp.special.gammaln(n+1) - (sp.special.gammaln(k+1) + sp.special.gammaln(n-k+1))) def _beta_binomial_logpmf(x, n, phi, tau): alpha = phi / tau beta = 1/tau - alpha return log_binomial_coeff(n, x)\ + sp.special.betaln(x + alpha, n - x + beta)\ - sp.special.betaln(alpha, beta) def beta_binomial_pmf(x, n, phi, tau): return np.exp(_beta_binomial_logpmf(x, n, phi, tau)) class Estimator(mixin_init_parameters.MixinInitParameters): def __init__(self, T, B, C_normal, C_tumor_tot, C_tumor_minor, D, p, J, maxiter=10000, pi=None, phi=None, xi=None, rho=None, tau=None, verbose=False, inputMU=None, nu=None, save_trace=False): """ est = Estimator(...) est.init_params(...) est.fit(...) """ self.T = T self.B = B self.C_normal = C_normal self.C_tumor_tot = C_tumor_tot self.C_tumor_minor = C_tumor_minor self.C_tumor_major = (self.C_tumor_tot - self.C_tumor_minor).astype(int) self.C_est_tumor_mut = C_tumor_minor.copy() # this is a choice self.C_est_tumor_mut[self.C_est_tumor_mut == 0] = \ self.C_tumor_tot[self.C_est_tumor_mut == 0] # alternative could be # self.C_est_tumor_mut[self.C_est_tumor_mut==0] = 1 self.Mmax = max(self.C_tumor_major) self.D = D self.p = p self.J = J self.N = len(B) if inputMU is None: self.mu_matrix = self.default_mu() else: self.mu_matrix = inputMU self.mu_matrix = self._remove_zeros_distrib(self.mu_matrix) self.mu = np.moveaxis(np.tile(self.mu_matrix[:, self.T.astype(int)], (self.J, self.Mmax, 1, 1)), [0, 1, 2, 3], [1, 3, 2, 0]) self.L = self.mu.shape[2] self.Fs = list() self.maxiter = maxiter self.init_params(pi, phi, xi, rho, tau, spasePi=False) self.init_nu_param(nu) self.verbose = verbose ev, _ = np.linalg.eig(1-sp.spatial.distance.squareform(sp.spatial.distance.pdist(self.mu_matrix, 'cosine'))) self.dof = sum(ev > EV_DOF_THRESHOLD) self.save_trace = save_trace def init_nu_param(self, nu=None): if nu is None: self.nu = np.ones((self.N, self.Mmax)) * \ mixin_init_parameters.ZERO_PADDING for i in range(self.N): self.nu[i, :self.C_tumor_major[i]] = 1 / self.C_tumor_major[i] # self.nu[i, min(int(np.round(max(1, self.B[i]/self.D[i] * (self.pself.C_tumor_tot[i] + (1-self.p) self.C_normal[i]) / self.p))), self.C_tumor_major[i]) - 1] = 1 else: self.nu = nu # make sure there are no null values self.nu[self.nu == 0] = mixin_init_parameters.ZERO_PADDING self.nu = self.nu / self.nu.sum(axis=1)[:, np.newaxis] # get log(nu) in the right dimension (N * J * L * Mmax) for computation self.lognu_sig = np.moveaxis(np.tile(np.log(self.nu), [self.J, self.L, 1, 1]), [0, 1, 2], [1, 2, 0]) self.Mask = (self.nu > mixin_init_parameters.ZERO_PADDING * 10).astype(bool) self.Mask_sig = np.moveaxis(np.tile(self.Mask, [self.J, self.L, 1, 1]), [0, 1, 2], [1, 2, 0]) pre_eta = np.repeat(np.arange(1, self.Mmax+1).reshape([-1, 1]), self.N, axis=1).T self.eta = self.p * pre_eta / ((1 - self.p) * np.repeat(self.C_normal.reshape([-1, 1]), self.Mmax, axis=1) + self.p * np.repeat(self.C_tumor_tot.reshape([-1, 1]), self.Mmax, axis=1)) self.eta[~self.Mask] = 1 self.qun, self.vmnu, self.rnus = self.get_responsabilities @property def get_theta(self): return np.concatenate((self.xi.flatten(), self.pi.flatten(), self.phi.flatten(), [self.tau])) def get_log_xi_sig(self, xi): """ get log(xi) in the right dimension (N * J * L * Mmax) for computation """ return np.moveaxis(np.tile(np.log(xi).reshape(-1, 1).T, [self.N, self.L, self.Mmax, 1]), [1, 2, 3], [2, 3, 1]) def get_log_bb_sig(self, phi, tau): """ computes the logbinomial probability of Bn\|Dn for all point, in all clones in the right dimension (N * J * L * Mmax) for computation """ phi_un_bar = np.rollaxis(np.repeat([self.eta], self.J, axis=0), 0, 2) \ * np.tile(phi.reshape(-1, 1), [self.N, 1, self.Mmax]) log_bb = _beta_binomial_logpmf( np.rollaxis(np.tile(self.B.reshape(-1, 1), [self.J, 1, self.Mmax]), 1, 0), np.rollaxis(np.tile(self.D.reshape(-1, 1), [self.J, 1, self.Mmax]), 1, 0), phi_un_bar, tau) log_bb_sig = np.rollaxis(np.repeat([log_bb], self.L, axis=0), 0, 3) return log_bb_sig def get_log_pi_sig(self, pi): """ get log(pi) in the right dimension (N * J * L * Mmax) for computation """ return np.moveaxis(np.tile(np.log(pi), (self.N, self.Mmax, 1, 1)), [1, 2, 3], [3, 1, 2]) def compute_F(self, qun, rnus, vmnu, new_xi, new_pi, tau, phi, nu): q = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, tau, phi, nu) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) big_qun_sig = np.moveaxis(np.repeat([big_qun], self.L, axis=0), [0, 1, 2], [2, 0, 1]) big_rnus_m = np.moveaxis(np.repeat([rnus], self.Mmax, axis=0), [0, 1, 2, 3], [3, 0, 1, 2]) big_vmn_sig = np.moveaxis(np.repeat([vmnu], self.L, axis=0), [0, 1, 2], [2, 0, 1]) joint_dist = self._remove_zeros_joint( big_qun_sig * big_rnus_m * big_vmn_sig) # old version - (joint_dist * np.log(joint_dist)).sum() h = - (joint_dist * np.log(joint_dist)).sum() return q - h def compute_Q(self, qun, rnus, vmnu, new_xi, new_pi, tau, phi, nu): log_xi_sig = self.get_log_xi_sig(new_xi) log_mu = np.log(self.mu) log_pi = self.get_log_pi_sig(new_pi) combiln = log_binomial_coeff(self.D, self.B) bin_coeff = np.moveaxis(np.tile(combiln, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) phi_un_bar = np.repeat(self.eta.reshape(self.N, -1, self.Mmax), self.J, axis=1) \ np.swapaxes(np.tile(phi, (self.N, self.Mmax, 1)), 1, 2) big_b = np.moveaxis(np.tile(self.B, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) big_d = np.moveaxis(np.tile(self.D, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) term1 = sp.special.loggamma(big_b + phi_un_bar / tau) term2 = sp.special.loggamma((1 - phi_un_bar) / tau + big_d - big_b) term3 = sp.special.loggamma(np.ones((self.N, self.J, self.Mmax)) / tau) term4 = sp.special.loggamma(np.ones((self.N, self.J, self.Mmax)) / tau + big_d) term5 = sp.special.loggamma(phi_un_bar / tau) term6 = sp.special.loggamma((1 - phi_un_bar) / tau) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) big_qun_sig = np.moveaxis(np.repeat([big_qun], self.L, axis=0), [0, 1, 2], [2, 0, 1]) big_rnus_m = np.moveaxis(np.repeat([rnus], self.Mmax, axis=0), [0, 1, 2, 3], [3, 0, 1, 2]) big_vmn_sig = np.moveaxis(np.repeat([vmnu], self.L, axis=0), [0, 1, 2], [2, 0, 1]) Q = (big_qun vmnu * (bin_coeff + term1 + term2 + term3 - term4 - term5 - term6)).sum() + \ (big_qun_sig * big_rnus_m * big_vmn_sig * (log_xi_sig + log_mu + log_pi + self.lognu_sig)).sum() return -Q def compute_alternative_Q(self, qun, rnus, vmnu, new_xi, new_pi, tau, phi, nu): """ this function implements another way to compute Q on can then test self.compute_Q(qun, rnus, new_xi, new_pi, tau, phi) ==\ self.compute_alternative_Q(qun, rnus, new_xi, new_pi, tau, phi) """ log_xi_sig = self.get_log_xi_sig(new_xi) log_bb_sig = self.get_log_bb_sig(phi, tau) log_pi = self.get_log_pi_sig(new_pi) log_mu = np.log(self.mu) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) big_qun_sig = np.moveaxis(np.repeat([big_qun], self.L, axis=0), [0, 1, 2], [2, 0, 1]) big_rnus_m = np.moveaxis(np.repeat([rnus], self.Mmax, axis=0), [0, 1, 2, 3], [3, 0, 1, 2]) big_vmn_sig = np.moveaxis(np.repeat([vmnu], self.L, axis=0), [0, 1, 2], [2, 0, 1]) Q = (big_qun_sig * big_rnus_m * big_vmn_sig * (log_xi_sig + log_mu + log_pi + log_bb_sig + self.lognu_sig) * self.Mask_sig).sum() return -Q def compute_dQ(self, qun, vmnu, tau, phi): # convention : tau and then phi dQ = np.zeros(self.J + 1) # general stuff big_eta = np.repeat(self.eta.reshape(self.N, -1, self.Mmax), self.J, axis=1) phi_un_bar = big_eta \ np.swapaxes(np.tile(phi, (self.N, self.Mmax, 1)), 1, 2) big_b = np.moveaxis(np.tile(self.B, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) big_d = np.moveaxis(np.tile(self.D, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) # compute dQ/d\tau term1 = sp.special.psi(big_b + phi_un_bar / tau) term2 = sp.special.psi((1 - phi_un_bar) / tau + big_d - big_b) term3 = sp.special.psi(np.ones((self.N, self.J, self.Mmax)) / tau) term4 = sp.special.psi(np.ones((self.N, self.J, self.Mmax)) / tau + big_d) term5 = sp.special.psi(phi_un_bar / tau) term6 = sp.special.psi((1 - phi_un_bar) / tau) dQ[0] = (big_qun vmnu / (tau*2) (- phi_un_bar * term1 - (1 - phi_un_bar) * term2 - term3 + term4 + phi_un_bar * term5 + (1 - phi_un_bar) * term6)).sum() # compute dQ/f\phi_u factor = big_qun * vmnu * big_eta / tau dQ[1:] = (factor * (term1 - term2 - term5 + term6)).sum(axis=0).sum(axis=1) return -dQ def compute_alternative_dQ(self, qun, vmnu, tau, phi): """ this function implements another way to compute dQ on can then test self.compute_dQ(qun, rnus, new_xi, new_pi, tau, phi)[1:] == \ self.compute_alternative_dQ(qun, rnus, new_xi, new_pi, tau, phi)[1:] # only implemented for dQ/d\phi as \tau=1/\tau, so not the same """ dQ = np.zeros(self.J + 1) big_eta = np.repeat(self.eta.reshape(self.N, -1, self.Mmax), self.J, axis=1) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) for mut in range(self.N): bn = np.moveaxis(np.tile(np.arange(self.B[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) dn = np.moveaxis(np.tile(np.arange(self.D[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) phi_big1 = np.swapaxes(np.tile(phi, (int(self.B[mut]), self.Mmax, 1)), 1, 2) to_add_phi_1 = (big_eta[mut, :, :] / (big_eta[mut, :, :] * phi_big1 + bn * tau)).sum(axis=0) bndn = np.moveaxis(np.tile(np.arange(self.D[mut] - self.B[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) phi_big2 = np.swapaxes(np.tile(phi, (int(self.D[mut] - self.B[mut]), self.Mmax, 1)), 1, 2) to_add_phi_2 = (-big_eta[mut, :, :] / (1 - phi_big2 * big_eta[mut, :, :] + bndn * tau))\ .sum(axis=0) dQ[1:] += (big_qun[mut, :, :] * vmnu[mut, :, :] * (to_add_phi_1 + to_add_phi_2)).sum(axis=1) to_add_tau_1 = (bn / (big_eta[mut, :, :] * phi_big1 + bn * tau))\ .sum(axis=0) to_add_tau_2 = (bndn / (1 - phi_big2 * big_eta[mut, :, :] + bndn * tau))\ .sum(axis=0) to_add_tau_3 = (dn / (1 + dn * tau)).sum(axis=0) dQ[0] += (big_qun[mut, :, :] * vmnu[mut, :, :] * (to_add_tau_1 + to_add_tau_2 - to_add_tau_3)).sum() return -dQ def compute_dQ2(self, qun, vmnu, tau, phi): dQ2 = np.zeros((self.J + 1, self.J + 1)) big_eta = np.repeat(self.eta.reshape(self.N, -1, self.Mmax), self.J, axis=1) phi_un_bar = big_eta \ np.swapaxes(np.tile(phi, (self.N, self.Mmax, 1)), 1, 2) big_b = np.moveaxis(np.tile(self.B, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) big_d = np.moveaxis(np.tile(self.D, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) term1_0 = sp.special.psi(big_b + phi_un_bar / tau) term2_0 = sp.special.psi((1 - phi_un_bar) / tau + big_d - big_b) term3_0 = sp.special.psi(np.ones((self.N, self.J, self.Mmax)) / tau) term4_0 = sp.special.psi(np.ones((self.N, self.J, self.Mmax)) / tau + big_d) term5_0 = sp.special.psi(phi_un_bar / tau) term6_0 = sp.special.psi((1 - phi_un_bar) / tau) term1_1 = sp.special.polygamma(1, big_b + phi_un_bar / tau) term2_1 = sp.special.polygamma(1, (1 - phi_un_bar) / tau + big_d - big_b) term3_1 = sp.special.polygamma(1, np.ones((self.N, self.J, self.Mmax)) / tau) term4_1 = sp.special.polygamma(1, np.ones((self.N, self.J, self.Mmax)) / tau + big_d) term5_1 = sp.special.polygamma(1, phi_un_bar / tau) term6_1 = sp.special.polygamma(1, (1 - phi_un_bar) / tau) u_prime_v = (2 / (tau3)) (phi_un_bar * term1_0 + (1 - phi_un_bar) * term2_0 + term3_0 - term4_0 - phi_un_bar * term5_0 - (1 - phi_un_bar) * term6_0) v_prime_u = (phi_un_bar2 / tau4 * term1_1 + (1 - phi_un_bar)2 / tau4 * term2_1 + term3_1 / tau4 - term4_1 / tau4 - phi_un_bar2 / tau4 * term5_1 - (1 - phi_un_bar)2 / tau4 * term6_1) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) dQ2[0] = (big_qun * vmnu * (u_prime_v + v_prime_u)).sum() factor = big_qun * vmnu * big_eta / tau*2 dQ2[0, 1:] = dQ2[1:, 0] = (factor (- term1_0 - phi_un_bar / tau * term1_1 + term2_0 + (1 - phi_un_bar) / tau * term2_1 + term5_0 + phi_un_bar / tau * term5_1 - term6_0 - (1 - phi_un_bar) / tau * term6_1)).sum(axis=0).sum(axis=1) dQ2[np.arange(1, self.J+1), np.arange(1, self.J+1)] = \ (big_qun * vmnu * big_eta2 / tau2 * (term1_1 + term2_1 - term5_1 - term6_1)).sum(axis=0).sum(axis=1) return -dQ2 def compute_alternative_dQ2(self, qun, vmnu, tau, phi): """ this function implements another way to compute dQ on can then test self.compute_dQ(qun, rnus, new_xi, new_pi, tau, phi)[1:] == \ elf.compute_alternative_dQ(qun, rnus, new_xi, new_pi, tau, phi)[1:] # only implemented for dQ/d\phi as \tau=1/\tau, so not the same """ dQ2 = np.zeros((self.J + 1, self.J + 1)) big_eta = np.repeat(self.eta.reshape(self.N, -1, self.Mmax), self.J, axis=1) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) for mut in range(self.N): bn = np.moveaxis(np.tile(np.arange(self.B[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) dn = np.moveaxis(np.tile(np.arange(self.D[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) phi_big1 = np.swapaxes(np.tile(phi, (int(self.B[mut]), self.Mmax, 1)), 1, 2) to_add_phi_1 = (big_eta[mut, :, :]*2 / (big_eta[mut, :, :] phi_big1 + bn * tau)2).sum(axis=0) bndn = np.moveaxis(np.tile(np.arange(self.D[mut] - self.B[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) phi_big2 = np.swapaxes(np.tile(phi, (int(self.D[mut] - self.B[mut]), self.Mmax, 1)), 1, 2) to_add_phi_2 = (big_eta[mut, :, :]2 / (1 - phi_big2 * big_eta[mut, :, :] + bndn * tau)*2).sum(axis=0) dQ2[np.arange(1, self.J+1), np.arange(1, self.J+1)] += (big_qun[mut, :, :] vmnu[mut, :, :] * (- to_add_phi_1 - to_add_phi_2)).sum(axis=1) to_add_tau_1 = (bn*2 / ((big_eta[mut, :, :] phi_big1 + bn * tau))2).sum(axis=0) to_add_tau_2 = (bndn2 / ((1 - phi_big2 * big_eta[mut, :, :] + bndn * tau))2).sum(axis=0) to_add_tau_3 = (dn2 / ((1 + dn * tau))*2).sum(axis=0) dQ2[0, 0] += (big_qun[mut, :, :] vmnu[mut, :, :] * (- to_add_tau_1 - to_add_tau_2 + to_add_tau_3)).sum() to_add_phi_tau_1 = (big_eta[mut, :, :] * bn / ((big_eta[mut, :, :] * phi_big1 + bn * tau)*2)).sum(axis=0) to_add_phi_tau_2 = (big_eta[mut, :, :] bndn / ((1 - phi_big2 * self.eta[mut] + bndn * tau)*2)).sum(axis=0) dQ2[1:, 0] += (big_qun[mut, :, :] vmnu[mut, :, :] * (- to_add_phi_tau_1 + to_add_phi_tau_2)).sum(axis=1) dQ2[0, 1:] += (big_qun[mut, :, :] * vmnu[mut, :, :] * (- to_add_phi_tau_1 + to_add_phi_tau_2)).sum(axis=1) return -dQ2 @staticmethod def _remove_zeros_joint(joint): pre_joint = joint + mixin_init_parameters.ZERO_PADDING * (joint == 0) joint_norm = pre_joint / pre_joint.sum(axis=3).sum(axis=2).sum(axis=1)[:, np.newaxis, np.newaxis, np.newaxis] return joint_norm @staticmethod def _get_binded_variables(L, R, x, dQ, epsilon): B_left = (x <= L + epsilon) & (dQ > 0) B_right = (x >= R - epsilon) & (dQ < 0) return B_left \| B_right @staticmethod def _compute_right_term(x, x_new, B, dQ, dQ2, L, R): pre_lam = np.linalg.inv(dQ2).dot(dQ)[~B] left_part = np.sum((dQ[~B]).dot(pre_lam)) right_part = np.sum(((dQ[B]).dot((x - x_new)[B]))) return left_part, right_part @property def get_responsabilities(self): # compute qun log_xi_sig = self.get_log_xi_sig(self.xi) log_bb_sig = self.get_log_bb_sig(self.phi, self.tau) log_pi = self.get_log_pi_sig(self.pi) pre_altfinal = np.exp(log_xi_sig + log_bb_sig + log_pi + np.log(self.mu) + self.lognu_sig) pre_altfinal[pre_altfinal == 0] = 2 * sys.float_info.min final = pre_altfinal * self.Mask_sig pre_qun = final.sum(axis=3).sum(axis=2) row_sums = pre_qun.sum(axis=1) qun = pre_qun / row_sums[:, np.newaxis] # compute vmnu pre_vmnu = final.sum(axis=2) row_sums = pre_vmnu.sum(axis=2) vmnu = pre_vmnu / np.expand_dims(row_sums, axis=2) # compute rnus pre_rnus = np.exp(log_pi[:, :, :, 0] + np.log(self.mu)[:, :, :, 0]) row_sums = pre_rnus.sum(axis=2) rnus = pre_rnus / row_sums[:, :, np.newaxis] return qun, vmnu, rnus def fit(self, epsilon_em=None, epsilon_newton=10-6, epsilon_box=10-6, beta=0.5, sigma=0.25): if epsilon_em is None: epsilon_em = 10*-5 self.J * self.L new_theta = self.get_theta * 10000 em = 0 while (np.sqrt(np.sum((new_theta-self.get_theta)2)) > epsilon_em) and (em < self.maxiter): if self.verbose: print(em, self.xi, self.phi, self.tau, np.sqrt(np.sum((new_theta - self.get_theta)2))) em = em + 1 new_theta = self.get_theta ########### # E-phase # ########### qun, vmnu, rnus = self.get_responsabilities ########### # M-phase # ########### new_xi = qun.sum(axis=0) / self.N pre_new_pi = (rnusnp.rollaxis(np.repeat([qun], self.L, axis=0), 0, 3)).sum(axis=0)/np.rollaxis(np.repeat([qun], self.L, axis=0), 0, 3).sum(axis=0) new_pi = self._remove_zeros_distrib(pre_new_pi) L = np.zeros(self.J + 1) + 1e-5 R = np.ones(self.J + 1) - 1e-5 R[0] = 0.5 # newton method x_0 = np.concatenate(([self.tau], self.phi)) x = x_0 currentQ = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, x[0], x[1:], self.nu) newt = 0 while True: if self.verbose: print('newt', newt, x, currentQ) newt = newt + 1 dQ2 = self.compute_dQ2(qun, vmnu, x[0], x[1:]) dQ = self.compute_dQ(qun, vmnu, x[0], x[1:]) if (np.sum(dQ) == 0) & (em == 1): break # get epsilon tmp_new_x = _get_projected(L, R, x - dQ) eps_k = min(epsilon_box, np.sqrt(np.sum((x - tmp_new_x)2))) # get I^{sharp} (binded variables) B = self._get_binded_variables(L, R, x, dQ, eps_k) # get D dQ2[:, B] = dQ2[B, :] = 0 dQ2[B, B] = 1 # get alpha k m = 1 alpha = 1 x_new = _get_projected(L, R, x - alpha np.linalg.inv(dQ2).dot(dQ)) new_Q = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, x_new[0], x_new[1:], self.nu) left_part, right_part = self._compute_right_term(x, x_new, B, dQ, dQ2, L, R) # deal with cases where the hessian in indefinite! if left_part < 0: eigenvalues = np.linalg.eigvals(dQ2) if min(eigenvalues) < 0: to_add_eig = np.abs(min(eigenvalues)) + np.finfo(np.float32).eps dQ2 = dQ2 + to_add_eig * np.identity(len(x)) x_new = _get_projected(L, R, x - alpha * np.linalg.inv(dQ2).dot(dQ)) new_Q = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, x_new[0], x_new[1:], self.nu) left_part, right_part = \ self._compute_right_term(x, x_new, B, dQ, dQ2, L, R) # stopping criterion if left_part + right_part < epsilon_newton: break # line search right_term = sigma * (beta * alpha * left_part + right_part) if self.verbose: print('linesearch_before', m, currentQ, new_Q, right_term, x, x_new) while not ((currentQ - new_Q) >= right_term): if self.verbose: print('linesearch', m, currentQ, new_Q, right_term, x, x_new) m += 1 alpha = beta * alpha x_new = _get_projected(L, R, x - alpha * np.linalg.inv(dQ2).dot(dQ)) new_Q = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, x_new[0], x_new[1:], self.nu) left_part, right_part = self._compute_right_term(x, x_new, B, dQ, dQ2, L, R) right_term = sigma * (beta * alpha * left_part + right_part) x = x_new currentQ = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, x[0], x[1:], self.nu) new_tau = x[0] new_phi = x[1:] self.xi = new_xi self.pi = new_pi self.tau = new_tau self.phi = new_phi self.qun = qun self.rnus = rnus self.vmnu = vmnu currentQ = self.compute_F(qun, rnus, vmnu, new_xi, new_pi, x[0], x[1:], self.nu) if self.save_trace: self.Fs.append(currentQ) self.Fs.append(currentQ) @property def get_k(self): return self.J * (self.L - 1 + 2) @property def get_k_cn(self): """ k is the number of parameters of the model using this function to test several values wrt data. True value is self.J * (self.L - 1 + 2) (self.L - 1) because of 1 degree of liberty in pi 2 for phi and xi -1 because xi lacks a degree of freedom 1 on top for tau/rho np.mean(self.C_tumor_major) to account for the extra degree of freedom of the model due to fitting the copy number. """ return self.J * (self.L - 1 + 2) * np.mean(self.C_tumor_major) @property def get_k_dof_cn(self): """ same as get_k_cn but with the degree of freedom of the input signature matrix instead of the number of signatures. """ return self.J * (self.dof - 1 + 2) * np.mean(self.C_tumor_major) def get_bic(self, dof=False, cn=False): if not cn: k = self.get_k else: if dof: k = self.get_k_dof_cn else: k = self.get_k_cn return - k * np.log(self.N) / 2 + self.get_loglikelihood def get_bic_heuristics(self, dof=True, factor=0.042, cn=False): """ the factor is valid for dof=True (O.O65 for a subset, O.O34 for 47 signatures) otherwise, we advise factor around 0.015 for the 47 signatures or around 0.040 for a subset of signatures. """ if not cn: k = self.get_k else: if dof: k = self.get_k_dof_cn else: k = self.get_k_cn return - factor * k *	np.log(self.N)	numpy.log
# This module has been generated automatically from space group information # obtained from the Computational Crystallography Toolbox # """ Space groups This module contains a list of all the 230 space groups that can occur in a crystal. The variable space_groups contains a dictionary that maps space group numbers and space group names to the corresponding space group objects. .. moduleauthor:: <NAME> <<EMAIL>> """ #----------------------------------------------------------------------------- # Copyright (C) 2013 The Mosaic Development Team # # Distributed under the terms of the BSD License. The full license is in # the file LICENSE.txt, distributed as part of this software. #----------------------------------------------------------------------------- import numpy as N class SpaceGroup(object): """ Space group All possible space group objects are created in this module. Other modules should access these objects through the dictionary space_groups rather than create their own space group objects. """ def __init__(self, number, symbol, transformations): """ :param number: the number assigned to the space group by international convention :type number: int :param symbol: the Hermann-Mauguin space-group symbol as used in PDB and mmCIF files :type symbol: str :param transformations: a list of space group transformations, each consisting of a tuple of three integer arrays (rot, tn, td), where rot is the rotation matrix and tn/td are the numerator and denominator of the translation vector. The transformations are defined in fractional coordinates. :type transformations: list """ self.number = number self.symbol = symbol self.transformations = transformations self.transposed_rotations = N.array([N.transpose(t[0]) for t in transformations]) self.phase_factors = N.exp(N.array([(-2jN.pit[1])/t[2] for t in transformations])) def __repr__(self): return "SpaceGroup(%d, %s)" % (self.number, repr(self.symbol)) def __len__(self): """ :return: the number of space group transformations :rtype: int """ return len(self.transformations) def symmetryEquivalentMillerIndices(self, hkl): """ :param hkl: a set of Miller indices :type hkl: Scientific.N.array_type :return: a tuple (miller_indices, phase_factor) of two arrays of length equal to the number of space group transformations. miller_indices contains the Miller indices of each reflection equivalent by symmetry to the reflection hkl (including hkl itself as the first element). phase_factor contains the phase factors that must be applied to the structure factor of reflection hkl to obtain the structure factor of the symmetry equivalent reflection. :rtype: tuple """ hkls = N.dot(self.transposed_rotations, hkl) p = N.multiply.reduce(self.phase_factors**hkl, -1) return hkls, p space_groups = {} transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(1, 'P 1', transformations) space_groups[1] = sg space_groups['P 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(2, 'P -1', transformations) space_groups[2] = sg space_groups['P -1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(3, 'P 1 2 1', transformations) space_groups[3] = sg space_groups['P 1 2 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(4, 'P 1 21 1', transformations) space_groups[4] = sg space_groups['P 1 21 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(5, 'C 1 2 1', transformations) space_groups[5] = sg space_groups['C 1 2 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(6, 'P 1 m 1', transformations) space_groups[6] = sg space_groups['P 1 m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(7, 'P 1 c 1', transformations) space_groups[7] = sg space_groups['P 1 c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(8, 'C 1 m 1', transformations) space_groups[8] = sg space_groups['C 1 m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(9, 'C 1 c 1', transformations) space_groups[9] = sg space_groups['C 1 c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(10, 'P 1 2/m 1', transformations) space_groups[10] = sg space_groups['P 1 2/m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(11, 'P 1 21/m 1', transformations) space_groups[11] = sg space_groups['P 1 21/m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(12, 'C 1 2/m 1', transformations) space_groups[12] = sg space_groups['C 1 2/m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(13, 'P 1 2/c 1', transformations) space_groups[13] = sg space_groups['P 1 2/c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(14, 'P 1 21/c 1', transformations) space_groups[14] = sg space_groups['P 1 21/c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(15, 'C 1 2/c 1', transformations) space_groups[15] = sg space_groups['C 1 2/c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(16, 'P 2 2 2', transformations) space_groups[16] = sg space_groups['P 2 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(17, 'P 2 2 21', transformations) space_groups[17] = sg space_groups['P 2 2 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(18, 'P 21 21 2', transformations) space_groups[18] = sg space_groups['P 21 21 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(19, 'P 21 21 21', transformations) space_groups[19] = sg space_groups['P 21 21 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(20, 'C 2 2 21', transformations) space_groups[20] = sg space_groups['C 2 2 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(21, 'C 2 2 2', transformations) space_groups[21] = sg space_groups['C 2 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(22, 'F 2 2 2', transformations) space_groups[22] = sg space_groups['F 2 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(23, 'I 2 2 2', transformations) space_groups[23] = sg space_groups['I 2 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(24, 'I 21 21 21', transformations) space_groups[24] = sg space_groups['I 21 21 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(25, 'P m m 2', transformations) space_groups[25] = sg space_groups['P m m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(26, 'P m c 21', transformations) space_groups[26] = sg space_groups['P m c 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(27, 'P c c 2', transformations) space_groups[27] = sg space_groups['P c c 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(28, 'P m a 2', transformations) space_groups[28] = sg space_groups['P m a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(29, 'P c a 21', transformations) space_groups[29] = sg space_groups['P c a 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(30, 'P n c 2', transformations) space_groups[30] = sg space_groups['P n c 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(31, 'P m n 21', transformations) space_groups[31] = sg space_groups['P m n 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(32, 'P b a 2', transformations) space_groups[32] = sg space_groups['P b a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(33, 'P n a 21', transformations) space_groups[33] = sg space_groups['P n a 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(34, 'P n n 2', transformations) space_groups[34] = sg space_groups['P n n 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(35, 'C m m 2', transformations) space_groups[35] = sg space_groups['C m m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(36, 'C m c 21', transformations) space_groups[36] = sg space_groups['C m c 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(37, 'C c c 2', transformations) space_groups[37] = sg space_groups['C c c 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(38, 'A m m 2', transformations) space_groups[38] = sg space_groups['A m m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(39, 'A b m 2', transformations) space_groups[39] = sg space_groups['A b m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(40, 'A m a 2', transformations) space_groups[40] = sg space_groups['A m a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(41, 'A b a 2', transformations) space_groups[41] = sg space_groups['A b a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(42, 'F m m 2', transformations) space_groups[42] = sg space_groups['F m m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,1,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,1,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(43, 'F d d 2', transformations) space_groups[43] = sg space_groups['F d d 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(44, 'I m m 2', transformations) space_groups[44] = sg space_groups['I m m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(45, 'I b a 2', transformations) space_groups[45] = sg space_groups['I b a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(46, 'I m a 2', transformations) space_groups[46] = sg space_groups['I m a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(47, 'P m m m', transformations) space_groups[47] = sg space_groups['P m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(48, 'P n n n :2', transformations) space_groups[48] = sg space_groups['P n n n :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(49, 'P c c m', transformations) space_groups[49] = sg space_groups['P c c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(50, 'P b a n :2', transformations) space_groups[50] = sg space_groups['P b a n :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(51, 'P m m a', transformations) space_groups[51] = sg space_groups['P m m a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(52, 'P n n a', transformations) space_groups[52] = sg space_groups['P n n a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(53, 'P m n a', transformations) space_groups[53] = sg space_groups['P m n a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(54, 'P c c a', transformations) space_groups[54] = sg space_groups['P c c a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(55, 'P b a m', transformations) space_groups[55] = sg space_groups['P b a m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(56, 'P c c n', transformations) space_groups[56] = sg space_groups['P c c n'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(57, 'P b c m', transformations) space_groups[57] = sg space_groups['P b c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(58, 'P n n m', transformations) space_groups[58] = sg space_groups['P n n m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(59, 'P m m n :2', transformations) space_groups[59] = sg space_groups['P m m n :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(60, 'P b c n', transformations) space_groups[60] = sg space_groups['P b c n'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(61, 'P b c a', transformations) space_groups[61] = sg space_groups['P b c a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(62, 'P n m a', transformations) space_groups[62] = sg space_groups['P n m a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(63, 'C m c m', transformations) space_groups[63] = sg space_groups['C m c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(64, 'C m c a', transformations) space_groups[64] = sg space_groups['C m c a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(65, 'C m m m', transformations) space_groups[65] = sg space_groups['C m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(66, 'C c c m', transformations) space_groups[66] = sg space_groups['C c c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(67, 'C m m a', transformations) space_groups[67] = sg space_groups['C m m a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(68, 'C c c a :2', transformations) space_groups[68] = sg space_groups['C c c a :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(69, 'F m m m', transformations) space_groups[69] = sg space_groups['F m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([4,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([4,4,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([4,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([4,4,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,3,3]) trans_den = N.array([1,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([4,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,1]) trans_den = N.array([4,4,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,1,1]) trans_den = N.array([4,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,1,1]) trans_den = N.array([4,4,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([2,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,0,3]) trans_den = N.array([4,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,1,1]) trans_den = N.array([4,4,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,-1,1]) trans_den = N.array([2,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([4,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,-1,1]) trans_den = N.array([4,4,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,3,1]) trans_den = N.array([2,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,1,1]) trans_den = N.array([4,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,0]) trans_den = N.array([4,4,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([4,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([4,4,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(70, 'F d d d :2', transformations) space_groups[70] = sg space_groups['F d d d :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(71, 'I m m m', transformations) space_groups[71] = sg space_groups['I m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(72, 'I b a m', transformations) space_groups[72] = sg space_groups['I b a m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(73, 'I b c a', transformations) space_groups[73] = sg space_groups['I b c a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(74, 'I m m a', transformations) space_groups[74] = sg space_groups['I m m a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(75, 'P 4', transformations) space_groups[75] = sg space_groups['P 4'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(76, 'P 41', transformations) space_groups[76] = sg space_groups['P 41'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(77, 'P 42', transformations) space_groups[77] = sg space_groups['P 42'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(78, 'P 43', transformations) space_groups[78] = sg space_groups['P 43'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(79, 'I 4', transformations) space_groups[79] = sg space_groups['I 4'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(80, 'I 41', transformations) space_groups[80] = sg space_groups['I 41'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(81, 'P -4', transformations) space_groups[81] = sg space_groups['P -4'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(82, 'I -4', transformations) space_groups[82] = sg space_groups['I -4'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(83, 'P 4/m', transformations) space_groups[83] = sg space_groups['P 4/m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(84, 'P 42/m', transformations) space_groups[84] = sg space_groups['P 42/m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(85, 'P 4/n :2', transformations) space_groups[85] = sg space_groups['P 4/n :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(86, 'P 42/n :2', transformations) space_groups[86] = sg space_groups['P 42/n :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(87, 'I 4/m', transformations) space_groups[87] = sg space_groups['I 4/m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-3,-3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,5,5]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,-1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(88, 'I 41/a :2', transformations) space_groups[88] = sg space_groups['I 41/a :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(89, 'P 4 2 2', transformations) space_groups[89] = sg space_groups['P 4 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(90, 'P 4 21 2', transformations) space_groups[90] = sg space_groups['P 4 21 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(91, 'P 41 2 2', transformations) space_groups[91] = sg space_groups['P 41 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(92, 'P 41 21 2', transformations) space_groups[92] = sg space_groups['P 41 21 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(93, 'P 42 2 2', transformations) space_groups[93] = sg space_groups['P 42 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(94, 'P 42 21 2', transformations) space_groups[94] = sg space_groups['P 42 21 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(95, 'P 43 2 2', transformations) space_groups[95] = sg space_groups['P 43 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(96, 'P 43 21 2', transformations) space_groups[96] = sg space_groups['P 43 21 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(97, 'I 4 2 2', transformations) space_groups[97] = sg space_groups['I 4 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(98, 'I 41 2 2', transformations) space_groups[98] = sg space_groups['I 41 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(99, 'P 4 m m', transformations) space_groups[99] = sg space_groups['P 4 m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(100, 'P 4 b m', transformations) space_groups[100] = sg space_groups['P 4 b m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(101, 'P 42 c m', transformations) space_groups[101] = sg space_groups['P 42 c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(102, 'P 42 n m', transformations) space_groups[102] = sg space_groups['P 42 n m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(103, 'P 4 c c', transformations) space_groups[103] = sg space_groups['P 4 c c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(104, 'P 4 n c', transformations) space_groups[104] = sg space_groups['P 4 n c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(105, 'P 42 m c', transformations) space_groups[105] = sg space_groups['P 42 m c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(106, 'P 42 b c', transformations) space_groups[106] = sg space_groups['P 42 b c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(107, 'I 4 m m', transformations) space_groups[107] = sg space_groups['I 4 m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(108, 'I 4 c m', transformations) space_groups[108] = sg space_groups['I 4 c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(109, 'I 41 m d', transformations) space_groups[109] = sg space_groups['I 41 m d'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(110, 'I 41 c d', transformations) space_groups[110] = sg space_groups['I 41 c d'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(111, 'P -4 2 m', transformations) space_groups[111] = sg space_groups['P -4 2 m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(112, 'P -4 2 c', transformations) space_groups[112] = sg space_groups['P -4 2 c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(113, 'P -4 21 m', transformations) space_groups[113] = sg space_groups['P -4 21 m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(114, 'P -4 21 c', transformations) space_groups[114] = sg space_groups['P -4 21 c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(115, 'P -4 m 2', transformations) space_groups[115] = sg space_groups['P -4 m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(116, 'P -4 c 2', transformations) space_groups[116] = sg space_groups['P -4 c 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(117, 'P -4 b 2', transformations) space_groups[117] = sg space_groups['P -4 b 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(118, 'P -4 n 2', transformations) space_groups[118] = sg space_groups['P -4 n 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(119, 'I -4 m 2', transformations) space_groups[119] = sg space_groups['I -4 m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(120, 'I -4 c 2', transformations) space_groups[120] = sg space_groups['I -4 c 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(121, 'I -4 2 m', transformations) space_groups[121] = sg space_groups['I -4 2 m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(122, 'I -4 2 d', transformations) space_groups[122] = sg space_groups['I -4 2 d'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(123, 'P 4/m m m', transformations) space_groups[123] = sg space_groups['P 4/m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(124, 'P 4/m c c', transformations) space_groups[124] = sg space_groups['P 4/m c c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(125, 'P 4/n b m :2', transformations) space_groups[125] = sg space_groups['P 4/n b m :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(126, 'P 4/n n c :2', transformations) space_groups[126] = sg space_groups['P 4/n n c :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(127, 'P 4/m b m', transformations) space_groups[127] = sg space_groups['P 4/m b m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(128, 'P 4/m n c', transformations) space_groups[128] = sg space_groups['P 4/m n c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(129, 'P 4/n m m :2', transformations) space_groups[129] = sg space_groups['P 4/n m m :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(130, 'P 4/n c c :2', transformations) space_groups[130] = sg space_groups['P 4/n c c :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(131, 'P 42/m m c', transformations) space_groups[131] = sg space_groups['P 42/m m c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(132, 'P 42/m c m', transformations) space_groups[132] = sg space_groups['P 42/m c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(133, 'P 42/n b c :2', transformations) space_groups[133] = sg space_groups['P 42/n b c :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(134, 'P 42/n n m :2', transformations) space_groups[134] = sg space_groups['P 42/n n m :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(135, 'P 42/m b c', transformations) space_groups[135] = sg space_groups['P 42/m b c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(136, 'P 42/m n m', transformations) space_groups[136] = sg space_groups['P 42/m n m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(137, 'P 42/n m c :2', transformations) space_groups[137] = sg space_groups['P 42/n m c :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(138, 'P 42/n c m :2', transformations) space_groups[138] = sg space_groups['P 42/n c m :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(139, 'I 4/m m m', transformations) space_groups[139] = sg space_groups['I 4/m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(140, 'I 4/m c m', transformations) space_groups[140] = sg space_groups['I 4/m c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,3,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-3,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-3,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,5,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,5]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,5,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,3,5]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,-1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,-1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(141, 'I 41/a m d :2', transformations) space_groups[141] = sg space_groups['I 41/a m d :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-3,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-3,-3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,5,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,5]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,5,5]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,-1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,-1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(142, 'I 41/a c d :2', transformations) space_groups[142] = sg space_groups['I 41/a c d :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(143, 'P 3', transformations) space_groups[143] = sg space_groups['P 3'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(144, 'P 31', transformations) space_groups[144] = sg space_groups['P 31'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(145, 'P 32', transformations) space_groups[145] = sg space_groups['P 32'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(146, 'R 3 :H', transformations) space_groups[146] = sg space_groups['R 3 :H'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(147, 'P -3', transformations) space_groups[147] = sg space_groups['P -3'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(148, 'R -3 :H', transformations) space_groups[148] = sg space_groups['R -3 :H'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(149, 'P 3 1 2', transformations) space_groups[149] = sg space_groups['P 3 1 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(150, 'P 3 2 1', transformations) space_groups[150] = sg space_groups['P 3 2 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(151, 'P 31 1 2', transformations) space_groups[151] = sg space_groups['P 31 1 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(152, 'P 31 2 1', transformations) space_groups[152] = sg space_groups['P 31 2 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(153, 'P 32 1 2', transformations) space_groups[153] = sg space_groups['P 32 1 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(154, 'P 32 2 1', transformations) space_groups[154] = sg space_groups['P 32 2 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(155, 'R 3 2 :H', transformations) space_groups[155] = sg space_groups['R 3 2 :H'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(156, 'P 3 m 1', transformations) space_groups[156] = sg space_groups['P 3 m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(157, 'P 3 1 m', transformations) space_groups[157] = sg space_groups['P 3 1 m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(158, 'P 3 c 1', transformations) space_groups[158] = sg space_groups['P 3 c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(159, 'P 3 1 c', transformations) space_groups[159] = sg space_groups['P 3 1 c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den =	N.array([3,3,3])	numpy.array
import numpy as np from sklearn.metrics import accuracy_score, f1_score class Particle: def __init__(self, resource, feature_num, k): self.resource = resource self.k = k # features to be selected if k is not None: self.feature_selected = np.random.choice(feature_num, self.k, replace=False).tolist() self.feature_selected.sort() self.position = indices2binary(self.feature_selected, feature_num) else: self.position = np.random.randint(0, 2, size=feature_num) self.feature_selected = binary2indices(self.position) self.performance = None """ evaluate the fitness/metrics of a particle """ def evaluate(self, x, y, clf, metrics="accuracy"): x_train, x_valid = x y_train, y_valid = y indices = self.feature_selected clf.fit(x_train[:, indices], y_train) y_pred = clf.predict(x_valid[:, indices]) if metrics == "accuracy": self.performance = accuracy_score(y_valid, y_pred) if metrics == "f1": self.performance = f1_score(y_valid, y_pred) def mutation(self, global_best, sample_best): # d = len(self.position) # c1 = 0.5 # c2 = 1.0 - c1 # r1 = np.random.rand(d) # r2 = np.random.rand(d) # # # update the position # v = c1 * r1 * (global_best.position - self.position) + \ # c2 * r2 * (sample_best[self.resource].position - self.position) # s = 1 / (1 + np.exp(-v)) # self.position = np.array([1 if si >= 0.5 else 0 for si in s]) # self.feature_selected = binary2indices(self.position) d = len(self.position) m = int(d * 0.1 / 2) for _ in range(m): bit1, bit2 = np.random.choice(d, 2, replace=False) if self.position[bit1] + self.position[bit2] == 1: self.position[bit1] = 1 - self.position[bit1] self.position[bit2] = 1 - self.position[bit2] def __lt__(self, other): return self.performance < other.performance def __le__(self, other): return self.performance <= other.performance def __gt__(self, other): return self.performance > other.performance def __ge__(self, other): return self.performance >= other.performance def __str__(self): return "resource = " + str(self.resource) + ", performance = " + str(self.performance) def crossover(chromosome1, chromosome2, m): assert len(chromosome1) == len(chromosome2) chromosome1 = list(chromosome1) chromosome2 = list(chromosome2) cutting_points = np.random.choice(len(chromosome1), m, replace=False).tolist() cutting_points.append(len(chromosome1)) cutting_points.sort() result = [] index = 0 flag = 1 for point in cutting_points: if flag > 0: result = result + chromosome1[index:point] index = point flag = -1 elif flag < 0: result = result + chromosome2[index:point] index = point flag = 1 return np.array(result, dtype=int) def mutation_ga(chromosome, pm, k): n1 = sum(chromosome) n0 = len(chromosome) - n1 p1 = pm p0 = pm * n1 / n0 for i, g in enumerate(chromosome): r = np.random.rand() if g == 1 and r < p1: chromosome[i] = 0 elif g == 0 and r < p0: chromosome[i] = 1 if k is not None: # ensure the number of the selected features to be same selected_num = sum(chromosome) if selected_num == k: return if selected_num > k: one_index = [index for index in range(len(chromosome)) if chromosome[index] == 1] remove_index = np.random.choice(one_index, selected_num - k, replace=False) chromosome[remove_index] = 0 if selected_num < k: zero_index = [index for index in range(len(chromosome)) if chromosome[index] == 0] remove_index = np.random.choice(zero_index, k - selected_num) chromosome[remove_index] = 1 def binary2indices(binary): indices = [] for i, bit in enumerate(binary): if bit == 1: indices.append(i) return indices def indices2binary(indices, feature_num): binary = [0] * feature_num for indice in indices: binary[indice] = 1 return np.array(binary, dtype=int) def local_mutation(particle): position = list(particle.position) d = len(position) m = int(d * 0.1 / 2) for _ in range(m): bit1, bit2 = np.random.choice(d, 2, replace=False) if position[bit1] + position[bit2] == 1: position[bit1] = 1 - position[bit1] position[bit2] = 1 - position[bit2] p = Particle(particle.resource, d, particle.k) p.position =	np.array(position)	numpy.array
#!/usr/bin/env python3 # -- coding: utf-8 -- """ model ocean waves with kayak sitting on top. Created on Sat Sep 4 15:59:49 2021 @author: jimlux """ import numpy as np from matplotlib import cm import matplotlib.pyplot as plt Earthg = 9.8 """ define swells period in seconds height in feet (because that's what the reports give) direction is "from" in degrees """ swells = [{"period":5,"height":.9,"direction":250}, {"period":13,"height":.8,"direction":210}, {"period":16,"height":.9,"direction":215}] nswells = len(swells) """ calculate wavelength (lambda) and speed and convert height to meters, and unipolar and convert direction to radians""" for swell in swells: swell["speed"] = Earthg/(2. * np.pi) swell["period"] swell["wavelength"] = swell["speed"] swell["period"] swell["height"] = swell["height"]/3.28/2 swell["direction"] = (90-swell["direction"])np.pi/180. gridspacing = 2.0 gridsize = 400 xrange = np.arange(0,gridsizegridspacing,gridspacing) yrange = np.arange(0,gridsizegridspacing,gridspacing) X,Y = np.meshgrid(xrange,yrange) phases = np.zeros((gridsize,gridsize,nswells)) for idx,swell in enumerate(swells): swellcos = np.cos(swell["direction"]) swellsin = np.sin(swell["direction"]) wavelength = swell["wavelength"] print(wavelength) """ there's got to be a better way to do this """ dswell1 = Xswellcos + Yswellsin phases[:,:,idx]=np.pi2.0 * dswell1/wavelength """for i in range(gridsize): for j in range(gridsize): dx = igridspacing dy = jgridspacing dswell = dx * swellcos + dyswellsin phases[i,j,idx]=np.pi 2 * dswell/wavelength """ #fig,ax = plt.subplots(subplot_kw={"projection": "3d"}) #plt.figure() #plt.title("swell #%d"%idx) #surf = ax.plot_surface(xrange, yrange, phases[:,:,idx], cmap=cm.coolwarm, # linewidth=0, antialiased=False) #seaheight = np.cos(phases[:,:,idx]) #img = plt.imshow(seaheight) makefigs = True for t in	np.arange(0,60,step=1)	numpy.arange
#!/usr/bin/env python # -- coding: utf-8 -- # @Time : 2021/2/19 10:38 # @Author : xiaorun # @Site : # @File : stereo.py # @Software: PyCharm import cv2 import numpy as np #####################左右相机外参################################################# ###########左相机世界坐标与图像坐标 object_3d_points_L = np.array(([-1300, 1200, 0], [1400, 1360, 0], [500, 2100, 0], [-700, 2400, 0], [-300,3000,0], [1300,3100,0], [-800,3350,0], [-1200,4260,0], [100,4650,0], [1350,4920,0]),dtype=np.double) object_2d_point_L= np.array(([535, 662], [1038, 594], [839, 494], [641, 471], [694,403], [909,383], [619,373], [567,304], [718,273], [847,250]),dtype=np.double) ##########左相机世界图像坐标 ###########右相机世界坐标与图像坐标 object_3d_points_R= np.array(([-1300, 1200, 0], [1400, 1360, 0], [500, 2100, 0], [-700, 2400, 0], [-300,3000,0], [1300,3100,0], [-800,3350,0], [-1200,4260,0], [100,4650,0], [1350,4920,0]),dtype=np.double) object_2d_point_R= np.array(([271, 619], [792, 693], [629, 504], [449, 457], [529, 397], [758,400], [470,361], [451,289], [612,270], [756,259]),dtype=np.double) ##########右相机世界图像坐标 ######################################################################## #################左右相机内参############################################### camera_matrix_L = np.array(([548.2646, 0, 666.9979], [0, 549.5398, 494.3196], [0, 0, 1.0]), dtype=np.double) dist_coefs_L = np.array([0.0119, -0.0018, 0, 0], dtype=np.double) rotM_L=np.array(([ 0.99647804, -0.0835722, 0.00687046], [-0.06215961, -0.7911815, -0.60841435], [ 0.05628231, 0.60584447, -0.79358981]),dtype=np.double) tvec_L=np.array(([ 734.59639027], [ 1704.06440486], [ 2086.22518369]),dtype=np.double) RT_leftcamera=np.hstack((rotM_L,tvec_L)) m_left=np.matmul(camera_matrix_L ,RT_leftcamera) camera_matrix_R=np.array(([554.9156,0,635.8487], [0,555.5995,509.9433], [0,0,1.0]),dtype=np.double) dist_coefs_R=np.array([0.0256,-0.0133,0,0],dtype=np.double) rotM_R=np.array(([ 0.99353492, 0.1123029, 0.01662595], [ 0.10227696, -0.82186833, -0.56042115], [-0.04927258, 0.55849843, -0.82804089]),dtype=np.double) tvec_R=np.array(([ -773.49701673], [ 1739.81170979], [ 2156.35612044]),dtype=np.double) RT_rightcamera=np.hstack((rotM_R,tvec_R)) #右相机M矩阵 # [u1] \|X\| [u2] \|X\| # Z[v1\| = Ml\|Y\| Z\|v2\| = Mr\|Y\| # [ 1] \|Z\| [ 1] \|Z\| # \|1\| \|1\| m_right=	np.matmul(camera_matrix_R ,RT_rightcamera)	numpy.matmul
import numpy as np import os import re import requests import sys import time from netCDF4 import Dataset import pandas as pd from bs4 import BeautifulSoup from tqdm import tqdm # setup constants used to access the data from the different M2M interfaces BASE_URL = 'https://ooinet.oceanobservatories.org/api/m2m/' # base M2M URL SENSOR_URL = '12576/sensor/inv/' # Sensor Information # setup access credentials AUTH = ['OOIAPI-853A3LA6QI3L62', '<KEY>'] def M2M_Call(uframe_dataset_name, start_date, end_date): options = '?beginDT=' + start_date + '&endDT=' + end_date + '&format=application/netcdf' r = requests.get(BASE_URL + SENSOR_URL + uframe_dataset_name + options, auth=(AUTH[0], AUTH[1])) if r.status_code == requests.codes.ok: data = r.json() else: return None # wait until the request is completed print('Waiting for OOINet to process and prepare data request, this may take up to 20 minutes') url = [url for url in data['allURLs'] if re.match(r'.async_results.', url)][0] check_complete = url + '/status.txt' with tqdm(total=400, desc='Waiting') as bar: for i in range(400): r = requests.get(check_complete) bar.update(1) if r.status_code == requests.codes.ok: bar.n = 400 bar.last_print_n = 400 bar.refresh() print('\nrequest completed in %f minutes.' % elapsed) break else: time.sleep(3) elapsed = (i * 3) / 60 return data def M2M_Files(data, tag=''): """ Use a regex tag combined with the results of the M2M data request to collect the data from the THREDDS catalog. Collected data is gathered into an xarray dataset for further processing. :param data: JSON object returned from M2M data request with details on where the data is to be found for download :param tag: regex tag to use in discriminating the data files, so we only collect the correct ones :return: the collected data as an xarray dataset """ # Create a list of the files from the request above using a simple regex as a tag to discriminate the files url = [url for url in data['allURLs'] if re.match(r'.thredds.', url)][0] files = list_files(url, tag) return files def list_files(url, tag=''): """ Function to create a list of the NetCDF data files in the THREDDS catalog created by a request to the M2M system. :param url: URL to user's THREDDS catalog specific to a data request :param tag: regex pattern used to distinguish files of interest :return: list of files in the catalog with the URL path set relative to the catalog """ page = requests.get(url).text soup = BeautifulSoup(page, 'html.parser') pattern = re.compile(tag) return [node.get('href') for node in soup.find_all('a', text=pattern)] def M2M_Data(nclist,variables): thredds = 'https://opendap.oceanobservatories.org/thredds/dodsC/ooi/' #nclist is going to contain more than one url eventually for jj in range(len(nclist)): url=nclist[jj] url=url[25:] dap_url = thredds + url + '#fillmismatch' openFile = Dataset(dap_url,'r') for ii in range(len(variables)): dum = openFile.variables[variables[ii].name] variables[ii].data = np.append(variables[ii].data, dum[:].data) tmp = variables[0].data/60/60/24 time_converted = pd.to_datetime(tmp, unit='D', origin=pd.Timestamp('1900-01-01')) return variables, time_converted class var(object): def __init__(self): """A Class that generically holds data with a variable name and the units as attributes""" self.name = '' self.data = np.array([]) self.units = '' def __repr__(self): return_str = "name: " + self.name + '\n' return_str += "units: " + self.units + '\n' return_str += "data: size: " + str(self.data.shape) return return_str class structtype(object): def __init__(self): """ A class that imitates a Matlab structure type """ self._data = [] def __getitem__(self, index): """implement index behavior in the struct""" if index == len(self._data): self._data.append(var()) return self._data[index] def __len__(self): return len(self._data) def M2M_URLs(platform_name,node,instrument_class,method): var_list = structtype() #MOPAK if platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #METBK elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' #FLORT elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/06-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/06-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' #FDCHP elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/08-FDCHPA000/telemetered/fdchp_a_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #DOSTA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' #ADCP elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/01-ADCPTA000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/01-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/01-ADCPTA000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/01-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/04-ADCPTM000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/04-ADCPTM000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/04-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/04-ADCPSJ000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' #ZPLSC elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #WAVSS elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' #VELPT elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' #PCO2W elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' #PHSEN elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' #SPKIR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' #PRESF elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/02-PRESFA000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/02-PRESFA000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/02-PRESFB000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/02-PRESFC000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' #CTDBP elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/06-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/06-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/03-CTDBPE000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' #VEL3D elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' #VEL3DK elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' #PCO2A elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' #PARAD elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' #OPTAA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/01-OPTAAC000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #NUTNR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' ## #MOPAK elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/SBD17/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/SBD17/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #METBK elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' #FLORT elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/SBD17/06-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/SBD17/06-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' #FDCHP elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/08-FDCHPA000/recovered_host/fdchp_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #DOSTA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD37/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD37/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD37/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD37/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' #ADCP elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID26/01-ADCPTA000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID26/01-ADCPTC000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID26/01-ADCPTA000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID26/01-ADCPTC000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD35/04-ADCPTM000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD35/04-ADCPTM000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD35/04-ADCPTC000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD35/04-ADCPSJ000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' #WAVSS elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_statistics_recovered' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_statistics_recovered' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_statistics_recovered' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_statistics_recovered' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' #VELPT elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/SBD17/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': #uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/recovered_host/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' #PCO2W elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' #PHSEN elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' #SPKIR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' #PRESF elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD35/02-PRESFA000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD35/02-PRESFA000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD35/02-PRESFB000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD35/02-PRESFC000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' #CTDBP elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD37/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/SBD17/06-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD37/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/SBD17/06-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD37/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD37/03-CTDBPE000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' #VEL3D elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD35/01-VEL3DD000/recovered_host/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD35/01-VEL3DD000/recovered_host/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD35/01-VEL3DD000/recovered_host/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD35/01-VEL3DD000/recovered_host/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' #PCO2A elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' #OPTAA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD37/01-OPTAAC000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #NUTNR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD37/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/SBD17/06-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD37/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/SBD17/06-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD37/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD37/03-CTDBPE000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CE09OSPM/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/RID26/01-ADCPTA000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/RID26/01-ADCPTC000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/RID26/01-ADCPTA000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/RID26/01-ADCPTC000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/04-ADCPTM000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/04-ADCPTM000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD35/04-ADCPTC000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD35/04-ADCPSJ000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/SBD17/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/SBD11/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/SBD11/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/SBD17/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/SBD11/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/SBD11/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CE09OSPM/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/01-VEL3DD000/recovered_inst/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/01-VEL3DD000/recovered_inst/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD35/01-VEL3DD000/recovered_inst/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD35/01-VEL3DD000/recovered_inst/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/02-PRESFA000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/02-PRESFA000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD35/02-PRESFB000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD35/02-PRESFC000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CE09OSPM/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/SBD12/08-FDCHPA000/recovered_inst/fdchp_a_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/SBD17/06-FLORTD000/recovered_inst/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/SBD17/06-FLORTD000/recovered_inst/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CE09OSPM/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CE09OSPM/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD37/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD37/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD37/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD37/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/04-ADCPTM000/recovered_inst/adcpt_m_instrument_log9_recovered' var_list[0].name = 'time' var_list[1].name = 'significant_wave_height' var_list[2].name = 'peak_wave_period' var_list[3].name = 'peak_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'seconds' var_list[3].units = 'degrees' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/04-ADCPTM000/recovered_inst/adcpt_m_instrument_log9_recovered' var_list[0].name = 'time' var_list[1].name = 'significant_wave_height' var_list[2].name = 'peak_wave_period' var_list[3].name = 'peak_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'seconds' var_list[3].units = 'degrees' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'CTD' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/06-CTDBPN106/streamed/ctdbp_no_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_no_seawater_pressure' var_list[5].name = 'ctdbp_no_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'CTD' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/06-CTDBPO108/streamed/ctdbp_no_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_no_seawater_pressure' var_list[5].name = 'ctdbp_no_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'DOSTA' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/06-CTDBPN106/streamed/ctdbp_no_sample' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'DOSTA' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/06-CTDBPO108/streamed/ctdbp_no_sample' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'PHSEN' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/10-PHSEND103/streamed/phsen_data_record' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'PHSEN' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/10-PHSEND107/streamed/phsen_data_record' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'PCO2W' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/09-PCO2WB103/streamed/pco2w_b_sami_data_record' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'PCO2W' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/09-PCO2WB104/streamed/pco2w_b_sami_data_record' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'ADCP' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/05-ADCPTB104/streamed/adcp_velocity_beam' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'ADCP' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/05-ADCPSI103/streamed/adcp_velocity_beam' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'VEL3D' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/07-VEL3DC108/streamed/vel3d_cd_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'VEL3D' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/07-VEL3DC107/streamed/vel3d_cd_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'OPTAA' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/08-OPTAAD106/streamed/optaa_sample' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'OPTAA' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/08-OPTAAC104/streamed/optaa_sample' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #CSPP Data below elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/08-FLORTJ000/telemetered/flort_dj_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/08-FLORTJ000/recovered_cspp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/08-FLORTJ000/telemetered/flort_dj_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/08-FLORTJ000/recovered_cspp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/02-DOSTAJ000/telemetered/dosta_abcdjm_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/02-DOSTAJ000/recovered_cspp/dosta_abcdjm_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/02-DOSTAJ000/telemetered/dosta_abcdjm_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/02-DOSTAJ000/recovered_cspp/dosta_abcdjm_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/09-CTDPFJ000/telemetered/ctdpf_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/09-CTDPFJ000/recovered_cspp/ctdpf_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/09-CTDPFJ000/telemetered/ctdpf_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/09-CTDPFJ000/recovered_cspp/ctdpf_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/10-PARADJ000/telemetered/parad_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/10-PARADJ000/recovered_cspp/parad_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/10-PARADJ000/telemetered/parad_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/10-PARADJ000/recovered_cspp/parad_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'NUTNR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/06-NUTNRJ000/recovered_cspp/nutnr_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'salinity_corrected_nitrate' var_list[2].name = 'nitrate_concentration' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'NUTNR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/06-NUTNRJ000/recovered_cspp/nutnr_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'salinity_corrected_nitrate' var_list[2].name = 'nitrate_concentration' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/07-SPKIRJ000/telemetered/spkir_abj_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/07-SPKIRJ000/recovered_cspp/spkir_abj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/07-SPKIRJ000/telemetered/spkir_abj_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/07-SPKIRJ000/recovered_cspp/spkir_abj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/05-VELPTJ000/telemetered/velpt_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/05-VELPTJ000/recovered_cspp/velpt_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/05-VELPTJ000/telemetered/velpt_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/05-VELPTJ000/recovered_cspp/velpt_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'OPTAA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/04-OPTAAJ000/recovered_cspp/optaa_dj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'OPTAA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/04-OPTAAJ000/recovered_cspp/optaa_dj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/07-FLORTJ000/recovered_cspp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/07-FLORTJ000/recovered_cspp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/01-DOSTAJ000/recovered_cspp/dosta_abcdjm_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/01-DOSTAJ000/recovered_cspp/dosta_abcdjm_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/08-CTDPFJ000/recovered_cspp/ctdpf_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/08-CTDPFJ000/recovered_cspp/ctdpf_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/09-PARADJ000/recovered_cspp/parad_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/09-PARADJ000/recovered_cspp/parad_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'NUTNR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/05-NUTNRJ000/recovered_cspp/nutnr_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'salinity_corrected_nitrate' var_list[2].name = 'nitrate_concentration' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'NUTNR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/05-NUTNRJ000/recovered_cspp/nutnr_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'salinity_corrected_nitrate' var_list[2].name = 'nitrate_concentration' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/06-SPKIRJ000/recovered_cspp/spkir_abj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/06-SPKIRJ000/recovered_cspp/spkir_abj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/02-VELPTJ000/recovered_cspp/velpt_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/02-VELPTJ000/recovered_cspp/velpt_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'OPTAA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/04-OPTAAJ000/recovered_cspp/optaa_dj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'OPTAA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/04-OPTAAJ000/recovered_cspp/optaa_dj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL386/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL386/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL384/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL384/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL383/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL383/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL382/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL382/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL381/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL381/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL327/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL327/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL326/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL326/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL320/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL320/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL319/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL319/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL312/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL312/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL311/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL311/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL247/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL247/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL386/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL386/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL384/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL384/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL383/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL383/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL382/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL382/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL381/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL381/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL327/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL327/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL326/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL326/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL320/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL320/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL319/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL319/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL312/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL312/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL311/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL311/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL247/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL247/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL386/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL386/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL384/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL384/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL383/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL383/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL382/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL382/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL381/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL381/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL327/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL327/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL326/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL326/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL320/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL320/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL319/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL319/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL312/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL312/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL311/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL311/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL247/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL247/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL386/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL386/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL384/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL384/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL383/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL383/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL382/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL382/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL381/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL381/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL327/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL327/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL326/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL326/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL320/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL320/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL319/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL319/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL312/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL312/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL311/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL311/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL247/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL247/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL386/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL384/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL383/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL382/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL381/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL327/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL326/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL320/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL319/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL312/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL311/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL247/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_mean_directional' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_mean_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_mean_directional' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_mean_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_mean_directional' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_mean_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_mean_directional' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_mean_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_non_directional' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_non_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_non_directional' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_non_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_non_directional' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_non_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_non_directional' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_non_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_motion' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_motion_recovered' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_motion' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_motion_recovered' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_motion' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_motion_recovered' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_motion' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_motion_recovered' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_fourier' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_fourier_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_fourier' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_fourier_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_fourier' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_fourier_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_fourier' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_fourier_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/2A-CTDPFA107/streamed/ctdpf_sbe43_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'seawater_pressure' var_list[5].name = 'seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSPD/DP01B/01-CTDPFL105/recovered_inst/dpc_ctd_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'dpc_ctd_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CE04OSPD/DP01B/01-CTDPFL105/recovered_wfp/dpc_ctd_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'dpc_ctd_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/2A-CTDPFA107/streamed/ctdpf_sbe43_sample' var_list[0].name = 'time' var_list[1].name = 'corrected_dissolved_oxygen' var_list[2].name = 'seawater_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSPD/DP01B/06-DOSTAD105/recovered_inst/dpc_optode_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CE04OSPD/DP01B/06-DOSTAD105/recovered_wfp/dpc_optode_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/3A-FLORTD104/streamed/flort_d_data_record' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSPD/DP01B/04-FLNTUA103/recovered_inst/dpc_flnturtd_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'flntu_x_mmp_cds_fluorometric_chlorophyll_a' var_list[2].name = 'flntu_x_mmp_cds_total_volume_scattering_coefficient ' var_list[3].name = 'flntu_x_mmp_cds_bback_total' var_list[4].name = 'flcdr_x_mmp_cds_fluorometric_cdom' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'ug/L' var_list[2].units = 'm-1 sr-1' var_list[3].units = 'm-1' var_list[4].units = 'ppb' var_list[5].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CE04OSPD/DP01B/03-FLCDRA103/recovered_wfp/dpc_flcdrtd_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'flntu_x_mmp_cds_fluorometric_chlorophyll_a' var_list[2].name = 'flntu_x_mmp_cds_total_volume_scattering_coefficient ' var_list[3].name = 'flntu_x_mmp_cds_bback_total' var_list[4].name = 'flcdr_x_mmp_cds_fluorometric_cdom' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'ug/L' var_list[2].units = 'm-1 sr-1' var_list[3].units = 'm-1' var_list[4].units = 'ppb' var_list[5].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'PHSEN' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/2B-PHSENA108/streamed/phsen_data_record' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'ph_seawater' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/3C-PARADA102/streamed/parad_sa_sample' var_list[0].name = 'time' var_list[1].name = 'par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/3D-SPKIRA102/streamed/spkir_data_record' var_list[0].name = 'time' var_list[1].name = 'spkir_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'NUTNR' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/4A-NUTNRA102/streamed/nutnr_a_sample' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'PCO2W' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/4F-PCO2WA102/streamed/pco2w_a_sami_data_record' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' var_list[3].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/4B-VELPTD106/streamed/velpt_velocity_data' var_list[0].name = 'time' var_list[1].name = 'velpt_d_eastward_velocity' var_list[2].name = 'velpt_d_northward_velocity' var_list[3].name = 'velpt_d_upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[9].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' var_list[9].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSPD/DP01B/02-VEL3DA105/recovered_inst/dpc_acm_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_a_eastward_velocity' var_list[2].name = 'vel3d_a_northward_velocity' var_list[3].name = 'vel3d_a_upward_velocity_ascending' var_list[4].name = 'vel3d_a_upward_velocity_descending' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'm/s' var_list[5].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CE04OSPD/DP01B/02-VEL3DA105/recovered_wfp/dpc_acm_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_a_eastward_velocity' var_list[2].name = 'vel3d_a_northward_velocity' var_list[3].name = 'vel3d_a_upward_velocity_ascending' var_list[4].name = 'vel3d_a_upward_velocity_descending' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'm/s' var_list[5].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PLATFORM200M' and instrument_class == 'CTD' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/PC01B/4A-CTDPFA109/streamed/ctdpf_optode_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'seawater_pressure' var_list[5].name = 'seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSPS' and node == 'PLATFORM200M' and instrument_class == 'DOSTA' and method == 'Streamed': #uframe_dataset_name = 'CE04OSPS/PC01B/4A-DOSTAD109/streamed/ctdpf_optode_sample' uframe_dataset_name = 'CE04OSPS/PC01B/4A-CTDPFA109/streamed/ctdpf_optode_sample' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'seawater_pressure' #also use this for the '4A-DOSTAD109/streamed/ctdpf_optode_sample' stream var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PLATFORM200M' and instrument_class == 'PHSEN' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/PC01B/4B-PHSENA106/streamed/phsen_data_record' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSPS' and node == 'PLATFORM200M' and instrument_class == 'PCO2W' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/PC01B/4D-PCO2WA105/streamed/pco2w_a_sami_data_record' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' #Coastal Pioneer CSM Data Streams elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK2' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK2' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' #WAVSS elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_statistics_recovered' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_mean_directional' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_mean_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_non_directional' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_non_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_motion' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_motion_recovered' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_fourier' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_fourier_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' #PCO2A elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' #PCO2A elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' #FDCHP elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/SBD12/08-FDCHPA000/recovered_inst/fdchp_a_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/08-FDCHPA000/telemetered/fdchp_a_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/08-FDCHPA000/recovered_host/fdchp_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK2-hr' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK2-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD37/03-CTDBPE000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD37/03-CTDBPE000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD37/03-CTDBPE000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD37/03-CTDBPD000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD37/03-CTDBPD000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD37/03-CTDBPD000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD37/03-CTDBPD000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD37/03-CTDBPD000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD37/03-CTDBPD000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD35/02-PRESFB000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD35/02-PRESFB000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD35/02-PRESFB000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD35/02-PRESFB000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD35/02-PRESFB000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD35/02-PRESFB000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD35/02-PRESFC000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD35/02-PRESFC000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD35/02-PRESFC000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD35/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD35/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD35/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD35/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD35/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD35/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD35/04-VELPTB000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD35/04-VELPTB000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD35/04-VELPTB000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD37/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD37/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD37/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD37/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD37/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD37/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD35/01-ADCPTF000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD35/01-ADCPTF000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD35/01-ADCPTF000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD35/01-ADCPTF000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD35/01-ADCPSJ000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD35/01-ADCPSJ000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' #Coastal Pioneer WireFollowing Profilers (WFP elif platform_name == 'CP04OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/SBS11/02-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSPM/SBS11/02-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP04OSPM/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP04OSPM/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP04OSPM/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP04OSPM/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP04OSPM/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP01CNPM/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP01CNPM/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP01CNPM/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP01CNPM/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP01CNPM/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCI/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCI/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCI/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCI/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCI/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCI/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCO/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCO/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCO/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCO/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCO/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCO/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUI/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUI/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUI/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUI/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUI/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUI/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUO/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUO/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUO/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUO/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUO/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUO/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP03ISPM/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP03ISPM/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP03ISPM/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP03ISPM/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP03ISPM/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSPM/RII01/02-ADCPSL010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP04OSPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSPM/RII01/02-ADCPSL010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP04OSPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/RII01/02-ADCPSL010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP01CNPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNPM/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP01CNPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNPM/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP01CNPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP02PMCI/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCI/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP02PMCO/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCO/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP02PMUI/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUI/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP02PMUO/RII01/02-ADCPSL010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUO/RII01/02-ADCPSL010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/RII01/02-ADCPSL010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP03ISPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISPM/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP03ISPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISPM/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP03ISPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL336/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL336/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL336/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL336/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL336/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL336/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL336/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL336/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL336/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL388/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL388/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL388/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL388/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL388/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL388/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL388/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL388/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL388/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL335/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL335/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL335/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL335/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL335/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL335/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL335/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL335/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL335/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL339/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL339/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL339/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL339/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL339/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL339/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL339/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL339/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL339/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL340/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL340/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL340/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL340/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL340/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL340/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL340/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL340/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL340/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL374/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL374/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL374/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL374/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL374/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL374/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL374/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL374/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL374/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL375/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL375/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL375/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL375/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL375/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL375/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL375/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL375/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL375/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL376/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL376/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL376/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL376/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL376/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL376/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL376/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL376/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL376/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL379/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL379/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL379/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL379/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL379/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL379/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL379/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL379/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL379/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL380/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL380/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL380/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL380/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL380/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL380/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL380/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL380/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL380/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL387/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL387/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL387/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL387/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL387/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL387/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL387/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL387/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL387/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL389/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL389/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL389/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL389/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL389/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL389/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL389/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL389/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL389/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL514/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL514/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL514/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL514/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL514/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL514/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL514/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL514/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL514/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCI/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCO/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUI/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUO/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data =	np.array([])	numpy.array
from functools import reduce from math import exp, isclose, log, pi from os import makedirs, path import matplotlib.pyplot as plt import numpy as np from scipy import special working_dir = path.dirname(path.abspath(__file__)) makedirs(path.join(working_dir, 'plots'), exist_ok=True) try: data = np.load(path.join(working_dir, 'data.npy')) except FileNotFoundError: data = np.load(path.join(working_dir, 'task4.npy')) def hist(x_array, n_bins, continuous=True, normalize=True): min_val = x_array.min() max_val = x_array.max() count = np.zeros(int(n_bins)) for x in x_array: bin_number = int((n_bins - 1) * ((x - min_val) / (max_val - min_val))) count[bin_number] += 1 # normalize the distribution if normalize: count /= x_array.shape[0] if continuous: count /= ((max_val - min_val) / n_bins) return count, np.linspace(min_val, max_val, num=n_bins) num_bins = 100 counts, bins = hist(data, num_bins, continuous=False, normalize=False) plt.bar(bins, counts, width=0.5, align='edge', color='gray') plt.xlabel('x') plt.ylabel(r'$P\left(x\right)$') plt.savefig(path.join(working_dir, 'plots/hist.eps'), bbox_inches='tight') plt.close() counts, bins = hist(data, num_bins, continuous=False, normalize=True) plt.bar(bins, counts, width=0.5, align='edge', color='gray') plt.xlabel('x') plt.ylabel(r'$P\left(x\right)$') plt.savefig( path.join(working_dir, 'plots/hist_normalized.eps'), bbox_inches='tight' ) def poisson_likelihood(x, lambda_): n = x.shape[0] lambda_x = reduce( lambda y, z: y * z, (lambda_ ** x).tolist() ) x_factorial = reduce( lambda y, z: y * z, special.factorial(x, exact=True).tolist() ) return exp(- lambda_ * n) * lambda_x / x_factorial def poisson_log_likelihood(x, lambda_): n = x.shape[0] log_lambda_x = log(lambda_) * np.sum(x) log_x_factorial = np.sum(np.log(special.factorial(x, exact=True))) return (- lambda_ * n) + log_lambda_x - log_x_factorial # Poisson MLE lambda_hat = np.mean(data) def gaussian_likelihood(x, mu, var): n = x.shape[0] normalization_factor = (2. * pi * var) ** (-.5 * n) x_minus_mu_squared = np.sum((x - mu) ** 2) return normalization_factor * exp(- x_minus_mu_squared / (2. * var)) def gaussian_log_likelihood(x, mu, var): n = x.shape[0] log_normalization_factor = (-.5 * n) * log(2. * pi * var) x_minus_mu_squared = np.sum((x - mu) ** 2) return log_normalization_factor - x_minus_mu_squared / (2. * var) # Gaussian MLE mu_hat = np.mean(data) var_hat = np.var(data) assert isclose( gaussian_log_likelihood(data, mu_hat, var_hat), -40287.57, abs_tol=1e-2 ) assert np.equal( exp(gaussian_log_likelihood(data, mu_hat, var_hat)), gaussian_likelihood(data, mu_hat, var_hat) ) print('Poisson') print('Lambda (MLE): \t\t', lambda_hat) try: print('Likelihood: \t\t', poisson_likelihood(data, lambda_hat)) except OverflowError: print('Likelihood: \t\t', exp(poisson_log_likelihood(data, lambda_hat))) print('Log-Likelihood: \t', poisson_log_likelihood(data, lambda_hat)) print() print('Gaussian') print('Mu (MLE): \t\t', mu_hat) print('Var (MLE): \t\t', var_hat) print('Likelihood: \t\t', gaussian_likelihood(data, mu_hat, var_hat)) print('Log-Likelihood: \t', gaussian_log_likelihood(data, mu_hat, var_hat)) # plot gaussian distribution data_range = np.arange(0, np.max(data), step=1) gaussian_densities = list( map( lambda x: gaussian_likelihood(	np.array([x])	numpy.array
""" Plot anomalies for Arctic and Antarctic sea ice extents of the current year from Sea Ice Index 3 (NSIDC). Total anomaly (global) is also shown. Website : ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/daily/data/ Author : <NAME> Date : 5 September 2016 """ ### Import modules import numpy as np import urllib.request import urllib as UL import datetime import matplotlib.pyplot as plt ### Directory and time directoryfigure = './Figures/' now = datetime.datetime.now() currentmn = str(now.month) currentdy = str(now.day) currentyr = str(now.year) currenttime = currentmn + '_' + currentdy + '_' + currentyr currentdoy = now.timetuple().tm_yday ### Load url url = 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/daily/data/' \ 'N_seaice_extent_daily_v3.0.csv' ### Read Arctic file raw_data = UL.request.urlopen(url) dataset = np.genfromtxt(raw_data, skip_header=2,delimiter=',', usecols=[0,1,2,3,4]) print('\nCompleted: Read sea ice data!') ### Set missing data to nan dataset[np.where(dataset==-9999)] = np.nan ### Variables year = dataset[:,0] month = dataset[:,1] day = dataset[:,2] iceAR = dataset[:,3] missing = dataset[:,4] ### Find current year yr2018 = np.where(year == 2018)[0] iceAR18 = iceAR[yr2018] ### Ice unit Conversion icevalAR = iceAR18 * 1e6 ########################################################################### ########################################################################### ########################################################################### ### Reads in 1981-2010 means ### Load url url2 = 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/daily/data/' \ 'N_seaice_extent_climatology_1981-2010_v3.0.csv' ### Read file raw_data2 = UL.request.urlopen(url2) dataset2 = np.genfromtxt(raw_data2, skip_header=2,delimiter=',', usecols=[0,1,2]) ### Create variables doy = dataset2[:,0] meaniceAR = dataset2[:,1] * 1e6 std = dataset2[:,2] ### Anomalies currentanomAR = icevalAR-meaniceAR[:currentdoy-1] ########################################################################### ########################################################################### ########################################################################### ### Antarctic file ### Load url url = 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/south/daily/data/' \ 'S_seaice_extent_daily_v3.0.csv' ### Read file raw_data = UL.request.urlopen(url) dataset = np.genfromtxt(raw_data, skip_header=2,delimiter=',', usecols=[0,1,2,3,4]) print('\nCompleted: Read sea ice data!') ### Set missing data to nan dataset[np.where(dataset==-9999)] = np.nan ### Variables year = dataset[:,0] month = dataset[:,1] day = dataset[:,2] iceAA = dataset[:,3] missing = dataset[:,4] ### Find current year yr2018 = np.where(year == 2018)[0] iceAA18 = iceAA[yr2018] ### Ice Conversion icevalAA = iceAA18 * 1e6 ########################################################################### ########################################################################### ########################################################################### ### Reads in 1981-2010 means ### Load url url2 = 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/south/daily/data/' \ 'S_seaice_extent_climatology_1981-2010_v3.0.csv' ### Read file raw_data2 = UL.request.urlopen(url2) dataset2 = np.genfromtxt(raw_data2, skip_header=2,delimiter=',', usecols=[0,1,2]) ### Create variables doy = dataset2[:,0] meaniceAA = dataset2[:,1] * 1e6 ### Anomalies currentanomAA = icevalAA-meaniceAA[:currentdoy-1] ########################################################################### ########################################################################### ########################################################################### ### Total Anomaly totalanom = (currentanomAR + currentanomAA) / 1e6 currentanomAR = currentanomAR/1e6 currentanomAA = currentanomAA/1e6 print('Completed script!') ########################################################################### ########################################################################### ########################################################################### ### Create plot plt.rc('text',usetex=True) plt.rc('font',**{'family':'sans-serif','sans-serif':['Avant Garde']}) plt.rc('savefig',facecolor='black') plt.rc('axes',edgecolor='white') plt.rc('xtick',color='white') plt.rc('ytick',color='white') plt.rc('axes',labelcolor='white') plt.rc('axes',facecolor='black') fig = plt.figure() ax = plt.subplot(111) xlabels = [r'Jan',r'Feb',r'Mar',r'Apr',r'May',r'Jun',r'Jul', r'Aug',r'Sep',r'Oct',r'Nov',r'Dec',r'Jan'] plt.xticks(	np.arange(0,366,30.4)	numpy.arange
import numpy as np from scipy.optimize import curve_fit import logging class stfmrSpectraFitting: def __init__(self, hArray, vArray, frequency, inputFileName): self.fieldArray = hArray self.amplitudeArray = vArray #self.fieldArray, self.amplitudeArray = self.assignFieldAndAmpArray(hArray, vArray, minRange, maxRange) self.deltaH = self.findDh(self.fieldArray, self.amplitudeArray) #Units: T self.Hres = self.fieldArray[int(len(self.fieldArray)/2)] #Units: T self.frequency = frequency #Units : Hz self.V0 = (self.amplitudeArray[0] + self.amplitudeArray[-1])/2 self.V1 = 0 self.Vsym = np.amax(self.amplitudeArray) + np.amin(self.amplitudeArray) - 2*self.V0 self.Vas = np.amax(self.amplitudeArray) -	np.amin(self.amplitudeArray)	numpy.amin
# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import division import unittest import numpy as np from singa import loss from singa import tensor class TestLoss(unittest.TestCase): def setUp(self): self.x_np = np.asarray([[0.9, 0.2, 0.1], [0.1, 0.4, 0.5], [0.2, 0.4, 0.4]], dtype=np.float32) self.y_np = np.asarray([[1, 0, 1], [0, 1, 1], [1, 0, 0]], dtype=np.float32) self.x = tensor.from_numpy(self.x_np) self.y = tensor.from_numpy(self.y_np) def test_sigmoid_cross_entropy(self): sig = loss.SigmoidCrossEntropy() l1 = sig.forward(True, self.x, self.y) sig.backward() l2 = sig.evaluate(True, self.x, self.y) p = 1.0 / (1 + np.exp(-self.x_np)) l = - (self.y_np * np.log(p) + (1 - self.y_np) * np.log(1 - p)) self.assertAlmostEqual(l1.l1(), l2) self.assertAlmostEqual(l1.l1(),	np.average(l)	numpy.average
"""A collection of core functions.""" import logging import os from collections import defaultdict from typing import Union import numpy as np import xarray as xr from scipy.spatial.distance import pdist, squareform logger = logging.getLogger(os.path.basename(__file__)) def area_weighted_mean(data_array: 'xr.DataArray') -> 'xr.DataArray': """Calculate area mean weighted by the latitude. Returns a data array consisting of N values, where N == number of ensemble members. """ weights_lat = np.cos(np.radians(data_array.lat)) means = data_array.weighted(weights_lat).mean(dim=['lat', 'lon']) return means def distance_matrix(values: 'np.ndarray', weights: 'np.ndarray' = None) -> 'np.ndarray': """Calculate the pairwise distance between model members. Takes a dataset with ensemble member/lon/lat. Flattens lon/lat into a single dimension. Calculates the distance between every ensemble member. If weights are passed, they should have the same shape as values. Returns 2D NxN array, where N == number of ensemble members. """ n_members = values.shape[0] values = values.reshape(n_members, -1) # pdist does not work with NaN not_nan = np.where(np.all(np.isfinite(values), axis=0))[0] values = values[:, not_nan] if weights is not None: # Reshape weights to match values array weights = weights.reshape(n_members, -1) weights = weights[:, not_nan] weights = weights[0] # Weights are equal along first dim d_matrix = squareform(pdist(values, metric='euclidean', w=weights)) return d_matrix def calculate_model_distances( data_array: 'xr.DataArray', dimension: str = 'model_ensemble_reference') -> 'xr.DataArray': """Calculate pair-wise distances between all values in data_array. Distances are calculated as the area weighted euclidean distance between each pair of models in data_array. Returned is a square matrix with where the number of elements along each edge equals the number of ensemble members. Parameters ---------- data_array : array_like, shape (N,...) Array of (2 dimensional) model fields. dimension : string Name of the newly created reference dimension (default: 'model_ensemble_reference'. Must not be equal to the existing model dimension ('model_ensemble')! Returns ------- distances : array_like, shape (N, N) Symmetric matrix of pairwise model distances. """ assert dimension != 'model_ensemble', f'{dimension} != "model_ensemble"' weights = np.cos(np.radians(data_array.lat)) weights, _ = xr.broadcast(weights, data_array) diff = xr.apply_ufunc( distance_matrix, data_array, weights, input_core_dims=[['model_ensemble', 'lat', 'lon'], ['model_ensemble', 'lat', 'lon']], output_core_dims=[[dimension, 'model_ensemble']], ) diff.name = f'd{data_array.name}' diff.attrs['variable_group'] = data_array.name diff.attrs["units"] = data_array.units diff[dimension] = diff.model_ensemble.values return diff def compute_overall_mean(dataset: 'xr.Dataset', weights: dict) -> 'xr.DataArray': """Normalize all variables in a dataset and return their weighted mean. Relative weights for each variable group are passed via the recipe. """ normalized = dataset / dataset.median() weights_selected = xr.DataArray( [weights[variable_group] for variable_group in dataset], coords={'variable_group': list(dataset)}, dims='variable_group') overall_mean = normalized.to_array( dim='variable_group').weighted(weights_selected).mean('variable_group') overall_mean.name = 'overall_mean' overall_mean.attrs['variable_group'] = 'overall_mean' overall_mean.attrs['units'] = '1' return overall_mean def combine_ensemble_members( dataset: Union['xr.DataArray', None], dimensions: Union[str, list] = 'model_ensemble', ) -> (Union['xr.DataArray', None], dict): """Combine ensemble members of the same model. Parameters ---------- dataset : None or data_array, shape (N,) or (N, N) A vector containing model-observations distances or a matrix containing model-model distances. dimensions : string or list of up to two strings Spezifies the dimensions along which ensemble members are combined. Returns ------- dataset : None or data_array, shape (M,), (M, L) with M, L <= N data_array where ensemble members along the given dimensions are combined by averaging. groups : dict of form {string: list} Dictionary mapping the combined model names (keys) to the original ensemble member names (values). """ if isinstance(dimensions, str): dimensions = [dimensions] assert len( dimensions) <= 2, 'dimensions can contain a maximum of two strings' if dataset is None: return None, {} groups = defaultdict(list) models = [] for name in dataset['model_ensemble'].values: model = name.split('_')[0] groups[model].append(name) models.append(model) for dimension in dimensions: if dimension in dataset.dims: model = xr.DataArray(models, dims=dimension) dataset = dataset.groupby(model).mean(keep_attrs=True).rename( {'group': dimension}) if len(dimensions) == 2: # need to set the diagonal elements back to zero after averaging dataset.values[np.diag_indices(dataset['model_ensemble'].size)] = 0 return dataset, groups def calculate_weights_data( performance: Union['np.array', None], independence: Union['np.array', None], performance_sigma: Union[float, None], independence_sigma: Union[float, None]) -> 'np.array': """Calculate normalized weights for each model N. Parameters ---------- performance : array_like, shape (N,) or None Array specifying the model performance. None is mutually exclusive with independence being None. Single values in performance can be nan, then they will be excluded from the independence calculation as well (used for the perfect model test). independence : array_like, shape (N, N) or None Array specifying the model independence. None is mutually exclusive with performance being None. performance_sigma : float or None Sigma value defining the form of the weighting function for the performance. Can be one only if performance is also None. independence_sigma : float or None Sigma value defining the form of the weighting function for the independence. Can be one only if independence is also None. Returns ------- weights : ndarray, shape (N,) """ numerator = 1 not_nan = True denominator = 1 if performance is not None: numerator = np.exp(-((performance / performance_sigma)2)) # nans in the performance vector indicate models to be excluded not_nan = np.isfinite(performance) if independence is not None: # don't consider nan models for independence of other models! exp = np.exp(-((independence[:, not_nan] / independence_sigma)2)) # Note diagonal = exp(0) = 1, thus this is equal to 1 + sum(i!=j) denominator = exp.sum(axis=1) weights = numerator / denominator weights /= weights.sum(where=not_nan) return weights def calculate_weights( performance: Union['xr.DataArray', None], independence: Union['xr.DataArray', None], performance_sigma: Union[float, None], independence_sigma: Union[float, None]) -> 'xr.DataArray': """Xarray wrapper for calculate_weights_data.""" performance_core_dims = [] if performance is None else ['model_ensemble'] independence_core_dims = [] if independence is None else [ 'model_ensemble', 'model_ensemble_reference' ] weights = xr.apply_ufunc( calculate_weights_data, performance, independence, performance_sigma, independence_sigma, input_core_dims=[ performance_core_dims, independence_core_dims, [], [] ], output_core_dims=[['model_ensemble']], vectorize=True, ) weights.name = 'weight' weights.attrs['variable_group'] = 'weight' # used in barplot weights.attrs['units'] = '1' return weights def weighted_quantile(values: list, quantiles: list, weights: list = None) -> 'np.array': """Calculate weighted quantiles. Analogous to np.quantile, but supports weights. Based on: https://stackoverflow.com/a/29677616/6012085 Parameters ---------- values: array_like List of input values. quantiles: array_like List of quantiles between 0.0 and 1.0. weights: array_like List with same length as `values` containing the weights. Returns ------- np.array Numpy array with computed quantiles. """ values = np.array(values) quantiles = np.array(quantiles) if weights is None: weights = np.ones(len(values)) weights = np.array(weights) # remove nans not_nan = np.where((np.isfinite(values) &	np.isfinite(weights)	numpy.isfinite
import random import numpy as np import cv2 import matplotlib.pyplot as plt # from matplotlib import pyplot as plt import scipy from scipy import signal from PIL import Image from scipy.ndimage import median_filter # 由于卷积核的大小一般是奇数，因此这里假设卷积核是奇数的 ''' #################### 图像处理的基本函数 #################### ''' # 图像加框 def addBoundary(img, kernel): ''' 给图像添加边界 :param img: 输入图像 :param kernel:卷积核 :return: 加边界后的图像 ''' kernel_size = kernel.shape[0] addLine = (int)((kernel_size - 1) / 2) img_ = cv2.copyMakeBorder(img, addLine, addLine, addLine, addLine, cv2.BORDER_CONSTANT, value=0); return img_ def convolve1(img, kernel, filter_type, mode='same'): ''' 单通道图像与卷积核的卷积，主要用于灰度图 :param img: 输入单通道图像矩阵 :param kernel: 卷积核 :param model: medium,gauss,mean, 即选择中值滤波、高斯滤波、还是均值滤波，其他滤波方式以后添加 :return: 卷积后的图像 ''' if mode == 'same': img_ = addBoundary(img, kernel) kernel_height = kernel.shape[0] kernel_width = kernel.shape[1] # 横向卷积、纵向卷积的次数 conv_height = img_.shape[0] - kernel_height + 1 conv_width = img_.shape[1] - kernel_width + 1 # 卷积结果存储在conv中 conv = np.zeros((conv_height, conv_width), dtype='uint8') for i in range(conv_height): for j in range(conv_width): conv[i][j] = wise_element_sum(img_[i:i + kernel_height, j:j + kernel_width], kernel, filter_type) return conv def wise_element_sum(img, kernel, filter_type): ''' 对于某一次卷积结果的取值 :param img: 输入的图片片段矩阵 :param kernel: 卷积核 :param modle: medium,gauss,mean, 即选择中值滤波、高斯滤波、还是均值滤波，其他滤波方式以后添加 :return: 返回该像素值 ''' if filter_type == 'medium_Filter': temp = img * kernel list = [] for i in range(temp.shape[0]): for j in range(temp.shape[1]): list.append(temp[i][j]) list.sort() if list[int(len(list) / 2)] > 255: return 255 elif list[int(len(list) / 2)] < 0: return 0 else: return list[int(len(list) / 2)] # 均值、高斯滤波等 else: result = (img * kernel).sum() if result < 0: return 0 elif result > 255: return 255 else: return result def convolve(img, kernel, filter_type, mode='same'): ''' 三通道卷积，主要用于彩色图 :param img: 输入图像矩阵 :param kernel: 卷积核 :param mode: medium,gauss,mean, 即选择中值滤波、高斯滤波、还是均值滤波，其他滤波方式以后添加 :return: 卷积后的图像矩阵 ''' R = np.mat(img[:, :, 0]) G = np.mat(img[:, :, 1]) B = np.mat(img[:, :, 2]) conv_B = convolve1(img[:, :, 0], kernel, filter_type, mode) conv_G = convolve1(img[:, :, 1], kernel, filter_type, mode) conv_R = convolve1(img[:, :, 2], kernel, filter_type, mode) conv_img = np.dstack([conv_B, conv_G, conv_R]) return conv_img ''' ############################################ 噪声函数脉冲噪声：add_PulseNoise(img, SNR) 椒盐噪声：add_Salt_PepperNoise(img, SNR) 高斯噪声：add_Gauss_Noise(img, mean, sigma) ############################################# ''' # 添加脉冲噪声 def add_PulseNoise(img, SNR): ''' 给图像添加脉冲噪声 :param img: 输入图像 :param SNR: 信噪比，决定添加多少噪声 :return: 添加噪声后的图像 ''' rows, cols, dims = img.shape # 创建与图像大小一样的矩阵 R =	np.mat(img[:, :, 0])	numpy.mat
#!/usr/bin/env python # -- coding: utf-8 -- # Evaluation config: line 489-497 for mitigated embedding, p822-824 for original embedding import json, codecs, time, re, os import gensim import logging import numpy as np import pandas as pd from gensim.models import word2vec, FastText from gensim.test.utils import datapath from sklearn.decomposition import PCA from sklearn import svm, metrics from sklearn.metrics import accuracy_score, roc_auc_score, precision_score, recall_score, confusion_matrix from collections import OrderedDict, defaultdict from copy import deepcopy # for visualizing import matplotlib.pyplot as plt from sklearn.manifold import TSNE import random import config, mitigating_stereotypes, base_words from config import SOURCE_DIR start_time = time.time() logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) # Basic constants DEFAULT_ARGUMENTS_W2V = dict(workers=4, sg=1, size=300, window=5, min_count=5, sample=10^-4, negative=5, seed=1, iter=2) DEFAULT_ARGUMENTS_FT = dict(*DEFAULT_ARGUMENTS_W2V, min_n=3, max_n=6) SVM_Cs = [10] ### UCI setting ### SMALL_UCI_NUM = 32561 def load_professions(fname): with codecs.open(fname, 'r', encoding='utf-8', errors='ignore') as f: professions = json.load(f) print('Loaded professions\n' + 'Format:\n' + 'word,\n' + 'definitional female -1.0 -> definitional male 1.0\n' + 'stereotypical female -1.0 -> stereotypical male 1.0') return professions sensitive_pair, neutral_word_list = config.load_analogy_pair(SOURCE_DIR + 'minority_groups.txt') def load_UCI(): X_train, y_train, X_test, y_test = [], [], [], [] with codecs.open(SOURCE_DIR + 'UCI_adult_dataset.txt', 'r', encoding='utf-8', errors='ignore') as f: for i, line in enumerate(re.split('[\r\n]+', f.read())): line = re.sub(r' ', '', line) tokens = re.split(r',', line) if len(tokens) == 15: X_train.append(tokens[:-1]) y_train.append(tokens[-1]) with codecs.open(SOURCE_DIR + 'UCI_adult_test.txt', 'r', encoding='utf-8', errors='ignore') as f: for i, line in enumerate(re.split('[\r\n]+', f.read())): if i == 0: continue line = re.sub(r' ', '', line) tokens = re.split(r',', line) if len(tokens) == 15: X_test.append(tokens[:-1]) y_test.append(tokens[-1]) print("### UCI train set statistics ###") UCI_stats_by_gender(X_train[:SMALL_UCI_NUM], y_train[:SMALL_UCI_NUM]) print("### UCI test set statistics ###") UCI_stats_by_gender(X_test, y_test) return (X_train, y_train), (X_test, y_test) def word2rep(_X_train, _y_train, model): X_train, y_train = [], [] avg_fnl_weight = np.array([int(e[2]) for e in _X_train]).mean() avg_hpw_weight = np.array([int(e[12]) for e in _X_train]).mean() for X, y in zip(_X_train, _y_train): tmp_X = np.array([]) for token in X: if not re.search(r'[a-zA-Z\-?]+', token): #tmp_X = np.append(tmp_X, np.array([float(token)/10000])) tmp_X = np.append(tmp_X, np.array([float(token)avg_hpw_weight/avg_fnl_weight])) tmp_X = np.append(tmp_X, np.zeros(np.shape(model.syn0[1])[0] - 1)) elif not config.CONSIDER_GENDER and (token == 'Male' or token == 'Female'): continue elif token in model.vocab: tmp_X = np.append(tmp_X, model[token]) # compound with '-': only select first vocab without oov for regulating sizes of all X elif re.search(r'-', token): add_tokens = re.split(r'-', token) i = 1 for add_token in add_tokens: if add_token in model.vocab: tmp_X = np.append(tmp_X, model[add_token]) i = 0 break else: continue if i: tmp_X = np.append(tmp_X, np.zeros(np.shape(model.syn0[1]), dtype=float)) else: tmp_X = np.append(tmp_X, np.zeros(np.shape(model.syn0[1]), dtype=float)) if np.shape(tmp_X)[0] > 0: X_train.append(tmp_X) if re.search(r'>', y): y_train.append(1) else: y_train.append(0) return np.array(X_train), np.array(y_train) def identify_index_by_gender(X, y): stats_dict = {} stats_dict['Male'] = [] stats_dict['Female'] = [] for i, (tokens, y) in enumerate(zip(X, y)): stats_dict[tokens[9]].append(i) return np.array(stats_dict['Male']), np.array(stats_dict['Female']) def identify_index_by_race(X, y): stats_dict = {} stats_dict['Amer-Indian-Eskimo'] = [] stats_dict['Asian-Pac-Islander'] = [] stats_dict['Black'] = [] stats_dict['White'] = [] stats_dict['Other'] = [] for i, (tokens, y) in enumerate(zip(X, y)): stats_dict[tokens[8]].append(i) return np.array(stats_dict['Amer-Indian-Eskimo']), np.array(stats_dict['Asian-Pac-Islander']), \ np.array(stats_dict['Black']), np.array(stats_dict['White']), np.array(stats_dict['Other']) def UCI_stats_by_gender(X, y): stats_dict = {} stats_dict['Male'] = [0, 0] stats_dict['Female'] = [0, 0] for tokens, label in zip(X, y): stats_dict[tokens[9]][1 if re.search(r'>', label) else 0] += 1 print("<=50K Male:Female = {:.3f} / {:.3f} ({} / {})".format(stats_dict['Male'][0] / (stats_dict['Male'][0] + stats_dict['Female'][0]), stats_dict['Female'][0] / (stats_dict['Male'][0] + stats_dict['Female'][0]), stats_dict['Male'][0], stats_dict['Female'][0])) print(" >50K Male:Female = {:.3f} / {:.3f} ({} / {})".format(stats_dict['Male'][1] / (stats_dict['Male'][1] + stats_dict['Female'][1]), stats_dict['Female'][1] / (stats_dict['Male'][1] + stats_dict['Female'][1]), stats_dict['Male'][1], stats_dict['Female'][1])) return 0 def print_result(y_test, pred, test_male_index, test_female_index): acc, auc, pre, rec = accuracy_score(y_test, pred), roc_auc_score(y_test, pred), \ precision_score(y_test, pred, average=None), recall_score(y_test, pred, average=None) cnf_matrix = confusion_matrix(y_test, pred) male_cnf_matrix = confusion_matrix(y_test[test_male_index], pred[test_male_index]) female_cnf_matrix = confusion_matrix(y_test[test_female_index], pred[test_female_index]) print(acc, auc, pre, rec) print("<=50K Male:Female = {:.3f} / {:.3f} ({} / {})".format(np.sum(male_cnf_matrix, axis=0)[0] / np.sum(cnf_matrix, axis=0)[0], np.sum(female_cnf_matrix, axis=0)[0] / np.sum(cnf_matrix, axis=0)[0], np.sum(male_cnf_matrix, axis=0)[0], np.sum(female_cnf_matrix, axis=0)[0])) print(" >50K Male:Female = {:.3f} / {:.3f} ({} / {})".format(np.sum(male_cnf_matrix, axis=0)[1] / np.sum(cnf_matrix, axis=0)[1], np.sum(female_cnf_matrix, axis=0)[1] / np.sum(cnf_matrix, axis=0)[1], np.sum(male_cnf_matrix, axis=0)[1], np.sum(female_cnf_matrix, axis=0)[1])) fpr, fnr = print_cnf_matrix(cnf_matrix) male_fpr, male_fnr = print_cnf_matrix(male_cnf_matrix) female_fpr, female_fnr = print_cnf_matrix(female_cnf_matrix) print("fpr_bias_ratio: {:.2f}, fnr_bias_ratio: {:.2f}".format(male_fpr / female_fpr, male_fnr / female_fnr)) print('-' * 30) return fpr, fnr def print_cnf_matrix(cnf_matrix, normalize=True): print(cnf_matrix) fpr, fnr = 0, 0 if normalize: cnf_matrix = cnf_matrix.astype('float') / cnf_matrix.sum(axis=1)[:, np.newaxis] print(cnf_matrix) fpr = cnf_matrix[0, 1] fnr = cnf_matrix[1, 0] return fpr, fnr # means predicted:X, target:y def find_optimal_cutoff(predicted, target): fpr, tpr, threshold = metrics.roc_curve(target, predicted) i = np.arange(len(tpr)) roc = pd.DataFrame({'tf' : pd.Series(tpr-(1-fpr), index=i), 'threshold': pd.Series(threshold, index=i)}) roc_t = roc.ix[(roc.tf-0).abs().argsort()[:1]] return list(roc_t['threshold']).pop() class W2vModel(object): def __init__(self, vocab_limit=None): """ :param is_selected_gender_vocab: 'True' means selected_gender_vocab is prepared. :param remove_oov: remove words not in w2v.model vocab. """ # embedding models self.w2v_fname = config.WORD_EMBEDDING_NAME self.w2v_model = self.load_w2v_model(self.w2v_fname, vocab_limit) if not vocab_limit: self.w2v_model.init_sims() # for using wv.syn0norm def load_w2v_model(self, fname, vocab_limit): try: try: print('Loading W2v Model... in {0:.2f} seconds'.format(time.time() - start_time)) w2v_model = word2vec.Word2Vec.load(fname) if vocab_limit: # it uses KeyedVector class (Word2vec.wv). Do not point wv. tmp_w2v = gensim.models.KeyedVectors(vector_size=300) tmp_w2v.index2word = w2v_model.wv.index2word[:vocab_limit] tmp_w2v.vocab = {w: w2v_model.wv.vocab[w] for w in tmp_w2v.index2word} # check if the order of keyedvector is broken for i, w in enumerate(tmp_w2v.index2word): if tmp_w2v.vocab[w].index != i: print(w, tmp_w2v.vocab[w].index, i) tmp_w2v.syn0 = w2v_model.wv.syn0[:vocab_limit, :] w2v_model.wv.vocab = {} w2v_model.wv.index2word = [] w2v_model.wv.syn0 = np.zeros((10, 300)) print(tmp_w2v) return tmp_w2v print(w2v_model) except Exception as e: w2v_model = word2vec.Word2VecKeyedVectors.load_word2vec_format(fname, binary=False) if vocab_limit: # it uses KeyedVector class (Word2vec.wv). Do not point wv. tmp_w2v = gensim.models.KeyedVectors(vector_size=300) tmp_w2v.index2word = w2v_model.index2word[:vocab_limit] tmp_w2v.vocab = {w: w2v_model.vocab[w] for w in tmp_w2v.index2word} # check if the order of keyedvector is broken for i, w in enumerate(tmp_w2v.index2word): if tmp_w2v.vocab[w].index != i: print(w, tmp_w2v.vocab[w].index, i) tmp_w2v.syn0 = w2v_model.syn0[:vocab_limit, :] w2v_model.vocab = {} w2v_model.index2word = [] w2v_model.syn0 = np.zeros((10, 300)) print(tmp_w2v) print('Success to load W2v Model... in {0:.2f} seconds'.format(time.time() - start_time)) return tmp_w2v print(w2v_model) print('Success to load W2v Model... in {0:.2f} seconds'.format(time.time() - start_time)) return w2v_model except Exception as e: print('No existed model. Training W2v Model... in {0:.2f} seconds'.format(time.time() - start_time)) texts = '' if config.MODEL_NAME == 'wiki': texts = config.WikiCorpus() elif config.MODEL_NAME == 'reddit': texts = config.RedditCorpus() else: print("please select corpus for training model.") exit(1) print('training w2v with {} corpus ... in {:.2f} seconds'.format(config.MODEL_NAME, config.whattime())) w2v_model = word2vec.Word2Vec(texts, DEFAULT_ARGUMENTS_W2V) # init_sims: reduce memory but cannot continue training (because original vectors are removed.) w2v_model.init_sims(replace=True) #w2v_model.save(fname) # save model self.w2v_model.save_word2vec_format(fname, binary=False) print('Success to load W2v Model... in {0:.2f} seconds'.format(time.time() - start_time)) return w2v_model.wv def test_intrinsic(self): try: self.w2v_model.wv.accuracy(SOURCE_DIR+'questions-words.txt', restrict_vocab=300000) """ analogy_score, result_list = self.w2v_model.wv.evaluate_word_analogies(datapath('questions-words.txt')) print("score: {:.2f}".format(analogy_score)) for result_dict in result_list: print("{}: True {} / False {}".format(result_dict['section'], result_dict['correct'][:3], result_dict['incorrect'][:3])) """ except Exception as e: self.w2v_model.accuracy(SOURCE_DIR + 'questions-words.txt', restrict_vocab=300000) try: similarities = self.w2v_model.wv.evaluate_word_pairs(datapath('wordsim353.tsv'), restrict_vocab=300000) except Exception as e: similarities = self.w2v_model.evaluate_word_pairs(datapath('wordsim353.tsv'), restrict_vocab=300000) def test_UCI(self, uci_dataset, small_train=True): (_X_train, _y_train), (_X_test, _y_test) = uci_dataset test_male_index, test_female_index = identify_index_by_gender(_X_test, _y_test) # test_amer_index, test_asian_index, test_black_index, test_white_index, test_other_index = identify_index_by_race(_X_test, _y_test) (X_train, y_train), (X_test, y_test) = word2rep(_X_train, _y_train, self.w2v_model), word2rep(_X_test, _y_test, self.w2v_model) assert len(X_train) == len(y_train) assert len(X_test) == len(y_test) print("num of tests / num of labels: {} {} / {} {} in {:.2f} sec".format( len(X_train), len(X_test), len(set(y_train)), len(set(y_test)), time.time() - start_time)) for c in SVM_Cs: clf = svm.SVC(C=c) if small_train: clf.fit(X_train[:SMALL_UCI_NUM], y_train[:SMALL_UCI_NUM]) else: clf.fit(X_train, y_train) pred = clf.predict(X_test) if not os.path.exists(SOURCE_DIR + 'pred_UCI'): os.makedirs(SOURCE_DIR + 'pred_UCI') with codecs.open(SOURCE_DIR + 'pred_UCI/w2v_' + config.MODEL_NAME + str(c) + '_pred.txt', 'w', encoding='utf-8', errors='ignore') as f: for tokens, label in zip(_X_test, pred): f.write('\t'.join(tokens) + '\t' + str(label) + '\n') print_result(y_test, pred, test_male_index, test_female_index) return 0 def test_analogy(self): for w1, w2 in sensitive_pair: for word in neutral_word_list: try: print('{}:{} = {}:{}'.format( w1, w2, word, self.w2v_model.most_similar(positive=[w2, word], negative=[w1], topn=10))) except Exception as e: continue def save(self, fname): self.w2v_model.save_word2vec_format(fname, binary=False) def save_vocab(self): """ Setting 4: remove noun particle / foreign words / digit and gender_specific suffix / prefix. After that, only remain the data between upper and lower cut off based on frequency. :return: """ with codecs.open(SOURCE_DIR + '{}_vocabs.txt'.format(config.MODEL_NAME), "w", encoding='utf-8', errors='ignore') as write_file: tmp_vocab = OrderedDict() for word, vocab_obj in sorted(self.w2v_model.wv.vocab.items(), key=lambda item: -item[1].count): if re.search(r'^[a-zA-Z][a-zA-Z0-9]{0,}$', word): tmp_vocab[word] = vocab_obj write_file.write('{0}\t{1}\n'.format(word, vocab_obj.count)) print("Success to save wiki vocabulary.") self.w2v_vocab = tmp_vocab def get_keyedvectors(self): return self.w2v_model class FtModel(object): def __init__(self): """ :param is_selected_gender_vocab: 'True' means selected_gender_vocab is prepared. :param remove_oov: remove words not in w2v.model vocab. """ # embedding models self.ft_fname = config.MODEL_DIR + 'ft_{0}_sg_300_neg5_it2.model'.format(config.MODEL_NAME) self.ft_model = self.load_ft_model(self.ft_fname) def load_ft_model(self, fname): """ class FastText(sentences=None, sg=0, hs=0, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, word_ngrams=1, sample=0.001, seed=1, workers=3, min_alpha=0.0001, negative=5, cbow_mean=1, hashfxn=hash, iter=5, null_word=0, min_n=3, max_n=6, sorted_vocab=1, bucket=2000000, trim_rule=None, batch_words=MAX_WORDS_IN_BATCH) min_n : int Min length of char ngrams to be used for training word representations. max_n : int Max length of char ngrams to be used for training word representations. Set max_n to be lesser than min_n to avoid char ngrams being used. word_ngrams : int {1,0} If 1, uses enriches word vectors with subword(ngrams) information. If 0, this is equivalent to word2vec. bucket : int Character ngrams are hashed into a fixed number of buckets, in order to limit the memory usage of the model. This option specifies the number of buckets used by the model. """ print('Loading Fasttext Model... in {0:.2f} seconds'.format(time.time() - start_time)) try: fasttext_model = FastText.load(fname) print(fasttext_model) except IOError: print('No existed model. Training Ft Model... in {0:.2f} seconds'.format(time.time() - start_time)) texts = config.WikiCorpus() fasttext_model = FastText(texts, DEFAULT_ARGUMENTS_FT) fasttext_model.save(fname) print('Success to load Fasttext Model... in {0:.2f} seconds'.format(time.time() - start_time)) return fasttext_model def test(self): self.ft_model.wv.accuracy(SOURCE_DIR + 'questions-words.txt') similarities = self.ft_model.wv.evaluate_word_pairs(datapath('wordsim353.tsv')) # print(similarities) class MyModel(object): def __init__(self, threshold=None, space_order=[1, 1]): """ :param is_selected_gender_vocab: 'True' means selected_gender_vocab is prepared. :param remove_oov: remove words not in w2v.model vocab. """ # embedding models self.my_fname = config.MITIGATED_EMBEDDING_NAME self.my_model = self.load_w2v_model(self.my_fname) self.init_modulate = np.shape(self.my_model.syn0)[1] self._modulate_vector_linalg(dim=1, dim2=1) self.threshold = threshold self.space_order = space_order self.modulated_number = 0 def load_w2v_model(self, fname, arranged_savfile=True): try: print('Loading My Model... in {0:.2f} seconds'.format(time.time() - start_time)) if not arranged_savfile: w2v_model = gensim.models.KeyedVectors.load(fname) wi = {w: i for i, w in enumerate(w2v_model.index2word)} w2v_model.vocab = {word: config.Vocab(count=count, index=wi[word]) for word, count in w2v_model.vocab.items()} w2v_model.save_word2vec_format(fname, binary=False) my_model = word2vec.Word2VecKeyedVectors.load_word2vec_format(fname, binary=False) #my_model = word2vec.Word2Vec.load(fname + 'w2vf') print(my_model) except IOError: print('No existed model. Training My Model... in {0:.2f} seconds'.format(time.time() - start_time)) print("constructing") exit() print('Success to load My Model... in {0:.2f} seconds'.format(time.time() - start_time)) return my_model def _modulate_vector_linalg(self, dim=1, dim2=1): self.my_model.syn0[:, :dim + dim2] = self.my_model.syn0[:, :dim + dim2] / self.init_modulate def modulate_sentiment(self, dim=1, dim2=1, intensity=1): assert len(self.space_order) < 3, "please set space_order with type 'list' (e.g. [1, 1])." if self.threshold and self.space_order[1] == 1: # modulate sentiment only for entity words self.my_model.syn0[:, :dim] = np.multiply(self.my_model.syn0[:, :dim], np.where(self.my_model.syn0[:, dim:dim + dim2] >= (self.threshold / self.init_modulate), intensity, 1)) elif self.threshold and self.space_order[1] == -1: # modulate sentiment only for entity words self.my_model.syn0[:, :dim] = np.multiply(self.my_model.syn0[:, :dim], np.where(self.my_model.syn0[:, dim:dim + dim2] <= -(self.threshold / self.init_modulate), intensity, 1)) else: # modulate sentiment for entire words self.my_model.syn0[:, :dim] = self.my_model.syn0[:, :dim] * intensity self.my_model.syn0norm = (self.my_model.syn0 / np.sqrt((self.my_model.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) self.modulated_number += intensity1 # self.my_model.init_sims(replace=True) # it makes syn0 and vectors to be also normalized (same as syn0norm and vectors_norm) def modulate_all(self, dim=1, dim2=1, intensity=1): if intensity < 1: assert len(self.space_order) < 3, "please set space_order with type 'list' (e.g. [1, 1])." self.my_model.syn0[:, :dim+dim2] = self.my_model.syn0[:, :dim+dim2] intensity # self.my_model.init_sims(replace=True) # it makes syn0 and vectors to be also normalized (same as syn0norm and vectors_norm) self.my_model.syn0norm = ( self.my_model.syn0 / np.sqrt((self.my_model.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) def test(self, uci_dataset, intensity_order=1): for i, intensity in enumerate([1, 10]): #, 10, 10]): if i == 0 and intensity_order < 0: continue print("Model with intensity 10^{}, threshold {}".format(iintensity_order, self.threshold)) self.modulate_sentiment(intensity=intensityintensity_order) #self.test_analogy() self.test_UCI(uci_dataset) self.test_intrinsic() #self.show_vocab_tsnescatterplot() #self.show_topn_embedding() print("Model with intensity 0, threshold {}".format(self.threshold)) self.modulate_sentiment(intensity=0) #self.test_analogy() self.test_UCI(uci_dataset) self.test_intrinsic() def test_intrinsic(self): self.my_model.accuracy(SOURCE_DIR + 'questions-words.txt', restrict_vocab=300000) similarities = self.my_model.evaluate_word_pairs(datapath('wordsim353.tsv'), restrict_vocab=300000) print(similarities) def test_analogy(self): for w1, w2 in sensitive_pair: for word in neutral_word_list: try: print('{}:{} = {}:{}'.format( w1, w2, word, self.my_model.most_similar(positive=[w2, word], negative=[w1], topn=10))) except Exception as e: continue def test_UCI(self, uci_dataset, small_train=True): (_X_train, _y_train), (_X_test, _y_test) = uci_dataset test_male_index, test_female_index = identify_index_by_gender(_X_test, _y_test) (X_train, y_train), (X_test, y_test) = word2rep(_X_train, _y_train, self.my_model), word2rep(_X_test, _y_test, self.my_model) assert len(X_train) == len(y_train) assert len(X_test) == len(y_test) print("num of tests / num of labels: {} {} / {} {} in {:.2f} sec".format( len(X_train), len(X_test), len(set(y_train)), len(set(y_test)), time.time() - start_time)) for c in SVM_Cs: clf = svm.SVC(C=c) if small_train: clf.fit(X_train[:SMALL_UCI_NUM], y_train[:SMALL_UCI_NUM]) else: clf.fit(X_train, y_train) pred = clf.predict(X_test) with codecs.open(SOURCE_DIR + 'pred_UCI\\my' + str(self.modulated_number) + '_' + config.MODEL_NAME + str(c) + '_pred.txt', 'w', encoding='utf-8', errors='ignore') as f: for tokens, label in zip(_X_test, pred): f.write('\t'.join(tokens) + '\t' + str(label) + '\n') print_result(y_test, pred, test_male_index, test_female_index) return 0 def show_topn_affect(self, dim=1, dim2=1, topn=50): sort_index_sum = np.ndarray.flatten(self.my_model.vectors[:, :dim]).argsort() sort_index = np.prod(self.my_model.vectors[:, :dim+dim2], axis=1).argsort() cond = np.ndarray.flatten(self.my_model.vectors[sort_index, dim:dim+dim2]) >= ( self.threshold / self.init_modulate) if self.space_order[1] == 1 else \ np.ndarray.flatten(self.my_model.vectors[sort_index, dim:dim+dim2]) <= -( self.threshold / self.init_modulate) print("< top {} positive stereotypes >".format(topn)) if self.space_order[0] self.space_order[1] == 1: for index in sort_index[cond][:-1-topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) else: for index in sort_index[cond][:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) print("< top {} negative stereotypes >".format(topn)) if self.space_order[0] * self.space_order[1] == 1: for index in sort_index[cond][:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) else: for index in sort_index[cond][:-1-topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) def show_vocab_tsnescatterplot(self, dim=1, dim2=1, shown_word=60, top=False): sort_index = np.prod(self.my_model.vectors[:, :dim + dim2], axis=1).argsort() cond = np.ndarray.flatten(self.my_model.vectors[sort_index, dim:dim + dim2]) >= ( self.threshold / self.init_modulate) if self.space_order[1] == 1 else \ np.ndarray.flatten(self.my_model.vectors[sort_index, dim:dim + dim2]) <= -( self.threshold / self.init_modulate) # get random words # close_words = model.similar_by_word(word) if top: entity_words = list(sort_index[cond][::self.space_order[1]])[:int(shown_word / 2)] notity_words = list(sort_index[np.logical_not(cond)][::-self.space_order[1]])[:int(shown_word / 2)] else: entity_words = random.sample(list(sort_index[cond]), int(shown_word / 2)) notity_words = random.sample(list(sort_index[np.logical_not(cond)]), int(shown_word / 2)) # add the vector for each of the closest words to the array arr, word_labels = np.empty((0, 300), dtype='f'), [] for index in entity_words + notity_words: wrd_vector = self.my_model.syn0norm[index] word_labels.append(self.my_model.index2word[index]) arr = np.append(arr, np.array([wrd_vector]), axis=0) # find tsne coords for 1 dimensions tsne = TSNE(n_components=1, random_state=0) np.set_printoptions(suppress=True) x_coords = arr[:, 1] y_coords = arr[:, 0] # display scatter plot plt.scatter(x_coords, y_coords) for label, x, y in zip(word_labels, x_coords, y_coords): plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points') plt.xlim(x_coords.min() + 0.05, x_coords.max() + 0.05) plt.ylim(y_coords.min() + 0.05, y_coords.max() + 0.05) plt.show() def show_topn_embedding(self, dim=1, dim2=1, topn=30): sort_index_sent = np.sum(self.my_model.vectors[:, :dim], axis=1).argsort() if self.space_order[0] == -1: print("< top {} positive stereotypes >".format(topn)) for index in sort_index_sent[:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) print("< top {} negative stereotypes >".format(topn)) for index in sort_index_sent[:-1-topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) else: print("< top {} positive stereotypes >".format(topn)) for index in sort_index_sent[:-1-topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) print("< top {} negative stereotypes >".format(topn)) for index in sort_index_sent[:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) sort_index_sent = np.sum(self.my_model.vectors[:, dim:dim+dim2], axis=1).argsort() if self.space_order[1] == -1: print("< top {} entity stereotypes >".format(topn)) for index in sort_index_sent[:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) print("< top {} notity stereotypes >".format(topn)) for index in sort_index_sent[:-1-topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) else: print("< top {} entity stereotypes >".format(topn)) for index in sort_index_sent[:-1 - topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim + dim2]) print("< top {} notity stereotypes >".format(topn)) for index in sort_index_sent[:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim + dim2]) class DebiasModel(object): def __init__(self, bias_model, same_env=True): """ :param is_selected_gender_vocab: 'True' means selected_gender_vocab is prepared. :param remove_oov: remove words not in w2v.model vocab. """ # embedding models print("same_env: {}".format(same_env)) if same_env: self.model = self.debias_we_same_env(bias_model) else: self.model = self.debias_we(bias_model) def debias_we(self, E): print('Loading Debias Model... in {0:.2f} seconds'.format(time.time() - start_time)) with open(SOURCE_DIR + 'definitional_pairs.json', "r") as f: definitional = json.load(f) with open(SOURCE_DIR + 'equalize_pairs.json', "r") as f: equalize = json.load(f) with open(SOURCE_DIR + 'gender_specific_seed.json', "r") as f: gender_specific_words = json.load(f) tmp_w2v = gensim.models.KeyedVectors(vector_size=300) tmp_w2v.index2word = E.index2word tmp_w2v.vocab = E.vocab tmp_w2v.syn0 = E.syn0 tmp_w2v.syn0norm = (E.syn0 / np.sqrt((E.syn0 2).sum(-1))[..., np.newaxis]).astype(float) gender_direction = self.doPCA(definitional, tmp_w2v).components_[0] specific_set = set(gender_specific_words) for i, w in enumerate(tmp_w2v.index2word): if w not in specific_set: tmp_w2v.syn0[i] = self.drop(tmp_w2v.syn0[i], gender_direction) tmp_w2v.syn0norm = (tmp_w2v.syn0 / np.sqrt((tmp_w2v.syn0 2).sum(-1))[..., np.newaxis]).astype(float) candidates = {x for e1, e2 in equalize for x in [(e1.lower(), e2.lower()), (e1.title(), e2.title()), (e1.upper(), e2.upper())]} print(candidates) for (a, b) in candidates: if (a in tmp_w2v.index2word and b in tmp_w2v.index2word): y = self.drop((tmp_w2v[a] + tmp_w2v[b]) / 2, gender_direction) z = np.sqrt(1 - np.linalg.norm(y) ** 2) if (tmp_w2v[a] - tmp_w2v[b]).dot(gender_direction) < 0: z = -z tmp_w2v.syn0[tmp_w2v.vocab[a].index] = z * gender_direction + y tmp_w2v.syn0[tmp_w2v.vocab[b].index] = -z * gender_direction + y tmp_w2v.syn0norm = (tmp_w2v.syn0 / np.sqrt((tmp_w2v.syn0 2).sum(-1))[..., np.newaxis]).astype(float) print('Success to load Debias Model... in {0:.2f} seconds'.format(time.time() - start_time)) return tmp_w2v def debias_we_same_env(self, E, random_sent_pair=False): print('Loading Debias (same env.) Model... in {0:.2f} seconds'.format(time.time() - start_time)) print('example: {} \n {}'.format(np.array(E['Male']), np.array(E['Female']))) lexicon = config.load_sent_lexicon() lexicon2, lexicon2_vocab = config.load_entity_lexicon() num = int(config.BASE_WORD_NUM) if random_sent_pair: positive_seeds, negative_seeds = mitigating_stereotypes.generate_random_seeds(lexicon, num=num) else: positive_seeds, negative_seeds = base_words.sent_seeds(10) print(positive_seeds, negative_seeds) entity_seeds, notity_seeds = mitigating_stereotypes.generate_random_seeds(lexicon2, num=num) definitional = zip(positive_seeds, negative_seeds) random_pos_seeds, random_neg_seeds = mitigating_stereotypes.generate_random_seeds(lexicon, num=num) equalize = zip(random_pos_seeds, random_neg_seeds) #notity_specific_words = notity_seeds notity_specific_words = [item[0] for item in lexicon2.items() if item[1] == -1] tmp_w2v = gensim.models.KeyedVectors(vector_size=300) tmp_w2v.index2word = E.index2word tmp_w2v.vocab = E.vocab tmp_w2v.syn0 = E.syn0 tmp_w2v.syn0norm = (E.syn0 / np.sqrt((E.syn0 2).sum(-1))[..., np.newaxis]).astype(float) gender_direction = self.doPCA(definitional, tmp_w2v).components_[0] specific_set = set(notity_specific_words) for i, w in enumerate(tmp_w2v.index2word): if w not in specific_set: tmp_w2v.syn0[i] = self.drop(tmp_w2v.syn0[i], gender_direction) tmp_w2v.syn0norm = (tmp_w2v.syn0 / np.sqrt((tmp_w2v.syn0 2).sum(-1))[..., np.newaxis]).astype(float) candidates = {x for e1, e2 in equalize for x in [(e1.lower(), e2.lower()), (e1.title(), e2.title()), (e1.upper(), e2.upper())]} print(candidates) for (a, b) in candidates: if (a in tmp_w2v.index2word and b in tmp_w2v.index2word): y = self.drop((tmp_w2v[a] + tmp_w2v[b]) / 2, gender_direction) z = np.sqrt(1 - np.linalg.norm(y) 2) if (tmp_w2v[a] - tmp_w2v[b]).dot(gender_direction) < 0: z = -z tmp_w2v.syn0[tmp_w2v.vocab[a].index] = z * gender_direction + y tmp_w2v.syn0[tmp_w2v.vocab[b].index] = -z * gender_direction + y tmp_w2v.syn0norm = (tmp_w2v.syn0 / np.sqrt((tmp_w2v.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) print('Success to load Debias (same env.) Model... in {0:.2f} seconds'.format(time.time() - start_time)) print('example: {} \n {}'.format(np.array(E['Male']),	np.array(E['Female'])	numpy.array
import numpy as np import pandas as pd import seaborn as sns import pickle import warnings from tqdm import tqdm import os from collections import defaultdict import matplotlib.pyplot as plt from sklearn.linear_model import LogisticRegression pd.set_option('display.max_columns', 500) warnings.filterwarnings('ignore') def sigmoid(x): return 1 / (1 + np.exp(-x)) def cal_accuracy(y_pred, y_true): y_pred = np.where(y_pred >= 0.5, 1, 0) return (y_pred == y_true).mean() def l2_logistic_regression(x, y, lr, lbd, dev_x, dev_y): w = np.zeros(x.shape[1]) epsilon = 1e-8 min_loss = float('inf') best_train_acc = 0 best_dev_acc = 0 train_acc_history = [] dev_acc_history = [] for i in tqdm(range(10000)): gradient = ((x.multiply(y - sigmoid(x.dot(w)), axis=0)).mean()) w = w + lr * gradient w[1:] = w[1:] - lr * lbd * w[1:] train_acc = cal_accuracy(sigmoid(x.dot(w)), y) dev_acc = cal_accuracy(sigmoid(dev_x.dot(w)), dev_y) train_acc_history.append(train_acc) dev_acc_history.append(dev_acc) best_train_acc = max(best_train_acc, train_acc) best_dev_acc = max(best_dev_acc, dev_acc) loss = ((-1 * y * np.log(sigmoid(x.dot(w)))) - ((np.ones(x.shape[0]) - y) * np.log( np.ones(x.shape[0]) - sigmoid(x.dot(w))))).mean() + lbd * np.sum(np.power(w[1:], 2)) # print("iter={}, loss={}, train_acc={}, dev_acc={}".format(i + 1, loss, train_acc, dev_acc)) min_loss = min(min_loss, loss) if np.linalg.norm(gradient) <= epsilon: # print("lr={}, min_loss={}".format(lr, min_loss)) break loss = ((-1 * y * np.log(sigmoid(x.dot(w)))) - ((np.ones(x.shape[0]) - y) * np.log( np.ones(x.shape[0]) - sigmoid(x.dot(w))))).mean() + lbd * np.sum(np.power(w[1:], 2)) train_acc = cal_accuracy(sigmoid(x.dot(w)), y) dev_acc = cal_accuracy(sigmoid(dev_x.dot(w)), dev_y) print("lr={}, lambda={}, min_loss={}, best_train_acc={}, best_dev_acc={}, loss={}, train_acc={}, dev_acc={}".format( lr, lbd, min_loss, best_train_acc, best_dev_acc, loss, train_acc, dev_acc)) return w, train_acc_history, dev_acc_history def l1_logistic_regression(x, y, lr, lbd, dev_x, dev_y): w = np.zeros(x.shape[1]) epsilon = 1e-8 min_loss = float('inf') best_train_acc = 0 best_dev_acc = 0 train_acc_history = [] dev_acc_history = [] for i in tqdm(range(10000)): gradient = ((x.multiply(y - sigmoid(x.dot(w)), axis=0)).mean()) w = w + lr * gradient w[1:] = np.sign(w[1:]) * np.maximum(np.abs(w[1:]) - (lr * lbd), np.zeros(w[1:].shape)) train_acc = cal_accuracy(sigmoid(x.dot(w)), y) dev_acc = cal_accuracy(sigmoid(dev_x.dot(w)), dev_y) train_acc_history.append(train_acc) dev_acc_history.append(dev_acc) best_train_acc = max(best_train_acc, train_acc) best_dev_acc = max(best_dev_acc, dev_acc) loss = ((-1 * y * np.log(sigmoid(x.dot(w)))) - ((np.ones(x.shape[0]) - y) * np.log( np.ones(x.shape[0]) - sigmoid(x.dot(w))))).mean() + lbd * np.sum(np.abs(w[1:])) # print("iter={}, loss={}, train_acc={}, dev_acc={}".format(i + 1, loss, train_acc, dev_acc)) min_loss = min(min_loss, loss) if np.linalg.norm(gradient) <= epsilon: # print("lr={}, min_loss={}".format(lr, min_loss)) break loss = ((-1 * y * np.log(sigmoid(x.dot(w)))) - ((	np.ones(x.shape[0])	numpy.ones
import os import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np from common.utils import compute_std_of_mean SAVE_ROOT = '../../figs_sigcomm22' plt.style.use('seaborn-deep') plt.rcParams['font.family'] = 'Arial' # plt.rcParams['font.size'] = 42 # plt.rcParams['axes.labelsize'] = 42 # plt.rcParams['legend.fontsize'] = 42 # plt.rcParams['figure.figsize'] = (11, 9) plt.rcParams['svg.fonttype'] = 'none' HATCHES = ['/', '\\', 'x', 'o', '.', 'O', '-', '*', '+'] WIDTH = 0.3 bbr_reward, bbr_tput, bbr_tail_lat, bbr_loss = 192.81, 32.94, 368.93, 0.03 copa_reward, copa_tput, copa_tail_lat, copa_loss = 183.89, 25.70, 265.02, 0.01 cubic_reward, cubic_tput, cubic_tail_lat, cubic_loss = -19.16, 33.99, 802.69, 0.02 vivace_reward, vivace_tput, vivace_tail_lat, vivace_loss = -547.01, 21.71, 947.25, 0.13 vivace_latency_reward, vivace_latency_tput, vivace_latency_tail_lat, vivace_latency_loss = -548.64, 21.84, 1010.43, 0.13 vivace_loss_reward, vivace_loss_tput, vivace_loss_tail_lat, vivace_loss_loss = -825.15, 28.89, 1125.94, 0.26 genet_reward = 223.88 genet_reward_err = 8.05 genet_tput, genet_tail_lat, genet_loss = 31.77, 183.75, 0.02 udr1_reward = 136.81 udr1_reward_err = 23.61 udr1_tput, udr1_tail_lat, udr1_loss = 23.16, 204.23, 0.03 udr2_reward = 158.48 udr2_reward_err = 17.71 udr2_tput, udr2_tail_lat, udr2_loss = 23.09, 185.58, 0.02 udr3_reward = 159.34 udr3_reward_err = 22.83 udr3_tput, udr3_tail_lat, udr3_loss = 22.72, 179.06, 0.02 real_reward = 191.61 real_reward_err = 3.88 # 26.39 250.47 0.02 cl1_reward = 143.86 cl1_reward_err = 7.64 # 22.53 206.07 0.02 cl2_reward = 177.97 cl2_reward_err = 4.55 # 23.17 204.86 0.01 udr3_real_5percent_ethernet_rewards = [177.2, 209.8, 95.2] udr3_real_10percent_ethernet_rewards = [139, 175, 173] udr3_real_20percent_ethernet_rewards = [133, 125, 151] udr3_real_50percent_ethernet_rewards = [162, 124, 78] column_wid = 0.7 capsize_wid = 8 eline_wid = 2 def generalization_test_ethernet(): plt.rcParams['font.size'] = 36 plt.rcParams['axes.labelsize'] = 36 plt.rcParams['axes.titlesize'] = 36 plt.rcParams['legend.fontsize'] = 36 fig, ax = plt.subplots(figsize=(9, 5)) # plt.bar([1, 2], [bbr_reward, cubic_reward], hatch=HATCHES[:2]) bars = ax.bar([1, 2, 3, 4], [udr1_reward, udr2_reward, udr3_reward, real_reward], yerr=[udr1_reward_err, udr2_reward_err, udr3_reward_err, real_reward_err], color='C0', width=column_wid, error_kw=dict( lw=eline_wid, capsize=capsize_wid)) # bars = ax.bar([1, 2, 3, 4], # [udr1_reward, udr2_reward, udr3_reward, real_reward], # color=None, edgecolor='white') for bar, pat in zip(bars, HATCHES): bar.set_hatch(pat) ax.bar([5], [genet_reward], yerr=[genet_reward_err], capsize=8, width=column_wid, color='C2', error_kw=dict( lw=eline_wid, capsize=capsize_wid)) # plt.title('Ethernet') ax.set_xticks([1, 2, 3, 4, 5]) ax.set_xticklabels(['RL1', 'RL2', 'RL3', 'RL-real', 'Genet'], rotation=20) ax.set_ylabel('Test reward') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.tick_params( axis='x', # changes apply to the x-axis which='both', # both major and minor ticks are affected bottom=False, # ticks along the bottom edge are off top=False) # ticks along the top edge are off # plt.tight_layout() svg_file = os.path.join(SAVE_ROOT, 'evaluation_generalization_test_ethernet.svg') pdf_file = os.path.join(SAVE_ROOT, 'evaluation_generalization_test_ethernet.pdf') fig.savefig(svg_file, bbox_inches='tight') os.system("inkscape {} --export-pdf={}".format(svg_file, pdf_file)) os.system("pdfcrop --margins 1 {} {}".format(pdf_file, pdf_file)) def asymptotic_real(): plt.rcParams['font.size'] = 34 plt.rcParams['axes.labelsize'] = 34 plt.rcParams['axes.titlesize'] = 34 plt.rcParams['legend.fontsize'] = 34 fig, ax = plt.subplots(figsize=(9.5, 5)) bbr_reward = 192.81 #32.94 368.93 0.03 udr_real_synthetic_reward = 171.16 # 23.67 194.00 0.02 udr_real_synthetic_reward_err = 24.22 genet_real_synthetic_reward = 239.39 # 30.93 208.04 0.02 genet_real_synthetic_reward_err = 7.34 cubic_reward = 97.16 # 33.99 802.69 0.02 # plt.bar([1, 2], [bbr_reward, cubic_reward]) # plt.bar([3.5, 4.5,], ax.bar([1, 2.2, 3.4, 4.6, 5.8], [udr_real_synthetic_reward, # 1% np.mean(udr3_real_10percent_ethernet_rewards),	np.mean(udr3_real_20percent_ethernet_rewards)	numpy.mean
# -- coding: utf-8 - """ --------------------- SOFTWARE DESCRIPTION: --------------------- Written October 2018 -- <NAME> Typeset in Python 3 This python class is specifically made for the spectroscopic data reduction of the Shelyak eShel spectrograph which is installed at the Hertzsprung SONG node telescope at Tenrife, Spain. The software is originally built from structures of the 'SONGWriter' which is SONG's spectroscopic data reduction pipeline, and by others is inspired by the data reduction pipeline 'FIESTools' of the FIES spectrograph at the NOT on La Palma. """ # Numpy: import numpy as np # Astropy: from astropy.io import fits from astropy.time import Time from astropy.coordinates import SkyCoord, EarthLocation # PyAstronomy: import PyAstronomy as pyas # SciPy: import scipy import scipy.constants import scipy.io import scipy.ndimage from scipy.ndimage import median_filter # Matplotlib: import matplotlib.pyplot as plt from matplotlib import gridspec from tikzplotlib import save as tikz_save # Others: import math, sys, time, glob, pylab, heapq import bottleneck from skimage import feature as skfeature # Error of propagation (nominal_value, std_dev): import uncertainties.unumpy as up from uncertainties import ufloat def val(x): return up.nominal_values(x) def err(x): return up.std_devs(x) # Own functions: import Plot_Tools as pt # Global settings for out-print to terminal (allow more digits and nice coloum ordering): np.set_printoptions(suppress=True, formatter={'float_kind':'{:7.5f}'.format}, linewidth=100) ############################################################################################################ # DEFINE CLASS # ############################################################################################################ class BlueSONG(object): # INITILIZE THE CLASS: def __init__(self, path, img_name): #------------------------------- # DEFINE GLOBAL VARIABLES (DGV): #------------------------------- # USER DEFINED VARIABLES: self.img_name = img_name # Name of image files self.path = path # Directory path to data self.path_img = '/home/nicholas/Dropbox/thesis/latex/pictures/' self.path_blues = '/home/nicholas/Dropbox/Software/Python/blues/' self.cross_cut = [50, 500] # Cut of spectral region in cross dispersion self.orders = [1, 2] self.n_orders = len(self.orders) # File handling: self.img_files = np.sort(glob.glob('{}{}'.format(self.path, self.img_name))) self.hdul = np.array([fits.open(str(files)) for files in self.img_files]) # Extract headers and sepearte files: self.img_type = [self.hdul[i][0].header['IMAGETYP'] for i in range(len(self.img_files))] self.BF_dex = np.where(np.array(self.img_type)=='bias')[0] self.DF_dex = np.where(np.array(self.img_type)=='dark')[0] self.FF_dex = np.where(np.array(self.img_type)=='flat')[0] self.TA_dex = np.where(np.array(self.img_type)=='thar')[0] self.SF_dex = np.where(np.array(self.img_type)=='star')[0] # Open header of object: header = self.hdul[self.SF_dex[0]][0].header # Observation information: self.datetime = header['DATE-OBS'] # Date and time of observation (string) self.date = self.datetime[:10] # Date of observation (string) self.jdate = header['JD-DATE'] # Julian date (float) self.altitude = header['OBJ-ALT'] # [deg] Initial altitude of target during obs (float) self.seeing = header['SEEING2'] # [arcsec] Running mean seeing on slit guiders (float) # Target information: self.target = header['OBJECT'] # Name of target (string) self.ra = header['OBJ-RA'] # Object Right Accension (string) self.dec = header['OBJ-DEC'] # Object Declination (string) self.magnitude = header['OBJ-MAG'] # Magnitude of object (float) # Dimension constants and arrays: self.len_disp = header['NAXIS1'] # [pixel] Height of image (int) self.len_cross = header['NAXIS2'] # [pixel] Width of image (int) self.cen_disp = int(self.len_disp/2) # [pixel] Center position of disp (int) self.cen_cross = int(self.len_cross/2) # [pixel] Center position of cross (int) self.disp = np.arange(self.len_disp) # [pixel] Integers spanning disp (array) # eShel and CCD setup constants: self.res_power = 10000 # Resolving power self.gain = 0.27 # [e-/ADU] Gain at -10 degC self.ron = 20 # [e-] Read-out-noise self.pixel_size= header['PIXSIZE1'] # [micro m] # HK survey constants: self.V = 3901.000 # [Å] V quasi-continuum center self.K = 3933.664 # [Å] Ca II K line self.H = 3968.470 # [Å] Ca II H line self.R = 4001.000 # [Å] R quasi-continuum center self.bands = [self.V, self.K, self.H, self.R] self.VR_bandpass = 20.0 # [Å] Bandpass of V and R continuums self.HK_bandpass = 1.09 # [Å] Bandpass of K and H lines ###################################################################################################### # CALIBRATION # ###################################################################################################### def image_reduction(self, redo=0, plot=0): """ This routine takes data path and loads all image files given in the directory. It combines the bias, dark, flat, and ThAr frames and make master frames used for the image reduction of the science frames. The routine checks if master calibrations frames already exists: (1) if they do it terminates, (2) if not it continues the image reduction. All calibration frames are saved with an extensions of the date, and science frames with the extension of the date and time of observation. ---------------------------- INPUT : ---------------------------- path (string): Path to data plot (integer): Plot flag activated by 1 ---------------------------- OUTPUT : ---------------------------- BF_XX-XX-XX (fits): Master bias DF_XX-XX-XX (fits): Master dark FF_XX-XX-XX (fits): Master flat TA_XX-XX-XX (fits): Master flat SF_XX-XX-XXTXX:XX:XX (fits): Science frame(s): Bias and dark frame calibrated light frames. """ #------------------------------------------ # TEST IF CALIBRATION IMAGES ALREADY EXIST: #------------------------------------------ try: BF = fits.open('{}BF_{}.fits'.format(self.path, self.date))[0].data DF = fits.open('{}DF_{}.fits'.format(self.path, self.date))[0].data FF = fits.open('{}FF_{}.fits'.format(self.path, self.date))[0].data TA = fits.open('{}TA_{}.fits'.format(self.path, self.date))[0].data SF = fits.open('{}SF_{}.fits'.format(self.path, self.datetime))[0].data except IOError: BF = [] #------------------------- # ELSE USE AVAILABLE DATA: #------------------------- if BF==[] or redo==1: # Find all calibration images: BF_i = np.array([fits.getdata(str(self.img_files[i])) for i in self.BF_dex]) DF_i = np.array([fits.getdata(str(self.img_files[i])) for i in self.DF_dex]) FF_i = np.array([fits.getdata(str(self.img_files[i])) for i in self.FF_dex]) TA_i = np.array([fits.getdata(str(self.img_files[i])) for i in self.TA_dex]) SF_i = np.array([fits.getdata(str(self.img_files[i])) for i in self.SF_dex]) # Exposure times: DF_exptimes = [self.hdul[self.DF_dex[i]][0].header['EXPTIME'] for i in range(len(self.DF_dex))] FF_exptimes = [self.hdul[self.FF_dex[i]][0].header['EXPTIME'] for i in range(len(self.FF_dex))] TA_exptimes = [self.hdul[self.TA_dex[i]][0].header['EXPTIME'] for i in range(len(self.TA_dex))] SF_exptimes = [self.hdul[self.SF_dex[i]][0].header['EXPTIME'] for i in range(len(self.SF_dex))] # Test if exposure times are the same: if int(np.sum(np.diff(DF_exptimes))) is not 0: print('ERROR: Dark exposure times are different!'); sys.exit() if int(np.sum(np.diff(FF_exptimes))) is not 0: print('ERROR: Flat exposure times are different!'); sys.exit() if int(np.sum(np.diff(TA_exptimes))) is not 0: print('ERROR: ThAr exposure times are different!'); sys.exit() #--------------------- # PERFORM CALIBRATION: #--------------------- # Make master bias: BF = np.median(BF_i, axis=0) # Make scaled master dark: DF_current = np.median(DF_i - BF, axis=0) DF_FF = (FF_exptimes[0]/DF_exptimes[0]) DF_current DF_TA = (TA_exptimes[0]/DF_exptimes[0]) * DF_current DF = (SF_exptimes[0]/DF_exptimes[0]) * DF_current # Make master flat: FF = np.median(FF_i - BF - DF_FF, axis=0) # Make master ThAr: TA = np.median(TA_i - BF - DF_TA, axis=0) # Calibrate science frames: SF = (SF_i - BF - DF)#/(FF/np.max(FF)) #-------------------- # SAVE MASTER FRAMES: #-------------------- # Find hdulists: BF_hdul = self.hdul[self.BF_dex[0]][0].header DF_hdul = self.hdul[self.DF_dex[0]][0].header FF_hdul = self.hdul[self.FF_dex[0]][0].header TA_hdul = self.hdul[self.TA_dex[0]][0].header # Save master calibration images: fits.writeto('{}BF_{}.fits'.format(self.path, self.date), BF, BF_hdul, overwrite=True) fits.writeto('{}DF_{}.fits'.format(self.path, self.date), DF, DF_hdul, overwrite=True) fits.writeto('{}FF_{}.fits'.format(self.path, self.date), FF, FF_hdul, overwrite=True) fits.writeto('{}TA_{}.fits'.format(self.path, self.date), TA, TA_hdul, overwrite=True) # Save calibrated science frames one by one: for i in range(len(self.SF_dex)): SF_hdul = self.hdul[self.SF_dex[i]][0].header header = self.hdul[self.SF_dex[i]][0].header['DATE-OBS'] fits.writeto('{}SF_{}.fits'.format(self.path, header), SF[0], SF_hdul, overwrite=True) # Only use first image if routine is running furter: SF = SF[0] #----------------------------- # LOAD RV AMPLITUDE OF OBJECT: #----------------------------- file_object = glob.glob('{}SF'.format(self.path)) hdul_object = fits.open(str(file_object[0])) self.rv_amp = hdul_object[0].header['OBJ-RV'] # [km/s] CDS RV amplitude (float) #----------------------------------------------------------- if plot==1: pt.plot_image_reduction(BF, DF, FF, TA, SF) #----------------------------------------------------------- # Select spectral region of interest: self.BF = BF; self.DF = DF; self.FF = FF; self.TA = TA self.F_calib = FF[self.cross_cut[0]:self.cross_cut[1], :].T self.T_calib = TA[self.cross_cut[0]:self.cross_cut[1], :].T self.S_calib = SF[self.cross_cut[0]:self.cross_cut[1], :].T self.noise = np.sqrt(np.mean(BF2)) #----------------------------------------------------------- return self.S_calib, self.F_calib, self.T_calib ######################################################################################################## # FIND ORDERS # ######################################################################################################## def trace_orders(self, data=None, smooth_win=10, exclude_border=10, min_order_width=40, \ threshold_abs=0, disp_gap_tol=5, num_orders=5, num_peaks=10, plot=0): """ This function find the orders in an eshel spectrum by tracing the maximum light distribution along each order. First the function finds a center order position and use this as a reference. Next the function finds the ridges of the specified number of order 'num_orders' using the skfeature package. Lastely, each order is the discribed by a 5 order polynomial and returned as output. ---------------------------- INPUT : ---------------------------- data (array): A single image smooth_win (float): Smooth value to enhance orders exclude_border (float): Border edges that should be exluded order_min_width (float): Minimum distance to locate where the orders are threshold_abs (float): Threshold used to locate peaks with skfeature disp_gap_tol (float): Tolerance for how big a gap there may be num_orders (float): User specified number of orders the program should find num_peaks (float): Number of peaks found for each bin ---------------------------- OUTPUT : ---------------------------- order_traces (dict): Orders within 'order x' and corresponding array with polynomials """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if data==None: data = self.F_calib #---------------------------------- # FIND CENTRAL REFERENCE POSITIONS: #---------------------------------- # Central position interval ref_int = [self.cen_disp-5, self.cen_disp+6] ref_cen_pos = self.find_ref_cen_pos(data, ref_int, smooth_win, exclude_border, min_order_width,\ threshold_abs, num_orders, plot) #------------------------ # TRACE THE ORDER RIDGES: #------------------------ ridge_pos_cross, ridge_pos_disp = self.find_order_ridges(data, smooth_win, exclude_border,\ min_order_width, threshold_abs, num_peaks) #------------------------------------ # FILL IN DATA INTO THE FOUND RIDGES: #------------------------------------ # Make dict placeholders: order_traced = {} order_trace = {} for i, order_pos in enumerate(np.sort(ref_cen_pos)[::-1]): # Here "order_pos" is the cross dispersion center value. order_pos[0] simply chooses one # value and not the increasing list within the loop. # Using ridges trace each order in each direction: min_order_width = 10 order_trace_cross, order_trace_disp = self.find_order_outliers(self.cen_disp, order_pos[0],\ ridge_pos_disp, ridge_pos_cross,\ min_order_width, disp_gap_tol) # Fit ridges with polynomial: poly_coefs = np.polyfit(order_trace_disp, order_trace_cross, 5) order_traced['order_{}'.format(i)] = poly_coefs order_trace['order_{}'.format(i)] = [order_trace_disp, order_trace_cross] #----------------------------------------------------------------------------- if plot==1: pt.plot_trace_order(ridge_pos_disp, ridge_pos_cross, order_trace, order_traced, \ order_trace_disp, self.cen_disp, ref_cen_pos) #----------------------------------------------------------------------------- self.ref_cen_pos = ref_cen_pos self.trace = order_traced #----------------------------------------------------------------------------- return order_traced def find_ref_cen_pos(self, data, ref_int, smooth_win, exclude_border, min_distance, threshold_abs, \ num_peaks, plot): """ This function finds the center order position used as a reference. """ # Collapse in disp direction to reduce cosmic ray contamination: # (FIXME done to make this robust against cosmics - maybe it is not needed) center_rows_median = np.median(data[ref_int[0]:ref_int[1], :], axis=0) # Smooth cross_dispersion direction to prepare for the peak-detection algorithm: center_row_median_convolved = bottleneck.move_sum(center_rows_median.astype(np.float), \ smooth_win, min_count=1) # Find orders using a peak detection function from scikit-image: order_centres = skfeature.peak_local_max(center_row_median_convolved, \ exclude_border=exclude_border,\ min_distance=min_distance, threshold_rel=0,\ threshold_abs=threshold_abs, num_peaks=num_peaks) # Peaks detected minus the smooth window applied (simply due to the moving sum of bottleneck): ref_cen_pos = order_centres - int(smooth_win/2) #------------------------------------------------------------------------------ if plot==1: pt.plot_find_ref_cen_pos(center_rows_median, center_row_median_convolved, \ self.len_cross, smooth_win, ref_cen_pos) #------------------------------------------------------------------------------ return ref_cen_pos def find_order_ridges(self, data, smooth_win, exclude_border, min_distance, threshold_abs, num_peaks): """ This function finds the ridge of each order. It does so by making a slice in cross dispersion and colvolve that with a smooth filter such as the "bottleneck.move_sum". It then finds the local max for each slice and saves the position """ # Placeholders: ridge_indices_disp = [] ridge_indices_cross = [] # Loop over the dispersion length (i) and the cross order row: for i, crossorder in enumerate(data): # Collapse in dispersion axis: # TODO should smoothing be handled separately? top_hat_conv = bottleneck.move_sum(crossorder.astype(np.float), smooth_win, min_count=1) # Again find the peaks as done in "find_ref_cen_pos": peaks = skfeature.peak_local_max(top_hat_conv, exclude_border=exclude_border,\ min_distance=min_distance, threshold_rel=0,\ threshold_abs=threshold_abs, indices=True, num_peaks=num_peaks) # Convert peaks to a list covering the ridges: peaks -= int(smooth_win/2) ridge_indices_cross = np.append(ridge_indices_cross, peaks) ridge_indices_disp = np.append(ridge_indices_disp, np.ones(peaks.shape[0]) i) #----------------------------------------------------- return ridge_indices_cross, ridge_indices_disp def find_order_outliers(self, cen_disp, ref_cen_cross, all_orders_x, all_orders_y, order_width,\ disp_gap_tol): """ This utility takes the found reference positions in cross dispersion and the traced ridges and locate all the outliers defined by 'order_width' threshold. If center_row is not an integer this will fail! """ # To simplify the code we make some abbreviations: x = np.unique(all_orders_x) y_last = ref_cen_cross x_last = x[cen_disp] cross_gap_tol = int(order_width/2.) # Placeholders for outliers: cross = [] disp = [] # Outliers on the left side of cen_disp: for xi in x[cen_disp:]: index_xi = all_orders_x == xi orders_y = all_orders_y[index_xi] min_dist_index = np.argmin(np.abs(orders_y - y_last)) new_y_pos = orders_y[min_dist_index] if (np.abs(new_y_pos - y_last) < cross_gap_tol) & (np.abs(xi - x_last) < disp_gap_tol): cross.append(new_y_pos) y_last = cross[-1] disp.append(xi) x_last = disp[-1] y_last = ref_cen_cross x_last = x[cen_disp] # Outliers on the right side of cen_disp: for xi in x[cen_disp-1::-1]: index_xi = all_orders_x == xi orders_y = all_orders_y[index_xi] min_dist_index = np.argmin(np.abs(orders_y - y_last)) new_y_pos = orders_y[min_dist_index] if (np.abs(new_y_pos - y_last) < cross_gap_tol) & (np.abs(xi - x_last) < disp_gap_tol): cross.append(new_y_pos) y_last = cross[-1] disp.append(xi) x_last = disp[-1] index = np.argsort(disp) #--------------------------------------------------- return np.array(cross)[index], np.array(disp)[index] ######################################################################################################## # INTER-ORDER MASK # ######################################################################################################## def inter_order_mask(self, data=None, order_traces=None, order_width=None, \ low_nudge=0, high_nudge=0, plot=0): """ This function is used to determine the background flux which will be used to correct for scattered light, wignetting, etc. The function looks at the flux level in between the orders ("inter-order") and make and return a mask with ones for which is inter-order and zero elsewhere. The function uses the result from the previos subdroutine "traced orders". ---------------------------- INPUT : ---------------------------- order_width (dict) : Traced orders found from the function 'trace' order_traces (int, float): Width of inter-order mask low_nudge (int, float): Number of pixels used below the traced orders high_nudge (int, float): Number of pixels used above the traced orders plot (int, float): Plot result if you like ---------------------------- OUTPUT : ---------------------------- inter_order_mask (dict) : Orders within 'order x' and corresponding array with polynomials """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if data ==None: data = self.F_calib if order_traces==None: order_traces = self.trace if order_width ==None: order_width = self.find_optimal_width(plot=plot) # FUNCTION CALL! # Check if the inter-order width is odd integer: inter_order_width = int(order_width * 4/3) if inter_order_width % 2 == 0: inter_order_width = inter_order_width - 1 # Constants and placeholders: inter_order_mask = data * 0 + 1 # Initial image mask of ones disp = np.arange(self.len_disp) # Number pixel interval in dispersion order_no = sorted(order_traces.keys()) # Orders numbers (string) cross_order_center = [] #----------------------- # FIND ALL INTER-ORDERS: #----------------------- # First loop through each order: for order in order_no: # Get the coefficients from the trace function: coefs = order_traces[order] cross_order_position = np.polyval(coefs, disp) # Polyfit to each order cross_order_center = np.append(cross_order_center, cross_order_position[int(self.len_disp/2)]) # Each inter order is found: for disp_i in range(self.len_disp): lower_order_edge =int(np.round(cross_order_position[disp_i]-inter_order_width/2-low_nudge)) upper_order_edge =int(np.round(cross_order_position[disp_i]+inter_order_width/2+high_nudge)) inter_order_mask[int(disp_i), lower_order_edge:upper_order_edge] = 0 # Distance/size of each inter order: inter_order_size = cross_order_center[1:] - cross_order_center[:-1] - inter_order_width \ - low_nudge - high_nudge #----------------------- # REMOVE 'GHOST' ORDERS: #----------------------- # Predict inter_order_size: xx = np.arange(len(cross_order_center)-1) inter_order_size_fit = np.polyfit(xx, inter_order_size, 2) size_before = np.polyval(inter_order_size_fit, -1) size_after = np.polyval(inter_order_size_fit, len(cross_order_center)) # Remove 'ghost orders' before first order: coefs = order_traces[order_no[0]] cross_order_position = np.polyval(coefs, disp) for disp_i in range(self.len_disp): lower_inter_order_edge = np.round(cross_order_position[disp_i] - inter_order_width/2 \ - low_nudge - size_before).astype(int) # Remove orders below edges: if lower_inter_order_edge < 0: lower_inter_order_edge = 0 inter_order_mask[disp_i, :lower_inter_order_edge] = 0 # Remove 'ghost orders' after last order: coefs = order_traces[order_no[-1]] cross_order_position = np.polyval(coefs, disp) for disp_i in range(self.len_disp): upper_inter_order_edge = np.round(cross_order_position[disp_i] + inter_order_width/2 \ + high_nudge + size_after).astype(int) # Remove orders above edges: if upper_inter_order_edge > self.len_cross+50: upper_inter_order_edge = 0 inter_order_mask[disp_i, upper_inter_order_edge:] = 0 #-------------------------------------------------------------- if plot==1: pt.plot_inter_order_mask(data, inter_order_mask) #-------------------------------------------------------------- self.inter_order_width = inter_order_width self.inter_order_mask = inter_order_mask #-------------------------------------------------------------- return self.inter_order_mask ######################################################################################################## # BACKGROUND IMAGE # ######################################################################################################## def background(self, image, inter_order_mask=None, order_ref=None, \ poly_order_y=2, poly_order_x=4, filter_size=5, plot=0): """ This function estimates the background flux of scattered light and subtract it. It uses the inter_order_mask to perform this removal. ---------------------------- INPUT : ---------------------------- mask (2d array): Background mask with ones and zeros poly_order_x (int, float): Order of polynomy to fits background flux in dispersion poly_order_y (int, float): Order of polynomy to fits background flux in cross dispersion nsteps (int, float): Number of steps orderdef (int, float): ---------------------------- OUTPUT : ---------------------------- background_image (2d array): """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if inter_order_mask==None: inter_order_mask = self.inter_order_mask #---------------------------- # CONSTANTS AND PLACEHOLDERS: #---------------------------- # Create a background image: (ysize, xsize) = image.shape background_image = np.zeros((ysize, xsize), dtype=np.float64) # Data size in arange: xx = np.arange(xsize, dtype=np.float64) yy = np.arange(ysize, dtype=np.float64) # Array to withhold fitted y values: xfitarr = np.zeros((len(yy), xsize), dtype=np.float64) # Step size and range: yvals = np.arange(len(yy)) # Constants: ycount = 0 niter = 0 sigma_limit = 3 # For plots: s_disp = [500, 1500, int(yvals[-1])] # Slices in disp s_cros = [50, 200, int(xx[-1])] # Slices in cross #---------------------------- # FIT IN Y-DIRECTION (CROSS): #---------------------------- for i in yvals: # Cut out slice in cross dispersion with width determined by 'filter_size': ymin_ind = np.max([i - filter_size, 0]) ymax_ind = np.min([i + filter_size, ysize-1]) y_slice = image[ymin_ind:ymax_ind, :] # Collapse in dispersion to a single cross row: y_mean = np.mean(y_slice, axis=0) # Indices/image of inter-order mask in cross row: y_image = np.where(inter_order_mask[i, :] == 1)[0] # Perform fitting with sigma-clipping: while 1: # Make polynomial fit: coefs = np.polyfit(y_image, y_mean[y_image], poly_order_y) xfit = np.polyval(coefs, y_image) # Find sigma: sigma = (y_mean[y_image] - xfit) / np.std(y_mean[y_image] - xfit) rejected = np.extract(sigma > sigma_limit, y_image) y_image = np.extract(sigma < sigma_limit, y_image) # Loop until all image are within sigma or niter is reached: niter = niter + 1 if niter == 5 or rejected.size == 0: break # Final polynomial fit: xfit = np.polyval(coefs, xx) # fitted line xfitarr[ycount, :] = xfit # Array with fit constants for each slice ycount = ycount + 1 # Save values for plotting: if i==s_disp[0]: yi0, ym0, yfit0 = y_image, y_mean[y_image], xfit if i==s_disp[1]: yi1, ym1, yfit1 = y_image, y_mean[y_image], xfit if i==s_disp[2]: yi2, ym2, yfit2 = y_image, y_mean[y_image], xfit #--------------------------- # FIT IN X-DIRECTION (DISP): #--------------------------- goodind = np.arange(len(yy)) for i in np.arange(xsize): # Perform fitting with sigma-clipping: while 1: # Make polynomial fit: coefs = np.polyfit(yvals.take(goodind), xfitarr[goodind, i], poly_order_x) yfit = np.polyval(coefs, yvals[goodind]) # Find sigma: sigma = (xfitarr[goodind, i] - yfit) / np.std(xfitarr[goodind, i] - yfit) rejected = np.extract(sigma > sigma_limit, goodind) goodind = np.extract(sigma < sigma_limit, goodind) # Loop until all image are within sigma: niter = niter + 1 if niter == 3 or rejected.size == 0 or goodind.size == 0: break # In case the image quality is higher than sigma_limit (poor quality image): if goodind.size == 0: print("Error: no points left when y-fitting the background") coefs = np.polyfit(xfitarr[:, i]) # Final background image is constructed: background_image[:, i] = np.polyval(coefs, yy) # Save values for plotting: if i==s_cros[0]: xi0, xm0, xfit0 = yvals[goodind], xfitarr[goodind, i], background_image[:,i] if i==s_cros[1]: xi1, xm1, xfit1 = yvals[goodind], xfitarr[goodind, i], background_image[:,i] if i==s_cros[2]: xi2, xm2, xfit2 = yvals[goodind], xfitarr[goodind, i], background_image[:,i] #--------------------- # SUBTRACT BACKGROUND: #--------------------- corrected_image = image - background_image #-------------------------------------------------------------- if plot is 1: pt.plot_background_fits(s_disp, s_cros, poly_order_y, poly_order_x, \ xx, yi0, yi1, yi2, ym0, ym1, ym2, yfit0, yfit1, yfit2, \ yy, xi0, xi1, xi2, xm0, xm1, xm2, xfit0, xfit1, xfit2) pt.plot_background(background_image) #-------------------------------------------------------------- return corrected_image, background_image ######################################################################################################## # EXTRACT SPECTRUM # ######################################################################################################## def spectral_extraction(self, S, F, T, trace=None, order_width=None, plot=0): """ This function uses the 'order_width' estimated earlier and first cut the spectrum in question using the utility 'cut_out_order'. All order of relevance is cut out, and a simple-sum over the spatial profile is used to get the 1D spectrum. Next clear cosmic hits are removed using the utility 'locate_outliers' and finally the normalized flat blaze from each order is used to de-blaze each spectral order. ---------------------------- INPUT : ---------------------------- S (2d array): Stellar spectrum F (2d array): Flat spectrum T (2d array): ThAr arc spectrum trace (bib): subfunction with poly-fits to all the orders order_width (int): Spatial order width for cutting out the order ---------------------------- OUTPUT : ---------------------------- s_deblaze (1d array): De-blazed 1D stellar spectral orders T_orders (2d array): ThAr arc image orders """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if trace ==None: trace = self.trace if order_width==None: order_width = self.order_width #---------------------------------------- # FIRST ITERATION WITH LINEAR EXTRACTION: #---------------------------------------- # Make sure that the order width is a odd number: if order_width % 2 == 0: order_width = order_width - 1 # Cut out orders with spatial size (order number is coundt bottom-up): S_orders = [self.cut_out_order(S, np.polyval(trace['order_{}'.format(self.orders[i])], self.disp), \ order_width) for i in range(self.n_orders)] F_orders = [self.cut_out_order(F, np.polyval(trace['order_{}'.format(self.orders[i])], self.disp), \ order_width) for i in range(self.n_orders)] T_orders = [self.cut_out_order(T, np.polyval(trace['order_{}'.format(self.orders[i])], self.disp), \ order_width) for i in range(self.n_orders)] # Linear extraction object and blaze: s_orders = [S_orders[i].sum(axis=1) for i in range(self.n_orders)] f_orders = [F_orders[i].sum(axis=1) for i in range(self.n_orders)] #------------------------------ # HEREAFTER OPTIMAL EXTRACTION: #------------------------------ # Initial variance image: #V = V0 + np.abs(S_calib, axis=0)/Q # # Cut out orders with spatial size 5FWHM: # S = self.cut_out_order(np.polyval(trace['order_2'], self.disp), S_calib) # # Find extracted spectrum: # s = np.zeros(np.shape(s)) # for i in range(self.len_disp): # # Variance image: # V = self.V0 + np.abs(sP+S_sky, axis=0)/self.Q # # Linear image: # s[i] = np.sum((PS_sky/V)/(P2/V), axis=1) #-------------------------------------------------------------- #if plot==1: pt.plot_optimal_extraction(S_orders[1][:, 900:950].T) #-------------------------------------------------------------- self.S_orders = S_orders; self.F_orders = F_orders; self.T_orders = T_orders self.s_orders = s_orders; self.f_orders = f_orders #-------------------------------------------------------------- return [self.S_orders, self.F_orders, self.T_orders] def cut_out_order(self, image, traced_order, cross_order_width=21): """ This utility takes the polynomial describtion 'traced_order' and the relevant order and a spectrum, and cuts out the spectrum in total dispersion length and 'cross_order_width' pixels in cross dispersion around this spectral order. It the returns the bandpass image 'order_cut' and positions which can be used for an easy way of flotting the result. 'cross_order_width' needs to be odd number. """ # Conatant and placeholders: half_width = int(cross_order_width/2.) order_cut = np.zeros((self.len_disp, cross_order_width)) cross_order_positions = np.zeros((self.len_disp, cross_order_width)) # This loop cuts out the order: for d in np.arange(self.len_disp): position = traced_order[d] rounded_position = int(np.round(position)) # Fill in the columns of the order: cp = image[d, rounded_position - half_width:rounded_position + half_width + 1] order_cut[d,:] = cp # Fill in the cross order position: x = np.arange(-half_width, half_width + 1) + rounded_position cross_order_positions[d, :] = x #-------------------------------------------------------------- return order_cut ######################################################################################################## # WAVELENGTH CALIBRATION # ######################################################################################################## def wavelength_calib(self, T_orders=None, poly_order=3, plot=0): """ This utility performs the wavelength calibration. This is done first using the Ca II lines as a initial reference of the wavelength scale. Thus, the utility 'calcium_lines_identifier' finds the peaks values of the H and K lines, then 'peak_finder' finds all ThAr lines in the order above a certain threshold, and lastly a FIES ThAr atlas is used to set the real wavelength scale of the order, which is returned as output. The terminology here is that (p) is pixel wavelength, (l) is wavelenght in Å, and (x) is the spatial direction. ---------------------------- INPUT : ---------------------------- s_orders (1d array): Stellar spectrum for each order T_orders (2d array): Extracted ThAr image of each order poly_order (int): Order of polynomial function for fitting wavelength relation ---------------------------- OUTPUT : ---------------------------- [l0, l1] (1d arrays): New wavelength scale for each order """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if T_orders==None: T_orders = self.T_orders #------------------------- # FIND OBSERVED ARC LINES: #------------------------- # Identify lines from different sigma levels: COF, radii = np.zeros(self.n_orders), np.zeros(self.n_orders) for i in range(self.n_orders): COF_i, radii_i, _ = self.peak_finder(T_orders[i], sigma=0.5, plot=0) COF = [COF[i], COF_i] radii = [radii[i], radii_i] # Only keep disp values: l_cof0 = COF[0][:,0] l_cof1 = COF[1][:,0] #--------------------------------------------------- # ITER 1: USE KNOWN FIES LINES AS INITIAL REFERENCE: TODO! automate the first iteration #--------------------------------------------------- # Cacium lines: l_ca = [self.K, self.H] # Load FIES arc atlas: l_fies0 = [3868.5284, 3873.8224, 3916.4176, 3925.7188, 3928.6233, 3948.9789] l_fies1 = [3925.7188, 3928.6233, 3948.9789, 3950.3951, 3979.3559, 4013.8566] # Pixel coordinats to known identified FIES peaks: l_pix0 = np.array([1091, 1190, 2025, 2217, 2279, 2723]) l_pix1 = np.array([ 900, 952, 1326, 1352, 1910, 2624]) # Find COF lines closest to known pixel coordinates: l_cof0_ini = [min(l_cof0, key=lambda x:abs(x-l_pix0[i])) for i in range(len(l_pix0))] l_cof1_ini = [min(l_cof1, key=lambda x:abs(x-l_pix1[i])) for i in range(len(l_pix1))] # Find wavelenght relations: r0_1 = self.find_arc_scale(self.disp, l_cof0_ini, l_fies0, poly_order, l_cof0, \ param=['1. ITER: 58', T_orders[0], COF[0], radii[0]], plot=plot) r1_1 = self.find_arc_scale(self.disp, l_cof1_ini, l_fies1, poly_order, l_cof1, \ param=['1. ITER: 57', T_orders[1], COF[1], radii[1]], plot=plot) l0_ini, l_cof0_ini, res0_ini, l_cof0_all = r0_1[0], r0_1[1], r0_1[2], r0_1[3] l1_ini, l_cof1_ini, res1_ini, l_cof1_all = r1_1[0], r1_1[1], r1_1[2], r1_1[3] #-------------------------------------- # ITER 2: CALIBRATE WITH PHOTRON ATLAS: #-------------------------------------- # Load Photron arc atlas (http://iraf.noao.edu/specatlas/thar_photron/thar_photron.html): thar_atlas_phot = np.loadtxt('dependencies/thar_atlas_photron.txt') l_atlas_phot = thar_atlas_phot[:,2] # Find wavelenght relations: r0_2 = self.find_arc_scale(l0_ini, l_cof0_all, l_atlas_phot, poly_order, threshold=0.02, \ param=['2. ITER: 58', T_orders[0], COF[0], radii[0]], plot=plot) r1_2 = self.find_arc_scale(l1_ini, l_cof1_all, l_atlas_phot, poly_order, threshold=0.02, \ param=['2. ITER: 57', T_orders[1], COF[1], radii[1]], plot=plot) l0, l_cof0, std0 = r0_2[0], r0_2[1], r0_2[2] l1, l_cof1, std1 = r1_2[0], r1_2[1], r1_2[2] #-------------------------------------------------------------- if plot==1: pt.plot_arc_check([l0, l1], T_orders, l_ca, 'FINAL RESULT') #-------------------------------------------------------------- self.l_orders = [l0, l1] self.sigma_w = [std0, std1] #-------------------------------------------------------------- return self.l_orders def find_arc_scale(self, l_scale, l_obs, l_atlas, poly_order, l_all=None, \ threshold=None, param=None, plot=None): """ This utility takes observed and atlas arc lines and find a wavelength solution given a threshold for comparing when the lines are close enough to be identified as a match. Thus, this utility works only if given a fair initial wavelength solution for the observed lines. If no threshold is given it will be assumed that the this is the initial step going from pixel to wavelenght space, where the exact coordinate matches are known. """ if threshold is not None: # Find atlas lines closest to observed lines (COF lines): l_atlas_match = [min(l_atlas, key=lambda x:abs(x-l_obs[i])) for i in range(len(l_obs))] # Find value difference between matched lines and keep only if diff < 1 Å: dex_goodin = np.where(abs(l_obs - l_atlas_match) < threshold)[0] l_atlas_good = [l_atlas_match[dex_goodin[i]] for i in range(len(dex_goodin))] l_obs_good = [l_obs[dex_goodin[i]] for i in range(len(dex_goodin))] else: l_obs_good = l_obs.copy() l_atlas_good = l_atlas.copy() # Find new pixel-wavelength relation: coefs = np.polyfit(l_obs_good, l_atlas_good, poly_order) # Copy solution to scale and observed lines: ipoly = np.arange(poly_order+1)[::-1] l_scale_new = np.sum([coefs[i]l_scale*ipoly[i] for i in range(poly_order+1)], axis=0) l_obs_new = np.sum([coefs[i]l_obs*ipoly[i] for i in range(poly_order+1)], axis=0) if l_all is not None: l_all_new = np.sum([coefs[i]l_all*ipoly[i] for i in range(poly_order+1)], axis=0) else: l_all_new = None # Calculate fit parameters: poly = np.poly1d(coefs) xp = np.linspace(min(l_obs_good), max(l_obs_good), 1e3) residuals = poly(l_obs_good)-l_atlas_good chi2r = 1-residuals/(len(l_obs_good)-poly_order+1) sigma = np.std(residuals) #-------------------------------------------------------------- if plot is 1: # Activate only illustration and save: #pt.plot_arc_illustration(l_obs, l_atlas, l_obs_good, l_atlas_good, l_scale, param) pt.plot_arc_fit(l_obs_good, l_atlas_good, coefs, poly_order, residuals, chi2r, sigma, param[0]) pt.plot_arc_scale(l_obs, l_atlas, l_obs_good, l_atlas_good, l_scale, param) #-------------------------------------------------------------- return l_scale_new, l_obs_new, sigma, l_all_new ######################################################################################################## # DE-BLAZING # ######################################################################################################## def deblazing(self, F_orders=None, l_orders=None, f_orders=None, s_orders=None, plot=0): """ This spectral order. ---------------------------- INPUT : ---------------------------- T (2d array): ThAr arc spectrum trace (bib): subfunction with poly-fits to all the orders order_width (int): Spatial order width for cutting out the order ---------------------------- OUTPUT : ---------------------------- s_deblaze (1d array): De-blazed 1D stellar spectral orders T_orders (2d array): ThAr arc image orders """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if F_orders==None: F_orders = self.F_orders if l_orders==None: l_orders = self.l_orders if f_orders==None: f_orders = self.f_orders if s_orders==None: s_orders = self.s_orders #---------------------- # CORRECT FOR COMSMICS: TODO! IF using optimal extraction this can be fixed at once #---------------------- # Do not work for arc images ask it is a peak detection algorithm: f_lincor = [self.locate_outliers(f_orders[i], convolve_step=3, cutoff=2e-2, plot=0) \ for i in range(self.n_orders)] #---------------------------- # CORRECT FOR BLAZE FUNCTION: #---------------------------- # Perform blaze correction: s_deblaz = [(s_orders[i]/f_lincor[i]) for i in range(self.n_orders)] #----------------------------------- # SCALE FLAT BALZE TO STELLAR BALZE: #----------------------------------- # Find maximum of each blaze: dex_blaze_max = [np.nanargmax(self.f_orders[i]) for i in range(self.n_orders)] # Remove all cosmics from spectra to be used only for scaling: s_coscor = [self.locate_outliers(s_orders[i], convolve_step=3, cutoff=1e-1, plot=0) \ for i in range(self.n_orders)] # With cosmics removed now scale to maximum difference: continuum_cor = 0.85 # Correction factor only valid for solar type stars dif_max = [continuum_cornp.max(self.f_orders[i])/np.max(s_coscor[i]) for i in range(self.n_orders)] #-------------------------------------------------------------- if plot==1: pt.plot_blaze(s_orders, f_orders, f_lincor, dif_max) pt.plot_deblaze(s_deblaz) #-------------------------------------------------------------- self.f_orders = f_lincor; self.s_deblaz = s_deblaz self.dif_max = dif_max #-------------------------------------------------------------- return self.f_orders, self.s_deblaz def norm_blaze_function(self, F_order): #TODO! this is not used but may be useful in future """ This utility finds the blaze function which recides from the fact that an échelle spectrum is bowed along the dispersion and thus... To find the blaze function here the order is collapsed in cross dispersion to a one dimentional array using a simple sum. The 'normalized_order' also gives an estimate of scatter within the order. """ # Use simple sum to collapse order: f_blaze = np.sum(F_order, axis=1) # Normalize the spectrum: F_norm_order = np.zeros(F_order.shape) for i in range(F_order.shape[1]): F_norm_order[:, i] = F_order[:, i] / f_blaze #-------------------------------------------------------------- return F_norm_order #F_norm = [self.norm_blaze_function(F_orders[i]) for i in range(self.n_orders)] #S_deblaze = [self.S_orders[i]/F_norm[i] for i in range(self.n_orders)] #s_deblaz_norm = [S_deblaze[i].sum(axis=1) for i in range(self.n_orders)] ##################################################################################################### # SCRUNCH, MERGE, AND CLIP # ##################################################################################################### def scrunch_and_merge(self, l_orders=None, s_deblaz=None, plot=0): """ This function estimates ---------------------------- INPUT : ---------------------------- mask (2d array): Background mask with ones and zeros ---------------------------- OUTPUT : ---------------------------- background_image (2d array): """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if l_orders==None: l_orders = self.l_orders if s_deblaz==None: s_deblaz = self.s_deblaz #---------------- # SCRUNCH ORDERS: #---------------- # Prepare uniform wavelength grid: dl = np.median([np.median(np.diff(l_orders[i])) for i in range(self.n_orders)]) dl_orders = [np.arange(l_orders[i][0], l_orders[i][-1]+dl, dl) for i in range(self.n_orders)] # Linear interpolate to uniform grid: s_grids = [scipy.interpolate.griddata(l_orders[i], s_deblaz[i], dl_orders[i], method='nearest') \ for i in range(self.n_orders)] #-------------- # MERGE ORDERS: #-------------- # Find index of merge boarders: dex0_min = np.where(dl_orders[0].astype(int)==3880)[0][0] dex0_max = np.where(dl_orders[0].astype(int)==3915)[0][0] dex1_min = np.where(dl_orders[1].astype(int)==3915)[0][0] # Merge wavelength axis: l_merge0 = dl_orders[0][dex0_min:dex0_max] l_merge1 = dl_orders[1][dex1_min::] l_merge = np.append(l_merge0, l_merge1) # Merge flux axis: s_merge0 = s_grids[0][dex0_min:dex0_max] s_merge1 = s_grids[1][dex1_min::] s_merge = np.append(s_merge0, s_merge1) #-------------------------------------------------------------- if plot is 1: pt.plot_merge(s_merge, l_merge, [self.H, self.K]) #-------------------------------------------------------------- self.s = s_merge; self.l = l_merge #-------------------------------------------------------------- return self.s, self.l ##################################################################################################### # RV CORRECTION # ##################################################################################################### def rv_correction(self, s=None, l=None, plot=0): """ This function is used to create a transit model for the Cross-Correlation (CC). To create the model the subrutine called 'model' is used. To perform the CC the CC coefficient is also needed and this is calculated in the subroutine 'cc_coefficients'. For the precision needed here future test of RV correction is needed. For now Astropy's find the projected RV component including the stellar motion, the baryocentric motion, and Earth's heliocentric and rotational velocities. ---------------------------- INPUT : ---------------------------- mask (2d array): ---------------------------- OUTPUT : ---------------------------- background_image (2d array): """ # Check if program parameters is defined: if s==None: s = self.s if l==None: l = self.l #--------------- # RV CORRECTION: #--------------- # Use astropy for Barycentric: obstime = Time(self.datetime) target = SkyCoord.from_name(self.target) song = EarthLocation.of_site('roque de los muchachos') # Closest observatory to Teide rv_baryc = target.radial_velocity_correction(obstime=obstime, location=song).to('km/s').value # Use baryocentric + stellar RV amplitude as RV correction: rv_shift = self.rv_amp - rv_baryc - self.rv_amp * rv_baryc / (scipy.constants.c/1e3) # Use standard equation for RV shift (dl/l = v/c) calculated in [km/s]: c = scipy.constants.c/1e3 delta_lambda = rv_shift / c * l # Perform wavelenght shift: l = l - delta_lambda # Calculate this for approx results: delta_l3950 = rv_shift / (scipy.constants.c/1e3) * 3950 delta_p3950 = delta_l3950 / np.diff(l)[0] #--------------------------- # HANDLING IDL SUN SPECTRUM: #--------------------------- # # Save IDL format to python: # import scipy.io # s_sun = scipy.io.readsav('{}sun_reference/ARCTURUS.IDL'.format(self.path_blues)) # np.savetxt('{}sun_reference/sun_python.txt'.format(self.path_blues), s_sun['atlas']) # # Import sun spectrum and save smaller spectral domain: # sun = np.loadtxt('{}/sun_reference/sun_python.txt'.format(self.path_blues)) # l_sun = np.array([sun[i][0] for i in range(len(sun))]) # s_sun = np.array([sun[i][2] for i in range(len(sun))]) # plt.figure() # plt.plot(l_sun, s_sun, 'k-', linewidth=0.1) # plt.show() # # Find index of merge boarders: # borders = [3880, 4020] # i_min = np.where(l_sun.astype(int)==borders[0])[0][0] # i_max = np.where(l_sun.astype(int)==borders[1])[0][0] # # Merge wavelength axis and save data: # l_sun = l_sun[i_min:i_max] # s_sun = s_sun[i_min:i_max] # np.savetxt('{}sun_reference/sun.txt'.format(self.path_blues), np.vstack([l_sun, s_sun]).T) #------------------------- # TRANSFORM AND NORMALIZE: #------------------------- # # Load sun data: # sun = np.loadtxt('{}/sun_reference/sun.txt'.format(self.path_blues)) # l_sun = sun[:,0] # s_sun = sun[:,1] # # Tranform to regular grid: # dl_sun = np.diff(l_sun)[0] # l_gsun = np.arange(l_sun[0], l_sun[-1], dl_sun) # # Interpolate: # s_gsun = scipy.interpolate.griddata(l_sun, s_sun, l_gsun, method='cubic') # Sun # s_gobs = scipy.interpolate.griddata(l, s, l_gsun, method='nearest') # Star observed # # Flux normalize signals: # s_gobs = s_gobs/np.max(s_gobs) # # Inverted and Normalized signal MUST be used: # x = (1-s_gobs) - np.mean(1-s_gobs) # y = (1-s_gsun) - np.mean(1-s_gsun) # #x = self.convolve(x, 'median', 1000) # #y = self.convolve(y, 'median', 1000) # print(np.diff(l_gsun)) # print(len(l_gsun)) # l_gsun_new = np.roll(l_gsun, 200) # plt.figure() # plt.plot(l_gsun, s_gsun, 'r-') # plt.plot(l_gsun_new, s_gsun, 'b-') # plt.plot(l_gsun, s_gobs, 'k-') # plt.show() #sys.exit() #--------------------------- # PERFORM CROSS CORRELATION: #--------------------------- # # Prepare indices for spectrum shift: # dx = 200 # dy = np.arange(-dx, dx) # # Perform Cross-correlation: # cc = np.zeros(len(dy)) # for i in dy: # y = np.roll(y, -i) # r_cc = self.cc_coefficient(x, y) # cc[i] = r_cc # # Find peaks maximum: # peaks_dex, _ = scipy.signal.find_peaks(cc) # dy_peaks = dy[peaks_dex] # cc_peaks = cc[peaks_dex] # # Choose only resonable RV shifts (<200 km/s) and good cc (>0.8): # peaks_good_dex = (dy_peaks>-200) * (dy_peaks<200) * (cc_peaks>0.8) # dy_peaks = dy_peaks[peaks_good_dex] # cc_peaks = cc_peaks[peaks_good_dex] # Find max peak corresponding to RV: # cc_max_dex = np.argmax(cc_peaks) # dy_max = dy_peaks[cc_max_dex] # cc_max = cc_peaks[cc_max_dex] # plt.figure() # plt.plot(dy, cc, 'k-') # plt.plot(dy_peaks, cc_peaks, 'r+') #plt.title('RV shift = {} km/s'.format(rv_mean)) #plt.axvline(max(cc), color='r') #plt.plot(l_sun, y, 'k-') #plt.plot(ll, x, 'b-') #plt.xlim(-200, 200) # plt.show() #-------------------------------------------------------------- #if plot is 1: pt.plot_merge(s_merge, l_merge, [self.H, self.K]) #-------------------------------------------------------------- self.delta_v_baryc = rv_baryc self.delta_v = rv_shift self.delta_l = delta_l3950 self.delta_p = delta_p3950 self.l = l #-------------------------------------------------------------- return self.l def cc_coefficient(self, x, y): """ This function find the cross-correlation coefficienten between two datasets. Here x is the data have an offset and y is the data that is cross correlated for every small step. """ cor = np.sum( (x-np.mean(x)) * (y-np.mean(y)) ) norm = np.sqrt( np.sum((x-np.mean(x))*2) np.sum((x-np.mean(x))*2) ) r = cor/norm #-------------------------------------------------------------- return r ######################################################################################################## # CONTINUUM NORMALIZATION # ######################################################################################################## def continuum_norm(self, l=None, s=None, rv_amp=0, plot=0): """ This function estimates ---------------------------- INPUT : ---------------------------- mask (2d array): Background mask with ones and zeros - uses rv corrected data only! ---------------------------- OUTPUT : ---------------------------- background_image (2d array): """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if l==None: l = self.l if s==None: s = self.s #---------------------------- # NORMALIZE WITH POINTS ONLY: #---------------------------- # Pseudo-continuum points used for the SSS included in the Ca H & K order: ps_min, ps_max = 3912, 4000 # Find central index for wavelength: dex_min = np.where(l.astype(int)==ps_min)[0][0] dex_max = np.where(l.astype(int)==ps_max)[0][0] # Values for points used to linear relation: l_point = [l[dex_min], l[dex_max]] s_point = [s[dex_max], s[dex_max]] # Find linear relation: coefs_point = np.polyfit(l_point, s_point, 0) poly_point = np.poly1d(coefs_point) s_norm_point = s/poly_point(l) #----------------------------- # NORMALIZE WITH HIGHEST PEAK: #----------------------------- # Find max peak around pseudo peaks: # (This function needs a initial guess for the RV shift!) dex_peak_min = self.find_peak_in_noise(s, dex_min, plot=0) dex_peak_max = self.find_peak_in_noise(s, dex_max, plot=0) # Values for fit: l_peak = [l[dex_peak_min], l[dex_peak_max]] s_peak = [s[dex_peak_min], s[dex_peak_max]] # Find linear relation: coefs_peak = np.polyfit(l_peak, s_peak, 1) poly_peak = np.poly1d(coefs_peak) s_norm_peak = s/poly_peak(l) #------------------------------ # NORMALIZE WITH MEAN BANDPASS: #------------------------------ # Another methods is to use pseudo-continuum bandpass': bold_band_min = (l > ps_min-0.5)(l < ps_min+0.5) bold_band_max = (l > ps_max-0.5)(l < ps_max+0.5) # Find meadian bandpass values: l_mean = [np.median(l[bold_band_min]), np.median(l[bold_band_max])] s_mean = [np.median(s[bold_band_min]), np.median(s[bold_band_max])] # Find linear relation: coefs_mean = np.polyfit(l_mean, s_mean, 1) poly_mean = np.poly1d(coefs_mean) s_norm_mean = s/poly_mean(l) #-------------------------------------------------------------- if plot is 1: pt.plot_continuum_norm_all(l, s, [l_point, l_peak, l_mean],[s_point, s_peak, s_mean], \ [s_norm_point, s_norm_peak, s_norm_mean], \ [poly_point, poly_peak, poly_mean], [self.K, self.H]) #-------------------------------------------------------------- self.l = l self.s = [s_norm_peak, s_norm_point, s_norm_mean] #-------------------------------------------------------------- return self.l, self.s ######################################################################################################## # S-INDEX # ######################################################################################################## def eshel_sindex(self, S, F, trace=None, order_width=None, l_orders=None, f_orders=None, \ l=None, s=None, plot=0): """ This function estimates ---------------------------- INPUT : ---------------------------- mask (2d array): Background mask with ones and zeros ---------------------------- OUTPUT : ---------------------------- background_image (2d array): """ #-------------------------- # CHECK PROGRAM PARAMETERS: #-------------------------- if trace==None: trace = self.trace if order_width==None: order_width = self.order_width if l_orders==None: l_orders = self.l_orders if f_orders==None: f_orders = self.f_orders if l ==None: l = self.l if s ==None: s = self.s #------------------ # FIND UNCERTAINTY: #------------------ self.uncertainty(S, F, trace, order_width, l_orders, f_orders, l, s[0], plot=1) #--------------------------------------------- # FIND FLUX AND UNCERTAINTY FOR EACH BANDPASS: #-------------------------------------------- result0 = self.find_bandpass_fluxes(l, s[0]) result1 = self.find_bandpass_fluxes(l, s[1]) result2 = self.find_bandpass_fluxes(l, s[2]) results = [result0, result1, result2] #-------------- # FIND S INDEX: #-------------- sindices = [self.sindex(results[i], l, save=i) for i in range(len(results))] # Find fractional difference between each continuum method: sindex_diff12 = abs(sindices[0] - sindices[1]) / sindices[0] 100 sindex_diff13 = abs(sindices[0] - sindices[2]) / sindices[0] * 100 print(sindex_diff12) print(sindex_diff13) #-------------------------------------------------------------- if plot is 1: self.sindex = sindices self.results() #-------------------------------------------------------------- return def uncertainty(self, F, S, trace, order_width, l_orders, f_orders, l, s, plot=0): """ This utility estimates the mean flux-uncertainty in a given bandpass using S/N ratio. """ #------------------------------------- # S/N RATIO OF ALONG EACH ORDER BLAZE: #------------------------------------- # Calculate mean sky background from background subtracted image: self.f_flux_sky, _ = self.mean_background(F, trace, plot=0) self.s_flux_sky, _ = self.mean_background(S, trace, plot=0) # Scale flat orders to that of the stellar orders: f_flux_obj = f_orders s_flux_obj = [f_flux_obj[i]/self.dif_max[i] for i in range(self.n_orders)] # Convolve data for smoothing: f_conv_obj = [self.convolve(f_flux_obj[i], 'mean', 10) for i in range(self.n_orders)] s_conv_obj = [self.convolve(s_flux_obj[i], 'mean', 10) for i in range(self.n_orders)] # Find SNR along the orders: f_snr_orders = np.zeros(self.n_orders) s_snr_orders = np.zeros(self.n_orders) for i in range(self.n_orders): fsnr = [self.signal_to_noise(f_conv_obj[i][j], order_width, self.f_flux_sky) \ for j in range(self.len_disp)] ssnr = [self.signal_to_noise(s_conv_obj[i][j], order_width, self.s_flux_sky) \ for j in range(self.len_disp)] f_snr_orders = [f_snr_orders[i], fsnr] s_snr_orders = [s_snr_orders[i], ssnr] # Find SNR peak maxima: self.f_snr_max = [max(f_snr_orders[i]) for i in range(self.n_orders)] self.s_snr_max = [max(s_snr_orders[i]) for i in range(self.n_orders)] #---------------------------- # UNCERTAINTY FROM S/N RATIO: #---------------------------- # Min and max indices for each bandpass: l_min = [self.V-self.VR_bandpass, self.K-self.HK_bandpass, \ self.H-self.HK_bandpass, self.R-self.VR_bandpass] l_max = [self.V+self.VR_bandpass, self.K+self.HK_bandpass, \ self.H+self.HK_bandpass, self.R+self.VR_bandpass] # Order nr to loop over: j = [0, 1, 1, 1] # Find mean S/N and uncertainty in each bandpass: self.f_snr_X = np.zeros(4) self.s_snr_X = np.zeros(4) self.sigma_f_snr = np.zeros(4) self.sigma_s_snr = np.zeros(4) self.std_f = np.zeros(4) for i in range(4): # Find min and max bandpass pixel indices: X_pix_min = np.where(min(l_orders[j[i]], key=lambda x:abs(x-(l_min[i])))==l_orders[j[i]])[0][0] X_pix_max = np.where(min(l_orders[j[i]], key=lambda x:abs(x-(l_max[i])))==l_orders[j[i]])[0][0] # Find number of pixels used in bandpass: n_pix_X = len(range(X_pix_min, X_pix_max)) # Estimate flux uncertainty from each bandpass: f_snr_X = np.sum(f_snr_orders[j[i]][X_pix_min:X_pix_max]) / n_pix_X s_snr_X = np.sum(s_snr_orders[j[i]][X_pix_min:X_pix_max]) / n_pix_X # Estimate S/N and uncertainties: self.sigma_f_snr[i] = 1/f_snr_X self.sigma_s_snr[i] = 1/s_snr_X self.f_snr_X[i] = f_snr_X self.s_snr_X[i] = s_snr_X # Estimate flat scatter: f = f_orders[j[i]]/np.max(f_orders[j[i]]) f_std0 = self.convolve(f, 'std', 2) f_std = self.convolve(f_std0, 'mean', 100) self.std_f[i] = np.mean(f_std[X_pix_min:X_pix_max]) # plt.figure() # plt.plot(l_orders[j[i]], f_std0, 'k-', linewidth=1.0, label=r'$\sigma_i$') # plt.plot(l_orders[j[i]], f_std, 'r-', linewidth=1.2, label=r'$\sigma_i/\mu_i$') # plt.show() # Uncertainty internally from spectrum: s_mea = self.convolve(s, 'mean', 2) s_dif = s/s_mea - 1 s_std0 = self.convolve(s, 'std', 2) s_std = self.convolve(s_std0, 'mean', 100) #---------------------------- # ALL UNCERTAINTY CONSIDERED: #---------------------------- # Shot Noise from flat blaze: self.sigma_f = np.sum(self.std_f) # Three lines from order #57 and one from order #58 is used: self.sigma_w = ( 1/4self.sigma_w[0] + 3/4self.sigma_w[1] )/2 # Find bandpass indices: _, _, bands = self.find_bandpass_fluxes(l, s, plot) V_indices, R_indices = bands[0], bands[1] K1_indices, H1_indices = bands[2], bands[3] Km_indices, Hm_indices = bands[4], bands[5] K2_indices, H2_indices = bands[6], bands[7] # Translate into flux uncertainty from each bandpass: self.std_V = np.sum(s_std[V_indices]) / len(V_indices) # Continuum bands self.std_R = np.sum(s_std[R_indices]) / len(R_indices) self.std_K1 = np.sum(s_std[K1_indices]) / len(K1_indices) # Used for 1.09 Å square band fluxes self.std_H1 = np.sum(s_std[H1_indices]) / len(H1_indices) self.std_Km = np.sum(s_std[Km_indices]) / len(Km_indices) # Used for mean fluxes per wavelength self.std_Hm = np.sum(s_std[Hm_indices]) / len(Hm_indices) self.std_K2 = np.sum(s_std[K2_indices]) / len(K2_indices) # Used for triangular integrated fluxes self.std_H2 = np.sum(s_std[H2_indices]) / len(H2_indices) self.sigma_bands = [s_std[K2_indices], s_std[H2_indices]] # Used for triangular norm fluxes # Combined uncertainties to be used for error propagation: x = self.sigma_w + self.sigma_f self.sigma_V = self.std_V + x self.sigma_R = self.std_R + x self.sigma_K1 = self.std_K1 + x; self.sigma_Km = self.std_Km + x; self.sigma_K2 = self.std_K2 + x self.sigma_H1 = self.std_H1 + x; self.sigma_Hm = self.std_Hm + x; self.sigma_H2 = self.std_H2 + x #-------------------------------------------------------------- if plot is 1: pt.plot_sindex_scatter(l, s_dif, s_std0, s_std, self.bands) #-------------------------------------------------------------- return s_std def find_bandpass_fluxes(self, l, s, plot=0): """ This utility simply find the bandpass indices and fluxes given the bandpass widths. """ # Shortwrite parameters: HK = self.HK_bandpass VR = self.VR_bandpass #------------------------------------------------ # FIND INITIAL INDICES NEEDED FOR ALL BANDPASSES: #------------------------------------------------ # Find central bandpass index: V_dex_cen = (np.abs(l-(self.V))).argmin() K_dex_cen = (np.abs(l-(self.K))).argmin() H_dex_cen = (np.abs(l-(self.H))).argmin() R_dex_cen = (np.abs(l-(self.R))).argmin() # Find wavelength indices for each bandpass: V_dex = (np.abs(l-(self.V+VR/2))).argmin() - (np.abs(l-(self.V-VR/2))).argmin() R_dex = (np.abs(l-(self.R+VR/2))).argmin() - (np.abs(l-(self.R-VR/2))).argmin() # Find wavelength indices for 1.09 Å bandpass: K1_dex = (np.abs(l-(self.K+HK/2))).argmin() - (np.abs(l-(self.K-HK/2))).argmin() H1_dex = (np.abs(l-(self.H+HK/2))).argmin() - (np.abs(l-(self.H-HK/2))).argmin() # Find wavelength indices for 2 x 1.09 Å lower widths: K2_dex = (np.abs(l-(self.K+HK))).argmin() - (np.abs(l-(self.K-HK))).argmin() H2_dex = (np.abs(l-(self.H+HK))).argmin() - (np.abs(l-(self.H-HK))).argmin() # Select only even bandswidths: for VR_dex_i in [V_dex, R_dex]: if VR_dex_i % 2 == 0: VR_dex = int(VR_dex_i / 2) else: VR_dex = int((VR_dex_i - 1) / 2) for HK1_dex_i in [K1_dex, H1_dex]: if HK1_dex_i % 2 == 0: HK1_dex = int(HK1_dex_i / 2) else: HK1_dex = int((HK1_dex_i - 1) / 2) for HK2_dex_i in [K2_dex, H2_dex]: if HK2_dex_i % 2 == 0: HK2_dex = int(HK2_dex_i / 2) else: HK2_dex = int((HK2_dex_i - 1) / 2) # Find square bandpass indices: V_indices = np.arange(V_dex_cen-VR_dex, V_dex_cen+VR_dex) R_indices = np.arange(R_dex_cen-VR_dex, R_dex_cen+VR_dex) # Covert to a full-range indices: K1_indices = np.arange(K_dex_cen-HK1_dex, K_dex_cen+HK1_dex) H1_indices = np.arange(H_dex_cen-HK1_dex, H_dex_cen+HK1_dex) # Covert to a full-range indices: K2_indices = np.arange(K_dex_cen-HK2_dex, K_dex_cen+HK2_dex+1) H2_indices = np.arange(H_dex_cen-HK2_dex, H_dex_cen+HK2_dex+1) #-------------------------------------- # DEFINE TRIANGULAR H AND K BANDPASSES: #-------------------------------------- # Split out index ranges: k1_indices = np.arange(K_dex_cen-HK2_dex, K_dex_cen) k2_indices = np.arange(K_dex_cen, K_dex_cen+HK2_dex+1) h1_indices = np.arange(H_dex_cen-HK2_dex, H_dex_cen) h2_indices = np.arange(H_dex_cen, H_dex_cen+HK2_dex+1) # Compute linear relations on either side triangle: coefs_k1 = np.polyfit([K_dex_cen-HK2_dex, K_dex_cen], [0, 1], 1); poly_k1 = np.poly1d(coefs_k1) coefs_k2 = np.polyfit([K_dex_cen, K_dex_cen+HK2_dex], [1, 0], 1); poly_k2 = np.poly1d(coefs_k2) coefs_h1 = np.polyfit([H_dex_cen-HK2_dex, H_dex_cen], [0, 1], 1); poly_h1 = np.poly1d(coefs_h1) coefs_h2 = np.polyfit([H_dex_cen, H_dex_cen+HK2_dex], [1, 0], 1); poly_h2 = np.poly1d(coefs_h2) # Find count values for each line: s_tri_k1 = poly_k1(k1_indices) s_tri_k2 = poly_k2(k2_indices) s_tri_h1 = poly_h1(h1_indices) s_tri_h2 = poly_h2(h2_indices) # Combine triangular count values: s_tri_K = np.append(s_tri_k1, s_tri_k2) s_tri_H = np.append(s_tri_h1, s_tri_h2) # Define finer regular grid: l_k1_grid = np.linspace(l[k1_indices[0]], l[k1_indices[-1]], 1e4) l_k2_grid = np.linspace(l[k2_indices[0]], l[k2_indices[-1]], 1e4) l_h1_grid = np.linspace(l[h1_indices[0]], l[h1_indices[-1]], 1e4) l_h2_grid = np.linspace(l[h2_indices[0]], l[h2_indices[-1]], 1e4) # Interpolate triangular data function: s_tri_k1_grid = scipy.interpolate.griddata(l[k1_indices], s_tri_k1, l_k1_grid, method='linear') s_tri_k2_grid = scipy.interpolate.griddata(l[k2_indices], s_tri_k2, l_k2_grid, method='linear') s_tri_h1_grid = scipy.interpolate.griddata(l[h1_indices], s_tri_h1, l_h1_grid, method='linear') s_tri_h2_grid = scipy.interpolate.griddata(l[h2_indices], s_tri_h2, l_h2_grid, method='linear') # Interpolate spectral data: s_spc_k1_grid = scipy.interpolate.griddata(l[k1_indices], s[k1_indices], l_k1_grid, method='linear') s_spc_k2_grid = scipy.interpolate.griddata(l[k2_indices], s[k2_indices], l_k2_grid, method='linear') s_spc_h1_grid = scipy.interpolate.griddata(l[h1_indices], s[h1_indices], l_h1_grid, method='linear') s_spc_h2_grid = scipy.interpolate.griddata(l[h2_indices], s[h2_indices], l_h2_grid, method='linear') # Find wavelength value of intersection: dex_k1_inter = np.where(np.abs(s_spc_k1_grid - s_tri_k1_grid) < 5e-4)[0][0] dex_k2_inter = np.where(np.abs(s_spc_k2_grid - s_tri_k2_grid) < 5e-4)[0][-1] dex_h1_inter = np.where(np.abs(s_spc_h1_grid - s_tri_h1_grid) < 5e-4)[0][0] dex_h2_inter = np.where(np.abs(s_spc_h2_grid - s_tri_h2_grid) < 5e-4)[0][-1] # Find coordinates of intersection points: l_k1_inter, s_k1_inter = l_k1_grid[dex_k1_inter], s_tri_k1_grid[dex_k1_inter] l_k2_inter, s_k2_inter = l_k2_grid[dex_k2_inter], s_tri_k2_grid[dex_k2_inter] l_h1_inter, s_h1_inter = l_h1_grid[dex_h1_inter], s_tri_h1_grid[dex_h1_inter] l_h2_inter, s_h2_inter = l_h2_grid[dex_h2_inter], s_tri_h2_grid[dex_h2_inter] # Find closest wavelength index to intersection point: dex_spc_k1_inter = (np.abs(l_k1_inter - l[k1_indices])).argmin() dex_spc_k2_inter = (np.abs(l_k2_inter - l[k2_indices])).argmin() dex_spc_h1_inter = (np.abs(l_h1_inter - l[h1_indices])).argmin() dex_spc_h2_inter = (np.abs(l_h2_inter - l[h2_indices])).argmin() #------------------ # FIND FLUX VALUES: #------------------ # Find fluxes in each continuum passbands: V_fluxes = s[V_indices] R_fluxes = s[R_indices] # Find final count values for retangular 1.09Å filter: K1_fluxes = s[K1_indices] H1_fluxes = s[H1_indices] # Find final count values for mean filter: Km_indices = np.arange(k1_indices[dex_spc_k1_inter], k2_indices[dex_spc_k2_inter]) Hm_indices = np.arange(h1_indices[dex_spc_h1_inter], h2_indices[dex_spc_h2_inter]) Km_fluxes = s[Km_indices] Hm_fluxes = s[Hm_indices] # Find final count values for triangular filter: k1_fluxes = s_tri_k1[:dex_spc_k1_inter].tolist() h1_fluxes = s_tri_h1[:dex_spc_h1_inter].tolist() k2_fluxes = s_tri_k2[dex_spc_k2_inter:].tolist() h2_fluxes = s_tri_h2[dex_spc_h2_inter:].tolist() K2_fluxes = np.array(k1_fluxes + Km_fluxes.tolist() + k2_fluxes) H2_fluxes = np.array(h1_fluxes + Hm_fluxes.tolist() + h2_fluxes) # Construct coordinate array for precise polygon-area calculation: Kp_wave = np.array([l[K2_indices[0]], l_k1_inter] + l[Km_indices].tolist() + \ [l_k2_inter, l[K2_indices[-1]] ]) Hp_wave = np.array([l[H2_indices[0]], l_h1_inter] + l[Hm_indices].tolist() + \ [l_h2_inter, l[H2_indices[-1]] ]) Kp_fluxes = np.array([0, s_k1_inter] + Km_fluxes.tolist() + [s_k2_inter, 0]) Hp_fluxes = np.array([0, s_h1_inter] + Hm_fluxes.tolist() + [s_h2_inter, 0]) Kp_coors = np.array([Kp_wave, Kp_fluxes]) Hp_coors = np.array([Hp_wave, Hp_fluxes]) # Combine to return: band_fluxes = [V_fluxes, R_fluxes, K1_fluxes, H1_fluxes, Km_fluxes, Hm_fluxes, \ K2_fluxes, H2_fluxes, Kp_coors, Hp_coors] tri_function = [s_tri_K, s_tri_H] band_indices = [V_indices, R_indices, K1_indices, H1_indices, Km_indices, Hm_indices, \ K2_indices, H2_indices, K_dex_cen, H_dex_cen] #-------------------------------------------------------------- if plot is 1: pt.plot_sindex_bands(l, s, s_tri_K, s_tri_H, K2_indices, H2_indices, K2_fluxes, H2_fluxes, \ l_k1_inter, l_k2_inter, l_h1_inter, l_h2_inter, \ s_k1_inter, s_k2_inter, s_h1_inter, s_h2_inter, \ Kp_wave, Hp_wave, Kp_fluxes, Hp_fluxes, Km_indices, Hm_indices, \ self.K, self.H, K1_indices, H1_indices) pt.plot_sindex_fluxes(l, s, band_indices, band_fluxes, self.bands) #-------------------------------------------------------------- return band_fluxes, tri_function, band_indices def sindex(self, flux_results, l, save=None): """ This utility is a general function to calculate the S index in (1) the standard way and (2) using mean bandpass fluxes if 'dex_meanband' is available. """ # Unpack flux results: bandfluxes, tri_func, bandindices = flux_results[0], flux_results[1], flux_results[2] # Find V an R bandpass fluxes: val_V = np.sum(bandfluxes[0]); V = ufloat(val_V, val_Vself.sigma_V) val_R = np.sum(bandfluxes[1]); R = ufloat(val_R, val_Rself.sigma_R) #-------------------------------------------- # FIND S INDEX FROM 1.09 Å INTEGRATED FLUXES: #-------------------------------------------- val_K1 = np.sum(bandfluxes[2]); K1 = ufloat(val_K1, val_K1self.sigma_K1) val_H1 = np.sum(bandfluxes[3]); H1 = ufloat(val_H1, val_H1self.sigma_H1) # Calculate S index: sindex_HK1 = 8 * (H1 + K1)/(R + V) 2.4 #-------------------------------------------- # FIND S INDEX FROM MEAN FLUX PER WAVELENGTH: #-------------------------------------------- val_Vm = np.mean(bandfluxes[0]); Vm = ufloat(val_Vm, val_Vmself.sigma_V) val_Rm = np.mean(bandfluxes[1]); Rm = ufloat(val_Rm, val_Rmself.sigma_R) val_Km = np.mean(bandfluxes[6]); Km = ufloat(val_Km, val_Kmself.sigma_Km) val_Hm = np.mean(bandfluxes[7]); Hm = ufloat(val_Hm, val_Hmself.sigma_Hm) # val_Km = np.mean(bandfluxes[4]); Km = ufloat(val_Km, val_Kmself.sigma_Km) # val_Hm = np.mean(bandfluxes[5]); Hm = ufloat(val_Hm, val_Hmself.sigma_Hm) # Calculate S index: sindex_HKm = 8 (Hm + Km)/(Rm + Vm) * self.HK_bandpass/self.VR_bandpass * 2.4 #------------------------------------------------------ # FIND S INDEX FROM TRIANGULAR BANDSPASS NORMALIZATION: #------------------------------------------------------ val_Kn = np.sum(bandfluxes[6] * tri_func[0]) val_Hn = np.sum(bandfluxes[7] * tri_func[1]) sigma_Kn = np.sum(self.sigma_bands[0] * tri_func[0]) sigma_Hn = np.sum(self.sigma_bands[1] * tri_func[1]) Kn = ufloat(val_Kn, val_Knsigma_Kn) Hn = ufloat(val_Hn, val_Hnsigma_Hn) # Calculate S index: sindex_HKn = 8 * (Hn + Kn)/(R + V) 2.4 #------------------------------------------------ # FIND S INDEX FROM TRIANGULAR INTEGRATED FLUXES: #------------------------------------------------ val_K2 = np.sum(bandfluxes[6]); K2 = ufloat(val_K2, val_K2self.sigma_K2) val_H2 = np.sum(bandfluxes[7]); H2 = ufloat(val_H2, val_H2self.sigma_H2) # Calculate S index: sindex_HK2 = 8 (H2 + K2)/(R + V) #---------------------------------------------- # FIND S INDEX FROM INCLOSED POLYGON FLUX AREA: #---------------------------------------------- # Unpack, reverse, add starting point: l_V = l[bandindices[0]].tolist(); lr_V = l_V[::-1]; lr_V = [l_V[0]] + [l_V[-1]] + lr_V l_R = l[bandindices[1]].tolist(); lr_R = l_R[::-1]; lr_R = [l_R[0]] + [l_R[-1]] + lr_R l_K = bandfluxes[8][0].tolist(); lr_K = l_K[::-1]; lr_K = [l_K[0]] + lr_K l_H = bandfluxes[9][0].tolist(); lr_H = l_H[::-1]; lr_H = [l_H[0]] + lr_H s_V = bandfluxes[0].tolist(); sr_V = s_V[::-1]; sr_V = [0] + [0] + sr_V s_R = bandfluxes[1].tolist(); sr_R = s_R[::-1]; sr_R = [0] + [0] + sr_R s_K = bandfluxes[8][1].tolist(); sr_K = s_K[::-1]; sr_K = [0] + sr_K s_H = bandfluxes[9][1].tolist(); sr_H = s_H[::-1]; sr_H = [0] + sr_H # list for calculation: val_Vp = self.polygon_area(np.array([lr_V, sr_V]).T) val_Kp = self.polygon_area(np.array([lr_K, sr_K]).T) val_Hp = self.polygon_area(np.array([lr_H, sr_H]).T) val_Rp = self.polygon_area(np.array([lr_R, sr_R]).T) # plt.figure() # plt.plot(lr_V, sr_V, 'b-') # plt.plot(lr_V, sr_V, 'r') # plt.show() # Combine with uncertainty: Vp = ufloat(val_Vp, val_Vpself.sigma_V) Kp = ufloat(val_Kp, val_Kpself.sigma_K2) Hp = ufloat(val_Hp, val_Hpself.sigma_H2) Rp = ufloat(val_Rp, val_Rpself.sigma_R) # Calculate S index: sindex_HKp = 8 (Hp + Kp)/(Rp + Vp) #-------------------------------------------------------------- if save is 0: self.s1 = ['1:', val_V, val_K1, val_H1, val_R, sindex_HK1] self.sn = ['n:', val_V, val_Kn, val_Hn, val_R, sindex_HKn] self.sm = ['m:', val_Vm, val_Km, val_Hm, val_Rm, sindex_HKm] self.s2 = ['2:', val_V, val_K2, val_H2, val_R, sindex_HK2] self.sp = ['p:', val_Vp, val_Kp, val_Hp, val_Rp, sindex_HKp] return np.array([sindex_HK1, sindex_HKn, sindex_HKm, sindex_HK2, sindex_HKp]) def polygon_area(self, xy, plot=0): """ This utility takes coordinates (x, y) ordered in an array and calculates the polygon area enclosed. The coordinates needs to be ordered in a counter-clock-wise manner since the circuference using Green's theorem is used to equate the polygon area. """ l = len(xy) s = 0.0 for i in range(l): j = (i+1)%l # keep index in [0,l) s += (xy[j,0] - xy[i,0])(xy[j,1] + xy[i,1]) #-------------------------------------------------------------- return -0.5s def results(self): print('################################################') print(' {} - {} '.format(self.target, self.date)) print('################################################') head_SF = self.hdul[self.SF_dex[0]][0].header head_FF = self.hdul[self.FF_dex[0]][0].header print('Magnitude = {}, Seeing = {}'.format(self.magnitude, self.seeing)) print('Exptime flat: t = {} s'.format(head_SF['EXPTIME'])) print('Exptime star: t = {} s'.format(head_FF['EXPTIME'])) print('------------------------------------------------') print(' CCD NOISE PROPERTIES ') print('------------------------------------------------') BF_mean, BF_std = np.mean(self.BF), np.std(self.BF) DF_mean, DF_std = np.mean(self.DF), np.std(self.DF) print('Bias master : mean = {:.4g}, std = {:.4g}'.format(BF_mean, BF_std)) print('Dark current: mean = {:.4g}, std = {:.4g}'.format(DF_mean, DF_std)) print('GAIN = {:.3g} e-/ADU'.format(self.gain)) print('RON = {:.3g} ADU'.format(BF_std)) print('VAR = {:.3g} ADU (=<RON^2>)'.format(BF_std*2)) print('------------------------------------------------') print(' BACKGROUND SKY & SCATTER ') print('------------------------------------------------') print('Flat mean background counts: {:.1f}'.format(self.f_flux_sky)) print('Star mean background counts: {:.1f}'.format(self.s_flux_sky)) print('------------------------------------------------') print(' RV CORRECTION ') print('------------------------------------------------') print('Barycentric RV correction: {:.2f} km/s'.format(self.delta_v_baryc)) print('Star motion RV Correction: {:.2f} km/s'.format(self.rv_amp)) print('Correction in velocity : {:.2f} km/s'.format(self.delta_v)) print('Correction in wavelength : {:.2f} Å'.format(self.delta_l)) print('Correction in pixelspace : {:.2f}'.format(self.delta_p)) print('------------------------------------------------') print(' SNR & UNCERTAINTIES ') print('------------------------------------------------') f_snr_max, s_snr_max = self.f_snr_max, self.s_snr_max print('S/N in order #57: {:.1f} (flat), {:.1f} (star)'.format(f_snr_max[1], s_snr_max[1])) print('S/N in order #58: {:.1f} (flat), {:.1f} (star)'.format(f_snr_max[0], s_snr_max[0])) print('------------------------------------------------') snx = [self.s_snr_X[0], self.s_snr_X[1], self.s_snr_X[2], self.s_snr_X[3]] snr =[self.sigma_s_snr[0]100,self.sigma_s_snr[1]100,self.sigma_s_snr[2]100,self.sigma_s_snr[3]100] std = [self.sigma_V100, self.sigma_K1100, self.sigma_H1100, self.sigma_R100] print('Bandpass : V K H R \| Total') print('S/N : {:.3g} {:.3g} {:.3g} {:.3g} \|'.format(snx[0], snx[1], snx[2], snx[3])) print('sigma(S/N): {:.3g}% {:.3g}% {:.3g}% {:.3g}% \| {:.1f}%'.format(snr[0], snr[1], snr[2],\ snr[3], np.sum(snr))) print('sigma(std): {:.3g}% {:.3g}% {:.3g}% {:.3g}% \| {:.1f}%'.format(std[0], std[1], std[2],\ std[3], np.sum(std))) print('sigma(wav): \| {:.2f}%'.format(self.sigma_w100)) print('sigma(fla): \| {:.2f}%'.format(self.sigma_f100)) print('------------------------------------------------') print(' S INDEX ') print('------------------------------------------------') print(self.s1) print(self.sn) print(self.sm) print(self.s2) print(self.sp) print('------------------------------------------------') ######################################################################################################## # OPTIMAL WIDTHS # ######################################################################################################## def find_optimal_width(self, image=None, trace=None, plot=0): """ This utility takes most preferably a reduced flat image and the polynomial describtion traced, and first cut out a bandpass defined by disp_lenght and cross_width. Looping through increasing spatials widths the S/N ratio is found for each, and the spatial width asigned to the highest S/N ratio is optimal for linear extraction. To return the results in terms of FWHM a Gauss function is fitted to the spatial width of maximum flux. """ # Check if 'image' and 'trace' is defined: if image==None: image = self.F_calib if trace==None: trace = self.trace # Cut out order: widths = np.arange(1, 40) order = self.cut_out_order(image, np.polyval(trace['order_2'], self.disp), widths[-1]) # Find maximum of blaze function: blaze = order.sum(axis=1) blaze_max = np.max(blaze) index_max = np.nanargmax(blaze) # Find mean sky background along disp direction used for S/N ratio: flux_inter, _ = self.mean_background(image, trace, plot=0) # Loop over spatial widths: snr = np.zeros(len(widths)) for w in widths: order_w = order[index_max, widths[-1]-1-w:widths[-1]-1+w] flux_order = np.sum(order_w) snr[w-1] = self.signal_to_noise(flux_order, len(order_w), flux_order) # Find highest S/N ratio optimal order width: index_max_snr = np.argmax(snr) optimal_order_width = widths[index_max_snr] # Find residual inter-order width: order_distance = int(((self.ref_cen_pos[1] - self.ref_cen_pos[2]) + \ (self.ref_cen_pos[2] - self.ref_cen_pos[3]))/2) #optimal_inter_order_width = int(order_distance - 2.5optimal_order_width) #-------------------------------------------------------------- if plot is 1: pt.plot_optimal_width(widths, order, blaze_max, index_max, flux_inter, snr, optimal_order_width) #-------------------------------------------------------------- self.order_width = optimal_order_width #-------------------------------------------------------------- return self.order_width def mean_background(self, image, trace, plot=0): """ This utility use 'trace' and 'cut_out_order' to select the pixel sky-background in a bandpass on both sides of the order of interest. In spatial direction on each side the median pixel value is found, and lastly the mean value of each side is then computed. Returned is a 1D spectrum describing the background (e.g. used by the 'signal_to_noise' utility). """ # Find midpoint of inter orders: midpoint_below = (self.ref_cen_pos[1] - self.ref_cen_pos[2])/2 midpoint_above = (self.ref_cen_pos[2] - self.ref_cen_pos[3])/2 # Move fit to the midpoint of inter orders: yfit_below = np.polyval(trace['order_1'], self.disp) + np.ones(len(self.disp))midpoint_below yfit_above = np.polyval(trace['order_2'], self.disp) + np.ones(len(self.disp))midpoint_above yfit_order = np.polyval(trace['order_2'], self.disp) + np.ones(len(self.disp)) # Set cross width for background cut to half the distance between orders: # (here the position of the order is a limitation) cross_order_width = math.floor(yfit_below[0])2 - 1 # (else if order are moved up use) #cross_order_width = int((self.ref_cen_pos[1] - self.ref_cen_pos[2])[0]/2 - 1) # Cut out stellar background on both sides: back_below = self.cut_out_order(image, yfit_below, cross_order_width) back_above = self.cut_out_order(image, yfit_above, cross_order_width) # Sum order to 1D spectrum and mean them: l_sky = (np.median(back_below, axis=1) + np.median(back_above, axis=1))/2. flux_sky_mean = abs(l_sky.mean()) #-----------------------------------------------------------: if plot is 1: pt.plot_sky_background(image, self.disp, yfit_below, yfit_above, yfit_order, l_sky) #-------------------------------------------------------------- return flux_sky_mean, l_sky def signal_to_noise(self, flux_star, n_pix_star, flux_sky): """ This function calculates the S/N ratio using the 1D spectrum of the object and sky-background. Purely by statistics with and increasing number of pixel used to define the object 'n_pix_object', the S/N ratio will decrease. The noise sources describing a CCD are the 'gain' (e-/ADU) and 'ron', read-out-noise (e-). """ # See Schroeder (1999) p. 317 or Bradt (2004) p. 163: signal = flux_starself.gain noise = np.sqrt(flux_starself.gain + flux_skyself.gainn_pix_star + self.ronn_pix_star) #-------------------------------------------------------------- return signal / noise ######################################################################################################## # GENERAL UTILITIES SPECIALIZED TO THIS SOFTWARE # ######################################################################################################## def blue_moves(self, path, plot=0): """ This routine measures the drift of the spectrum over time by using ThAr lines in the same order as the Ca II H & K lines. (Fun fact: the software name comes from 'Blue Moves' which is the eleventh studio album release by <NAME>, released in October 1976. """ # Load all files from same folder: img_files = np.sort(glob.glob('{}{}'.format(path, self.img_name))) hdu = np.array([fits.open(str(files)) for files in img_files]) n = len(img_files) # Find time scaling to utc time and Julian Date time = [hdu[i][0].header['JD-DATE'] for i in range(n)] # Loop through all ThAr images: move_x = np.zeros(n) move_y = np.zeros(n) sigma_x = np.zeros(n-1) sigma_y = np.zeros(n-1) for i in range(n): # Open and close one image at a time: with fits.open(str(img_files[i])) as hdu_i: # Select focused spectral region: T_i = hdu_i[0].data[300:480, 420:2270].T # UTILITY CALL: Locate coordinates of lines: COF_i, _, _ = self.peak_finder(T_i, sigma=5, plot=0) # UTILITY CALL: Remove lines too close to borders: COF_i, N_lines = self.image_border(T_i, COF_i) # UTILITY CALL: Only use same lines each time: if i==0: #COF_0, _, _ = self.peak_finder(T_i, sigma=5, plot=0) COF_0 = COF_i if i is not 0: indices0, indices1 = self.match_coordinates(COF_0, COF_i, threshold=5, plot=1) # Find scatter of the drift for each line: if i > 1: diff_x = COF_i[indices1,0] - x diff_y = COF_i[indices1,1] - y sigma_x[i-1] = np.std(diff_x) sigma_y[i-1] = np.std(diff_y) # Find coordinates (x and y needs to be after if < 1 statement): x = COF_i[indices1,0] y = COF_i[indices1,1] move_x[i] = x.mean() move_y[i] = y.mean() # Print to bash: pt.compilation(i, n, 'Blue Moves') print # Convert to relative changes: move_x = move_x[1::] - move_x[1::].mean() move_y = move_y[1::] - move_y[1::].mean() time = time[1::] #----------------------------------------------------------- if plot is 1: np.savetxt('{}bluemoves.txt'.format(self.path), np.vstack([time, move_y, sigma_y]).T) pt.plot_rv_stability(time, move_y, sigma_y) #----------------------------------------------------------- return def image_border(self, image, pixel_coor, border_edge=20): """ This utility takes an array of pixel coordinates and finds coordinates that is closer than 20 pixels to the image 'border_edge'. These coordinates are then removed from the array and a new array, 'new_pixel_coor', is returned together with the new (lower) number of coordinates 'N_coor'. """ # Unpack pixel coordinates: x = pixel_coor[:,0] y = pixel_coor[:,1] # Check if stellar coordinates are too close to borders: i_x1 = np.where(x < border_edge)[0] i_y1 = np.where(y < border_edge)[0] i_x2 = np.where(x > np.shape(image)[0]-border_edge)[0] i_y2 = np.where(y > np.shape(image)[1]-border_edge)[0] i_xy = np.hstack([i_x1, i_x2, i_y1, i_y2]) # Discard these coordinates: x_new = np.delete(x, i_xy) y_new = np.delete(y, i_xy) N_coor = len(x) #----------------------------------------------------------- return np.array([x_new, y_new]).T, N_coor ######################################################################################################## # GENERAL STRUCTURAL ALGORITHMS # ######################################################################################################## def peak_finder(self, pixel_array, min_pix=7, sigma=1, plot=0): """ This utility takes a pixel array and use the 'scipy.ndimage' package to find local maxima within an image. These are determined upon the number of standard deviations, 'sigma', and a minimum of pixels, 'min_pix', a structure should be considered. From the returned structure the same package determines the Center Of Flux ('COF') in coordinate space (x, y), and the circular 'radius' for each, together with the number of local maximum structures, 'N_struct', detected within the pixel_array. """ # FIND CENTER OF FLUX FOR STARS ABOVE THRESHOLD: # Define threshold as a number of standard deviations above the mean: threshold = np.mean(pixel_array) + sigmanp.std(pixel_array) # Find all pixels above the threshold: above_threshold = np.where(pixel_array > threshold, 1, 0) # Label the structures (where starmap = 1 that are adjacent to others): labels, N_structs = scipy.ndimage.label(above_threshold, structure = np.ones((3,3))) # Sum the number of elements in each structure: sums = scipy.ndimage.sum(above_threshold, labels, range(1,N_structs+1)) # Choose only structures with more than min_pix elements (+1 for index mismatch): structs = np.where(sums > min_pix)[0] + 1 # Define starmap as 0 where there are no stars and 1 where there are stars: struct_map = np.zeros(np.shape(pixel_array)) for struct in structs: struct_map = struct_map + np.where(labels == struct, 1, 0) # Label all the structures again: labels, N_structs = scipy.ndimage.label(struct_map, structure = np.ones((3,3))) # Find the center of flux of all the structures found above threshold: COF = scipy.ndimage.center_of_mass(pixel_array, labels, range(1, N_structs+1)) # Estimate the radius of the structures in pixels: radius = np.sqrt(sums[structs-1]/np.pi) # From tuple to array: COF = np.asarray(COF) #-------------------------------------------------------------- if plot is 1: # NEEDS ACTIVATION FROM SOURCE plt.figure() plt.imshow(pt.linear(pixel_array.T), cmap='Blues', origin='lower') plt.scatter(COF[:,0], COF[:,1], s=radius12, facecolors='none', edgecolors='r', marker='s') plt.show() #-------------------------------------------------------------- return COF, radius, N_structs def find_peak_in_noise(self, s, peak_coor, plot=0): """ This utility identifies the """ # Define limits for peak search: limits = [int(peak_coor-25), int(peak_coor+25)] # Different conditions: conv = self.res_power 1e-3 # Smooth-filter scale linear with resolving power width = self.len_disp/10 # Width scales likewise with the pixel scale # Find all peaks: peaks_all_dex, _ = scipy.signal.find_peaks(s) peaks_all_val = s[peaks_all_dex] # Find all approx peaks from convolved spectrum: s_conv = self.convolve(s, 'median', int(conv)) s_conv = self.convolve(s_conv, 'mean', int(conv)) peaks_conv_dex, _ = scipy.signal.find_peaks(s_conv) peaks_conv_val = s_conv[peaks_conv_dex] # Select peaks inside disp limits range: ndarray = (peaks_conv_dex > limits[0]) * (peaks_conv_dex < limits[1]) peaks_limit_dex = peaks_conv_dex[ndarray] peaks_limit_val = peaks_conv_val[ndarray] # Find x highest peaks from convolved spectrum: peak_conv_dex = heapq.nlargest(1, np.arange(len(peaks_limit_val)), key=peaks_limit_val.__getitem__)[0] peak_conv_pix = peaks_limit_dex[peak_conv_dex] # Make bold array around peak with dobbelt the width of conv: peak_dex = (peaks_all_dex>peak_conv_pix-conv)(peaks_all_dex<peak_conv_pix+conv) # Select peak: peak = peaks_all_dex[peak_dex][np.argmax(peaks_all_val[peak_dex])] #-------------------------------------------------------------- if plot is 1: pt.plot_arc_peak(s, s_conv, peaks_limit_dex, peaks_limit_val, peaks_all_dex, \ peaks_all_val, peak, limits) #-------------------------------------------------------------- return peak def convolve(self, data0, filtertype, n): """ This function can be used to correct for slow trends using e.g. a "moving mean" filter. The utility takes the flatten data, a string with the desired filter, and n number of points is should smooth the data with. Compared to the bottleneck package this function do not leave a offset. """ # Constants: data = data0.copy() # Avoid overwritting data: data_new = np.zeros(len(data)) # To pick up new data nzero = np.zeros(2n+1) # optimization constant # Available filters: if filtertype=='mean': moving_filter = np.mean if filtertype=='median': moving_filter = np.median if filtertype=='sum': moving_filter = np.sum if filtertype=='std': moving_filter = np.std # Interval: d[n, 1+n, ... , N-1, N-n] for i in range(len(data)-2n): data_new[n+i] = moving_filter(data[range((n+i)-n, (n+i)+n+1)]) for i in range(n): # Interval: d[-n, -(n-1), ... , n-1, n] - Low end of data low = nzero low[range(n-i)] = data[0]np.ones(n-i) low[-(n+1+i):] = data[range(0, n+1+i)] data_new[i] = moving_filter(low) # Interval: d[N-n, N-(n-1), ... , N+(n-1), N+n] - High end of data high = nzero high[range(n+1+i)] = data[range(len(data)-(n+i+1), len(data))] high[-(n-i):] = data[-1]*np.ones(n-i) data_new[len(data)-1-i] = moving_filter(high) #-------------------------------------------------------------- return data_new def match_coordinates(self, array1, array2, threshold=10, plot=0): """ This function match two set of coordinates. This is done by a purely geometrical technique and looking at the histogram. It finds the minimum distance from i'th array1 star to every other array2 star. Here indices is the rows with all the indices of matching. To select only common coordinates, then use 'indices2'. """ # Placeholders: value_min = np.zeros(len(array1)) index_min = {} # FIND MINIMUM DISTANCE WITH PYTHAGOREAN GEOMETRY: for i in range(len(array1)): d =	np.sqrt( (array2[:,0]-array1[i,0])2 + (array2[:,1]-array1[i,1])2 )	numpy.sqrt
# Copyright (c) 2015-2018 by the parties listed in the AUTHORS file. # All rights reserved. Use of this source code is governed by # a BSD-style license that can be found in the LICENSE file. # from memory_profiler import profile import os from toast_planck.reproc_modules.destripe_tools import (fast_hit_binning, fast_binning) import scipy.signal from toast import qarray as qa from toast.mpi import MPI from toast_planck.preproc_modules import MapSampler, flagged_running_average import astropy.io.fits as pf import numpy as np import toast.fod as tf import toast.timing as timing class OpNoiseEstim(): def __init__( self, signal=None, flags=None, detmask=1, commonmask=3, out=None, maskfile=None, mapfile=None, rimo=None, pol=True, nbin_psd=1000, lagmax=100000, stationary_period=86400., nosingle=False, no_spin_harmonics=False, calibrate_signal_estimate=False, nsum=10, naverage=100): self._signal = signal self._flags = flags self._detmask = detmask self._commonmask = commonmask self._out = out self._maskfile = maskfile self._mapfile = mapfile if rimo is None: raise RuntimeError('OpNoiseEstim: You must provide a RIMO') self._rimo = rimo self._pol = pol self._nbin_psd = nbin_psd self._lagmax = lagmax self._stationary_period = stationary_period self._nosingle = nosingle self._no_spin_harmonics = no_spin_harmonics self._calibrate_signal_estimate = calibrate_signal_estimate # Parameters for downsampling the data self._nsum = nsum self._naverage = naverage def exec(self, data): cworld = data.comm.comm_world rank = cworld.Get_rank() masksampler = None if self._maskfile: masksampler = MapSampler(self._maskfile, comm=cworld) mapsampler = None if self._mapfile: mapsampler = MapSampler(self._mapfile, comm=cworld, pol=True) for obs in data.obs: tod = obs['tod'] local_intervals = tod.local_intervals(obs['intervals']) dets = tod.local_dets ndet = len(dets) timestamps = tod.local_timestamps() commonflags = tod.local_common_flags() commonflags = (commonflags & self._commonmask != 0) fsample = self.subtract_signal( tod, cworld, rank, masksampler, mapsampler, local_intervals) # Extend the gap between intervals to prevent sample pairs # that cross the gap. intervals = obs['intervals'] gap_min = np.int(self._lagmax) + 1 # Downsampled data requires longer gaps gap_min_nsum = np.int(self._lagmax * self._nsum) + 1 offset, nsamp = tod.local_samples gapflags = np.zeros_like(commonflags) gapflags_nsum = np.zeros_like(commonflags) for ival1, ival2 in zip(intervals[:-1], intervals[1:]): gap_start = ival1.last + 1 gap_stop = max(gap_start + gap_min, ival2.first) gap_stop_nsum = max(gap_start + gap_min_nsum, ival2.first) if gap_start < offset + nsamp and gap_stop > offset: gap_start = max(0, gap_start - offset) gap_stop = min(offset + nsamp, gap_stop - offset) gapflags[gap_start:gap_stop] = True gap_stop_nsum = min(offset + nsamp, gap_stop_nsum - offset) gapflags_nsum[gap_start:gap_stop_nsum] = True for idet1 in range(ndet): for idet2 in range(idet1, ndet): det1 = dets[idet1] det2 = dets[idet2] if det1 == det2 and self._nosingle: continue signal1 = tod.local_signal(det1) flags1 = tod.local_flags(det1, name=self._flags) flags = (flags1 & self._detmask != 0) signal2 = None flags2 = None if det1 != det2: signal2 = tod.local_signal(det2) flags2 = tod.local_flags(det2, name=self._flags) flags[flags2 & self._detmask != 0] = True flags[commonflags] = True self.process_noise_estimate( signal1, signal2, flags, gapflags, gapflags_nsum, timestamps, fsample, cworld, rank, 'noise', det1, det2, local_intervals) return def subtract_signal(self, tod, cworld, rank, masksampler, mapsampler, local_intervals): """ Subtract a signal estimate from the TOD and update the flags for noise estimation. """ start_signal_subtract = MPI.Wtime() for det in tod.local_dets: if rank == 0: print('Subtracting signal for {}'.format(det), flush=True) tod.cache.report() fsample = self._rimo[det].fsample epsilon = self._rimo[det].epsilon eta = (1 - epsilon) / (1 + epsilon) signal = tod.local_signal(det, name=self._signal) flags = tod.local_flags(det, name=self._flags) flags &= self._detmask for ival in local_intervals: ind = slice(ival.first, ival.last + 1) sig = signal[ind] flg = flags[ind] quat = tod.local_pointing(det)[ind] if self._pol: theta, phi, psi = qa.to_angles(quat) iw = np.ones_like(theta) qw = eta * np.cos(2 * psi) uw = eta * np.sin(2 * psi) iquw = np.column_stack([iw, qw, uw]) else: theta, phi = qa.to_position(quat) if masksampler is not None: maskflg = masksampler.at(theta, phi) < 0.5 flg[maskflg] \|= 255 if mapsampler is not None: if self._pol: bg = mapsampler.atpol(theta, phi, iquw) else: bg = mapsampler.at(theta, phi) if self._calibrate_signal_estimate: good = flg == 0 ngood = np.sum(good) if ngood > 1: templates = np.vstack([np.ones(ngood), bg[good]]) invcov = np.dot(templates, templates.T) cov = np.linalg.inv(invcov) proj = np.dot(templates, sig[good]) coeff = np.dot(cov, proj) bg = coeff[0] + coeff[1] * bg sig -= bg cworld.barrier() stop_signal_subtract = MPI.Wtime() if rank == 0: print('TOD signal-subtracted in {:.2f} s'.format( stop_signal_subtract - start_signal_subtract), flush=True) return fsample def decimate(self, x, flg, gapflg, local_intervals): # Low-pass filter with running average, then downsample xx = x.copy() flags = flg.copy() for ival in local_intervals: ind = slice(ival.first, ival.last + 1) xx[ind], flags[ind] = flagged_running_average( x[ind], flg[ind], self._naverage, return_flags=True) return xx[::self._nsum].copy(), (flags + gapflg)[::self._nsum].copy() """ def highpass(self, x, flg): # Flagged real-space high pass filter xx = x.copy() j = 0 while j < x.size and flg[j]: j += 1 alpha = .999 for i in range(j+1, x.size): if flg[i]: xx[i] = x[j] else: xx[i] = alpha(xx[j] + x[i] - x[j]) j = i xx /= alpha return xx """ def log_bin(self, freq, nbin=100, fmin=None, fmax=None): if np.any(freq == 0): raise Exception('Logarithmic binning should not include ' 'zero frequency') if fmin is None: fmin = np.amin(freq) if fmax is None: fmax = np.amax(freq) bins = np.logspace(np.log(fmin), np.log(fmax), num=nbin + 1, endpoint=True, base=np.e) bins[-1] = 1.01 # Widen the last bin not to have a bin with one entry locs = np.digitize(freq, bins).astype(np.int32) hits = np.zeros(nbin + 2, dtype=np.int32) fast_hit_binning(locs, hits) return locs, hits def bin_psds(self, my_psds, fmin=None, fmax=None): my_binned_psds = [] my_times = [] binfreq0 = None for i in range(len(my_psds)): t0, _, freq, psd = my_psds[i] good = freq != 0 if self._no_spin_harmonics: # Discard the bins containing spin harmonics and their # immediate neighbors for i0 in range(1, 3): f0 = i0 / 60. for i in range(1, 30): fmask = f0 * i imin = np.argmin(np.abs(freq - fmask)) if i == 1: # The first bin has a wider neighborhood good[imin - 2:imin + 3] = False else: good[imin - 1:imin + 2] = False if self._nbin_psd is not None: locs, hits = self.log_bin(freq[good], nbin=self._nbin_psd, fmin=fmin, fmax=fmax) binfreq = np.zeros(hits.size) fast_binning(freq[good], locs, binfreq) binfreq = binfreq[hits != 0] / hits[hits != 0] else: binfreq = freq hits = np.ones(len(binfreq)) if binfreq0 is None: binfreq0 = binfreq else: if np.any(binfreq != binfreq0): raise Exception('Binned PSD frequencies change') if self._nbin_psd is not None: binpsd = np.zeros(hits.size) fast_binning(psd[good], locs, binpsd) binpsd = binpsd[hits != 0] / hits[hits != 0] else: binpsd = psd my_times.append(t0) my_binned_psds.append(binpsd) return my_binned_psds, my_times, binfreq0 def discard_spin_harmonics(self, binfreq, all_psds): ind = binfreq != 0 for i0 in range(1, 3): f0 = i0 / 60. for i in range(1, 10): fmask = f0 * i imin = np.argmin(np.abs(binfreq - fmask)) if i == 1: ind[imin - 1:imin + 2] = False else: ind[imin] = False binfreq = binfreq[ind] all_psds = all_psds[:, ind] return binfreq, all_psds def discard_outliers(self, binfreq, all_psds, all_times): all_psds = list(all_psds) all_times = list(all_times) nrow, ncol = np.shape(all_psds) # Discard empty PSDs i = 1 nbad = 0 all_psds = list(all_psds) all_times = list(all_times) while i < nrow: p = all_psds[i] if np.all(p == 0) or np.any(np.isnan(p)): del all_psds[i] del all_times[i] nrow -= 1 nbad += 1 else: i += 1 if nbad > 0: print('Discarded {} empty or NaN psds'.format(nbad), flush=True) # Throw away outlier PSDs by comparing the PSDs in specific bins if nrow > 10: all_good = np.isfinite(np.sum(all_psds, 1)) for col in range(ncol - 1): if binfreq[col] < .001: continue # Local outliers psdvalues = np.array([x[col] for x in all_psds]) smooth_values = scipy.signal.medfilt(psdvalues, 11) good = np.ones(psdvalues.size, dtype=np.bool) good[psdvalues == 0] = False for i in range(10): # Local test diff = np.zeros(psdvalues.size) diff[good] = np.log(psdvalues[good]) - \ np.log(smooth_values[good]) sdev =	np.std(diff[good])	numpy.std
from __future__ import absolute_import from __future__ import division from __future__ import print_function import nibabel as nib from dltk.io.preprocessing import whitening,normalise_zero_one from sklearn import preprocessing import matplotlib.pyplot as plt import matplotlib.image as mpimg import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import os import matplotlib.pyplot as plt import numpy as np import fnmatch import AD_Constants as constants import requests import sys from imgaug import augmenters as iaa from numpy import random import time import re import tensorflow as tf from tensorflow.python.keras.api.keras.preprocessing.image import ImageDataGenerator,img_to_array, load_img,array_to_img class Dataset_Import(): def __init__(self): self.main_directory=constants.main_image_directory self.training_part=constants.training_frac self.i = 0 self.auto_shuffling_state=False self.trainer_shuffling_state = False self.train_ad_fnames = None self.train_mci_fnames = None self.train_nc_fnames = None self.source=constants.source self.target=constants.target self.img_shape_tuple=constants.img_shape_tuple self.img_channel=constants.img_channel self.shu_control_state=False self.data_augmentation=constants.augment_data self.image_group=constants.chosen_epi_format self.strict_match=constants.strict_match self.pic_index =constants.pic_index self.train_dir =constants.train_dir self.validation_dir =constants.validation_dir self.set_checker=0 self.classify_group=constants.classify_group # Directory with our training AD dataset self.train_ad_dir =constants.train_ad_dir # Directory with our training MCI dataset self.train_mci_dir = constants.train_mci_dir # Directory with our training NC dataset self.train_nc_dir =constants.train_nc_dir # Directory with our validation AD dataset self.validation_ad_dir = constants.validation_ad_dir # Directory with our validation MCI dataset self.validation_mci_dir =constants.validation_mci_dir # Directory with our validation NC dataset self.validation_nc_dir =constants.validation_nc_dir self.nrows =constants.nrows self.ncols = constants.ncols def statistics(self): #read_directory_file print('total training AD Data:', self.readNiiFiles(self.train_ad_dir,augment_data=True,gan_train=True), end="\n") # print('total training MCI Data:', len(self.readNiiFiles(self.train_mci_dir,augment_data=False)), end="\n") # print('total training NC Data:', len(self.readNiiFiles(self.train_nc_dir,augment_data=True)), end="\n") # print('total validation AD Data:', len(self.readNiiFiles(self.validation_ad_dir,augment_data=True)), end="\n") # print('total validation MCI Data:', len(self.readNiiFiles(self.validation_mci_dir,augment_data=False)), end="\n") # print('total validation NC Data:', len(self.readNiiFiles(self.validation_nc_dir,augment_data=False)), end="\n") #print('total main validation AD Data:', len(self.readNiiFiles(constants.main_validation_ad_dir, augment_data=False,checker=9)), end="\n") #print('total main validation MCI Data:', len(self.readNiiFiles(constants.main_validation_mci_dir, augment_data=False,checker=9)), end="\n") #print('total main validation NC Data:', len(self.readNiiFiles(constants.main_validation_nc_dir, augment_data=False,checker=9)), end="\n") def readNiiFiles(self,original_dir,augment_data=False,checker=8,gan_train=False): # creating destinations folders counter = 0 group_data = [] ad_class =original_dir.split(os.sep)[-1] domain_class = original_dir.split(os.sep)[-2] if domain_class =='train': source = '1.5T' elif domain_class =='validation': source='3.0T' elif domain_class == 'model_validate': source='1.5T' checker_file="" for root, dir, f_names in os.walk(original_dir): for f in f_names: if f.lower().find(".nii") > -1: sourcefile = os.path.join(root, f) if sourcefile.find("skull_workflow") <= -1 : for fgroup in self.image_group: if fgroup in sourcefile: if sourcefile.split(os.sep)[checker] != checker_file: checker_file = sourcefile.split(os.sep)[checker] try: label =self.get_nii_group(ad_class) source_label =self.get_nii_source(source) if gan_train == False : group_data.append([sourcefile, label, source_label]) if augment_data == True: for l in range(constants.augment_factor): aug_source_file = sourcefile.replace('.nii',str(l+1)+'_aug.nii') group_data.append([aug_source_file, label, source_label]) #print("data" ,[sourcefile, label, source_label]) except (IOError, OSError) as e: print(e.errno) sys.exit(2) return group_data def readNiiFiles_2(self,original_dir,augment_data=False,gan_train=False,augment_factor=constants.augment_factor,limit_number=0): # creating destinations folders group_data = [] ad_class =original_dir.split(os.sep)[-1] domain_class = original_dir.split(os.sep)[-2] augment_counter=0 augment_pause=False augment_limit=constants.max_class_value-limit_number if domain_class =='train': source = '1.5T' elif domain_class =='validation': source='3.0T' elif domain_class == 'model_validate': source='1.5T' else: source='1.5T' for root, dir, f_names in os.walk(original_dir): for f in f_names: if f.lower().find(".nii") > -1: sourcefile = os.path.join(root, f) #check=sourcefile.split(os.sep)[9] # check.lower().find("localizer") < 0 if True: try: label =self.get_nii_group(ad_class) source_label =self.get_nii_source(source) if gan_train == False : group_data.append([sourcefile, label, source_label]) if augment_data == True: if augment_pause==False: aug_source_file = sourcefile.replace('.nii',str(1)+'_aug.nii') group_data.append([aug_source_file, label, source_label]) augment_counter =augment_counter+1 if augment_counter==limit_number: augment_pause=True #print("data" ,[sourcefile, label, source_label]) except (IOError, OSError) as e: print(e.errno) sys.exit(2) augment_pause=False augment_counter=0 return group_data def read_directory_file(self,original_dir,dx_group,source=None): group_data = [] try: if original_dir is not None: for file in os.listdir(original_dir): if os.path.isdir(os.path.join(original_dir,file)): filepath = os.path.join(original_dir, file) for sub_file in os.listdir(filepath): if os.path.isdir(os.path.join(filepath,sub_file)): filepath2 = os.path.join(filepath, sub_file) for sub_file2 in os.listdir(filepath2): decision_check=None if self.strict_match : decision_check= sub_file2.strip() in self.image_group #str(self.image_group) == str(sub_file.strip()) if decision_check==True : modal_groupings_source=os.path.join(filepath2 ,sub_file2) files_in_time = len(os.listdir(modal_groupings_source)) if files_in_time > 1: time_file=max(os.listdir(modal_groupings_source)) else: time_file=os.listdir(modal_groupings_source)[0] time_grouping_source=os.path.join(modal_groupings_source ,time_file) files_in_time=len(os.listdir(modal_groupings_source)) for image_file in os.listdir(time_grouping_source): image_grouping_source = os.path.join(time_grouping_source, image_file) if os.path.isdir(os.path.join(image_grouping_source)): if os.path.isdir(os.path.join(image_grouping_source, "skull_workflow")): image_grouping_source=os.path.join(image_grouping_source,"skull_workflow") if os.path.isdir(os.path.join(image_grouping_source,"BSE")): image_grouping_source = os.path.join(image_grouping_source,"BSE") for image_file in os.listdir(image_grouping_source): pattern=".bse.nii.gz" if fnmatch.fnmatch(image_file,pattern): image_grouping_source_file=os.path.join(image_grouping_source,image_file) label=self.get_nii_group(dx_group) source_label=self.get_nii_source(source) group_data.append([image_grouping_source_file,label,source_label]) image_grouping_source="" image_grouping_source_file="" except OSError as e: print('Error: %s' % e) return group_data def try_replace(self): st='/HDD/public/data/ADNI_NEW_DATASET/train/MCI/016_S_11493_aug.nii' #st.replace('_aug.nii', '.nii') if st.find("_aug.nii") > -1: st = re.sub("\d_aug.nii",'.nii',st) return st def convert_nii_to_image_data(self,nii_path): if nii_path.find("_aug.nii") > -1: nii_path = re.sub("\d_aug.nii",'.nii',nii_path) image_load = nib.load(nii_path, mmap=False) img_data = self.dataAugment(np.asarray(image_load.get_data()[:,:,:,0])) else: image_load = nib.load(nii_path, mmap=False) #use_shape=image_load img_data = np.asarray(image_load.get_data()[:,:,:,0]) shape = img_data.shape #print(shape) #img_data = img_data[65:shape[0]-65,30:shape[1]-30, int(shape[2]/2)-40:int(shape[2]/2)+40] #img_data = img_data[45:105, 40:170, 55:115] #img_data = img_data[int(shape[2] / 2) - 65:int(shape[2] / 2) + 65,int(shape[2] / 2) - 70:int(shape[2] / 2) + 50, int(shape[2] / 2) - 40:int(shape[2] / 2) + 40] #img_data = img_data[0:90, 0:90,:] img_data = img_data.astype(np.float32) img_data -= np.mean(img_data) img_data/=np.maximum(np.std(img_data),10(-5)) img_data = np.pad(img_data, pad_width=((int(np.ceil(1.0 (constants.img_shape_tuple[0] - shape[0]) / 2)), int(np.floor(1.0 * (constants.img_shape_tuple[0] - shape[0]) / 2))), ( int(np.ceil(1.0 * (constants.img_shape_tuple[1] - shape[1]) / 2)), int(np.floor(1.0 * (constants.img_shape_tuple[1] - shape[1]) / 2))), (int(np.ceil(1.0 * (constants.img_shape_tuple[2] - shape[2]) / 2)), int(np.floor(1.0 * (constants.img_shape_tuple[2] - shape[2]) / 2)))), mode="constant", constant_values=(0, 0)) #print("shape",img_data.shape) # img_to_array # seg=iaa.Sequential([ # iaa.BilateralBlur(d=(3,10), sigma_color=(10,255), sigma_space=(10,255)) # ]) # # #print(np.resize(img_data,constants.img_bilateral_filter).shape) # # filter_bilateral=seg.augment_image(np.resize(np.uint8(img_data),constants.img_bilateral_filter)) #determine_shape = np.resize(img_data, self.img_shape_tuple) #print("data",normalise_zero_one(determine_shape+ np.random.normal(0, 0.05, determine_shape.shape))) normalise_zero_one(img_data) return img_data #normalise_zero_one( #determine_shape(1./255) def convert_nii_to_image_dataK(self,nii_path): if nii_path.find("_aug.nii") > -1: nii_path = re.sub("\d_aug.nii",'.nii',nii_path) image_load = nib.load(nii_path, mmap=False) img_data = self.dataAugment(np.asarray(image_load.get_data())) else: image_load = nib.load(nii_path, mmap=False) img_data = np.asarray(image_load.get_data()) K_SHAPE=(self.img_shape_tuple[0],self.img_shape_tuple[1],self.img_shape_tuple[2],self.img_channel) #img_to_array determine_shape = np.resize(img_data,K_SHAPE) #print("data",normalise_zero_one(determine_shape+ np.random.normal(0, 0.05, determine_shape.shape))) return normalise_zero_one(determine_shape) def convert_nii_to_image_real(self,nii_path): if nii_path.find("_aug.nii") > -1: nii_path = re.sub("\d_aug.nii",'.nii',nii_path) image_load = nib.load(nii_path, mmap=False) img_data = np.asarray(image_load.get_data()) else: image_load = nib.load(nii_path, mmap=False) img_data = np.asarray(image_load.get_data()) #img_to_array determine_shape = np.resize(img_data, self.img_shape_tuple) #print("data",normalise_zero_one(determine_shape+ np.random.normal(0, 0.05, determine_shape.shape))) return normalise_zero_one(determine_shape) #normalise_zero_one( #determine_shape(1./255) def one_hot_encode(self,vec): ''' For use to one-hot encode the 3- possible labels ''' n_classes=len(constants.classify_group) return np.eye(n_classes)[vec] def one_hot_encode_d(self,vec): ''' For use to one-hot encode the 3- possible labels ''' n_classes=2 return np.eye(n_classes)[vec] def get_nii_group(self,nii_path): img_label=nii_path if img_label=="AD": label=1 elif img_label=="NC": label=2 elif img_label=="MCI": label=0 return label def get_nii_source(self, source_target): source_label = source_target if source_label == "1.5T": source_label = 0 elif source_label == "3.0T": source_label = 1 return source_label def all_source_data(self,augment_data=False): if "AD" in constants.classify_group: all_ad_train = self.readNiiFiles_2(self.train_ad_dir,augment_data=augment_data,limit_number=264) if "MCI" in constants.classify_group: all_mci_train = self.readNiiFiles_2(self.train_mci_dir,augment_data=augment_data,limit_number=320) if "NC" in constants.classify_group: all_nc_train = self.readNiiFiles_2(self.train_nc_dir,augment_data=augment_data,limit_number=320) data_feed=[] for groups in constants.classify_group: if groups=="AD": data_feed=data_feed+all_ad_train elif groups=="MCI": data_feed = data_feed + all_mci_train elif groups=="NC": data_feed=data_feed + all_nc_train all_source = [img_path for i, img_path in enumerate(data_feed)] all_source = np.array(all_source) self.set_random_seed(random.random_integers(1200)) return self.shuffle(all_source) def all_source_gan(self, augment_data=False): if "AD" in constants.classify_group: all_ad_train = self.readNiiFiles_2(self.train_ad_dir, augment_data=False,gan_train=False) if "MCI" in constants.classify_group: all_mci_train = self.readNiiFiles_2(self.train_mci_dir, augment_data=False,gan_train=False) if "NC" in constants.classify_group: all_nc_train = self.readNiiFiles_2(self.train_nc_dir, augment_data=False,gan_train=False) data_feed = [] for groups in constants.classify_group: if groups == "AD": data_feed = data_feed + all_ad_train elif groups == "MCI": data_feed = data_feed + all_mci_train elif groups == "NC": data_feed = data_feed + all_nc_train all_source = [img_path for i, img_path in enumerate(data_feed)] all_source = np.array(all_source) self.set_random_seed(random.random_integers(1200)) return self.shuffle(all_source) def all_main_validate(self, augment_data=False): if "AD" in constants.classify_group: all_ad_val = self.readNiiFiles_2(constants.main_validation_ad_dir, augment_data=augment_data) if "MCI" in constants.classify_group: all_mci_val = self.readNiiFiles_2(constants.main_validation_mci_dir, augment_data=augment_data) if "NC" in constants.classify_group: all_nc_val = self.readNiiFiles_2(constants.main_validation_nc_dir, augment_data=augment_data) data_feed = [] for groups in constants.classify_group: if groups == "AD": data_feed = data_feed + all_ad_val elif groups == "MCI": data_feed = data_feed + all_mci_val elif groups == "NC": data_feed = data_feed + all_nc_val all_val = [img_path for i, img_path in enumerate(data_feed)] all_val = np.array(all_val) self.set_random_seed(	random.random_integers(1200)	numpy.random.random_integers
import pytest pytest.importorskip('numpy') import numpy as np import pytest import dask.array as da from dask.array.utils import assert_eq def test_linspace(): darr = da.linspace(6, 49, chunks=5) nparr = np.linspace(6, 49) assert_eq(darr, nparr) darr = da.linspace(1.4, 4.9, chunks=5, num=13) nparr = np.linspace(1.4, 4.9, num=13) assert_eq(darr, nparr) darr = da.linspace(6, 49, chunks=5, dtype=float) nparr = np.linspace(6, 49, dtype=float) assert_eq(darr, nparr) darr = da.linspace(1.4, 4.9, chunks=5, num=13, dtype=int) nparr = np.linspace(1.4, 4.9, num=13, dtype=int) assert_eq(darr, nparr) assert (sorted(da.linspace(1.4, 4.9, chunks=5, num=13).dask) == sorted(da.linspace(1.4, 4.9, chunks=5, num=13).dask)) assert (sorted(da.linspace(6, 49, chunks=5, dtype=float).dask) == sorted(da.linspace(6, 49, chunks=5, dtype=float).dask)) def test_arange(): darr = da.arange(77, chunks=13) nparr = np.arange(77) assert_eq(darr, nparr) darr = da.arange(2, 13, chunks=5) nparr = np.arange(2, 13) assert_eq(darr, nparr) darr = da.arange(4, 21, 9, chunks=13) nparr = np.arange(4, 21, 9) assert_eq(darr, nparr) # negative steps darr = da.arange(53, 5, -3, chunks=5) nparr = np.arange(53, 5, -3) assert_eq(darr, nparr) darr = da.arange(77, chunks=13, dtype=float) nparr = np.arange(77, dtype=float) assert_eq(darr, nparr) darr = da.arange(2, 13, chunks=5, dtype=int) nparr = np.arange(2, 13, dtype=int) assert_eq(darr, nparr) assert (sorted(da.arange(2, 13, chunks=5).dask) == sorted(da.arange(2, 13, chunks=5).dask)) assert (sorted(da.arange(77, chunks=13, dtype=float).dask) == sorted(da.arange(77, chunks=13, dtype=float).dask)) # 0 size output darr = da.arange(0, 1, -0.5, chunks=20) nparr = np.arange(0, 1, -0.5) assert_eq(darr, nparr) darr = da.arange(0, -1, 0.5, chunks=20) nparr = np.arange(0, -1, 0.5) assert_eq(darr, nparr) def test_arange_has_dtype(): assert da.arange(5, chunks=2).dtype ==	np.arange(5)	numpy.arange
import glob import subprocess import numpy as np import dynapack.LSwrite as LSw from fnmatch import filter as fltr import matplotlib.pyplot as plt """ Functions for post-processing of LS-DYNA files """ def readrcforc( Inputfilename): """ Function to read rcforc files from LS-DYNA Run as rc = readrcforc('rcforc') Plot as plt.plot(rc[contactID,forcedir,:]) """ ncontacts=0 contactnames = [] f = open(Inputfilename,'r') for i in range(0,5): dummy = f.readline() # Read to find number of contacts temp=0 while temp>-1: line = f.readline() if 'END' in line: temp = -5 line = f.readline() else: ncontacts = ncontacts + 1 s=' ' seq = line.strip().split()[1:] contactnames.append(s.join(seq)) # Create 2d list for data storage rcforc= [[] for i in range(ncontacts)] # Read reaction forces for each contact temp=0 while temp>-1: for i in range(0,ncontacts2): line = f.readline() if 'master' in line: #print line contactID = int(float(line.strip().split()[1])) time = float(line.strip().split()[3]) Fx = float(line.strip().split()[5]) Fy = float(line.strip().split()[7]) Fz = float(line.strip().split()[9]) Mx = float(line.strip().split()[13]) My = float(line.strip().split()[15]) Mz = float(line.strip().split()[17]) Fres = np.sqrt(Fx2 + Fy2 + Fz*2) rcforc[contactID-1].append([Fx, Fy, Fz, Mx, My, Mz, time, Fres]) if len(line)==0: temp=-5 f.close() # Sort into better array def column(matrix, i): #Extract column from array return [row[i] for row in matrix] def rclen(rc): # Find length of rcforc file rclengths = np.zeros(len(rcforc)) for i in range(0,len(rcforc)): rclengths[i] = len(column(rcforc[i],0)) return int(np.max(rclengths)) temp = np.zeros((ncontacts, 8, rclen(rcforc))) for i in range(0,ncontacts): if len(rcforc[i])>0: for j in range(0,8): temp[i,j,:] = column(rcforc[i],j) return temp def readnodout( Inputfilename, stringname = ''): """ Script to read nodout-files from LS-DYNA Run as nodout = readnodout('nodout') nodoutID = readnodout('nodout', 'stringname') Plot as plt.plot(nodout[:,nodeID,variableID]) Variables : 0 : time 1 : nodal_point 2 : x-disp 3 : y-disp 4 : z-disp 5 : x-vel 6 : y-vel 7 : z-vel 8 : x-accl 9 : y-accl 10 : z-accl 11 : x-coor 12 : y-coor 13 : z-coor """ import numpy as np def chunks(l, n): """Yield successive n-sized chunks from l.""" for i in range(0, len(l), n): yield l[i:i + n] # Read file to get node index heading f = open(Inputfilename,'r') temp = 0 val = [] while temp==0: line = f.readline() if '{BEGIN LEGEND}' in line: temp=2 line = f.readline() # heading while temp==2: line = f.readline() if '{END LEGEND}' in line: temp=3 else: val.append(line.strip()) elif 'n o d a l p r i n t o u t f o r' in line: # abort if no names temp=1 # Read file to get number of elements f = open(Inputfilename,'r') temp = 0 nodes = [] while temp==0: line = f.readline() if 'nodal point' in line: temp=2 nnode = 0 while temp==2: line = f.readline() if len(line.split()) == 0: temp=3 else: nnode += 1 nodes.append(int(line.split()[0])) nodes = np.array(nodes) f.close() # Find match between element ID and index selval = [] if len(val) == nnode: # Then all elements have title for ID, v in enumerate(val): if stringname in v: selval.append(ID) else: for ID, v in enumerate(val): # Then we find matching node index if stringname in v: selval.append(np.where(nodes == int(v.split()[0]))[0][0]) if stringname == '': values = [] # Read data f = open(Inputfilename,'r') temp = 0 while temp==0: line = f.readline() if 'n o d a l' in line: # Then we have a new time step time = line.split()[-2] vval = [] for i in range(0,2): dummy = f.readline() for i in range(0,nnode): line = f.readline() nodeID = line.split()[0] templist = list(chunks(line[10:],12))[:-1] templist = [float(i) for i in np.concatenate(([float(time), nodeID], templist))] vval.append(templist) teststop = 0 for i in range(0,5): dummy = f.readline() teststop += len(dummy) if teststop == 0: temp=3 #end of file values.append(vval) if len(line) == 0: temp=3 f.close() if stringname == '': #then return nodout values ret =	np.array(values)	numpy.array
""" sparse tables ============= Might look like this: level 1 level 2 level 3 columns columns columns idx a b c d e f idx g h i j k l idx m n o p ___ _ _ _ _ _ _ ___ _ _ _ _ _ _ \|_0_\|_\|_\|_\|_\|_\|_\|\|_0_\|_\|_\|_\|_\|_\|_\| \|_1_\|_\|_\|_\|_\|_\|_\| \|_2_\|_\|_\|_\|_\|_\|_\| ___ _ _ _ _ _ _ \|_3_\|_\|_\|_\|_\|_\|_\|\|_3_\|_\|_\|_\|_\|_\|_\| \|_4_\|_\|_\|_\|_\|_\|_\|\|_4_\|_\|_\|_\|_\|_\|_\| ___ _ _ _ _ \|_5_\|_\|_\|_\|_\|_\|_\|\|_5_\|_\|_\|_\|_\|_\|_\|\|_5_\|_\|_\|_\|_\| \|_6_\|_\|_\|_\|_\|_\|_\| \|_7_\|_\|_\|_\|_\|_\|_\| \|_8_\|_\|_\|_\|_\|_\|_\| ___ _ _ _ _ _ _ \|_9_\|_\|_\|_\|_\|_\|_\|\|_9_\|_\|_\|_\|_\|_\|_\| \|10_\|_\|_\|_\|_\|_\|_\|\|10_\|_\|_\|_\|_\|_\|_\| \|11_\|_\|_\|_\|_\|_\|_\| ___ _ _ _ _ _ _ ___ _ _ _ _ \|12_\|_\|_\|_\|_\|_\|_\|\|12_\|_\|_\|_\|_\|_\|_\|\|12_\|_\|_\|_\|_\| \|13_\|_\|_\|_\|_\|_\|_\| ___ _ _ _ _ _ _ \|14_\|_\|_\|_\|_\|_\|_\|\|14_\|_\|_\|_\|_\|_\|_\| Can be represented in memory like this: level 1 level 2 level 3 columns columns columns idx a b c d e f idx g h i j k l idx m n o p ___ _ _ _ _ _ _ ___ _ _ _ _ _ _ ___ _ _ _ _ \|_0_\|_\|_\|_\|_\|_\|_\| \|_0_\|_\|_\|_\|_\|_\|_\| \|_5_\|_\|_\|_\|_\| \|_1_\|_\|_\|_\|_\|_\|_\| \|_3_\|_\|_\|_\|_\|_\|_\| \|12_\|_\|_\|_\|_\| \|_2_\|_\|_\|_\|_\|_\|_\| \|_4_\|_\|_\|_\|_\|_\|_\| \|_3_\|_\|_\|_\|_\|_\|_\| \|_9_\|_\|_\|_\|_\|_\|_\| \|_4_\|_\|_\|_\|_\|_\|_\| \|10_\|_\|_\|_\|_\|_\|_\| \|_6_\|_\|_\|_\|_\|_\|_\| \|12_\|_\|_\|_\|_\|_\|_\| \|_7_\|_\|_\|_\|_\|_\|_\| \|14_\|_\|_\|_\|_\|_\|_\| \|_8_\|_\|_\|_\|_\|_\|_\| \|_9_\|_\|_\|_\|_\|_\|_\| \|10_\|_\|_\|_\|_\|_\|_\| \|11_\|_\|_\|_\|_\|_\|_\| \|12_\|_\|_\|_\|_\|_\|_\| \|13_\|_\|_\|_\|_\|_\|_\| \|14_\|_\|_\|_\|_\|_\|_\| Written to tape-archive table.tar \|_ level_1/idx \|_ level_1/column_a \|_ level_1/column_b \|_ level_1/column_c \|_ level_1/column_d \|_ level_1/column_e \|_ level_1/column_f \|_ level_2/idx \|_ level_2/column_g \|_ level_2/column_h \|_ level_2/column_i \|_ level_2/column_j \|_ level_2/column_k \|_ level_2/column_l \|_ level_3/idx \|_ level_3/column_m \|_ level_3/column_n \|_ level_3/column_o \|_ level_3/column_p """ import pandas as pd import numpy as np import tarfile import io import shutil import tempfile import os IDX = "idx" IDX_DTYPE = "<u8" LEVEL_COLUMN_DELIMITER = "/" FILEAME_TEMPLATE = "{:s}" + LEVEL_COLUMN_DELIMITER + "{:s}.{:s}" DTYPES = [ "<u1", "<u2", "<u4", "<u8", "<i1", "<i2", "<i4", "<i8", "<f2", "<f4", "<f8", ] # logical operations # ================== def intersection(list_of_lists_of_indices): """ Returns the common indices among the lists of indices. Example ------- [4, 5, 6] = intersection([[1,2,3,4,5,6], [3,4,5,6,7,8], [4,5,6,7,8,9,10]]) """ inter = list_of_lists_of_indices[0] for i in range(len(list_of_lists_of_indices)): inter = np.intersect1d(inter, list_of_lists_of_indices[i]) return inter def cut_level_on_indices(level, indices, column_keys=None): """ Returns a level (recarray) only containing the row-indices in 'indices'. Parameters ---------- level : recarray A level in a sparse table. indices : list The row-indices to be written to the output-level. column_keys : list of strings (None) When specified, only these columns will be in the output-level. """ if column_keys is None: column_keys = list(level.dtype.names) column_keys.append(IDX) _part = {} for column_key in column_keys: _part[column_key] = level[column_key] part_df = pd.DataFrame(_part) del _part common_df = pd.merge( part_df, pd.DataFrame(dict_to_recarray({IDX: indices})), on=IDX, how="inner", ) del part_df return common_df.to_records(index=False) def cut_table_on_indices(table, common_indices, level_keys=None): """ Returns table but only with the rows listed in common_indices. Parameters ---------- table : dict of recarrays. The sparse numeric table. common_indices : list of indices The row-indices to cut on. Only row-indices in this list will go in the output-table. level_keys : list of strings (None) When provided, the output-table will only contain these levels. """ if level_keys is None: level_keys = list(table.keys()) out = {} for level_key in level_keys: out[level_key] = cut_level_on_indices( level=table[level_key], indices=common_indices, ) return out def sort_table_on_common_indices(table, common_indices): """ Returns a table with all row-indices ordered same as common_indices. table : dict of recarrays. The table. But must be rectangular, i.e. not sparse. common_indices : list of indices The row-indices to sort by. """ common_order_args =	np.argsort(common_indices)	numpy.argsort
""" Produce calibrated light curves. ``SDTlcurve`` is a script that, given a list of cross scans from different sources, is able to recognize calibrators and use them to convert the observed counts into a density flux value in Jy. """ import os import sys import glob import re import warnings import traceback import configparser import copy import numpy as np from astropy import log import astropy.units as u from scipy.optimize import curve_fit from astropy.table import Table, Column from .scan import Scan, list_scans from .read_config import read_config, sample_config_file, get_config_file from .fit import fit_baseline_plus_bell from .io import mkdir_p from .utils import standard_string, standard_byte, compare_strings from .utils import HAS_STATSM, calculate_moments, scantype try: import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec HAS_MPL = True except ImportError: HAS_MPL = False CALIBRATOR_CONFIG = None __all__ = ["CalibratorTable", "read_calibrator_config"] def _constant(x, p): return p FLUX_QUANTITIES = {"Jy/beam": "Flux", "Jy/pixel": "Flux Integral", "Jy/sr": "Flux Integral"} def _get_flux_quantity(map_unit): try: return FLUX_QUANTITIES[map_unit] except Exception: raise ValueError("Incorrect map_unit for flux conversion. Use one " "of {}".format(list(FLUX_QUANTITIES.keys()))) def read_calibrator_config(): """Read the configuration of calibrators in data/calibrators. Returns ------- configs : dict Dictionary containing the configuration for each calibrator. Each key is the name of a calibrator. Each entry is another dictionary, in one of the following formats: 1) {'Kind' : 'FreqList', 'Frequencies' : [...], 'Bandwidths' : [...], 'Fluxes' : [...], 'Flux Errors' : [...]} where 'Frequencies' is the list of observing frequencies in GHz, 'Bandwidths' is the list of bandwidths in GHz, 'Fluxes' is the list of flux densities in Jy from the literature and 'Flux Errors' are the uncertainties on those fluxes. 2) {'Kind' : 'CoeffTable', 'CoeffTable': {'coeffs' : 'time, a0, a0e, a1, a1e, a2, a2e, a3, a3e\n2010.0,0 ...}} where the 'coeffs' key contains a dictionary with the table of coefficients a la Perley & Butler ApJS 204, 19 (2013), as a comma-separated string. See Also -------- srttools.calibration.flux_function Examples -------- >>> calibs = read_calibrator_config() # doctest: +ELLIPSIS INFO... >>> calibs['DummyCal']['Kind'] 'CoeffTable' >>> 'coeffs' in calibs['DummyCal']['CoeffTable'] True """ flux_re = re.compile(r'^Flux') curdir = os.path.dirname(__file__) calibdir = os.path.join(curdir, 'data', 'calibrators') calibrator_file_list = glob.glob(os.path.join(calibdir, '.ini')) configs = {} for cfile in calibrator_file_list: cparser = configparser.ConfigParser() cparser.read(cfile) log.info(f"Reading {cfile}") if 'CoeffTable' not in list(cparser.sections()): configs[cparser.get("Info", "Name")] = {"Kind": "FreqList", "Frequencies": [], "Bandwidths": [], "Fluxes": [], "Flux Errors": []} for section in cparser.sections(): if not flux_re.match(section): continue configs[cparser.get("Info", "Name")]["Frequencies"].append( float(cparser.get(section, "freq"))) configs[cparser.get("Info", "Name")]["Bandwidths"].append( float(cparser.get(section, "bwidth"))) configs[cparser.get("Info", "Name")]["Fluxes"].append( float(cparser.get(section, "flux"))) configs[cparser.get("Info", "Name")]["Flux Errors"].append( float(cparser.get(section, "eflux"))) else: configs[cparser.get("Info", "Name")] = \ {"CoeffTable": dict(cparser.items("CoeffTable")), "Kind": "CoeffTable"} return configs def _get_calibrator_flux(calibrator, frequency, bandwidth=1, time=0): global CALIBRATOR_CONFIG log.info(f"Getting calibrator flux from {calibrator}") if CALIBRATOR_CONFIG is None: CALIBRATOR_CONFIG = read_calibrator_config() calibrators = CALIBRATOR_CONFIG.keys() for cal in calibrators: if cal == calibrator: calibrator = cal break else: return None, None conf = CALIBRATOR_CONFIG[calibrator] # find closest value among frequencies if conf["Kind"] == "FreqList": idx = (np.abs(np.array(conf["Frequencies"]) - frequency)).argmin() return conf["Fluxes"][idx], \ conf["Flux Errors"][idx] elif conf["Kind"] == "CoeffTable": return _calc_flux_from_coeffs(conf, frequency, bandwidth, time) def _treat_scan(scan_path, plot=False, kwargs): scandir, sname = os.path.split(scan_path) if plot and HAS_MPL: outdir = os.path.splitext(sname)[0] + "_scanfit" outdir = os.path.join(scandir, outdir) mkdir_p(outdir) try: # For now, use nosave. HDF5 doesn't store meta, essential for # this scan = Scan(scan_path, norefilt=True, nosave=True, kwargs) except KeyError as e: log.warning( "Missing key. Bad file? {}: {}".format(sname, str(e)) ) return False, None except Exception as e: log.warning( "Error while processing {}: {}".format(sname, str(e)) ) log.warning(traceback.format_exc()) return False, None feeds = np.arange(scan['ra'].shape[1]) chans = scan.chan_columns() chan_nums = np.arange(len(chans)) F, N = np.meshgrid(feeds, chan_nums) F = F.flatten() N = N.flatten() rows = [] for feed, nch in zip(F, N): channel = chans[nch] ras = np.degrees(scan['ra'][:, feed]) decs = np.degrees(scan['dec'][:, feed]) els = np.degrees(scan['el'][:, feed]) azs = np.degrees(scan['az'][:, feed]) time = np.mean(scan['time'][:]) el = np.mean(els) az = np.mean(azs) source = scan.meta['SOURCE'] pnt_ra = np.degrees(scan.meta['RA']) pnt_dec = np.degrees(scan.meta['Dec']) frequency = scan[channel].meta['frequency'] bandwidth = scan[channel].meta['bandwidth'] temperature = scan[channel + '-Temp'] y = scan[channel] # Fit for gain curves x, _ = scantype(ras, decs, els, azs) temperature_model, _ = \ fit_baseline_plus_bell(x, temperature, kind='gauss') source_temperature = temperature_model['Bell'].amplitude.value # Fit RA and/or Dec x, scan_type = scantype(ras, decs) model, fit_info = fit_baseline_plus_bell(x, y, kind='gauss') try: uncert = fit_info['param_cov'].diagonal() * 0.5 except Exception: message = fit_info['message'] warnings.warn( "Fit failed in scan {s}: {m}".format(s=sname, m=message)) continue bell = model['Bell'] baseline = model['Baseline'] # pars = model.parameters pnames = model.param_names counts = model.amplitude_1.value backsub = y - baseline(x) moments = calculate_moments(backsub) skewness = moments['skewness'] kurtosis = moments['kurtosis'] if scan_type.startswith("RA"): fit_ra = bell.mean.value fit_width = bell.stddev.value * np.cos(np.radians(pnt_dec)) fit_dec = None ra_err = fit_ra * u.degree - pnt_ra dec_err = None fit_mean = fit_ra fit_label = 'RA' pnt = pnt_ra elif scan_type.startswith("Dec"): fit_ra = None fit_dec = bell.mean.value fit_width = bell.stddev.value dec_err = fit_dec * u.degree - pnt_dec ra_err = None fit_mean = fit_dec fit_label = 'Dec' pnt = pnt_dec else: raise ValueError("Unknown scan type") index = pnames.index("amplitude_1") counts_err = uncert[index] index = pnames.index("stddev_1") width_err = uncert[index] flux_density, flux_density_err = 0, 0 flux_over_counts, flux_over_counts_err = 0, 0 calculated_flux, calculated_flux_err = 0, 0 new_row = [scandir, sname, scan_type, source, channel, feed, time, frequency, bandwidth, counts, counts_err, fit_width, width_err, flux_density, flux_density_err, el, az, source_temperature, flux_over_counts, flux_over_counts_err, flux_over_counts, flux_over_counts_err, calculated_flux, calculated_flux_err, pnt_ra, pnt_dec, fit_ra, fit_dec, ra_err, dec_err, skewness, kurtosis] rows.append(new_row) if plot and HAS_MPL: fig = plt.figure("Fit information") gs = GridSpec(2, 1, height_ratios=(3, 1)) ax0 = plt.subplot(gs[0]) ax1 = plt.subplot(gs[1], sharex=ax0) ax0.plot(x, y, label="Data") ax0.plot(x, bell(x), label="Fit: Amp: {}, Wid: {}".format(counts, fit_width)) ax1.plot(x, y - bell(x)) ax0.axvline(fit_mean, label=fit_label + " Fit", ls="-") ax0.axvline(pnt.to(u.deg).value, label=fit_label + " Pnt", ls="--") ax0.set_xlim([min(x), max(x)]) ax1.set_xlabel(fit_label) ax0.set_ylabel("Counts") ax1.set_ylabel("Residual (cts)") ax0.legend() ax1.legend() plt.savefig(os.path.join(outdir, "Feed{}_chan{}.png".format(feed, nch))) plt.close(fig) fig = plt.figure("Fit information - temperature") gs = GridSpec(2, 1, height_ratios=(3, 1)) ax0 = plt.subplot(gs[0]) ax1 = plt.subplot(gs[1], sharex=ax0) ax0.plot(x, temperature, label="Data") ax0.plot(x, temperature_model['Bell'](x), label="Fit") ax1.plot(x, temperature - temperature_model['Bell'](x)) ax0.axvline(pnt.to(u.deg).value, label=fit_label + " Pnt", ls="--") ax0.set_xlim([min(x), max(x)]) ax1.set_xlabel(fit_label) ax0.set_ylabel("Counts") ax1.set_ylabel("Residual (cts)") plt.legend() plt.savefig(os.path.join(outdir, "Feed{}_chan{}_temp.png".format(feed, nch))) plt.close(fig) return True, rows class CalibratorTable(Table): """Table composed of fitted and tabulated fluxes.""" def __init__(self, args, kwargs): """Initialize the object.""" Table.__init__(self, args, *kwargs) self.calibration_coeffs = {} self.calibration_uncerts = {} self.calibration = {} names = ["Dir", "File", "Scan Type", "Source", "Chan", "Feed", "Time", "Frequency", "Bandwidth", "Counts", "Counts Err", "Width", "Width Err", "Flux", "Flux Err", "Elevation", "Azimuth", "Source_temperature", "Flux/Counts", "Flux/Counts Err", "Flux Integral/Counts", "Flux Integral/Counts Err", "Calculated Flux", "Calculated Flux Err", "RA", "Dec", "Fit RA", "Fit Dec", "RA err", "Dec err", "Skewness", "Kurtosis"] dtype = ['S200', 'S200', 'S200', 'S200', 'S200', np.int, np.double, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float] for n, d in zip(names, dtype): if n not in self.keys(): self.add_column(Column(name=n, dtype=d)) def from_scans(self, scan_list=None, debug=False, freqsplat=None, config_file=None, nofilt=False, plot=False): """Load source table from a list of scans. For each scan, a fit is performed. Since we are assuming point-like sources here, the fit is a Gaussian plus a slope. The centroid, width and amplitude of the fit fill out new rows of the CalibratorTable ('Fit RA' or 'Fit Dec', 'Width' and 'Counts' respectively). Parameters ---------- scan_list : list of str List of files containing cross scans to be fitted config_file : str File containing the configuration (list of directories etc.) Other parameters ---------------- debug : bool Throw debug information freqsplat : str List of frequencies to be merged into one. See :func:`srttools.scan.interpret_frequency_range` nofilt : bool Do not filter the noisy channels of the scan. See :class:`srttools.scan.clean_scan_using_variability` plot : bool Plot diagnostic plots? Default False, True if debug is True. Returns ------- retval : bool True if at least one scan was correctly processed See Also -------- srttools.scan.interpret_frequency_range """ if debug is True: plot = True if scan_list is None: if config_file is None: config_file = get_config_file() config = read_config(config_file) scan_list = \ list_scans(config['datadir'], config['list_of_directories']) + \ list_scans(config['datadir'], config['calibrator_directories']) scan_list.sort() nscan = len(scan_list) out_retval = False for i_s, s in enumerate(scan_list): log.info('{}/{}: Loading {}'.format(i_s + 1, nscan, s)) retval, rows = _treat_scan(s, plot=plot, debug=debug, freqsplat=freqsplat, nofilt=nofilt) if retval: out_retval = True for r in rows: self.add_row(r) return out_retval def write(self, fname, args, *kwargs): """Same as Table.write, but adds path information for HDF5.""" if fname.endswith('.hdf5'): super(CalibratorTable, self).write(fname, args, *kwargs) else: super(CalibratorTable, self).write(fname, args, *kwargs) def check_not_empty(self): """Check that table is not empty. Returns ------- good : bool True if all checks pass, False otherwise. """ if len(self["Flux/Counts"]) == 0: warnings.warn("The calibrator table is empty!") return False return True def check_up_to_date(self): """Check that the calibration information is up to date. Returns ------- good : bool True if all checks pass, False otherwise. """ if not self.check_not_empty(): return False if np.any(self["Flux/Counts"] == 0): warnings.warn("The calibrator table needs an update!") self.update() return True def update(self): """Update the calibration information. Execute ``get_fluxes``, ``calibrate`` and ``compute_conversion_function`` """ if not self.check_not_empty(): return self.get_fluxes() self.calibrate() self.compute_conversion_function() def get_fluxes(self): """Get the tabulated flux of the source, if listed as calibrators. Updates the table. """ if not self.check_not_empty(): return for it, t in enumerate(self['Time']): source = self['Source'][it] frequency = self['Frequency'][it] / 1000 bandwidth = self['Bandwidth'][it] / 1000 flux, eflux = \ _get_calibrator_flux(source, frequency, bandwidth, time=t) self['Flux'][it] = flux self['Flux Err'][it] = eflux def calibrate(self): """Calculate the calibration constants. The following conversion functions are calculated for each tabulated cross scan belonging to a calibrator: + 'Flux/Counts' and 'Flux/Counts Err': Tabulated flux density divided by the _height_ of the fitted Gaussian. This is used, e.g. to calibrate images in Jy/beam, as it calibrates the local amplitude to the flux density + 'Flux Integral/Counts' and 'Flux Integral/Counts Err': Tabulated flux density divided by the _volume_ of the 2D Gaussian corresponding to the fitted cross scans, assuming a symmetrical beam (which is generally not the case, but a good approximation). This is used, e.g., to perform the calibration in Jy/pixel: Each pixel will be normalized to the expected total flux in the corresponding pixel area See Also -------- srttools.calibration.CalibratorTable.from_scans """ if not self.check_not_empty(): return flux = self['Flux'] u.Jy eflux = self['Flux Err'] * u.Jy counts = self['Counts'] * u.ct ecounts = self['Counts Err'] * u.ct width = np.radians(self['Width']) * u.radian ewidth = np.radians(self['Width Err']) * u.radian # Volume in a beam: For a 2-d Gaussian with amplitude A and sigmas sx # and sy, this is 2 pi A sx sy. total = 2 * np.pi * counts * width ** 2 etotal = 2 * np.pi * ecounts * width ** 2 flux_integral_over_counts = flux / total flux_integral_over_counts_err = \ (etotal / total + eflux / flux + 2 * ewidth / width) * flux_integral_over_counts flux_over_counts = flux / counts flux_over_counts_err = \ (ecounts / counts + eflux / flux) * flux_over_counts self['Flux/Counts'][:] = \ flux_over_counts.to(u.Jy / u.ct).value self['Flux/Counts Err'][:] = \ flux_over_counts_err.to(u.Jy / u.ct).value self['Flux Integral/Counts'][:] = \ flux_integral_over_counts.to(u.Jy / u.ct / u.steradian).value self['Flux Integral/Counts Err'][:] = \ flux_integral_over_counts_err.to(u.Jy / u.ct / u.steradian).value def compute_conversion_function(self, map_unit="Jy/beam", good_mask=None): """Compute the conversion between Jy and counts. Try to get a meaningful second-degree polynomial fit over elevation. Revert to the rough function :func:`Jy_over_counts_rough` in case ``statsmodels`` is not installed. In this latter case, only the baseline value is given for flux conversion and error. These values are saved in the ``calibration_coeffs`` and ``calibration_uncerts`` attributes of ``CalibratorTable``, and a dictionary called ``calibration`` is also created. For each channel, this dictionary contains either None or an object. This object is the output of a ``fit`` procedure in ``statsmodels``. The method object.predict(X) returns the calibration corresponding to elevation X. """ if not HAS_STATSM: channels = list(set(self["Chan"])) for channel in channels: fc, fce = self.Jy_over_counts_rough(channel=channel, map_unit=map_unit, good_mask=None) self.calibration_coeffs[channel] = [fc, 0, 0] self.calibration_uncerts[channel] = \ [fce, 0, 0] self.calibration[channel] = None return else: import statsmodels.api as sm if good_mask is None: good_mask = self['Flux'] > 0 flux_quantity = _get_flux_quantity(map_unit) channels = list(set(self["Chan"])) for channel in channels: good_chans = (self["Chan"] == channel) & good_mask f_c_ratio = self[flux_quantity + "/Counts"][good_chans] f_c_ratio_err = self[flux_quantity + "/Counts Err"][good_chans] elvs = np.radians(self["Elevation"][good_chans]) good_fc = (f_c_ratio == f_c_ratio) & (f_c_ratio > 0) good_fce = (f_c_ratio_err == f_c_ratio_err) & (f_c_ratio_err >= 0) good = good_fc & good_fce x_to_fit = np.array(elvs[good]) y_to_fit = np.array(f_c_ratio[good]) ye_to_fit = np.array(f_c_ratio_err[good]) order = np.argsort(x_to_fit) x_to_fit = x_to_fit[order] y_to_fit = y_to_fit[order] ye_to_fit = ye_to_fit[order] X = np.column_stack((np.ones(len(x_to_fit)), x_to_fit)) # X = np.c_[np.ones(len(x_to_fit)), X] # X = sm.add_constant(X) model = sm.RLM(y_to_fit, X, missing='drop') results = model.fit() self.calibration_coeffs[channel] = results.params self.calibration_uncerts[channel] = \ results.cov_params().diagonal()0.5 self.calibration[channel] = results def Jy_over_counts(self, channel=None, elevation=None, map_unit="Jy/beam", good_mask=None): """Compute the Jy/Counts conversion corresponding to a given map unit. Parameters ---------- channel : str Channel name (e.g. 'Feed0_RCP', 'Feed0_LCP' etc.) elevation : float or array-like The elevation or a list of elevations map_unit : str A valid unit for the calibrated map (See the keys of FLUX_QUANTITIES) good_mask : array of bools, default None This mask can be used to specify the valid entries of the table. If None, the mask is set to an array of True values Returns ------- fc : float or array-like One conversion value for each elevation fce : float or array-like the uncertainties corresponding to each ``fc`` """ rough = False if not HAS_STATSM: rough = True if good_mask is None: good_mask = self['Flux'] > 0 flux_quantity = _get_flux_quantity(map_unit) if channel not in self.calibration.keys(): self.compute_conversion_function(map_unit, good_mask=good_mask) if elevation is None or rough is True or channel is None: elevation = np.array(elevation) fc, fce = self.Jy_over_counts_rough(channel=channel, map_unit=map_unit, good_mask=good_mask) if elevation.size > 1: fc = np.zeros_like(elevation) + fc fce = np.zeros_like(elevation) + fce return fc, fce X = np.column_stack((np.ones(np.array(elevation).size), np.array(elevation))) fc = self.calibration[channel].predict(X) goodch = self["Chan"] == channel good = good_mask & goodch fce = np.sqrt(np.mean( self[flux_quantity + "/Counts Err"][good]2)) + np.zeros_like(fc) if len(fc) == 1: fc, fce = fc[0], fce[0] return fc, fce def Jy_over_counts_rough(self, channel=None, map_unit="Jy/beam", good_mask=None): """Get the conversion from counts to Jy. Other parameters ---------------- channel : str Name of the data channel map_unit : str A valid unit for the calibrated map (See the keys of FLUX_QUANTITIES) good_mask : array of bools, default None This mask can be used to specify the valid entries of the table. If None, the mask is set to an array of True values Returns ------- fc : float flux density /count ratio fce : float uncertainty on ``fc`` """ self.check_up_to_date() flux_quantity = _get_flux_quantity(map_unit) if good_mask is None: good_mask = self['Flux'] > 0 good_chans = np.ones(len(self["Time"]), dtype=bool) if channel is not None: good_chans = self['Chan'] == channel good_chans = good_chans & good_mask f_c_ratio = self[flux_quantity + "/Counts"][good_chans] f_c_ratio_err = self[flux_quantity + "/Counts Err"][good_chans] times = self["Time"][good_chans] good_fc = (f_c_ratio == f_c_ratio) & (f_c_ratio > 0) good_fce = (f_c_ratio_err == f_c_ratio_err) & (f_c_ratio_err >= 0) good = good_fc & good_fce x_to_fit = np.array(times[good]) y_to_fit = np.array(f_c_ratio[good]) ye_to_fit = np.array(f_c_ratio_err[good]) p = [np.median(y_to_fit)] pcov = np.array([[np.median(ye_to_fit)*2]]) first = True print(x_to_fit, y_to_fit, ye_to_fit) while 1: bad = np.abs((y_to_fit - _constant(x_to_fit, p)) / ye_to_fit) > 5 if not np.any(bad) and not first: break if len(x_to_fit[bad]) > len(x_to_fit) - 5: warnings.warn("Calibration fit is shaky") break xbad = x_to_fit[bad] ybad = y_to_fit[bad] for xb, yb in zip(xbad, ybad): log.warning("Outliers: {}, {}".format(xb, yb)) good = np.logical_not(bad) x_to_fit = x_to_fit[good] y_to_fit = y_to_fit[good] ye_to_fit = ye_to_fit[good] p, pcov = curve_fit(_constant, x_to_fit, y_to_fit, sigma=ye_to_fit, p0=p) first = False fc = p[0] fce = np.sqrt(pcov[0, 0]) return fc, fce def calculate_src_flux(self, channel=None, map_unit="Jy/beam", source=None): """Calculate source flux and error, pointing by pointing. Uses the conversion factors calculated from the tabulated fluxes for all sources but the current, and the fitted Gaussian amplitude for the current source. Updates the calibrator table and returns the average flux Parameters ---------- channel : str or list of str Data channel map_unit : str Units in the map (default Jy/beam) source : str Source name. Must match one of the sources in the table. Default Returns ------- mean_flux : array of floats Array with as many channels as the input ones mean_flux_err : array of floats Uncertainties corresponding to mean_flux """ if source is None: good_source = np.ones_like(self['Flux'], dtype=bool) else: good_source = self['Source'] == source non_source = np.logical_not(good_source) if channel is None: channels = [s for s in set(self['Chan'])] else: channels = [channel] mean_flux = [] mean_flux_err = [] for channel in channels: good_chan = self['Chan'] == channel good = good_source & good_chan elevation = np.radians(self['Elevation'][good]) fc, fce = self.Jy_over_counts(channel=channel, elevation=elevation, map_unit=map_unit, good_mask=non_source) calculated_flux = copy.deepcopy(self['Calculated Flux']) calculated_flux_err = copy.deepcopy(self['Calculated Flux Err']) counts = np.array(self['Counts']) counts_err = np.array(self['Counts Err']) calculated_flux[good] = counts[good] fc calculated_flux_err[good] = \ (counts_err[good] / counts[good] + fce / fc) * \ calculated_flux[good] self['Calculated Flux'][:] = calculated_flux self['Calculated Flux Err'][:] = calculated_flux_err mean_flux.append(np.mean(calculated_flux[good])) mean_flux_err.append( np.sqrt(np.mean(calculated_flux_err[good] ** 2))) return mean_flux, mean_flux_err def check_consistency(self, channel=None, epsilon=0.05): """Check the consistency of calculated and fitted flux densities. For each source in the ``srttools``' calibrator list, use ``calculate_src_flux`` to calculate the source flux ignoring the tabulated value, and compare the calculated and tabulated values. Returns ------- retval : bool True if, for all calibrators, the tabulated and calculated values of the flux are consistent. False otherwise. """ is_cal = (~np.isnan(self['Flux']))&(self['Flux'] > 0) calibrators = list(set(self['Source'][is_cal])) for cal in calibrators: self.calculate_src_flux(channel=channel, source=cal) if channel is None: good_chan = np.ones_like(self['Chan'], dtype=bool) else: good_chan = self['Chan'] == channel calc_fluxes = self['Calculated Flux'][is_cal & good_chan] biblio_fluxes = self['Flux'][is_cal & good_chan] names = self['Source'][is_cal & good_chan] times = self['Time'][is_cal & good_chan] consistent = \ np.abs(biblio_fluxes - calc_fluxes) < epsilon * biblio_fluxes for n, t, b, c, cons, in zip( names, times, biblio_fluxes, calc_fluxes, consistent): if not cons: warnings.warn("{}, MJD {}: Expected {}, " "measured {}".format(n, t, b, c)) return consistent def beam_width(self, channel=None): """Calculate the (weighted) mean beam width, in radians. Checks for invalid (nan and such) values. """ goodch = np.ones(len(self), dtype=bool) if channel is not None: goodch = self['Chan'] == channel allwidths = self[goodch]['Width'] allwidth_errs = self[goodch]['Width Err'] good = (allwidth_errs > 0) & (allwidth_errs == allwidth_errs) allwidths = allwidths[good] allwidth_errs = allwidth_errs[good] # Weighted mean width =	np.sum(allwidths/allwidth_errs)	numpy.sum
# General Packages from math import atan2, degrees from datetime import datetime from pathlib import Path import time import pprint import numpy as np import pandas as pd import pickle # Plotting import matplotlib.pyplot as plt import matplotlib.ticker as mtick from matplotlib.dates import date2num import seaborn as sns # Scaling from sklearn.preprocessing import StandardScaler settings = { # # audit settings 'data_name': 'credit', 'method_name': 'logreg', 'normalize_data': True, 'force_rational_actions': False, # # script flags 'audit_recourse': True, 'plot_audits': True, 'print_flag': True, 'save_flag': True, 'randomseed': 2338, # # placeholders 'method_suffixes': [''], 'audit_suffixes': [''], } # Paths repo_dir = Path(__file__).absolute().parent.parent paper_dir = repo_dir / 'paper/' # directory containing paper related info data_dir = paper_dir / 'data/' # directory containing data files results_dir = paper_dir / 'results/' # directory containing results # create directories that don't exist for d in [data_dir, results_dir]: d.mkdir(exist_ok = True) # Formatting Options np.set_printoptions(precision = 4, suppress = False) pd.set_option('display.max_columns', 30) pd.options.mode.chained_assignment = None pp = pprint.PrettyPrinter(indent = 4) # Plotting Settings sns.set(style="white", palette="muted", color_codes = True) plt.rcParams['font.size'] = 20 plt.rcParams['axes.labelsize'] = 24 plt.rcParams['axes.spines.top'] = False plt.rcParams['axes.spines.right'] = False plt.rcParams['xtick.labelsize'] = 20 plt.rcParams['ytick.labelsize'] = 20 plt.rc('legend', fontsize = 20) # file names output_dir = results_dir / settings['data_name'] output_dir.mkdir(exist_ok = True) if settings['normalize_data']: settings['method_suffixes'].append('normalized') if settings['force_rational_actions']: settings['audit_suffixes'].append('rational') # set file header settings['dataset_file'] = '%s/%s_processed.csv' % (data_dir, settings['data_name']) settings['file_header'] = '%s/%s_%s%s' % (output_dir, settings['data_name'], settings['method_name'], '_'.join(settings['method_suffixes'])) settings['audit_file_header'] = '%s%s' % (settings['file_header'], '_'.join(settings['audit_suffixes'])) settings['model_file'] = '%s_models.pkl' % settings['file_header'] settings['audit_file'] = '%s_audit_results.pkl' % settings['audit_file_header'] # Recourse Objects from recourse.action_set import ActionSet from recourse.builder import RecourseBuilder from recourse.auditor import RecourseAuditor from recourse.flipset import Flipset ### Helper Functions for Experimental Script def load_data(): """Helper function to load in data, and output that and optionally a scaler object: Output: data: dict with the following fields outcome_name: Name of the outcome variable (inferred as the first column.) variable_names: A list of names indicating input columns. X: The input features for our model. y: The column of the dataframe indicating our outcome variable. scaler: The sklearn StandardScaler used to normalize the dataset, if we wish to scale. X_scaled: Scaled version of X, if we wish to scale X_train: The training set: set to the whole dataset if not scaled. Set to X_scaled if we do scale. scaler: Object used to scale data. If "scale" is set to None, then this is returned as None. """ # data set data_df = pd.read_csv(settings['dataset_file']) data = { 'outcome_name': data_df.columns[0], 'variable_names': data_df.columns[1:].tolist(), 'X': data_df.iloc[:, 1:], 'y': data_df.iloc[:, 0] } scaler = None data['X_train'] = data['X'] data['scaler'] = None if settings['normalize_data']: from sklearn.preprocessing import StandardScaler scaler = StandardScaler(copy=True, with_mean=True, with_std=True) data['X_scaled'] = pd.DataFrame(scaler.fit_transform(data['X'].to_numpy(dtype=float), data['y'].values), columns=data['X'].columns) data['X_train'] = data['X_scaled'] data['scaler'] = scaler return data, scaler def undo_coefficient_scaling(clf = None, coefficients = None, intercept = 0.0, scaler = None): """ given coefficients and data for scaled data, returns coefficients and intercept for unnormalized data w = w_scaled / sigma b = b_scaled - (w_scaled / sigma).dot(mu) = b_scaled - w.dot(mu) :param sklearn linear classifier :param coefficients: vector of coefficients :param intercept: scalar for the intercept function :param scaler: sklearn.Scaler or :return: coefficients and intercept for unnormalized data """ if coefficients is None: assert clf is not None assert intercept == 0.0 assert hasattr(clf, 'coef_') coefficients = clf.coef_ intercept = clf.intercept_ if hasattr(clf, 'intercept_') else 0.0 if scaler is None: w = np.array(coefficients) b = float(intercept) else: isinstance(scaler, StandardScaler) x_shift = np.array(scaler.mean_) x_scale = np.sqrt(scaler.var_) w = coefficients / x_scale b = intercept -	np.dot(w, x_shift)	numpy.dot
import zipfile import os import PIL.Image from typing import List, Tuple, Dict import numpy as np from deep500.utils.download import real_download, unrar from deep500.lv2.dataset import FileListDataset from deep500.utils.onnx_interop.losses import SoftmaxCrossEntropy # Optionally import PyAV try: import av except (ImportError, ModuleNotFoundError) as ex: av = None def ucf101_shape(): return (101, None, 3, 240, 320) def ucf101_loss(): return SoftmaxCrossEntropy def download_ucf101_and_get_file_paths(folder='', split='01'): """ Download ucf101 from University of Central Florida The archive contains the videos of different action classes :return: paths to different files """ base_url = "https://www.crcv.ucf.edu/data/UCF101/" filenames = [('ucf101', 'UCF101.rar'), ('ucf101_split','UCF101TrainTestSplits-RecognitionTask.zip')] sub_folder = '/ucf101' local_files = real_download(base_url, filenames, sub_folder, output_dir=folder) files = unrar(local_files['ucf101']) zip = zipfile.ZipFile(local_files['ucf101_split']) path = os.path.dirname(os.path.abspath(local_files['ucf101']))+'/UCF-101/' train_files = [] with zip.open('ucfTrainTestlist/trainlist{}.txt'.format(split)) as file_split: for line in file_split: file = path + bytes.decode(line.split()[0]) if file in files: train_files.append(file) test_files = [] with zip.open('ucfTrainTestlist/testlist{}.txt'.format(split)) as file_split: for line in file_split: file = path + bytes.decode(line.strip()) if file in files: test_files.append(file) label_list = {} with zip.open('ucfTrainTestlist/classInd.txt') as labels: for line in labels: line = bytes.decode(line.strip()) label = line.split()[1] idx = int(line.split()[0]) - 1 label_list[label] = idx return train_files, test_files, label_list ucf101_mean = (0.39607886, 0.37930175, 0.351559) ucf101_std = (0.28261574, 0.27613039, 0.28061599) class ucf101_loader(): def __init__(self, normalize=True, max_length=1777, skip_frames=10): if av is None: raise ImportError('Cannot load ucf101 videos without PyAV. Please see ' 'https://github.com/mikeboers/PyAV for installation instructions.') self.normalize = normalize self.max_length = max_length self.skip_frames = skip_frames def _video_loader(self, video_path): container = av.open(video_path) container.streams.video[0].thread_type = 'AUTO' _data = [frame.to_ndarray(format='rgb24') for frame in container.decode(video=0)] if _data[0].shape != (240, 320, 3): _data = [np.array(PIL.Image.fromarray(img, 'RGB').resize((320,240))) for img in _data] _data = np.asarray(_data, dtype=np.float32) if self.normalize: for ch in range(3): _data[:,:,:,ch] -= ucf101_mean[ch] _data[:,:,:,ch] /= ucf101_std[ch] return _data def __call__(self, data_path): #load multiple videos if type(data_path) is np.ndarray: data = [self._video_loader(path) for path in data_path] max_frames = max([x.shape[0] for x in data]) data = [np.pad(x, ((max_frames-x.shape[0],0), (0,0), (0,0), (0,0)), 'constant') for x in data] data = np.vstack([	np.expand_dims(x, axis=0)	numpy.expand_dims
#!/home/daniel/anaconda3/bin/python # -- coding: utf-8 -- """ ================================================ main_extract_trt ================================================ This program extracts individual TRT cell data from the original files and puts it in a separate file for each cell """ # Author: fvj # License: BSD 3 clause import datetime import argparse import atexit import os import numpy as np from pyrad.io import get_trtfile_list, read_trt_data, write_trt_cell_data print(__doc__) def main(): """ """ # parse the arguments parser = argparse.ArgumentParser( description='Entry to Pyrad processing framework') # positional arguments parser.add_argument( 'start_times', type=str, help=('Start times of the data to process. Format YYYYMMDDhhmmss.' + 'Coma separated')) parser.add_argument( 'end_times', type=str, help=('End times of the data to process. Format YYYYMMDDhhmmss.' + 'Coma separated')) # keyword arguments parser.add_argument( '--raw_trtbase', type=str, default='/store/msrad/radar/rad4alp/TRT/', help='name of folder containing the TRT cell data') parser.add_argument( '--proc_trtbase', type=str, default='/store/msrad/radar/trt/', help='name of folder containing the TRT cell data') parser.add_argument( '--nsteps_min', type=int, default=3, help=('Minimum number of time steps to consider the TRT cell ' + 'worth processing')) args = parser.parse_args() print("====== TRT cell extraction started: %s" % datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")) atexit.register(_print_end_msg, "====== TRT cell extraction finished: ") start_time_list = args.start_times.split(',') end_time_list = args.end_times.split(',') for i, start_time_str in enumerate(start_time_list): end_time_str = end_time_list[i] starttime = datetime.datetime.strptime(start_time_str, '%Y%m%d%H%M%S') endtime = datetime.datetime.strptime(end_time_str, '%Y%m%d%H%M%S') data_input_path = ( args.raw_trtbase+starttime.strftime('%y%j/TRTC%y%j/')) data_output_path = ( args.proc_trtbase+starttime.strftime('%Y-%m-%d')+'/TRTC_cell/') if not os.path.isdir(data_output_path): os.makedirs(data_output_path) flist = get_trtfile_list(data_input_path, starttime, endtime) if flist is None: continue traj_ID =	np.array([], dtype=int)	numpy.array
import numpy as np import pandas as pd import itertools import sys import math import Constant class Parameter: USER_NUM_CONST = 40 CAPACITY_CONST = 10 def __init__(self, seed): np.random.seed(seed) self.USER_NUM = Parameter.USER_NUM_CONST self.CAPACITY = Parameter.CAPACITY_CONST df_server = pd.read_csv("../../network/kanto.csv") self.SERVER_NUM = len(df_server) def create_input(self): self.e_u = list(itertools.product(range(self.USER_NUM), range(self.SERVER_NUM))) self.e_s = list(itertools.combinations(list(range(0, self.SERVER_NUM)), 2)) self.m_s = np.full(self.SERVER_NUM, self.CAPACITY) df_server = pd.read_csv("../../network/kanto.csv") self.d_st = self.get_d_st(df_server) self.d_us = self.get_d_us(df_server) def get_d_st(self, df): d_st = [] for link in self.e_s: city_1, city_2 = link[0], link[1] x_1, y_1 = df.iloc[city_1]["latitude"], df.iloc[city_1]["longitude"] x_2, y_2 = df.iloc[city_2]["latitude"], df.iloc[city_2]["longitude"] d_st.append(Parameter.get_distance(x_1, y_1, x_2, y_2)) return np.array(d_st) def get_lower_and_upper(df, col): return df[col].min() - 0.3, df[col].max() + 0.3 def get_d_us(self, df_server): # range lati_lower, lati_upper = Parameter.get_lower_and_upper(df_server, "latitude") longi_lower, longi_upper = Parameter.get_lower_and_upper(df_server, "longitude") # create users location lati_array = (lati_upper - lati_lower) *	np.random.rand(self.USER_NUM)	numpy.random.rand
#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Sun Feb 18 12:05:56 2018 @author: fabrizio """ import os import os.path import numpy as np import sys from prody import execDSSP from Amino import Amino as aminoacido from Protein import Protein as proteina from DSSPData import DSSPData as DSSP from keras.utils import to_categorical class PdbParser: def __init__(self): #default folders with pdbs and (saved) DSSPs: self.pdbdir='data/cullpdb/pdbs/' self.dsspdir='data/cullpdb/dssp/' if not os.path.exists(self.dsspdir): os.makedirs(self.dsspdir) def convertSomePDBtoDSSP(self,quantity): #converts a given number of pdb to DSSP for root, dirs, files in os.walk(self.pdbdir): for filename in files: quantity-=1 if(quantity<0): break print(quantity,' )',filename,' converted to dssp') # here uses from prody import execDSSP execDSSP(self.pdbdir+filename,outputdir=self.dsspdir) break def convertiSinglePDBtoDSSP(self,file): # con estensione if(os.path.isfile(self.pdbdir+file)): execDSSP(self.pdbdir+file,outputdir=self.dsspdir) else: print("File "+self.pdbdir+file+" not found..\n") def getDSSPInfo(self,file): # for debug if(os.path.isfile(self.dsspdir+file)): print('-'7,'DSSP ',file,'-'7) dsspData = DSSP() dsspData.parseDSSP(self.dsspdir+file) dsspACC = np.array(dsspData.getACC()) print('ACC: ',dsspACC) print(dsspACC.shape) getAAs = np.array(dsspData.getAAs()) print('ACC: ',getAAs) print(getAAs.shape) if(dsspACC.shape[0]>700): return 0 else: return 1 else: print('dssp not found..searching in pdbs folder') self.convertiSinglePDBtoDSSP(file.replace('dssp','pdb')) if(os.path.isfile(self.dsspdir+file)): self.getDSSPInfo(file) else: print('file not found: ',file) def getAllDSSPInfo(self,c=8000): count=0 for root, dirs, files in os.walk(self.dsspdir): for filename in files: #count += self.getDSSPInfo(filename) dssps = self.extractSSfromDSSP(filename) print([x[1] for x in dssps]) count +=1 if(count>=c): break break print('count: ',count) def convertDSSPtoSample(self,file): if(os.path.isfile(self.dsspdir+file)): #print('-'7,'CONVERTING DSSP ',file,' TO SAMPLE','-'7) primaryArray=['A', 'C', 'E', 'D', 'G', 'F', 'I', 'H', 'K', 'M', 'L', 'N', 'Q', 'P', 'S', 'R', 'T', 'W', 'V', 'Y', 'X'] secondaryArray=['L', 'B', 'E', 'G', 'I', 'H', 'S', 'T'] dssp = self.extractSSfromDSSP(file) dsspData = DSSP() dsspData.parseDSSP(self.dsspdir+file) dsspPrimary = np.array(dsspData.getAAs()) dsspSecondary =	np.array([x[1] for x in dssp])	numpy.array
"""deals with data for project""" import re import json import os import sys import math import random import tarfile import numpy as np import pandas as pd from PIL import Image from filecmp import dircmp from collections import deque from Reco3D.third_party import binvox_rw from Reco3D.lib import utils, dataset from sklearn import model_selection from keras.utils import to_categorical from numpy.random import randint, permutation, shuffle from natsort import natsorted from skimage.transform import resize import boto3 def load_obj_id(obj_id): data_path, label_path = id_to_path(obj_id) return load_imgs_from_dir(data_path), np.squeeze(load_voxs_from_dir(label_path)) def id_to_path(obj_id, data_dir="./data/ShapeNetRendering/", label_dir="./data/ShapeNetVox32/"): regex = re.search("(.)_(.)", obj_id) ret_1 = os.path.join(data_dir, regex.group(1), regex.group(2)) ret_2 = os.path.join(label_dir, regex.group(1), regex.group(2)) return ret_1, ret_2 def resize_img(img): max_size = max(img.size) ratio = 137/max_size size = tuple([int(x*ratio) for x in img.size]) img.thumbnail(size,Image.ANTIALIAS) # loading functions def load_img(img_path): img = Image.open(img_path) if img.size != (137, 137, 137): resize_img(img) return np.array(img) def load_vox(vox_path): with open(vox_path, 'rb') as f: voxel = binvox_rw.read_as_3d_array(f).data #if False: #if np.shape(voxel) != (32, 32, 32): # voxel = resize(voxel, (32, 32, 32), anti_aliasing=True,anti_aliasing_sigma=0.01)>0 return to_categorical(voxel) def load_imgs(img_path_list): assert(isinstance(img_path_list, (list, np.ndarray))) ret = [] for p in img_path_list: ret.append(load_img(p)) return np.stack(ret) def load_voxs(vox_path_list): assert(isinstance(vox_path_list, (list, np.ndarray))) ret = [] for p in vox_path_list: ret.append(load_vox(p)) return np.stack(ret) def load_imgs_from_dir(img_dir): img_path_list = construct_file_path_list_from_dir(img_dir, [".png"]) return load_imgs(img_path_list) def load_voxs_from_dir(vox_dir): vox_path_list = construct_file_path_list_from_dir(vox_dir, [".binvox"]) return load_voxs(vox_path_list) # # dataset loading functions def load_data(data_samples): if isinstance(data_samples, str): data_samples = [data_samples] return load_imgs(data_samples) def load_label(label_samples): if isinstance(label_samples, str): label_samples = [label_samples] return np.squeeze(load_voxs(label_samples)) def load_data_Pix3D(): ''' Read Pix3D data. This dataset includes real imageas and binvox''' data_path = utils.read_params()['DIRS']['DATA_PIX3D'] data_all = sorted(construct_file_path_list_from_dir(data_path, ['_x.npy'])) label_all = sorted(construct_file_path_list_from_dir(data_path, ['_y.npy'])) return np.array(data_all), np.array(label_all) def load_random_data_Pix3D(): data, label = load_data_Pix3D() while True: i = np.random.randint(0, len(data)) data_np, label_np = np.load(data[i]), np.load(label[i]) if data_np.shape[-1] == 3: return data_np, label_np # load preprocessed data and labels def load_preprocessed_dataset(): data_preprocessed_dir = utils.read_params( )["DIRS"]["DATA_PREPROCESSED"] data_all = sorted( dataset.construct_file_path_list_from_dir(data_preprocessed_dir, ["_x.npy"])) label_all = sorted( dataset.construct_file_path_list_from_dir(data_preprocessed_dir, ["_y.npy"])) return np.array(data_all), np.array(label_all) def load_random_sample(): data, label = load_preprocessed_dataset() i = randint(0, len(data)) return np.load(data[i]), np.load(label[i]) def load_testset(model_dir): try: X_test = np.load( "{}/X_test.npy".format(model_dir)) y_test = np.load( "{}/y_test.npy".format(model_dir)) except: model_dir = os.path.dirname(model_dir) X_test = np.load( "{}/X_test.npy".format(model_dir)) y_test = np.load( "{}/y_test.npy".format(model_dir)) return X_test, y_test def shuffle_batchs(data, label, batch_size): # print(data, label, batch_size) assert(len(data) == len(label)) num_of_batches = math.ceil(len(data)/batch_size) perm = permutation(len(data)) data_batchs = np.array_split(data[perm], num_of_batches) label_batchs = np.array_split(label[perm], num_of_batches) return deque(data_batchs), deque(label_batchs) def train_val_test_split(data, label, split=0.1): # split into training and test set X_train, X_test, y_train, y_test = model_selection.train_test_split( data, label, test_size=split) # shuffled # split of validation set X_train, X_val, y_train, y_val = model_selection.train_test_split( X_train, y_train, test_size=split) # shuffled return X_train, y_train, X_val, y_val, X_test, y_test def setup_dir(): params = utils.read_params() DIR = params["DIRS"] for d in DIR.values(): if 'ikea' in d: continue utils.make_dir(d) def construct_file_path_list_from_dir(dir, file_filter): if isinstance(file_filter, str): file_filter = [file_filter] paths = [[] for _ in range(len(file_filter))] for root, _, files in os.walk(dir): for f_name in files: for i, f_substr in enumerate(file_filter): if f_substr in f_name: (paths[i]).append(root + '/' + f_name) for i, p in enumerate(paths): paths[i] = natsorted(p) if len(file_filter) == 1: return paths[0] return tuple(paths) def create_path_csv(data_dir, label_dir): print("creating path csv for {} and {}".format(data_dir, label_dir)) params = utils.read_params() common_paths = [] for dir_top, subdir_cmps in dircmp(data_dir, label_dir).subdirs.items(): for dir_bot in subdir_cmps.common_dirs: common_paths.append(os.path.join(dir_top, dir_bot)) mapping = pd.DataFrame(common_paths, columns=["common_dirs"]) mapping['data_dirs'] = mapping.apply( lambda data_row: os.path.join(data_dir, data_row.common_dirs), axis=1) mapping['label_dirs'] = mapping.apply( lambda data_row: os.path.join(label_dir, data_row.common_dirs), axis=1) table = [] for n, d, l in zip(common_paths, mapping.data_dirs, mapping.label_dirs): data_row = [os.path.dirname(n)+"_"+os.path.basename(n)] data_row += construct_file_path_list_from_dir(d, [".png"]) data_row += construct_file_path_list_from_dir(l, [".binvox"]) if not construct_file_path_list_from_dir(l, [".binvox"]): continue table.append(data_row) paths = pd.DataFrame(table) paths.to_csv("{}/paths.csv".format(params["DIRS"]["OUTPUT"])) return paths def download_from_link(link): download_folder = os.path.splitext(os.path.basename(link))[0] archive = download_folder + ".tgz" if not os.path.isfile(archive): os.system('wget -c {0}'.format(link)) os.system("tar -xvzf {0}".format(archive)) os.rename(download_folder, "data/{}".format(download_folder)) # os.system("rm -f {0}".format(archive)) def download_dataset(): LABEL_LINK = 'ftp://cs.stanford.edu/cs/cvgl/ShapeNetVox32.tgz' DATA_LINK = "ftp://cs.stanford.edu/cs/cvgl/ShapeNetRendering.tgz" if not os.path.isdir("data/ShapeNetVox32"): download_from_link(LABEL_LINK) if not os.path.isdir("data/ShapeNetRendering"): download_from_link(DATA_LINK) # download data from s3 bucket def download_from_s3_folder(s3_bucket='shapenetv1'): #s3_bucket_name = 'shapenetv1' #s3 = boto3.resource('s3') #print ("Downloading the data {} from s3 to {}".format("shapenetv1.tar", "./data")) #s3.meta.client.download_file(s3_bucket, 'data/shapenetv1.tar', './data/shapenetv1.tar') LINK = 'https://shapenetv1.s3-us-west-2.amazonaws.com/data/shapenetv1.tar' os.system('wget -c {0} -P ./data'.format(LINK)) def prepare_dataset(): archive = 'data/shapenetv1.tar' if not os.path.isfile(archive) and not os.path.isdir("data/ShapeNetVox32"): download_from_s3_folder() if not os.path.isdir("data/ShapeNetVox32") or not os.path.isdir('data/ShapeNetRendering') : os.system("tar -xvzf {0} -C ./data/".format(archive)) def preprocess_dataset(is_high_res=False): params = utils.read_params() dataset_size = params["DATASET_SIZE"] output_dir = params["DIRS"]["OUTPUT"] data_preprocessed_dir = params["DIRS"]["DATA_PREPROCESSED"] data_dir = params["DIRS"]["DATA"] if not os.path.isfile("{}/paths.csv".format(output_dir)): if is_high_res: dataset.create_path_csv( "{}/ShapeNetRendering".format(data_dir), "{}/ShapeNetVox64".format(data_dir)) else: dataset.create_path_csv( "{}/ShapeNetRendering".format(data_dir), "{}/ShapeNetVox32".format(data_dir)) path_list = pd.read_csv( "{}/paths.csv".format(output_dir), index_col=0).as_matrix() # randomly pick examples from dataset shuffle(path_list) if dataset_size <= 0 or dataset_size >= len(path_list): dataset_size = len(path_list) for i in range(dataset_size): model_name = path_list[i, 0] utils.to_npy('{}/{}_x'.format(data_preprocessed_dir, model_name), load_data(path_list[i, 1:-1])) utils.to_npy('{}/{}_y'.format(data_preprocessed_dir, model_name), load_label(path_list[i, -1])) def render_dataset(dataset_dir="ShapeNet", num_of_examples=None, render_count=24): print("[load_dataset] loading from {0}".format(dataset_dir)) pathlist_tuple = construct_file_path_list_from_dir( dataset_dir, ['.obj', '.mtl']) pathlist = pathlist_tuple[0] # DANGER, RANDOM pathlist = pathlist[:num_of_examples] if num_of_examples is not None else pathlist render_list = [] for mesh_path in pathlist: if not os.path.isfile(mesh_path): continue try: mesh_obj = trimesh.load_mesh(mesh_path) except: print("failed to load {}".format(mesh_path)) continue if isinstance(mesh_obj, list): compund_mesh = mesh_obj.pop(0) for m in mesh_obj: compund_mesh += m else: compund_mesh = mesh_obj render_dir = "./ShapeNet_Renders" renders = os.path.dirname( str.replace(mesh_path, dataset_dir, render_dir)) if os.path.isdir(renders) and os.listdir(renders) != []: render_list.append(load_imgs_from_dir(renders)) else: write_renders_to_disk(compund_mesh, renders, render_count) render_list.append(load_imgs_from_dir(renders)) return render_list def write_renders_to_disk(mesh, renders, render_count=10): print("[write_renders_to_disk] writing renders to {0} ... ".format( renders)) # FIXME: stupid but clean os.system("rm -rf {}".format(renders)) utils.make_dir(renders) scene = mesh.scene() for i in range(render_count): angle = math.radians(random.randint(15, 30)) axis = random.choice([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) rotate = trimesh.transformations.rotation_matrix( angle, axis, scene.centroid) camera_old, _geometry = scene.graph['camera'] camera_new =	np.dot(camera_old, rotate)	numpy.dot
## Copyright 2020 <NAME> ## Licensed under the Apache License, Version 2.0 (the "License"); ## you may not use this file except in compliance with the License. ## You may obtain a copy of the License at ## http://www.apache.org/licenses/LICENSE-2.0 ## Unless required by applicable law or agreed to in writing, software ## distributed under the License is distributed on an "AS IS" BASIS, ## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ## See the License for the specific language governing permissions and ## limitations under the License. ########################################################################### ########################################################################### import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import json class Validation: def __init__(self,config): self.config = config self.cut_off = config["cut_off"] self.data = {} self.result_dir = config["result_dir"] def validate(self,model,data): # model.to(device) criterion_bce = nn.BCELoss() # criterion = nn.BCEWithLogitsLoss() criterion_mse = nn.MSELoss() model.eval() correct = 0 # false_safe = 0 under_approx = 0 over_approx = 0 total = 0 metric_mse = [] metric_bce = [] for i in range(data.n_all_batches): state, safe = data.giveBatch(i) safe_model = model(state).view(-1) safe_model_max = (safe_model >= self.cut_off).type(torch.FloatTensor) metric_mse.append(criterion_mse(safe_model, safe).item()) metric_bce.append(criterion_bce(safe_model, safe).item()) total += safe.size(0) correct += (safe_model_max == safe).sum().item() under_approx += (safe_model_max < safe).sum().item() over_approx += (safe_model_max > safe).sum().item() print('\tMSE: %.4f, BCE: %.4f, Acc: %.4f, UnderApprox: %.4f, OverApprox: %.4f' % (np.mean(metric_mse), np.mean(metric_bce), correct / total, under_approx / total, over_approx / total)) self.data['full_set'] = [] self.data['full_set'].append({ 'acc': correct / total, 'under': under_approx / total, 'over': over_approx / total, 'total': total, 'correct': correct, 'mse': np.mean(metric_mse), 'bce': np.mean(metric_bce) }) def validateTest(self,model,data): criterion_bce = nn.BCELoss() criterion_mse = nn.MSELoss() model.eval() correct = 0 # false_safe = 0 under_approx = 0 over_approx = 0 total = 0 metric_mse = [] metric_bce = [] for i in range(data.n_train_batches,data.n_all_batches): state, safe = data.giveBatch(i) safe_model = model(state).view(-1) safe_model_max = (safe_model >= self.cut_off).type(torch.FloatTensor) metric_mse.append(criterion_mse(safe_model, safe).item()) metric_bce.append(criterion_bce(safe_model, safe).item()) total += safe.size(0) correct += (safe_model_max == safe).sum().item() under_approx += (safe_model_max < safe).sum().item() over_approx += (safe_model_max > safe).sum().item() print('\tMSE: %.4f, BCE: %.4f, Acc: %.4f, UnderApprox: %.4f, OverApprox: %.4f' % (np.mean(metric_mse),	np.mean(metric_bce)	numpy.mean
import os import folium # Importing explicitly the module from 'folium' - 'folium.plugins' import folium.plugins as plugins ''' ************************************* Generating Folium Base Map **********************************''' # Generating a 'Leaflet' map for the location in interest by passing through the coordinates # Calling the 'folium.folium.Map' object Site_Coord = [4.145825, 108.3035] m_folium = folium.Map(location = Site_Coord, zoom_start = 5) ''' *********************************** Adding Minimap onto Folium Base Map **********************************''' # Activating the 'folium.plugins' to include a minimap at the bottomright of the main map m_minimap_Batu_Kawan = plugins.MiniMap(toggle_display = True, width=200, height=200, zoom_level_fixed=None, minimized=True) m_folium.add_child(m_minimap_Batu_Kawan) ''' ************************* Extracting G&P Geotechnics Project with Coordinates ***************************''' import pyexcel as pyex import numpy as np DATA = pyex.get_book(file_name = '2020_Gallery.xlsx') ''' **************************************** 2008 **********************************************************''' # Data extraction for projects secured in year 2008 DATA_2008 = np.array(DATA.sheet_by_name('2008')) lat_2008 = np.ndarray.tolist(DATA_2008[1:,1]) lgn_2008 = np.ndarray.tolist(DATA_2008[1:,0]) pgn_2008 = np.ndarray.tolist(DATA_2008[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2008 = folium.FeatureGroup("2008 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2008, lgn_2008, pgn_2008): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2008.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='black',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2008) ''' **************************************** 2009 **********************************************************''' # Data extraction for projects secured in year 2009 DATA_2009 = np.array(DATA.sheet_by_name('2009')) lat_2009 = np.ndarray.tolist(DATA_2009[1:,1]) lgn_2009 = np.ndarray.tolist(DATA_2009[1:,0]) pgn_2009 = np.ndarray.tolist(DATA_2009[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2009 = folium.FeatureGroup("2009 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2009, lgn_2009, pgn_2009): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2009.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='black',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2009) ''' **************************************** 2010 **********************************************************''' # Data extraction for projects secured in year 2010 DATA_2010 = np.array(DATA.sheet_by_name('2010')) lat_2010 = np.ndarray.tolist(DATA_2010[1:,1]) lgn_2010 = np.ndarray.tolist(DATA_2010[1:,0]) pgn_2010 = np.ndarray.tolist(DATA_2010[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2010 = folium.FeatureGroup("2010 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2010, lgn_2010, pgn_2010): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2010.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='black',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2010) ''' **************************************** 2011 **********************************************************''' # Data extraction for projects secured in year 2011 DATA_2011 = np.array(DATA.sheet_by_name('2011')) lat_2011 = np.ndarray.tolist(DATA_2011[1:,1]) lgn_2011 = np.ndarray.tolist(DATA_2011[1:,0]) pgn_2011 = np.ndarray.tolist(DATA_2011[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2011 = folium.FeatureGroup("2011 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2011, lgn_2011, pgn_2011): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2011.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='orange',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2011) ''' **************************************** 2012 **********************************************************''' # Data extraction for projects secured in year 2012 DATA_2012 = np.array(DATA.sheet_by_name('2012')) lat_2012 = np.ndarray.tolist(DATA_2012[1:,1]) lgn_2012 = np.ndarray.tolist(DATA_2012[1:,0]) pgn_2012 = np.ndarray.tolist(DATA_2012[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2012 = folium.FeatureGroup("2012 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2012, lgn_2012, pgn_2012): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2012.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='orange',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2012) ''' **************************************** 2013 **********************************************************''' # Data extraction for projects secured in year 2013 DATA_2013 = np.array(DATA.sheet_by_name('2013')) lat_2013 = np.ndarray.tolist(DATA_2013[1:,1]) lgn_2013 = np.ndarray.tolist(DATA_2013[1:,0]) pgn_2013 = np.ndarray.tolist(DATA_2013[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2013 = folium.FeatureGroup("2013 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2013, lgn_2013, pgn_2013): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2013.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='orange',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2013) ''' **************************************** 2014 **********************************************************''' # Data extraction for projects secured in year 2014 DATA_2014 = np.array(DATA.sheet_by_name('2014')) lat_2014 = np.ndarray.tolist(DATA_2014[1:,1]) lgn_2014 = np.ndarray.tolist(DATA_2014[1:,0]) pgn_2014 = np.ndarray.tolist(DATA_2014[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2014 = folium.FeatureGroup("2014 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2014, lgn_2014, pgn_2014): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2014.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='orange',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2014) ''' **************************************** 2015 **********************************************************''' # Data extraction for projects secured in year 2015 DATA_2015 = np.array(DATA.sheet_by_name('2015')) lat_2015 = np.ndarray.tolist(DATA_2015[1:,1]) lgn_2015 = np.ndarray.tolist(DATA_2015[1:,0]) pgn_2015 = np.ndarray.tolist(DATA_2015[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2015 = folium.FeatureGroup("2015 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2015, lgn_2015, pgn_2015): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2015.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='orange',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2015) ''' **************************************** 2016 **********************************************************''' # Data extraction for projects secured in year 2016 DATA_2016 = np.array(DATA.sheet_by_name('2016')) lat_2016 = np.ndarray.tolist(DATA_2016[1:,1]) lgn_2016 = np.ndarray.tolist(DATA_2016[1:,0]) pgn_2016 = np.ndarray.tolist(DATA_2016[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2016 = folium.FeatureGroup("2016 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2016, lgn_2016, pgn_2016): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2016.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='red',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2016) ''' **************************************** 2017 **********************************************************''' # Data extraction for projects secured in year 2017 DATA_2017 = np.array(DATA.sheet_by_name('2017')) lat_2017 = np.ndarray.tolist(DATA_2017[1:,1]) lgn_2017 = np.ndarray.tolist(DATA_2017[1:,0]) pgn_2017 = np.ndarray.tolist(DATA_2017[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2017 = folium.FeatureGroup("2017 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2017, lgn_2017, pgn_2017): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2017.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='red',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2017) ''' **************************************** 2018 ************************************************************''' # Data extraction for projects secured in year 2018 DATA_2018 = np.array(DATA.sheet_by_name('2018')) lat_2018 = np.ndarray.tolist(DATA_2018[1:,1]) lgn_2018 =	np.ndarray.tolist(DATA_2018[1:,0])	numpy.ndarray.tolist
''' Layers for NN-models (forward+backward pass). Written by <NAME> (https://github.com/SLotAbr). BSD License ''' # from multiprocessing import Process import numpy as np import pickle from tools.functions import string_softmax from tools.optimizers import AdaM as AdaM class token_embedding: def __init__(self, vocabulary_size, d_model, context_size, optim_param): self.TE_table = np.random.randn(vocabulary_size, d_model) * 1e-3 self.vocabulary_size = vocabulary_size self.d_model = d_model self.context_size = context_size self.input_field = 0 self.optim = AdaM(optim_param) def __call__(self, index_list): # form X matrix from tokens indexes # We should use 2D array for further concatenation self.input_indexes = index_list context =[[self.TE_table[j] for j in index_list]] return np.concatenate(context, axis=1) def update_weights(self, dX, dTE_linear): # dTE_linear - the second part of TE derivative # TE derivative have 2 parts - so, we'll get it by external source dTE = np.zeros((self.vocabulary_size, self.d_model)) for i in range(self.context_size): dTE[self.input_indexes[i]]+= dX[i] dTE += dTE_linear self.TE_table = self.optim.weights_update(self.TE_table, dTE) def linear(self, x): ''' using token_embeddings as linear layer with bias=0 we'll use it for finding out output token probabilities :x.shape = [context_size; d_model] :output.shape = [context_size; vocabulary_size] ''' self.input_field = x return [email protected]_table.T def linear_backward(self, dl): # returns derivatives for input signal and TE_table return [email protected]_table, (self.input_field.T@dl).T def save_weights(self, path): with open(path, 'wb') as f: pickle.dump([self.TE_table, self.optim], f) def restore_weights(self, path): with open(path, 'rb') as f: self.TE_table, self.optim = pickle.load(f) class linear: def __init__(self, hidden_units, number_of_neurons, optim_param): # mean = 0, var = 1 self.W = np.random.randn(hidden_units, number_of_neurons) * 1e-3 self.b = np.zeros(number_of_neurons) self.input_field = 0 # Memory for backpropagation self.w_optim = AdaM(optim_param) self.b_optim = AdaM(optim_param) def __call__(self, x): self.input_field = x return (x @ self.W + self.b) #np.dot(x, w) + b def backward(self, dl): dw = self.input_field.T @ dl db = dl.sum(axis=0) # Updating weights self.W = self.w_optim.weights_update(self.W, dw) self.b = self.b_optim.weights_update(self.b, db) # returns dl for previous layers return dl @ self.W.T def save_weights(self, path): with open(path, 'wb') as f: pickle.dump([self.W, self.b, self.w_optim, self.b_optim], f) def restore_weights(self, path): with open(path, 'rb') as f: self.W, self.b, self.w_optim, self.b_optim = pickle.load(f) class ReLU: def __call__(self, x): result = np.maximum(0, x) self.mask = result>0 return result def backward(self, dl): return dl * self.mask class LayerNormalization: def __init__(self, context_size): self.context_size = context_size def __call__(self, x, phase='train'): ''' I'll delete if-else construction and replace it more eficient version for evaluation phase later. There is the same construction in MH_attention_mechanism (__call__ field) ''' if phase == 'train': context_size = self.context_size else: context_size = x.shape[0] x_mean = (x.mean(axis=1).reshape(1,context_size)).T self.x_var = (x.var(axis=1).reshape(1,context_size)).T return (x-x_mean)/np.sqrt(self.x_var+1e-12) def backward(self, dl): l_mean = (dl.mean(axis=1).reshape(1,self.context_size)).T return (dl - l_mean)/np.sqrt(self.x_var+1e-12) class MH_attention_mechanism: def __init__(self, context_size, d_model, H): self.d_k = 1/np.sqrt(d_model/H) self.context_size = context_size self.H = H # matrix with 'True' values above the main diagonal # We'll use it for replacing elements in dot product of Q and K self.mask=(np.tril(np.ones((context_size, context_size)))==0) self.backward_mask=np.tril(np.ones((context_size, context_size))) def __call__(self, x, phase='train'): self.Q, self.K, self.V = np.split(x, 3, axis=1) self.Q = np.split(self.Q, self.H, axis=1) self.K = np.split(self.K, self.H, axis=1) self.V = np.split(self.V, self.H, axis=1) # When we generate text ('eval phase'), context_size always different if phase == 'train': context_size = self.context_size else: context_size = x.shape[0] # Replace it by pre-init fields for faster implementation? C = [0 for h in range(self.H)] self.S = [0 for h in range(self.H)] Z = [0 for h in range(self.H)] # https://docs.python.org/3/library/multiprocessing.html for h in range(self.H): # Attention formula C[h] = self.Q[h] @ self.K[h].T * self.d_k if phase == 'train': C[h][self.mask]=-1e12 else: # We've got different context_size during evaluation mask = (np.tril(np.ones((context_size, context_size)))==0) C[h][mask]=-1e12 self.S[h] = string_softmax(C[h], context_size) # print('softmax\'s state:\n', self.S[h]) Z[h] = self.S[h]@self.V[h] # print('Z\'s state:\n', Z[h]) return np.concatenate(Z, axis=1) def backward(self, dl): dZ =	np.split(dl, self.H, axis=1)	numpy.split
################################################################################ # UNIVERSIDADE FEDERAL DE CATALÃO (UFCAT) # <NAME>, ENG. CIVIL / PROF (UFCAT) # <NAME> ENG. CIVIL / PROF (UFCAT) # <NAME>, ENG. CIVIL (UFCAT) ################################################################################ ################################################################################ # DESCRIÇÃO ALGORITMO: # BIBLIOTECA DE DIMENSIONAMENTO DE VIGAS PRÉ-FABRICADAS E PROTENDIDAS DESENVOL- # VIDA PELO GRUPO DE PESQUISA E ESTUDOS EM ENGENHARIA (GPEE) ################################################################################ ################################################################################ # BIBLIOTECAS NATIVAS PYTHON import numpy as np ################################################################################ # BIBLIOTECAS DESENVOLVEDORES GPEE def PROP_GEOMETRICA_I(H, B_FS, B_FI, B_W, H_S, H_I, H_SI, H_II): """ Esta função determina as propriedades geométricas de uma seção I. Entrada: H \| Altura da viga \| m \| float B_FS \| Base de mesa superior da viga \| m \| float B_FI \| Base de mesa inferior da viga \| m \| float B_W \| Base de alma da viga \| m \| float H_S \| Altura de mesa superior da viga \| m \| float H_I \| Altura de mesa inferior da viga \| m \| float H_SI \| Altura inclinada de mesa superior da viga \| m \| float H_II \| Altura inclinada de mesa inferior da viga \| m \| float Saída: A_C \| Área da seção transversal da viga \| m² \| float I_C \| Inércia da viga \| m^4 \| float Y_SUP \| Ordenada da fibra superior \| m \| float Y_INF \| Ordenada da fibra inferior \| m \| float W_SUP \| Modulo de resistência superior \| m³ \| float W_INF \| Modulo de resistência inferior \| m³ \| float """ A_1 = B_W * H A_2 = (B_FS - B_W) * H_S A_3 = ((B_FS - B_W) * H_SI) / 2 A_4 = (B_FI - B_W) * H_I A_5 = ((B_FI - B_W) * H_I)/2 A_C = A_1 + A_2 + A_3 + A_4 + A_5 Y_CG = (A_1 * (H/2) + A_2 * (H - H_S / 2) + A_3 * (H - H_S - H_SI / 2) + A_4 * (H_I / 2) + A_5 * (H_I + H_II)) / A_C I_1 = (B_W * H*3) / 12 + A_1 (H / 2 - Y_CG)*2 I_2 = ((B_FS - B_W) H_S*3) / 12 + A_2 (H - H_S/2 - Y_CG)*2 I_3 = ((B_FS - B_W) H_SI*3) / 36 + A_3 (H - H_S - H_SI / 3 - Y_CG)*2 I_4 = ((B_FI - B_W) H_I*3) / 12 + A_4 (Y_CG - H_I / 2)*2 I_5 = ((B_FI - B_W) H_II*3) / 36 + A_5 (Y_CG - H_I - H_II / 3)*2 I_C = I_1 + I_2 + I_3 + I_4 + I_5 Y_SUP = H - Y_CG Y_INF = Y_CG W_SUP = I_C / Y_SUP W_INF = I_C / Y_INF return A_C, I_C, Y_SUP, Y_INF, W_SUP, W_INF def PROP_GEOMETRICA_RET(B_W, H): """ Esta função determina as propriedades geométricas de uma seção retangular. Entrada: B_W \| Largura da viga \| m \| float H \| Altura da viga \| m \| float Saída: A_C \| Área da seção transversal da viga \| m² \| float I_C \| Inércia da viga \| m^4 \| float Y_SUP \| Ordenada da fibra superior \| m \| float Y_INF \| Ordenada da fibra inferior \| m \| float W_SUP \| Modulo de resistência superior \| m³ \| float W_INF \| Modulo de resistência inferior \| m³ \| float """ A_C = B_W H I_C = (B_W * H ** 3) / 12 Y_SUP = H / 2 Y_INF = H / 2 W_SUP = I_C / Y_SUP W_INF = I_C / Y_INF return A_C, I_C, Y_SUP, Y_INF, W_SUP, W_INF def FATOR_BETA1(TEMPO, CIMENTO): """ Esta função calcula o valor de BETA_1 que representa a função de crescimento da resistência do cimento. Entrada: TEMPO \| Tempo \| dias \| float CIMENTO \| Cimento utilizado \| \| string \| 'CP1' - Cimento portland 1 \| \| \| 'CP2' - Cimento portland 2 \| \| \| 'CP3' - Cimento portland 3 \| \| \| 'CP4' - Cimento portland 4 \| \| \| 'CP5' - Cimento portland 5 \| \| Saída: BETA_1 \| Parâmetro de crescimento da resistência \| \| float """ if TEMPO < 28 : if CIMENTO == 'CP1' or CIMENTO == 'CP2': S = 0.25 elif CIMENTO == 'CP3' or CIMENTO == 'CP4': S = 0.38 elif CIMENTO == 'CP5': S = 0.20 BETA_1 = np.exp(S * (1 - (28 / TEMPO) ** 0.50)) else : BETA_1 = 1 return BETA_1 def MODULO_ELASTICIDADE_CONCRETO(AGREGADO, F_CK, F_CKJ): """ Esta função calcula os módulos de elasticidade do concreto. Entrada: AGREGADO \| Tipo de agragado usado no traço do cimento \| \| string \| 'BAS' - Agregado de Basalto \| \| \| 'GRA' - Agregado de Granito \| \| \| 'CAL' - Agregado de Calcário \| \| \| 'ARE' - Agregado de Arenito \| \| F_CK \| Resistência característica à compressão \| kN/m² \| float F_CKJ \| Resistência característica à compressão idade J \| kN/m² \| float Saída: E_CIJ \| Módulo de elasticidade tangente \| kN/m² \| float E_CSJ \| Módulo de elasticidade do secante \| kN/m² \| float """ # Determinação do módulo tangente E_CI idade T if AGREGADO == 'BAS': ALFA_E = 1.2 elif AGREGADO == 'GRA': ALFA_E = 1.0 elif AGREGADO == 'CAL': ALFA_E = 0.9 elif AGREGADO == 'ARE': ALFA_E = 0.7 F_CK /= 1E3 if F_CK <= 50: E_CI = ALFA_E * 5600 * np.sqrt(F_CK) elif F_CK > 50: E_CI = 21.5 * (10 ** 3) * ALFA_E * (F_CK / 10 + 1.25) ** (1 / 3) ALFA_I = 0.8 + 0.2 * F_CK / 80 if ALFA_I > 1: ALFA_I = 1 # Determinação do módulo secante E_CS idade T E_CS = E_CI * ALFA_I if F_CK <= 45 : F_CK = 1E3 E_CIJ = E_CI (F_CKJ / F_CK) ** 0.5 elif F_CK > 45 : F_CK = 1E3 E_CIJ = E_CI (F_CKJ / F_CK) ** 0.3 E_CSJ = E_CIJ * ALFA_I E_CIJ = 1E3 E_CSJ = 1E3 return E_CIJ, E_CSJ def PROP_MATERIAL(F_CK, TEMPO, CIMENTO, AGREGADO): """ Esta função determina propriedades do concreto em uma idade TEMPO. Entrada: F_CK \| Resistência característica à compressão \| kN/m² \| float TEMPO \| Tempo \| dias \| float CIMENTO \| Cimento utilizado \| \| string \| 'CP1' - Cimento portland 1 \| \| \| 'CP2' - Cimento portland 2 \| \| \| 'CP3' - Cimento portland 3 \| \| \| 'CP4' - Cimento portland 4 \| \| \| 'CP5' - Cimento portland 5 \| \| AGREGADO \| Tipo de agragado usado no traço do cimento \| \| string \| 'BAS' - Agregado de Basalto \| \| \| 'GRA' - Agregado de Granito \| \| \| 'CAL' - Agregado de Calcário \| \| \| 'ARE' - Agregado de Arenito \| \| Saída: F_CKJ \| Resistência característica à compressão idade J \| kN/m² \| float F_CTMJ \| Resistência média caracteristica a tração idade J \| kN/m² \| float F_CTKINFJ \| Resistência média caracteristica a tração inf idade J \| kN/m² \| float F_CTKSUPJ \| Resistência média caracteristica a tração sup idade J \| kN/m² \| float E_CIJ \| Módulo de elasticidade tangente \| kN/m² \| float E_CSJ \| Módulo de elasticidade do secante \| kN/m² \| float """ # Propriedades em situação de compressão F_C idade TEMPO em dias BETA_1 = FATOR_BETA1(TEMPO, CIMENTO) F_CKJ = F_CK * BETA_1 F_CKJ /= 1E3 F_CK /= 1E3 if F_CKJ < 21 : F_CKJ = 21 # Propriedades em situação de tração F_CT idade TEMPO em dias if F_CK <= 50: F_CTMJ = 0.3 * F_CKJ ** (2/3) elif F_CK > 50: F_CTMJ = 2.12 *	np.log(1 + 0.11 * F_CKJ)	numpy.log
import sys import warnings import itertools import platform import pytest from decimal import Decimal import numpy as np from numpy.core import umath from numpy.random import rand, randint, randn from numpy.testing import ( assert_, assert_equal, assert_raises, assert_raises_regex, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_warns, HAS_REFCOUNT ) class TestResize(object): def test_copies(self): A = np.array([[1, 2], [3, 4]]) Ar1 = np.array([[1, 2, 3, 4], [1, 2, 3, 4]]) assert_equal(np.resize(A, (2, 4)), Ar1) Ar2 = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) assert_equal(np.resize(A, (4, 2)), Ar2) Ar3 = np.array([[1, 2, 3], [4, 1, 2], [3, 4, 1], [2, 3, 4]]) assert_equal(np.resize(A, (4, 3)), Ar3) def test_zeroresize(self): A = np.array([[1, 2], [3, 4]]) Ar = np.resize(A, (0,)) assert_array_equal(Ar, np.array([])) assert_equal(A.dtype, Ar.dtype) Ar = np.resize(A, (0, 2)) assert_equal(Ar.shape, (0, 2)) Ar = np.resize(A, (2, 0)) assert_equal(Ar.shape, (2, 0)) def test_reshape_from_zero(self): # See also gh-6740 A = np.zeros(0, dtype=[('a', np.float32)]) Ar = np.resize(A, (2, 1)) assert_array_equal(Ar, np.zeros((2, 1), Ar.dtype)) assert_equal(A.dtype, Ar.dtype) class TestNonarrayArgs(object): # check that non-array arguments to functions wrap them in arrays def test_choose(self): choices = [[0, 1, 2], [3, 4, 5], [5, 6, 7]] tgt = [5, 1, 5] a = [2, 0, 1] out = np.choose(a, choices) assert_equal(out, tgt) def test_clip(self): arr = [-1, 5, 2, 3, 10, -4, -9] out = np.clip(arr, 2, 7) tgt = [2, 5, 2, 3, 7, 2, 2] assert_equal(out, tgt) def test_compress(self): arr = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] tgt = [[5, 6, 7, 8, 9]] out = np.compress([0, 1], arr, axis=0) assert_equal(out, tgt) def test_count_nonzero(self): arr = [[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]] tgt = np.array([2, 3]) out = np.count_nonzero(arr, axis=1) assert_equal(out, tgt) def test_cumproduct(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.all(np.cumproduct(A) == np.array([1, 2, 6, 24, 120, 720]))) def test_diagonal(self): a = [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] out = np.diagonal(a) tgt = [0, 5, 10] assert_equal(out, tgt) def test_mean(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.mean(A) == 3.5) assert_(np.all(np.mean(A, 0) == np.array([2.5, 3.5, 4.5]))) assert_(np.all(np.mean(A, 1) == np.array([2., 5.]))) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.mean([]))) assert_(w[0].category is RuntimeWarning) def test_ptp(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.ptp(a, axis=0), 15.0) def test_prod(self): arr = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] tgt = [24, 1890, 600] assert_equal(np.prod(arr, axis=-1), tgt) def test_ravel(self): a = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] tgt = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] assert_equal(np.ravel(a), tgt) def test_repeat(self): a = [1, 2, 3] tgt = [1, 1, 2, 2, 3, 3] out = np.repeat(a, 2) assert_equal(out, tgt) def test_reshape(self): arr = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] tgt = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]] assert_equal(np.reshape(arr, (2, 6)), tgt) def test_round(self): arr = [1.56, 72.54, 6.35, 3.25] tgt = [1.6, 72.5, 6.4, 3.2] assert_equal(np.around(arr, decimals=1), tgt) def test_searchsorted(self): arr = [-8, -5, -1, 3, 6, 10] out = np.searchsorted(arr, 0) assert_equal(out, 3) def test_size(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.size(A) == 6) assert_(np.size(A, 0) == 2) assert_(np.size(A, 1) == 3) def test_squeeze(self): A = [[[1, 1, 1], [2, 2, 2], [3, 3, 3]]] assert_equal(np.squeeze(A).shape, (3, 3)) assert_equal(np.squeeze(np.zeros((1, 3, 1))).shape, (3,)) assert_equal(np.squeeze(np.zeros((1, 3, 1)), axis=0).shape, (3, 1)) assert_equal(np.squeeze(np.zeros((1, 3, 1)), axis=-1).shape, (1, 3)) assert_equal(np.squeeze(np.zeros((1, 3, 1)), axis=2).shape, (1, 3)) assert_equal(np.squeeze([np.zeros((3, 1))]).shape, (3,)) assert_equal(np.squeeze([np.zeros((3, 1))], axis=0).shape, (3, 1)) assert_equal(np.squeeze([np.zeros((3, 1))], axis=2).shape, (1, 3)) assert_equal(np.squeeze([np.zeros((3, 1))], axis=-1).shape, (1, 3)) def test_std(self): A = [[1, 2, 3], [4, 5, 6]] assert_almost_equal(np.std(A), 1.707825127659933) assert_almost_equal(np.std(A, 0), np.array([1.5, 1.5, 1.5])) assert_almost_equal(np.std(A, 1), np.array([0.81649658, 0.81649658])) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.std([]))) assert_(w[0].category is RuntimeWarning) def test_swapaxes(self): tgt = [[[0, 4], [2, 6]], [[1, 5], [3, 7]]] a = [[[0, 1], [2, 3]], [[4, 5], [6, 7]]] out = np.swapaxes(a, 0, 2) assert_equal(out, tgt) def test_sum(self): m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] tgt = [[6], [15], [24]] out = np.sum(m, axis=1, keepdims=True) assert_equal(tgt, out) def test_take(self): tgt = [2, 3, 5] indices = [1, 2, 4] a = [1, 2, 3, 4, 5] out = np.take(a, indices) assert_equal(out, tgt) def test_trace(self): c = [[1, 2], [3, 4], [5, 6]] assert_equal(np.trace(c), 5) def test_transpose(self): arr = [[1, 2], [3, 4], [5, 6]] tgt = [[1, 3, 5], [2, 4, 6]] assert_equal(np.transpose(arr, (1, 0)), tgt) def test_var(self): A = [[1, 2, 3], [4, 5, 6]] assert_almost_equal(np.var(A), 2.9166666666666665) assert_almost_equal(np.var(A, 0), np.array([2.25, 2.25, 2.25])) assert_almost_equal(np.var(A, 1), np.array([0.66666667, 0.66666667])) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.var([]))) assert_(w[0].category is RuntimeWarning) B = np.array([None, 0]) B[0] = 1j assert_almost_equal(np.var(B), 0.25) class TestIsscalar(object): def test_isscalar(self): assert_(np.isscalar(3.1)) assert_(np.isscalar(np.int16(12345))) assert_(np.isscalar(False)) assert_(np.isscalar('numpy')) assert_(not np.isscalar([3.1])) assert_(not np.isscalar(None)) # PEP 3141 from fractions import Fraction assert_(np.isscalar(Fraction(5, 17))) from numbers import Number assert_(np.isscalar(Number())) class TestBoolScalar(object): def test_logical(self): f = np.False_ t = np.True_ s = "xyz" assert_((t and s) is s) assert_((f and s) is f) def test_bitwise_or(self): f = np.False_ t = np.True_ assert_((t \| t) is t) assert_((f \| t) is t) assert_((t \| f) is t) assert_((f \| f) is f) def test_bitwise_and(self): f = np.False_ t = np.True_ assert_((t & t) is t) assert_((f & t) is f) assert_((t & f) is f) assert_((f & f) is f) def test_bitwise_xor(self): f = np.False_ t = np.True_ assert_((t ^ t) is f) assert_((f ^ t) is t) assert_((t ^ f) is t) assert_((f ^ f) is f) class TestBoolArray(object): def setup(self): # offset for simd tests self.t = np.array([True] * 41, dtype=bool)[1::] self.f = np.array([False] * 41, dtype=bool)[1::] self.o = np.array([False] * 42, dtype=bool)[2::] self.nm = self.f.copy() self.im = self.t.copy() self.nm[3] = True self.nm[-2] = True self.im[3] = False self.im[-2] = False def test_all_any(self): assert_(self.t.all()) assert_(self.t.any()) assert_(not self.f.all()) assert_(not self.f.any()) assert_(self.nm.any()) assert_(self.im.any()) assert_(not self.nm.all()) assert_(not self.im.all()) # check bad element in all positions for i in range(256 - 7): d = np.array([False] * 256, dtype=bool)[7::] d[i] = True assert_(np.any(d)) e = np.array([True] * 256, dtype=bool)[7::] e[i] = False assert_(not np.all(e)) assert_array_equal(e, ~d) # big array test for blocked libc loops for i in list(range(9, 6000, 507)) + [7764, 90021, -10]: d = np.array([False] * 100043, dtype=bool) d[i] = True assert_(np.any(d), msg="%r" % i) e = np.array([True] * 100043, dtype=bool) e[i] = False assert_(not np.all(e), msg="%r" % i) def test_logical_not_abs(self): assert_array_equal(~self.t, self.f) assert_array_equal(np.abs(~self.t), self.f) assert_array_equal(np.abs(~self.f), self.t) assert_array_equal(np.abs(self.f), self.f) assert_array_equal(~np.abs(self.f), self.t) assert_array_equal(~np.abs(self.t), self.f) assert_array_equal(np.abs(~self.nm), self.im) np.logical_not(self.t, out=self.o) assert_array_equal(self.o, self.f) np.abs(self.t, out=self.o) assert_array_equal(self.o, self.t) def test_logical_and_or_xor(self): assert_array_equal(self.t \| self.t, self.t) assert_array_equal(self.f \| self.f, self.f) assert_array_equal(self.t \| self.f, self.t) assert_array_equal(self.f \| self.t, self.t) np.logical_or(self.t, self.t, out=self.o) assert_array_equal(self.o, self.t) assert_array_equal(self.t & self.t, self.t) assert_array_equal(self.f & self.f, self.f) assert_array_equal(self.t & self.f, self.f) assert_array_equal(self.f & self.t, self.f) np.logical_and(self.t, self.t, out=self.o) assert_array_equal(self.o, self.t) assert_array_equal(self.t ^ self.t, self.f) assert_array_equal(self.f ^ self.f, self.f) assert_array_equal(self.t ^ self.f, self.t) assert_array_equal(self.f ^ self.t, self.t) np.logical_xor(self.t, self.t, out=self.o) assert_array_equal(self.o, self.f) assert_array_equal(self.nm & self.t, self.nm) assert_array_equal(self.im & self.f, False) assert_array_equal(self.nm & True, self.nm) assert_array_equal(self.im & False, self.f) assert_array_equal(self.nm \| self.t, self.t) assert_array_equal(self.im \| self.f, self.im) assert_array_equal(self.nm \| True, self.t) assert_array_equal(self.im \| False, self.im) assert_array_equal(self.nm ^ self.t, self.im) assert_array_equal(self.im ^ self.f, self.im) assert_array_equal(self.nm ^ True, self.im) assert_array_equal(self.im ^ False, self.im) class TestBoolCmp(object): def setup(self): self.f = np.ones(256, dtype=np.float32) self.ef = np.ones(self.f.size, dtype=bool) self.d = np.ones(128, dtype=np.float64) self.ed = np.ones(self.d.size, dtype=bool) # generate values for all permutation of 256bit simd vectors s = 0 for i in range(32): self.f[s:s+8] = [i & 2x for x in range(8)] self.ef[s:s+8] = [(i & 2x) != 0 for x in range(8)] s += 8 s = 0 for i in range(16): self.d[s:s+4] = [i & 2x for x in range(4)] self.ed[s:s+4] = [(i & 2x) != 0 for x in range(4)] s += 4 self.nf = self.f.copy() self.nd = self.d.copy() self.nf[self.ef] = np.nan self.nd[self.ed] = np.nan self.inff = self.f.copy() self.infd = self.d.copy() self.inff[::3][self.ef[::3]] = np.inf self.infd[::3][self.ed[::3]] = np.inf self.inff[1::3][self.ef[1::3]] = -np.inf self.infd[1::3][self.ed[1::3]] = -np.inf self.inff[2::3][self.ef[2::3]] = np.nan self.infd[2::3][self.ed[2::3]] = np.nan self.efnonan = self.ef.copy() self.efnonan[2::3] = False self.ednonan = self.ed.copy() self.ednonan[2::3] = False self.signf = self.f.copy() self.signd = self.d.copy() self.signf[self.ef] = -1. self.signd[self.ed] = -1. self.signf[1::6][self.ef[1::6]] = -np.inf self.signd[1::6][self.ed[1::6]] = -np.inf self.signf[3::6][self.ef[3::6]] = -np.nan self.signd[3::6][self.ed[3::6]] = -np.nan self.signf[4::6][self.ef[4::6]] = -0. self.signd[4::6][self.ed[4::6]] = -0. def test_float(self): # offset for alignment test for i in range(4): assert_array_equal(self.f[i:] > 0, self.ef[i:]) assert_array_equal(self.f[i:] - 1 >= 0, self.ef[i:]) assert_array_equal(self.f[i:] == 0, ~self.ef[i:]) assert_array_equal(-self.f[i:] < 0, self.ef[i:]) assert_array_equal(-self.f[i:] + 1 <= 0, self.ef[i:]) r = self.f[i:] != 0 assert_array_equal(r, self.ef[i:]) r2 = self.f[i:] != np.zeros_like(self.f[i:]) r3 = 0 != self.f[i:] assert_array_equal(r, r2) assert_array_equal(r, r3) # check bool == 0x1 assert_array_equal(r.view(np.int8), r.astype(np.int8)) assert_array_equal(r2.view(np.int8), r2.astype(np.int8)) assert_array_equal(r3.view(np.int8), r3.astype(np.int8)) # isnan on amd64 takes the same code path assert_array_equal(np.isnan(self.nf[i:]), self.ef[i:]) assert_array_equal(np.isfinite(self.nf[i:]), ~self.ef[i:]) assert_array_equal(np.isfinite(self.inff[i:]), ~self.ef[i:]) assert_array_equal(np.isinf(self.inff[i:]), self.efnonan[i:]) assert_array_equal(np.signbit(self.signf[i:]), self.ef[i:]) def test_double(self): # offset for alignment test for i in range(2): assert_array_equal(self.d[i:] > 0, self.ed[i:]) assert_array_equal(self.d[i:] - 1 >= 0, self.ed[i:]) assert_array_equal(self.d[i:] == 0, ~self.ed[i:]) assert_array_equal(-self.d[i:] < 0, self.ed[i:]) assert_array_equal(-self.d[i:] + 1 <= 0, self.ed[i:]) r = self.d[i:] != 0 assert_array_equal(r, self.ed[i:]) r2 = self.d[i:] != np.zeros_like(self.d[i:]) r3 = 0 != self.d[i:] assert_array_equal(r, r2) assert_array_equal(r, r3) # check bool == 0x1 assert_array_equal(r.view(np.int8), r.astype(np.int8)) assert_array_equal(r2.view(np.int8), r2.astype(np.int8)) assert_array_equal(r3.view(np.int8), r3.astype(np.int8)) # isnan on amd64 takes the same code path assert_array_equal(np.isnan(self.nd[i:]), self.ed[i:]) assert_array_equal(	np.isfinite(self.nd[i:])	numpy.isfinite
#! /usr/bin/env python # Copyright 2021 <NAME> # # This file is part of WarpX. # # License: BSD-3-Clause-LBNL import os import sys import yt sys.path.insert(1, '../../../../warpx/Regression/Checksum/') import checksumAPI import numpy as np import scipy.constants as scc ## This script performs various checks for the proton boron nuclear fusion module. The simulation ## that we check is made of 5 different tests, each with different proton, boron and alpha species. ## ## The first test is performed in the proton-boron center of mass frame. It could correspond to the ## physical case of a proton beam colliding with a boron beam. The kinetic energy of the colliding ## particles depends on the cell number in the z direction and varies in the few keV to few MeV ## range. All the particles within a cell have the exact same momentum, which allows detailed ## checks of the energy of produced alpha particles. The proton and boron species have the same ## density and number of particles in this test. The number of produced alphas is much smaller than ## the initial number of protons and borons. ## ## The second test is performed in the boron rest frame. It corresponds to the physical case of a ## low density proton beam colliding with a high-density proton+boron target. The energy of the ## proton beam is varied in the few keV to few MeV range, depending on the cell number in the z ## direction. As in the previous case, all the particles within a cell have the exact same ## momentum, which allows detailed checks of the energy of produced alpha particles. In this test, ## there are 100 immobile boron and 100 immobile proton macroparticles per cell, as well as 900 ## beam proton macroparticles per cell. The density of the immobile particles is 6 orders of ## magnitude higher than the number of beam particles, which means that they have a much higher ## weight. This test is similar to the example given in section 3 of Higginson et al., ## Journal of Computation Physics, 388 439–453 (2019), which was found to be sensitive to the way ## unsampled pairs are accounted for. As before, the number of produced alphas is much smaller than ## the initial number of protons and borons. ## ## The third test corresponds to a Maxwellian plasma with a 44 keV temperature. The alpha yield is ## directly compared to the analytical fits of <NAME> and <NAME>, Nuclear Fusion, 40, 865 ## (2000) for a thermal plasma. ## ## The fourth test corresponds to a plasma with an extremely small boron density, so that all boron ## macroparticles should have disappeared by the end of the simulation, which we verify. ## ## The fifth test is exactly the same as the fourth test, except that the ## fusion_probability_threshold parameter is increased to an excessive value. Because of that, we ## severely underestimate the fusion yield and boron macroparticles remain at the end of the ## simulation, which we verify. ## ## In all simulations, we check particle number, charge, momentum and energy conservation and ## perform basic checks regarding the produced particles. When possible, we also compare the number ## of produced macroparticles, fusion yield and energy of the produced particles to theoretical ## values. ## ## Please be aware that the relative tolerances are often set empirically in this analysis script, ## so it would not be surprising that some tolerances need to be increased in the future. default_tol = 1.e-12 # Default relative tolerance ## Some physical parameters keV_to_Joule = scc.e1e3 MeV_to_Joule = scc.e1e6 barn_to_square_meter = 1.e-28 m_p = scc.m_p # Proton mass m_b = 10.9298m_p # Boron 11 mass m_reduced = m_pm_b/(m_p+m_b) m_a = 3.97369m_p # Alpha mass m_be = 7.94748m_p # Beryllium 8 mass Z_boron = 5. Z_proton = 1. E_Gamow = (Z_boronZ_protonnp.piscc.fine_structure)22.m_reducedscc.c*2 E_Gamow_MeV = E_Gamow/MeV_to_Joule E_Gamow_keV = E_Gamow/keV_to_Joule E_fusion = 8.59009MeV_to_Joule # Energy released during p + B -> alpha + Be E_decay = 0.0918984MeV_to_Joule # Energy released during Be -> 2alpha E_fusion_total = E_fusion + E_decay # Energy released during p + B -> 3alpha ## Some numerical parameters for this test size_x = 8 size_y = 8 size_z = 16 dV_total = size_xsize_ysize_z # Total simulation volume # Volume of a slice corresponding to a single cell in the z direction. In tests 1 and 2, all the # particles of a given species in the same slice have the exact same momentum dV_slice = size_xsize_y dt = 1./(scc.cnp.sqrt(3.)) # In test 1 and 2, the energy in cells number i (in z direction) is typically Energy_step i*2 Energy_step = 22.keV_to_Joule def is_close(val1, val2, rtol=default_tol, atol=0.): ## Wrapper around numpy.isclose, used to override the default tolerances. return np.isclose(val1, val2, rtol=rtol, atol=atol) def add_existing_species_to_dict(yt_ad, data_dict, species_name, prefix, suffix): data_dict[prefix+"_px_"+suffix] = yt_ad[species_name, "particle_momentum_x"].v data_dict[prefix+"_py_"+suffix] = yt_ad[species_name, "particle_momentum_y"].v data_dict[prefix+"_pz_"+suffix] = yt_ad[species_name, "particle_momentum_z"].v data_dict[prefix+"_w_"+suffix] = yt_ad[species_name, "particle_weight"].v data_dict[prefix+"_id_"+suffix] = yt_ad[species_name, "particle_id"].v data_dict[prefix+"_cpu_"+suffix] = yt_ad[species_name, "particle_cpu"].v data_dict[prefix+"_z_"+suffix] = yt_ad[species_name, "particle_position_z"].v def add_empty_species_to_dict(data_dict, species_name, prefix, suffix): data_dict[prefix+"_px_"+suffix] = np.empty(0) data_dict[prefix+"_py_"+suffix] = np.empty(0) data_dict[prefix+"_pz_"+suffix] = np.empty(0) data_dict[prefix+"_w_"+suffix] = np.empty(0) data_dict[prefix+"_id_"+suffix] = np.empty(0) data_dict[prefix+"_cpu_"+suffix] = np.empty(0) data_dict[prefix+"_z_"+suffix] = np.empty(0) def add_species_to_dict(yt_ad, data_dict, species_name, prefix, suffix): try: ## If species exist, we add its data to the dictionary add_existing_species_to_dict(yt_ad, data_dict, species_name, prefix, suffix) except yt.utilities.exceptions.YTFieldNotFound: ## If species does not exist, we avoid python crash and add empty arrays to the ## dictionnary. Currently, this happens for the boron species in test number 4, which ## entirely fuses into alphas. add_empty_species_to_dict(data_dict, species_name, prefix, suffix) def check_particle_number_conservation(data): total_w_proton_start = np.sum(data["proton_w_start"]) total_w_proton_end = np.sum(data["proton_w_end"]) total_w_boron_start = np.sum(data["boron_w_start"]) total_w_boron_end = np.sum(data["boron_w_end"]) consumed_proton = total_w_proton_start - total_w_proton_end consumed_boron = total_w_boron_start - total_w_boron_end created_alpha = np.sum(data["alpha_w_end"]) assert(consumed_proton >= 0.) assert(consumed_boron >= 0.) assert(created_alpha >= 0.) ## Check that number of consumed proton and consumed boron are equal assert_scale = max(total_w_proton_start, total_w_boron_start) assert(is_close(consumed_proton, consumed_boron, rtol = 0., atol = default_tolassert_scale)) ## Check that number of consumed particles corresponds to number of produced alpha ## Factor 3 is here because each nuclear fusion reaction produces 3 alphas assert(is_close(total_w_proton_start, total_w_proton_end + created_alpha/3.)) assert(is_close(total_w_boron_start, total_w_boron_end + created_alpha/3.)) def compute_energy_array(data, species_name, suffix, m): ## Relativistic computation of kinetic energy for a given species psq_array = data[species_name+'_px_'+suffix]2 + data[species_name+'_py_'+suffix]2 + \ data[species_name+'_pz_'+suffix]2 rest_energy = mscc.c*2 return np.sqrt(psq_arrayscc.c2 + rest_energy2) - rest_energy def check_energy_conservation(data): proton_energy_start = compute_energy_array(data, "proton", "start", m_p) proton_energy_end = compute_energy_array(data, "proton", "end", m_p) boron_energy_start = compute_energy_array(data, "boron", "start", m_b) boron_energy_end = compute_energy_array(data, "boron", "end", m_b) alpha_energy_end = compute_energy_array(data, "alpha", "end", m_a) total_energy_start = np.sum(proton_energy_startdata["proton_w_start"]) + \ np.sum(boron_energy_startdata["boron_w_start"]) total_energy_end = np.sum(proton_energy_enddata["proton_w_end"]) + \ np.sum(boron_energy_enddata["boron_w_end"]) + \ np.sum(alpha_energy_enddata["alpha_w_end"]) ## Factor 3 is here because each nuclear fusion reaction produces 3 alphas n_fusion_reaction = np.sum(data["alpha_w_end"])/3. assert(is_close(total_energy_end, total_energy_start + n_fusion_reactionE_fusion_total, rtol = 1.e-8)) def check_momentum_conservation(data): proton_total_px_start = np.sum(data["proton_px_start"]data["proton_w_start"]) proton_total_py_start = np.sum(data["proton_py_start"]data["proton_w_start"]) proton_total_pz_start = np.sum(data["proton_pz_start"]data["proton_w_start"]) proton_total_px_end = np.sum(data["proton_px_end"]data["proton_w_end"]) proton_total_py_end = np.sum(data["proton_py_end"]data["proton_w_end"]) proton_total_pz_end = np.sum(data["proton_pz_end"]data["proton_w_end"]) boron_total_px_start = np.sum(data["boron_px_start"]data["boron_w_start"]) boron_total_py_start = np.sum(data["boron_py_start"]data["boron_w_start"]) boron_total_pz_start = np.sum(data["boron_pz_start"]data["boron_w_start"]) boron_total_px_end = np.sum(data["boron_px_end"]data["boron_w_end"]) boron_total_py_end = np.sum(data["boron_py_end"]data["boron_w_end"]) boron_total_pz_end = np.sum(data["boron_pz_end"]data["boron_w_end"]) alpha_total_px_end = np.sum(data["alpha_px_end"]data["alpha_w_end"]) alpha_total_py_end = np.sum(data["alpha_py_end"]data["alpha_w_end"]) alpha_total_pz_end = np.sum(data["alpha_pz_end"]data["alpha_w_end"]) total_px_start = proton_total_px_start + boron_total_px_start total_py_start = proton_total_py_start + boron_total_py_start total_pz_start = proton_total_pz_start + boron_total_pz_start total_px_end = proton_total_px_end + boron_total_px_end + alpha_total_px_end total_py_end = proton_total_py_end + boron_total_py_end + alpha_total_py_end total_pz_end = proton_total_pz_end + boron_total_pz_end + alpha_total_pz_end ## Absolute tolerance is needed because sometimes the initial momentum is exactly 0 assert(is_close(total_px_start, total_px_end, atol=1.e-15)) assert(is_close(total_py_start, total_py_end, atol=1.e-15)) assert(is_close(total_pz_start, total_pz_end, atol=1.e-15)) def check_id(data): ## Check that all created particles have unique id + cpu identifier (two particles with ## different cpu can have the same id) complex_id = data["alpha_id_end"] + 1jdata["alpha_cpu_end"] assert(complex_id.shape == np.unique(complex_id).shape) def basic_product_particles_check(data): ## For each nuclear fusion reaction in the code, we create 6 alpha macroparticles. So the ## total number of alpha macroparticles must be a multiple of 6. num_alpha = data["alpha_w_end"].shape[0] assert(num_alpha%6 == 0) ## The weight of the 6 macroparticles coming from a single fusion event should be the same. ## We verify this here. assert(np.array_equal(data["alpha_w_end"][::6], data["alpha_w_end"][1::6])) assert(np.array_equal(data["alpha_w_end"][::6], data["alpha_w_end"][2::6])) assert(np.array_equal(data["alpha_w_end"][::6], data["alpha_w_end"][3::6])) assert(np.array_equal(data["alpha_w_end"][::6], data["alpha_w_end"][4::6])) assert(np.array_equal(data["alpha_w_end"][::6], data["alpha_w_end"][5::6])) ## When we create 6 macroparticles, the first has the exact same momentum as the second, the ## third has the same as the fourth and the fifth has the same as the sixth. We verify this ## here assert(np.array_equal(data["alpha_px_end"][::6], data["alpha_px_end"][1::6])) assert(np.array_equal(data["alpha_py_end"][::6], data["alpha_py_end"][1::6])) assert(np.array_equal(data["alpha_pz_end"][::6], data["alpha_pz_end"][1::6])) assert(np.array_equal(data["alpha_px_end"][2::6], data["alpha_px_end"][3::6])) assert(np.array_equal(data["alpha_py_end"][2::6], data["alpha_py_end"][3::6])) assert(np.array_equal(data["alpha_pz_end"][2::6], data["alpha_pz_end"][3::6])) assert(np.array_equal(data["alpha_px_end"][4::6], data["alpha_px_end"][5::6])) assert(np.array_equal(data["alpha_py_end"][4::6], data["alpha_py_end"][5::6])) assert(np.array_equal(data["alpha_pz_end"][4::6], data["alpha_pz_end"][5::6])) def generic_check(data): check_particle_number_conservation(data) check_energy_conservation(data) check_momentum_conservation(data) check_id(data) basic_product_particles_check(data) def check_isotropy(data, relative_tolerance): ## Checks that the alpha particles are emitted isotropically average_px_sq = np.average(data["alpha_px_end"]data["alpha_px_end"]) average_py_sq = np.average(data["alpha_py_end"]data["alpha_py_end"]) average_pz_sq = np.average(data["alpha_pz_end"]data["alpha_pz_end"]) assert(is_close(average_px_sq, average_py_sq, rtol = relative_tolerance)) assert(is_close(average_px_sq, average_pz_sq, rtol = relative_tolerance)) def astrophysical_factor_lowE(E): ## E is in keV ## Returns astrophysical factor in MeV b using the low energy fit in the range E < 400 keV ## described in equation (2) of <NAME> and <NAME>, Nuclear Fusion, 40, 865 (2000) C0 = 197. C1 = 0.24 C2 = 2.31e-4 AL = 1.82e4 EL = 148. dEL = 2.35 return C0 + C1E + C2E2 + AL/((E-EL)2 + dEL2) def astrophysical_factor_midE(E): ## E is in keV ## Returns astrophysical factor in MeV b using the mid energy fit in the range ## 400 keV < E < 642 keV described in equation (3) of <NAME> and <NAME>, ## Nuclear Fusion, 40, 865 (2000) D0 = 330. D1 = 66.1 D2 = -20.3 D5 = -1.58 E_400 = 400. E_100 = 100. E_norm = (E - E_400)/E_100 return D0 + D1E_norm + D2E_norm2 + D5E_norm5 def astrophysical_factor_highE(E): ## E is in keV ## Returns astrophysical factor in MeV b using the high energy fit in the range ## 642 keV < E < 3500 keV described in equation (4) of <NAME> and <NAME>, ## Nuclear Fusion, 40, 865 (2000) A0 = 2.57e6 A1 = 5.67e5 A2 = 1.34e5 A3 = 5.68e5 E0 = 581.3 E1 = 1083. E2 = 2405. E3 = 3344. dE0 = 85.7 dE1 = 234. dE2 = 138. dE3 = 309. B = 4.38 return A0/((E-E0)2 + dE02) + A1/((E-E1)2 + dE12) + \ A2/((E-E2)2 + dE22) + A3/((E-E3)2 + dE3*2) + B def astrophysical_factor(E): ## E is in keV ## Returns astrophysical factor in MeV b using the fits described in <NAME> ## and <NAME>, Nuclear Fusion, 40, 865 (2000) conditions = [E <= 400, E <= 642, E > 642] choices = [astrophysical_factor_lowE(E), astrophysical_factor_midE(E), astrophysical_factor_highE(E)] return np.select(conditions, choices) def pb_cross_section_buck_fit(E): ## E is in MeV ## Returns cross section in b using a power law fit of the data presented in Buck et al., ## Nuclear Physics A, 398(2), 189-202 (1983) in the range E > 3.5 MeV. E_start_fit = 3.5 ## Cross section at E = E_start_fit = 3.5 MeV cross_section_start_fit = 0.2168440845211521 slope_fit = -2.661840717596765 return cross_section_start_fit(E/E_start_fit)*slope_fit def pb_cross_section(E): ## E is in keV ## Returns cross section in b using the fits described in <NAME> and <NAME>, ## Nuclear Fusion, 40, 865 (2000) for E < 3.5 MeV and a power law fit of the data presented in ## Buck et al., Nuclear Physics A, 398(2), 189-202 (1983) for E > 3.5 MeV. E_MeV = E/1.e3 conditions = [E <= 3500, E > 3500] choices = [astrophysical_factor(E)/E_MeV np.exp(-np.sqrt(E_Gamow_MeV / E_MeV)), pb_cross_section_buck_fit(E_MeV)] return np.select(conditions, choices) def E_com_to_p_sq_com(m1, m2, E): ## E is the total (kinetic+mass) energy of a two particle (with mass m1 and m2) system in ## its center of mass frame, in J. ## Returns the square norm of the momentum of each particle in that frame. return E*2/(4.scc.c2) - (m12 + m2*2)scc.c2/2. + \ scc.c6/(4.E2)((m12 - m22)*2) def compute_relative_v_com(E): ## E is the kinetic energy of proton+boron in the center of mass frame, in keV ## Returns the relative velocity between proton and boron in this frame, in m/s E_J = EkeV_to_Joule + (m_p + m_b)scc.c2 p_sq = E_com_to_p_sq_com(m_p, m_b, E_J) p = np.sqrt(p_sq) gamma_p = np.sqrt(1. + p_sq / (m_pscc.c)*2) gamma_b = np.sqrt(1. + p_sq / (m_bscc.c)*2) v_p = p/(gamma_pm_p) v_b = p/(gamma_bm_b) return v_p+v_b def expected_alpha_weight_com(E_com, proton_density, boron_density, dV, dt): ## Computes expected number of produced alpha particles as a function of energy E_com in the ## center of mass frame. E_com is in keV. assert(np.all(E_com>=0)) ## Case E_com == 0 is handled manually to avoid division by zero conditions = [E_com == 0, E_com > 0] ## Necessary to avoid division by 0 warning when pb_cross_section is evaluated E_com_never_zero = np.clip(E_com, 1.e-15, None) choices = [0., pb_cross_section(E_com_never_zero)compute_relative_v_com(E_com_never_zero)] sigma_times_vrel = np.select(conditions, choices) ## Factor 3 is here because each fusion reaction produces 3 alphas return 3.proton_densityboron_densitysigma_times_vrelbarn_to_square_meterdVdt def check_macroparticle_number(data, fusion_probability_target_value, num_pair_per_cell): ## Checks that the number of macroparticles is as expected for the first and second tests ## The first slice 0 < z < 1 does not contribute to alpha creation numcells = dV_total - dV_slice ## In these tests, the fusion_multiplier is so high that the fusion probability per pair is ## equal to the parameter fusion_probability_target_value fusion_probability_per_pair = fusion_probability_target_value expected_fusion_number = numcellsnum_pair_per_cellfusion_probability_per_pair ## Each fusion event produces 6 alpha macroparticles expected_macroparticle_number = 6.expected_fusion_number std_macroparticle_number = 6.np.sqrt(expected_fusion_number) actual_macroparticle_number = data["alpha_w_end"].shape[0] # 5 sigma test that has an intrinsic probability to fail of 1 over ~2 millions assert(is_close(actual_macroparticle_number, expected_macroparticle_number, rtol = 0., atol = 5.std_macroparticle_number)) ## used in subsequent function return expected_fusion_number def p_sq_boron_frame_to_E_COM_frame(p_proton_sq): # Takes the proton square norm of the momentum in the boron rest frame and returns the total # kinetic energy in the center of mass frame. Everything is in SI units. # Total (kinetic + mass) energy in lab frame E_lab = np.sqrt(p_proton_sqscc.c*2 + (m_pscc.c2)2) + m_bscc.c2 # Use invariant E2 - p2c2 of 4-momentum norm to compute energy in center of mass frame E_com = np.sqrt(E_lab2 - p_proton_sqscc.c*2) # Corresponding kinetic energy E_com_kin = E_com - (m_b+scc.m_p)scc.c*2 return E_com_kin def p_sq_to_kinetic_energy(p_sq, m): ## Returns the kinetic energy of a particle as a function of its squared momentum. ## Everything is in SI units. return np.sqrt(p_sqscc.c*2 + (mscc.c2)2) - (mscc.c2) def compute_E_com1(data): ## Computes kinetic energy (in Joule) in the center of frame for the first test ## Square norm of the momentum of proton/boron as a function of cell number in z direction p_sq = 2.m_reduced(Energy_stepnp.arange(size_z)*2) return p_sq_to_kinetic_energy(p_sq, m_b) + p_sq_to_kinetic_energy(p_sq, m_p) def compute_E_com2(data): ## Computes kinetic energy (in Joule) in the center of frame for the second test ## Square norm of the momentum of the proton as a function of cell number in z direction p_proton_sq = 2.m_p(Energy_stepnp.arange(size_z)*2) return p_sq_boron_frame_to_E_COM_frame(p_proton_sq) def check_alpha_yield(data, expected_fusion_number, E_com, proton_density, boron_density): ## Checks that the fusion yield is as expected for the first and second tests. ## Proton and boron densities are in m^-3. alpha_weight_theory = expected_alpha_weight_com(E_com/keV_to_Joule, proton_density, boron_density, dV_slice, dt) alpha_weight_simulation = np.histogram(data["alpha_z_end"], bins=size_z, range=(0, size_z), weights = data["alpha_w_end"])[0] ## -1 is here because the first slice 0 < z < 1 does not contribute to alpha creation expected_fusion_number_per_slice = expected_fusion_number/(size_z-1) relative_std_alpha_weight = 1./np.sqrt(expected_fusion_number_per_slice) # 5 sigma test that has an intrinsic probability to fail of 1 over ~2 millions assert(np.all(is_close(alpha_weight_theory, alpha_weight_simulation, rtol = 5.relative_std_alpha_weight))) def check_initial_energy1(data, E_com): ## In WarpX, the initial momentum of the alphas is computed assuming that the fusion process ## takes place in two steps: ## (1): proton + boron 11 -> alpha + beryllium 8 ## (2): beryllium 8 -> alpha + alpha ## The alpha generated in the first step (labeled alpha1) generally has a different initial ## energy distribution than the alphas generated in the second step (labeled alpha2 and ## alpha3). ## In the first test, we are in the center of mass frame. Therefore, the momentum of alpha1 is ## entirely determined by the energy in the center of mass frame, so we check in this function ## that the energy of the alpha1 macroparticles is as expected. On the other hand, the energy ## of alpha2 and alpha3 follows a continuous distribution within a given range. In this test, ## we check that this range is as expected by comparing the maximum and minimum energy of the ## obtained macroparticles to the theoretical maximum and minimum. ## Note that in the simulations, 6 macroparticles are generated during for each fusion event. ## The first and second macroparticles are alpha1 particles. The third and fourth are alpha2. ## The fifth and sixth are alpha3. energy_alpha_simulation = compute_energy_array(data, "alpha", "end", m_a) z_alpha = data["alpha_z_end"] # Loop over all slices (i.e. cells in the z direction) for slice_number in range(1, size_z): ## Kinetic energy in the lab frame before fusion E_kinetic_com_before = E_com[slice_number] ## Total (kinetic + mass) energy in the lab frame after ## proton + boron 11 -> alpha + beryllium 8 E_total_com_after = E_kinetic_com_before + E_fusion + (m_a + m_be)scc.c2 ## Corresponding momentum norm squared of alpha1/beryllium p_sq_after = E_com_to_p_sq_com(m_a, m_be, E_total_com_after) ## Corresponding kinetic energy for alpha1 energy_alpha1_theory = p_sq_to_kinetic_energy(p_sq_after, m_a) ## Corresponding kinetic energy for beryllium energy_beryllium_theory = p_sq_to_kinetic_energy(p_sq_after, m_be) ## Corresponding kinetic energy for alpha2 + alpha3 after beryllium decay energy_alpha2_plus_3_theory = energy_beryllium_theory + E_decay ## Compute the theoretical maximum and minimum energy of alpha2 and alpha3. This ## calculation is done nonrelativistically, by noting that the maximum (minimum) energy ## corresponds to an alpha emitted exactly in the (opposite) direction of the beryllium ## in the center of mass frame. This calculation involves solving a polynomial equation of ## order 2 in p_alpha23. max_p_alpha23 = 0.5(np.sqrt(p_sq_after) + \ np.sqrt(4m_aenergy_alpha2_plus_3_theory - p_sq_after)) min_p_alpha23 = 0.5(np.sqrt(p_sq_after) - \ np.sqrt(4m_aenergy_alpha2_plus_3_theory - p_sq_after)) max_energy_alpha23 = max_p_alpha232/(2.m_a) min_energy_alpha23 = min_p_alpha23*2/(2.m_a) ## Get the energy of all alphas in the slice energy_alpha_slice = energy_alpha_simulation[(z_alpha >= slice_number)* \ (z_alpha < (slice_number + 1))] ## Energy of alphas1 (here, first macroparticle of each fusion event) in the slice energy_alpha1_simulation = energy_alpha_slice[::6] ## Energy of alphas2 (here, third macroparticle of each fusion event) in the slice energy_alpha2_simulation = energy_alpha_slice[2::6] ## Energy of alphas3 (here, fifth macroparticle of each fusion event) in the slice energy_alpha3_simulation = energy_alpha_slice[4::6] assert(np.all(is_close(energy_alpha1_simulation, energy_alpha1_theory, rtol=5.e-8))) assert(is_close(np.amax(energy_alpha2_simulation), max_energy_alpha23, rtol=1.e-2)) assert(is_close(np.amin(energy_alpha2_simulation), min_energy_alpha23, rtol=1.e-2)) assert(is_close(np.amax(energy_alpha3_simulation), max_energy_alpha23, rtol=1.e-2)) assert(is_close(np.amin(energy_alpha3_simulation), min_energy_alpha23, rtol=1.e-2)) def check_initial_energy2(data): ## In WarpX, the initial momentum of the alphas is computed assuming that the fusion process ## takes place in two steps: ## (1): proton + boron 11 -> alpha + beryllium 8 ## (2): beryllium 8 -> alpha + alpha ## The alpha generated in the first step (labeled alpha1) generally has a different initial ## energy distribution than the alphas generated in the second step (labeled alpha2 and ## alpha3). ## In the second test, we are in the boron rest frame. In this case, the momentum of each alpha ## follows a continuous distribution within a given range. In this function, we verify that ## this range is as expected by comparing the maximum and minimum energy of the obtained ## macroparticles to the theoretical maximum and minimum. Be aware that the range for alpha1 ## is not the same as the range for alpha2 and alpha3 (typically alpha1 particles will carry ## more energy). ## Note that in the simulations, 6 macroparticles are generated during for each fusion event. ## The first and second macroparticles are alpha1 particles. The third and fourth are alpha2. ## The fifth and sixth are alpha3. energy_alpha_simulation = compute_energy_array(data, "alpha", "end", m_a) z_alpha = data["alpha_z_end"] # Loop over all slices (i.e. cells in the z direction) for slice_number in range(1, size_z): ## For simplicity, all the calculations in this functino are done nonrelativistically ## Proton kinetic energy in the lab frame before fusion E_proton_nonrelativistic = Energy_stepslice_number2 ## Corresponding square norm of proton momentum p_proton_sq = 2.scc.m_pE_proton_nonrelativistic ## Kinetic energy in the lab frame after ## proton + boron 11 -> alpha + beryllium 8 E_after_fusion = E_proton_nonrelativistic + E_fusion ## Compute the theoretical maximum and minimum energy of alpha1 in the lab frame. This ## calculation is done by noting that the maximum (minimum) energy corresponds to an alpha ## emitted exactly in the (opposite) direction of the proton in the lab frame. This ## calculation involves solving a polynomial equation of order 2 in p_alpha1. max_p_alpha1 = (m_a/m_benp.sqrt(p_proton_sq) + \ np.sqrt(-m_a/m_bep_proton_sq + 2.E_after_fusionm_a(m_a/m_be + 1.))) / \ (m_a/m_be + 1.) min_p_alpha1 = (m_a/m_benp.sqrt(p_proton_sq) - \ np.sqrt(-m_a/m_bep_proton_sq + 2.E_after_fusionm_a(m_a/m_be + 1.))) / \ (m_a/m_be + 1.) max_energy_alpha1 = max_p_alpha12/(2m_a) min_energy_alpha1 = min_p_alpha1*2/(2m_a) ## Corresponding max/min kinetic energy of Beryllium in the lab frame max_E_beryllium = E_after_fusion - min_energy_alpha1 min_E_beryllium = E_after_fusion - max_energy_alpha1 ## Corresponding max/min momentum square of Beryllium in the lab frame max_p_sq_beryllium = 2.m_bemax_E_beryllium min_p_sq_beryllium = 2.m_bemin_E_beryllium ## Corresponding max/min kinetic energy in the lab frame for alpha2 + alpha3 after ## Beryllium decay max_energy_alpha2_plus_3 = max_E_beryllium + E_decay min_energy_alpha2_plus_3 = min_E_beryllium + E_decay ## Compute the theoretical maximum and minimum energy of alpha2 and alpha3 in the lab ## frame. This calculation is done by noting that the maximum (minimum) energy corresponds ## to an alpha emitted exactly in the (opposite) direction of a beryllium with energy ## max_E_beryllium (min_E_beryllium). This calculation involves solving a polynomial ## equation of order 2 in p_alpha23. max_p_alpha23 = 0.5(np.sqrt(max_p_sq_beryllium) + \ np.sqrt(4m_amax_energy_alpha2_plus_3 - max_p_sq_beryllium)) min_p_alpha23 = 0.5(np.sqrt(min_p_sq_beryllium) - \ np.sqrt(4m_amin_energy_alpha2_plus_3 - min_p_sq_beryllium)) max_energy_alpha23 = max_p_alpha23*2/(2m_a) min_energy_alpha23 = min_p_alpha23*2/(2m_a) ## Get the energy of all alphas in the slice energy_alpha_slice = energy_alpha_simulation[(z_alpha >= slice_number)* \ (z_alpha < (slice_number + 1))] ## Energy of alphas1 (here, first macroparticle of each fusion event) in the slice energy_alpha1_simulation = energy_alpha_slice[::6] ## Energy of alphas2 (here, third macroparticle of each fusion event) in the slice energy_alpha2_simulation = energy_alpha_slice[2::6] ## Energy of alphas3 (here, fifth macroparticle of each fusion event) in the slice energy_alpha3_simulation = energy_alpha_slice[4::6] assert(is_close(np.amax(energy_alpha1_simulation), max_energy_alpha1, rtol=1.e-2)) assert(is_close(np.amin(energy_alpha1_simulation), min_energy_alpha1, rtol=1.e-2)) ## Tolerance is quite high below because we don't have a lot of alphas to produce good ## statistics and an event like alpha1 emitted exactly in direction of proton & alpha2 ## emitted exactly in direction opposite to Beryllium is somewhat rare. assert(is_close(np.amax(energy_alpha2_simulation), max_energy_alpha23, rtol=2.5e-1)) assert(is_close(np.amin(energy_alpha2_simulation), min_energy_alpha23, rtol=2.5e-1)) assert(is_close(np.amax(energy_alpha3_simulation), max_energy_alpha23, rtol=2.5e-1)) assert(is_close(np.amin(energy_alpha3_simulation), min_energy_alpha23, rtol=2.5e-1)) def check_xy_isotropy(data): ## Checks that the alpha particles are emitted isotropically in x and y average_px_sq = np.average(data["alpha_px_end"]*data["alpha_px_end"]) average_py_sq =	np.average(data["alpha_py_end"]*data["alpha_py_end"])	numpy.average
"""""" # -- coding: utf-8 -- # date: 2021 # author: AllChooseC import os import numpy as np from tensorboardX import SummaryWriter import torch from tqdm import tqdm from utils import EarlyStopping, class_penalty class_distribution = [59.68, 8.68, 28.55, 3.08] # 2017 class_distribution = [59.22, 8.65, 28.80, 3.33] def loss_batch(model, loss_func, xb, yb, opt=None, metric=None): """Calculates the loss and metric value for a batch of data, and optionally performs gradient descent if an optimizer is provided.""" preds = model(xb) loss = loss_func(preds, yb, weight=class_penalty(class_distribution, class_penalty=0.2)) if opt is not None: loss.backward() # Compute gradients opt.step() # Update parameters opt.zero_grad() # Reset gradients metric_result = None if metric is not None: metric_result = metric(preds, yb) # Compute the metric return loss.item(), len(xb), metric_result def evaluate(model, loss_func, valid_dl, metric=None): """""" with torch.no_grad(): # Pass each batch through the model results = [loss_batch(model, loss_func, xb, yb, metric=metric) for xb, yb in tqdm(valid_dl)] losses, nums, metrics = zip(results) # Separate losses, counts and metrics total = np.sum(nums) # Total size of the dataset avg_loss = np.sum(np.multiply(losses, nums)) / total avg_metric = None if metric is not None: avg_metric = np.sum(np.multiply(metrics, nums)) / total return avg_loss, total, avg_metric def learn_on_batch(model, loss_func, train_dl, opt=None, metric=None): """""" # Pass each batch through the model results = [loss_batch(model, loss_func, xb, yb, opt, metric=metric) for xb, yb in tqdm(train_dl)] losses, nums, metrics = zip(results) # Separate losses, counts and metrics total = np.sum(nums) # Total size of the dataset avg_loss = np.sum(	np.multiply(losses, nums)	numpy.multiply
# This code is part of Qiskit. # # (C) Copyright IBM 2019, 2021. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """Analytical Quantum Gradient Descent (AQGD) optimizer.""" import logging from typing import Callable, Tuple, List, Dict, Union import numpy as np from qiskit.utils.validation import validate_range_exclusive_max from .optimizer import Optimizer, OptimizerSupportLevel from ..exceptions import AlgorithmError logger = logging.getLogger(__name__) class AQGD(Optimizer): """Analytic Quantum Gradient Descent (AQGD) with Epochs optimizer. Performs gradient descent optimization with a momentum term, analytic gradients, and customized step length schedule for parametrized quantum gates, i.e. Pauli Rotations. See, for example: * <NAME>, <NAME>, <NAME>, and <NAME>. (2018). Quantum circuit learning. Phys. Rev. A 98, 032309. https://arxiv.org/abs/1803.00745 * <NAME>, <NAME>, <NAME>, <NAME>, <NAME>. (2019). Evaluating analytic gradients on quantum hardware. Phys. Rev. A 99, 032331. https://arxiv.org/abs/1811.11184 for further details on analytic gradients of parametrized quantum gates. Gradients are computed "analytically" using the quantum circuit when evaluating the objective function. """ _OPTIONS = ['maxiter', 'eta', 'tol', 'disp', 'momentum', 'param_tol', 'averaging'] def __init__(self, maxiter: Union[int, List[int]] = 1000, eta: Union[float, List[float]] = 1.0, tol: float = 1e-6, # this is tol momentum: Union[float, List[float]] = 0.25, param_tol: float = 1e-6, averaging: int = 10) -> None: """ Performs Analytical Quantum Gradient Descent (AQGD) with Epochs. Args: maxiter: Maximum number of iterations (full gradient steps) eta: The coefficient of the gradient update. Increasing this value results in larger step sizes: param = previous_param - eta * deriv tol: Tolerance for change in windowed average of objective values. Convergence occurs when either objective tolerance is met OR parameter tolerance is met. momentum: Bias towards the previous gradient momentum in current update. Must be within the bounds: [0,1) param_tol: Tolerance for change in norm of parameters. averaging: Length of window over which to average objective values for objective convergence criterion Raises: AlgorithmError: If the length of ``maxiter``, `momentum``, and ``eta`` is not the same. """ super().__init__() if isinstance(maxiter, int): maxiter = [maxiter] if isinstance(eta, (int, float)): eta = [eta] if isinstance(momentum, (int, float)): momentum = [momentum] if len(maxiter) != len(eta) or len(maxiter) != len(momentum): raise AlgorithmError("AQGD input parameter length mismatch. Parameters `maxiter`, " "`eta`, and `momentum` must have the same length.") for m in momentum: validate_range_exclusive_max('momentum', m, 0, 1) self._eta = eta self._maxiter = maxiter self._momenta_coeff = momentum self._param_tol = param_tol self._tol = tol self._averaging = averaging # state self._avg_objval = None self._prev_param = None self._eval_count = 0 # function evaluations self._prev_loss = [] # type: List[float] self._prev_grad = [] # type: List[List[float]] def get_support_level(self) -> Dict[str, OptimizerSupportLevel]: """ Support level dictionary Returns: Dict[str, int]: gradient, bounds and initial point support information that is ignored/required. """ return { 'gradient': OptimizerSupportLevel.ignored, 'bounds': OptimizerSupportLevel.ignored, 'initial_point': OptimizerSupportLevel.required } def _compute_objective_fn_and_gradient(self, params: List[float], obj: Callable) -> Tuple[float, np.array]: """ Obtains the objective function value for params and the analytical quantum derivatives of the objective function with respect to each parameter. Requires 2(number parameters) + 1 objective evaluations Args: params: Current value of the parameters to evaluate the objective function obj: Objective function of interest Returns: Tuple containing the objective value and array of gradients for the given parameter set. """ num_params = len(params) param_sets_to_eval = params + np.concatenate( (np.zeros((1, num_params)), # copy of the parameters as is np.eye(num_params) np.pi / 2, # copy of the parameters with the positive shift -np.eye(num_params) * np.pi / 2), # copy of the parameters with the negative shift axis=0) # Evaluate, # reshaping to flatten, as expected by objective function values = np.array(obj(param_sets_to_eval.reshape(-1))) # Update number of objective function evaluations self._eval_count += 2 * num_params + 1 # return the objective function value obj_value = values[0] # return the gradient values gradient = 0.5 * (values[1:num_params + 1] - values[1 + num_params:]) return obj_value, gradient def _update(self, params: np.array, gradient: np.array, mprev: np.array, step_size: float, momentum_coeff: float) -> Tuple[List[float], List[float]]: """ Updates full parameter array based on a step that is a convex combination of the gradient and previous momentum Args: params: Current value of the parameters to evaluate the objective function at gradient: Gradient of objective wrt parameters mprev: Momentum vector for each parameter step_size: The scaling of step to take momentum_coeff: Bias towards previous momentum vector when updating current momentum/step vector Returns: Tuple of the updated parameter and momentum vectors respectively. """ # Momentum update: # Convex combination of previous momentum and current gradient estimate mnew = (1 - momentum_coeff) * gradient + momentum_coeff * mprev params -= step_size * mnew return params, mnew def _converged_objective(self, objval: float, tol: float, window_size: int) -> bool: """ Tests convergence based on the change in a moving windowed average of past objective values Args: objval: Current value of the objective function tol: tolerance below which (average) objective function change must be window_size: size of averaging window Returns: Bool indicating whether or not the optimization has converged. """ # If we haven't reached the required window length, # append the current value, but we haven't converged if len(self._prev_loss) < window_size: self._prev_loss.append(objval) return False # Update last value in list with current value self._prev_loss.append(objval) # (length now = n+1) # Calculate previous windowed average # and current windowed average of objective values prev_avg = np.mean(self._prev_loss[:window_size]) curr_avg = np.mean(self._prev_loss[1:window_size + 1]) self._avg_objval = curr_avg # Update window of objective values # (Remove earliest value) self._prev_loss.pop(0) if np.absolute(prev_avg - curr_avg) < tol: # converged logger.info("Previous obj avg: %f\nCurr obj avg: %f", prev_avg, curr_avg) return True return False def _converged_parameter(self, parameter: List[float], tol: float) -> bool: """ Tests convergence based on change in parameter Args: parameter: current parameter values tol: tolerance for change in norm of parameters Returns: Bool indicating whether or not the optimization has converged """ if self._prev_param is None: self._prev_param = np.copy(parameter) return False order = np.inf p_change =	np.linalg.norm(self._prev_param - parameter, ord=order)	numpy.linalg.norm
import os import sys file_dir = os.path.dirname(__file__) sys.path.append(file_dir) import numpy as np import torch from pytorch_resnet import ResNet43 import torch.nn.functional as F import kornia as K import torchvision from matplotlib import pyplot as plt class Attention: def __init__(self,in_shape,n_rotations,preprocess,device,lite=False): # TODO BY HAOJIE: add lite model self.device = device self.preprocess = preprocess self.n_rotations = n_rotations max_dim = np.max(in_shape[:2]) max_dim = 480 self.padding = np.zeros((3, 2), dtype=int) pad = (max_dim - np.array(in_shape[:2])) / 2 self.padding[:2] = pad.reshape(2, 1) in_shape = np.array(in_shape) in_shape += np.sum(self.padding, axis=1) in_shape = tuple(in_shape) self.in_type = in_shape[-1] # self.in_type = 6, self.outdim=1 # get the location self.model = ResNet43(self.in_type,outdim=1,include_batch_normal=False).to(self.device) # use the location as pivot to rotate the image and get the angle #self.angle_model = ResNet43(self.in_type,outdim=1,include_batch_normal=False).to(self.device) self.optim = torch.optim.Adam(self.model.parameters(),lr=1e-5) #self.pad_2 = (80,80,80,80) def forward(self,in_img,softmax=True,train=True): #print('padding',self.padding) #print('img',in_img.shape) in_data = np.pad(in_img, self.padding, mode='constant') #print('indata',in_data.shape) in_data = self.preprocess(in_data) in_shape = (1,) + in_data.shape in_data = in_data.reshape(in_shape).transpose(0, 3, 1, 2) in_data = torch.from_numpy(in_data).to(self.device) #print(in_data.size()) # rotate image pivot = torch.as_tensor([in_data.shape[-2]/2,in_data.shape[-1]/2]) pivot =pivot.to(self.device).repeat(self.n_rotations//2,1).to(torch.float32) in_data = in_data.repeat(self.n_rotations//2,1,1,1) in_data = K.geometry.rotate(in_data,torch.from_numpy(-np.linspace(0., 360., self.n_rotations, endpoint=False, dtype=np.float32))[0:18].to(self.device), mode='nearest',center=pivot) #print('indata rotate 36/2',in_data.shape) #self.imshow(in_data,size=(36,12),name='rotation') if not train: self.model.eval() with torch.no_grad(): logits = self.model(in_data) else: logits = self.model(in_data) #print('logits',logits.shape) # rotate back logits = K.geometry.rotate(logits,torch.from_numpy(np.linspace(0., 360., self.n_rotations, endpoint=False,dtype=np.float32))[0:18].to(self.device), mode='nearest',center=pivot) #print('atenion logits1',logits.shape) #self.imshow(logits) #self.imshow(logits,size=(36,12),name='rotation_back') #logits = logits[:,:,80:-80,80:-80] #print('first crop',logits.size()) c0 = self.padding[:2, 0] c1 = c0 + in_img.shape[:2] #print('crop',c0) #print('crop',c1) logits = logits[:, :, c0[0]:c1[0], c0[1]:c1[1]] #print('second crop',logits.size()) #print('attention logits',logits.shape) #self.imshow(logits) output = logits.reshape(1,-1) if softmax: output = F.softmax(output,dim=-1) output = output.reshape(logits.shape[0],logits.shape[-2],logits.shape[-1]).cpu().detach().numpy() #print('output',output.shape) output = output.transpose(1,2,0) return output def train(self,in_img,p,theta,backprop=True): self.model.train() self.optim.zero_grad() output = self.forward(in_img,softmax=False) # Get label theta = (theta + 2np.pi)%(2np.pi) if theta >= np.pi: theta = theta -np.pi # angle label # dgree interval: 10 theta_i = theta / (2 * np.pi / 36) # theta_i is in range [0,17] theta_i = np.int32(	np.round(theta_i)	numpy.round
import numpy as np import pandas as pd from numpy.linalg.linalg import LinAlgError from numpy.linalg import norm import matplotlib.pyplot as plt import sys from ctypes import CDLL, POINTER from ctypes import c_int, c_double # Load the library I created for extra speed mylib = CDLL("./mylib.so") # C-type corresponding to numpy 2-dimensional array (matrix) ND_POINTER_1 = np.ctypeslib.ndpointer(dtype=np.float64, ndim=1, flags="C") ND_POINTER_2 = np.ctypeslib.ndpointer(dtype=np.float64, ndim=2, flags="C") ND_POINTER_3 = np.ctypeslib.ndpointer(dtype=np.float64, ndim=3, flags="C") # define the prototypes of the functions mylib.lennard_jones_function.argtypes = [ND_POINTER_2, c_int, c_double, c_double] mylib.lennard_jones_function.restype = c_double mylib.evaluate.argtypes = [ND_POINTER_3, ND_POINTER_2, c_int, c_int] mylib.evaluate.restype = None # For Genetic Algorithms # Evaluation def evaluate_population(population, number_of_atoms): values = np.zeros(shape=(population.shape[0], 1), dtype=np.float64) mylib.evaluate(population, values, population.shape[0], number_of_atoms) x_best_index = np.argmin(values) return values, x_best_index, values.min() # Selection def roulette_wheel_selection(population, evaluations, selective_pressure): descenting_order = np.argsort(evaluations, axis=0)[::-1] population = population[descenting_order] N = evaluations.shape[0] fitness_scores = np.zeros(shape=(N, 1)) random_vector = np.random.uniform(low=0, high=1, size=(N, 1)) selected_indexs = np.zeros(shape=(N, 1), dtype=int) for i, _ in enumerate(fitness_scores): fitness_scores[i] = 2 - selective_pressure + 2 * (selective_pressure - 1) * (i - 1) / (N - 1) selection_probabilities = fitness_scores / np.sum(fitness_scores) for rn_index, random_number in enumerate(random_vector): probability_sum = 0 for sp_index, selection_probability in enumerate(selection_probabilities): probability_sum += selection_probability if random_number <= probability_sum: selected_indexs[rn_index] = sp_index break return np.squeeze(population[selected_indexs]) def tournament_selection(population, evaluations, tournament_size, dtype): N = population.shape[0] tournament_winners = np.zeros(shape=population.shape, dtype=dtype) for i in range(0, N): random_choices = np.random.choice(N, size=tournament_size, replace=False) tournament_winner_index = evaluations[random_choices].argmin() tournament_winners[i] = population[random_choices][tournament_winner_index] return tournament_winners def new_population_top_N(population, mutated_population, population_evaluations, mutated_population_evaluations): N = population.shape[0] all_population = np.stack((population, mutated_population), axis=0) all_population = all_population.reshape((2 * population.shape[0], population.shape[1])) all_evaluations = np.stack((population_evaluations, mutated_population_evaluations)) all_evaluations = all_evaluations.reshape((2 * population_evaluations.shape[0], 1)) ascending_order = np.argsort(all_evaluations, axis=0) all_evaluations = all_evaluations[ascending_order] all_evaluations = all_evaluations.reshape((all_evaluations.shape[0], 1)) all_population = all_population[ascending_order] all_population = np.squeeze(all_population) return all_population[0:N], all_evaluations[0:N] # Genetic Algorithm Binary def calculate_number_of_bits(Umin, Umax, error): length_of_space = Umax - Umin possible_numbers = 1 + length_of_space / error for n in range(1, 64): if np.power(2, n-1) < possible_numbers <= np.power(2, n): return n def calculate_base_10(binary_number): number_base_10 = 0 for i, bi in enumerate(binary_number): number_base_10 += bi * np.power(2, i) return number_base_10 def calculate_number_base_10_in_feasible_space(Umin, Umax, n_bits, number_base_10): length_of_space = Umax - Umin return Umin + number_base_10 * length_of_space / (np.power(2, n_bits) - 1) def decoder(population, Umin, Umax, number_of_atoms, dimensionality, n_bits): population_base_10 = np.zeros(shape=(population.shape[0], number_of_atoms, dimensionality)) for i, pi in enumerate(population): pi = np.array_split(pi, number_of_atoms) for j, pij in enumerate(pi): pij = np.array_split(pij, dimensionality) pij_base_10 = list() for binary_number in pij: number_base_10 = calculate_base_10(binary_number) number_base_10_fs = calculate_number_base_10_in_feasible_space(Umin, Umax, n_bits, number_base_10) pij_base_10.append(number_base_10_fs) population_base_10[i][j] = np.asarray(pij_base_10) return population_base_10 def initialize_binary_population(population_size, number_of_atoms, dimensionality, n_bits): population = np.random.randint(low=0, high=2, size=(population_size, number_of_atoms, dimensionality * n_bits)) population = population.reshape(population_size, number_of_atoms * dimensionality * n_bits) return population def crossover_binary_population(selected_population, crossover_rate, crossover_points): # crossover_rate = [0, 1] # crossover_points = m - 1, where m is the length of the dna N = selected_population.shape[0] to_crossover = np.random.uniform(low=0, high=1, size=(N, 1)) < crossover_rate to_crossover_indexes = np.where(np.any(to_crossover==True, axis=1))[0] crossover_population = np.array(selected_population) if to_crossover_indexes.shape[0] % 2 != 0: random_choice = np.random.randint(low=0, high=N) to_crossover_indexes = np.append(to_crossover_indexes, random_choice) parents = selected_population[to_crossover_indexes] children = np.zeros(shape=(parents.shape[0], parents.shape[1]), dtype=int) if parents.shape[0] == 0: return selected_population points_of_crossover = np.arange(1, selected_population.shape[1]) np.random.shuffle(points_of_crossover) points_of_crossover = points_of_crossover[:crossover_points] points_of_crossover = np.sort(points_of_crossover, axis=0) for i in range(0, parents.shape[0], 2): parent_0 = np.array_split(parents[i], points_of_crossover) parent_1 = np.array_split(parents[i + 1], points_of_crossover) child_0, child_1 = list(), list() for j in range(0, crossover_points + 1): if j % 2 == 0: child_0.append(parent_0[j]) child_1.append(parent_1[j]) else: child_0.append(parent_1[j]) child_1.append(parent_0[j]) child_0 = np.asarray(child_0, dtype=object) child_1 = np.asarray(child_1, dtype=object) children[i] = np.concatenate(child_0, axis=None) children[i + 1] = np.concatenate(child_1, axis=None) # Replace parents with their children for child_index, parent_index in enumerate(to_crossover_indexes): crossover_population[parent_index] = children[child_index] return crossover_population def mutation_binary_population(crossover_population, mutation_rate): # mutation_rate = [0, 1] mutated_population = np.array(crossover_population) for i, pi in enumerate(mutated_population): to_mutate = np.random.uniform(low=0, high=1, size=(pi.shape[0], 1)) < mutation_rate to_mutate_indexes = np.where(np.any(to_mutate==True, axis=1))[0] for j in to_mutate_indexes: pi[j] = 1 - pi[j] mutated_population[i] = pi return mutated_population def Genetic_Algorithm_Binary(Umin, Umax, number_of_atoms, selection_method): # Algorithm parameters population_size = 1000 selective_pressure = 1.3 # selective_pressure = [1, 2] tournament_size = 100 # tournament_size = [1, population_size] crossover_rate = 0.5 # crossover_rate = [0, 1] crossover_points = 6 # crossover_points = [0, m-1] mutation_rate = 0.1 # mutation_rate = [0, 1] # Do not change error = 1e-3 dimensionality = 3 # 3D space n_bits = calculate_number_of_bits(Umin, Umax, error) iteration = population_size best_iteration = iteration max_iterations = number_of_atoms * 1e+5 population = initialize_binary_population(population_size, number_of_atoms, dimensionality, n_bits) decoded_population = decoder(population, Umin, Umax, number_of_atoms, dimensionality, n_bits) population_evaluations, x_best_index, min_value = evaluate_population(decoded_population, number_of_atoms) x_best = population[x_best_index] x_best_fvalue = min_value while(True): if (max_iterations <= iteration): break iteration += population_size if(selection_method == "rw"): selected_population = roulette_wheel_selection(population, population_evaluations, selective_pressure) else: selected_population = tournament_selection(population, population_evaluations, tournament_size, int) crossover_population = crossover_binary_population(selected_population, crossover_rate, crossover_points) mutated_population = mutation_binary_population(crossover_population, mutation_rate) decoded_population = decoder(mutated_population, Umin, Umax, number_of_atoms, dimensionality, n_bits) mutated_population_evaluations, _, _ = evaluate_population(decoded_population, number_of_atoms) population, population_evaluations = new_population_top_N(population, mutated_population, population_evaluations, mutated_population_evaluations) if population_evaluations[0] < x_best_fvalue: x_best, x_best_fvalue = population[0], population_evaluations[0] best_iteration = iteration print("Iterations: %d/%d Lennard-Jones potential: %.10f!!!" % (iteration, max_iterations, x_best_fvalue)) else: print("Iterations: %d/%d Lennard-Jones potential: %.10f" % (iteration, max_iterations, x_best_fvalue)) x_best = x_best.reshape((1, x_best.size)) population = np.append(population, x_best, axis=0) decoded_population = decoder(population, Umin, Umax, number_of_atoms, dimensionality, n_bits) population_evaluations, x_best_index, x_best_fvalue = evaluate_population(decoded_population, number_of_atoms) x_best = decoded_population[x_best_index] return x_best, x_best_fvalue, decoded_population, population_evaluations, best_iteration # Genetic Algorithm Real def initialize_real_population(Umin, Umax, population_size, number_of_atoms, dimensionality): population = np.random.uniform(low=Umin, high=Umax, size=(population_size, number_of_atoms, dimensionality)) return population def crossover_real_population(selected_population, crossover_rate, delta=0.25): # crossover_rate = [0, 1] # delta > 0 N = selected_population.shape[0] to_crossover = np.random.uniform(low=0, high=1, size=(N, 1)) < crossover_rate to_crossover_indexes = np.where(np.any(to_crossover==True, axis=1))[0] crossover_population = np.array(selected_population) if to_crossover_indexes.shape[0] % 2 != 0: random_choice = np.random.randint(low=0, high=N) to_crossover_indexes = np.append(to_crossover_indexes, random_choice) parents = selected_population[to_crossover_indexes] children = np.zeros(shape=parents.shape, dtype=float) if parents.shape[0] == 0: return selected_population for i in range(0, parents.shape[0], 2): # Create a pair of children for a pair of parents for j in range(0, 2): random_vector = np.random.uniform(low=-delta, high=1+delta, size=selected_population.shape[1]) child = np.multiply(random_vector, parents[i]) + np.multiply((1 - random_vector), parents[i + 1]) children[i + j] = child # Replace parents with their children for child_index, parent_index in enumerate(to_crossover_indexes): crossover_population[parent_index] = children[child_index] return crossover_population def mutation_real_population(crossover_population, mutation_rate, Umin, Umax): # mutation_rate = [0, 1] mutated_population = np.array(crossover_population) for i, pi in enumerate(mutated_population): to_mutate = np.random.uniform(low=0, high=1, size=(pi.shape[0], 1)) < mutation_rate to_mutate_indexes = np.where(np.any(to_mutate==True, axis=1))[0] for j in to_mutate_indexes: distance_from_Umin = abs(abs(pi[j]) - abs(Umin)) distance_from_Umax = abs(abs(pi[j]) - abs(Umax)) min_distance = min(distance_from_Umin, distance_from_Umax) sigma = min_distance / 3 zj = np.random.normal(0, sigma) pi[j] = pi[j] + zj mutated_population[i] = pi return mutated_population def Genetic_Algorithm_Real(Umin, Umax, number_of_atoms, selection_method): # Algorithm parameters population_size = 1000 selective_pressure = 1.3 tournament_size = 100 crossover_rate = 0.5 mutation_rate = 0.1 # Do not change dimensionality = 3 iteration = population_size best_iteration = iteration max_iterations = number_of_atoms * 1e+5 population = initialize_real_population(Umin, Umax, population_size, number_of_atoms, dimensionality) population_evaluations, x_best_index, min_value = evaluate_population(population, number_of_atoms) # vectorize population for the alogrithm population = population.reshape(population_size, number_of_atoms * dimensionality) x_best = population[x_best_index] x_best_fvalue = min_value while(True): if (max_iterations <= iteration): break iteration += population_size if selection_method == "rw": selected_population = roulette_wheel_selection(population, population_evaluations, selective_pressure) else: selected_population = tournament_selection(population, population_evaluations, tournament_size, float) crossover_population = crossover_real_population(selected_population, crossover_rate) mutated_population = mutation_real_population(crossover_population, mutation_rate, Umin, Umax) # create mutated_population as array for evaluation array_mutated_population = mutated_population.reshape(mutated_population.shape[0], number_of_atoms, dimensionality) mutated_population_evaluations, _, _ = evaluate_population(array_mutated_population, number_of_atoms) population, population_evaluations = new_population_top_N(population, mutated_population, population_evaluations, mutated_population_evaluations) if population_evaluations[0] < x_best_fvalue: x_best, x_best_fvalue = population[0], population_evaluations[0] best_iteration = iteration print("Iterations: %d/%d Lennard-Jones potential: %.10f!!!" % (iteration, max_iterations, x_best_fvalue)) else: print("Iterations: %d/%d Lennard-Jones potential: %.10f" % (iteration, max_iterations, x_best_fvalue)) x_best = x_best.reshape(number_of_atoms, dimensionality) population = population.reshape(population.shape[0], number_of_atoms, dimensionality) return x_best, x_best_fvalue.item(), population, population_evaluations, best_iteration # Particle Swarm Optimization def initialize_velocity(swarm_size, number_of_atoms, dimensionality, max_velocity): velocity = np.random.uniform(low=-max_velocity, high=max_velocity, size=(swarm_size, number_of_atoms, dimensionality)) return velocity def create_neighborhoods(swarm_size, neighborhood_radius): # neighborhood_radius = [0, N/2] neighborhoods = list() for i in range(0, swarm_size): neighborhood_i = list() for j in range(i - neighborhood_radius, i + neighborhood_radius + 1): neighborhood_i.append(j % swarm_size) neighborhoods.append(np.asarray(neighborhood_i)) return np.asarray(neighborhoods) def update_velocity(swarm, velocity, best_positions, best_positions_evaluations, neighborhoods, c1=2.05, c2=2.05, x=0.729): rgn_0 = np.random.uniform(low=0, high=1) rgn_1 = np.random.uniform(low=0, high=1) best_neighbors = np.zeros(shape=swarm.shape) for particle_i, neighbors_i in enumerate(neighborhoods): best_neighbor_index = 0 best_neighbor_f_value = best_positions_evaluations[neighbors_i[0]] for j, neighbor_ij in enumerate(neighbors_i): neighborij_f_value = best_positions_evaluations[neighbor_ij] if neighborij_f_value < best_neighbor_f_value: best_neighbor_index = j best_neighbor_f_value = neighborij_f_value best_neighbors[particle_i] = swarm[neighbors_i[best_neighbor_index]] velocity = x * (velocity + rgn_0 * c1 * (best_positions - swarm) + rgn_1 * c2 * (best_neighbors - swarm)) return velocity def update_particles(swarm, velocity): return swarm + velocity def check_velocity_bounds(velocity, max_velocity): for i, vi in enumerate(velocity): for j, vij in enumerate(vi): if vij < -max_velocity: vij = -max_velocity elif max_velocity < vij: vij = max_velocity vi[j] = vij velocity[i] = vi return velocity def check_particles_bounds(swarm, Umin, Umax): for i, pi in enumerate(swarm): for j, pij in enumerate(pi): if pij < Umin: pij = Umin elif Umax < pij: pij = Umax pi[j] = pij swarm[i] = pi return swarm def update_best_positions(best_positions, best_positions_evaluations, swarm, swarm_evaluations): for i, _ in enumerate(swarm_evaluations): if swarm_evaluations[i] < best_positions_evaluations[i]: best_positions[i] = swarm[i] best_positions_evaluations[i] = swarm_evaluations[i] return best_positions, best_positions_evaluations def Particle_Swarm_Optimization(Umin, Umax, number_of_atoms, model): # Algorithm parameters swarm_size = 1000 alpha = 0.5 # alpha = [0, 1] # neighborhood_radius = [0, swarm_size / 2] if model == "lbest": neighborhood_radius = 5 else: neighborhood_radius = int(swarm_size / 2) # Do not change max_velocity = alpha * (Umax - Umin) iteration = swarm_size best_iteration = iteration max_iterations = number_of_atoms * 1e+5 dimensionality = 3 # Initializations swarm = initialize_real_population(Umin, Umax, swarm_size, number_of_atoms, dimensionality) velocity = initialize_velocity(swarm_size, number_of_atoms, dimensionality, max_velocity) best_positions =	np.array(swarm)	numpy.array
import numpy as np from collections import defaultdict import torch import pandas as pd from scipy.sparse import csr_matrix from scipy.sparse.csgraph import connected_components import cosypose.utils.tensor_collection as tc from cosypose.lib3d.transform_ops import invert_T, compute_transform_from_pose9d from cosypose.lib3d.camera_geometry import project_points from cosypose.lib3d.symmetric_distances import symmetric_distance_reprojected from .ransac import make_obj_infos from cosypose.utils.logging import get_logger from cosypose.utils.timer import Timer logger = get_logger(__name__) def make_view_groups(pairs_TC1C2): views = pairs_TC1C2.infos.loc[:, ['view1', 'view2']].values.T views = np.unique(views.reshape(-1)) view_df = pd.DataFrame(dict(view_id=views, view_local_id=np.arange(len(views)))) view_to_id = view_df.set_index('view_id') view1 = view_to_id.loc[pairs_TC1C2.infos.loc[:, 'view1'], 'view_local_id'].values view2 = view_to_id.loc[pairs_TC1C2.infos.loc[:, 'view2'], 'view_local_id'].values data = np.ones(len(view1)) n_views = len(views) graph = csr_matrix((data, (view1, view2)), shape=(n_views, n_views)) n_components, ids = connected_components(graph, directed=True, connection='strong') view_df['view_group'] = ids view_df = view_df.drop(columns=['view_local_id']) return view_df class SamplerError(Exception): pass class MultiviewRefinement: def __init__(self, candidates, cameras, pairs_TC1C2, mesh_db): self.device, self.dtype = candidates.device, candidates.poses.dtype self.mesh_db = mesh_db cameras = cameras.to(self.device).to(self.dtype) pairs_TC1C2 = pairs_TC1C2.to(self.device).to(self.dtype) view_ids = np.unique(candidates.infos['view_id']) keep_ids = np.logical_and( np.isin(pairs_TC1C2.infos['view1'], view_ids), np.isin(pairs_TC1C2.infos['view2'], view_ids), ) pairs_TC1C2 = pairs_TC1C2[	np.where(keep_ids)	numpy.where
import numpy as np import tensorflow as tf from tqdm import tqdm import copy from flearn.models.client import Client from flearn.utils.model_utils import Metrics from flearn.utils.tf_utils import process_grad, norm_grad, norm_grad_sparse class BaseFedarated(object): def __init__(self, params, learner, dataset): # transfer parameters to self for key, val in params.items(): setattr(self, key, val); # create worker nodes tf.reset_default_graph() self.client_model = learner(params['model_params'], self.q, self.inner_opt, self.seed) self.clients = self.setup_clients(dataset, self.dynamic_lam, self.client_model) print('{} Clients in Total'.format(len(self.clients))) self.latest_model = copy.deepcopy(self.client_model.get_params()) # initialize system metrics self.metrics = Metrics(self.clients, params) def __del__(self): self.client_model.close() def setup_clients(self, dataset, dynamic=0, model=None): '''instantiates clients based on given train and test data directories Return: list of Clients ''' users, groups, train_data, test_data = dataset if len(groups) == 0: groups = [None for _ in users] all_clients = [Client(u, g, train_data[u], test_data[u], dynamic, model) for u, g in zip(users, groups)] return all_clients def train_error(self): num_samples = [] tot_correct = [] losses = [] self.client_model.set_params(self.latest_model) for c in self.clients: ct, cl, ns = c.train_error() tot_correct.append(ct1.0) losses.append(cl * 1.0) num_samples.append(ns) return np.array(num_samples), np.array(tot_correct), np.array(losses) def test(self): '''tests self.latest_model on given clients ''' num_samples = [] tot_correct = [] losses = [] self.client_model.set_params(self.latest_model) for c in self.clients: ct, cl, ns = c.test() tot_correct.append(ct1.0) num_samples.append(ns) losses.append(cl 1.0) return np.array(num_samples), np.array(tot_correct), np.array(losses) def validate(self): '''tests self.latest_model on given clients ''' num_samples = [] tot_correct = [] self.client_model.set_params(self.latest_model) for c in self.clients: ct, ns = c.validate() tot_correct.append(ct1.0) num_samples.append(ns) return np.array(num_samples), np.array(tot_correct) def test_resulting_model(self): num_samples = [] tot_correct = [] # self.client_model.set_params(self.latest_model) for c in self.clients: ct, ns = c.test() tot_correct.append(ct1.0) num_samples.append(ns) ids = [c.id for c in self.clients] groups = [c.group for c in self.clients] return ids, groups, num_samples, tot_correct def save(self): pass def select_clients(self, round, corrupt_id, num_clients=20): '''selects num_clients clients weighted by number of samples from possible_clients Args: num_clients: number of clients to select; default 20 note that within function, num_clients is set to min(num_clients, len(possible_clients)) Return: indices: an array of indices self.clients[] ''' num_clients = min(num_clients, len(self.clients)) # number of selected clients per round np.random.seed(round+4) non_corrupt_id = np.setdiff1d(range(len(self.clients)), corrupt_id) corrupt_fraction = len(corrupt_id) / len(self.clients) num_selected_corrupted = int(num_clients * corrupt_fraction) if self.sampling == 0: indices = np.random.choice(range(len(self.clients)), num_clients, replace=False, p=pk) return indices, np.asarray(self.clients)[indices] elif self.sampling == 1: num_samples = [] for client in self.clients: num_samples.append(client.train_samples) total_samples = np.sum(np.asarray(num_samples)) pk = [item * 1.0 / total_samples for item in num_samples] indices1 = np.random.choice(corrupt_id, num_selected_corrupted, replace=False, p=np.asarray(pk)[corrupt_id] / sum(np.asarray(pk)[corrupt_id])) indices2 = np.random.choice(non_corrupt_id, num_clients-num_selected_corrupted, replace=False, p=np.asarray(pk)[non_corrupt_id] / sum(np.asarray(pk)[non_corrupt_id])) indices = np.concatenate((indices1, indices2)) #print(indices1, indices2) return indices,	np.asarray(self.clients)	numpy.asarray
import numpy as np import logging _FFMPEG_INSTALLED = True try: import ffmpeg except Exception: _FFMPEG_INSTALLED = False logger = logging.getLogger(__name__) def video_write(fn, images, framerate=60, vcodec="libx264"): """ Save list of images to a video file. Source: https://github.com/kkroening/ffmpeg-python/issues/246#issuecomment-520200981 Modified so that framerate is given to .input(), as suggested in the thread, to avoid skipping frames. Parameters ---------- fn : string filename images : list or np.array list of images to save to a video. framerate : int """ global _FFMPEG_INSTALLED try: if len(images) == 0: logger.warning("Calling video_write() with empty images.") return if not _FFMPEG_INSTALLED: logger.error( "video_write(): Unable to save video, ffmpeg-python \ package required (https://github.com/kkroening/ffmpeg-python)" ) return if not isinstance(images, np.ndarray): images =	np.asarray(images)	numpy.asarray
# Copyright 2022 <NAME> # # Permission is hereby granted, free of charge, to any person obtaining a copy of this software # and associated documentation files (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all copies or # substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT # LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. import numpy as np class Component: def __init__(self, name, mode_name, current_ma): self.name = name self.mode_name = mode_name self.current_ma = current_ma class Stage: def __init__(self, delta_t_sec, components): self.delta_t_sec = delta_t_sec self.components = components class Thread: def __init__(self, name, stages): self.name = name self.stages = stages self.num_stages = len(stages) self.last_stage_change_t = 0.0 self.next_stage_change_t = self.last_stage_change_t + stages[0].delta_t_sec self.stage_index = 0 class SolarPanel: def __init__(self, rated_power_W, charge_efficiency=0.7, t_offset_sec = 0.0, clouds_tau=3600.0, clouds_cover = 1.0): self.rated_power_W = rated_power_W self.charge_efficiency = charge_efficiency self.t_offset_sec = t_offset_sec self.clouds_tau = clouds_tau self.clouds_cover = clouds_cover self.random_walk_val = 0.0 self.last_time_s = 0.0 self.time = [] self.power_history_W = [] self.random_walk_vals = [] def calculate_power(self, t): dt = t - self.last_time_s if(dt > 0.0): f = np.exp(-dt/self.clouds_tau) self.random_walk_val = fself.random_walk_val + np.sqrt(1.0-f2.0) np.random.randn() power = self.rated_power_W(1.0/0.65)(np.sin(((2.0np.pi)/86400)(t-self.t_offset_sec)) - 0.35) if power < 0.0: power = 0.0 # Calculate threshold based on normal distribution if	np.abs(self.random_walk_val)	numpy.abs
# coding: utf8 # !/usr/env/python # This file has tests for the old style output writers to ensure backwards # compatibility. All of the existing tests for output writers are kept as is. # There are a few new ones too. import glob import os import numpy as np from terrainbento import Basic, NotCoreNodeBaselevelHandler from terrainbento.utilities import filecmp _TEST_OUTPUT_DIR = os.path.join(os.curdir, "output") _TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), "data") def get_output_filepath(filename): return os.path.join(_TEST_OUTPUT_DIR, filename) def cleanup_files(searchpath): files = glob.glob(searchpath) for f in files: os.remove(f) # Some output writers def output_writer_function_a(model): average_elevation =	np.mean(model.z[model.grid.core_nodes])	numpy.mean
import argparse import glob import os import sys import time from itertools import product, permutations import matplotlib.pyplot as plt import multiprocessing as mp import numpy as np import pandas as pd import seaborn as sns import statsmodels.nonparametric.api as smnp import swifter N_PROC = 1 CHUNK = 25 MIX = False BASE_DIR = '/home/jmcbride/Scales/Compared_data' RAW_DIR = '/home/jmcbride/Scales/Toy_model/Data/Raw/' PRO_DIR = '/home/jmcbride/Scales/Toy_model/Data/Processed/' DIST_DIR = '/home/jmcbride/Scales/Toy_model/Data/None_dist/' REAL_DIR = '/home/jmcbride/Scales/Real_scales' TEMP_MIN = 50. TEMP_MAX = 300. TEMP_LOW_MARGIN = 0.50 TEMP_HI_MARGIN = 1.50 N_TRIALS = 50 ALPHA_W = 0.1 def parse_arguments(): parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument('--partabase', action='store', default='None', type=str) parser.add_argument('-f', action='store', default='None', dest='fName', type=str) parser.add_argument('--sample', action='store_true', default=False, dest='sample',) return parser.parse_args() args = parse_arguments() def get_scale_from_pair_ints(pair_ints): ints = [int(y) for y in pair_ints.split(';')] return ';'.join(['0'] + [str(y) for y in np.cumsum(ints)]) def calculate_most_harmonic_neighbour(int_cents, sim_only=False, CENT_DIFF_MAX=22): best_ratio = [1,1] max_similarity = 0.0 cents = 0.0 for x in np.arange(1,75, dtype=float): cent_diff = 1200.*	np.log10((x+1.)/x)	numpy.log10
import occultquad import numpy as np def s2(r, b): if (b >= 1 + r): return (2 * np.pi / 3) elif (b <= r - 1): return 0 r_ = np.array([r], dtype=float) b_ = np.array([b], dtype=float) flux =	np.empty(1)	numpy.empty
''' Pull out HI properties (and/or others) from a set of point sources. Create a distance map as a function of distance from the nearest source. ''' import astropy.coordinates as coord from astropy.table import Table, Column import astropy.units as u import astropy.constants as const import numpy as np from galaxies import Galaxy import scipy.ndimage as nd from astropy.io import fits from spectral_cube import SpectralCube, Projection from spectral_cube.analysis_utilities import stack_spectra from astropy.utils.console import ProgressBar import matplotlib.pyplot as plt from plotting_styles import default_figure from constants import hi_freq, hi_mass_conversion # from paths import allfigs_path def distance_map_from_catalogue(gal, tab, header, ra_key="RA", dec_key="Dec", diam_key=None): ''' Create a distance map from a set of sky location in a catalogue. ''' if not isinstance(gal, Galaxy): raise TypeError("gal must be a Galaxy instance.") ra = tab[ra_key] dec = tab[dec_key] coords = coord.SkyCoord(ra, dec, frame='icrs', unit=(u.deg, u.deg)) # Assumes that the table column has a unit attached that Table can distinguish if diam_key is not None: # Assume pc. Lost units in the save table?? diams = tab[diam_key].quantity * u.pc radii = gal.radius(header=header) coord_map = gal.skycoord_grid(header=header) object_mask = np.zeros_like(coord_map.ra.value, dtype=int) # Loop through and mask points belonging at a remnant, or the nearest point for i, co in enumerate(coords): mask_index = np.unravel_index(coord_map.separation(co).argmin(), object_mask.shape) if diam_key is not None: # Major axis diameter diam_rad = (diams[i].to(u.pc) / gal.distance).to(u.dimensionless_unscaled).value * u.rad diam_pix = diam_rad.to(u.deg).value / np.abs(header['CDELT2']) # Gather all pixels with a circular region yy, xx = np.mgrid[-(int(diam_pix)//2 + 1):int(diam_pix)//2 + 1, -(int(diam_pix)//2 + 1):int(diam_pix)//2 + 1] # Find all pixels within the diameter valids = np.where(np.sqrt(yy2 + xx2) < diam_pix / 2.) y_pts = valids[0] + mask_index[0] x_pts = valids[1] + mask_index[1] mask_index = (y_pts, x_pts) object_mask[mask_index] = i + 1 # print(object_mask[mask_index]) # print(mask_index) # print((object_mask > 0).sum()) dist_transf = nd.distance_transform_edt(~(object_mask > 0)) return object_mask, dist_transf def find_bubble_props(dist_bins, int_profile, lwidth_profile, obj_diam, disk_height=100 * u.pc / np.cos(55.1 * u.deg), mass_conv_factor=None): ''' Dumb estimations of bubble properties based on integrated intensity and line width profiles. ''' # Define the shell radius based on the distance of the peak arg_max = np.argmax(int_profile) # If the centre is the peak, assume it is unresolved if arg_max == 0: shell_rad = obj_diam / 2. else: shell_rad = obj_diam / 2. + dist_bins[arg_max] # Assume a disk scale height and check if the radius of the shell # exceeds it if shell_rad > disk_height: # It has maybe broken out of the disk. Adjust volume as needed # Subtract off caps of the sphere vol = (4 * np.pi / 3.) * shell_rad*3 - \ (2 np.pi / 3.) * (shell_rad - disk_height)*2 (2 * shell_rad + disk_height) else: # Likely still contained within the disk vol = (4 * np.pi / 3.) * shell_rad*3 # Awful estimations of the velocity expansion. Assume velocity dispersion # is exactly the same... # Don't know how to do that with any sort of logic applied, so let it be # the dispersion in the peak bin. v_exp = lwidth_profile[arg_max] # Now the integ intensity. If unresolved, we don't have an estimate of the # background. Assume the last distance bin as a background?? Otherwise take # the larger of the innermost and outermost when resolved. peak_int = int_profile[arg_max] if arg_max == 0: bkg_int = int_profile[-1] else: bkg_int = max(int_profile[0], int_profile[-1]) hole_mass = np.pi shell_rad*2 bkg_int shell_mass = np.pi * shell_rad*2 \ (peak_int - bkg_int) if mass_conv_factor is not None: hole_mass = mass_conv_factor shell_mass = mass_conv_factor # Estimate an avg volume density within the hole. Don't do this # for unresolved holes if arg_max == 0: energy = np.NaN * u.erg vol_dens = np.NaN * u.cm*-3 else: # Chevalier 74 expansion energy formula vol_dens = ((shell_mass / (1.4 const.m_p)) / vol).to(u.cm*-3) energy = 5.3e43 vol_dens.value*1.12 \ shell_rad.to(u.pc).value*3.12 v_exp.to(u.km / u.s).value*1.4 u.erg return shell_rad, vol, v_exp, hole_mass, shell_mass, vol_dens, energy default_figure() # Update this for the server files (geometry should be the same though) gal = Galaxy("M33") gal.distance = 840 * u.kpc hi_cube = SpectralCube.read("/Volumes/Travel_Data/M33_2/HI/M33_14B-088_HI.clean.image.GBT_feathered.pbcov_gt_0.5_masked.fits") peak_vel = Projection.from_hdu(fits.open("/Volumes/Travel_Data/M33_2/HI/M33_14B-088_HI.clean.image.GBT_feathered.pbcov_gt_0.5_masked.peakvels.fits")) mom0 = Projection.from_hdu(fits.open("/Volumes/Travel_Data/M33_2/HI/M33_14B-088_HI.clean.image.GBT_feathered.pbcov_gt_0.5_masked.mom0.fits")) beam = mom0.beam moment0_Kkm_s = beam.jtok(hi_freq).value * mom0.value / 1000. moment0_surfdens = moment0_Kkm_s * hi_mass_conversion * (u.K * u.km / u.s) * np.cos(55.1 * u.deg) lwidth = Projection.from_hdu(fits.open("/Volumes/Travel_Data/M33_2/HI/M33_14B-088_HI.clean.image.GBT_feathered.pbcov_gt_0.5_masked.lwidth.fits")) snr_tab = Table.read("/Volumes/Travel_Data/M33_2/MMT_SNR_catalogue_long18_combined.txt", format='ascii') # Also consider weighting by something like ~1/sqrt(L) to place distances # on a common "scale" index_mask, dist_transf = \ distance_map_from_catalogue(gal, snr_tab, hi_cube.header, diam_key='D') # Get all points within ~100 pc. dist_limit = np.arange(10) * 100 * u.pc stacked_spectra = [] lwidth_bins = [] intint_bins = [] # Pick out individual regions num = index_mask.max() for n in ProgressBar(range(1, num + 1)): reg_mask = index_mask == n dist_transf_reg = nd.distance_transform_edt(~reg_mask) lwidth_reg = [] intint_reg = [] # Calculate avg properties within the region lwidth_reg.append([np.nanmean(lwidth[reg_mask].value), np.nanstd(lwidth[reg_mask].value) / np.sqrt(reg_mask.sum() / 41.)]) intint_reg.append([np.nanmean(moment0_surfdens[reg_mask].value), np.nanstd(moment0_surfdens[reg_mask].value) / np.sqrt(reg_mask.sum() / 41.)]) for i, (low, high) in enumerate(zip(dist_limit[:-1], dist_limit[1:])): # print("On bin {}".format(i + 1)) dist_ang_low = (low / gal.distance.to(u.pc)).value * u.rad dist_pix_low = dist_ang_low.to(u.deg).value / np.abs(hi_cube.header['CDELT2']) dist_ang_high = (high / gal.distance.to(u.pc)).value * u.rad dist_pix_high = dist_ang_high.to(u.deg).value / np.abs(hi_cube.header['CDELT2']) dist_mask = np.logical_and(dist_transf_reg > dist_pix_low, dist_transf_reg <= dist_pix_high) num_beams = dist_mask.sum() / 41. intint_reg.append([np.nanmean(moment0_surfdens[dist_mask].value),	np.nanstd(moment0_surfdens[dist_mask].value)	numpy.nanstd
import numpy as np from numba import * from numba.typed import Dict from numba.types import bool_ import timeit from time import process_time @njit def to_dependency_network(U: np.array, force_select: np.array, extended=False): """Form a dependency network such that the set of nodes S contains all graphlets and graphlets s,k \in U are connected if they share a force selected node v in their intersection. Edge weight $w$ is defined as $$ w_{sk} = \|v \cap s \cap k\|$$. Parameter `extended` controls for which type of dependency is used. By default the criteria is relaxed such that only graphlets that share force selected node(s) will be considered. Parameters ---------- U : weighted adjacency matrix for the dependency network force_select : set of nodes (as numpy array) that each graphlet configuration is required to contain extended : by default dependency edge is added only if graphlets contain same force selected node(s). Set True for building a network such that it takes into account all overlapping seed nodes Returns ------- U_out : output graphlets as rows of an array with shape (n_out, k_max) where elements are node indices in the adjacency matrix of the input network. Rows are padded from the right with -1 for graphlet sizes < k_max. (Note that n_out <= n) E_const : weighted adjacency matrix of the dependency network where nodes represent graphlets and edges dependencies between graphlets. Numpy array dimensions are (n_out,n_out) fs_map : mapping from force selected nodes to graphlets, numpy array dimensions: (n_out, n_fs) idxs : numpy array of row indeces that can be used for selecting the rows in U_out from U """ n = len(U) n_fs = len(force_select) E_const = np.zeros((n,n)) fs_map = np.zeros((n, n_fs), dtype=bool_) fs_idsn = Dict() fs = set(force_select) for k, v in zip(force_select, np.arange(n_fs)): fs_idsn[k] = v for i, Si in enumerate(U): fs_Si = fs & set(Si) if len(fs_Si) > 0: for u in fs_Si: fs_map[i,fs_idsn[u]] = True for i,Si in enumerate(U): for j,Sj in enumerate(U): if i != j: Si_s = set(Si) Sj_s = set(Sj) l_ij = len(Si_s & Sj_s) if extended else len(Si_s & Sj_s & fs) E_const[j,i] = len(Si_s & Sj_s) if l_ij > 0 else 0 idxs = np.array([(E_const[i,:] > 0).sum() != 0 for i in range(E_const.shape[0])]) E_const = E_const[idxs,:][:,idxs] fs_map = fs_map[idxs,:] return U[idxs,:], E_const, fs_map, idxs @njit def maximal_independent_set(A: np.array) -> np.array: """Sample maximal independent set (numpy implementation) using a greedy strategy. An independent set is a set of nodes such that the subgraph of G induced by these nodes contains no edges. A maximal independent set is an independent set such that it is not possible to add a new node and still get an independent set. Parameters ---------- A : weighted adjacency matrix for the dependency network Returns ------- s : indices of nodes in the found maximal independent set """ n = A.shape[0] D = np.array([False for _ in range(n)]) I = np.array([False for _ in range(n)]) seed_node = np.random.choice(n) neighbors = np.where(A[seed_node,:])[0] D[seed_node] = I[seed_node] = True D[neighbors] = True while np.sum(D) < n: node = np.random.choice(np.where(D != True)[0]) I[node] = True node_neighbors = np.where(A[node,:])[0] D[node_neighbors] = True D[node] = True s = np.where(I)[0] return s def sample_fs_configurations(A: np.array, U, fs, fs_map, target_time=30, adaptive=True, tol=0.99, n_min_target=10000, c_limit: int=10, verbose=False) -> list: """Sample maximal independent sets to determine a configuration of force selected nodes that guarantees near-minimal overlap. Employs adaptive enumeration, where the run length is determined by target_time parameter (and n_min_target sets the absolute minimum requirement). Parameters ---------- A : weighted adjacency matrix of the input network U : input graphlets as rows of an array with shape (n, k_max) where elements are node indices in the adjacency matrix of the input network. Rows are padded from the right with -1 for graphlet sizes < k_max fs : set of nodes that each graphlet configuration is required to contain fs_map : mapping of network nodes to graphlets (see `to_dependency_network` method) target_time : max time threshold for the run (unit in seconds) n_min_target : minimum number of configurations that should be generated Returns ------- ss (list of lists): configurations fs (np.array): updated set of force selected nodes Notes ----- An independent set is a set of nodes such that the subgraph of G induced by these nodes contains no edges. A maximal independent set is an independent set such that it is not possible to add a new node and still get an independent set. """ map_fs = lambda x: np.unique(np.where(fs_map[x,:])[1]) t = timeit.timeit(lambda: maximal_independent_set(A), number=100) n_sample = np.max([np.int32(100 / t), 1000]) rng = np.random.default_rng() u_sel = np.arange(U.shape[0]) n_fsa = len(fs) if verbose: print(':: Sampling configurations of s-nodes for force selection') print(':: --> Targeting {:.0f}s, threshold set @ {:.2f}%' .format(target_time, tol100)) ls_exs = [] removed = [] enums = set() c = np.zeros(n_fsa, dtype=int) i = n_ss0 = exs0 = i0 = i0a = c_stop = 0 t0 = pt0 = process_time() while pt0 - t0 < target_time or len(enums) < n_min_target: for _ in range(n_sample): s = maximal_independent_set(A) fs_idxs = map_fs(s) c[fs_idxs] += 1 i+=1 l_so = len(set(fs_idxs)) if l_so == len(fs): s_prime = u_sel[s] enums.add(frozenset(s_prime)) else: ls_exs.append(l_so) if verbose: pt0, n_ss0, i0a = __output_stats(fs, enums, i, i0a, c, t0, pt0, n_ss0) # EVALUATE c_stop = c_stop + 1 if len(enums) == n_ss0 else 0 if c_stop == c_limit: if verbose: print(f'Run terminated due to {c_limit} successive empty batches.') break exs_frac = (len(ls_exs)-exs0) / (i - i0) if exs_frac > tol: if not adaptive: raise Exception('Configurations that satisfy the complete set of ' \ 'force selected nodes appear too infrequently. ' \ 'Either remove the problematic nodes or enable '\ 'adaptive running mode by setting `adaptive=True`.') fs, c, fs_map, A, u_sel, removed = __drop(fs, c, fs_map, A, u_sel, removed, verbose) i0 = i exs0 = len(ls_exs) enums = set() c_stop = 0 if verbose: print(':: Discarded {:.2f}% of the configurations'.format(100exs_frac)) tdelta = process_time() - t0 if verbose: print(':: GENERATION STATS:\n* {} generated\n* {} accepted\n' \ '* {} discarded\n* {:.2f}% (+/-) {:.2f} of the FS nodes included on avg ' \ '\n* elapsed time: {:.2f}s.'.format(i,i-len(ls_exs),len(ls_exs), np.mean(ls_exs) / n_fsa100, np.std(ls_exs) / n_fsa100, tdelta)) print(':: Found {} unique configurations, {} ({:.0f}%) force selected nodes were ' \ 'removed: {}'.format(len(enums),len(removed),len(removed)/n_fsa*100,removed)) ss = [[v for v in e] for e in enums] return ss, fs def __drop(fs, c, fs_map, A, u_sel, removed, verbose): """Drop the worst performing node in the current force selected set by occurrence in the generated configurations. """ c_argsort = np.argsort(c) drop = c_argsort[0] keep = c_argsort[1:] if verbose: print(':: Node', fs[drop],'removed.') idxs = np.unique(np.where(fs_map[:, keep])[0]) A = A[idxs,:][:,idxs] fs_map = fs_map[idxs,:][:,keep] c = c[keep] removed.append(fs[drop]) fs = fs[keep] u_sel = u_sel[idxs] return fs, c, fs_map, A, u_sel, removed def __output_stats(fs, enums, i, i0a, c, t0, pt0, n_ss0): """Print iteration statistics in the `sample_fs_configurations` method. """ if i % 10 == 0: pt1 = process_time() if pt1 - pt0 > 10.0: n_ss = len(enums) rate_1_sec = np.round((i-i0a) / (pt1-pt0), 2) rate_2_sec =	np.round((n_ss-n_ss0) / (pt1-pt0), 2)	numpy.round
# this is the python library created for using BigGAN in evolution. import sys from os.path import join sys.path.append("C:/Users/zhanq/OneDrive - Washington University in St. Louis/GitHub/pytorch-pretrained-BigGAN") # sys.path.append("E:\Github_Projects\pytorch-pretrained-BigGAN") from pytorch_pretrained_biggan import (BigGAN, one_hot_from_names, one_hot_from_int, truncated_noise_sample, convert_to_images) import torch import numpy as np import matplotlib.pylab as plt #%% #%% from numpy.linalg import norm def orthonorm(ref, vec2): res = vec2 - vec2 @ ref.T * ref / norm(ref, axis=1)*2 return res / norm(res) norm(ref) #%% from scipy.stats import truncnorm def convert_to_images_np(obj, scale=1.0): """ Convert an output tensor from BigGAN in a list of images. Params: obj: tensor or numpy array of shape (batch_size, channels, height, width) Output: list of Pillow Images of size (height, width) """ try: from PIL import Image except ImportError: raise ImportError("Please install Pillow to use images: pip install Pillow") if not isinstance(obj, np.ndarray): obj = obj.detach().numpy() obj = obj.transpose((0, 2, 3, 1)) obj = np.clip(((obj + 1) / 2.0) * scale, 0, scale) img = [] for i, out in enumerate(obj): img.append(out) return img def truncated_noise_sample(batch_size=1, dim_z=128, truncation=1., seed=None): """ Create a truncated noise vector. Params: batch_size: batch size. dim_z: dimension of z truncation: truncation value to use seed: seed for the random generator Output: array of shape (batch_size, dim_z) """ state = None if seed is None else np.random.RandomState(seed) values = truncnorm.rvs(-2, 2, size=(batch_size, dim_z), random_state=state).astype(np.float32) return truncation * values #%% # Load pre-trained model tokenizer (vocabulary) model = BigGAN.from_pretrained('biggan-deep-256') model.to('cuda') def BigGAN_render(class_vector, noise_vector, truncation): if class_vector.shape[0] == 1: class_vector = np.tile(class_vector, [noise_vector.shape[0], 1]) if noise_vector.shape[0] == 1: noise_vector = np.tile(noise_vector, [class_vector.shape[0], 1]) class_vector = torch.from_numpy(class_vector.astype(np.float32)).to('cuda') noise_vector = torch.from_numpy(noise_vector.astype(np.float32)).to('cuda') with torch.no_grad(): output = model(noise_vector, class_vector, truncation) imgs = convert_to_images(output.cpu()) return imgs def BigGAN_embed_render(embed_vecs, noise_vecs=None, truncation=0.7, scale=255.0, batch=5): if embed_vecs.shape[1] == 256: input_vecs = torch.from_numpy(embed_vecs) elif embed_vecs.shape[1] == 128: if noise_vecs is None: embed_vecs = torch.from_numpy(embed_vecs) input_vecs = torch.cat((torch.zeros_like(embed_vecs), embed_vecs), dim=1) else: assert noise_vecs.shape[1] == 128 if noise_vecs.shape[0] == embed_vecs[0]: input_vecs = torch.cat((torch.from_numpy(noise_vecs), torch.from_numpy(embed_vecs)), dim=1) else: assert noise_vecs.shape[0] == 1 noise_vecs = np.tile(noise_vecs, [embed_vecs.shape[0], 1]) input_vecs = torch.cat((torch.from_numpy(noise_vecs), torch.from_numpy(embed_vecs)), dim=1) sample_n = input_vecs.shape[0] imgs_all = [] csr = 0 csr_end = 0 while csr_end < sample_n: csr_end = min(csr + batch, sample_n) with torch.no_grad(): output = model.generator(input_vecs[csr:csr_end, :].float().cuda(), truncation).cpu() # imgs = convert_to_images(output.cpu()) # imgs = [np.array(img).astype(np.float64) / 255 * scale for img in imgs] imgs = convert_to_images_np(output, scale) imgs_all.extend(imgs) csr = csr_end return imgs_all if __name__=="__main__": # %% # Prepare a input batch_size = 3 truncation = 0.5 class_vector = one_hot_from_names(['soap bubble', 'coffee', 'mushroom'], batch_size=batch_size) noise_vector = truncated_noise_sample(truncation=truncation, batch_size=batch_size) #noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1) # All in tensors #noise_vector = torch.from_numpy(np.ones([3, 128]).astype(np.float32)) # noise_vector = torch.from_numpy(noise_vector) class_vector = torch.from_numpy(class_vector) # If you have a GPU, put everything on cuda noise_vector = noise_vector.to('cuda') class_vector = class_vector.to('cuda') model.to('cuda') # Generate an image with torch.no_grad(): output = model(noise_vector, class_vector, truncation) imgs = convert_to_images(output.cpu()) #%% 1d interpolation truncation = 0.7 batch_size = 11 class_vector = one_hot_from_names(['mushroom']batch_size, batch_size=1) noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1) scale_vec = np.arange(-1, 1.1, 0.2) noise_vec_scale = scale_vec.reshape([-1, 1])noise_vector imgs = BigGAN_render(class_vector, noise_vec_scale, truncation=truncation) #% figh = plt.figure(figsize=[25, 3]) gs = figh.add_gridspec(1, len(imgs)) # 1d interpolation for i, img in enumerate(imgs): plt.subplot(gs[i]) plt.imshow(img) plt.axis('off') plt.title("{0:.2f}".format(scale_vec[i]), fontsize=15,) plt.show() #%% savedir = r"C:\Users\zhanq\OneDrive - Washington University in St. Louis\Generator_Testing\BigGAN256" truncation = 0.7 # batch_size = 11 classname = 'goldfish' class_vector = one_hot_from_names([classname], batch_size=1) #%% 1d interpolation and save truncation = 0.7 noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1) scale_UL = 1; scale_BL = -scale_UL; sample_n = 11 scale_vec = np.linspace(scale_BL, scale_UL, sample_n) # scale_vec = np.linspace(-2.5, -0.9, sample_n) noise_vec_scale = scale_vec.reshape([-1, 1])noise_vector imgs = BigGAN_render(class_vector, noise_vec_scale, truncation=truncation) figh = plt.figure(figsize=[25, 3]) gs = figh.add_gridspec(1, len(imgs)) # 1d interpolation for i, img in enumerate(imgs): plt.subplot(gs[i]) plt.imshow(img) plt.axis('off') plt.title("{0:.1f}".format(scale_vec[i]), fontsize=15,) plt.savefig(join(savedir, "%s_UL%.1f_BL%.1f_trunc%.1f_%04d.png" % (classname, scale_UL, scale_BL, truncation, np.random.randint(10000)))) plt.show() #%% 2d linear interpolation through center savedir = r"C:\Users\zhanq\OneDrive - Washington University in St. Louis\Generator_Testing\BigGAN256" truncation = 0.7 # batch_size = 11 classname = 'goldfish' class_vector = one_hot_from_names([classname], batch_size=1) truncation = 0.7 noise_vector = truncated_noise_sample(truncation=truncation, batch_size=2) vec1 = noise_vector[0:1, :] vec2 = orthonorm(vec1, noise_vector[1:2, :]) xlim = (-1, 1) ylim = (-1, 1); sample_n = 11 x_scale_vec = np.linspace(xlim, sample_n) y_scale_vec = np.linspace(ylim, sample_n) # scale_vec = np.linspace(-2.5, -0.9, sample_n) imgs = [] for ydeg in y_scale_vec: noise_vec_scale = x_scale_vec[:, np.newaxis] vec1 + ydeg * vec2 img_row = BigGAN_render(class_vector, noise_vec_scale, truncation=truncation) imgs.append(img_row) #% figh = plt.figure(figsize=[25, 25]) gs = figh.add_gridspec(len(y_scale_vec), len(x_scale_vec)) # 2d interpolation for i, img_row in enumerate(imgs): for j, img in enumerate(img_row): plt.subplot(gs[i, j]) plt.imshow(img) plt.axis('off') plt.title("%.1f, %.1f"%(x_scale_vec[i], y_scale_vec[j]), fontsize=15,) plt.tight_layout() plt.savefig(join(savedir, "%s_[%.1f-%.1f]_[%.1f-%.1f]_trunc%.1f_%04d.png" % (classname, xlim, ylim, truncation, np.random.randint(10000)))) plt.show() #%% 2d interpolation in sphere savedir = r"C:\Users\zhanq\OneDrive - Washington University in St. Louis\Generator_Testing\BigGAN256" truncation = 0.4 # batch_size = 11 classname = 'goldfish' class_vector = one_hot_from_names([classname], batch_size=1) truncation = 0.4 noise_vector = truncated_noise_sample(truncation=truncation, batch_size=3) vec1 = noise_vector[0:1, :] vec2 = orthonorm(vec1, noise_vector[1:2, :]) vec3 = orthonorm(vec2, noise_vector[2:3, :]) vec3 = orthonorm(vec1, vec3) sample_n = 11 phi_scale_vec =	np.linspace(-90, 90, sample_n)	numpy.linspace
import numpy as np from pkg.dist import dist def weights_c(X, C): w = np.zeros(C.shape[0]) for i in range(len(X)): if X[i] not in C: ind = np.argmin([dist(X[i],c,0) for c in C]) w[ind] +=1 return w def weighted_kmeans(Cen,w,k,random_state=42): rs = np.random.RandomState(random_state) ran = rs.randint(len(Cen)) C1 = Cen[ran,:].reshape(1,Cen.shape[1]) X1 = np.delete(Cen, ran, 0) w=	np.delete(w,ran)	numpy.delete
import copy import glob import os import time import pickle import matchingpennies from collections import deque import gym import gym_compete import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from a2c_ppo_acktr import algo, utils from a2c_ppo_acktr.algo import gail from a2c_ppo_acktr.arguments import get_args from a2c_ppo_acktr.envs import make_vec_envs from a2c_ppo_acktr.model import Policy from a2c_ppo_acktr.storage import RolloutStorage from evaluation import evaluate, evaluate_multi, eval_movie from stable_baselines3.common.running_mean_std import RunningMeanStd class MpPolicy: def __init__(self): self.vals = np.array([1.1,0.1]) self.counts = np.array([1,1]) self.eps = .2 #self.vals = np.zeros(2) + 1e-10 def update(self, obs, action, reward): if isinstance(action, list): for a, r in zip(action, reward): self.vals[action] += reward self.counts[action] += 1 else: self.vals[action] += reward self.counts[action] += 1 @property def probs(self): probs = np.exp(self.vals/self.counts) / np.exp(self.vals/self.counts).sum() return probs def act(self, obs): probs = self.probs actions = [] if isinstance(obs, list): for _ in obs: if np.random.rand() < self.eps: actions.append(np.random.choice([0,1])) else: actions.append(np.random.choice([0,1],p=probs)) else: actions.append(np.random.choice([0,1],p=probs)) return np.array(actions) class Agent: def __init__(self, playerid): self.playerid = playerid self.policies = [] def add_policy(self, policy): # TODO: need to add a wrapper that adds the obs_rms to each actor as we run it... self.policies.append(policy) self.weights = np.arange(1, len(self.policies)+1).astype(float) self.weights[-1] = 2 self.weights /= self.weights.sum() def sample_policy(self): ind = np.random.choice(range(len(self.policies)), p=self.weights) return self.policies[ind] def init(self, num_envs): self.env_policies = [] for _ in range(num_envs): policy = self.sample_policy() self.env_policies.append(policy) def new_policy(self, ind): policy = self.sample_policy() self.env_policies[ind] = policy def act(self, obs, args, *kwargs): values = [] actions = [] logprobs = [] rhs = [] with torch.no_grad(): for i, o in enumerate(obs): policy = self.env_policies[i] action = policy.act(o) actions.append(action) actions = np.array(actions) #if len(actions) > 1: # actions = np.concatenate(actions) return actions class EnvWrapper: def __init__(self, env_name, num_envs): self.envs = [] for _ in range(num_envs): self.envs.append(gym.make(env_name)) def step(self, actions): steps = [] for i, a in enumerate(actions): steps.append(self.envs[i].step(a)) obs = [s[0] for s in steps] r = [s[1] for s in steps] dones = [s[2] for s in steps] infos = [s[3] for s in steps] return obs, np.array(r).T, dones, infos def reset(self): obs = [] for env in self.envs: obs.append(env.reset()) return obs def main(): args = get_args() torch.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) if args.cuda and torch.cuda.is_available() and args.cuda_deterministic: torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.set_num_threads(1) device = torch.device("cuda:0" if args.cuda else "cpu") envs = EnvWrapper(args.env_name, args.num_processes) #envs = make_vec_envs(args.env_name, args.seed, args.num_processes, # args.gamma, args.log_dir, device, False, dense=args.dense) envs.reset() agent0 = MpPolicy() agent1 = MpPolicy() player0_policies = [] player1_policies = [] obs_rms_cache = [] episode_rewards0 = deque(maxlen=10) episode_rewards1 = deque(maxlen=10) train_stats = [] eval_p0 = [] eval_p1 = [] cache_freq = args.cache_freq learn_agent0 = agent0 old_agent0 = Agent(0) old_agent0.add_policy(copy.deepcopy(agent0)) old_agent0.init(args.num_processes//2) learn_agent1 = agent1 old_agent1 = Agent(1) old_agent1.add_policy(copy.deepcopy(agent1)) old_agent1.init(args.num_processes//2) p0numep = 0 p1numep = 0 #num_updates = int( # args.num_env_steps) // args.num_steps // args.num_processes num_updates = 50 for j in range(num_updates): eps = -1 p0draws, p1draws = 0, 0 w0, w1 = 0, 0 epoch_p0ep, epoch_p1ep = 0, 0 while eps < args.num_episodes: start = time.time() for step in range(args.num_steps): # Sample actions with torch.no_grad(): action0a = agent0.act([None for _ in range(args.num_processes//2)]) action0b = old_agent0.act([None for _ in range(args.num_processes//2)]) action1a = old_agent1.act([None for _ in range(args.num_processes//2)]) action1b = agent1.act([None for _ in range(args.num_processes//2)]) action0 = np.concatenate((action0a.squeeze(),action0b.squeeze())) action1 = np.concatenate((action1a.squeeze(),action1b.squeeze())) #action0 = torch.tensor(action0)[:,None] #action1 = torch.tensor(action1)[:,None] # Obser reward and next obs obs, reward, done, infos = envs.step(zip(action0, action1)) envs.reset() agent0.update(None, action0, reward[0]) agent1.update(None, action1, reward[1]) for i, info in enumerate(infos[:args.num_processes//2]): d = True if 'episode' in info[0].keys(): old_agent1.new_policy(i) epoch_p0ep += 1 if "winner" in info[0].keys(): d = False w0 += 1 if "winner" in info[1].keys(): d = False eps += 1 episode_rewards0.append(info[0]['episode']['r']) if d: p0draws += 1 p0numep += 1 if p0numep % cache_freq == 0: print(f"ADDING POLICY !! Current length {len(old_agent0.policies)}") old_agent0.add_policy(copy.deepcopy(agent0)) for i, info in enumerate(infos[args.num_processes//2:]): if 'episode' in info[1].keys(): old_agent0.new_policy(i) epoch_p1ep += 1 if "winner" in info[0].keys(): d = False if "winner" in info[1].keys(): d = False w1 += 1 episode_rewards1.append(info[1]['episode']['r']) if d: p1draws += 1 p1numep += 1 if p1numep % cache_freq == 0: old_agent1.add_policy(copy.deepcopy(agent1)) if j % args.log_interval == 0 and len(episode_rewards0) > 1: total_num_steps = args.num_steps args.num_processes end = time.time() print("=============================================================================================") print(f"Updates {j} player0 rewards {	np.mean(episode_rewards0)	numpy.mean
# module import import gc import os import copy import random import platform import numpy as np import pickle as p import pandas as pd import multiprocessing as mp from numpy.matlib import repmat # scipy module imports from scipy.stats import norm, linregress from scipy.spatial.distance import * from scipy.interpolate import PchipInterpolator as pchip from scipy.interpolate import InterpolatedUnivariateSpline as IUS from scipy.interpolate import interp1d from scipy.optimize import curve_fit from scipy.signal import periodogram, hamming, boxcar, find_peaks # sklearn module imports from sklearn.linear_model import LinearRegression from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA # statsmodels module imports from statsmodels.nonparametric.smoothers_lowess import lowess # pyqt5 module import from PyQt5.QtCore import QThread, pyqtSignal # custom module imports import analysis_guis.common_func as cf import analysis_guis.calc_functions as cfcn import analysis_guis.rotational_analysis as rot from analysis_guis.dialogs.rotation_filter import RotationFilteredData from analysis_guis.cluster_read import ClusterRead from probez.spike_handling import spike_io # other parameters dcopy = copy.deepcopy default_dir_file = os.path.join(os.getcwd(), 'default_dir.p') interp_arr = lambda xi, y: np.vstack([interp1d(np.linspace(0, 1, len(x)), x, kind='nearest')(xi) for x in y]) cell_perm_ind = lambda n_cell_tot, n_cell: np.sort(np.random.permutation(n_cell_tot)[:n_cell]) set_sf_cell_perm = lambda spd_sf, n_pool, n_cell: [x[:, :, cell_perm_ind(n_pool, n_cell)] for x in spd_sf] grp_expt_indices = lambda i_expt0: [np.where(i_expt0 == i)[0] for i in np.unique(i_expt0)] # lambda function declarations lin_func = lambda x, a: a * x ######################################################################################################################## ######################################################################################################################## class WorkerThread(QThread): # creates the signal object work_started = pyqtSignal() work_progress = pyqtSignal(str, float) work_finished = pyqtSignal(object) work_error = pyqtSignal(str, str) work_plot = pyqtSignal(object) def __init__(self, parent=None, main_gui=None): # creates the worker object super(WorkerThread, self).__init__(parent) self.update_pbar = True self.is_running = False self.forced_quit = False self.sub_job = None self.is_ok = True self.data = None # other initialisations self.main_gui = main_gui self.thread_job_primary = None self.thread_job_secondary = None self.thread_job_para = None def set_worker_func_type(self, thread_job_primary, thread_job_secondary=None, thread_job_para=None): ''' :param func_type: :return: ''' # updates the worker primary/secondary job type and parameters self.thread_job_primary = thread_job_primary self.thread_job_secondary = thread_job_secondary self.thread_job_para = thread_job_para def run(self): ''' :return: ''' # initialisations w_prog, w_err = self.work_progress, self.work_error # updates the running/forced quit flagsv self.is_running = True self.forced_quit = False self.is_ok = True # updates the running parameter and enables the progress group parameters self.work_started.emit() # runs the job based on the type thread_data = None if self.thread_job_primary == 'init_data_file': # case is initialising the data file self.init_cluster_data() elif self.thread_job_primary == 'init_pool_object': # case is initialising the pool worker object thread_data = self.init_pool_worker() ################################## #### DATA I/O FUNCTIONS #### ################################## elif self.thread_job_primary == 'load_data_files': # case is loading the data files thread_data = self.load_data_file() elif self.thread_job_primary == 'save_multi_expt_file': # retrieves the parameters data, out_info = self.thread_job_para[0], self.thread_job_para[1] # case is loading the data files thread_data = self.save_multi_expt_file(data, out_info) elif self.thread_job_primary == 'save_multi_comp_file': # retrieves the parameters data, out_info = self.thread_job_para[0], self.thread_job_para[1] # case is loading the data files thread_data = self.save_multi_comp_file(data, out_info) elif self.thread_job_primary == 'run_calc_func': # case is the calculation functions calc_para, plot_para = self.thread_job_para[0], self.thread_job_para[1] data, pool, g_para = self.thread_job_para[2], self.thread_job_para[3], self.thread_job_para[4] ################################################ #### CLUSTER CLASSIFICATION FUNCTIONS #### ################################################ if self.thread_job_secondary == 'Fixed/Free Cluster Matching': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['clust']) # case is determining the cluster matches self.det_cluster_matches(data, calc_para, w_prog) elif self.thread_job_secondary == 'Cluster Cross-Correlogram': # case is the cc-gram type determinations thread_data = self.calc_ccgram_types(calc_para, data.cluster) ###################################### #### AHV ANALYSIS FUNCTIONS #### ###################################### elif ' (Fixed)' in self.thread_job_secondary or \ (self.thread_job_secondary == 'Correlation Significance Overlap'): # ensures the smoothing window is an odd integer (if smoothing) if calc_para['is_smooth']: if calc_para['n_smooth'] % 2 != 1: # if not, then output an error message to screen e_str = 'The median smoothing filter window span must be an odd integer.' w_err.emit(e_str, 'Incorrect Smoothing Window Span') # sets the error flag and exits the function self.is_ok = False self.work_finished.emit(thread_data) return # initialises the rotation filter class object (if not already set) if plot_para['rot_filt'] is None: plot_para['rot_filt'] = cf.init_rotation_filter_data(False) # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['vel', 'vel_sf_fix'], other_para=False) # calculates the shuffled kinematic spiking frequencies cfcn.calc_binned_kinemetic_spike_freq(data, plot_para, dcopy(calc_para), w_prog, roc_calc=False) cfcn.calc_shuffled_kinematic_spike_freq(data, dcopy(calc_para), w_prog) # runs any specific additional function fit_func = ['Correlation Comparison (Fixed)', 'Correlation Fit Parameters (Fixed)', 'Individual Cell Correlation (Fixed)'] if self.thread_job_secondary in fit_func: # case is the correlation fit parameters self.calc_corr_fit_para(data, plot_para, dcopy(calc_para), w_prog) elif (' (Freely Moving)' in self.thread_job_secondary): # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['vel_sf_free'], other_para=False) # updates the bin velocity data.rotation.vel_bin_corr = calc_para['vel_bin'] elif 'Fixed/Free Spiking Correlation' in self.thread_job_secondary: # determines if the freely moving data file has been loaded if not hasattr(data.externd, 'free_data'): # if the data-file has not been loaded then output an error to screen and exit e_str = 'The freely moving spiking frequency/statistics data file must be loaded ' \ 'before being able to run this function.\n\nPlease load this data file and try again.' w_err.emit(e_str, 'Freely Moving Data Missing?') # exits the function with an error flag self.is_ok = False self.work_finished.emit(thread_data) return # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['ff_corr', 'vel'], other_para=False) # calculates the shuffled kinematic spiking frequencies cfcn.calc_binned_kinemetic_spike_freq(data, plot_para, calc_para, w_prog, roc_calc=False, use_raw=True) # calculates the fixed/free correlations (if not already set) if not data.comp.ff_corr.is_set: self.calc_fix_free_correlation(data, calc_para, w_prog) ################################################ #### FREELY MOVING ANALYSIS FUNCTIONS #### ################################################ elif self.thread_job_secondary == 'Freely Moving Cell Fit Residual': # ensures the calculation fields are self.calc_cell_fit_residual(data, calc_para, w_prog) ###################################### #### EYE TRACKING FUNCTIONS #### ###################################### elif self.thread_job_secondary in ['Eye Movement Event Signals']: # check to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['eye_track']) # calculates the eye-tracking metrics (if not calculated) if len(data.externd.eye_track.t_evnt) == 0: self.calc_eye_track_metrics(data, calc_para, w_prog) elif 'Eye Movement Correlation' in self.thread_job_secondary: # check to see if any parameters have been altered/ self.check_altered_para(data, calc_para, plot_para, g_para, ['eye_track']) # calculates the eye-tracking metrics (if not calculated) if len(data.externd.eye_track.t_evnt) == 0: self.calc_eye_track_metrics(data, calc_para, w_prog) # calculates the eye-tracking metrics if len(data.externd.eye_track.t_sp_h) == 0: self.calc_eye_track_corr(data, calc_para, w_prog) ###################################### #### ROC ANALYSIS FUNCTIONS #### ###################################### elif self.thread_job_secondary == 'Direction ROC Curves (Single Cell)': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition']) # case is the shuffled cluster distances if not self.calc_cond_roc_curves(data, pool, calc_para, plot_para, g_para, False, 100.): self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Direction ROC Curves (Whole Experiment)': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition', 'phase']) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(data, pool, calc_para, plot_para, g_para, False, 33.): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(data, calc_para, 66.) self.calc_phase_roc_significance(calc_para, g_para, data, pool, 100.) elif self.thread_job_secondary in ['Direction ROC AUC Histograms', 'Direction ROC Spiking Rate Heatmap']: # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition']) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(data, pool, calc_para, plot_para, g_para, True, 100., True): self.is_ok = False self.work_finished.emit(thread_data) return elif 'Velocity ROC Curves' in self.thread_job_secondary: # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['vel'], other_para=True) # calculates the binned kinematic spike frequencies cfcn.calc_binned_kinemetic_spike_freq(data, plot_para, calc_para, w_prog) self.calc_kinematic_roc_curves(data, pool, calc_para, g_para, 50.) elif self.thread_job_secondary == 'Velocity ROC Significance': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['vel'], other_para=True) # calculates the binned kinematic spike frequencies cfcn.calc_binned_kinemetic_spike_freq(data, plot_para, calc_para, w_prog) # calculates the kinematic roc curves and their significance self.calc_kinematic_roc_curves(data, pool, calc_para, g_para, 0.) self.calc_kinematic_roc_significance(data, calc_para, g_para) elif self.thread_job_secondary == 'Condition ROC Curve Comparison': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['phase']) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(data, pool, calc_para, plot_para, g_para, True, 33.): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(data, calc_para, 66.) self.calc_phase_roc_significance(calc_para, g_para, data, pool, 100.) elif self.thread_job_secondary == 'Direction ROC Significance': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition', 'phase']) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(data, pool, calc_para, plot_para, g_para, True, 33., force_black_calc=True): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(data, calc_para, 66.) self.calc_phase_roc_significance(calc_para, g_para, data, pool, 100.) if cf.det_valid_vis_expt(data, True): if not self.calc_dirsel_group_types(data, pool, calc_para, plot_para, g_para): self.is_ok = False self.work_finished.emit(thread_data) return ############################################### #### COMBINED ANALYSIS LDA FUNCTIONS #### ############################################### elif self.thread_job_secondary == 'Rotation/Visual Stimuli Response Statistics': # calculates the phase roc curve/significance values self.calc_phase_roc_curves(data, calc_para, 50.) # calculates the direction/selection group types if not self.calc_dirsel_group_types(data, pool, calc_para, plot_para, g_para): self.is_ok = False self.work_finished.emit(thread_data) elif self.thread_job_secondary == 'Combined Direction ROC Curves (Whole Experiment)': # checks that the conditions are correct for running the function if not self.check_combined_conditions(calc_para, plot_para): self.is_ok = False self.work_finished.emit(thread_data) return # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition', 'phase', 'visual']) # initisalises the rotational filter (if not initialised already) if plot_para['rot_filt'] is None: plot_para['rot_filt'] = cf.init_rotation_filter_data(False) # adds motordrifting (if the visual expt type) _plot_para, _calc_para = dcopy(plot_para), dcopy(calc_para) if calc_para['vis_expt_type'] == 'MotorDrifting': _plot_para['rot_filt']['t_type'].append('MotorDrifting') # resets the flags to use the full rotation/visual phases _calc_para['use_full_rot'], _calc_para['use_full_vis'] = True, True # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(data, pool, _calc_para, _plot_para, g_para, False, 33.): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(data, _calc_para, 66.) if (calc_para['vis_expt_type'] == 'UniformDrifting') and \ (calc_para['grp_stype'] != 'Wilcoxon Paired Test'): # sets up the visual rotation filter r_filt_v = cf.init_rotation_filter_data(False) r_filt_v['t_type'], r_filt_v['is_ud'], r_filt_v['t_cycle'] = ['UniformDrifting'], [True], ['15'] # retrieves the visual filter object plot_exp_name, plot_all_expt = plot_para['plot_exp_name'], plot_para['plot_all_expt'] r_obj_vis, ind_type = cf.split_unidrift_phases(data, r_filt_v, None, plot_exp_name, plot_all_expt, 'Whole Experiment', 2.) # calculates the full uniform-drifting curves self.calc_ud_roc_curves(data, r_obj_vis, ind_type, 66.) # calculates the direction selection types if not self.calc_dirsel_group_types(data, pool, _calc_para, _plot_para, g_para): self.is_ok = False # calculates the partial roc curves self.calc_partial_roc_curves(data, calc_para, plot_para, 66.) elif self.thread_job_secondary in ['Normalised Kinematic Spiking Frequency']: # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['vel'], other_para=False) # calculates the binned kinematic spike frequencies cfcn.calc_binned_kinemetic_spike_freq(data, plot_para, calc_para, w_prog, roc_calc=False) ###################################################### #### DEPTH-BASED SPIKING ANALYSIS FUNCTIONS #### ###################################################### elif self.thread_job_secondary == 'Depth Spiking Rate Comparison': # make a copy of the plotting/calculation parameters _plot_para, _calc_para, r_data = dcopy(plot_para), dcopy(calc_para), data.depth _plot_para['plot_exp_name'] = None # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition', 'phase', 'visual']) # reduces the data clusters to only include the RSPd/RSPg cells _data = cfcn.get_rsp_reduced_clusters(data) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(_data, pool, _calc_para, _plot_para, g_para, True, 33., r_data=r_data, force_black_calc=True): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(_data, _calc_para, 66., r_data=r_data) ############################################ #### SPIKING FREQUENCY CALCULATION #### ############################################ # initialisations r_filt = _plot_para['rot_filt'] r_data.ch_depth, r_data.ch_region, r_data.ch_layer = \ cfcn.get_channel_depths_tt(_data._cluster, r_filt['t_type']) t_ofs, t_phase = cfcn.get_rot_phase_offsets(calc_para) # rotation filtered object calculation r_obj_rot = RotationFilteredData(_data, r_filt, None, None, True, 'Whole Experiment', False, t_ofs=t_ofs, t_phase=t_phase) # calculates the individual trial/mean spiking rates and sets up the plot/stats arrays sp_f0_rot, sp_f_rot = cf.calc_phase_spike_freq(r_obj_rot) s_plt, _, sf_stats, ind = cf.setup_spike_freq_plot_arrays(r_obj_rot, sp_f0_rot, sp_f_rot, None, 3) r_data.plt, r_data.stats, r_data.ind, r_data.r_filt = s_plt, sf_stats, ind, dcopy(r_filt) elif self.thread_job_secondary == 'Depth Spiking Rate Comparison (Multi-Sensory)': # checks that the conditions are correct for running the function if not self.check_combined_conditions(calc_para, plot_para): self.is_ok = False self.work_finished.emit(thread_data) return else: # otherwise, make a copy of the plotting/calculation parameters _plot_para, _calc_para, r_data = dcopy(plot_para), dcopy(calc_para), data.depth _plot_para['plot_exp_name'], r_filt = None, _plot_para['rot_filt'] t_ofs, t_phase = cfcn.get_rot_phase_offsets(calc_para) # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition', 'phase', 'visual']) # adds motordrifting (if it is the visual expt type) if calc_para['vis_expt_type'] == 'MotorDrifting': _plot_para['rot_filt']['t_type'].append('MotorDrifting') # reduces the data clusters to only include the RSPd/RSPg cells _data = cfcn.get_rsp_reduced_clusters(data) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(_data, pool, _calc_para, _plot_para, g_para, False, 33., r_data=r_data): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(_data, _calc_para, 66., r_data=r_data) if (calc_para['vis_expt_type'] == 'UniformDrifting'): # sets up the visual rotation filter r_filt_v = cf.init_rotation_filter_data(False) r_filt_v['t_type'], r_filt_v['is_ud'], r_filt_v['t_cycle'] = ['UniformDrifting'], [True], ['15'] # retrieves the visual filter object r_obj_vis, ind_type = cf.split_unidrift_phases(_data, r_filt_v, None, None, True, 'Whole Experiment', 2., t_phase, t_ofs) # calculates the full uniform-drifting curves self.calc_ud_roc_curves(_data, r_obj_vis, ind_type, 66., r_data=r_data) # calculates the individual trial/mean spiking rates and sets up the plot/stats arrays sp_f0, sp_f = cf.calc_phase_spike_freq(r_obj_vis) s_plt, _, sf_stats, ind = cf.setup_spike_freq_plot_arrays(r_obj_vis, sp_f0, sp_f, ind_type, 2) r_data.plt_vms, r_data.stats_vms, r_data.ind_vms = s_plt, sf_stats, ind, r_filt_v r_data.r_filt_vms = dcopy(r_filt_v) else: # resets the uniform drifting fields r_data.plt_vms, r_data.stats_vms, r_data.ind_vms, r_data.r_filt_vms = None, None, None, None ############################################ #### SPIKING FREQUENCY CALCULATION #### ############################################ # rotation filtered object calculation r_obj_rot = RotationFilteredData(_data, r_filt, None, None, True, 'Whole Experiment', False, t_phase=t_phase, t_ofs=t_ofs) r_data.ch_depth_ms, r_data.ch_region_ms, r_data.ch_layer_ms = \ cfcn.get_channel_depths_tt(_data._cluster, r_filt['t_type']) # calculates the individual trial/mean spiking rates and sets up the plot/stats arrays sp_f0_rot, sp_f_rot = cf.calc_phase_spike_freq(r_obj_rot) s_plt, _, sf_stats, ind = cf.setup_spike_freq_plot_arrays(r_obj_rot, sp_f0_rot, sp_f_rot, None, 3) r_data.plt_rms, r_data.stats_rms, r_data.ind_rms = s_plt, sf_stats, ind r_data.r_filt_rms = dcopy(r_filt) ########################################################## #### ROTATION DISCRIMINATION ANALYSIS FUNCTIONS #### ########################################################## elif self.thread_job_secondary == 'Rotation Direction LDA': # if the solver parameter have not been set, then initalise them d_data = data.discrim.dir # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=d_data) # sets up the lda values r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, d_data, w_prog, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not cfcn.run_rot_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, d_data=d_data, w_prog=w_prog): self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Temporal Duration/Offset LDA': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.temp) # if the temporal data parameters have changed/has not been initialised then calculate the values if data.discrim.temp.lda is None: # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.temp) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.temp, w_prog, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return # if an update in the calculations is required, then run the temporal LDA analysis if status == 2: if not self.run_temporal_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max): # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Individual LDA': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.indiv) self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.dir) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.dir, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not cfcn.run_rot_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, d_data=data.discrim.dir, w_prog=w_prog): self.is_ok = False self.work_finished.emit(thread_data) return # if the individual data parameters have changed/has not been initialised then calculate the values if data.discrim.indiv.lda is None: # runs the individual LDA if not self.run_individual_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max): # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Shuffled LDA': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.shuffle) self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.dir) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.dir, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not cfcn.run_rot_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, d_data=data.discrim.dir, w_prog=w_prog): self.is_ok = False self.work_finished.emit(thread_data) return # runs the shuffled LDA if not self.run_shuffled_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max): # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Pooled Neuron LDA': # resets the minimum cell count and checks if the pooled parameters have been altered # calc_para['lda_para']['n_cell_min'] = calc_para['n_cell_min'] self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.part) # if the pooled data parameters have changed/has not been initialised then calculate the values if data.discrim.part.lda is None: # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.dir) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.dir, w_prog, True, w_err=w_err) if not calc_para['pool_expt']: if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return # elif status == 2: # # if an update in the calculations is required, then run the rotation LDA analysis # if not cfcn.run_rot_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, # d_data=data.discrim.dir, w_prog=w_prog): # self.is_ok = False # self.work_finished.emit(thread_data) # return # runs the partial LDA if not self.run_pooled_lda(pool, data, calc_para, r_filt, i_expt, i_cell, n_trial_max): # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Individual Cell Accuracy Filtered LDA': # check to see if the individual LDA calculations have been performed if data.discrim.indiv.lda is None: # if the individual LDA has not been run, then output an error to screen e_str = 'The Individual LDA must be run first before this analysis can be performed' w_err.emit(e_str, 'Missing Individual LDA Data') # sets the ok flag to false and exit the function self.is_ok = False self.work_finished.emit(thread_data) return # _calc_para = dcopy(calc_para) _calc_para['comp_cond'] = dcopy(data.discrim.indiv.ttype) ######################################### #### ROTATION LDA CALCULATIONS #### ######################################### # sets the min/max accuracy values _calc_para['lda_para']['y_acc_min'] = 0 _calc_para['lda_para']['y_acc_max'] = 100 # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, _calc_para, g_para, ['lda'], other_para=data.discrim.dir) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, _calc_para, data.discrim.dir, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not cfcn.run_rot_lda(data, _calc_para, r_filt, i_expt, i_cell, n_trial_max, d_data=data.discrim.dir, w_prog=w_prog, pW=50.): self.is_ok = False self.work_finished.emit(thread_data) return ######################################### #### FILTERED LDA CALCULATIONS #### ######################################### # sets the min/max accuracy values _calc_para['lda_para']['y_acc_min'] = _calc_para['y_acc_min'] _calc_para['lda_para']['y_acc_max'] = _calc_para['y_acc_max'] # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, _calc_para, g_para, ['lda'], other_para=data.discrim.filt) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, _calc_para, data.discrim.filt, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not cfcn.run_rot_lda(data, _calc_para, r_filt, i_expt, i_cell, n_trial_max, d_data=data.discrim.filt, w_prog=w_prog, pW=50., pW0=50.): self.is_ok = False self.work_finished.emit(thread_data) return else: # otherwise, update the calculation parameters data.discrim.filt.yaccmn = _calc_para['y_acc_min'] data.discrim.filt.yaccmx = _calc_para['y_acc_max'] elif self.thread_job_secondary == 'LDA Group Weightings': # checks to see if the data class as changed parameters d_data, w_prog = data.discrim.wght, self.work_progress self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=d_data) # sets up the lda values r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, d_data, w_prog, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not self.run_wght_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max): self.is_ok = False self.work_finished.emit(thread_data) return ####################################################### #### SPEED DISCRIMINATION ANALYSIS FUNCTIONS #### ####################################################### elif self.thread_job_secondary == 'Speed LDA Accuracy': # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.spdacc) # if the pooled data parameters have changed/has not been initialised then calculate the values if data.discrim.spdc.lda is None: # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.spdacc, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: if not self.run_speed_lda_accuracy(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, w_prog): self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Speed LDA Comparison (Individual Experiments)': # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.spdc) # if the pooled data parameters have changed/has not been initialised then calculate the values if data.discrim.spdc.lda is None: # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.spdc, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not self.run_kinematic_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, w_prog): self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Speed LDA Comparison (Pooled Experiments)': # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.spdcp) # if the pooled data parameters have changed/has not been initialised then calculate the values if data.discrim.spdcp.lda is None: # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.spdcp, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return # elif status == 2:/ # if an update in the calculations is required, then run the rotation LDA analysis if not self.run_pooled_kinematic_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, w_prog): self.is_ok = False self.work_finished.emit(thread_data) return # # calculates the psychometric curves # w_prog.emit('Calculating Pyschometric Curves', 100.) # cfcn.calc_all_psychometric_curves(data.discrim.spdcp, float(calc_para['vel_bin']), calc_para['use_all']) elif self.thread_job_secondary == 'Velocity Direction Discrimination LDA': # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.spddir) # if the pooled data parameters have changed/has not been initialised then calculate the values if data.discrim.spddir.lda is None: # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.spddir, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: if not self.run_speed_dir_lda_accuracy(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, w_prog): self.is_ok = False self.work_finished.emit(thread_data) return ####################################### #### MISCELLANEOUS FUNCTIONS #### ####################################### elif self.thread_job_secondary == 'Velocity Multilinear Regression Dataframe Output': # checks to see if any base spiking frequency dataframe parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['spikedf'], other_para=data.spikedf) # checks to see if the overlap duration is less than the time bin size if calc_para['t_over'] >= calc_para['bin_sz']: # if not, then output an error to screen e_str = 'Bin Overlap Duration must be less than the Time Bin Size.\n' \ 'Reset these parameters before running this function.' w_err.emit(e_str, 'Incorrect Function Parameters') # exits the function with an error flag self.is_ok = False self.work_finished.emit(thread_data) return # only continue if the spiking frequency dataframe has not been set up if not data.spikedf.is_set: self.setup_spiking_freq_dataframe(data, calc_para) elif self.thread_job_secondary == 'Autocorrelogram Theta Index Calculations': # case to see if any parameters have changed self.check_altered_para(data, calc_para, plot_para, g_para, ['theta'], other_para=data.theta_index) # only continue if the theta index dataframe has not been setup if not data.theta_index.is_set: self.calc_auto_ccgram_fft(data, calc_para) ############################### #### OTHER FUNCTIONS #### ############################### elif self.thread_job_secondary == 'Shuffled Cluster Distances': # case is the shuffled cluster distances thread_data = self.calc_shuffled_cluster_dist(calc_para, data.cluster) elif self.thread_job_primary == 'update_plot': pass # emits the finished work signal self.work_finished.emit(thread_data) ############################################ #### THREAD CALCULATION FUNCTIONS #### ############################################ def load_data_file(self): ''' :param exp_file: :return: ''' # retrieves the job parameters load_dlg, loaded_exp, is_multi = self.thread_job_para[0], self.thread_job_para[1], self.thread_job_para[2] if not np.any([not x in loaded_exp for x in load_dlg.exp_name]): # if there are no new experiments to load, then exit the function return None else: n_file = len(load_dlg.exp_files) dpw, p_rlx, data = 1.0 / n_file, 0.05, [] _, f_extn = os.path.splitext(load_dlg.exp_files[0]) # for i_file in range(n_file): if not self.is_running: # if the user cancelled, then exit return None else: # updates the progress bar string p_str, pw0 = 'Loading File {0} of {1}'.format(i_file+1, n_file), i_file / n_file self.work_progress.emit(p_str, 100.0 * pw0) # sets the experiment file and name if load_dlg.exp_name[i_file] not in loaded_exp: # loads the data from the data file with open(load_dlg.exp_files[i_file], 'rb') as fp: data_nw = p.load(fp) # setting of other fields if isinstance(data_nw, dict): data_nw['expFile'] = load_dlg.exp_files[i_file] # re-calculates the signal features (single experiment only) if f_extn == '.cdata': if np.shape(data_nw['sigFeat'])[1] == 5: # memory allocation for the signal features xi = np.array(range(data_nw['nPts'])) sFeat = np.zeros((data_nw['nC'], 2)) for i in range(data_nw['nC']): # creates the piecewise-polynomial of the mean signal pp, t_max = pchip(xi, data_nw['vMu'][:, i]), data_nw['sigFeat'][i, 2] t_min = np.argmin(data_nw['vMu'][int(t_max):, i]) + t_max v_max_2 = data_nw['vMu'][int(t_max), i] / 2.0 v_min = np.min(data_nw['vMu'][int(t_max):, i]) v_half = data_nw['vMu'][int(data_nw['sigFeat'][i, 1]), i] / 2.0 ################################################## #### POST-STIMULI SPIKE HALF-WIDTH TIME #### ################################################## # determines the point/voltage of the pmaximum proceding the minimum bnd_1 = [(data_nw['sigFeat'][i, 0], data_nw['sigFeat'][i, 1])] bnd_2 = [(data_nw['sigFeat'][i, 1], data_nw['sigFeat'][i, 2])] bnd_3 = [(data_nw['sigFeat'][i, 2], t_min)] # determines the location of the half-width points t_hw1_lo = cfcn.opt_time_to_y0((pp, v_half), bnd_1) t_hw1_hi = cfcn.opt_time_to_y0((pp, v_half), bnd_2) t_hw2_lo = cfcn.opt_time_to_y0((pp, v_max_2), bnd_2) t_hw2_hi = cfcn.opt_time_to_y0((pp, v_max_2), bnd_3) t_rlx = cfcn.opt_time_to_y0((pp, v_min + p_rlx * (v_max_2 - v_min)), bnd_3) # determine if it is feasible to find the 2nd peak half-width point if (t_hw2_hi is None) or (t_rlx is None): # if not, then linearly extrapolate past the end point of the signal xi2 = np.array(range(2xi[-1])) ppL = IUS(xi, data_nw['vMu'][:, i], k=1) # determines the half-width/relaxtion time from the extrapolated signal bnd_4 = [(data_nw['sigFeat'][i, 2], xi2[-1])] t_hw2_hi = cfcn.opt_time_to_y0((ppL, v_max_2), bnd_4) t_rlx = cfcn.opt_time_to_y0((ppL, v_min + p_rlx (v_max_2 - v_min)), bnd_4) # calculates the new signal features data_nw['sigFeat'][i, 3] = t_hw1_lo data_nw['sigFeat'][i, 4] = t_hw1_hi sFeat[i, 0] = t_hw2_hi - t_hw2_lo sFeat[i, 1] = t_rlx - t_max # concatenates the new signal feature date data_nw['sigFeat'] = np.concatenate((data_nw['sigFeat'], sFeat), axis=1) # sets the cell cluster include indices (if not already set) if 'clInclude' not in data_nw['expInfo']: data_nw['expInfo']['clInclude'] = np.ones(data_nw['nC'], dtype=bool) # appends the new data dictionary to the overall data list data.append(data_nw) # appends the current filename to the data dictionary and returns the object return data def save_multi_expt_file(self, data, out_info): ''' :return: ''' # updates the progressbar self.work_progress.emit('Saving Data To File...', 50.0) # sets the file extension (based on the data type) if hasattr(data.comp, 'data'): f_extn = 'mdata' if len(data.comp.data) == 0 else 'mcomp' else: f_extn = 'mdata' # sets the output file name out_file = os.path.join(out_info['inputDir'], '{0}.{1}'.format(out_info['dataName'], f_extn)) # outputs the data to file with open(out_file, 'wb') as fw: p.dump(data, fw) # updates the progressbar self.work_progress.emit('Data Save Complete!', 100.0) def save_multi_comp_file(self, data, out_info): ''' :return: ''' # updates the progressbar self.work_progress.emit('Saving Data To File...', 50.0) # memory allocation n_file = len(out_info['exptName']) # sets the output file name out_file = os.path.join(out_info['inputDir'], '{0}.mcomp'.format(out_info['dataName'])) # output data file data_out = { 'data': np.empty((n_file, 2), dtype=object), 'c_data': np.empty(n_file, dtype=object), 'ff_corr': data.comp.ff_corr if hasattr(data.comp, 'ff_corr') else None, 'f_data': data.externd.free_data if hasattr(data.externd, 'free_data') else None } for i_file in range(n_file): # retrieves the index of the data field corresponding to the current experiment fix_file = out_info['exptName'][i_file].split('/')[0] i_comp = cf.det_comp_dataset_index(data.comp.data, fix_file) # creates the multi-experiment data file based on the type data_out['c_data'][i_file] = data.comp.data[i_comp] data_out['data'][i_file, 0], data_out['data'][i_file, 1] = \ cf.get_comp_datasets(data, c_data=data_out['c_data'][i_file], is_full=True) # outputs the data to file with open(out_file, 'wb') as fw: p.dump(data_out, fw) # updates the progressbar self.work_progress.emit('Data Save Complete!', 100.0) def init_pool_worker(self): ''' :return: ''' # creates the pool worker object p = mp.Pool(int(np.floor(1.5 * mp.cpu_count()))) # returns the object return p def init_cluster_data(self): ''' :return: ''' def map_cluster_depths(): ''' :param cluster_depth: :return: ''' # retrieves the cluster depths from the spike I/O class object cluster_depth = sp_io.get_cluster_depths(cluster_ids) # sets the mapped cluster depths based on the file type if (exp_info['dmapFile'] is None) or (len(exp_info['dmapFile']) == 0): # no map is given so return the original depth values return cluster_depth, None else: # otherwise, map the cluster depth values from the probe to actual values data = np.array(pd.read_csv(exp_info['dmapFile'])) if np.size(data, axis=1) < 4: # if the mapping file is not correct, then output an error to screen e_str = 'Channel mapping file does not have the correct format.\n\n' \ 'Re-select a valid file before attempting to initialise the combined data files.' self.work_error.emit(e_str, 'Invalid Channel Mapping File') # return none values indicating the error return None, None else: # otherwise, return the mapped channel depths and the other mapping values return np.array([data[data[:, 1] == x, 0][0] for x in cluster_depth]), data[:, :4] # retrieves the job parameters exp_info, out_name, g_para = self.thread_job_para[0], self.thread_job_para[1], self.thread_job_para[2] # sets the global parameters n_hist = int(g_para['n_hist']) n_spike = int(g_para['n_spike']) cluster_ids = None # retrieves the spike I/O data and sets the cluster IDs based on the cluster type sp_io = spike_io.SpikeIo(exp_info['srcDir'], exp_info['traceFile'], int(exp_info['nChan'])) if exp_info['clusterType'] == 'Good': # case is the good clusters if hasattr(sp_io, 'good_cluster_ids'): cluster_ids = sp_io.good_cluster_ids elif exp_info['clusterType'] == 'MUA': # case is the multi-unit clusters if hasattr(sp_io, 'MUA_cluster_ids'): cluster_ids = sp_io.MUA_cluster_ids if cluster_ids is None: e_str = 'Cluster group file is missing? Please re-run with cluster-group file in the source data directory' self.work_error.emit(e_str, 'Cluster Group File Missing!') return # retrieves the clusters spike data and channel depths self.work_progress.emit('Reshaping Cluster Data...', 0.0) clusters = [ClusterRead(sp_io, cid) for cid in cluster_ids] # determines the channel depths mapping depth, channel_map_data = map_cluster_depths() if depth is None: # if the file has an incorrect format, then exit the function return # determines if the mapping values were set correctly if channel_map_data is not None: # if so, then determine the region/recording layers y_coords = channel_map_data[:, 3] depthLo, depthHi = np.array(exp_info['depthLo']).astype(int), np.array(exp_info['depthHi']).astype(int) indD = np.array([next((i for i in range(len(depthHi)) if x <= depthHi[i]), len(depthHi)-1) for x in y_coords]) chRegion = np.array(exp_info['regionName'])[indD][depth.astype(int)] chLayer = np.array(exp_info['recordLayer'])[indD][depth.astype(int)] else: # otherwise, return N/A for the region/recording layers chRegion, chLayer = ['N/A'] * len(clusters), ['N/A'] * len(clusters) depthLo, depthHi = None, None # sets the signal point-wise/ISI bin vectors xi_pts_H = np.linspace(-200, 100, n_hist + 1) xi_isi_H = np.linspace(0, 1000, n_hist + 1) # creates the recording/experimental information sub-dictionaries expInfo = {'name': exp_info['expName'], 'date': exp_info['expDate'], 'cond': exp_info['expCond'], 'type': exp_info['expType'], 'sex': exp_info['expSex'], 'age': exp_info['expAge'], 'probe': exp_info['expProbe'], 'lesion': exp_info['lesionType'], 'channel_map': channel_map_data, 'cluster_type': exp_info['clusterType'], 'other_info': exp_info['otherInfo'], 'record_state': exp_info['recordState'], 'record_coord': exp_info['recordCoord'], 'depth_lo': depthLo, 'depth_hi': depthHi} # memory allocation pW0, pW1, nFeat = 20.0, 60.0, 5 nC, nSample = len(clusters), np.size(sp_io.traces, axis=0) sFreq, vGain = float(exp_info['sFreq']), float(exp_info['vGain']) # sets the data file dictionary object A = { 'vSpike': np.empty(nC, dtype=object), 'tSpike': np.empty(nC, dtype=object), 'vMu': None, 'vSD': None, 'ccGram': None, 'ccGramXi': None, 'sigFeat': np.zeros((nC, nFeat)), 'clustID': cluster_ids, 'expInfo': expInfo, 'chDepth': depth, 'chRegion': chRegion, 'chLayer': chLayer, 'sFreq': sFreq, 'nC': nC, 'nPts': None, 'tExp': nSample / sFreq, 'vGain': vGain, 'isiHist': np.empty(nC, dtype=object), 'isiHistX': xi_isi_H, 'ptsHist': np.empty(nC, dtype=object), 'ptsHistX': xi_pts_H, 'rotInfo': None, } # sets up the rotation analysis data dictionary A['rotInfo'] = rot.load_rot_analysis_data(A, exp_info, sp_io, w_prog=self.work_progress, pW0=pW0) # sets up the sub-job flags self.sub_job = np.zeros(nC, dtype=bool) # retrieves the cluster data for i, c in enumerate(clusters): if not self.is_running: # if the user cancelled, then exit the function return else: # updates the main gui progressnbar pW = pW0 + pW1 * (i + 1) / nC self.work_progress.emit('Processing Cluster {0} of {1}'.format(i + 1, nC), pW) ################################################### #### DATA RETRIEVAL & MEMORY ALLOCATIONS #### ################################################### # retrieves the spike voltage/timing v_spike = c.channel_waveforms t_spike = 1000.0 * sp_io.get_spike_times_in_cluster(cluster_ids[i]) / sFreq # memory allocation (only for the first cluster) if i == 0: A['nPts'] = np.size(v_spike, axis=0) A['vMu'] = np.zeros((A['nPts'], nC), dtype=float) A['vSD'] = np.zeros((A['nPts'], nC), dtype=float) xi = np.array(range(A['nPts'])) ############################################### #### MAIN METRIC CALCULATION/STORAGE #### ############################################### # sets the values into the final array A['vSpike'][i] = v_spike[:, :n_spike] * vGain A['tSpike'][i] = t_spike[:np.size(v_spike, axis=1)] # calculates the mean/standard deviation of the voltage spikes A['vMu'][:, i] = np.mean(v_spike, axis=1) * vGain A['vSD'][:, i] = np.std(v_spike, axis=1) * vGain ###################################### #### HISTOGRAM CALCULATIONS #### ###################################### # calculates the point-wise histograms A['ptsHist'][i] = np.zeros((A['nPts'], n_hist), dtype=int) for iPts in range(A['nPts']): H = np.histogram(v_spike[iPts, :], bins=xi_pts_H) A['ptsHist'][i][iPts, :] = H[0] # calculates the ISI histograms dT = np.diff(A['tSpike'][i]) dT = dT[dT <= xi_isi_H[-1]] H_isi = np.histogram(dT, bins=xi_isi_H, range=(xi_isi_H[0], xi_isi_H[-1])) A['isiHist'][i] = H_isi[0] ########################################### #### SIGNAL FEATURE CALCULATIONS #### ########################################### # creates the piecewise-polynomial of the mean signal pp = pchip(xi, A['vMu'][:, i]) # determines the point/voltage of the pmaximum proceding the minimum i_min = np.argmin(A['vMu'][:, i]) i_max1 = np.argmax(A['vMu'][:i_min, i]) i_max2 = np.argmax(A['vMu'][i_min:, i]) + i_min # determines the location of the half-width points v_half = (min(pp(i_max1), pp(i_max2)) + pp(i_min)) / 2.0 t_lo = cfcn.opt_time_to_y0((pp, v_half), [(i_max1, i_min)]) t_hi = cfcn.opt_time_to_y0((pp, v_half), [(i_min, i_max2)]) # sets the signal features into the final array A['sigFeat'][i, :] = [i_max1, i_min, i_max2, t_lo, t_hi] # memory garbage collection gc.collect() ###################################################### #### CLUSTER CROSS-CORRELOGRAM CALCULATIONS #### ###################################################### # memory allocation win_size = 50 # calculates the cross-correlation between each signal from each cluster for i_row in range(nC): if not self.is_running: # if the user cancelled, then exit the function return else: # updates the main gui progressbar pW = (pW0 + pW1) + (100.0 - (pW0 + pW1)) * (i_row + 1) / (nC + 1) self.work_progress.emit('Calculating CC-Grams...', pW) # calculates the cross-correlograms between each of the other clusters for j_row in range(nC): if (i_row == 0) and (j_row == 0): # case is the first cluster so allocate memory and set the time bin array ccGram, A['ccGramXi'] = cfcn.calc_ccgram(A['tSpike'][i_row], A['tSpike'][j_row], win_size) A['ccGram'] = np.zeros((nC, nC, len(ccGram))) A['ccGram'][i_row, j_row, :] = ccGram else: # otherwise, set the new values directly into the array A['ccGram'][i_row, j_row, :], _ = cfcn.calc_ccgram(A['tSpike'][i_row], A['tSpike'][j_row], win_size) ################################# #### FINAL DATA OUTPUT #### ################################# # dumps the cluster data to file self.work_progress.emit('Outputting Data To File...', 99.0) cf.save_single_file(out_name, A) ########################################## #### CLUSTER MATCHING FUNCTIONS #### ########################################## def det_cluster_matches(self, data, calc_para, w_prog): ''' :param exp_name: :param comp_dlg: :return: ''' # retrieves the comparison dataset i_comp = cf.det_comp_dataset_index(data.comp.data, calc_para['calc_comp']) c_data, data.comp.last_comp = data.comp.data[i_comp], i_comp # if there is no further calculation necessary, then exit the function if c_data.is_set: return # updates the cluster matching parameters c_data.is_set = True c_data.d_max = calc_para['d_max'] c_data.r_max = calc_para['r_max'] c_data.sig_corr_min = calc_para['sig_corr_min'] c_data.isi_corr_min = calc_para['isi_corr_min'] c_data.sig_diff_max = calc_para['sig_diff_max'] c_data.sig_feat_min = calc_para['sig_feat_min'] c_data.w_sig_feat = calc_para['w_sig_feat'] c_data.w_sig_comp = calc_para['w_sig_comp'] c_data.w_isi = calc_para['w_isi'] # retrieves the fixed/free cluster dataframes data_fix, data_free = cf.get_comp_datasets(data, c_data=c_data, is_full=True) def det_overall_cluster_matches(is_feas, D): ''' :param data_fix: :param data_free: :param D: :return: ''' # calculates the pair-wise SS distances between each the fixed/free mean signals iDsort, n_rows = np.argsort(D.T, axis=None), np.size(D, axis=0) # memory allocation isFix = np.zeros(data_fix['nC'], dtype=bool) isFree = np.zeros(data_free['nC'], dtype=bool) i_match = -np.ones(data_fix['nC'], dtype=int) # determines the overall unique for i in range(len(iDsort)): # determines the indices of the next best match iR, iC = cfcn.ind2sub(n_rows, iDsort[i]) if not (isFix[iR] or isFree[iC]) and is_feas[iR, iC]: # if there is not already a match, then update the match arrays i_match[iR] = iC isFix[iR], isFree[iC] = True, True if all(isFix) or all(isFree): # if all matches are found, then exit the loop break # returns the final match array return i_match def det_cluster_matches_old(c_data, is_feas, d_depth): ''' :param data_fix: :param data_free: :return: ''' # parameters z_max = 1.0 # calculates the inter-signal euclidean distances DD = cdist(data_fix['vMu'].T, data_free['vMu'].T) # determines the matches based on the signal euclidean distances c_data.i_match_old = det_overall_cluster_matches(is_feas, DD) # calculates the correlation coefficients between the best matching signals for i in range(data_fix['nC']): # calculation of the z-scores i_match = c_data.i_match_old[i] if i_match >= 0: # z-score calculations dW = data_fix['vMu'][:, i] - data_free['vMu'][:, i_match] c_data.z_score[:, i] = np.divide(dW, data_fix['vSD'][:, i]) # calculates the correlation coefficient CC = np.corrcoef(data_fix['vMu'][:, i], data_free['vMu'][:, i_match]) c_data.sig_corr_old[i] = CC[0, 1] c_data.sig_diff_old[i] = DD[i, i_match] c_data.d_depth_old[i] = d_depth[i, i_match] # sets the acceptance flag. for a cluster to be accepted, the following must be true: # * the maximum absolute z-score must be < z_max # * the correlation coefficient between the fixed/free signals must be > sig_corr_min c_data.is_accept_old[i] = np.max(np.abs(c_data.z_score[:, i])) < z_max and \ c_data.sig_corr_old[i] > c_data.sig_corr_min else: # sets NaN values for all the single value metrics c_data.sig_corr[i] = np.nan c_data.d_depth_old[i] = np.nan # ensures the group is rejected c_data.is_accept_old[i] = False def det_cluster_matches_new(c_data, is_feas, d_depth, r_spike, w_prog): ''' :param data_fix: :param data_free: :return: ''' # parameters pW = 100.0 / 7.0 # memory allocation signal_metrics = np.zeros((data_fix['nC'], data_free['nC'], 4)) isi_metrics = np.zeros((data_fix['nC'], data_free['nC'], 3)) isi_metrics_norm = np.zeros((data_fix['nC'], data_free['nC'], 3)) total_metrics = np.zeros((data_fix['nC'], data_free['nC'], 3)) # initialises the comparison data object w_prog.emit('Calculating Signal DTW Indices', pW) c_data = cfcn.calc_dtw_indices(c_data, data_fix, data_free, is_feas) # calculates the signal feature metrics w_prog.emit('Calculating Signal Feature Metrics', 2.0 * pW) signal_feat = cfcn.calc_signal_feature_diff(data_fix, data_free, is_feas) # calculates the signal direct matching metrics w_prog.emit('Calculating Signal Comparison Metrics', 3.0 * pW) cc_dtw, dd_dtw, dtw_scale = \ cfcn.calc_signal_corr(c_data.i_dtw, data_fix, data_free, is_feas) signal_metrics[:, :, 0] = cc_dtw signal_metrics[:, :, 1] = 1.0 - dd_dtw signal_metrics[:, :, 2] = dtw_scale signal_metrics[:, :, 3] = \ cfcn.calc_signal_hist_metrics(data_fix, data_free, is_feas, cfcn.calc_hist_intersect, max_norm=True) # calculates the ISI histogram metrics w_prog.emit('Calculating ISI Histogram Comparison Metrics', 4.0 * pW) isi_metrics[:, :, 0], isi_metrics_norm[:, :, 0] = \ cfcn.calc_isi_corr(data_fix, data_free, is_feas) isi_metrics[:, :, 1], isi_metrics_norm[:, :, 1] = \ cfcn.calc_isi_hist_metrics(data_fix, data_free, is_feas, cfcn.calc_hist_intersect, max_norm=True) # isi_metrics[:, :, 2], isi_metrics_norm[:, :, 2] = \ # cfcn.calc_isi_hist_metrics(data_fix, data_free, is_feas, cfcn.calc_wasserstein, max_norm=False) # isi_metrics[:, :, 3], isi_metrics_norm[:, :, 3] = \ # cfcn.calc_isi_hist_metrics(data_fix, data_free, is_feas, cfcn.calc_bhattacharyya, max_norm=True) # sets the isi relative spiking rate metrics isi_metrics[:, :, 2] = np.nan for i_row in range(np.size(r_spike, axis=0)): isi_metrics[i_row, is_feas[i_row, :], 2] = r_spike[i_row, is_feas[i_row, :]] isi_metrics_norm[:, :, 2] = cfcn.norm_array_rows(isi_metrics[:, :, 2], max_norm=False) # calculates the array euclidean distances (over all measures/clusters) weight_array = [c_data.w_sig_feat, c_data.w_sig_comp, c_data.w_isi] total_metrics[:, :, 0] = cfcn.calc_array_euclidean(signal_feat) total_metrics[:, :, 1] = cfcn.calc_array_euclidean(signal_metrics) total_metrics[:, :, 2] = cfcn.calc_array_euclidean(isi_metrics_norm) total_metrics_mean = cfcn.calc_weighted_mean(total_metrics, W=weight_array) # determines the unique overall cluster matches w_prog.emit('Determining Overall Cluster Matches', 5.0 * pW) c_data.i_match = det_overall_cluster_matches(is_feas, -total_metrics_mean) # matches which are from different regions are to be removed ii = np.where(c_data.i_match >= 0)[0] same_region = data_fix['chRegion'][ii] == data_free['chRegion'][c_data.i_match[ii]] c_data.i_match[ii[~same_region]] = -1 # calculates the correlation coefficients between the best matching signals w_prog.emit('Setting Final Match Metrics', 6.0 * pW) for i in range(data_fix['nC']): # calculation of the z-scores i_match = c_data.i_match[i] if i_match >= 0: # sets the signal feature metrics c_data.match_intersect[:, i] = cfcn.calc_single_hist_metric(data_fix, data_free, i, i_match, True, cfcn.calc_hist_intersect) c_data.match_wasserstain[:, i] = cfcn.calc_single_hist_metric(data_fix, data_free, i, i_match, True, cfcn.calc_wasserstein) c_data.match_bhattacharyya[:, i] = cfcn.calc_single_hist_metric(data_fix, data_free, i, i_match, True, cfcn.calc_bhattacharyya) # sets the signal difference metrics c_data.d_depth[i] = d_depth[i, i_match] c_data.dtw_scale[i] = dtw_scale[i, i_match] c_data.sig_corr[i] = cc_dtw[i, i_match] c_data.sig_diff[i] = max(0.0, 1 - dd_dtw[i, i_match]) c_data.sig_intersect[i] = signal_metrics[i, i_match, 2] # sets the isi metrics c_data.isi_corr[i] = isi_metrics[i, i_match, 0] c_data.isi_intersect[i] = isi_metrics[i, i_match, 1] # sets the total match metrics c_data.signal_feat[i, :] = signal_feat[i, i_match, :] c_data.total_metrics[i, :] = total_metrics[i, i_match, :] c_data.total_metrics_mean[i] = total_metrics_mean[i, i_match] # sets the acceptance flag. for a cluster to be accepted, the following must be true: # * the ISI correlation coefficient must be > isi_corr_min # * the signal correlation coefficient must be > sig_corr_min # * the inter-signal euclidean distance must be < sig_diff_max # * all signal feature metric similarity scores must be > sig_feat_min c_data.is_accept[i] = (c_data.isi_corr[i] > c_data.isi_corr_min) and \ (c_data.sig_corr[i] > c_data.sig_corr_min) and \ (c_data.sig_diff[i] > (1 - c_data.sig_diff_max)) and \ (np.all(c_data.signal_feat[i, :] > c_data.sig_feat_min)) else: # sets NaN values for all the single value metrics c_data.d_depth[i] = np.nan c_data.dtw_scale[i] = np.nan c_data.sig_corr[i] = np.nan c_data.sig_diff[i] = np.nan c_data.sig_intersect[i] = np.nan c_data.isi_corr[i] = np.nan c_data.isi_intersect[i] = np.nan c_data.signal_feat[i, :] = np.nan c_data.total_metrics[i, :] = np.nan c_data.total_metrics_mean[i] = np.nan # ensures the group is rejected c_data.is_accept[i] = False # determines the number of spikes n_spike_fix = [len(x) / data_fix['tExp'] for x in data_fix['tSpike']] n_spike_free = [len(x) / data_free['tExp'] for x in data_free['tSpike']] # calculates the relative spiking rates (note - ratios are coverted so that they are all > 1) r_spike = np.divide(repmat(n_spike_fix, data_free['nC'], 1).T, repmat(n_spike_free, data_fix['nC'], 1)) r_spike[r_spike < 1] = 1 / r_spike[r_spike < 1] # calculates the pair-wise distances between the fixed/free probe depths d_depth = np.abs(np.subtract(repmat(data_fix['chDepth'], data_free['nC'], 1).T, repmat(data_free['chDepth'], data_fix['nC'], 1))) # determines the feasible fixed/free cluster groupings such that: # 1) the channel depth has to be <= d_max # 2) the relative spiking rates between clusters is <= r_max is_feas = np.logical_and(r_spike <= c_data.r_max, d_depth <= c_data.d_max) # determines the cluster matches from the old/new methods det_cluster_matches_old(c_data, is_feas, d_depth) det_cluster_matches_new(c_data, is_feas, d_depth, r_spike, w_prog) def calc_ccgram_types(self, calc_para, data): ''' :return: ''' # determines the indices of the experiment to be analysed if calc_para['calc_all_expt']: # case is all experiments are to be analysed i_expt = list(range(len(data))) else: # case is a single experiment is being analysed i_expt = [cf.get_expt_index(calc_para['calc_exp_name'], data)] # memory allocation d_copy = copy.deepcopy A, B, C = np.empty(len(i_expt), dtype=object), [[] for _ in range(5)], [[] for _ in range(4)] c_type, t_dur, t_event, ci_lo, ci_hi, ccG_T = d_copy(A), d_copy(A), d_copy(A), d_copy(A), d_copy(A), d_copy(A) # for i_ex in i_expt: # sets the experiment ID info based on the number of experiments being analysed if len(i_expt) == 1: # only one experiment is being analysed expt_id = None else: # multiple experiments are being analysed expt_id = [(i_ex+1), len(i_expt)] # retrieves the cluster information t_dur[i_ex], t_event[i_ex] = d_copy(C), d_copy(C) c_type[i_ex], ci_lo[i_ex], ci_hi[i_ex], ccG_T[i_ex] = d_copy(B), d_copy(B), d_copy(B), d_copy(B) ccG, ccG_xi, t_spike = data[i_ex]['ccGram'], data[i_ex]['ccGramXi'], data[i_ex]['tSpike'] c_id = data[i_ex]['clustID'] # runs the cc-gram type calculation function c_type0, t_dur[i_ex], t_event[i_ex], ci_hi0, ci_lo0, ccG_T0 = cfcn.calc_ccgram_types( ccG, ccG_xi, t_spike, calc_para=calc_para, expt_id=expt_id, w_prog=self.work_progress, c_id=c_id) # sets the final values into their respective groupings for i in range(5): # sets the final type values and lower/upper bound confidence interval signals if len(c_type0[i]): # c_type[i_ex][i] =	np.vstack(c_type0[i])	numpy.vstack
import numpy as np from numba import vectorize # Size 为列表,为神经网络结构，比如[3，5，5，4，2]，3是输入层神经元个数，中间为隐藏层每层神经元个数，2为输出层个数 class nn_Creat(): def __init__(self,Size,active_fun='sigmoid',learning_rate=1.5,batch_normalization=1,objective_fun='MSE', output_function='sigmoid',optimization_method='normal',weight_decay=0): self.Size=Size # 初始化网络参数，并进行打印 print('the structure of the NN is \n', self.Size) self.active_fun=active_fun print('active function is %s '% active_fun) self.learning_rate=learning_rate print('learning_rate is %s '% learning_rate) self.batch_normalization=batch_normalization print('batch_normalization is %d '% batch_normalization) self.objective_fun=objective_fun print('objective_function is %s '% objective_fun) self.optimization_method=optimization_method print('optimization_method is %s '% optimization_method) self.weight_decay = weight_decay print('weight_decay is %f '% weight_decay) # 初始化网络权值和梯度 self.vecNum=0 self.depth=len(Size) self.W=[] self.b=[] self.W_grad=[] self.b_grad=[] self.cost=[] if self.batch_normalization: # 是否运用批量归一化，如果用，则引入期望E和方差S，以及缩放因子Gamma、Beta self.E = [] self.S = [] self.Gamma = [] self.Beta = [] if objective_fun=='Cross Entropy': # 目标函数是否为交叉墒函数 self.output_function='softmax' else: self.output_function='sigmoid' print('output_function is %s \n'% self.output_function) print('Start training NN \n') for item in range(self.depth-1): width=self.Size[item] height=self.Size[item+1] q=2np.random.rand(height,width)/np.sqrt(width)-1/np.sqrt(width) #初始化权系数W self.W.append(q) if self.active_fun=='relu': # 判断激活函数是否为relu函数，以决定b的初始化形式 self.b.append(np.random.rand(height,1)+0.01) else: self.b.append(2np.random.rand(height,1)/np.sqrt(width)-1/np.sqrt(width)) if self.optimization_method=='Momentum': #优化方向是否使用矩形式，即为之前梯度的叠加 if item!=0: self.vW.append(np.zeros([height,width])) self.vb.append(np.zeros([height, 1])) else: self.vW=[] self.vb=[] self.vW.append(np.zeros([height, width])) self.vb.append(np.zeros([height, 1])) if self.optimization_method=='AdaGrad'or optimization_method=='RMSProp' or optimization_method=='Adam': #优化方法是否使用上述方法 if item!=0: self.rW.append(np.zeros([height,width])) self.rb.append(np.zeros([height, 1])) else: self.rW=[] self.rb=[] self.rW.append(np.zeros([height, width])) self.rb.append(np.zeros([height, 1])) if self.optimization_method == 'Adam': #优化方法是否为Adam方法 if item!=0: self.sW.append(np.zeros([height, width])) self.sb.append(np.zeros([height, 1])) else: self.sW = [] self.sb = [] self.sW.append(np.zeros([height, width])) self.sb.append(np.zeros([height, 1])) if self.batch_normalization: #是否对每层进行归一化 self.Gamma.append(	np.array([1])	numpy.array
# ________ # / # \ / # \ / # \/ import random import textwrap import emd_mean import AdvEMDpy import emd_basis import emd_utils import numpy as np import pandas as pd import cvxpy as cvx import seaborn as sns import matplotlib.pyplot as plt from scipy.integrate import odeint from scipy.ndimage import gaussian_filter from emd_utils import time_extension, Utility from scipy.interpolate import CubicSpline from emd_hilbert import Hilbert, hilbert_spectrum from emd_preprocess import Preprocess from emd_mean import Fluctuation from AdvEMDpy import EMD # alternate packages from PyEMD import EMD as pyemd0215 import emd as emd040 sns.set(style='darkgrid') pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001) pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time) pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series) # plot 0 - addition fig = plt.figure(figsize=(9, 4)) ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title('First Iteration of Sifting Algorithm') plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1) plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()], pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()], c='r', label=r'$M(t_i)$', zorder=2) plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4) plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()], pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()], c='c', label=r'$m(t_j)$', zorder=3) plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5) plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5) plt.yticks(ticks=[-2, -1, 0, 1, 2]) plt.xticks(ticks=[0, np.pi, 2 * np.pi], labels=[r'0', r'$\pi$', r'$2\pi$']) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/pseudo_algorithm.png') plt.show() knots = np.arange(12) time = np.linspace(0, 11, 1101) basis = emd_basis.Basis(time=time, time_series=time) b_spline_basis = basis.cubic_b_spline(knots) chsi_basis = basis.chsi_basis(knots) # plot 1 plt.title('Non-Natural Cubic B-Spline Bases at Boundary') plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $') plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $') plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $') plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $') plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $') plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $']) plt.xlim(4.4, 6.6) plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') plt.legend(loc='upper left') plt.savefig('jss_figures/boundary_bases.png') plt.show() # plot 1a - addition knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001) knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time) knots_uniform = np.linspace(0, 2 * np.pi, 51) emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series) imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0] fig, axs = plt.subplots(3, 1) fig.subplots_adjust(hspace=0.6) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Time Series and Uniform Knots') axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100) axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].set_title('IMF 1 and Uniform Knots') axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[2].set_title('IMF 2 and Uniform Knots') axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100) axs[2].set_yticks(ticks=[-2, 0, 2]) axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[0].legend(loc='lower left') axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') for i in range(3): for j in range(1, len(knots_uniform)): axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey') plt.savefig('jss_figures/knot_uniform.png') plt.show() # plot 1b - addition knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001) knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time) emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series) imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric', optimise_knots=1, verbose=False) fig, axs = plt.subplots(3, 1) fig.subplots_adjust(hspace=0.6) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Time Series and Statically Optimised Knots') axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100) axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].set_title('IMF 1 and Statically Optimised Knots') axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[2].set_title('IMF 2 and Statically Optimised Knots') axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100) axs[2].set_yticks(ticks=[-2, 0, 2]) axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[0].legend(loc='lower left') axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') for i in range(3): for j in range(1, len(knots)): axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey') plt.savefig('jss_figures/knot_1.png') plt.show() # plot 1c - addition knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001) knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time) emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series) imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric', optimise_knots=2, verbose=False) fig, axs = plt.subplots(3, 1) fig.subplots_adjust(hspace=0.6) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Time Series and Dynamically Optimised Knots') axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100) axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].set_title('IMF 1 and Dynamically Knots') axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[2].set_title('IMF 2 and Dynamically Knots') axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100) axs[2].set_yticks(ticks=[-2, 0, 2]) axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[0].legend(loc='lower left') axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') for i in range(3): for j in range(1, len(knots[i])): axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey') plt.savefig('jss_figures/knot_2.png') plt.show() # plot 1d - addition window = 81 fig, axs = plt.subplots(2, 1) fig.subplots_adjust(hspace=0.4) figure_size = plt.gcf().get_size_inches() factor = 0.8 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Preprocess Filtering Demonstration') axs[1].set_title('Zoomed Region') preprocess_time = pseudo_alg_time.copy() np.random.seed(1) random.seed(1) preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time)) for i in random.sample(range(1000), 500): preprocess_time_series[i] += np.random.normal(0, 1) preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series) axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12)) axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13)) axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12)) axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize interpolation filter', 14)) axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey', label=textwrap.fill('Quantile window', 12)) axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey') axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black', label=textwrap.fill('Zoomed region', 10)) axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black') axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12)) axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13)) axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12)) axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize interpolation filter', 14)) axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey', label=textwrap.fill('Quantile window', 12)) axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey') axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi) axs[1].set_ylim(-3, 3) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[np.pi]) axs[1].set_xticklabels(labels=[r'$\pi$']) box_0 = axs[0].get_position() axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15)) box_1 = axs[1].get_position() axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height]) plt.savefig('jss_figures/preprocess_filter.png') plt.show() # plot 1e - addition fig, axs = plt.subplots(2, 1) fig.subplots_adjust(hspace=0.4) figure_size = plt.gcf().get_size_inches() factor = 0.8 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Preprocess Smoothing Demonstration') axs[1].set_title('Zoomed Region') axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[0].plot(preprocess_time, preprocess.hp()[1], label=textwrap.fill('Hodrick-Prescott smoothing', 12)) axs[0].plot(preprocess_time, preprocess.hw(order=51)[1], label=textwrap.fill('Henderson-Whittaker smoothing', 13)) downsampled_and_decimated = preprocess.downsample() axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1], label=textwrap.fill('Downsampled & decimated', 11)) downsampled = preprocess.downsample(decimate=False) axs[0].plot(downsampled[0], downsampled[1], label=textwrap.fill('Downsampled', 13)) axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black', label=textwrap.fill('Zoomed region', 10)) axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black') axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[1].plot(preprocess_time, preprocess.hp()[1], label=textwrap.fill('Hodrick-Prescott smoothing', 12)) axs[1].plot(preprocess_time, preprocess.hw(order=51)[1], label=textwrap.fill('Henderson-Whittaker smoothing', 13)) axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1], label=textwrap.fill('Downsampled & decimated', 13)) axs[1].plot(downsampled[0], downsampled[1], label=textwrap.fill('Downsampled', 13)) axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi) axs[1].set_ylim(-3, 3) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[np.pi]) axs[1].set_xticklabels(labels=[r'$\pi$']) box_0 = axs[0].get_position() axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height]) axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15)) box_1 = axs[1].get_position() axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height]) plt.savefig('jss_figures/preprocess_smooth.png') plt.show() # plot 2 fig, axs = plt.subplots(1, 2, sharey=True) axs[0].set_title('Cubic B-Spline Bases') axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1') axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2') axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3') axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4') axs[0].legend(loc='upper left') axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-') axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-') axs[0].set_xticks([5, 6]) axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $']) axs[0].set_xlim(4.5, 6.5) axs[1].set_title('Cubic Hermite Spline Bases') axs[1].plot(time, chsi_basis[10, :].T, '--') axs[1].plot(time, chsi_basis[11, :].T, '--') axs[1].plot(time, chsi_basis[12, :].T, '--') axs[1].plot(time, chsi_basis[13, :].T, '--') axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') axs[1].set_xticks([5, 6]) axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $']) axs[1].set_xlim(4.5, 6.5) plt.savefig('jss_figures/comparing_bases.png') plt.show() # plot 3 a = 0.25 width = 0.2 time = np.linspace(0, (5 - a) * np.pi, 1001) time_series = np.cos(time) + np.cos(5 * time) utils = emd_utils.Utility(time=time, time_series=time_series) max_bool = utils.max_bool_func_1st_order_fd() maxima_x = time[max_bool] maxima_y = time_series[max_bool] min_bool = utils.min_bool_func_1st_order_fd() minima_x = time[min_bool] minima_y = time_series[min_bool] max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101) max_dash = maxima_y[-1] * np.ones_like(max_dash_time) min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101) min_dash = minima_y[-1] * np.ones_like(min_dash_time) dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101) dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101) max_discard = maxima_y[-1] max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1] max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101) max_discard_dash = max_discard * np.ones_like(max_discard_dash_time) dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101) dash_2 = np.linspace(minima_y[-1], max_discard, 101) end_point_time = time[-1] end_point = time_series[-1] time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101) time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi, (5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi, (5 - a) * np.pi, 101))) time_series_anti_reflect = time_series_reflect[0] - time_series_reflect utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect) anti_max_bool = utils.max_bool_func_1st_order_fd() anti_max_point_time = time_reflect[anti_max_bool] anti_max_point = time_series_anti_reflect[anti_max_bool] utils = emd_utils.Utility(time=time, time_series=time_series_reflect) no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()] no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()] point_1 = 5.4 length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101) length_distance_time = point_1 * np.pi * np.ones_like(length_distance) length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101) length_top = maxima_y[-1] * np.ones_like(length_time) length_bottom = minima_y[-1] * np.ones_like(length_time) point_2 = 5.2 length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101) length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2) length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101) length_top_2 = time_series[-1] * np.ones_like(length_time_2) length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2) symmetry_axis_1_time = minima_x[-1] * np.ones(101) symmetry_axis_2_time = time[-1] * np.ones(101) symmetry_axis = np.linspace(-2, 2, 101) end_time = np.linspace(time[-1] - width, time[-1] + width, 101) end_signal = time_series[-1] * np.ones_like(end_time) anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101) anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time) ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.plot(time, time_series, LineWidth=2, label='Signal') plt.title('Symmetry Edge Effects Example') plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10)) plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2, label=textwrap.fill('Anti-symmetric signal', 10)) plt.plot(max_dash_time, max_dash, 'k-') plt.plot(min_dash_time, min_dash, 'k-') plt.plot(dash_1_time, dash_1, 'k--') plt.plot(dash_2_time, dash_2, 'k--') plt.plot(length_distance_time, length_distance, 'k--') plt.plot(length_distance_time_2, length_distance_2, 'k--') plt.plot(length_time, length_top, 'k-') plt.plot(length_time, length_bottom, 'k-') plt.plot(length_time_2, length_top_2, 'k-') plt.plot(length_time_2, length_bottom_2, 'k-') plt.plot(end_time, end_signal, 'k-') plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1) plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1) plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1) plt.text(5.1 * np.pi, -0.7, r'$\beta$L') plt.text(5.34 * np.pi, -0.05, 'L') plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima') plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima') plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10)) plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10)) plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10)) plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10)) plt.xlim(3.9 * np.pi, 5.5 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/edge_effects_symmetry_anti.png') plt.show() # plot 4 a = 0.21 width = 0.2 time = np.linspace(0, (5 - a) * np.pi, 1001) time_series = np.cos(time) + np.cos(5 * time) utils = emd_utils.Utility(time=time, time_series=time_series) max_bool = utils.max_bool_func_1st_order_fd() maxima_x = time[max_bool] maxima_y = time_series[max_bool] min_bool = utils.min_bool_func_1st_order_fd() minima_x = time[min_bool] minima_y = time_series[min_bool] max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101) max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101) max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1) max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1) min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101) min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101) min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1) min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1) dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101) dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101) dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101) dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101) s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1]) slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2]) slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1 max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1) max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101) dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101) dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101) s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1]) slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2]) slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2 min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1) min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101) dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time) dash_4 = np.linspace(slope_based_maximum, slope_based_minimum) maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101) maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash) maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash) maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash) maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101) maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time) minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101) minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash) minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash) minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash) minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101) minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time) # slightly edit signal to make difference between slope-based method and improved slope-based method more clear time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \ time_series[time == minima_x[-1]] improved_slope_based_maximum_time = time[-1] improved_slope_based_maximum = time_series[-1] improved_slope_based_minimum_time = slope_based_minimum_time improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time - improved_slope_based_maximum_time) min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101) min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4) dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101) dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101) ax = plt.subplot(111) figure_size = plt.gcf().get_size_inches() factor = 0.9 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.gcf().subplots_adjust(bottom=0.10) plt.plot(time, time_series, LineWidth=2, label='Signal') plt.title('Slope-Based Edge Effects Example') plt.plot(max_dash_time_1, max_dash_1, 'k-') plt.plot(max_dash_time_2, max_dash_2, 'k-') plt.plot(max_dash_time_3, max_dash_3, 'k-') plt.plot(min_dash_time_1, min_dash_1, 'k-') plt.plot(min_dash_time_2, min_dash_2, 'k-') plt.plot(min_dash_time_3, min_dash_3, 'k-') plt.plot(min_dash_time_4, min_dash_4, 'k-') plt.plot(maxima_dash_time_1, maxima_dash, 'k-') plt.plot(maxima_dash_time_2, maxima_dash, 'k-') plt.plot(maxima_dash_time_3, maxima_dash, 'k-') plt.plot(minima_dash_time_1, minima_dash, 'k-') plt.plot(minima_dash_time_2, minima_dash, 'k-') plt.plot(minima_dash_time_3, minima_dash, 'k-') plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$') plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$') plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$') plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$') plt.text(4.30 * np.pi, 0.35, r'$s_1$') plt.text(4.43 * np.pi, -0.20, r'$s_2$') plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$') plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]), -0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$') plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]), 1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$') plt.plot(minima_line_dash_time, minima_line_dash, 'k--') plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--') plt.plot(dash_1_time, dash_1, 'k--') plt.plot(dash_2_time, dash_2, 'k--') plt.plot(dash_3_time, dash_3, 'k--') plt.plot(dash_4_time, dash_4, 'k--') plt.plot(dash_final_time, dash_final, 'k--') plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima') plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima') plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4, label=textwrap.fill('Slope-based maximum', 11)) plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4, label=textwrap.fill('Slope-based minimum', 11)) plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4, label=textwrap.fill('Improved slope-based maximum', 11)) plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4, label=textwrap.fill('Improved slope-based minimum', 11)) plt.xlim(3.9 * np.pi, 5.5 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/edge_effects_slope_based.png') plt.show() # plot 5 a = 0.25 width = 0.2 time = np.linspace(0, (5 - a) * np.pi, 1001) time_series = np.cos(time) + np.cos(5 * time) utils = emd_utils.Utility(time=time, time_series=time_series) max_bool = utils.max_bool_func_1st_order_fd() maxima_x = time[max_bool] maxima_y = time_series[max_bool] min_bool = utils.min_bool_func_1st_order_fd() minima_x = time[min_bool] minima_y = time_series[min_bool] A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2 A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2 P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2]) P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1]) Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1] Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1] Coughlin_time = Huang_time Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0])) Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2]) Average_max = (maxima_y[-2] + maxima_y[-1]) / 2 Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2]) Average_min = (minima_y[-2] + minima_y[-1]) / 2 utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave) Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd() Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd() utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave) Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd() Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd() Huang_max_time = Huang_time[Huang_max_bool] Huang_max = Huang_wave[Huang_max_bool] Huang_min_time = Huang_time[Huang_min_bool] Huang_min = Huang_wave[Huang_min_bool] Coughlin_max_time = Coughlin_time[Coughlin_max_bool] Coughlin_max = Coughlin_wave[Coughlin_max_bool] Coughlin_min_time = Coughlin_time[Coughlin_min_bool] Coughlin_min = Coughlin_wave[Coughlin_min_bool] max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101) max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101) max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time) min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101) min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101) min_2_x = minima_y[-2] * np.ones_like(min_2_x_time) dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101) dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x) max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101) max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101) max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y) min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101) min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101) min_2_y_time = minima_x[-2] * np.ones_like(min_2_y) dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101) dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time) max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101) max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101) max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time) min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101) min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101) min_1_x = minima_y[-1] * np.ones_like(min_1_x_time) dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101) dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x) max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101) max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101) max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y) min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101) min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101) min_1_y_time = minima_x[-1] * np.ones_like(min_1_y) dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101) dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time) ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title('Characteristic Wave Effects Example') plt.plot(time, time_series, LineWidth=2, label='Signal') plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10)) plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10)) plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4, label=textwrap.fill('Coughlin maximum', 14)) plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4, label=textwrap.fill('Coughlin minimum', 14)) plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4, label=textwrap.fill('Average maximum', 14)) plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4, label=textwrap.fill('Average minimum', 14)) plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima') plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima') plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14)) plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14)) plt.plot(max_2_x_time, max_2_x, 'k-') plt.plot(max_2_x_time_side, max_2_x, 'k-') plt.plot(min_2_x_time, min_2_x, 'k-') plt.plot(min_2_x_time_side, min_2_x, 'k-') plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--') plt.text(5.16 * np.pi, 0.85, r'$2a_2$') plt.plot(max_2_y_time, max_2_y, 'k-') plt.plot(max_2_y_time, max_2_y_side, 'k-') plt.plot(min_2_y_time, min_2_y, 'k-') plt.plot(min_2_y_time, min_2_y_side, 'k-') plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--') plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$') plt.plot(max_1_x_time, max_1_x, 'k-') plt.plot(max_1_x_time_side, max_1_x, 'k-') plt.plot(min_1_x_time, min_1_x, 'k-') plt.plot(min_1_x_time_side, min_1_x, 'k-') plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--') plt.text(5.42 * np.pi, -0.1, r'$2a_1$') plt.plot(max_1_y_time, max_1_y, 'k-') plt.plot(max_1_y_time, max_1_y_side, 'k-') plt.plot(min_1_y_time, min_1_y, 'k-') plt.plot(min_1_y_time, min_1_y_side, 'k-') plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--') plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$') plt.xlim(3.9 * np.pi, 5.6 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/edge_effects_characteristic_wave.png') plt.show() # plot 6 t = np.linspace(5, 95, 100) signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200) util_nn = emd_utils.Utility(time=t, time_series=signal_orig) maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()] minima = signal_orig[util_nn.min_bool_func_1st_order_fd()] cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima) cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima) time = np.linspace(0, 5 * np.pi, 1001) lsq_signal = np.cos(time) + np.cos(5 * time) knots = np.linspace(0, 5 * np.pi, 101) time_extended = time_extension(time) time_series_extended = np.zeros_like(time_extended) / 0 time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal neural_network_m = 200 neural_network_k = 100 # forward -> P = np.zeros((int(neural_network_k + 1), neural_network_m)) for col in range(neural_network_m): P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))] P[-1, col] = 1 # for additive constant t = lsq_signal[-neural_network_m:] # test - top seed_weights = np.ones(neural_network_k) / neural_network_k weights = 0 * seed_weights.copy() train_input = P[:-1, :] lr = 0.01 for iterations in range(1000): output = np.matmul(weights, train_input) error = (t - output) gradients = error * (- train_input) # guess average gradients average_gradients = np.mean(gradients, axis=1) # steepest descent max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients))) adjustment = - lr * average_gradients # adjustment = - lr * max_gradient_vector weights += adjustment # test - bottom weights_right = np.hstack((weights, 0)) max_count_right = 0 min_count_right = 0 i_right = 0 while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1): time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \ sum(weights_right * np.hstack((time_series_extended[ int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right): int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1))) i_right += 1 if i_right > 1: emd_utils_max = \ emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)], time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)]) if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0: max_count_right += 1 emd_utils_min = \ emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)], time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)]) if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0: min_count_right += 1 # backward <- P = np.zeros((int(neural_network_k + 1), neural_network_m)) for col in range(neural_network_m): P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)] P[-1, col] = 1 # for additive constant t = lsq_signal[:neural_network_m] vx = cvx.Variable(int(neural_network_k + 1)) objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary prob = cvx.Problem(objective) result = prob.solve(verbose=True, solver=cvx.ECOS) weights_left = np.array(vx.value) max_count_left = 0 min_count_left = 0 i_left = 0 while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1): time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \ 2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left): int(len(lsq_signal) - 1 - i_left + neural_network_k)], 1))) + 1 i_left += 1 if i_left > 1: emd_utils_max = \ emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))], time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))]) if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0: max_count_left += 1 emd_utils_min = \ emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))], time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))]) if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0: min_count_left += 1 lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal) utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended) maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()] maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()] maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1] maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1] minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()] minima_time = time[lsq_utils.min_bool_func_1st_order_fd()] minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:] minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:] ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title('Single Neuron Neural Network Example') plt.plot(time, lsq_signal, zorder=2, label='Signal') plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12)) plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima') plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima') plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3, label=textwrap.fill('Extrapolated maxima', 12)) plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4, label=textwrap.fill('Extrapolated minima', 12)) plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k', label=textwrap.fill('Neural network inputs', 13)) plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100), -2.75 * np.ones(100), c='k') plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100), 2.75 * np.ones(100), c='k') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2), ((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2), ((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k') plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k') plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed', label=textwrap.fill('Neural network targets', 13)) plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100), -2.75 * np.ones(100), c='gray') plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100), 2.75 * np.ones(100), c='gray') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2), ((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2), ((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray') plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed') plt.xlim(3.4 * np.pi, 5.6 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/neural_network.png') plt.show() # plot 6a np.random.seed(0) time = np.linspace(0, 5 * np.pi, 1001) knots_51 = np.linspace(0, 5 * np.pi, 51) time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time) noise = np.random.normal(0, 1, len(time_series)) time_series += noise advemdpy = EMD(time=time, time_series=time_series) imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3, edge_effect='symmetric_anchor', verbose=False)[:3] knots_31 = np.linspace(0, 5 * np.pi, 31) imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2, edge_effect='symmetric_anchor', verbose=False)[:3] knots_11 = np.linspace(0, 5 * np.pi, 11) imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1, edge_effect='symmetric_anchor', verbose=False)[:3] fig, axs = plt.subplots(3, 1) plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40)) plt.subplots_adjust(hspace=0.1) axs[0].plot(time, time_series, label='Time series') axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21)) print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}') for knot in knots_51: axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1) axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots') axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi]) axs[0].set_xticklabels(['', '', '', '', '', '']) axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--') axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--') axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--') axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region') box_0 = axs[0].get_position() axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8) axs[1].plot(time, time_series, label='Time series') axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19)) axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19)) print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}') for knot in knots_31: axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1) axs[1].plot(knot * np.ones(101),	np.linspace(-5, 5, 101)	numpy.linspace
import sys # We need sys so that we can pass argv to QApplication from pathlib import Path from dataclasses import dataclass import numpy as np from PyQt5 import QtWidgets, QtCore, QtGui, uic import pyqtgraph as pg import easyqc.qt COLOR_PLOTS = (pg.mkColor((31, 119, 180)),) class EasyQC(QtWidgets.QMainWindow): """ This is the view in the MVC approach """ layers = None # used for additional scatter layers @staticmethod def _instances(): app = QtWidgets.QApplication.instance() return [w for w in app.topLevelWidgets() if isinstance(w, EasyQC)] @staticmethod def _get_or_create(title=None): eqc = next(filter(lambda e: e.isVisible() and e.windowTitle() == title, EasyQC._instances()), None) if eqc is None: eqc = EasyQC() eqc.setWindowTitle(title) return eqc def __init__(self, args, kwargs): super(EasyQC, self).__init__(args, *kwargs) # wave by <NAME> from the Noun Project self.layers = {} self.ctrl = Controller(self) uic.loadUi(Path(__file__).parent.joinpath('easyqc.ui'), self) self.setWindowIcon(QtGui.QIcon(str(Path(__file__).parent.joinpath('easyqc.svg')))) background_color = self.palette().color(self.backgroundRole()) # init the seismic density display self.plotItem_seismic.setAspectLocked(False) self.imageItem_seismic = pg.ImageItem() self.plotItem_seismic.setBackground(background_color) self.plotItem_seismic.addItem(self.imageItem_seismic) self.viewBox_seismic = self.plotItem_seismic.getPlotItem().getViewBox() self._init_cmenu() # init the header display and link X and Y axis with density display self.plotDataItem_header_h = pg.PlotDataItem() self.plotItem_header_h.addItem(self.plotDataItem_header_h) self.plotItem_seismic.setXLink(self.plotItem_header_h) self.plotDataItem_header_v = pg.PlotDataItem() self.plotItem_header_h.setBackground(background_color) self.plotItem_header_v.addItem(self.plotDataItem_header_v) self.plotItem_header_v.setBackground(background_color) self.plotItem_seismic.setYLink(self.plotItem_header_v) # set the ticks so that they don't auto scale and ruin the axes link ax = self.plotItem_seismic.getAxis('left') ax.setStyle(tickTextWidth=60, autoReduceTextSpace=False, autoExpandTextSpace=False) ax = self.plotItem_header_h.getAxis('left') ax.setStyle(tickTextWidth=60, autoReduceTextSpace=False, autoExpandTextSpace=False) ax = self.plotItem_header_v.getAxis('left') ax.setStyle(showValues=False) # prepare placeholders for hover windows self.hoverPlotWidgets = {'Trace': None, 'Spectrum': None, 'Spectrogram': None} # connect signals and slots s = self.viewBox_seismic.scene() # vb.scene().sigMouseMoved.connect(self.mouseMoveEvent) self.proxy = pg.SignalProxy(s.sigMouseMoved, rateLimit=60, slot=self.mouseMoveEvent) s.sigMouseClicked.connect(self.mouseClick) self.lineEdit_gain.returnPressed.connect(self.editGain) self.lineEdit_sort.returnPressed.connect(self.editSort) self.comboBox_header.activated[str].connect(self.ctrl.set_header) self.viewBox_seismic.sigRangeChanged.connect(self.on_sigRangeChanged) self.horizontalScrollBar.sliderMoved.connect(self.on_horizontalSliderChange) self.verticalScrollBar.sliderMoved.connect(self.on_verticalSliderChange) def _init_cmenu(self): """ Setup context menus - on instantiation only """ self.viewBox_seismic.scene().contextMenu = None # this gets rid of the export context menu self.plotItem_seismic.plotItem.ctrlMenu = None # this gets rid of the plot context menu for act in self.viewBox_seismic.menu.actions(): if act.text() == 'View All': continue self.viewBox_seismic.menu.removeAction(act) # and add ours self.viewBox_seismic.menu.addSeparator() act = QtWidgets.QAction("View Trace", self.viewBox_seismic.menu) act.triggered.connect(self.cmenu_ViewTrace) self.viewBox_seismic.menu.addAction(act) act = QtWidgets.QAction("View Spectrum", self.viewBox_seismic.menu) act.triggered.connect(self.cmenu_ViewSpectrum) self.viewBox_seismic.menu.addAction(act) act = QtWidgets.QAction("View Spectrogram", self.viewBox_seismic.menu) act.triggered.connect(self.cmenu_ViewSpectrogram) self.viewBox_seismic.menu.addAction(act) """ View Methods """ def closeEvent(self, event): self.destroy() def keyPressEvent(self, e): """ page-up / ctrl + a : gain up page-down / ctrl + z : gain down ctrl + p : propagate display to current windows up/down/left/right arrows: pan using keys :param e: """ k, m = (e.key(), e.modifiers()) # page up / ctrl + a if k == QtCore.Qt.Key_PageUp or ( m == QtCore.Qt.ControlModifier and k == QtCore.Qt.Key_A): self.ctrl.set_gain(self.ctrl.gain - 3) # page down / ctrl + z elif k == QtCore.Qt.Key_PageDown or ( m == QtCore.Qt.ControlModifier and k == QtCore.Qt.Key_Z): self.ctrl.set_gain(self.ctrl.gain + 3) # control + P: propagate elif m == QtCore.Qt.ControlModifier and k == QtCore.Qt.Key_P: self.ctrl.propagate() # arrows keys move seismic elif k in (QtCore.Qt.Key_Up, QtCore.Qt.Key_Left, QtCore.Qt.Key_Right, QtCore.Qt.Key_Down): self.translate_seismic(k, m == QtCore.Qt.ControlModifier) # ctrl + s: screenshot to clipboard elif m == QtCore.Qt.ControlModifier and k == QtCore.Qt.Key_S: qtapp = QtWidgets.QApplication.instance() qtapp.clipboard().setPixmap(self.grab()) def editGain(self): self.ctrl.set_gain() def editSort(self): keys = self.lineEdit_sort.text().split(' ') self.ctrl.sort(keys) def mouseClick(self, event): if not event.double(): return qxy = self.imageItem_seismic.mapFromScene(event.scenePos()) tr, s = (qxy.x(), qxy.y()) print(tr, s) def mouseMoveEvent(self, scenepos): if isinstance(scenepos, tuple): scenepos = scenepos[0] else: return qpoint = self.imageItem_seismic.mapFromScene(scenepos) c, t, a, h = self.ctrl.cursor2timetraceamp(qpoint) self.label_x.setText(f"{c:.0f}") self.label_t.setText(f"{t:.4f}") self.label_amp.setText(f"{a:2.2E}") htxt = h if isinstance(h, str) else f"{h:.4f}" self.label_h.setText(htxt) for key in self.hoverPlotWidgets: if self.hoverPlotWidgets[key] is not None and self.hoverPlotWidgets[key].isVisible(): self.ctrl.update_hover(qpoint, key) def translate_seismic(self, k, cm): """ Resizes vertical or horizontal on a KeyPress :param k: translate by 1./7 :param cm (bool): if the control modifier has been pressed, translate by 1./2 :return: """ r = self.viewBox_seismic.viewRect() xlim, ylim = self.ctrl.limits() FAC = 1 / 2 if cm else 1 / 7 dy = FAC r.height() dx = FAC * r.width() if k == QtCore.Qt.Key_Down: yr = np.array([r.y(), r.y() + r.height()]) + dy yr += np.min([0, ylim[1] - yr[1]]) self.viewBox_seismic.setYRange(yr[0], yr[1], padding=0) elif k == QtCore.Qt.Key_Left: xr = np.array([r.x(), r.x() + r.width()]) - dx xr += np.max([0, xlim[0] - xr[0]]) self.viewBox_seismic.setXRange(xr[0], xr[1], padding=0) elif k == QtCore.Qt.Key_Right: xr = np.array([r.x(), r.x() + r.width()]) + dx xr += np.min([0, xlim[1] - xr[1]]) self.viewBox_seismic.setXRange(xr[0], xr[1], padding=0) elif k == QtCore.Qt.Key_Up: yr = np.array([r.y(), r.y() + r.height()]) - dy yr += np.max([0, ylim[0] - yr[0]]) self.viewBox_seismic.setYRange(yr[0], yr[1], padding=0) def on_sigRangeChanged(self, r): def set_scroll(sb, r, b): # sb: scroll bar object, r: current range, b: axes limits (bounds) # cf. https://doc.qt.io/qt-5/qscrollbar.html range = (r[1] - r[0]) doclength = (b[1] - b[0]) maximum = int((doclength - range) / doclength * 65536) sb.setMaximum(maximum) sb.setPageStep(65536 - maximum) sb.setValue(int((r[0] - b[0]) / doclength * 65536)) xr, yr = self.viewBox_seismic.viewRange() xl, yl = self.ctrl.limits() set_scroll(self.horizontalScrollBar, xr, xl) set_scroll(self.verticalScrollBar, yr, yl) def on_horizontalSliderChange(self, r): b = self.ctrl.limits()[0] r = self.viewBox_seismic.viewRange()[0] x = float(self.horizontalScrollBar.value()) / 65536 * (b[1] - b[0]) + b[0] self.viewBox_seismic.setXRange(x, x + r[1] - r[0], padding=0) def on_verticalSliderChange(self, r): b = self.ctrl.limits()[1] r = self.viewBox_seismic.viewRange()[1] y = float(self.verticalScrollBar.value()) / 65536 * (b[1] - b[0]) + b[0] self.viewBox_seismic.setYRange(y, y + r[1] - r[0], padding=0) def _cmenu_hover(self, key, image=False): """Creates the plot widget for a given key: could be 'Trace', 'Spectrum', or 'Spectrogram'""" if self.hoverPlotWidgets[key] is None: from easyqc.pgtools import ImShowItem if image: self.hoverPlotWidgets[key] = ImShowItem().plotwidget else: self.hoverPlotWidgets[key] = pg.plot([0], [0], pen=pg.mkPen(color=COLOR_PLOTS[0])) self.hoverPlotWidgets[key].setBackground(pg.mkColor('#ffffff')) self.hoverPlotWidgets[key].setVisible(True) def cmenu_ViewTrace(self): self._cmenu_hover('Trace') def cmenu_ViewSpectrum(self): self._cmenu_hover('Spectrum') def cmenu_ViewSpectrogram(self): self._cmenu_hover('Spectrogram', image=True) class Controller: def __init__(self, view): self.view = view self.model = Model(None, None) self.order = None self.transform = None # affine transform image indices 2 data domain self.trace_indices = None self.hkey = None def remove_all_layers(self): layers_dict = self.view.layers.copy() for label in layers_dict: self.remove_layer_from_label(label) def remove_layer_from_label(self, label): current_layer = self.view.layers.get(label) if current_layer is not None: current_layer['layer'].clear() self.view.plotItem_seismic.removeItem(current_layer['layer']) self.view.layers.pop(label) def add_scatter(self, x, y, rgb=None, label='default'): """ Adds a scatter layer to the display (removing any previous one if any) """ rgb = rgb or (0, 255, 0) self.remove_layer_from_label(label) new_scatter = pg.ScatterPlotItem() self.view.layers[label] = {'layer': new_scatter, 'type': 'scatter'} self.view.plotItem_seismic.addItem(new_scatter) new_scatter.setData(x=x, y=y, brush=pg.mkBrush(rgb), name=label) def cursor2timetraceamp(self, qpoint): """Used for the mouse hover function over seismic display, returns trace, time, amplitude,and header""" ixy = self.cursor2ind(qpoint) a = self.model.data[ixy[0], ixy[1]] xy_ = np.matmul(self.transform, np.array([ixy[0], ixy[1], 1])) t = xy_[self.model.taxis] c = xy_[self.model.caxis] h = self.model.header[self.hkey][ixy[self.model.caxis]] return c, t, a, h def cursor2ind(self, qpoint): """ image coordinates over the seismic display""" ix = np.max((0, np.min((int(np.floor(qpoint.x())), self.model.nx - 1)))) iy = np.max((0, np.min((int(np.round(qpoint.y())), self.model.ny - 1)))) return ix, iy def limits(self): # returns the xlims and ylims of the data in the data space (time, trace) ixlim = [0, self.model.nx] iylim = [0, self.model.ny] x, y, _ = np.matmul(self.transform, np.c_[ixlim, iylim, [1, 1]].T) return x, y def propagate(self): """ set all the eqc instances at the same position/gain scales for flip comparisons """ eqcs = self.view._instances() for eqc in eqcs: if eqc is self.view: continue else: eqc.setGeometry(self.view.geometry()) eqc.ctrl.set_gain(self.gain) eqc.plotItem_seismic.setXLink(self.view.plotItem_seismic) eqc.plotItem_seismic.setYLink(self.view.plotItem_seismic) # also propagate sorting eqc.lineEdit_sort.setText(self.view.lineEdit_sort.text()) eqc.ctrl.sort(eqc.lineEdit_sort.text()) def redraw(self): """ redraw seismic and headers with order and selection""" # np.take could look neater but it's actually much slower than straight indexing if self.model.taxis == 1: self.view.imageItem_seismic.setImage(self.model.data[self.trace_indices, :]) elif self.model.taxis == 0: self.view.imageItem_seismic.setImage(self.model.data[:, self.trace_indices]) self.set_header() self.set_gain() def set_gain(self, gain=None): if gain is None: gain = self.gain levels = 10 ** (gain / 20) * 4 * np.array([-1, 1]) self.view.imageItem_seismic.setLevels(levels) self.view.lineEdit_gain.setText(f"{gain:.1f}") @property def gain(self): return float(self.view.lineEdit_gain.text()) or self.model.auto_gain() def set_header(self): key = self.view.comboBox_header.currentText() if key not in self.model.header.keys(): return self.hkey = key traces = np.arange(self.trace_indices.size) values = self.model.header[self.hkey][self.trace_indices] # skip the plotting part for non-numeric arrays if not np.issubdtype(values.dtype, np.number): return if self.model.taxis == 1: self.view.plotDataItem_header_h.setData(x=traces, y=values) elif self.model.taxis == 0: self.view.plotDataItem_header_v.setData(y=traces, x=values) def sort(self, keys): if not(set(keys).issubset(set(self.model.header.keys()))): print("Wrong input") return elif len(keys) == 0: return self.trace_indices = np.lexsort([self.model.header[k] for k in keys]) self.redraw() def update_data(self, data, h=None, si=.002, gain=None, x0=0, t0=0, taxis=1): """ data is a 2d array [ntr, nsamples] if 3d the first dimensions are merged in ntr and the last is nsamples update_data(self, data=None, h=0.002, gain=None) """ # reshape a 3d+ array in 2d to plot as an image self.remove_all_layers() # if the data has the same shape as the current model data, keep axis all the same update_axis = self.model.data is None or self.model.data.shape != data.shape if data.ndim >= 3: data = np.reshape(data, (-1, data.shape[-1])) self.model.set_data(data, si=si, header=h, x0=x0, t0=t0, taxis=taxis) self.trace_indices = np.arange(self.model.ntr) # this will contain selection and sort clim = [x0 - .5, x0 + self.model.ntr - .5] tlim = [t0, t0 + self.model.ns * self.model.si] if taxis == 0: # time is the 0 dimension and the horizontal axis xlim, ylim = (tlim, clim) transform = [si, 0., 0., 0., 1, 0., t0 - si / 2, x0 - .5, 1.] self.view.imageItem_seismic.setImage(data[:, self.trace_indices]) elif taxis == 1: # time is the 1 dimension and vertical axis xlim, ylim = (clim, tlim) transform = [1., 0., 0., 0., si, 0., x0 - .5, t0 - si / 2, 1.] self.view.imageItem_seismic.setImage(data[self.trace_indices, :]) self.view.plotItem_seismic.invertY() else: ValueError('taxis must be 0 (horizontal axis) or 1 (vertical axis)') self.transform = np.array(transform).reshape((3, 3)).T self.view.imageItem_seismic.setTransform(QtGui.QTransform(transform)) self.view.plotItem_header_h.setLimits(xMin=xlim[0], xMax=xlim[1]) self.view.plotItem_header_v.setLimits(yMin=ylim[0], yMax=ylim[1]) self.view.plotItem_seismic.setLimits(xMin=xlim[0], xMax=xlim[1], yMin=ylim[0], yMax=ylim[1]) # reset the view if update_axis: xlim, ylim = self.limits() self.view.viewBox_seismic.setXRange(xlim, padding=0) self.view.viewBox_seismic.setYRange(ylim, padding=0) # set the header combo box keys if isinstance(self.model.header, dict): self.view.comboBox_header.clear() for hname in self.model.header.keys(): self.view.comboBox_header.addItem(hname) self.set_gain(gain=gain) self.set_header() def update_hover(self, qpoint, key): c, _, _, _ = self.cursor2timetraceamp(qpoint) if key == 'Trace': plotitem = self.view.hoverPlotWidgets[key].getPlotItem() plotitem.items[0].setData(self.model.tscale, self.model.get_trace(c)) plotitem.setXRange(self.trange) elif key == 'Spectrum': plotitem = self.view.hoverPlotWidgets[key].getPlotItem() plotitem.items[0].setData(self.model.get_trace_spectrum(c, trange=self.trange)) elif key == 'Spectrogram': imageshowitem = self.view.hoverPlotWidgets[key].imageshowitem fscale, tscale, tf = self.model.get_trace_spectrogram(c, trange=self.trange) imageshowitem.set_image(tf, tscale, fscale) @property def trange(self): """ returns the current time range of the view :return: 2 floats list """ return self.view.viewBox_seismic.viewRange()[self.model.taxis] @property def crange(self): """ returns the current channel range of the view :return: 2 floats list """ return self.view.viewBox_seismic.viewRange()[self.model.caxis] @dataclass class Model: """Class for keeping track of the visualized data""" data: np.array header: np.array si: float = 1. def auto_gain(self) -> float: rmsnan = np.nansum(self.data * 2, axis=self.taxis) / np.sum( ~np.isnan(self.data), axis=self.taxis) return 20 * np.log10(np.median(np.sqrt(rmsnan))) def get_trace_spectrogram(self, c, trange=None): from scipy.signal import spectrogram tr = self.get_trace(c, trange=trange) fscale, tscale, tf = spectrogram(tr, fs=1 / self.si, nperseg=50, nfft=512, window='cosine', noverlap=48) tscale += trange[0] tf = 20 * np.log10(tf + np.finfo(float).eps) return fscale, tscale, tf def get_trace_spectrum(self, c, trange=None): tr = self.get_trace(c, trange=trange) psd = 20 * np.log10(np.abs(np.fft.rfft(tr)) -	np.finfo(float)	numpy.finfo
import numpy as np import os import re import requests import sys import time from netCDF4 import Dataset import pandas as pd from bs4 import BeautifulSoup from tqdm import tqdm # setup constants used to access the data from the different M2M interfaces BASE_URL = 'https://ooinet.oceanobservatories.org/api/m2m/' # base M2M URL SENSOR_URL = '12576/sensor/inv/' # Sensor Information # setup access credentials AUTH = ['OOIAPI-853A3LA6QI3L62', '<KEY>'] def M2M_Call(uframe_dataset_name, start_date, end_date): options = '?beginDT=' + start_date + '&endDT=' + end_date + '&format=application/netcdf' r = requests.get(BASE_URL + SENSOR_URL + uframe_dataset_name + options, auth=(AUTH[0], AUTH[1])) if r.status_code == requests.codes.ok: data = r.json() else: return None # wait until the request is completed print('Waiting for OOINet to process and prepare data request, this may take up to 20 minutes') url = [url for url in data['allURLs'] if re.match(r'.async_results.', url)][0] check_complete = url + '/status.txt' with tqdm(total=400, desc='Waiting') as bar: for i in range(400): r = requests.get(check_complete) bar.update(1) if r.status_code == requests.codes.ok: bar.n = 400 bar.last_print_n = 400 bar.refresh() print('\nrequest completed in %f minutes.' % elapsed) break else: time.sleep(3) elapsed = (i * 3) / 60 return data def M2M_Files(data, tag=''): """ Use a regex tag combined with the results of the M2M data request to collect the data from the THREDDS catalog. Collected data is gathered into an xarray dataset for further processing. :param data: JSON object returned from M2M data request with details on where the data is to be found for download :param tag: regex tag to use in discriminating the data files, so we only collect the correct ones :return: the collected data as an xarray dataset """ # Create a list of the files from the request above using a simple regex as a tag to discriminate the files url = [url for url in data['allURLs'] if re.match(r'.thredds.', url)][0] files = list_files(url, tag) return files def list_files(url, tag=''): """ Function to create a list of the NetCDF data files in the THREDDS catalog created by a request to the M2M system. :param url: URL to user's THREDDS catalog specific to a data request :param tag: regex pattern used to distinguish files of interest :return: list of files in the catalog with the URL path set relative to the catalog """ page = requests.get(url).text soup = BeautifulSoup(page, 'html.parser') pattern = re.compile(tag) return [node.get('href') for node in soup.find_all('a', text=pattern)] def M2M_Data(nclist,variables): thredds = 'https://opendap.oceanobservatories.org/thredds/dodsC/ooi/' #nclist is going to contain more than one url eventually for jj in range(len(nclist)): url=nclist[jj] url=url[25:] dap_url = thredds + url + '#fillmismatch' openFile = Dataset(dap_url,'r') for ii in range(len(variables)): dum = openFile.variables[variables[ii].name] variables[ii].data = np.append(variables[ii].data, dum[:].data) tmp = variables[0].data/60/60/24 time_converted = pd.to_datetime(tmp, unit='D', origin=pd.Timestamp('1900-01-01')) return variables, time_converted class var(object): def __init__(self): """A Class that generically holds data with a variable name and the units as attributes""" self.name = '' self.data = np.array([]) self.units = '' def __repr__(self): return_str = "name: " + self.name + '\n' return_str += "units: " + self.units + '\n' return_str += "data: size: " + str(self.data.shape) return return_str class structtype(object): def __init__(self): """ A class that imitates a Matlab structure type """ self._data = [] def __getitem__(self, index): """implement index behavior in the struct""" if index == len(self._data): self._data.append(var()) return self._data[index] def __len__(self): return len(self._data) def M2M_URLs(platform_name,node,instrument_class,method): var_list = structtype() #MOPAK if platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #METBK elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' #FLORT elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/06-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/06-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' #FDCHP elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/08-FDCHPA000/telemetered/fdchp_a_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #DOSTA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' #ADCP elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/01-ADCPTA000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/01-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/01-ADCPTA000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/01-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/04-ADCPTM000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/04-ADCPTM000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/04-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/04-ADCPSJ000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' #ZPLSC elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #WAVSS elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' #VELPT elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' #PCO2W elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' #PHSEN elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' #SPKIR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' #PRESF elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/02-PRESFA000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/02-PRESFA000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/02-PRESFB000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/02-PRESFC000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' #CTDBP elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/06-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/06-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/03-CTDBPE000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' #VEL3D elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' #VEL3DK elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' #PCO2A elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' #PARAD elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' #OPTAA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/01-OPTAAC000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #NUTNR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' ## #MOPAK elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/SBD17/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/SBD17/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #METBK elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data =	np.array([])	numpy.array
import numpy as np from scipy.optimize import minimize import math """Diagonalize the Jaynes-Cummings ladder of energies""" def get_order(omega_r, qubit_energy_list): """Get the order of eigenenergies that diagonalizer produces. Use the bare energy of the system as reference. Args: omega_r (float): Resonator frequency qubit_energy_list (List[float]): Qubit energies Returns: (List[int]): Indices giving the proper order of the elements in qubit_energy_list. """ tmax = len(qubit_energy_list) # Maximum number of transmon levels order = np.zeros(tmax) # Bare energies of the system diag_bare = np.array([-i*omega_r + qubit_energy_list[i] for i in range(tmax)]) # Eigenenergies in the order produced by diagonalizer eigensolver_order = np.linalg.eigvalsh(np.diag(diag_bare)) # Find where the diagonalizer puts the energies for i in range(tmax): index, =	np.where(eigensolver_order==diag_bare[i])	numpy.where
import os import numpy as np import matplotlib.pyplot as plt import cv2 import torch import torch.nn as nn import torchvision.models as models from model import Unet from utils.dataloader import read_data_path, MaskDataset from torch.utils.data import DataLoader from utils.config import Config from utils.loss import dice_score # Hyperparameter config = Config() TRAIN_TEST_SPLIT = config.TRAIN_TEST_SPLIT BATCH_SIZE_VALIDATION = config.BATCH_SIZE_VALIDATION BATCH_SIZE_TESTING = config.BATCH_SIZE_TESTING PRED_SAVE_DIR = config.PRED_SAVE_DIR os.makedirs(PRED_SAVE_DIR, exist_ok=True) INFERENCE_WEIGHT = config.INFERENCE_WEIGHT # Use torch cuda device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Import Resnet-50 as base network, modify first layer model_ft = models.resnet50(pretrained=True) model_ft.conv1 = nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(2, 2), padding=(3, 3), bias=False) # Add Residual layer in unet model = Unet(model_ft) model.to(device) if INFERENCE_WEIGHT: model.load_state_dict(torch.load(INFERENCE_WEIGHT)) # Read data path, make in dataloader """ read_data_path input: (float), the split of train and test return: (list, list, list), train & valid & test file path list list -> (img_path, mask_path) """ training_list, validation_list, testing_list = read_data_path(TRAIN_TEST_SPLIT) val_dataset = MaskDataset(validation_list) val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE_VALIDATION, shuffle=False, drop_last=True) # Confusion matrix, postive = abnormal; negative = normal TP = 0; FP = 0 FN = 0; TN = 0 """ TP => mask: abnormal, pred: abnormal FP => mask: normal, pred: abnormal FN => mask: abnormal, pred: normal TN => mask: normal, pred: normal """ dice_score_list = [] number = 0 with torch.no_grad(): for imgs, masks in val_loader: imgs_gpu = imgs.to(device) outputs = model(imgs_gpu) outputs = torch.round(outputs) * 255 masks = masks.to(device) # Dice score list dice_scores = dice_score(outputs, masks) dice_score_list.extend([dice_scores.item()]) for index in range(BATCH_SIZE_VALIDATION): img_origin = np.reshape(imgs_gpu[index].cpu().numpy(), (256, 256)) pred_img = np.reshape(outputs[index].cpu().numpy(), (256, 256)) mask_img = np.reshape(masks[index].cpu().numpy()*255, (256, 256)) # Confusion Matrix if np.sum(mask_img)!=0 and np.sum(pred_img)!=0: TP += 1 if np.sum(mask_img)==0 and np.sum(pred_img)!=0: FP += 1 if np.sum(mask_img)!=0 and np.sum(pred_img)==0: FN += 1 if np.sum(mask_img)==0 and np.sum(pred_img)==0: TN += 1 number += 1 print(number) if	np.all(mask_img==0)	numpy.all
#!/usr/bin/env python # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # <NAME> # California Institute of Technology # (C) 2006-2010 All Rights Reserved # # {LicenseText} # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # standalone = True import unittestX as unittest import journal debug = journal.debug( "Broadened_E_Q_Kernel_TestCase" ) warning = journal.warning( "Broadened_E_Q_Kernel_TestCase" ) import mcni from mccomposite import mccompositebp from mccomponents import mccomponentsbp class TestCase(unittest.TestCase): def test(self): E_Q = "Q*Q/3." S_Q = "1" sigma_Q = "Q/2." Qmin = 0; Qmax = 10 absorption_coefficient = scattering_coefficient = 1. kernel = mccomponentsbp.create_Broadened_E_Q_Kernel( E_Q, S_Q, sigma_Q, Qmin, Qmax, absorption_coefficient, scattering_coefficient, ) ei = 500 # meV from mcni.utils import conversion vil = conversion.e2v(ei) vi = (0,0,vil) import numpy.linalg as nl import numpy as np for i in range(10): event = mcni.neutron( r = (0,0,0), v = vi, prob = 1, time = 0 ) kernel.scatter( event ); vf = np.array(event.state.velocity) diffv = vi - vf Q = conversion.v2k(	nl.norm(diffv)	numpy.linalg.norm
import pytest, numbers, warnings import numpy as np from numpy.testing import assert_array_equal, assert_allclose, assert_equal from scipy.sparse import rand as sprand from scipy import optimize from pyuoi import UoI_L1Logistic from pyuoi.linear_model.logistic import (fit_intercept_fixed_coef, MaskedCoefLogisticRegression, LogisticInterceptFitterNoFeatures, _logistic_regression_path, _multinomial_loss_grad, _logistic_loss_and_grad) from sklearn.metrics import accuracy_score from sklearn.preprocessing import LabelEncoder, OneHotEncoder from sklearn.utils import (compute_class_weight, check_consistent_length, check_array) from sklearn.exceptions import ConvergenceWarning from pyuoi.datasets import make_classification from pyuoi.lbfgs import fmin_lbfgs, AllZeroLBFGSError def _logistic_regression_path_old(X, y, Cs=48, fit_intercept=True, max_iter=100, tol=1e-4, verbose=0, coef=None, class_weight=None, penalty='l2', multi_class='auto', check_input=True, sample_weight=None, l1_ratio=None, coef_mask=None): """Compute a Logistic Regression model for a list of regularization parameters. This is the original function used to check the new indexing-based version rather than the masking version implemented here. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Input data. y : array-like, shape (n_samples,) or (n_samples, n_targets) Input data, target values. Cs : int \| array-like, shape (n_cs,) List of values for the regularization parameter or integer specifying the number of regularization parameters that should be used. In this case, the parameters will be chosen in a logarithmic scale between 1e-4 and 1e4. fit_intercept : bool Whether to fit an intercept for the model. In this case the shape of the returned array is (n_cs, n_features + 1). max_iter : int Maximum number of iterations for the solver. tol : float Stopping criterion. For the newton-cg and lbfgs solvers, the iteration will stop when ``max{\|g_i \| i = 1, ..., n} <= tol`` where ``g_i`` is the i-th component of the gradient. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. coef : array-like, shape (n_features,), default None Initialization value for coefficients of logistic regression. Useless for liblinear solver. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. multi_class : str, {'multinomial', 'auto'}, default: 'auto' For 'multinomial' the loss minimised is the multinomial loss fit across the entire probability distribution, even when the data is binary. 'auto' selects binary if the data is binary and otherwise selects 'multinomial'. check_input : bool, default True If False, the input arrays X and y will not be checked. sample_weight : array-like, shape(n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. coef_mask : array-like, shape (n_features), (n_classes, n_features) optional Masking array for coef. Returns ------- coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1) List of coefficients for the Logistic Regression model. If fit_intercept is set to True then the second dimension will be n_features + 1, where the last item represents the intercept. For ``multiclass='multinomial'``, the shape is (n_classes, n_cs, n_features) or (n_classes, n_cs, n_features + 1). Cs : ndarray Grid of Cs used for cross-validation. n_iter : array, shape (n_cs,) Actual number of iteration for each Cs. """ if isinstance(Cs, numbers.Integral): Cs = np.logspace(-4, 4, Cs) # Preprocessing. if check_input: X = check_array(X, accept_sparse='csr', dtype=np.float64, accept_large_sparse=True) y = check_array(y, ensure_2d=False, dtype=None) check_consistent_length(X, y) _, n_features = X.shape classes = np.unique(y) if multi_class == 'auto': if len(classes) > 2: multi_class = 'multinomial' else: multi_class = 'ovr' # If sample weights exist, convert them to array (support for lists) # and check length # Otherwise set them to 1 for all examples if sample_weight is not None: sample_weight = np.array(sample_weight, dtype=X.dtype, order='C') check_consistent_length(y, sample_weight) else: sample_weight = np.ones(X.shape[0], dtype=X.dtype) # If class_weights is a dict (provided by the user), the weights # are assigned to the original labels. If it is "balanced", then # the class_weights are assigned after masking the labels with a OvR. le = LabelEncoder() if isinstance(class_weight, dict) or multi_class == 'multinomial': class_weight_ = compute_class_weight(class_weight, classes=classes, y=y) sample_weight = class_weight_[le.fit_transform(y)] # For doing a ovr, we need to mask the labels first. for the # multinomial case this is not necessary. if multi_class == 'ovr': coef_size = n_features w0 = np.zeros(n_features + int(fit_intercept), dtype=X.dtype) mask_classes = np.array([-1, 1]) mask = (y == 1) y_bin = np.ones(y.shape, dtype=X.dtype) y_bin[~mask] = -1. # for compute_class_weight if class_weight == "balanced": class_weight_ = compute_class_weight(class_weight, classes=mask_classes, y=y_bin) sample_weight = class_weight_[le.fit_transform(y_bin)] else: coef_size = classes.size * n_features lbin = OneHotEncoder(categories=[range(classes.size)], sparse=False) Y_multi = lbin.fit_transform(y[:, np.newaxis]) if Y_multi.shape[1] == 1: Y_multi = np.hstack([1 - Y_multi, Y_multi]) w0 = np.zeros((classes.size, n_features + int(fit_intercept)), dtype=X.dtype) w0[:, -1] = LogisticInterceptFitterNoFeatures(y, classes.size).intercept_ if coef is not None: # it must work both giving the bias term and not if multi_class == 'ovr': if coef.size not in (n_features, w0.size): raise ValueError( 'Initialization coef is of shape %d, expected shape ' '%d or %d' % (coef.size, n_features, w0.size)) w0[:coef.size] = coef else: w0[:, :coef.shape[1]] = coef # Mask initial array if coef_mask is not None: if multi_class == 'ovr': w0[:n_features] = coef_mask else: w0[:, :n_features] = coef_mask if multi_class == 'multinomial': # fmin_l_bfgs_b and newton-cg accepts only ravelled parameters. target = Y_multi if penalty == 'l2': w0 = w0.ravel() def func(x, args): return _multinomial_loss_grad(x, args)[0:2] else: w0 = w0.T.ravel().copy() def inner_func(x, args): return _multinomial_loss_grad(x, args)[0:2] def func(x, g, args): x = x.reshape(-1, classes.size).T.ravel() loss, grad = inner_func(x, args) grad = grad.reshape(classes.size, -1).T.ravel() g[:] = grad return loss else: target = y_bin if penalty == 'l2': func = _logistic_loss_and_grad else: def func(x, g, args): loss, grad = _logistic_loss_and_grad(x, args) g[:] = grad return loss coefs = list() n_iter = np.zeros(len(Cs), dtype=np.int32) for i, C in enumerate(Cs): iprint = [-1, 50, 1, 100, 101][ np.searchsorted(np.array([0, 1, 2, 3]), verbose)] if penalty == 'l2': w0, loss, info = optimize.fmin_l_bfgs_b( func, w0, fprime=None, args=(X, target, 1. / C, coef_mask, sample_weight), iprint=iprint, pgtol=tol, maxiter=max_iter) else: zeros_seen = [0] def zero_coef(x, args): if multi_class == 'multinomial': x = x.reshape(-1, classes.size)[:-1] else: x = x[:-1] now_zeros = np.array_equiv(x, 0.) if now_zeros: zeros_seen[0] += 1 else: zeros_seen[0] = 0 if zeros_seen[0] > 1: return -2048 try: w0 = fmin_lbfgs(func, w0, orthantwise_c=1. / C, args=(X, target, 0., coef_mask, sample_weight), max_iterations=max_iter, epsilon=tol, orthantwise_end=coef_size, progress=zero_coef) except AllZeroLBFGSError: w0 = 0. info = None if info is not None and info["warnflag"] == 1: warnings.warn("lbfgs failed to converge. Increase the number " "of iterations.", ConvergenceWarning) # In scipy <= 1.0.0, nit may exceed maxiter. # See https://github.com/scipy/scipy/issues/7854. if info is None: n_iter_i = -1 else: n_iter_i = min(info['nit'], max_iter) if multi_class == 'multinomial': n_classes = max(2, classes.size) if penalty == 'l2': multi_w0 = np.reshape(w0, (n_classes, -1)) else: multi_w0 = np.reshape(w0, (-1, n_classes)).T if coef_mask is not None: multi_w0[:, :n_features] = coef_mask coefs.append(multi_w0.copy()) else: if coef_mask is not None: w0[:n_features] = coef_mask coefs.append(w0.copy()) n_iter[i] = n_iter_i return np.array(coefs), np.array(Cs), n_iter def test_fit_intercept_fixed_coef(): """Test that the intercept in fit correctly for fixed coefficients.""" X = np.zeros((6, 5)) coef = np.ones((1, 5)) y = np.ones(6, dtype=int) y[:3] = 0 b = fit_intercept_fixed_coef(X, coef, y, 2) assert_allclose(b, 0.) X = np.zeros((7, 5)) y = np.ones(7, dtype=int) y[:3] = 0 b = fit_intercept_fixed_coef(X, coef, y, 3) assert_allclose(b.argmax(), 1) assert_allclose(b.argmin(), 2) def test_fit_intercept_no_features(): """Test that the intercept in fit correctly for fixed coefficients.""" X = np.zeros((5, 1)) y = np.ones(6, dtype=int) y[:3] = 0 LR = LogisticInterceptFitterNoFeatures(y, 1) b = LR.intercept_ assert_allclose(b, 0.) y = np.ones(7, dtype=int) y[:3] = 0 LR = LogisticInterceptFitterNoFeatures(y, 1) yhat = LR.predict(X) assert_allclose(yhat, 1) py = LR.predict_proba(X) assert np.all(py > .5) y = np.ones(7, dtype=int) y[:3] = 0 LR = LogisticInterceptFitterNoFeatures(y, 3) yhat = LR.predict(X) assert_allclose(yhat, 1) py = LR.predict_proba(X) assert_allclose(py.argmax(axis=1), 1) assert_allclose(py.argmin(axis=1), 2) def test_l1logistic_intercept(): """Test that binary L1 Logistic fits an intercept when run.""" for fi in [True, False]: X, y, w, b = make_classification(n_samples=100, random_state=11, n_features=4, w_scale=4., include_intercept=fi) l1log = UoI_L1Logistic(fit_intercept=fi, n_boots_sel=3, n_boots_est=3).fit(X, y) if not fi: assert_array_equal(l1log.intercept_, 0.) else: l1log.intercept_ def test_l1logistic_binary(): """Test that binary L1 Logistic runs in the UoI framework.""" n_inf = 10 X, y, w, b = make_classification(n_samples=200, random_state=6, n_informative=n_inf, n_features=20, w_scale=4., include_intercept=True) l1log = UoI_L1Logistic(random_state=10).fit(X, y) l1log = UoI_L1Logistic(random_state=10, fit_intercept=False).fit(X, y) l1log.predict_proba(X) l1log.predict_log_proba(X) y_hat = l1log.predict(X) assert_equal(accuracy_score(y, y_hat), l1log.score(X, y)) assert (np.sign(abs(w)) == np.sign(abs(l1log.coef_))).mean() >= .8 def test_l1logistic_binary_multinomial(): """Test that binary L1 Logistic runs in the UoI framework using multi_class='multinomial'.""" n_inf = 10 X, y, w, b = make_classification(n_samples=200, random_state=6, n_informative=n_inf, n_features=20, w_scale=4., include_intercept=True) UoI_L1Logistic(random_state=10, multi_class='multinomial').fit(X, y) UoI_L1Logistic(random_state=10, fit_intercept=False, multi_class='multinomial').fit(X, y) def test_l1logistic_no_ovr(): """Test that binary L1 Logistic model raises an error for multiclass='ovr'.""" with pytest.raises(ValueError): UoI_L1Logistic(multi_class='ovr') def test_l1logistic_multiclass(): """Test that multiclass L1 Logistic runs in the UoI framework when all classes share a support.""" n_features = 20 n_inf = 10 X, y, w, b = make_classification(n_samples=200, random_state=10, n_classes=5, n_informative=n_inf, n_features=n_features, shared_support=True, w_scale=4.) l1log = UoI_L1Logistic().fit(X, y) l1log.predict_proba(X) l1log.predict_log_proba(X) y_hat = l1log.predict(X) assert_equal(accuracy_score(y, y_hat), l1log.score(X, y)) assert (np.sign(abs(w)) == np.sign(abs(l1log.coef_))).mean() >= .8 def test_l1logistic_multiclass_not_shared(): """Test that multiclass L1 Logistic runs in the UoI framework when all classes share a support.""" n_features = 20 n_inf = 10 X, y, w, b = make_classification(n_samples=400, random_state=10, n_classes=5, n_informative=n_inf, n_features=n_features, shared_support=False, w_scale=4.) l1log = UoI_L1Logistic(shared_support=False).fit(X, y) l1log.predict_log_proba(X) y_hat = l1log.predict(X) assert_equal(accuracy_score(y, y_hat), l1log.score(X, y)) assert (np.sign(abs(w)) == np.sign(abs(l1log.coef_))).mean() >= .7 def test_masked_logistic(): """Test the masked logistic regression class.""" n_features = 20 n_inf = 10 for shared_support in [True, False]: for n_classes in [2, 3]: for intercept in [True, False]: X, y, w, b = make_classification(n_samples=200, random_state=10, n_classes=n_classes, n_informative=n_inf, n_features=n_features, shared_support=shared_support, include_intercept=intercept, w_scale=4.) mask = np.squeeze(np.logical_not(np.equal(w, 0))) for penalty in ['l1', 'l2']: lr = MaskedCoefLogisticRegression(penalty=penalty, C=10., warm_start=True, fit_intercept=intercept) lr.fit(X, y, coef_mask=mask) coef_idxs = np.flatnonzero(np.equal(lr.coef_, 0.)) coef_idxs = set(coef_idxs.tolist()) mask_idxs = np.flatnonzero(np.equal(mask, 0)) mask_idxs = set(mask_idxs.tolist()) assert mask_idxs.issubset(coef_idxs) lr.fit(X, y, coef_mask=mask) def test_masked_logistic_standardize(): """Test the masked logistic regression class with `standardize=True`.""" n_features = 20 n_inf = 10 for shared_support in [True, False]: for n_classes in [2, 3]: for intercept in [True, False]: X, y, w, b = make_classification(n_samples=200, random_state=10, n_classes=n_classes, n_informative=n_inf, n_features=n_features, shared_support=shared_support, include_intercept=intercept, w_scale=4.) mask = np.squeeze(np.logical_not(np.equal(w, 0))) for penalty in ['l1', 'l2']: lr = MaskedCoefLogisticRegression(penalty=penalty, C=10., warm_start=True, fit_intercept=intercept, standardize=True) lr.fit(X, y, coef_mask=mask) coef_idxs = np.flatnonzero(np.equal(lr.coef_, 0.)) coef_idxs = set(coef_idxs.tolist()) mask_idxs = np.flatnonzero(np.equal(mask, 0)) mask_idxs = set(mask_idxs.tolist()) assert mask_idxs.issubset(coef_idxs) lr.fit(X, y, coef_mask=mask) @pytest.mark.parametrize("n_classes,penalty,fit_intercept", [(3, "l2", True), (3, "l2", False), (3, "l1", True), (3, "l1", False), (2, "l2", True), (2, "l2", False), (2, "l1", True), (2, "l1", False)]) def test_masking_with_indexing(n_classes, penalty, fit_intercept): """Check that indexing the masks gives the same results as masking with logistic regression. """ X, y, w, intercept = make_classification(n_samples=1000, n_classes=n_classes, n_features=20, n_informative=10, random_state=0) mask = w != 0. if n_classes == 2: mask = mask.ravel() coefs, _, _ = _logistic_regression_path(X, y, [10.], coef_mask=mask, penalty=penalty, fit_intercept=fit_intercept) coefs_old, _, _ = _logistic_regression_path_old(X, y, [10.], coef_mask=mask, penalty=penalty, fit_intercept=fit_intercept) assert_allclose(coefs, coefs_old) coefs, _, _ = _logistic_regression_path(X, y, [10.], penalty=penalty, fit_intercept=fit_intercept) coefs_old, _, _ = _logistic_regression_path_old(X, y, [10.], penalty=penalty, fit_intercept=fit_intercept) assert_allclose(coefs, coefs_old) @pytest.mark.parametrize("n_classes,penalty,fit_intercept", [(3, "l2", True), (3, "l2", False), (3, "l1", True), (3, "l1", False), (2, "l2", True), (2, "l2", False), (2, "l1", True), (2, "l1", False)]) def test_all_masked_with_indexing(n_classes, penalty, fit_intercept): """Check masking all of the coef either works with intercept or raises an error. """ X, y, w, intercept = make_classification(n_samples=1000, n_classes=n_classes, n_features=20, n_informative=10, random_state=0) mask = np.zeros_like(w) if n_classes == 2: mask = mask.ravel() coefs, _, _ = _logistic_regression_path(X, y, [10.], coef_mask=mask, fit_intercept=fit_intercept) if fit_intercept: if n_classes == 2: assert_equal(coefs[0][:-1], 0.) else: assert_equal(coefs[0][:, :-1], 0.) else: assert_equal(coefs[0], 0.) def test_estimation_score_usage(): """Test the ability to change the estimation score in UoI L1Logistic""" methods = ('acc', 'log', 'BIC', 'AIC', 'AICc') X, y, w, b = make_classification(n_samples=200, random_state=6, n_informative=5, n_features=10) scores = [] for method in methods: l1log = UoI_L1Logistic(random_state=12, estimation_score=method, tol=1e-2, n_boots_sel=24, n_boots_est=24) assert_equal(l1log.estimation_score, method) l1log.fit(X, y) scores.append(l1log.scores_) scores = np.stack(scores) assert_equal(len(np.unique(scores, axis=0)), len(methods)) def test_set_random_state(): """Tests whether random states are handled correctly.""" X, y, w, b = make_classification(n_samples=100, random_state=60, n_informative=4, n_features=5, w_scale=4.) # same state l1log_0 = UoI_L1Logistic(random_state=13) l1log_1 = UoI_L1Logistic(random_state=13) l1log_0.fit(X, y) l1log_1.fit(X, y) assert_array_equal(l1log_0.coef_, l1log_1.coef_) # different state l1log_1 = UoI_L1Logistic(random_state=14) l1log_1.fit(X, y) assert not np.array_equal(l1log_0.coef_, l1log_1.coef_) # different state, not set l1log_0 = UoI_L1Logistic() l1log_1 = UoI_L1Logistic() l1log_0.fit(X, y) l1log_1.fit(X, y) assert not np.array_equal(l1log_0.coef_, l1log_1.coef_) def test_normalization_by_samples(): """Test that coef_ does not depend directly on the number of samples.""" n_features = 20 for n_classes in [2, 3]: X, y, w, b = make_classification(n_samples=200, random_state=10, n_classes=n_classes, n_informative=n_features, n_features=n_features, w_scale=4.) for penalty in ['l1', 'l2']: lr1 = MaskedCoefLogisticRegression(penalty=penalty, C=1e2) lr1.fit(X, y) lr3 = MaskedCoefLogisticRegression(penalty=penalty, C=1e2) lr3.fit(np.tile(X, (3, 1)), np.tile(y, 3)) assert_allclose(lr1.coef_, lr3.coef_) def test_l1logistic_binary_strings(): """Test that binary L1 Logistic runs in the UoI framework.""" n_inf = 10 X, y, w, b = make_classification(n_samples=200, random_state=6, n_informative=n_inf, n_features=20, w_scale=4., include_intercept=True) classes = ['a', 'b'] lb = LabelEncoder() lb.fit(classes) y = lb.inverse_transform(y) l1log = UoI_L1Logistic(random_state=10).fit(X, y) y_hat = l1log.predict(X) assert set(classes) >= set(y_hat) def test_l1logistic_multiclass_strings(): """Test that multiclass L1 Logistic runs in the UoI framework when all classes share a support.""" n_features = 20 n_inf = 10 X, y, w, b = make_classification(n_samples=200, random_state=10, n_classes=5, n_informative=n_inf, n_features=n_features, shared_support=True, w_scale=4.) classes = ['a', 'b', 'c', 'd', 'e'] lb = LabelEncoder() lb.fit(classes) y = lb.inverse_transform(y) l1log = UoI_L1Logistic(random_state=10).fit(X, y) y_hat = l1log.predict(X) assert set(classes) >= set(y_hat) def test_l1logistic_sparse_input(): """Test that multiclass L1 Logistic works when using sparse matrix inputs""" rs = np.random.RandomState(17) X = sprand(100, 100, random_state=rs) classes = ['abc', 'de', 'fgh'] y = np.array(classes)[rs.randint(3, size=100)] kwargs = dict( fit_intercept=False, random_state=rs, n_boots_sel=4, n_boots_est=4, n_C=7, ) l1log = UoI_L1Logistic(**kwargs).fit(X, y) y_hat = l1log.predict(X) assert set(classes) >= set(y_hat) def test_l1logistic_sparse_input_no_center(): """Test that multiclass L1 Logistic raises an error when asked to center sparse data. """ rs = np.random.RandomState(17) X = sprand(10, 10, random_state=rs) classes = ['abc', 'de', 'fgh'] y = np.array(classes)[rs.randint(3, size=10)] with pytest.raises(ValueError): UoI_L1Logistic(fit_intercept=True).fit(X, y) def test_l1logistic_bad_est_score(): """Test that multiclass L1 Logistic raises an error when given a bad estimation_score value. """ X =	np.random.randn(20, 5)	numpy.random.randn
# 'source /home/voanna/TimePrediction/src/bash/gpu_caffe_env_variables ') from __future__ import print_function import os import time_to_label import glob import math import numpy as np import scipy.io import json import argparse import HONHelpers as hon import random parser = argparse.ArgumentParser() parser.add_argument("webcam", help="either the name of the webcam you want to use from {} or 'all'".format(hon.webcams), type=str) parser.add_argument("GPU_ID", help="gpu core to run the caffe training on", type=int) args = parser.parse_args() np.random.seed(6) random.seed(6) if args.webcam == 'all': webcams = hon.webcams else: assert args.webcam in hon.webcams webcams = [args.webcam] CAFFE_PATH = hon.gpu_caffe_root CAFFE_MODEL = hon.VGG16_caffemodel_path DATA_ROOT = hon.hon_data_root EXPERIMENT_ROOT = os.path.join(os.path.dirname(os.path.realpath(__file__)), args.webcam) training_frac = 0.8 MIN_TEMPERATURE = 5 with open(os.path.join(EXPERIMENT_ROOT, 'train.txt'), 'w') as ftrain, \ open(os.path.join(EXPERIMENT_ROOT, 'val.txt'), 'w') as fval: for webcam in webcams: matfile = os.path.join(DATA_ROOT, webcam, 'train_data_aligned.mat') labels = scipy.io.loadmat(matfile) labels = labels['y'] labels = labels[~	np.isnan(labels)	numpy.isnan
import struct import numpy as np import random def norm_sgy(data): maxval=max(max(data)) minval=min(min(data)) return [[(float(i)-minval)/float(maxval-minval) for i in j] for j in data] def norm_sgy1(data): #maxval=max(max(data)) #minval=min(min(data)) # return [[(float(i)-min(j))/float(max(j)-min(j)) for i in j] for j in data] return [[float(i)/float(max(np.abs(j))) for i in j] for j in data] def norm_sgy2(data): maxval=np.max(np.abs(data),axis=1) index=[i for i in range(len(data))] return [data[j]/maxval[j] for j in index] def norm_tgy(data): # maxval=np.max(np.abs(data),axis=1) maxval=np.max(1000) index=[i for i in range(len(data))] return [data[j]/maxval for j in index] def norm_ydata(data): maxval=np.max(np.abs(data),axis=0) minval=np.min(np.abs(data),axis=0) # maxval=np.max(data) # minval=np.min(data) # return [(j-minval[np.where(j)])/(maxval[np.where(j)]-minval[np.where(j)]) for j in data], maxval, minval return [j/maxval for j in data], maxval, minval # return [[(float(i)-float(minval))/float(maxval-minval) for i in j] for j in data] def read_sgy(sgynam): # print "sgynam: "+sgynam try: binsgy = open(sgynam,'rb') except IOError: return 0,0,[] fhead=binsgy.read(3600); # print fhead[3213:3215] nr=struct.unpack(">H",fhead[3212:3214]) print (nr) nsmp=struct.unpack(">H",fhead[3220:3222]) print(nsmp) data = [] for ir in range(0,nr[0]): trchead=binsgy.read(240) trcdata=binsgy.read(nsmp[0]4) data1 = [] for i in range(0,nsmp[0]): # print(trcdata[i4:i4+4]) data1=data1+list(struct.unpack(">f",trcdata[i4:i4+4])) data.append(data1) print("read 1sgy end") binsgy.close() return nr,nsmp,data; def read_egy(egynam): try: binegy = open(egynam,'rb') except IOError: return 0,[] data=[] fbinegy=binegy.read() ndata=len(fbinegy)//4 for i in range(ndata): data1=[] data1=struct.unpack("f",fbinegy[i4:i4+4]) data.append(data1) print("read 1egy end") binegy.close() return ndata, data def read_tgy(tgynam): try: binegy = open(tgynam,'rb') except IOError: return 0,[] data=[] fbinegy=binegy.read() ndata=len(fbinegy)//4 for i in range(ndata): data1=[] data1=struct.unpack("f",fbinegy[i4:i4+4]) data.append(data1) print("read 1tgy end") binegy.close() return data def load_data(sgynam='sgy',sgyf1=0,sgyt1=300,sgyf2=0,sgyt2=300,shuffle='true'): data= [] ydata=[] for i in range(sgyf1,sgyt1): print(sgynam+"/event/event%04d.sgy" %(i)) nr,nsmp,data1 = read_sgy(sgynam+"/event/event%04d.sgy" %(i)); if nr != 0: data1=norm_sgy1(data1) data.append(data1) ydata.append(1); else: print('1 event sgy not found') for i in range(sgyf2,sgyt2): nr,nsmp,data1 = read_sgy(sgynam+"/noise/noise%04d.sgy" %(i)); if nr != 0: data1=norm_sgy1(data1) data.append(data1) ydata.append(0); else: print('1 noise sgy not found') index=[i for i in range(len(ydata))] random.seed(7) if shuffle == 'true': random.shuffle(index) data = [data[i] for i in index] ydata = [ydata[i] for i in index] data=np.array(data) ydata=np.array(ydata) return data.shape[0],(data.shape[1]data.shape[2]),data,ydata def load_sgylist(sgylist,floc,shuffle='false'): data= [] ydata=[] lines=open(sgylist,'r').readlines() lines2=open(floc,'r').readlines() for i in range(0,len(lines)): egynam=lines[i][:lines[i].find(' ')] print(egynam) ndata, data1 = read_egy(egynam) tgynam=lines2[i][:lines2[i].find(' ')] print(tgynam) data2 = read_tgy(tgynam); if ndata != 0: data2=norm_tgy(data2) data.append(data1) ydata.append(data2) else: print('1 event tgy not found') index=[i for i in range(len(ydata))] random.seed(7) if shuffle == 'true': random.shuffle(index) data = [data[i] for i in index] ydata = [ydata[i] for i in index] data=np.array(data) ydata=	np.array(ydata)	numpy.array
"""Collection of functions to run forwards and backwards algorithms on haploid genotype data, where the data is structured as samples x variants.""" import numba as nb import numpy as np @nb.jit def forwards_ls_hap(n, m, H, s, e, r, norm=True): """Matrix based haploid LS forward algorithm using numpy vectorisation.""" # Initialise F = np.zeros((n, m)) c =	np.ones(m)	numpy.ones
import unittest from ancb import NumpyCircularBuffer from ancb import ( # type: ignore star_can_broadcast, can_broadcast ) from numpy import array_equal, allclose, shares_memory from numpy import array, zeros, arange, ndarray, ones, empty from numpy.random import rand, randint from numpy import fill_diagonal, roll from itertools import zip_longest from operator import ( matmul, add, sub, mul, truediv, mod, floordiv, pow, rshift, lshift, and_, or_, xor, neg, pos, abs, inv, invert, iadd, iand, ifloordiv, ilshift, imod, imul, ior, ipow, irshift, isub, itruediv, ixor ) class TestBroadcastability(unittest.TestCase): def test_broadcastablity(self): x = zeros((1, 2, 3, 4, 5)) y = zeros((1, 1, 1, 4, 5)) z = zeros((1, 1, 1, 3, 5)) w = zeros(1) self.assertTrue(can_broadcast(x.shape, y.shape)) self.assertFalse(can_broadcast(x.shape, z.shape)) self.assertFalse(can_broadcast(y.shape, z.shape)) self.assertTrue(can_broadcast(x.shape, x.shape)) self.assertTrue(can_broadcast(y.shape, y.shape)) self.assertTrue(can_broadcast(z.shape, z.shape)) self.assertTrue(can_broadcast(w.shape, w.shape)) self.assertTrue(can_broadcast(x.shape, w.shape)) self.assertTrue(can_broadcast(y.shape, w.shape)) self.assertTrue(can_broadcast(z.shape, w.shape)) def test_star_broadcastablity(self): x = zeros((1, 2, 3, 4, 5)) y = zeros((1, 1, 1, 4, 5)) z = zeros((1, 1, 1, 3, 5)) w = zeros(1) starexpr = zip_longest(x.shape, y.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(x.shape, z.shape, fillvalue=1) self.assertFalse(star_can_broadcast(starexpr)) starexpr = zip_longest(y.shape, z.shape, fillvalue=1) self.assertFalse(star_can_broadcast(starexpr)) starexpr = zip_longest(x.shape, x.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(y.shape, y.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(z.shape, z.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(w.shape, w.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(x.shape, w.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(y.shape, w.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(y.shape, w.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(z.shape, w.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) class OperatorTestFactory(type): def __new__(cls, name, bases, dct): obj = super().__new__(cls, name, bases, dct) bin_operators = [ matmul, add, sub, mul, truediv, mod, floordiv, pow ] un_operators = [neg, pos, abs, invert, inv] bitbin_operators = [rshift, lshift, and_, or_, xor] i_operators = [ iadd, ifloordiv, imul, ipow, isub, itruediv ] bit_ioperators = [ ilshift, irshift, ior, iand, ixor, imod ] def unop_testcase(op): def f(self): data = zeros(3, dtype=int) test = -arange(3, dtype=int) buffer = NumpyCircularBuffer(data) buffer.append(0) buffer.append(-1) buffer.append(-2) res = op(buffer) self.assertIsInstance(res, ndarray) self.assertTrue(array_equal(res, op(test))) # unfrag buffer.append(-3) test -= 1 res = op(buffer) self.assertIsInstance(res, ndarray) self.assertTrue(array_equal(res, op(test))) # frag return f def bitbinop_testcase(op): def f(self): data = zeros(3, dtype=int) test = arange(1, 4, dtype=int) x = randint(3) buffer = NumpyCircularBuffer(data) buffer.append(1) buffer.append(2) buffer.append(3) res1 = op(buffer, x) res2 = op(x, buffer) self.assertIsInstance(res1, ndarray) self.assertIsInstance(res2, ndarray) self.assertTrue(array_equal(res1, op(test, x))) self.assertTrue(array_equal(res2, op(x, test))) buffer.append(4) test += 1 res1 = op(buffer, x) res2 = op(x, buffer) self.assertIsInstance(res1, ndarray) self.assertIsInstance(res2, ndarray) self.assertTrue(array_equal(res1, op(test, x))) self.assertTrue(array_equal(res2, op(x, test))) return f def binop_testcase(op): def f(self): data = zeros(3, dtype=float) test = arange(1, 4, dtype=float) x = rand(3) buffer = NumpyCircularBuffer(data) buffer.append(1) buffer.append(2) buffer.append(3) res1 = op(buffer, x) self.assertIsInstance(res1, ndarray) self.assertTrue(allclose(res1, op(test, x))) res2 = op(x, buffer) self.assertIsInstance(res2, ndarray) self.assertTrue(allclose(res2, op(x, test))) buffer.append(4) test += 1 res1 = op(buffer, x) self.assertIsInstance(res1, ndarray) self.assertTrue(allclose(res1, op(test, x))) res2 = op(x, buffer) self.assertIsInstance(res2, ndarray) self.assertTrue(allclose(res2, op(x, test))) return f def iop_testcase(op): def f(self): data = zeros(3, dtype=float) data2 = zeros(3, dtype=float) test1 = arange(1, 4, dtype=float) test2 = arange(2, 5, dtype=float) x = rand(3) buffer1 = NumpyCircularBuffer(data) buffer2 = NumpyCircularBuffer(data2) buffer1.append(1) buffer1.append(2) buffer1.append(3) buffer2.append(1) buffer2.append(2) buffer2.append(3) op(buffer1, x) op(test1, x) self.assertIsInstance(buffer1, NumpyCircularBuffer) self.assertTrue(array_equal(buffer1 + 0, test1)) buffer2.append(4) op(buffer2, x) op(test2, x) self.assertIsInstance(buffer2, NumpyCircularBuffer) self.assertTrue(array_equal(buffer2 + 0, test2)) return f def bitiop_testcase(op): def f(self): data = zeros(3, dtype=int) data2 = zeros(3, dtype=int) test1 = arange(1, 4, dtype=int) test2 = arange(2, 5, dtype=int) x = randint(low=1, high=100, size=3) buffer1 = NumpyCircularBuffer(data) buffer2 = NumpyCircularBuffer(data2) buffer1.append(1) buffer1.append(2) buffer1.append(3) buffer2.append(1) buffer2.append(2) buffer2.append(3) op(buffer1, x) op(test1, x) self.assertIsInstance(buffer1, NumpyCircularBuffer) self.assertTrue(allclose(buffer1 + 0, test1)) buffer2.append(4) op(buffer2, x) op(test2, x) self.assertIsInstance(buffer2, NumpyCircularBuffer) self.assertTrue(allclose(buffer2 + 0, test2)) return f for op in bin_operators: setattr(obj, 'test_{}'.format(op.__name__), binop_testcase(op)) for op in bitbin_operators: setattr(obj, 'test_{}'.format(op.__name__), bitbinop_testcase(op)) for op in un_operators: setattr(obj, 'test_{}'.format(op.__name__), unop_testcase(op)) for op in i_operators: setattr(obj, 'test_{}'.format(op.__name__), iop_testcase(op)) for op in bit_ioperators: setattr(obj, 'test_{}'.format(op.__name__), bitiop_testcase(op)) return(obj) class TestNumpyCircularBuffer( unittest.TestCase, metaclass=OperatorTestFactory ): """ NumpyCircularBuffer tests """ def test_init(self): data = zeros(3) buffer = NumpyCircularBuffer(data) self.assertTrue(array_equal(data, buffer)) def test_fragmentation(self): data = zeros(3) buffer = NumpyCircularBuffer(data) self.assertFalse(buffer.fragmented) buffer.append(0) self.assertFalse(buffer.fragmented) buffer.append(1) self.assertFalse(buffer.fragmented) buffer.append(2) self.assertFalse(buffer.fragmented) buffer.append(3) self.assertTrue(buffer.fragmented) buffer.append(4) self.assertTrue(buffer.fragmented) buffer.append(5) self.assertFalse(buffer.fragmented) buffer.pop() self.assertFalse(buffer.fragmented) buffer.pop() self.assertFalse(buffer.fragmented) buffer.pop() self.assertFalse(buffer.fragmented) def test_matmul_1d1d(self): """Tests buffer @ X where buffer.ndim == 1 and X.ndim == 1""" data = zeros(3) C = rand(3) buffer = NumpyCircularBuffer(data) buffer.append(0) self.assertTrue(allclose(buffer @ C[:1], arange(1) @ C[:1])) buffer.append(1) self.assertTrue(allclose(buffer @ C[:2], arange(2) @ C[:2])) buffer.append(2) self.assertTrue(allclose(buffer @ C, arange(3) @ C)) buffer.append(3) self.assertTrue(allclose(buffer @ C, (arange(1, 4)) @ C)) buffer.append(4) self.assertTrue(allclose(buffer @ C, (arange(2, 5)) @ C)) buffer.append(5) self.assertTrue(allclose(buffer @ C, (arange(3, 6)) @ C)) buffer.append(6) self.assertTrue(allclose(buffer @ C, (arange(4, 7)) @ C)) buffer.pop() self.assertTrue(allclose(buffer @ C[1:], (arange(5, 7)) @ C[1:])) buffer.pop() self.assertTrue(allclose(buffer @ C[2:], (arange(6, 7)) @ C[2:])) def test_matmul_1d2d(self): """Tests buffer @ X where buffer.ndim == 1 and X.ndim == 2""" data = zeros(3) A = zeros((3, 3)) B = rand(9).reshape(3, 3) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) buffer.append(0) buffer.append(1) buffer.append(2) res_a = buffer @ A res_b = buffer @ B self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, arange(3) @ A)) self.assertTrue(allclose(res_b, arange(3) @ B)) buffer.append(3) res_a = buffer @ A res_b = buffer @ B self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(allclose(res_a, arange(1, 4) @ A)) self.assertTrue(allclose(res_b, arange(1, 4) @ B)) def test_matmul_2d2d(self): """Tests buffer @ X where buffer.ndim == 2""" data = zeros((3, 3)) A = zeros(9).reshape(3, 3) B = rand(9).reshape(3, 3) fill_diagonal(A, arange(1, 4)) buffer = NumpyCircularBuffer(data) buffer.append(arange(3)) buffer.append(arange(3, 6)) buffer.append(arange(6, 9)) test = arange(9).reshape(3, 3) self.assertTrue(array_equal(buffer, test)) res_a = buffer @ A res_b = buffer @ B self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) buffer.append(arange(9, 12)) test += 3 res_a = buffer @ A res_b = buffer @ B self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) def test_matmul_ndnd(self): """Tests buffer @ X where X.ndim > 2 and buffer.ndim > 2""" data = zeros((3, 3, 3)) A = zeros((3, 3, 3)) B = rand(27).reshape(3, 3, 3) C = rand(12).reshape(3, 4) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) filler = arange(9).reshape(3, 3) buffer.append(filler) buffer.append(filler + 9) buffer.append(filler + 18) test = arange(27).reshape(3, 3, 3) res_a = buffer @ A res_b = buffer @ B self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) buffer.append(filler + 27) test += 9 res_a = buffer @ A res_b = buffer @ B res_c = buffer @ C self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) self.assertTrue(allclose(res_c, test @ C)) def test_rmatmul_1d1d(self): """Tests X @ buffer where X.ndim == 1 and buffer.ndim == 1""" data = zeros(3) C = rand(3) buffer = NumpyCircularBuffer(data) buffer.append(0) res_c = C[:1] @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C[:1] @ arange(1))) buffer.append(1) res_c = C[:2] @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C[:2] @ arange(2))) buffer.append(2) res_c = C @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C @ arange(3))) buffer.append(3) res_c = C @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C @ arange(1, 4))) buffer.append(4) res_c = C @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C @ arange(2, 5))) buffer.append(5) res_c = C @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C @ arange(3, 6))) buffer.append(6) res_c = C @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C @ arange(4, 7))) buffer.pop() res_c = C[1:] @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C[1:] @ arange(5, 7))) buffer.pop() res_c = C[2:] @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C[2:] @ arange(6, 7))) def test_rmatmul_nd1d(self): """Tests X @ buffer where X.ndim == 1 and buffer.ndim > 1""" data = zeros(3) A = zeros(9).reshape(3, 3) B = arange(9).reshape(3, 3) C = arange(3) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) buffer.append(0) buffer.append(1) buffer.append(2) res_a = A @ buffer self.assertIsInstance(res_a, ndarray) self.assertTrue(array_equal(A @ buffer, A @ array([0, 1, 2]))) buffer.append(3) res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ array([1, 2, 3]))) self.assertTrue(allclose(res_b, B @ array([1, 2, 3]))) self.assertTrue(allclose(res_c, C @ array([1, 2, 3]))) buffer.append(4) res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ arange(2, 5))) self.assertTrue(allclose(res_b, B @ arange(2, 5))) self.assertTrue(allclose(res_c, C @ arange(2, 5))) buffer.append(5) res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ arange(3, 6))) self.assertTrue(allclose(res_b, B @ arange(3, 6))) self.assertTrue(allclose(res_c, C @ arange(3, 6))) def test_rmatmul_1dnd(self): """Tests X @ buffer where X.ndim == 1 and buffer.ndim > 1""" data1 = zeros((3, 3)) data2 = zeros((3, 3, 3)) A = rand(3) test1 = arange(9).reshape(3, 3) test2 = arange(27).reshape(3, 3, 3) buffer1 = NumpyCircularBuffer(data1) buffer2 = NumpyCircularBuffer(data2) buffer1.append(arange(3)) buffer1.append(arange(3, 6)) buffer1.append(arange(6, 9)) buffer2.append(arange(9).reshape(3, 3)) buffer2.append(arange(9, 18).reshape(3, 3)) buffer2.append(arange(18, 27).reshape(3, 3)) res_buf1 = A @ buffer1 res_buf2 = A @ buffer2 self.assertIsInstance(res_buf1, ndarray) self.assertIsInstance(res_buf2, ndarray) self.assertTrue(allclose(res_buf1, A @ test1)) self.assertTrue(allclose(res_buf2, A @ test2)) buffer1.append(arange(9, 12)) buffer2.append(arange(27, 36).reshape(3, 3)) test1 += 3 test2 += 9 res_buf1 = A @ buffer1 res_buf2 = A @ buffer2 self.assertIsInstance(res_buf1, ndarray) self.assertIsInstance(res_buf2, ndarray) self.assertTrue(allclose(res_buf1, A @ test1)) self.assertTrue(allclose(res_buf2, A @ test2)) buffer1.append(arange(12, 15)) buffer2.append(arange(36, 45).reshape(3, 3)) test1 += 3 test2 += 9 res_buf1 = A @ buffer1 res_buf2 = A @ buffer2 self.assertIsInstance(res_buf1, ndarray) self.assertIsInstance(res_buf2, ndarray) self.assertTrue(allclose(res_buf1, A @ test1)) self.assertTrue(allclose(res_buf2, A @ test2)) buffer1.append(arange(15, 18)) buffer2.append(arange(45, 54).reshape(3, 3)) test1 += 3 test2 += 9 res_buf1 = A @ buffer1 res_buf2 = A @ buffer2 self.assertIsInstance(res_buf1, ndarray) self.assertIsInstance(res_buf2, ndarray) self.assertTrue(allclose(res_buf1, A @ test1)) self.assertTrue(allclose(res_buf2, A @ test2)) def test_rmatmul_2d2d(self): data = zeros((3, 3)) A = zeros(9).reshape(3, 3) B = rand(9).reshape(3, 3) C = rand(12).reshape(4, 3) fill_diagonal(A, arange(1, 4)) buffer = NumpyCircularBuffer(data) buffer.append(arange(3)) buffer.append(arange(3, 6)) buffer.append(arange(6, 9)) test = arange(9).reshape(3, 3) self.assertTrue(array_equal(buffer, test)) res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ test)) self.assertTrue(allclose(res_b, B @ test)) self.assertTrue(allclose(res_c, C @ test)) buffer.append([9, 10, 11]) test += 3 res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ test)) self.assertTrue(allclose(res_b, B @ test)) self.assertTrue(allclose(res_c, C @ test)) def test_rmatmul_ndnd(self): data = zeros((3, 3, 3)) A = zeros(27).reshape(3, 3, 3) B = arange(27).reshape(3, 3, 3) C = arange(383).reshape(3, 8, 3) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) filler = arange(9).reshape(3, 3) buffer.append(filler) buffer.append(filler + 9) buffer.append(filler + 18) test = arange(27).reshape(3, 3, 3) res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ test)) self.assertTrue(allclose(res_b, B @ test)) self.assertTrue(allclose(res_c, C @ test)) buffer.append(filler + 27) test += 9 res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ test)) self.assertTrue(allclose(res_b, B @ test)) self.assertTrue(allclose(res_c, C @ test)) def test_matmul2_1d1d(self): """Tests buffer @ X where buffer.ndim == 1 and X.ndim == 1""" data = zeros(3) C = rand(3) buffer = NumpyCircularBuffer(data) buffer.append(0) self.assertTrue(allclose( buffer.matmul(C[:1], empty(1)), arange(1) @ C[:1] ) ) buffer.append(1) self.assertTrue(allclose( buffer.matmul(C[:2], empty(2)), arange(2) @ C[:2] ) ) buffer.append(2) self.assertTrue(allclose( buffer.matmul(C, empty(3)), arange(3) @ C ) ) buffer.append(3) self.assertTrue(allclose( buffer.matmul(C, empty(3)), arange(1, 4) @ C ) ) buffer.append(4) self.assertTrue(allclose( buffer.matmul(C, empty(3)), arange(2, 5) @ C ) ) buffer.append(5) self.assertTrue(allclose( buffer.matmul(C, empty(3)), arange(3, 6) @ C ) ) buffer.append(6) self.assertTrue(allclose( buffer.matmul(C, empty(3)), arange(4, 7) @ C ) ) buffer.pop() self.assertTrue(allclose( buffer.matmul(C[1:], empty(2)), arange(5, 7) @ C[1:] ) ) buffer.pop() self.assertTrue(allclose( buffer.matmul(C[2:], empty(1)), arange(6, 7) @ C[2:] ) ) def test_matmul2_1d2d(self): """Tests buffer @ X where buffer.ndim == 1 and X.ndim == 2""" data = zeros(3) A = zeros((3, 3)) B = rand(9).reshape(3, 3) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) buffer.append(0) buffer.append(1) buffer.append(2) res_a = buffer.matmul(A, empty(3)) res_b = buffer.matmul(B, empty(3)) self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, arange(3) @ A)) self.assertTrue(allclose(res_b, arange(3) @ B)) buffer.append(3) res_a = buffer.matmul(A, empty(3)) res_b = buffer.matmul(B, empty(3)) self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(allclose(res_a, arange(1, 4) @ A)) self.assertTrue(allclose(res_b, arange(1, 4) @ B)) def test_matmul2_2d2d(self): """Tests buffer @ X where buffer.ndim == 2""" data = zeros((3, 3)) A = zeros(9).reshape(3, 3) B = rand(9).reshape(3, 3) fill_diagonal(A, arange(1, 4)) buffer = NumpyCircularBuffer(data) buffer.append(arange(3)) buffer.append(arange(3, 6)) buffer.append(arange(6, 9)) test = arange(9).reshape(3, 3) self.assertTrue(array_equal(buffer, test)) res_a = buffer.matmul(A, empty((3, 3))) res_b = buffer.matmul(B, empty((3, 3))) self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) buffer.append(arange(9, 12)) test += 3 res_a = buffer.matmul(A, empty((3, 3))) res_b = buffer.matmul(B, empty((3, 3))) self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) def test_matmul2_ndnd(self): """Tests buffer @ X where X.ndim > 2 and buffer.ndim > 2""" data = zeros((3, 3, 3)) A = zeros((3, 3, 3)) B = rand(27).reshape(3, 3, 3) C = rand(12).reshape(3, 4) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) filler =	arange(9)	numpy.arange
# Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections from functools import partial import itertools import operator import unittest from unittest import SkipTest from absl.testing import absltest from absl.testing import parameterized import numpy as np import jax import jax.numpy as jnp from jax import core from jax._src import dtypes from jax import lax from jax._src import test_util as jtu from jax import tree_util from jax._src import lax_reference from jax.test_util import check_grads import jax.util from jax._src.util import prod from jax._src.lax.lax import _device_put_raw from jax.config import config config.parse_flags_with_absl() ### lax tests # For standard unops and binops, we can generate a large number of tests on # arguments of appropriate shapes and dtypes using the following table. float_dtypes = jtu.dtypes.all_floating complex_elem_dtypes = jtu.dtypes.floating complex_dtypes = jtu.dtypes.complex inexact_dtypes = jtu.dtypes.all_inexact int_dtypes = jtu.dtypes.all_integer uint_dtypes = jtu.dtypes.all_unsigned bool_dtypes = jtu.dtypes.boolean default_dtypes = float_dtypes + int_dtypes all_dtypes = float_dtypes + complex_dtypes + int_dtypes + uint_dtypes + bool_dtypes python_scalar_types = [bool, int, float, complex] compatible_shapes = [[(3,)], [(3, 4), (3, 1), (1, 4)], [(2, 3, 4), (2, 1, 4)]] # We check cases where the preferred type is at least as wide as the input # type and where both are either both floating-point or both integral, # which are the only supported configurations. preferred_type_combinations = [ (np.float16, np.float16), (np.float16, np.float32), (np.float16, np.float64), (dtypes.bfloat16, dtypes.bfloat16), (dtypes.bfloat16, np.float32), (dtypes.bfloat16, np.float64), (np.float32, np.float32), (np.float32, np.float64), (np.float64, np.float64), (np.int8, np.int8), (np.int8, np.int16), (np.int8, np.int32), (np.int8, np.int64), (np.int16, np.int16), (np.int16, np.int32), (np.int16, np.int64), (np.int32, np.int32), (np.int32, np.int64), (np.int64, np.int64), (np.complex64, np.complex64), (np.complex64, np.complex128), (np.complex128, np.complex128)] OpRecord = collections.namedtuple( "OpRecord", ["op", "nargs", "dtypes", "rng_factory", "tol"]) def op_record(op, nargs, dtypes, rng_factory, tol=None): return OpRecord(op, nargs, dtypes, rng_factory, tol) LAX_OPS = [ op_record("neg", 1, default_dtypes + complex_dtypes, jtu.rand_small), op_record("sign", 1, default_dtypes + uint_dtypes, jtu.rand_small), op_record("floor", 1, float_dtypes, jtu.rand_small), op_record("ceil", 1, float_dtypes, jtu.rand_small), op_record("round", 1, float_dtypes, jtu.rand_default), op_record("nextafter", 2, [f for f in float_dtypes if f != dtypes.bfloat16], jtu.rand_default, tol=0), op_record("is_finite", 1, float_dtypes, jtu.rand_small), op_record("exp", 1, float_dtypes + complex_dtypes, jtu.rand_small), # TODO(b/142975473): on CPU, expm1 for float64 is only accurate to ~float32 # precision. op_record("expm1", 1, float_dtypes + complex_dtypes, jtu.rand_small, {np.float64: 1e-8}), op_record("log", 1, float_dtypes + complex_dtypes, jtu.rand_positive), op_record("log1p", 1, float_dtypes + complex_dtypes, jtu.rand_positive), # TODO(b/142975473): on CPU, tanh for complex128 is only accurate to # ~float32 precision. # TODO(b/143135720): on GPU, tanh has only ~float32 precision. op_record("tanh", 1, float_dtypes + complex_dtypes, jtu.rand_small, {np.float64: 1e-9, np.complex128: 1e-7}), op_record("sin", 1, float_dtypes + complex_dtypes, jtu.rand_default), op_record("cos", 1, float_dtypes + complex_dtypes, jtu.rand_default), op_record("atan2", 2, float_dtypes, jtu.rand_default), op_record("sqrt", 1, float_dtypes, jtu.rand_positive), op_record("sqrt", 1, complex_dtypes, jtu.rand_default), op_record("rsqrt", 1, float_dtypes, jtu.rand_positive), op_record("rsqrt", 1, complex_dtypes, jtu.rand_default), op_record("cbrt", 1, float_dtypes, jtu.rand_default), op_record("square", 1, float_dtypes + complex_dtypes, jtu.rand_default), op_record("reciprocal", 1, float_dtypes + complex_dtypes, jtu.rand_positive), op_record("tan", 1, float_dtypes + complex_dtypes, jtu.rand_default, {np.float32: 3e-5}), op_record("asin", 1, float_dtypes + complex_dtypes, jtu.rand_small), op_record("acos", 1, float_dtypes + complex_dtypes, jtu.rand_small), op_record("atan", 1, float_dtypes + complex_dtypes, jtu.rand_small), op_record("asinh", 1, float_dtypes + complex_dtypes, jtu.rand_default, tol={np.complex64: 1E-4, np.complex128: 1E-5}), op_record("acosh", 1, float_dtypes + complex_dtypes, jtu.rand_positive), # TODO(b/155331781): atanh has only ~float precision op_record("atanh", 1, float_dtypes + complex_dtypes, jtu.rand_small, {np.float64: 1e-9}), op_record("sinh", 1, float_dtypes + complex_dtypes, jtu.rand_default), op_record("cosh", 1, float_dtypes + complex_dtypes, jtu.rand_default), op_record("lgamma", 1, float_dtypes, jtu.rand_positive, {np.float32: 1e-3 if jtu.device_under_test() == "tpu" else 1e-5, np.float64: 1e-14}), op_record("digamma", 1, float_dtypes, jtu.rand_positive, {np.float64: 1e-14}), op_record("betainc", 3, float_dtypes, jtu.rand_positive, {np.float64: 1e-14}), op_record("igamma", 2, [f for f in float_dtypes if f not in [dtypes.bfloat16, np.float16]], jtu.rand_positive, {np.float64: 1e-14}), op_record("igammac", 2, [f for f in float_dtypes if f not in [dtypes.bfloat16, np.float16]], jtu.rand_positive, {np.float64: 1e-14}), op_record("erf", 1, float_dtypes, jtu.rand_small), op_record("erfc", 1, float_dtypes, jtu.rand_small), # TODO(b/142976030): the approximation of erfinf used by XLA is only # accurate to float32 precision. op_record("erf_inv", 1, float_dtypes, jtu.rand_small, {np.float64: 1e-9}), op_record("bessel_i0e", 1, float_dtypes, jtu.rand_default), op_record("bessel_i1e", 1, float_dtypes, jtu.rand_default), op_record("real", 1, complex_dtypes, jtu.rand_default), op_record("imag", 1, complex_dtypes, jtu.rand_default), op_record("complex", 2, complex_elem_dtypes, jtu.rand_default), op_record("conj", 1, complex_elem_dtypes + complex_dtypes, jtu.rand_default), op_record("abs", 1, default_dtypes + complex_dtypes, jtu.rand_default), op_record("pow", 2, float_dtypes + complex_dtypes, jtu.rand_positive), op_record("bitwise_and", 2, bool_dtypes, jtu.rand_small), op_record("bitwise_not", 1, bool_dtypes, jtu.rand_small), op_record("bitwise_or", 2, bool_dtypes, jtu.rand_small), op_record("bitwise_xor", 2, bool_dtypes, jtu.rand_small), op_record("population_count", 1, int_dtypes + uint_dtypes, jtu.rand_int), op_record("clz", 1, int_dtypes + uint_dtypes, jtu.rand_int), op_record("add", 2, default_dtypes + complex_dtypes, jtu.rand_small), op_record("sub", 2, default_dtypes + complex_dtypes, jtu.rand_small), op_record("mul", 2, default_dtypes + complex_dtypes, jtu.rand_small), op_record("div", 2, default_dtypes + complex_dtypes, jtu.rand_nonzero), op_record("rem", 2, default_dtypes, jtu.rand_nonzero), op_record("max", 2, all_dtypes, jtu.rand_small), op_record("min", 2, all_dtypes, jtu.rand_small), op_record("eq", 2, all_dtypes, jtu.rand_some_equal), op_record("ne", 2, all_dtypes, jtu.rand_small), op_record("ge", 2, default_dtypes, jtu.rand_small), op_record("gt", 2, default_dtypes, jtu.rand_small), op_record("le", 2, default_dtypes, jtu.rand_small), op_record("lt", 2, default_dtypes, jtu.rand_small), ] class LaxTest(jtu.JaxTestCase): """Numerical tests for LAX operations.""" @parameterized.named_parameters(itertools.chain.from_iterable( jtu.cases_from_list( {"testcase_name": jtu.format_test_name_suffix( rec.op, shapes, itertools.repeat(dtype)), "op_name": rec.op, "rng_factory": rec.rng_factory, "shapes": shapes, "dtype": dtype} for shape_group in compatible_shapes for shapes in itertools.combinations_with_replacement(shape_group, rec.nargs) for dtype in rec.dtypes) for rec in LAX_OPS)) def testOp(self, op_name, rng_factory, shapes, dtype): rng = rng_factory(self.rng()) args_maker = lambda: [rng(shape, dtype) for shape in shapes] op = getattr(lax, op_name) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(itertools.chain.from_iterable( jtu.cases_from_list( {"testcase_name": jtu.format_test_name_suffix( rec.op, shapes, itertools.repeat(dtype)), "op_name": rec.op, "rng_factory": rec.rng_factory, "shapes": shapes, "dtype": dtype, "tol": rec.tol} for shape_group in compatible_shapes for shapes in itertools.combinations_with_replacement(shape_group, rec.nargs) for dtype in rec.dtypes) for rec in LAX_OPS)) def testOpAgainstNumpy(self, op_name, rng_factory, shapes, dtype, tol): if (not config.x64_enabled and op_name == "nextafter" and dtype == np.float64): raise SkipTest("64-bit mode disabled") rng = rng_factory(self.rng()) args_maker = lambda: [rng(shape, dtype) for shape in shapes] op = getattr(lax, op_name) numpy_op = getattr(lax_reference, op_name) self._CheckAgainstNumpy(numpy_op, op, args_maker, tol=tol) # TODO test shift_left, shift_right_arithmetic, shift_right_logical @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_from_dtype={}_to_dtype={}_weak_type={}".format( from_dtype, to_dtype, weak_type), "from_dtype": from_dtype, "to_dtype": to_dtype, "weak_type": weak_type} for from_dtype, to_dtype in itertools.product( [None, np.float32, np.int32, "float32", "int32"], repeat=2) for weak_type in [True, False])) def testConvertElementType(self, from_dtype, to_dtype, weak_type): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng((2, 3), from_dtype)] op = lambda x: lax._convert_element_type(x, to_dtype, weak_type) self._CompileAndCheck(op, args_maker) x = rng((1,), from_dtype) out = op(x) self.assertEqual(out.dtype, dtypes.canonicalize_dtype(to_dtype or x.dtype)) self.assertEqual(out.aval.weak_type, weak_type) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_from_dtype={}_to_dtype={}" .format(from_dtype, to_dtype), "from_dtype": from_dtype, "to_dtype": to_dtype} for from_dtype, to_dtype in itertools.product( [np.float32, np.int32, "float32", "int32"], repeat=2))) def testConvertElementTypeAgainstNumpy(self, from_dtype, to_dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng((2, 3), from_dtype)] op = lambda x: lax.convert_element_type(x, to_dtype) numpy_op = lambda x: lax_reference.convert_element_type(x, to_dtype) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_from_dtype={}_to_dtype={}" .format(from_dtype, to_dtype), "from_dtype": from_dtype, "to_dtype": to_dtype} for from_dtype, to_dtype in itertools.product( [np.float32, np.int32, "float32", "int32"], repeat=2))) def testBitcastConvertType(self, from_dtype, to_dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng((2, 3), from_dtype)] op = lambda x: lax.bitcast_convert_type(x, to_dtype) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_from_dtype={}_to_dtype={}" .format(from_dtype, to_dtype), "from_dtype": from_dtype, "to_dtype": to_dtype} for from_dtype, to_dtype in itertools.product( [np.float32, np.int32, "float32", "int32"], repeat=2))) def testBitcastConvertTypeAgainstNumpy(self, from_dtype, to_dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng((2, 3), from_dtype)] op = lambda x: lax.bitcast_convert_type(x, to_dtype) numpy_op = lambda x: lax_reference.bitcast_convert_type(x, to_dtype) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_from_dtype={}_to_dtype={}_weak_type={}" .format(from_dtype, to_dtype, weak_type), "from_dtype": from_dtype, "to_dtype": to_dtype, "weak_type": weak_type} for from_dtype, to_dtype in itertools.product( [np.float32, np.int32, "float32", "int32"], repeat=2) for weak_type in [True, False])) def testBitcastConvertWeakType(self, from_dtype, to_dtype, weak_type): rng = jtu.rand_default(self.rng()) x_in = lax._convert_element_type(rng((2, 3), from_dtype), weak_type=weak_type) op = lambda x: lax.bitcast_convert_type(x, to_dtype) self.assertEqual(dtypes.is_weakly_typed(x_in), weak_type) x_out = op(x_in) self.assertEqual(dtypes.is_weakly_typed(x_out), False) x_out_jit = jax.jit(op)(x_in) self.assertEqual(dtypes.is_weakly_typed(x_out_jit), False) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_min_shape={}_operand_shape={}_max_shape={}".format( jtu.format_shape_dtype_string(min_shape, dtype), jtu.format_shape_dtype_string(operand_shape, dtype), jtu.format_shape_dtype_string(max_shape, dtype)), "min_shape": min_shape, "operand_shape": operand_shape, "max_shape": max_shape, "dtype": dtype} for min_shape, operand_shape, max_shape in [ [(), (2, 3), ()], [(2, 3), (2, 3), ()], [(), (2, 3), (2, 3)], [(2, 3), (2, 3), (2, 3)], ] for dtype in default_dtypes)) def testClamp(self, min_shape, operand_shape, max_shape, dtype): rng = jtu.rand_default(self.rng()) shapes = [min_shape, operand_shape, max_shape] args_maker = lambda: [rng(shape, dtype) for shape in shapes] self._CompileAndCheck(lax.clamp, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_min_shape={}_operand_shape={}_max_shape={}".format( jtu.format_shape_dtype_string(min_shape, dtype), jtu.format_shape_dtype_string(operand_shape, dtype), jtu.format_shape_dtype_string(max_shape, dtype)), "min_shape": min_shape, "operand_shape": operand_shape, "max_shape": max_shape, "dtype": dtype} for min_shape, operand_shape, max_shape in [ [(), (2, 3), ()], [(2, 3), (2, 3), ()], [(), (2, 3), (2, 3)], [(2, 3), (2, 3), (2, 3)], ] for dtype in default_dtypes)) def testClampAgainstNumpy(self, min_shape, operand_shape, max_shape, dtype): rng = jtu.rand_default(self.rng()) shapes = [min_shape, operand_shape, max_shape] args_maker = lambda: [rng(shape, dtype) for shape in shapes] self._CheckAgainstNumpy(lax_reference.clamp, lax.clamp, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_dim={}_baseshape=[{}]_dtype={}_narrs={}".format( dim, ",".join(str(d) for d in base_shape), np.dtype(dtype).name, num_arrs), "dim": dim, "base_shape": base_shape, "dtype": dtype, "num_arrs": num_arrs} for num_arrs in [3] for dtype in default_dtypes for base_shape in [(4,), (3, 4), (2, 3, 4)] for dim in range(len(base_shape)))) def testConcatenate(self, dim, base_shape, dtype, num_arrs): rng = jtu.rand_default(self.rng()) shapes = [base_shape[:dim] + (size,) + base_shape[dim+1:] for size, _ in zip(itertools.cycle([3, 1, 4]), range(num_arrs))] args_maker = lambda: [rng(shape, dtype) for shape in shapes] op = lambda args: lax.concatenate(args, dim) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_dim={}_baseshape=[{}]_dtype={}_narrs={}".format( dim, ",".join(str(d) for d in base_shape), np.dtype(dtype).name, num_arrs), "dim": dim, "base_shape": base_shape, "dtype": dtype, "num_arrs": num_arrs} for num_arrs in [3] for dtype in default_dtypes for base_shape in [(4,), (3, 4), (2, 3, 4)] for dim in range(len(base_shape)))) def testConcatenateAgainstNumpy(self, dim, base_shape, dtype, num_arrs): rng = jtu.rand_default(self.rng()) shapes = [base_shape[:dim] + (size,) + base_shape[dim+1:] for size, _ in zip(itertools.cycle([3, 1, 4]), range(num_arrs))] args_maker = lambda: [rng(shape, dtype) for shape in shapes] op = lambda args: lax.concatenate(args, dim) numpy_op = lambda args: lax_reference.concatenate(args, dim) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding} for lhs_shape, rhs_shape in [ ((b, i, 9, 10), (j, i, 4, 5)) for b, i, j in itertools.product([2, 3], repeat=3)] for dtype in float_dtypes for strides in [(1, 1), (1, 2), (2, 1)] for padding in ["VALID", "SAME"])) def testConv(self, lhs_shape, rhs_shape, dtype, strides, padding): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv(lhs, rhs, strides, padding) self._CompileAndCheck(fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_preferred_element_type={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), preferred_element_type.__name__), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "preferred_element_type": preferred_element_type} for lhs_shape, rhs_shape in [ ((b, i, 9, 10), (j, i, 4, 5)) for b, i, j in itertools.product([2, 3], repeat=3)] for dtype, preferred_element_type in preferred_type_combinations)) def testConvPreferredElement(self, lhs_shape, rhs_shape, dtype, preferred_element_type): if (not config.x64_enabled and (dtype == np.float64 or preferred_element_type == np.float64 or dtype == np.int64 or preferred_element_type == np.int64 or dtype == np.complex128 or preferred_element_type == np.complex128)): raise SkipTest("64-bit mode disabled") if jtu.device_under_test() == "gpu" and np.issubdtype(dtype, np.integer): # TODO(b/183565702): Support integer convolutions on CPU/GPU. raise SkipTest("Integer convolution not yet supported on GPU") if (jtu.device_under_test() == "tpu" and (dtype == np.complex128 or preferred_element_type == np.complex128)): raise SkipTest("np.complex128 is not yet supported on TPU") # x64 implementation is only accurate to ~float32 precision for this case. if dtype == np.complex64 and preferred_element_type == np.complex128: tol = 1e-5 else: tol = {np.float64: 1e-14} rng = jtu.rand_default(self.rng()) x = rng(lhs_shape, dtype) y = rng(rhs_shape, dtype) # We first compute the conv when both inputs are a lower-precision type and # preferred_element_type is a higher-precision type. We then compute results # where the inputs are first upcast to the higher-precision type and no # `preferred_element_type` is given. We expect the result to be extremely # similar given the semantics of `preferred_element_type`. result_with_preferred_type = lax.conv( x, y, (1, 1), "VALID", preferred_element_type=preferred_element_type) result_with_upcast_inputs = lax.conv( x.astype(preferred_element_type), y.astype(preferred_element_type), (1, 1), "VALID") self.assertArraysAllClose( result_with_preferred_type, result_with_upcast_inputs, rtol=tol, atol=tol) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding} for lhs_shape, rhs_shape in [ ((b, i, 9, 10), (j, i, 4, 5)) for b, i, j in itertools.product([2, 3], repeat=3)] for dtype in float_dtypes for strides in [(1, 1), (1, 2), (2, 1)] for padding in ["VALID", "SAME"])) def testConvAgainstNumpy(self, lhs_shape, rhs_shape, dtype, strides, padding): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] op = lambda lhs, rhs: lax.conv(lhs, rhs, strides, padding) numpy_op = lambda lhs, rhs: lax_reference.conv(lhs, rhs, strides, padding) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}" "_lhs_dilation={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, lhs_dilation, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "lhs_dilation": lhs_dilation, "rhs_dilation": rhs_dilation} for lhs_shape, rhs_shape in [ ((b, i, 9, 10), (j, i, 4, 5)) for b, i, j in itertools.product([1, 2, 3], repeat=3)] for dtype in float_dtypes for strides in [(1, 1), (1, 2), (2, 1)] for padding in [((0, 0), (0, 0)), ((1, 2), (2, 0))] for lhs_dilation, rhs_dilation in itertools.product( [(1, 1), (1, 2), (2, 2)], repeat=2))) def testConvWithGeneralPadding(self, lhs_shape, rhs_shape, dtype, strides, padding, lhs_dilation, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv_with_general_padding( lhs, rhs, strides, padding, lhs_dilation, rhs_dilation) self._CompileAndCheck(fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}" "_lhs_dilation={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, lhs_dilation, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "lhs_dilation": lhs_dilation, "rhs_dilation": rhs_dilation} for lhs_shape, rhs_shape in [ ((b, i, 9, 10), (j, i, 4, 5)) for b, i, j in itertools.product([1, 2, 3], repeat=3)] for dtype in [np.float32] for strides in [(1, 1), (1, 2), (2, 1)] for padding in [((0, 0), (0, 0)), ((1, 2), (2, 0))] for lhs_dilation, rhs_dilation in itertools.product( [(1, 1), (1, 2), (2, 2)], repeat=2))) def testConvWithGeneralPaddingAgainstNumpy( self, lhs_shape, rhs_shape, dtype, strides, padding, lhs_dilation, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv_with_general_padding( lhs, rhs, strides, padding, lhs_dilation, rhs_dilation, precision=lax.Precision.HIGHEST) def numpy_fun(lhs, rhs): return lax_reference.conv_with_general_padding( lhs, rhs, strides, padding, lhs_dilation, rhs_dilation) self._CheckAgainstNumpy(numpy_fun, fun, args_maker) @parameterized.named_parameters(jtu.named_cases_from_sampler(lambda s: ({ "testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}" "_lhs_dilation={}_rhs_dilation={}" "_dims={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, lhs_dilation, rhs_dilation, ",".join(dim_nums)), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "lhs_dilation": lhs_dilation, "rhs_dilation": rhs_dilation, "dimension_numbers": dim_nums, "feature_group_count": feature_group_count, "batch_group_count": batch_group_count, "perms": perms } for batch_group_count, feature_group_count in s([(1, 1), (2, 1), (1, 2)]) for lhs_shape, rhs_shape in s([ ((b batch_group_count, i * feature_group_count, 9, w), (j * feature_group_count * batch_group_count, i, 4, 5)) for w in [0, 10] for b, i, j in itertools.product([2, 3], repeat=3)]) for dtype in s(all_dtypes) for strides in s([(1, 1), (2, 1)]) for padding in s([((1, 2), (2, 0)), ((10, 8), (7, 13))]) for lhs_dilation, rhs_dilation in s(itertools.product( [(1, 1), (1, 2), (1, 4)], repeat=2)) for dim_nums, perms in s([ (("NCHW", "OIHW", "NCHW"), ([0, 1, 2, 3], [0, 1, 2, 3])), (("NHWC", "HWIO", "NHWC"), ([0, 2, 3, 1], [2, 3, 1, 0])), (("NCHW", "HWIO", "NHWC"), ([0, 1, 2, 3], [2, 3, 1, 0])), ])))) def testConvGeneralDilated(self, lhs_shape, rhs_shape, dtype, strides, padding, lhs_dilation, rhs_dilation, feature_group_count, batch_group_count, dimension_numbers, perms): if np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.bool_): # TODO(b/183565702): Support integer convolutions on CPU/GPU. if jtu.device_under_test() == "gpu": raise SkipTest("Integer convolution not yet supported on GPU") rng = jtu.rand_small(self.rng()) lhs_perm, rhs_perm = perms # permute to compatible shapes def args_maker(): return [lax.transpose(rng(lhs_shape, dtype), lhs_perm), lax.transpose(rng(rhs_shape, dtype), rhs_perm)] def fun(lhs, rhs): return lax.conv_general_dilated( lhs, rhs, strides, padding, lhs_dilation, rhs_dilation, dimension_numbers, feature_group_count=feature_group_count, batch_group_count=batch_group_count) self._CompileAndCheck(fun, args_maker) def testConvGeneralDilatedPatchesOverlapping1D(self): lhs = np.array([[1]], np.float32).reshape((1, 1)) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=(), window_strides=(), padding='SAME' ) self.assertAllClose(lhs, patches) dn = ('NHC', 'OIH', 'NHC') lhs = np.array([1, 2, 3, 4, 5], np.float32).reshape((1, -1, 1)) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=(2,), window_strides=(2,), padding='VALID', dimension_numbers=dn ) self.assertAllClose( np.array([[1, 2], [3, 4]], np.float32).reshape((1, 2, 2)), patches) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=(3,), window_strides=(1,), padding='SAME', dimension_numbers=dn ) self.assertAllClose( np.array([[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 0]], np.float32).reshape((1, 5, 3)), patches) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=(3,), window_strides=(1,), padding='SAME', rhs_dilation=(2,), dimension_numbers=dn ) self.assertAllClose( np.array([[0, 1, 3], [0, 2, 4], [1, 3, 5], [2, 4, 0], [3, 5, 0]], np.float32).reshape((1, 5, 3)), patches) def testConvGeneralDilatedPatchesOverlapping2D(self): lhs = np.array([[1, 2, 3], [4, 5, 6]], np.float32).reshape((1, 2, 3, 1)) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=(2, 2), window_strides=(1, 1), padding='SAME', dimension_numbers=('NHWC', 'OIHW', 'NHWC') ) self.assertAllClose(np.array([[1, 2, 4, 5], [2, 3, 5, 6], [3, 0, 6, 0], [4, 5, 0, 0], [5, 6, 0, 0], [6, 0, 0, 0]], np.float32).reshape((1, 2, 3, 4)), patches) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_filter_shape={}_strides={}_padding={}" "_dims={}_precision={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(filter_shape, dtype), strides, padding, "None" if dim_nums is None else ",".join(dim_nums), precision ), "lhs_shape": lhs_shape, "filter_shape": filter_shape, "dtype": dtype, "strides": strides, "padding": padding, "dimension_numbers": dim_nums, "precision": precision } for dtype in all_dtypes for lhs_shape, filter_shape, strides, padding, dim_nums in [ ((2, 5), (), (), [], ("NC", "OI", "CN")), ((2, 3, 4), (2,), (2,), [(0, 2)], ("CNH", "OHI", "HNC")), ((3, 1, 4, 5), (1, 3), (1, 3), [(3, 1), (2, 2)], ("NCHW", "OIHW", "NCHW")), ((3, 2, 5, 6), (4, 3), (4, 3), [(5, 2), (2, 4)], None), ((1, 2, 3, 4), (1, 1), (1, 1), [(0, 0), (0, 0)], ("NCWH", "OHWI", "CNHW")), ((1, 2, 3, 4), (3, 2), (1, 1), [(0, 0), (0, 0)], ("CWHN", "HOWI", "NCHW")), ((2, 3, 4, 5, 6), (2, 1, 3), (2, 1, 3), [(1, 2), (5, 3), (3, 5)], ("NHWDC", "HDIWO", "DCWNH")) ] for precision in [None, lax.Precision.DEFAULT, lax.Precision.HIGH, lax.Precision.HIGHEST] )) def testConvGeneralDilatedPatchesNonOverlapping(self, lhs_shape, filter_shape, dtype, strides, padding, dimension_numbers, precision): if np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.bool_): # TODO(b/183565702): Support integer convolutions on CPU/GPU. if jtu.device_under_test() == "gpu": raise SkipTest("Integer convolution not yet supported on GPU") rng = jtu.rand_small(self.rng()) lhs = rng(lhs_shape, dtype) if dimension_numbers is None: lhs_spec, rhs_spec, out_spec = "NCHW", "OIHW", "NCHW" else: lhs_spec, rhs_spec, out_spec = dimension_numbers filter_spec = ''.join(c for c in rhs_spec if c not in ('I', 'O')) patches_spec = out_spec.replace('C', 'C' + filter_spec.lower()) full_padding = [] for c in lhs_spec: if c in ('N', 'C'): full_padding += [(0, 0)] else: full_padding += [padding[filter_spec.index(c)]] lhs_padded = np.pad(lhs, full_padding, 'constant') out = lax.transpose(lhs_padded, [lhs_spec.index(c) for c in out_spec]) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=filter_shape, window_strides=strides, padding=padding, dimension_numbers=dimension_numbers, precision=precision ) source = [] # Test that output spatial shape is factored into `#patches x patch_size`. for c in out_spec: out_c = out.shape[out_spec.index(c)] patch_c = patches.shape[out_spec.index(c)] if c == 'N': self.assertEqual(out_c, patch_c) elif c == 'C': self.assertEqual(out_c * np.prod(filter_shape), patch_c) else: self.assertEqual(out_c, patch_c * filter_shape[filter_spec.index(c)]) source += [patches_spec.index(c), patches_spec.index(c.lower())] # Test that stacking patches together gives the source image, padded. c = out_spec.index('C') patches = patches.reshape(patches.shape[:c] + (lhs_shape[lhs_spec.index('C')],) + filter_shape + patches.shape[c + 1:] ) patches = np.moveaxis(patches, source, range(len(source))) for i in range(len(filter_shape)): patches = patches.reshape(patches.shape[:i] + (-1,) + patches.shape[2 + i:]) patches = np.moveaxis( patches, range(len(filter_shape)), [out_spec.index(c) for c in out_spec if c not in ('N', 'C')]) self.assertAllClose(out, patches) # TODO(mattjj): test conv_general_dilated against numpy def testConv0DIsDot(self): rng = jtu.rand_default(self.rng()) def args_maker(): return [rng((10, 5), np.float32), rng((5, 7), np.float32)] jnp_fun = partial(lax.conv_general_dilated, window_strides=(), padding='VALID', dimension_numbers=('NC', 'IO', 'NC')) self._CompileAndCheck(jnp_fun, args_maker) self._CheckAgainstNumpy(np.dot, jnp_fun, args_maker, tol=.1) def testGradConv0D(self): # Reproduces a failure in neural_tangents not caught in our presubmit tests # See cl/367416742. lhs = np.ones((2, 5), dtype=np.float32) rhs = np.ones((5, 10), dtype=np.float32) def f_jax(lhs, rhs): return lax.conv_general_dilated( lhs, rhs, window_strides=(), padding=(), lhs_dilation=(), rhs_dilation=(), dimension_numbers=lax.ConvDimensionNumbers((0, 1), (1, 0), (0, 1)), batch_group_count=1, feature_group_count=1, precision=None, preferred_element_type=None) res, pullback = jax.vjp(f_jax, lhs, rhs) grad = pullback(np.ones_like(res)) self.assertAllClose((lhs * 10., rhs * 2.), grad) @staticmethod def _conv_transpose_via_grad(data, kernel, strides, padding, rhs_dilation=None, dimension_numbers=None): """Helper method: calculates conv transpose via grad for testing.""" assert len(data.shape) == len(kernel.shape) nspatial = len(data.shape) - 2 one = (1,) * nspatial rhs_dilation = rhs_dilation or one dn = lax.conv_dimension_numbers(data.shape, kernel.shape, dimension_numbers) in_shape = np.take(data.shape, dn.lhs_spec) in_sdims = in_shape[2:] k_shape = np.take(kernel.shape, dn.rhs_spec) k_sdims = k_shape[2:] e_k_sdims = [(k-1) * r + 1 for k, r in zip(k_sdims, rhs_dilation)] if padding == 'VALID': o_sdims = [in_sdims[i]strides[i] + max(e_k_sdims[i]-strides[i],0) for i in range(nspatial)] elif padding == 'SAME': o_sdims = [in_sdims[i]strides[i] for i in range(nspatial)] o_shape = [in_shape[0], k_shape[1]] + o_sdims out_spec_inv = [x[0] for x in sorted(enumerate(dn.out_spec), key=lambda x: x[1])] o_layout = np.take(np.array(o_shape), out_spec_inv) placeholder = np.ones(o_layout, data.dtype) conv = lambda x: lax.conv_general_dilated(x, kernel, strides, padding, one, rhs_dilation, dn) _, g = jax.vjp(conv, placeholder) return g(data)[0] @staticmethod def _transpose_conv_kernel(data, kernel, dimension_numbers): dn = lax.conv_dimension_numbers(data.shape, kernel.shape, dimension_numbers) spatial_axes = np.array(dn.rhs_spec)[2:] for axis in spatial_axes: kernel = np.flip(kernel, axis) kernel = np.swapaxes(kernel, dn.rhs_spec[0], dn.rhs_spec[1]) return kernel @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "rhs_dilation": rhs_dilation, "dspec": dspec} for lhs_shape, rhs_shape in [ ((b, 9, 10, i), (k, k, j, i)) # NB: i,j flipped in RHS for transpose for b, i, j, k in itertools.product([2,3],[2,3],[2,3],[3,4,5])] for dtype in float_dtypes for strides in [(1, 1), (1, 2), (2, 1), (2, 2), (3, 3)] for padding in ["VALID", "SAME"] for dspec in [('NHWC', 'HWIO', 'NHWC'),] for rhs_dilation in [None, (2, 2)])) @jtu.skip_on_flag("jax_skip_slow_tests", True) def testConvTranspose2DT(self, lhs_shape, rhs_shape, dtype, strides, padding, dspec, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] # NB: this test calculates conv_transpose performing identically to the # lhs-grad of conv. def fun(lhs, rhs): return lax.conv_transpose(lhs, rhs, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec, transpose_kernel=True) def fun_via_grad(lhs, rhs): return self._conv_transpose_via_grad(lhs, rhs, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec) # NB: below just checks for agreement, we're not calling numpy. self._CheckAgainstNumpy(fun_via_grad, fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "rhs_dilation": rhs_dilation, "dspec": dspec} for lhs_shape, rhs_shape in [ ((b, 9, 10, i), (k, k, i, j)) for b, i, j, k in itertools.product([2,3],[2,3],[2,3],[3,4,5])] for dtype in float_dtypes for strides in [(1, 1), (1, 2), (2, 1), (2, 2), (3, 3)] for padding in ["VALID", "SAME"] for dspec in [('NHWC', 'HWIO', 'NHWC'),] for rhs_dilation in [None, (2, 2)])) @jtu.skip_on_flag("jax_skip_slow_tests", True) def testConvTranspose2D(self, lhs_shape, rhs_shape, dtype, strides, padding, dspec, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv_transpose(lhs, rhs, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec, transpose_kernel=False) def fun_via_grad(lhs, rhs): rhs_t = self._transpose_conv_kernel(lhs, rhs, dimension_numbers=dspec) return self._conv_transpose_via_grad(lhs, rhs_t, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec) # NB: below just checks for agreement, we're not calling numpy. self._CheckAgainstNumpy(fun_via_grad, fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "rhs_dilation": rhs_dilation, "dspec": dspec} for lhs_shape, rhs_shape in [ ((b, 10, i), (k, i, j)) for b, i, j, k in itertools.product([2,3],[2,3],[2,3],[3,4,5])] for dtype in float_dtypes for strides in [(1,), (2,), (3,)] for padding in ["VALID", "SAME"] for dspec in [('NHC', 'HIO', 'NHC'),] for rhs_dilation in [None, (2,)])) def testConvTranspose1D(self, lhs_shape, rhs_shape, dtype, strides, padding, dspec, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv_transpose(lhs, rhs, strides, padding, dimension_numbers=dspec, rhs_dilation=rhs_dilation, transpose_kernel=False) def fun_via_grad(lhs, rhs): rhs_t = self._transpose_conv_kernel(lhs, rhs, dimension_numbers=dspec) return self._conv_transpose_via_grad(lhs, rhs_t, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec) # NB: below just checks for agreement, we're not calling numpy. self._CheckAgainstNumpy(fun_via_grad, fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "rhs_dilation": rhs_dilation, "dspec": dspec} for lhs_shape, rhs_shape in [ ((b, i), (i, j)) for b, i, j in itertools.product([2,3],[2,3],[2,3])] for dtype in float_dtypes for strides in [()] for padding in ["VALID", "SAME"] for dspec in [('NC', 'IO', 'NC'),] for rhs_dilation in [None, ()])) def testConvTranspose0D(self, lhs_shape, rhs_shape, dtype, strides, padding, dspec, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv_transpose(lhs, rhs, strides, padding, dimension_numbers=dspec, rhs_dilation=rhs_dilation, transpose_kernel=False) def fun_via_grad(lhs, rhs): rhs_t = self._transpose_conv_kernel(lhs, rhs, dimension_numbers=dspec) return self._conv_transpose_via_grad(lhs, rhs_t, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec) # NB: below just checks for agreement, we're not calling numpy. self._CheckAgainstNumpy(fun_via_grad, fun, args_maker) def testConvTransposePaddingList(self): # Regression test for https://github.com/google/jax/discussions/8695 a = jnp.ones((28,28)) b = jnp.ones((3,3)) c = lax.conv_general_dilated(a[None, None], b[None, None], (1,1), [(0,0),(0,0)], (1,1)) self.assertArraysEqual(c, 9 * jnp.ones((1, 1, 26, 26))) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_precision={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), precision), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "precision": precision} for lhs_shape in [(3,), (4, 3)] for rhs_shape in [(3,), (3, 6)] for dtype in all_dtypes for precision in [None, lax.Precision.DEFAULT, lax.Precision.HIGH, lax.Precision.HIGHEST, (lax.Precision.DEFAULT, lax.Precision.HIGHEST)])) def testDot(self, lhs_shape, rhs_shape, dtype, precision): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] self._CompileAndCheck(partial(lax.dot, precision=precision), args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_preferred_element_type={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), jtu.format_shape_dtype_string((), preferred_element_type) ), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "preferred_element_type": preferred_element_type } for lhs_shape in [(3,), (4, 3)] for rhs_shape in [(3,), (3, 6)] for dtype, preferred_element_type in preferred_type_combinations)) def testDotPreferredElement(self, lhs_shape, rhs_shape, dtype, preferred_element_type): if (not config.x64_enabled and (dtype == np.float64 or preferred_element_type == np.float64 or dtype == np.int64 or preferred_element_type == np.int64)): raise SkipTest("64-bit mode disabled") if (jtu.device_under_test() == "tpu" and (dtype == np.complex128 or preferred_element_type == np.complex128)): raise SkipTest("np.complex128 is not yet supported on TPU") if jtu.device_under_test() == "gpu": # TODO(b/189287598) raise SkipTest("dot_general with preferred_element_type returns NaN non-deterministically on GPU") rng = jtu.rand_default(self.rng()) x = rng(lhs_shape, dtype) y = rng(rhs_shape, dtype) # We first compute the dot when both inputs are a lower-precision type and # preferred_element_type is a higher-precision type. We then compute results # where the inputs are first upcast to the higher-precision type and no # `preferred_element_type` is given. We expect the result to be extremely # similar given the semantics of `preferred_element_type`. result_with_preferred_type = lax.dot(x, y, preferred_element_type=preferred_element_type) result_with_upcast_inputs = lax.dot( x.astype(preferred_element_type), y.astype(preferred_element_type)) self.assertArraysAllClose(result_with_preferred_type, result_with_upcast_inputs) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype)), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype} for lhs_shape in [(3,), (4, 3)] for rhs_shape in [(3,), (3, 6)] for dtype in all_dtypes)) def testDotAgainstNumpy(self, lhs_shape, rhs_shape, dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] tol = { np.float16: 1e-2, np.float64: max(jtu.default_tolerance()[np.dtype(np.float64)], 1e-14), np.complex128: max(jtu.default_tolerance()[np.dtype(np.complex128)], 1e-14) } lax_op = partial(lax.dot, precision=lax.Precision.HIGHEST) self._CheckAgainstNumpy(lax_reference.dot, lax_op, args_maker, tol=tol) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_lhs_contracting={}_rhs_contracting={}" .format(jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), lhs_contracting, rhs_contracting), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "lhs_contracting": lhs_contracting, "rhs_contracting": rhs_contracting} for lhs_shape, rhs_shape, lhs_contracting, rhs_contracting in [ [(5,), (5,), [0], [0]], [(5, 7), (5,), [0], [0]], [(7, 5), (5,), [1], [0]], [(3, 5), (2, 5), [1], [1]], [(5, 3), (5, 2), [0], [0]], [(5, 3, 2), (5, 2, 4), [0], [0]], [(5, 3, 2), (5, 2, 4), [0,2], [0,1]], [(5, 3, 2), (3, 5, 2, 4), [0,2], [1,2]], [(1, 2, 2, 3), (1, 2, 3, 1), [1], [1]], [(3, 2), (2, 4), [1], [0]], ] for dtype in all_dtypes)) def testDotGeneralContractOnly(self, lhs_shape, rhs_shape, dtype, lhs_contracting, rhs_contracting): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] dimension_numbers = ((lhs_contracting, rhs_contracting), ([], [])) def fun(lhs, rhs): return lax.dot_general(lhs, rhs, dimension_numbers) self._CompileAndCheck(fun, args_maker, check_dtypes=False) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_dimension_numbers={}" .format(jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), dimension_numbers), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "dimension_numbers": dimension_numbers} for lhs_shape, rhs_shape, dimension_numbers in [ ((3, 3, 2), (3, 2, 4), (([2], [1]), ([0], [0]))), ((3, 3, 2), (2, 3, 4), (([2], [0]), ([0], [1]))), ((3, 4, 2, 4), (3, 4, 3, 2), (([2], [3]), ([0, 1], [0, 1]))), ] for dtype in all_dtypes)) def testDotGeneralContractAndBatch(self, lhs_shape, rhs_shape, dtype, dimension_numbers): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.dot_general(lhs, rhs, dimension_numbers) self._CompileAndCheck(fun, args_maker, check_dtypes=False) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_dimension_numbers={}" .format(jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), dimension_numbers), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "dimension_numbers": dimension_numbers} for lhs_shape, rhs_shape, dimension_numbers in [ ((3, 3, 2), (3, 2, 4), (([2], [1]), ([0], [0]))), ((3, 3, 2), (2, 3, 4), (([2], [0]), ([0], [1]))), ((3, 4, 2, 4), (3, 4, 3, 2), (([2], [3]), ([0, 1], [0, 1]))), ] for dtype in all_dtypes)) def testDotGeneralAgainstNumpy(self, lhs_shape, rhs_shape, dtype, dimension_numbers): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] op = lambda x, y: lax.dot_general(x, y, dimension_numbers) numpy_op = lambda x, y: lax_reference.dot_general(x, y, dimension_numbers) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_dtype={}_broadcast_sizes={}".format( shape, np.dtype(dtype).name, broadcast_sizes), "shape": shape, "dtype": dtype, "broadcast_sizes": broadcast_sizes} for shape in [(), (2, 3)] for dtype in default_dtypes for broadcast_sizes in [(), (2,), (1, 2)])) def testBroadcast(self, shape, dtype, broadcast_sizes): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.broadcast(x, broadcast_sizes) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_broadcast_sizes={}".format( jtu.format_shape_dtype_string(shape, dtype), broadcast_sizes), "shape": shape, "dtype": dtype, "broadcast_sizes": broadcast_sizes} for shape in [(), (2, 3)] for dtype in default_dtypes for broadcast_sizes in [(), (2,), (1, 2)])) def testBroadcastAgainstNumpy(self, shape, dtype, broadcast_sizes): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.broadcast(x, broadcast_sizes) numpy_op = lambda x: lax_reference.broadcast(x, broadcast_sizes) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_outshape={}_bcdims={}".format( jtu.format_shape_dtype_string(inshape, dtype), outshape, broadcast_dimensions), "inshape": inshape, "dtype": dtype, "outshape": outshape, "dimensions": broadcast_dimensions} for inshape, outshape, broadcast_dimensions in [ ([2], [2, 2], [0]), ([2], [2, 2], [1]), ([2], [2, 3], [0]), ([], [2, 3], []), ([1], [2, 3], [1]), ] for dtype in default_dtypes)) def testBroadcastInDim(self, inshape, dtype, outshape, dimensions): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(inshape, dtype)] op = lambda x: lax.broadcast_in_dim(x, outshape, dimensions) self._CompileAndCheck(op, args_maker) def testBroadcastInDimOperandShapeTranspose(self): # Regression test for https://github.com/google/jax/issues/5276 def f(x): return lax.broadcast_in_dim(x, (2, 3, 4), broadcast_dimensions=(0, 1, 2)).sum() def g(x): return lax.broadcast_in_dim(x.reshape((3,)), (2, 3, 4), broadcast_dimensions=(1,)).sum() x = np.ones((1, 3, 1)) self.assertArraysEqual(jax.grad(f)(x), jax.grad(g)(x)) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_outshape={}_bcdims={}".format( jtu.format_shape_dtype_string(inshape, np.float32), outshape, broadcast_dimensions), "inshape": inshape, "outshape": outshape, "broadcast_dimensions": broadcast_dimensions, "err_msg": err_msg} for inshape, outshape, broadcast_dimensions, err_msg in [ ([2], [2, 2], [0, 1], ('broadcast_dimensions must have length equal to ' 'operand ndim')), ([2, 2], [2], [0, 1], ('target broadcast shape must have equal or higher rank ' 'to the operand shape')), ([2], [2, 3], [2], ('broadcast_in_dim broadcast_dimensions must be a subset of output ' 'dimensions')), ([2], [3], [0], ('operand dimension sizes must either be 1, or be ' 'equal to their corresponding dimensions in the target broadcast shape')), ([2, 2], [2, 2], [1, 0], ('broadcast_dimensions must be strictly increasing')), ])) def testBroadcastInDimShapeCheck(self, inshape, outshape, broadcast_dimensions, err_msg): rng = jtu.rand_default(self.rng()) x = rng(inshape, np.float32) with self.assertRaisesRegex(TypeError, err_msg): lax.broadcast_in_dim(x, shape=outshape, broadcast_dimensions=broadcast_dimensions) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_outshape={}_bcdims={}".format( jtu.format_shape_dtype_string(inshape, dtype), outshape, broadcast_dimensions), "inshape": inshape, "dtype": dtype, "outshape": outshape, "dimensions": broadcast_dimensions} for inshape, outshape, broadcast_dimensions in [ ([2], [2, 2], [0]), ([2], [2, 2], [1]), ([2], [2, 3], [0]), ([], [2, 3], []), ([1], [2, 3], [1]), ] for dtype in default_dtypes)) def testBroadcastInDimAgainstNumpy(self, inshape, dtype, outshape, dimensions): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(inshape, dtype)] op = lambda x: lax.broadcast_in_dim(x, outshape, dimensions) numpy_op = lambda x: lax_reference.broadcast_in_dim(x, outshape, dimensions) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_dimensions={}".format( jtu.format_shape_dtype_string(inshape, np.float32), dimensions), "inshape": inshape, "dimensions": dimensions, "error_type": error_type, "err_msg": err_msg} for inshape, dimensions, error_type, err_msg in [ ((1, 2, 3), (0, 0), ValueError, 'dimensions are not unique'), ((1, 2, 3), (3,), ValueError, 'axis 3 is out of bounds'), ((1, 2, 3), (-4,), ValueError, 'axis -4 is out of bounds'), ((1, 2, 3), (1,), ValueError, 'cannot select an axis to squeeze out'), ((1, 2, 3), (None,), TypeError, 'cannot be interpreted as an integer'), ])) def testSqueezeShapeCheck(self, inshape, dimensions, error_type, err_msg): rng = jtu.rand_default(self.rng()) x = rng(inshape, np.float32) with self.assertRaisesRegex(error_type, err_msg): lax.squeeze(x, dimensions=dimensions) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_dimensions={}".format( jtu.format_shape_dtype_string(arg_shape, np.float32), dimensions), "arg_shape": arg_shape, "dimensions": dimensions} for arg_shape, dimensions in [ [(1,), (0,)], [(1,), (-1,)], [(2, 1, 4), (1,)], [(2, 1, 3, 1), (1,)], [(2, 1, 3, 1), (1, 3)], [(2, 1, 3, 1), (3,)], ])) def testSqueeze(self, arg_shape, dimensions): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(arg_shape, np.float32)] op = lambda x: lax.squeeze(x, dimensions) numpy_op = lambda x: lax_reference.squeeze(x, dimensions) self._CompileAndCheck(op, args_maker) self._CheckAgainstNumpy(numpy_op, op, args_maker) check_grads(op, args_maker(), 2, ["fwd", "rev"], eps=1.) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_outshape={}".format( jtu.format_shape_dtype_string(arg_shape, dtype), jtu.format_shape_dtype_string(out_shape, dtype)), "arg_shape": arg_shape, "out_shape": out_shape, "dtype": dtype} for dtype in default_dtypes for arg_shape, out_shape in [ [(3, 4), (12,)], [(2, 1, 4), (8,)], [(2, 2, 4), (2, 8)] ])) def testReshape(self, arg_shape, out_shape, dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(arg_shape, dtype)] op = lambda x: lax.reshape(x, out_shape) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_outshape={}".format( jtu.format_shape_dtype_string(arg_shape, dtype), jtu.format_shape_dtype_string(out_shape, dtype)), "arg_shape": arg_shape, "out_shape": out_shape, "dtype": dtype} for dtype in default_dtypes for arg_shape, out_shape in [ [(3, 4), (12,)], [(2, 1, 4), (8,)], [(2, 2, 4), (2, 8)] ])) def testReshapeAgainstNumpy(self, arg_shape, out_shape, dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(arg_shape, dtype)] op = lambda x: lax.reshape(x, out_shape) numpy_op = lambda x: lax_reference.reshape(x, out_shape) self._CheckAgainstNumpy(numpy_op, op, args_maker) def testRoundRoundingMethods(self): x = np.array([-2.5, -1.5, -0.5, 0.5, 1.5, 2.5], dtype=np.float32) self.assertAllClose(lax.round(x, lax.RoundingMethod.AWAY_FROM_ZERO), np.array([-3, -2, -1, 1, 2, 3], dtype=np.float32)) self.assertAllClose(lax.round(x, lax.RoundingMethod.TO_NEAREST_EVEN), np.array([-2, -2, 0, 0, 2, 2], dtype=np.float32)) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_pads={}" .format(jtu.format_shape_dtype_string(shape, dtype), pads), "shape": shape, "dtype": dtype, "pads": pads} for dtype in default_dtypes for shape, pads in [ ((0, 2), [(1, 2, 1), (0, 1, 0)]), ((2, 3), [(1, 2, 1), (0, 1, 0)]), ((2,), [(1, 2, 0)]), ((1, 2), [(1, 2, 0), (3, 4, 0)]), ((1, 2), [(0, 0, 0), (0, 0, 0)]), ((2,), [(1, 2, 3),]), ((3, 2), [(1, 2, 1), (3, 4, 2)]), ((2,), [(-1, 2, 0),]), ((4, 2), [(-1, -2, 0), (1, 2, 0)]), ((4, 2), [(-1, 2, 0), (1, 2, 2)]), ((5,), [(-1, -2, 2),]), ((4, 2), [(-1, -2, 1), (1, 2, 2)]) ])) def testPad(self, shape, dtype, pads): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(shape, dtype)] fun = lambda operand: lax.pad(operand, np.array(0, dtype), pads) self._CompileAndCheck(fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_pads={}" .format(jtu.format_shape_dtype_string(shape, dtype), pads), "shape": shape, "dtype": dtype, "pads": pads} for shape in [(2, 3)] for dtype in default_dtypes for pads in [ [(0, 0, 0), (0, 0, 0)], # no padding [(1, 1, 0), (2, 2, 0)], # only positive edge padding [(1, 2, 1), (0, 1, 0)], # edge padding and interior padding [(0, 0, 0), (-1, -1, 0)], # negative padding [(0, 0, 0), (-2, -2, 4)], # add big dilation then remove from edges [(0, 0, 0), (-2, -3, 1)], # remove everything in one dimension ])) def testPadAgainstNumpy(self, shape, dtype, pads): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.pad(x, np.array(0, dtype), pads) numpy_op = lambda x: lax_reference.pad(x, np.array(0, dtype), pads) self._CheckAgainstNumpy(numpy_op, op, args_maker) def testPadErrors(self): with self.assertRaisesRegex(ValueError, "padding_config"): lax.pad(np.zeros(2), 0., [(0, 1, 0), (0, 1, 0)]) with self.assertRaisesRegex(ValueError, "interior padding in padding_config must be nonnegative"): lax.pad(np.zeros(2), 0., [(0, 1, -1)]) with self.assertRaisesRegex(ValueError, "Dimension size after padding is not at least 0"): lax.pad(np.zeros(2), 0., [(-3, 0, 0)]) with self.assertRaisesRegex(ValueError, "Dimension size after padding is not at least 0"): lax.pad(np.zeros(2), 0., [(-4, 0, 1)]) def testReverse(self): rev = jax.jit(lambda operand: lax.rev(operand, dimensions)) dimensions = [] self.assertAllClose(np.array([0, 1, 2, 3]), rev(np.array([0, 1, 2, 3])), check_dtypes=False) dimensions = [0] self.assertAllClose(np.array([3, 2, 1]), rev(np.array([1, 2, 3])), check_dtypes=False) dimensions = [0, 1] self.assertAllClose(np.array([[6, 5, 4], [3, 2, 1]]), rev(np.array([[1, 2, 3], [4, 5, 6]])), check_dtypes=False) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_predshape={}_argshapes={}".format( jtu.format_shape_dtype_string(pred_shape, np.bool_), jtu.format_shape_dtype_string(arg_shape, arg_dtype)), "pred_shape": pred_shape, "arg_shape": arg_shape, "arg_dtype": arg_dtype} for arg_shape in [(), (3,), (2, 3)] for pred_shape in ([(), arg_shape] if arg_shape else [()]) for arg_dtype in default_dtypes)) def testSelect(self, pred_shape, arg_shape, arg_dtype): rng = jtu.rand_default(self.rng()) def args_maker(): return [rng(pred_shape, np.bool_), rng(arg_shape, arg_dtype), rng(arg_shape, arg_dtype)] return self._CompileAndCheck(lax.select, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_predshape={}_argshapes={}".format( jtu.format_shape_dtype_string(pred_shape, np.bool_), jtu.format_shape_dtype_string(arg_shape, arg_dtype)), "pred_shape": pred_shape, "arg_shape": arg_shape, "arg_dtype": arg_dtype} for arg_shape in [(), (3,), (2, 3)] for pred_shape in ([(), arg_shape] if arg_shape else [()]) for arg_dtype in default_dtypes)) def testSelectAgainstNumpy(self, pred_shape, arg_shape, arg_dtype): rng = jtu.rand_default(self.rng()) def args_maker(): return [rng(pred_shape, np.bool_), rng(arg_shape, arg_dtype), rng(arg_shape, arg_dtype)] return self._CheckAgainstNumpy(lax_reference.select, lax.select, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_limit_indices={}_strides={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, limit_indices, strides), "shape": shape, "dtype": dtype, "starts": indices, "limits": limit_indices, "strides": strides} for shape, indices, limit_indices, strides in [ [(3,), (1,), (2,), None], [(7,), (4,), (7,), None], [(5,), (1,), (5,), (2,)], [(8,), (1,), (6,), (2,)], [(5, 3), (1, 1), (3, 2), None], [(5, 3), (1, 1), (3, 1), None], [(7, 5, 3), (4, 0, 1), (7, 1, 3), None], [(5, 3), (1, 1), (2, 1), (1, 1)], [(5, 3), (1, 1), (5, 3), (2, 1)], ] for dtype in default_dtypes)) def testSlice(self, shape, dtype, starts, limits, strides): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.slice(x, starts, limits, strides) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_limit_indices={}_strides={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, limit_indices, strides), "shape": shape, "dtype": dtype, "starts": indices, "limits": limit_indices, "strides": strides} for shape, indices, limit_indices, strides in [ [(3,), (1,), (2,), None], [(7,), (4,), (7,), None], [(5,), (1,), (5,), (2,)], [(8,), (1,), (6,), (2,)], [(5, 3), (1, 1), (3, 2), None], [(5, 3), (1, 1), (3, 1), None], [(7, 5, 3), (4, 0, 1), (7, 1, 3), None], [(5, 3), (1, 1), (2, 1), (1, 1)], [(5, 3), (1, 1), (5, 3), (2, 1)], ] for dtype in default_dtypes)) def testSliceAgainstNumpy(self, shape, dtype, starts, limits, strides): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.slice(x, starts, limits, strides) numpy_op = lambda x: lax_reference.slice(x, starts, limits, strides) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_size_indices={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, size_indices), "shape": shape, "dtype": dtype, "indices": indices, "size_indices": size_indices} for shape, indices, size_indices in [ [(3,), np.array((1,)), (1,)], [(5, 3), (1, 1), (3, 1)], [(5, 3), np.array((1, 1)), (3, 1)], [(7, 5, 3), np.array((4, 1, 0)), (2, 0, 1)], ] for dtype in default_dtypes)) def testDynamicSlice(self, shape, dtype, indices, size_indices): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype), np.array(indices)] op = lambda x, starts: lax.dynamic_slice(x, starts, size_indices) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_size_indices={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, size_indices), "shape": shape, "dtype": dtype, "indices": indices, "size_indices": size_indices} for shape, indices, size_indices in [ [(3,), (1,), (1,)], [(5, 3), (1, 1), (3, 1)], [(7, 5, 3), (4, 1, 0), (2, 0, 1)], ] for dtype in default_dtypes)) def testDynamicSliceAgainstNumpy(self, shape, dtype, indices, size_indices): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype), np.array(indices)] op = lambda x, s: lax.dynamic_slice(x, s, size_indices) numpy_op = lambda x, s: lax_reference.dynamic_slice(x, s, size_indices) self._CheckAgainstNumpy(numpy_op, op, args_maker) def testDynamicSliceInDim(self): # Regression test for mixed type problem in dynamic_slice_in_dim. rng = jtu.rand_default(self.rng()) x = rng((6, 7), np.int32) np.testing.assert_equal(lax.dynamic_slice_in_dim(x, 2, 3), x[2:5]) def testDynamicSliceArraySliceSizes(self): rng = jtu.rand_default(self.rng()) x = rng((6, 7), np.int32) np.testing.assert_equal(lax.dynamic_slice(x, [2, 3], jnp.array([2, 2])), x[2:4, 3:5]) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_update_shape={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, update_shape), "shape": shape, "dtype": dtype, "indices": indices, "update_shape": update_shape} for shape, indices, update_shape in [ [(3,), (1,), (1,)], [(5, 3), (1, 1), (3, 1)], [(7, 5, 3), (4, 1, 0), (2, 0, 1)], ] for dtype in default_dtypes)) def testDynamicUpdateSlice(self, shape, dtype, indices, update_shape): rng = jtu.rand_default(self.rng()) def args_maker(): return [rng(shape, dtype), rng(update_shape, dtype), np.array(indices)] self._CompileAndCheck(lax.dynamic_update_slice, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_update_shape={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, update_shape), "shape": shape, "dtype": dtype, "indices": indices, "update_shape": update_shape} for shape, indices, update_shape in [ [(3,), (1,), (1,)], [(5, 3), (1, 1), (3, 1)], [(7, 5, 3), (4, 1, 0), (2, 0, 1)], ] for dtype in default_dtypes)) def testDynamicUpdateSliceAgainstNumpy(self, shape, dtype, indices, update_shape): rng = jtu.rand_default(self.rng()) def args_maker(): return [rng(shape, dtype), rng(update_shape, dtype), np.array(indices)] self._CheckAgainstNumpy(lax_reference.dynamic_update_slice, lax.dynamic_update_slice, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_perm={}".format( jtu.format_shape_dtype_string(shape, dtype), perm), "shape": shape, "dtype": dtype, "perm": perm} for shape, perm in [ [(3, 4), (1, 0)], [(3, 4), (0, 1)], [(3, 4, 5), (2, 1, 0)], [(3, 4, 5), (1, 0, 2)], ] for dtype in default_dtypes)) def testTranspose(self, shape, dtype, perm): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.transpose(x, perm) self._CompileAndCheck(op, args_maker) def testTransposeWithArrayPermutation(self): x = lax.transpose(np.ones((2, 3)), jnp.array([1, 0])) self.assertEqual((3, 2), x.shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_perm={}".format( jtu.format_shape_dtype_string(shape, dtype), perm), "shape": shape, "dtype": dtype, "perm": perm} for shape, perm in [ [(3, 4), (1, 0)], [(3, 4), (0, 1)], [(3, 4, 5), (2, 1, 0)], [(3, 4, 5), (1, 0, 2)], ] for dtype in default_dtypes)) def testTransposeAgainstNumpy(self, shape, dtype, perm): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.transpose(x, perm) numpy_op = lambda x: lax_reference.transpose(x, perm) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_op={}_inshape={}_reducedims={}_initval={}" .format(op.__name__, jtu.format_shape_dtype_string(shape, dtype), dims, init_val), "op": op, "init_val": init_val, "shape": shape, "dtype": dtype, "dims": dims} for init_val, op, types in [ (0, lax.add, default_dtypes), (1, lax.mul, default_dtypes), (0, lax.max, all_dtypes), # non-monoidal (-np.inf, lax.max, float_dtypes), (dtypes.iinfo(np.int32).min, lax.max, [np.int32]), (dtypes.iinfo(np.int64).min, lax.max, [np.int64]), (np.inf, lax.min, float_dtypes), (dtypes.iinfo(np.int32).max, lax.min, [np.int32]), (dtypes.iinfo(np.int64).max, lax.min, [np.int64]), (dtypes.iinfo(np.uint32).max, lax.min, [np.uint32]), (dtypes.iinfo(np.uint64).max, lax.min, [np.uint64]), ] for dtype in types for shape, dims in [ [(3, 4, 5), (0,)], [(3, 4, 5), (1, 2)], [(3, 4, 5), (0, 2)], [(3, 4, 5), (0, 1, 2)] ])) def testReduce(self, op, init_val, shape, dtype, dims): rng_factory = (jtu.rand_default if dtypes.issubdtype(dtype, np.integer) else jtu.rand_small) rng = rng_factory(self.rng()) init_val = np.asarray(init_val, dtype=dtype) fun = lambda operand, init_val: lax.reduce(operand, init_val, op, dims) args_maker = lambda: [rng(shape, dtype), init_val] self._CompileAndCheck(fun, args_maker) # we separately test the version that uses a concrete init_val because it # can hit different code paths fun = lambda operand: lax.reduce(operand, init_val, op, dims) args_maker = lambda: [rng(shape, dtype)] self._CompileAndCheck(fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_op={}.{}_arr_weak_type={}_init_weak_type={}" .format(op_namespace.__name__, op, arr_weak_type, init_weak_type), "op": op, "op_namespace": op_namespace, "arr_weak_type": arr_weak_type, "init_weak_type": init_weak_type} for op in ["add", "mul"] for op_namespace in [lax, operator] for arr_weak_type in [True, False] for init_weak_type in [True, False])) def testReduceWeakType(self, op_namespace, op, arr_weak_type, init_weak_type): op = getattr(op_namespace, op) arr = lax._convert_element_type(	np.arange(10)	numpy.arange
import controlSBML.constants as cn import controlSBML as ctl from controlSBML.option_management.option_manager import OptionManager from controlSBML.option_management.options import Options from docstring_expander.expander import Expander import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import urllib.request REPO_URL = "https://github.com/ModelEngineering/controlSBML/raw/main" MODULE_URLPAT = REPO_URL + "/controlSBML/%s.py" # Pattern for module URL MODEL_URLPAT = REPO_URL + "/models/%s" MODEL_823_FILE = "biomodels_823.ant" LOCAL_FILE = "local.txt" def calculateMatrixDistance(mat1, mat2): """ Calculates the distance between two matrices with the same shape. Parameters ---------- mat1 - np.array mat2 - np.array Returns ------- float """ if	np.shape(mat1)	numpy.shape
"""Write a python module (e.g. function) that returns movies randomly""" import numpy as np from sklearn.decomposition import NMF import pandas as pd import random import pickle MOVIES = [x[:-7] for x in (pd.read_csv('movies.csv'))['title'].values.tolist()] #MOVIES = ['Shawshank Redemption', # 'Wizard of Oz', # 'Pulp Fiction', # 'Kill Bill', # 'Rings, The Lord of (2002)', # 'RegEx: The Movie', # 'FuzzyWuzzy Bubbly Buddy', # 'Docker: Unleashed', # 'Docker: FML', # 'Flask Fun', # 'Django Girls Unchained'] # Add userinput as parameter to this FUNCTION and give the funcion a user user_input # transform user input so that it is a list of 3 with length equal to number of columns # replace the columns that represent the user id with the values the user has given # give that resulting list to line 40 (P = model.transform(user_input2)) # nR is going to be the new recommendation list # find top values in nR (np.argsort or np.argmax) # find the index (or movieId) of those columns # find the name for those movieIds (iloc) or pandas.columns def random_recommend(user_input_movies, user_input_ratings): ratings = pd.read_csv('ratings.csv') movies = pd.read_csv('movies.csv') moviesIdlist=movies['movieId'].tolist() MOVIES = [x[:-7] for x in (pd.read_csv('movies.csv'))['title'].values.tolist()] MOVIEID = [x for x in (pd.read_csv('movies.csv'))['movieId'].values.tolist()] movies['title'] = MOVIES ratings2=ratings.set_index(['userId']) ratings3 = ratings.drop(['timestamp'], axis=1) newmoviesratings = pd.merge(ratings3, movies, on='movieId') newmoviesratings2=newmoviesratings[['userId','title','rating']] # newf = newmoviesratings2.set_index('userId') newf = ratings3.pivot(index='userId', columns='movieId') actualmovieIdslist=ratings3['movieId'].tolist() newmoviesIDlist = [x for x in moviesIdlist if x in actualmovieIdslist] newf2=newf.fillna(3.000000) R=newf2.values model = pickle.load(open('finalized_model.sav', 'rb')) Q = model.components_ P = model.transform(R) nR =	np.dot(P, Q)	numpy.dot
# -- coding: utf-8 -- """ Created on 2020/8/12 @project: SPAIC @filename: Monitor @author: <NAME> @contact: <EMAIL> @description: 定义神经集群放电以及神经元状态量、连接状态量的仿真记录模块 """ from ..Network.Assembly import BaseModule, Assembly from ..Network.Connection import Connection from ..Backend.Backend import Backend import numpy as np import torch class Monitor(BaseModule): def __init__(self, target, var_name, index='full', dt=None, get_grad=False, nbatch=True): super().__init__() if isinstance(target, Assembly): self.target = target self.target_type = 'Assembly' elif isinstance(target, Connection): self.target = target self.target_type = 'Connection' elif target == None: self.target = None self.target_type = None else: raise ValueError("The target does not belong to types that can be watched (Assembly, Connection).") self.var_name = '{'+var_name+'}' self.index = index self.var_container = None self.get_grad = get_grad self.nbatch = nbatch self._nbatch_records = [] # all time window's record self._nbatch_times = [] self._records = [] # single time window's record self._times = [] self.dt = dt self.is_recording = True self.new_record = True def check_var_name(self, var_name): ''' Check if variable is in the traget model, and add the target id label to the variable name. Parameters ---------- var_name : original variable name Returns : modified variable name ------- ''' tar_var_name = None if var_name[1:-1] in self.backend._variables.keys(): tar_var_name = var_name[1:-1] else: for tar_name in self.target.get_var_names(): # 没有中间变量 if var_name in tar_name: tar_var_name = tar_name break if tar_var_name is not None: return tar_var_name else: raise ValueError(" Variable %s is not in the target model"%var_name) def get_str(self, level): pass def monitor_on(self): self.is_recording = True def monitor_off(self): self.is_recording = False def clear(self): NotImplementedError() def build(self, backend: Backend): NotImplementedError() def init_record(self): NotImplementedError() def update_step(self): NotImplementedError() def push_data(self, data, time): "push data to monitor by backend" self._records.append(data) self._times.append(time) class SpikeMonitor(Monitor): def __init__(self, target, var_name='O', index='full', dt=None, get_grad=False, nbatch=False): super().__init__(target=target, var_name=var_name, index=index, dt=dt, get_grad=get_grad, nbatch=nbatch) self._transform_len = 0 self._nbatch_index = [] # all time window's record self._nbatch_times = [] self._spk_index = [] self._spk_times = [] self._records = [] # single time window's record self._times = [] def build(self, backend: Backend): self.backend = backend self.backend._monitors.append(self) self.var_name = self.check_var_name(self.var_name) self.shape = self.backend._variables[self.var_name].shape if self.dt is None: self.dt = self.backend.dt def clear(self): self._transform_len = -1 self._nbatch_index = [] # all time window's record self._nbatch_times = [] self._spk_index = [] self._spk_times = [] self._records = [] # single time window's record self._times = [] def init_record(self): self.new_record = True if len(self._spk_index) > 0: if self.nbatch is True: if isinstance(self._spk_index[0], torch.Tensor): self._nbatch_index.append(torch.stack(self._spk_index[1:], dim=-1).cpu().detach().numpy()) else: self._nbatch_index.append(np.stack(self._spk_index[1:], axis=-1)) self._nbatch_times.append(self._times[1:]) elif self.nbatch > 0: if isinstance(self._spk_index[0], torch.Tensor): self._nbatch_index.append(torch.stack(self._spk_index[1:], dim=-1).cpu().detach().numpy()) else: self._nbatch_index.append(np.stack(self._spk_index[1:], axis=-1)) self._nbatch_times.append(self._times[1:]) if len(self._nbatch_times) > self.nbatch: self._nbatch_index = self._nbatch_index[-self.nbatch:] self._nbatch_times = self._nbatch_times[-self.nbatch:] self._records = [] # single time window's record self._times = [] self._transform_len = -1 def push_spike_train(self, spk_times, spk_index, batch_index=0): if len(self._spk_index) < batch_index+1: add_num = batch_index + 1 - len(self._spk_index) for _ in range(add_num): self._spk_index.append([]) self._spk_times.append([]) if isinstance(spk_times, list) or isinstance(spk_times, tuple): self._spk_times[batch_index].extend(spk_times) self._spk_index[batch_index].extend(spk_index) else: self._spk_times[batch_index].append(spk_times) self._spk_index[batch_index].append(spk_index) #to override the _spike_transform function when getting spk_times and spk_index self._transform_len = 1 def update_step(self, variables): ''' Recoding the variable values of the current step. Returns ------- ''' if self.is_recording is False: return if int(10000 * self.backend.time / self.dt) % 10000 == 0: record_value = variables[self.var_name] if self.get_grad: variables[self.var_name].retain_grad() if self.index == 'full': self._records.append(record_value) self._times.append(self.backend.time) else: if len(self.index) == record_value.ndim: self._records.append(record_value[self.index]) self._times.append(self.backend.time) else: assert len(self.index) == record_value.ndim -1 if self.backend.backend_name == 'pytorch': record_value = torch.movedim(record_value, 0, -1) indexed_value = record_value[tuple(self.index)] indexed_value = torch.movedim(indexed_value, -1, 0) else: record_value = np.array(record_value) record_value = np.moveaxis(record_value, 0, -1) indexed_value = record_value[tuple(self.index)] indexed_value = np.moveaxis(indexed_value, -1, 0) self._records.append(indexed_value) self._times.append(self.backend.time) def _spike_transform(self): batch_size = self.backend.get_batch_size() if len(self._records) > self._transform_len: self._transform_len = len(self._records) self._spk_index = [] self._spk_times = [] if isinstance(self._records[0], torch.Tensor): step = len(self._records) rec_spikes = torch.stack(self._records, dim=-1).cpu().detach() if '{[2]' in self.var_name: for ii in range(batch_size): rec_spikes_i = rec_spikes[ii,0,...].bool().reshape(-1) rec_spikes_t = rec_spikes[ii,1,...].reshape(-1) num = int(rec_spikes_i.size(0)/step) time_seq = torch.tensor(self._times).unsqueeze(dim=0).expand(num, -1).reshape(-1) indx_seq = torch.arange(0, num).unsqueeze(dim=1).expand(-1, step).reshape(-1) time_seq = (torch.masked_select(time_seq - rec_spikes_t, rec_spikes_i) ).numpy() indx_seq = torch.masked_select(indx_seq, rec_spikes_i).numpy() self._spk_index.append(indx_seq) self._spk_times.append(time_seq) else: for ii in range(batch_size): rec_spikes_i = rec_spikes[ii,...].bool().reshape(-1) num = int(rec_spikes_i.size(0)/step) time_seq = torch.tensor(self._times).unsqueeze(dim=0).expand(num, -1).reshape(-1) indx_seq = torch.arange(0, num).unsqueeze(dim=1).expand(-1, step).reshape(-1) time_seq = torch.masked_select(time_seq, rec_spikes_i).numpy() indx_seq = torch.masked_select(indx_seq, rec_spikes_i).numpy() self._spk_index.append(indx_seq) self._spk_times.append(time_seq) @property def spk_times(self): self._spike_transform() return self._spk_times @property def spk_index(self): self._spike_transform() return self._spk_index @property def spk_grad(self): pass return None @property def time_spk_rate(self): if isinstance(self._records[0], torch.Tensor): if '{[2]' in self.var_name: spike = torch.stack(self._records, dim=-1).cpu().detach()[:,0,...] else: spike = torch.stack(self._records, dim=-1).cpu().detach() return torch.mean(spike, dim=0).numpy() else: if '{[2]' in self.var_name: spike = np.stack(self._records, axis=-1)[:,0,...] else: spike = np.stack(self._records, axis=-1) return np.mean(spike, axis=0).numpy() @property def time(self): return np.stack(self._times, axis=-1) class StateMonitor(Monitor): def __init__(self, target, var_name, index='full', dt=None, get_grad=False, nbatch=False): # TODO: 初始化有点繁琐，需要知道record的变量，考虑采用更直接的监控函数 super().__init__(target=target, var_name=var_name, index=index, dt=dt, get_grad=get_grad, nbatch=nbatch) self._nbatch_records = [] # all time window's record self._nbatch_times = [] self._records = [] # single time window's record self._times = [] def build(self, backend: Backend): self.backend = backend self.backend._monitors.append(self) self.var_name = self.check_var_name(self.var_name) if self.index != 'full': self.index = tuple(self.index) if self.dt is None: self.dt = self.backend.dt def clear(self): self._nbatch_records = [] # all time window's record self._nbatch_times = [] self._records = [] # single time window's record self._times = [] def init_record(self): ''' Inite record of new trial Returns: ''' self.new_record = True if len(self._records) > 0: if self.nbatch is True: if isinstance(self._records[0], torch.Tensor): self._nbatch_records.append(torch.stack(self._records, dim=-1).cpu().detach().numpy()) else: self._nbatch_records.append(np.stack(self._records, axis=-1)) self._nbatch_times.append(self._times) elif self.nbatch > 0: if isinstance(self._records[0], torch.Tensor): self._nbatch_records.append(torch.stack(self._records, dim=-1).cpu().detach().numpy()) else: self._nbatch_records.append(	np.stack(self._records, axis=-1)	numpy.stack
import numpy as np from scipy.optimize import curve_fit from scipy.optimize import fsolve, brentq from scipy.interpolate import interp1d import scipy.integrate import sys import os import writeproperties.velociraptor_python_tools as vpt from scipy.spatial import cKDTree import h5py import re from constants import * from snapshot import * import copy import itertools def getHaloCoord(catalog, halo, z=0): #Mpc/h coords = np.zeros(3) coords[0] = (catalog['Xcmbp'][halo]+catalog['Xc'][halo])h(1+z) coords[1] = (catalog['Ycmbp'][halo]+catalog['Yc'][halo])h(1+z) coords[2] = (catalog['Zcmbp'][halo]+catalog['Zc'][halo])h(1+z) return coords def getHaloRadius(catalog, halo, z=0): #Mpc/h return catalog['R_200crit'][halo]h(1+z) def getHaloCoordCOM(catalog, halo, z=0): #Mpc/h coords = np.zeros(3) coords[0] = catalog['Xc'][halo]h(1+z) coords[1] = catalog['Yc'][halo]h(1+z) coords[2] = catalog['Zc'][halo]h(1+z) return coords def readHaloFile(halofile): atime,tree,numhalos,halodata,cosmodata,unitdata = vpt.ReadUnifiedTreeandHaloCatalog(halofile, desiredfields=[], icombinedfile=1,iverbose=0) return atime,tree,numhalos,halodata,cosmodata,unitdata def findSurroundingHaloProperties(hp, halolist, d_snap, boxsize=32.): coords = hp['Coord'] halotree = cKDTree(coords, boxsize=boxsize) for k in halolist: if hp['R200'][k] == -1: continue halostring = hp['HaloIndex'][k] length_of_neighbours = len(np.array(halotree.query_ball_point([hp['Coord'][k]], r=hp['R200'][k]5)[0])) distance, indices = halotree.query([hp['Coord'][k]], k=length_of_neighbours) indices = np.array(indices[0])[1:] distance = np.array(distance[0])[1:] hp['Neighbours'][halostring] = hp['HaloIndex'][indices] hp['Neighbour_distance'][halostring] = distance hp['Neighbour_Velrad'][halostring] = np.zeros(len(distance)) j=0 for i in indices: partindices = hp['Partindices'][hp['HaloIndex'][i]] hp['Neighbour_Velrad'][halostring][j] = np.sum(d_snap['File'].get_radialvelocity(hp['Coord'][k], indices=partindices))/len(partindices) j+=1 def fixSatelliteProblems(hp, Hydro=False, TEMPORALHALOIDVAL=1000000000000): halotree = cKDTree(hp['Coord'], boxsize=32) toolarge = np.where(hp['R200'] > hp['R200'][0]1.2)[0] #print(i, toolarge) if len(toolarge) != 0: for tl in toolarge: hp['M200'][tl] = -1 hp['R200'][tl] = -1 if Hydro: hp['DMFraction'][tl] = -1 hp['hostHaloIndex'][hp['HaloIndex'][tl]==hp['hostHaloIndex']] = -2 for halo in range(len(hp['M200'])): if hp['M200'][halo] == -1: continue buren = np.array(halotree.query_ball_point(hp['Coord'][halo], r = 2hp['R200'][halo])) if len(buren) <= 1: continue buren = buren[hp['R200'][buren] != -1] i_largest = np.argmax(hp['n_part'][buren]) index_largest = buren[i_largest] buren = np.delete(buren,i_largest) coords = hp['Coord'][buren] - hp['Coord'][index_largest] coords = np.where(np.abs(coords) > 0.532, coords - coords/np.abs(coords)32, coords) rad = np.sqrt(np.sum(coordscoords, axis=1)) burentemp = np.where(hp['R200'][buren]-rad+hp['R200'][index_largest] > 0)[0] if len(burentemp) == 0: continue buren = buren[burentemp] hp['hostHaloIndex'][buren] = index_largest hp['M200'][buren] = -1 hp['R200'][buren] = -1 if Hydro: hp['DMFraction'][buren] = -1 def findSubHaloFraction(hp, catalog): if len(hp['hostHaloIndex']) < 10: hp['Msub'] = np.zeros(len(hp['M200'])) return 0 i_hostH = np.where(hp['hostHaloIndex'] > -1)[0] hp['Msub'] = np.zeros(len(hp['M200'])) for i in i_hostH: isattemp = np.where(hp['HaloID'][i] == catalog['ID'])[0] hp['Msub'][hp['hostHaloIndex'][i]] += catalog['Mass_FOF'][isattemp] def buildHaloDictionary(Hydro=False, multiple=False, mainBranch=False): haloproperties = {} if Hydro: haloarray = (['HaloIndex', 'HaloID', 'Coord', 'R200', 'M200', 'redshift', 'snapshot', 'lambda', 'lambdaDM', 'lambdaH', 'DensityDM', 'DensityH', 'Npart', 'DMpartIDs', 'Partindices', 'HpartIDs', 'NpartDM_profile', 'Npart_profile', 'DMFraction', 'DMFraction_profile', 'MassH_profile', 'VelradDM', 'Velrad', 'VelradH', 'Vel', 'Temperature', 'Mass_profile', 'MassDM_profile', 'COM_offset', 'AngularMomentumDM', 'AngularMomentumH', 'AngularMomentum', 'Radius', 'MaxRadIndex', 'hostHaloIndex', 'n_part', 'Msub', 'CrossTime', 'Virial_ratio']) else: haloarray = (['HaloIndex', 'HaloID', 'Coord', 'R200', 'M200', 'redshift', 'snapshot', 'DMpartIDs', 'lambda', 'Density', 'Npart', 'AngularMomentum', 'Npart_profile', 'Radius', 'Velrad', 'Vel', 'MassDM_profile', 'Partindices', 'n_part', 'MaxRadIndex', 'Virial_ratio', 'COM_offset', 'Msub', 'CrossTime', 'hostHaloIndex']) if mainBranch: haloarray.append('Head') haloarray.append('Tail') haloarray.append('TreeBool') for key in haloarray: if multiple and (key == 'DMpartIDs' or key == 'HpartIDs' or key=='Partindices' or key=='Neighbours' or key=='Neighbour_distance' or key=='Neighbour_Velrad'): haloproperties[key] = {} else: haloproperties[key] = np.zeros(0) return haloproperties def findHaloPropertiesInSnap(catalog, snappath, snapshot, particledata=[], Hydro=False, Nhalo=100, startHalo=0, softeningLength=0.002, r200fac=1., mass=False, partlim=200, savePartData=False): print("Computing properties for %i haloes in snapshot %i" %(Nhalo, snapshot)) haloproperties = buildHaloDictionary(Hydro = Hydro, multiple=True) if len(catalog['Mass_tot']) == 0: return haloproperties sortorder = np.argsort(catalog['Mass_tot'][:]) d_snap = {} d_snap['snapshot'] = snapshot limiet = 0 if Hydro: d_snap['File'] = Snapshot(snappath, snapshot, useIDs=False, partType=7, softeningLength=softeningLength) d_snap['File'].makeCoordTree() else: d_snap['File'] = Snapshot(snappath, snapshot, useIDs=False, partType=1, softeningLength=softeningLength) d_snap['File'].makeCoordTree() for key in catalog.keys(): catalog[key][:] = catalog[key][sortorder] catalog[key][:] = catalog[key][::-1] if len(particledata) > 0: mass=True for key in particledata.keys(): particledata[key][:] = np.array(particledata[key])[sortorder] particledata[key][:] = particledata[key][::-1] for halo in range(startHalo, startHalo+Nhalo): masshier = False if mass: masshier=catalog['Mass_200crit'][halo]h if masshier <= 0.000001: startHalo += 1 limiet += 1 continue if halo > len(catalog['Xc'])-1 or limiet > 500: print("Halo limit reached: nhalo = %i, hlim = %i" %(halo, limiet)) print("Coordinates: ", coords) break halopropertiestemp = {} coords = getHaloCoord(catalog, halo, z=d_snap['File'].redshift) if coords[0] < 0 or coords[1] < 0 or coords[2] < 0 or coords[0]%32 < 0.5 or coords[1]%32 < 0.5 or coords[2]%32 < 0.5: startHalo += 1 continue coords = coords%32 radhier = getHaloRadius(catalog, halo, z=d_snap['File'].redshift) if ((coords[0] < 2.radhier) or (coords[1] < 2.radhier) or (coords[2] < 2.radhier) or (np.abs(coords[0]-32) < 2.radhier) or (np.abs(coords[1]-32) < 2.radhier) or (np.abs(coords[2]-32) < 2.radhier)): startHalo += 1 continue if (catalog['hostHaloID'][halo] != -1) or (catalog['npart'][halo] < 20) or (catalog['Mass_200crit'][halo]h == 0):# or ((catalog['Mass_200crit'][halo]/4.e3)*(1./3) < catalog['R_200crit'][halo]): startHalo += 1 continue halopropertiestemp = findHaloProperties(halo, catalog, d_snap, particledata=particledata, Hydro=Hydro, r200fac=r200fac, mass=masshier, partlim=partlim) if len(halopropertiestemp) == 0: startHalo += 1 limiet += 1 continue if halopropertiestemp['Npart'] < partlim: startHalo += 1 limiet += 1 continue limiet = 0 for key in haloproperties.keys(): if key == 'Neighbours' or key == 'Neighbour_distance' or key == 'Neighbour_Velrad': iets = 1 elif key == 'DMpartIDs' or key == 'HpartIDs' or key=='Partindices': if key=='Partindices': haloproperties[key][halopropertiestemp['HaloIndex']] = halopropertiestemp[key][:] elif savePartData: haloproperties[key][halopropertiestemp['HaloIndex']] = halopropertiestemp[key][:] elif halo == startHalo: haloproperties[key] = [halopropertiestemp[key]] else: haloproperties[key] = np.concatenate((haloproperties[key], [halopropertiestemp[key]])) #if startHalo + Nhalo >= len(catalog['npart']) and len(haloproperties['Npart']) > partlim: print("Compute temperatures outside haloes...") everythingOutside(haloproperties, d_snap) print("finding surrounding haloes...") if len(haloproperties['M200']) > 100: findSurroundingHaloProperties(haloproperties, np.arange(0, 100, 1).astype(int), d_snap) return haloproperties def findHaloPropertiesInSnap_fromUnifiedTreeCatalog(catalog, snappath, snapshot, Hydro=False, Nhalo=100, startHalo=0, softeningLength=0.002, Radius=1., partlim=200, savePartData=False, sortorder=[], boxsize=32, TEMPORALHALOIDVAL=1000000000000): print("Computing properties for %i haloes in snapshot %i" %(Nhalo, snapshot)) haloproperties = buildHaloDictionary(Hydro = Hydro, multiple=True, mainBranch=True) if len(catalog['Mass_tot']) == 0: return haloproperties if len(sortorder)==0: sortorder = np.argsort(catalog['Mass_tot'][:])[::-1] sortorderinvert = np.argsort(sortorder) else: sortorderinvert = np.argsort(sortorder) d_snap = {} d_snap['snapshot'] = snapshot limiet = 0 if Hydro: d_snap['File'] = Snapshot(snappath, snapshot, useIDs=False, partType=7, softeningLength=softeningLength) d_snap['File'].makeCoordTree() else: d_snap['File'] = Snapshot(snappath, snapshot, useIDs=False, partType=1, softeningLength=softeningLength) d_snap['File'].makeCoordTree() for key in catalog.keys(): catalog[key][:] = catalog[key][sortorder] #catalog[key][:] = catalog[key][::-1] #haloproperties['TreeBool'] = np.ones(len(tails), dtype=int) for halo in range(startHalo, startHalo+Nhalo): #start_time = time.time() #print(halo) #print(catalog['npart'][halo]) if halo%1000==0: print('Computing properties for halo %i-%i' %(halo, halo+1000)) if halo > len(catalog['Xc'])-1: print("Halo limit reached: nhalo = %i, hlim = %i" %(halo, limiet)) print("Coordinates: ", coords) break if limiet > 500: #Only computing sats if catalog['hostHaloID'][halo] == -1: # haloproperties['TreeBool'][halo] = 0 continue halopropertiestemp = {} coords = getHaloCoord(catalog, halo, z=d_snap['File'].redshift) coords = coords%32 radhier = getHaloRadius(catalog, halo, z=d_snap['File'].redshift) satellite = False if (catalog['npart'][halo] < 20) or (catalog['Mass_200crit'][halo]h == 0): startHalo += 1 # haloproperties['TreeBool'][halo] = 0 continue if (catalog['hostHaloID'][halo] != -1) and len(haloproperties['HaloID'])>1: haloindextemp = np.where((haloproperties['HaloID']%TEMPORALHALOIDVAL)==catalog['hostHaloID'][halo]%TEMPORALHALOIDVAL)[0] if len(haloindextemp) == 0: hostHaloIDtemp = -1 if catalog['npart'][halo] < 50: hostHaloIDtemp = -2 satellite = True else: afstandtemp = (haloproperties['Coord'][haloindextemp[0]]-coords) afstandtemp = np.where(np.abs(afstandtemp)>0.5boxsize, np.abs(afstandtemp) - boxsize, afstandtemp) afstandtemp = (np.sum(afstandtempafstandtemp))*0.5 if afstandtemp < haloproperties['R200'][haloindextemp[0]]: # and catalog['npart'][halo] > 50: #print(afstandtemp, haloproperties['R200'][haloindextemp[0]], haloproperties['Coord'][haloindextemp[0]], coords) hostHaloIDtemp = haloindextemp[0] satellite = True else: #print(afstandtemp, haloproperties['R200'][haloindextemp[0]], haloproperties['Coord'][haloindextemp[0]], coords) hostHaloIDtemp = -1 else: hostHaloIDtemp = -1 #All happens here halopropertiestemp = findHaloPropertiesFixedRadius(d_snap, halo, coords, np.logspace(-3, 0, 60)Radius, Hydro=Hydro, rad=radhier, mass=False, satellite=satellite, mainBranch=True) #print("--- %s seconds ---" % (time.time() - start_time), 'halopropertiestemp computed') if len(halopropertiestemp) == 0: startHalo += 1 limiet += 1 # haloproperties['TreeBool'][halo] = 0 continue if satellite == False and halopropertiestemp['Npart'] < partlim: startHalo += 1 limiet += 1 # haloproperties['TreeBool'][halo] = 0 continue limiet = 0 if satellite: halopropertiestemp['Npart'] = catalog['npart'][halo] #start_time = time.time() halopropertiestemp['n_part'] = catalog['npart'][halo] halopropertiestemp['HaloID'] = catalog['ID'][halo] halopropertiestemp['hostHaloIndex'] = hostHaloIDtemp if not satellite: afstandtemp = coords - getHaloCoordCOM(catalog, halo, z=d_snap['File'].redshift) rhier = np.where(np.abs(afstandtemp)>0.5boxsize, np.abs(afstandtemp) - boxsize, afstandtemp) halopropertiestemp['COM_offset'] = np.sqrt(np.sum(rhier2))/halopropertiestemp['R200'] halopropertiestemp['CrossTime'] = (2.halopropertiestemp['R200']Mpc_to_km / np.sqrt(G_Mpc_km2_Msi_si2halopropertiestemp['M200']1e10/halopropertiestemp['R200']))s_to_yr/1.e6 else: halopropertiestemp['COM_offset'] = -1 halopropertiestemp['CrossTime'] = -1 for key in haloproperties.keys(): if key == 'TreeBool' or key == 'Tail' or key == 'Head' or key == 'Radius': continue elif key == 'Neighbours' or key == 'Neighbour_distance' or key == 'Neighbour_Velrad': continue elif key == 'DMpartIDs' or key == 'HpartIDs' or key=='Partindices': if key=='Partindices': haloproperties[key][halopropertiestemp['HaloIndex']] = halopropertiestemp[key][:] elif savePartData: haloproperties[key][halopropertiestemp['HaloIndex']] = halopropertiestemp[key][:] elif halo == startHalo: haloproperties[key] = [halopropertiestemp[key]] else: #print(key) haloproperties[key] = np.concatenate((haloproperties[key], [halopropertiestemp[key]])) #print("--- %s seconds ---" % (time.time() - start_time), 'haloproperties updated') # print("Correcting hostHaloIndices...") # for i in range(len(haloproperties['HaloID'])): # if (haloproperties['hostHaloIndex'][i] != -1): # haloindextemp = np.where((haloproperties['HaloID']%TEMPORALHALOIDVAL)==haloproperties['hostHaloIndex'][i]%TEMPORALHALOIDVAL)[0] # if len(haloindextemp) == 0: # haloproperties['hostHaloIndex'][i] = -2 # else: # haloproperties['hostHaloIndex'][i] = haloindextemp[0] # else: # haloproperties['hostHaloIndex'][i] = -1 # haloproperties['Tail'] = tails # haloproperties['Head'] = heads haloproperties['Radius'] =	np.logspace(-3, 0, 60)	numpy.logspace
#!/usr/bin/env python ''' Filename: test_flags.py Description: unit tests to test flag_timepoints.py ''' __author__ = '<NAME>' __email__ = '<EMAIL>' __status__ = 'prototype' # standard imports import os import sys import unittest import numpy as np HERE = os.path.dirname(os.path.realpath(__file__)) sys.path.append(os.path.abspath(HERE +'/../../importing')) from flags_and_breaks import * class TestFlagTimepoints(unittest.TestCase): def test_consolidate_all_true_flags(self): all_flags = {'f1': [True, True, True], 'f2': [True, True, True],} flags = consolidate_flags(all_flags) soln = [True, True, True] self.assertEqual(flags, soln) def test_consolidate_all_False_flags(self): all_flags = {'f1': [False, False], 'f2': [False, False],} flags = consolidate_flags(all_flags) soln = [False, False] self.assertEqual(flags, soln) def test_consolidate_mixed_flags(self): all_flags = {'f1': [False, True, True, True], 'f2': [False, True, True, True], 'f2': [True, True, False, True]} flags = consolidate_flags(all_flags) soln = [False, True, False, True] self.assertEqual(flags, soln) def test_flag_no_outliers(self): fail_num = 0 for i in range(10): normal_dataset = np.random.normal(loc=5.0, scale=1.0, size=1000) flags = flag_outliers(normal_dataset) soln = [True for i in range(1000)] if flags != soln: fail_num += 1 self.assertTrue(fail_num <= 2) def test_flag_lage_and_small_outliers(self): N_normal, N_out1, N_out2 = 200, 50, 20 normal_dataset = np.random.normal(loc=10.0, scale=1.0, size=N_normal) outliers1 = np.random.normal(loc=20.0, scale=1.0, size=N_out1) outliers2 = np.random.normal(loc=1.0, scale=0.5, size=N_out2) test_set = list(normal_dataset) + list(outliers1) + list(outliers2) flags = flag_outliers(test_set) soln = ([True] * N_normal) + ([False] * (N_out1 + N_out2)) self.assertEqual(flags, soln) def test_flag_just_small_outliers(self): N_normal, N_out1, N_out2 = 200, 10, 30 normal_dataset = np.random.normal(loc=10.0, scale=1.0, size=N_normal) outliers1 = np.random.normal(loc=20.0, scale=1.0, size=N_out1) outliers2 = np.random.normal(loc=1.0, scale=0.5, size=N_out2) test_set = list(normal_dataset) + list(outliers1) + list(outliers2) flags = flag_outliers(test_set, options='short') soln = ([True] * (N_normal+N_out1)) + ([False] * N_out2) self.assertEqual(flags, soln) def test_flag_just_large_outliers(self): N_normal, N_out1, N_out2 = 200, 10, 20 normal_dataset = np.random.normal(loc=10.0, scale=1.0, size=N_normal) outliers1 = np.random.normal(loc=20.0, scale=1.0, size=N_out1) outliers2 = np.random.normal(loc=1.0, scale=0.5, size=N_out2) test_set = list(normal_dataset) + list(outliers1) + list(outliers2) flags = flag_outliers(test_set, options='long') soln = [True] * N_normal + [False] * N_out1 + [True] * N_out2 self.assertEqual(flags, soln) def test_flag_outliers_with_nulls(self): N_normal = 1000 normal_dataset =	np.random.normal(loc=10.0, scale=1.0, size=N_normal)	numpy.random.normal
# Copyright 2019 <NAME> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 import numpy as np from scipy.optimize import curve_fit from auspex.log import logger from copy import copy import matplotlib.pyplot as plt from .fits import AuspexFit, Auspex2DFit from .signal_analysis import KT_estimation class RabiAmpFit(AuspexFit): """A fit to a Rabi amplitude curve, assuming a cosine model. """ xlabel = "Amplitude" ylabel = r"<$\sigma_z$>" title = "Rabi Amp Fit" @staticmethod def _model(x, p): return p[0] - p[1]np.cos(2np.pip[2](x - p[3])) def _initial_guess(self): #seed Rabi frequency from largest FFT component N = len(self.ypts) yfft = np.fft.fft(self.ypts) f_max_ind = np.argmax(np.abs(yfft[1:N//2])) f_0 = 0.5 max([1, f_max_ind]) / self.xpts[-1] amp_0 = 0.5(self.ypts.max() - self.ypts.min()) offset_0 = np.mean(self.ypts) phase_0 = 0 if self.ypts[N//2 - 1] > offset_0: amp_0 = -amp_0 return [offset_0, amp_0, f_0, phase_0] def _fit_dict(self, p): return {"y0": p[0], "Api": p[1], "f": p[2], "phi": p[3]} def __str__(self): return "y0 - Apicos(2pif(t - phi))" @property def pi_amp(self): """Returns the pi-pulse amplitude of the fit. """ return 0.5/self.fit_params["f"] def annotation(self): return r"$A_\pi$ = {0:.2e} {1} {2:.2e}".format(self.pi_amp, chr(177), self.fit_errors["Api"]) class RabiWidthFit(AuspexFit): """Fit to a single-frequency decaying cosine for fitting Rabi-vs-time experiments """ xlabel = "Delay" ylabel = r"<$\sigma_z$>" title = "Rabi Width Fit" @staticmethod def _model(x, p): return p[0] + p[1]np.exp(-x/p[2])np.cos(2np.pip[3](x - p[4])) def _initial_guess(self): frabi, Tcs, amps = KT_estimation(self.ypts-np.mean(self.ypts), self.xpts, 1) offset = np.average(self.xpts) amp = np.max(self.ypts) trabi = self.xpts[np.size(self.ypts) // 3]# assume Trabi is 1/3 of the scan phase = 90.0 return [offset, amp, trabi, frabi[0], phase] def _fit_dict(self, p): return {"y0": p[0], "A": p[1], 'T': p[2], "f": p[3], "phi": p[4]} def __str__(self): return "y0 + Aexp(-x/T)cos(2pif(t - phi))" @property def t_rabi(self): return self.fit_params["T"] def annotation(self): return r"$T_\pi$ = {0:.2e} {1} {2:.2e}".format(self.fit_params["T"], chr(177), self.fit_errors["T"]) class T1Fit(AuspexFit): """Fit to a decaying exponential for T1 measurement experiments. """ xlabel = "Delay" ylabel = r"<$\sigma_z$>" title = r"$T_1$ Fit" @staticmethod def _model(x, p): return p[0]np.exp(-x/p[1]) + p[2] def _initial_guess(self): ## Initial guess using method of linear regression via integral equations ## https://www.scribd.com/doc/14674814/Regressions-et-equations-integrales N = len(self.xpts) S = np.zeros(N) for j in range(2, N): S[j] = S[j-1] + 0.5((self.ypts[j] + self.ypts[j-1]) (self.xpts[j] - self.xpts[j-1])) xs = self.xpts - self.xpts[0] ys = self.ypts - self.ypts[0] M = np.array([[np.sum(xs*2), np.sum(xs S)], [np.sum(xs * S), np.sum(S*2)]]) B1 = (np.linalg.inv(M) @ np.array([np.sum(ys xs), np.sum(ys * S)]).T)[1] theta = np.exp(B1 * self.xpts) M2 = np.array([[N, np.sum(theta)], [np.sum(theta), np.sum(theta*2)]]) A = np.linalg.inv(M2) @ np.array([np.sum(self.ypts), np.sum(self.ypts theta)]).T return [A[1], -1.0/B1, A[0]] def _fit_dict(self, p): return {"A": p[0], "T1": p[1], "A0": p[2]} def __str__(self): return "A0 + Aexp(-t/T1)" @property def T1(self): """Return the measured T1 (i.e. decay constant of exponential). """ return self.fit_params["T1"] def make_plots(self): """Create plot on both linear and semilog scale """ logger.info("Semilog plot of \|1> state probability requires calibrated data.") plt.figure(figsize=(26.4, 4.8)) plt.subplot(121) plt.plot(self.xpts, self.ypts, ".", markersize=15, label="Data") plt.plot(self.xpts, self.model(self.xpts), "-", linewidth=3, label="Fit") plt.xlabel(self.xlabel, fontsize=14) plt.ylabel(self.ylabel, fontsize=14) plt.annotate(self.annotation(), xy=(0.4, 0.10), xycoords='axes fraction', size=12) plt.subplot(122) plt.semilogy(self.xpts, -1/2(self.ypts - self.fit_params["A0"]), ".", markersize=15, label="Data") plt.semilogy(self.xpts, -1/2(self.model(self.xpts) - self.fit_params["A0"]), "-", linewidth=3, label="Fit") plt.xlabel(self.xlabel, fontsize=14) plt.ylabel('\|1> probability', fontsize=14) plt.suptitle(self.title, fontsize=14) def annotation(self): return r"$T_1$ = {0:.2e} {1} {2:.2e}".format(self.fit_params["T1"], chr(177), self.fit_errors["T1"]) class RamseyFit(AuspexFit): """Fit to a Ramsey experiment using either a one or two frequency decaying sine model. """ xlabel = "Delay" ylabel = r"<$\sigma_z$>" title = "Ramsey Fit" def __init__(self, xpts, ypts, two_freqs=True, AIC=True, make_plots=False, force=False, ax=None): """One or two frequency Ramsey experiment fit. If a two-frequency fit is selected by the user or by comparing AIC scores, fit parameters are returned as tuples instead of single numbers. Args: xpts (numpy.array): Time data points. ypts (numpy.array): Qubit measurements. two_freqs (Bool): If true, attempt a two-frequency fit of the data. AIC (Bool): Decide between one and two frequency fits using the Akaike information criterion. make_plots (Bool): Display a plot of data and fit result. ax (Axes, optional): Axes on which to draw plot. If None, new figure is created force (Bool): Force the selection of a two-frequency fit regardless of AIC score. """ self.AIC = AIC self.dict_option = two_freqs self.two_freqs = two_freqs self.force = force self.plots = make_plots self.ax = ax assert len(xpts) == len(ypts), "Length of X and Y points must match!" self.xpts = xpts self.ypts = ypts self._do_fit() def _initial_guess_1f(self): freqs, Tcs, amps = KT_estimation(self.ypts-np.mean(self.ypts), self.xpts, 1) return [freqs[0], abs(amps[0]), Tcs[0], np.angle(amps[0]), np.mean(self.ypts)] def _initial_guess_2f(self): freqs, Tcs, amps = KT_estimation(self.ypts-np.mean(self.ypts), self.xpts, 2) return [freqs, abs(amps), Tcs, np.angle(amps), np.mean(self.ypts)] @staticmethod def _ramsey_1f(x, f, A, tau, phi, y0): return Anp.exp(-x/tau)np.cos(2np.pifx + phi) + y0 @staticmethod def _model_2f(x, p): return (RamseyFit._ramsey_1f(x, p[0], p[2], p[4], p[6], p[8]) + RamseyFit._ramsey_1f(x, p[1], p[3], p[5], p[7], p[8])) @staticmethod def _model_1f(x, p): return RamseyFit._ramsey_1f(x, p[0], p[1], p[2], p[3], p[4]) def _aicc(self, e, k, n): return 2k+e+(2k(k+1))/(n-k-1) def _do_fit(self): if self.two_freqs: self.dict_option = True self._initial_guess = self._initial_guess_2f self._model = self._model_2f try: super()._do_fit() two_freq_chi2 = self.sq_error except: self.two_freqs = False logger.info("Two-frequency fit failed. Trying single-frequency fit.") if self.two_freqs and self.AIC: #Compare the one and two frequency fits self.dict_option = False self._initial_guess = self._initial_guess_1f self._model = self._model_1f super()._do_fit() one_freq_chi2 = self.sq_error aic = self._aicc(two_freq_chi2, 9, len(self.xpts)) - self._aicc(one_freq_chi2, 5, len(self.xpts)) if aic > 0 and not self.force: self.two_freqs = False rl = 100np.exp(-aic/2) logger.info(f"Selecting one-frequency fit with relative likelihood = {rl:.2f}%") if rl>33: logger.info("Relative likelihood of 2nd frequency high, take more averages or set force = True.") else: self.dict_option = True self._initial_guess = self._initial_guess_2f self._model = self._model_2f super()._do_fit() if not self.two_freqs: self.dict_option = False self._initial_guess = self._initial_guess_1f self._model = self._model_1f super()._do_fit() if self.plots: self.make_plots() def annotation(self): if self.two_freqs: return r"$T_2$ = {0:.2e} {1} {2:.2e} "'\n'"$T_2$ = {3:.2e} {4} {5:.2e}".format(self.fit_params["tau1"], chr(177), self.fit_errors["tau1"], self.fit_params["tau2"], chr(177), self.fit_errors["tau2"]) else: return r"$T_2$ = {0:.2e} {1} {2:.2e}".format(self.fit_params["tau"], chr(177), self.fit_errors["tau"]) @property def T2(self): if self.two_freqs: return self.fit_params["tau1"], self.fit_params["tau2"] else: return self.fit_params["tau"] @property def ramsey_freq(self): if self.two_freqs: return self.fit_params["f1"], self.fit_params["f2"] else: return self.fit_params["f"] def _fit_dict(self, p): if self.dict_option: return {"f1": p[0], "A1": p[2], "tau1": p[4], "phi1": p[6], "f2": p[1], "A2": p[3], "tau2": p[5], "phi2": p[7], "y0": p[8]} else: return {"f": p[0], "A": p[1], "tau": p[2], "phi": p[3], "y0": p[4]} class SingleQubitRBFit(AuspexFit): """Fit to an RB decay curve using the model A(r^n) + B """ ylabel = r"<$\sigma_z$>" title = "Single Qubit RB Fit" def __init__(self, lengths, data, make_plots=False, log_scale_x=True, smart_guess=True, bounded_fit=True, ax=None): self.lengths = sorted(list(set(lengths))) repeats = len(data) // len(self.lengths) xpts = np.array(self.lengths) ypts = np.mean(np.reshape(data,(len(self.lengths),repeats)),1) self.data = data self.data_points = np.reshape(data,(len(self.lengths),repeats)) self.errors = np.std(self.data_points, 1) self.log_scale_x = log_scale_x self.ax = ax self.smart_guess = smart_guess if log_scale_x: self.xlabel = r"$log_2$ Clifford Number" else: self.xlabel = "Clifford Number" if bounded_fit: self.bounds = ((0, -np.inf, 0), (1, np.inf, 1)) super().__init__(xpts, ypts, make_plots=make_plots, ax=ax) @staticmethod def _model(x, p): return p[0] (1-p[1])*x + p[2] def _initial_guess(self): if self.smart_guess: ## Initial guess using method of linear regression via integral equations ## https://www.scribd.com/doc/14674814/Regressions-et-equations-integrales N = len(self.xpts) S = np.zeros(N) for j in range(2, N): S[j] = S[j-1] + 0.5((self.ypts[j] + self.ypts[j-1]) * (self.xpts[j] - self.xpts[j-1])) xs = self.xpts - self.xpts[0] ys = self.ypts - self.ypts[0] M = np.array([[np.sum(xs*2), np.sum(xs S)], [	np.sum(xs * S)	numpy.sum
from typing import List import numpy as np from scipy.interpolate import interp1d import matplotlib.pyplot as plt import pandas as pd from modules.CD_parameters import * from modules.utilities import * # check if the output directory exists check_dir("".join((project_dir, '/figures/foo.eps'))) def flatten_list(list_of_lists: List) -> np.ndarray: return np.array([item for sub_list in list_of_lists for item in sub_list]) start_line_number = 93 # The first line end_line_number = 93 # The last line following_the_spectra_catlogue = True # Read spectrumIDs or read SampleIDs first? rows = np.array(range(start_line_number, end_line_number + 1)) - 2 # Read the files if following_the_spectra_catlogue: Sample_catalogue = pd.read_excel("".join((path_relab, 'Sample_Catalogue.xlsx')), index_col=None, na_values=['NA'], usecols="A, C, Ag", engine='openpyxl').to_numpy() Spectra_catalogue = pd.read_excel("".join((path_relab, 'Spectra_Catalogue.xlsx')), index_col=None, na_values=['NA'], usecols="A, B, F:H", engine='openpyxl').to_numpy()[rows] SpectrumIDs = Spectra_catalogue[:, 0] SampleIDs = Spectra_catalogue[:, 1] # Range for interpolation Start = np.array(Spectra_catalogue[:, 2]) Stop = np.array(Spectra_catalogue[:, 3]) Step = np.array(Spectra_catalogue[:, 4]) Weathering = flatten_list([Sample_catalogue[np.where(Sample_catalogue[:, 0] == SampleID)[0], 2] for SampleID in SampleIDs]) else: Sample_catalogue = pd.read_excel("".join((path_relab, 'Sample_Catalogue.xlsx')), index_col=None, na_values=['NA'], usecols="A, C, Ag", engine='openpyxl').to_numpy()[rows] Spectra_catalogue = pd.read_excel("".join((path_relab, 'Spectra_Catalogue.xlsx')), index_col=None, na_values=['NA'], usecols="A, B, F:H", engine='openpyxl').to_numpy() SampleIDs = np.array(Sample_catalogue[:, 0]) # Find Spectrum ID SpectrumIDs = flatten_list([Spectra_catalogue[np.where(Spectra_catalogue[:, 1] == SampleID)[0], 0] for SampleID in SampleIDs]) # Range for interpolation Start = flatten_list([Spectra_catalogue[np.where(Spectra_catalogue[:, 1] == SampleID)[0], 2] for SampleID in SampleIDs]) Stop = flatten_list([Spectra_catalogue[np.where(Spectra_catalogue[:, 1] == SampleID)[0], 3] for SampleID in SampleIDs]) Step = flatten_list([Spectra_catalogue[np.where(Spectra_catalogue[:, 1] == SampleID)[0], 4] for SampleID in SampleIDs]) # Find samples for the spectra (this is necessary because a spectrum can have same SampleID) SampleIDs = flatten_list([Spectra_catalogue[np.where(Spectra_catalogue[:, 0] == SpectrumID)[0], 1] for SpectrumID in SpectrumIDs]) Weathering = flatten_list([Sample_catalogue[np.where(Sample_catalogue[:, 0] == SampleID)[0], 2] for SampleID in SampleIDs]) # Take only these spectra mask = np.array(np.where((Start <= lambda_min) & (Stop >= lambda_max) & (Step <= resolution_max))).ravel() SpectrumIDs = SpectrumIDs[mask] SampleIDs = SampleIDs[mask] X = np.arange(lambda_min, lambda_max + resolution_final / 2, resolution_final) # Find corresponding PIs PIs = flatten_list([Sample_catalogue[np.where(Sample_catalogue[:, 0] == SampleID)[0], 1] for SampleID in SampleIDs]) """ # Sorting idx = np.argsort(SampleIDs) SampleIDs = SampleIDs[idx] SpectrumIDs = SpectrumIDs[idx] PIs = PIs[idx] """ if denoise: width = 9 cent = np.int(	np.round(width / 2)	numpy.round
import argparse import numpy as np import torch import plyfile import skimage.measure from tqdm import tqdm import yaml import os.path as osp import skimage import time def convert_sigma_samples_to_ply( input_3d_sigma_array: np.ndarray, voxel_grid_origin, volume_size, ply_filename_out, level=5.0, offset=None, scale=None,): """ Convert density samples to .ply :param input_3d_sdf_array: a float array of shape (n,n,n) :voxel_grid_origin: a list of three floats: the bottom, left, down origin of the voxel grid :volume_size: a list of three floats :ply_filename_out: string, path of the filename to save to This function adapted from: https://github.com/RobotLocomotion/spartan """ start_time = time.time() verts, faces, normals, values = skimage.measure.marching_cubes( input_3d_sigma_array, level=level, spacing=volume_size ) # transform from voxel coordinates to camera coordinates # note x and y are flipped in the output of marching_cubes mesh_points =	np.zeros_like(verts)	numpy.zeros_like
import os import pybullet as p import numpy as np import time from itertools import product from .utils import unit_pose, safe_zip, multiply, Pose, AABB, create_box, set_pose, get_all_links, LockRenderer, \ get_aabb, pairwise_link_collision, remove_body, draw_aabb, get_box_geometry, create_shape, create_body, STATIC_MASS, \ unit_quat, unit_point, CLIENT, create_shape_array, set_color, get_point, clip, load_model, TEMP_DIR, NULL_ID, elapsed_time MAX_TEXTURE_WIDTH = 418 # max square dimension MAX_PIXEL_VALUE = 255 MAX_LINKS = 125 # Max links seems to be 126 class VoxelGrid(object): # https://github.mit.edu/caelan/ROS/blob/master/sparse_voxel_grid.py # https://github.mit.edu/caelan/ROS/blob/master/base_navigation.py # https://github.mit.edu/caelan/ROS/blob/master/utils.py # https://github.mit.edu/caelan/ROS/blob/master/voxel_detection.py # TODO: can always display the grid in RVIZ after filtering # TODO: compute the maximum sized cuboid (rectangle) in a grid (matrix) def __init__(self, resolutions, color=(1, 0, 0, 0.5)): #def __init__(self, sizes, centers, pose=unit_pose()): #assert len(sizes) == len(centers) self.resolutions = resolutions self.occupied = set() self.world_from_grid = unit_pose() # TODO: support for real self.color = color #self.bodies = None # TODO: store voxels more intelligently spatially def __len__(self): return len(self.occupied) def voxel_from_point(self, point): return tuple(np.floor(np.divide(point, self.resolutions)).astype(int)) def voxels_from_aabb(self, aabb): lower_voxel, upper_voxel = map(self.voxel_from_point, aabb) return map(tuple, product([range(l, u + 1) for l, u in safe_zip(lower_voxel, upper_voxel)])) def lower_from_voxel(self, voxel): return np.multiply(voxel, self.resolutions) def center_from_voxel(self, voxel): return self.lower_from_voxel(voxel) + self.resolutions/2. def upper_from_voxel(self, voxel): return self.lower_from_voxel(voxel) + self.resolutions def pose_from_voxel(self, voxel): return multiply(self.world_from_grid, Pose(self.center_from_voxel(voxel))) def aabb_from_voxel(self, voxel): return AABB(self.lower_from_voxel(voxel), self.upper_from_voxel(voxel)) def is_occupied(self, voxel): return voxel in self.occupied def set_occupied(self, voxel): if self.is_occupied(voxel): return False self.occupied.add(voxel) return True def set_free(self, voxel): if not self.is_occupied(voxel): return False self.occupied.remove(voxel) return True def get_neighbors(self, index): for i in range(len(index)): direction = np.zeros(len(index), dtype=int) for n in (-1, +1): direction[i] = n yield tuple(np.array(index) + direction) def get_clusters(self, voxels=None): if voxels is None: voxels = self.occupied clusters = [] assigned = set() def dfs(current): if (current in assigned) or (not self.is_occupied(current)): return [] cluster = [current] assigned.add(current) for neighbor in self.get_neighbors(current): cluster.extend(dfs(neighbor)) return cluster for voxel in voxels: cluster = dfs(voxel) if cluster: clusters.append(cluster) return clusters # TODO: implicitly check collisions def create_box(self): color = (0, 0, 0, 0) #color = None box = create_box(self.resolutions, color=color) #set_color(box, color=color) set_pose(box, self.world_from_grid) return box def get_affected(self, bodies, occupied): assert self.world_from_grid == unit_pose() check_voxels = {} for body in bodies: for link in get_all_links(body): aabb = get_aabb(body, link) # TODO: pad using threshold for voxel in self.voxels_from_aabb(aabb): if self.is_occupied(voxel) == occupied: check_voxels.setdefault(voxel, []).append((body, link)) return check_voxels def check_collision(self, box, voxel, pairs, threshold=0.): box_pairs = [(box, link) for link in get_all_links(box)] set_pose(box, self.pose_from_voxel(voxel)) return any(pairwise_link_collision(body1, link1, body2, link2, max_distance=threshold) for (body1, link1), (body2, link2) in product(pairs, box_pairs)) def add_point(self, point): self.set_occupied(self.voxel_from_point(point)) def add_aabb(self, aabb): for voxel in self.voxels_from_aabb(aabb): self.set_occupied(voxel) def add_bodies(self, bodies, threshold=0.): # Otherwise, need to transform bodies check_voxels = self.get_affected(bodies, occupied=False) box = self.create_box() for voxel, pairs in check_voxels.items(): # pairs typically only has one element if self.check_collision(box, voxel, pairs, threshold=threshold): self.set_occupied(voxel) remove_body(box) def remove_bodies(self, bodies, threshold=1e-2): # TODO: could also just iterate over the voxels directly check_voxels = self.get_affected(bodies, occupied=True) box = self.create_box() for voxel, pairs in check_voxels.items(): if self.check_collision(box, voxel, pairs, threshold=threshold): self.set_free(voxel) remove_body(box) def draw_voxel_bodies(self): # TODO: transform into the world frame with LockRenderer(): handles = [] for voxel in sorted(self.occupied): handles.extend(draw_aabb(self.aabb_from_voxel(voxel), color=self.color[:3])) return handles def create_voxel_bodies1(self): start_time = time.time() geometry = get_box_geometry(self.resolutions) collision_id, visual_id = create_shape(geometry, color=self.color) bodies = [] for voxel in sorted(self.occupied): body = create_body(collision_id, visual_id) #scale = self.resolutions[0] #body = load_model('models/voxel.urdf', fixed_base=True, scale=scale) set_pose(body, self.pose_from_voxel(voxel)) bodies.append(body) # 0.0462474774444 / voxel print(elapsed_time(start_time)) return bodies def create_voxel_bodies2(self): geometry = get_box_geometry(self.resolutions) collision_id, visual_id = create_shape(geometry, color=self.color) ordered_voxels = sorted(self.occupied) bodies = [] for start in range(0, len(ordered_voxels), MAX_LINKS): voxels = ordered_voxels[start:start + MAX_LINKS] body = p.createMultiBody(#baseMass=STATIC_MASS, #baseCollisionShapeIndex=-1, #baseVisualShapeIndex=-1, #basePosition=unit_point(), #baseOrientation=unit_quat(), #baseInertialFramePosition=unit_point(), #baseInertialFrameOrientation=unit_quat(), linkMasses=len(voxels)[STATIC_MASS], linkCollisionShapeIndices=len(voxels)[collision_id], linkVisualShapeIndices=len(voxels)[visual_id], linkPositions=list(map(self.center_from_voxel, voxels)), linkOrientations=len(voxels)[unit_quat()], linkInertialFramePositions=len(voxels)[unit_point()], linkInertialFrameOrientations=len(voxels)[unit_quat()], linkParentIndices=len(voxels)[0], linkJointTypes=len(voxels)[p.JOINT_FIXED], linkJointAxis=len(voxels)[unit_point()], physicsClientId=CLIENT) set_pose(body, self.world_from_grid) bodies.append(body) # 0.0163199263677 / voxel return bodies def create_voxel_bodies3(self): ordered_voxels = sorted(self.occupied) geoms = [get_box_geometry(self.resolutions) for _ in ordered_voxels] poses = list(map(self.pose_from_voxel, ordered_voxels)) #colors = [list(self.color) for _ in self.voxels] # TODO: colors don't work colors = None collision_id, visual_id = create_shape_array(geoms, poses, colors) body = create_body(collision_id, visual_id) # Max seems to be 16 #dump_body(body) set_color(body, self.color) return [body] def create_voxel_bodies(self): with LockRenderer(): return self.create_voxel_bodies1() #return self.create_voxel_bodies2() #return self.create_voxel_bodies3() def project2d(self): # TODO: combine adjacent voxels into larger lines # TODO: greedy algorithm that combines lines/boxes tallest_voxel = {} for i, j, k in self.occupied: tallest_voxel[i, j] = max(k, tallest_voxel.get((i, j), k)) return {(i, j, k) for (i, j), k in tallest_voxel.items()} def create_height_map(self, plane, plane_size, width=MAX_TEXTURE_WIDTH, height=MAX_TEXTURE_WIDTH): min_z, max_z = 0., 2. plane_extent = plane_sizenp.array([1, 1, 0]) plane_lower = get_point(plane) - plane_extent/2. #plane_aabb = (plane_lower, plane_lower + plane_extent) #plane_aabb = get_aabb(plane) # TODO: bounding box is effectively empty #plane_lower, plane_upper = plane_aabb #plane_extent = (plane_upper - plane_lower) image_size = np.array([width, height]) # TODO: fix width/height order pixel_from_point = lambda point: np.floor( image_size (point - plane_lower)[:2] / plane_extent[:2]).astype(int) # TODO: last row/col doesn't seem to be filled height_map = np.zeros(image_size) for voxel in self.project2d(): voxel_aabb = self.aabb_from_voxel(voxel) #if not aabb_contains_aabb(aabb2d_from_aabb(voxel_aabb), aabb2d_from_aabb(plane_aabb)): # continue (x1, y1), (x2, y2) = map(pixel_from_point, voxel_aabb) if (x1 < 0) or (width <= x2) or (y1 < 0) or (height <= y2): continue scaled_z = (clip(voxel_aabb[1][2], min_z, max_z) - min_z) / max_z for c in range(x1, x2+1): for y in range(y1, y2+1): r = height - y - 1 # TODO: can also just set in bulk if using height_map height_map[r, c] = max(height_map[r, c], scaled_z) return height_map def create_textured_square(size, color=None, width=MAX_TEXTURE_WIDTH, height=MAX_TEXTURE_WIDTH): body = load_model('models/square.urdf', scale=size) if color is not None: set_color(body, color) path = os.path.join(TEMP_DIR, 'texture.png') image = MAX_PIXEL_VALUE*np.ones((width, height, 3), dtype=np.uint8) import scipy.misc scipy.misc.imsave(path, image) texture = p.loadTexture(path) p.changeVisualShape(body, NULL_ID, textureUniqueId=texture, physicsClientId=CLIENT) return body, texture def set_texture(texture, image): # Alias/WaveFront Material (.mtl) File Format # https://people.cs.clemson.edu/~dhouse/courses/405/docs/brief-mtl-file-format.html #print(get_visual_data(body)) width, height, channels = image.shape pixels = image.flatten().tolist() assert len(pixels) <= 524288 # b3Printf: uploadBulletFileToSharedMemory 747003 exceeds max size 524288 p.changeTexture(texture, pixels, width, height, physicsClientId=CLIENT) # TODO: it's important that width and height are the same as the original def rgb_interpolate(grey_image, min_color, max_color): width, height = grey_image.shape channels = 3 rgb_image =	np.zeros((width, height, channels), dtype=np.uint8)	numpy.zeros
# -- coding: utf-8 -- from __future__ import print_function from __future__ import absolute_import import os import sys import tempfile import warnings import numpy from numpy import testing as npt import tables from tables import Atom, ClosedNodeError, NoSuchNodeError from tables.utils import byteorders from tables.tests import common from tables.tests.common import allequal from tables.tests.common import unittest, test_filename from tables.tests.common import PyTablesTestCase as TestCase from six.moves import range #warnings.resetwarnings() class BasicTestCase(TestCase): """Basic test for all the supported typecodes present in numpy. All of them are included on pytables. """ endiancheck = False def write_read(self, testarray): a = testarray if common.verbose: print('\n', '-=' * 30) print("Running test for array with type '%s'" % a.dtype.type, end=' ') print("for class check:", self.title) # Create an instance of HDF5 file filename = tempfile.mktemp(".h5") try: with tables.open_file(filename, mode="w") as fileh: root = fileh.root # Create the array under root and name 'somearray' if self.endiancheck and a.dtype.kind != "S": b = a.byteswap() b.dtype = a.dtype.newbyteorder() a = b fileh.create_array(root, 'somearray', a, "Some array") # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: root = fileh.root # Read the saved array b = root.somearray.read() # Compare them. They should be equal. if common.verbose and not allequal(a, b): print("Write and read arrays differ!") # print("Array written:", a) print("Array written shape:", a.shape) print("Array written itemsize:", a.itemsize) print("Array written type:", a.dtype.type) # print("Array read:", b) print("Array read shape:", b.shape) print("Array read itemsize:", b.itemsize) print("Array read type:", b.dtype.type) if a.dtype.kind != "S": print("Array written byteorder:", a.dtype.byteorder) print("Array read byteorder:", b.dtype.byteorder) # Check strictly the array equality self.assertEqual(a.shape, b.shape) self.assertEqual(a.shape, root.somearray.shape) if a.dtype.kind == "S": self.assertEqual(root.somearray.atom.type, "string") else: self.assertEqual(a.dtype.type, b.dtype.type) self.assertEqual(a.dtype.type, root.somearray.atom.dtype.type) abo = byteorders[a.dtype.byteorder] bbo = byteorders[b.dtype.byteorder] if abo != "irrelevant": self.assertEqual(abo, root.somearray.byteorder) self.assertEqual(bbo, sys.byteorder) if self.endiancheck: self.assertNotEqual(bbo, abo) obj = root.somearray self.assertEqual(obj.flavor, 'numpy') self.assertEqual(obj.shape, a.shape) self.assertEqual(obj.ndim, a.ndim) self.assertEqual(obj.chunkshape, None) if a.shape: nrows = a.shape[0] else: # scalar nrows = 1 self.assertEqual(obj.nrows, nrows) self.assertTrue(allequal(a, b)) finally: # Then, delete the file os.remove(filename) def write_read_out_arg(self, testarray): a = testarray if common.verbose: print('\n', '-=' * 30) print("Running test for array with type '%s'" % a.dtype.type, end=' ') print("for class check:", self.title) # Create an instance of HDF5 file filename = tempfile.mktemp(".h5") try: with tables.open_file(filename, mode="w") as fileh: root = fileh.root # Create the array under root and name 'somearray' if self.endiancheck and a.dtype.kind != "S": b = a.byteswap() b.dtype = a.dtype.newbyteorder() a = b fileh.create_array(root, 'somearray', a, "Some array") # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: root = fileh.root # Read the saved array b = numpy.empty_like(a, dtype=a.dtype) root.somearray.read(out=b) # Check strictly the array equality self.assertEqual(a.shape, b.shape) self.assertEqual(a.shape, root.somearray.shape) if a.dtype.kind == "S": self.assertEqual(root.somearray.atom.type, "string") else: self.assertEqual(a.dtype.type, b.dtype.type) self.assertEqual(a.dtype.type, root.somearray.atom.dtype.type) abo = byteorders[a.dtype.byteorder] bbo = byteorders[b.dtype.byteorder] if abo != "irrelevant": self.assertEqual(abo, root.somearray.byteorder) self.assertEqual(abo, bbo) if self.endiancheck: self.assertNotEqual(bbo, sys.byteorder) self.assertTrue(allequal(a, b)) finally: # Then, delete the file os.remove(filename) def write_read_atom_shape_args(self, testarray): a = testarray atom = Atom.from_dtype(a.dtype) shape = a.shape byteorder = None if common.verbose: print('\n', '-=' * 30) print("Running test for array with type '%s'" % a.dtype.type, end=' ') print("for class check:", self.title) # Create an instance of HDF5 file filename = tempfile.mktemp(".h5") try: with tables.open_file(filename, mode="w") as fileh: root = fileh.root # Create the array under root and name 'somearray' if self.endiancheck and a.dtype.kind != "S": b = a.byteswap() b.dtype = a.dtype.newbyteorder() if b.dtype.byteorder in ('>', '<'): byteorder = byteorders[b.dtype.byteorder] a = b ptarr = fileh.create_array(root, 'somearray', atom=atom, shape=shape, title="Some array", # specify the byteorder explicitly # since there is no way to deduce # it in this case byteorder=byteorder) self.assertEqual(shape, ptarr.shape) self.assertEqual(atom, ptarr.atom) ptarr[...] = a # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: root = fileh.root # Read the saved array b = root.somearray.read() # Compare them. They should be equal. if common.verbose and not allequal(a, b): print("Write and read arrays differ!") # print("Array written:", a) print("Array written shape:", a.shape) print("Array written itemsize:", a.itemsize) print("Array written type:", a.dtype.type) # print("Array read:", b) print("Array read shape:", b.shape) print("Array read itemsize:", b.itemsize) print("Array read type:", b.dtype.type) if a.dtype.kind != "S": print("Array written byteorder:", a.dtype.byteorder) print("Array read byteorder:", b.dtype.byteorder) # Check strictly the array equality self.assertEqual(a.shape, b.shape) self.assertEqual(a.shape, root.somearray.shape) if a.dtype.kind == "S": self.assertEqual(root.somearray.atom.type, "string") else: self.assertEqual(a.dtype.type, b.dtype.type) self.assertEqual(a.dtype.type, root.somearray.atom.dtype.type) abo = byteorders[a.dtype.byteorder] bbo = byteorders[b.dtype.byteorder] if abo != "irrelevant": self.assertEqual(abo, root.somearray.byteorder) self.assertEqual(bbo, sys.byteorder) if self.endiancheck: self.assertNotEqual(bbo, abo) obj = root.somearray self.assertEqual(obj.flavor, 'numpy') self.assertEqual(obj.shape, a.shape) self.assertEqual(obj.ndim, a.ndim) self.assertEqual(obj.chunkshape, None) if a.shape: nrows = a.shape[0] else: # scalar nrows = 1 self.assertEqual(obj.nrows, nrows) self.assertTrue(allequal(a, b)) finally: # Then, delete the file os.remove(filename) def setup00_char(self): """Data integrity during recovery (character objects)""" if not isinstance(self.tupleChar, numpy.ndarray): a = numpy.array(self.tupleChar, dtype="S") else: a = self.tupleChar return a def test00_char(self): a = self.setup00_char() self.write_read(a) def test00_char_out_arg(self): a = self.setup00_char() self.write_read_out_arg(a) def test00_char_atom_shape_args(self): a = self.setup00_char() self.write_read_atom_shape_args(a) def test00b_char(self): """Data integrity during recovery (string objects)""" a = self.tupleChar filename = tempfile.mktemp(".h5") try: # Create an instance of HDF5 file with tables.open_file(filename, mode="w") as fileh: fileh.create_array(fileh.root, 'somearray', a, "Some array") # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: # Read the saved array b = fileh.root.somearray.read() if isinstance(a, bytes): self.assertEqual(type(b), bytes) self.assertEqual(a, b) else: # If a is not a python string, then it should be a list # or ndarray self.assertTrue(type(b) in [list, numpy.ndarray]) finally: # Then, delete the file os.remove(filename) def test00b_char_out_arg(self): """Data integrity during recovery (string objects)""" a = self.tupleChar filename = tempfile.mktemp(".h5") try: # Create an instance of HDF5 file with tables.open_file(filename, mode="w") as fileh: fileh.create_array(fileh.root, 'somearray', a, "Some array") # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: # Read the saved array b = numpy.empty_like(a) if fileh.root.somearray.flavor != 'numpy': self.assertRaises(TypeError, lambda: fileh.root.somearray.read(out=b)) else: fileh.root.somearray.read(out=b) self.assertTrue(type(b), numpy.ndarray) finally: # Then, delete the file os.remove(filename) def test00b_char_atom_shape_args(self): """Data integrity during recovery (string objects)""" a = self.tupleChar filename = tempfile.mktemp(".h5") try: # Create an instance of HDF5 file with tables.open_file(filename, mode="w") as fileh: nparr = numpy.asarray(a) atom = Atom.from_dtype(nparr.dtype) shape = nparr.shape if nparr.dtype.byteorder in ('>', '<'): byteorder = byteorders[nparr.dtype.byteorder] else: byteorder = None ptarr = fileh.create_array(fileh.root, 'somearray', atom=atom, shape=shape, byteorder=byteorder, title="Some array") self.assertEqual(shape, ptarr.shape) self.assertEqual(atom, ptarr.atom) ptarr[...] = a # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: # Read the saved array b = numpy.empty_like(a) if fileh.root.somearray.flavor != 'numpy': self.assertRaises(TypeError, lambda: fileh.root.somearray.read(out=b)) else: fileh.root.somearray.read(out=b) self.assertTrue(type(b), numpy.ndarray) finally: # Then, delete the file os.remove(filename) def setup01_char_nc(self): """Data integrity during recovery (non-contiguous character objects)""" if not isinstance(self.tupleChar, numpy.ndarray): a = numpy.array(self.tupleChar, dtype="S") else: a = self.tupleChar if a.ndim == 0: b = a.copy() else: b = a[::2] # Ensure that this numpy string is non-contiguous if len(b) > 1: self.assertEqual(b.flags.contiguous, False) return b def test01_char_nc(self): b = self.setup01_char_nc() self.write_read(b) def test01_char_nc_out_arg(self): b = self.setup01_char_nc() self.write_read_out_arg(b) def test01_char_nc_atom_shape_args(self): b = self.setup01_char_nc() self.write_read_atom_shape_args(b) def test02_types(self): """Data integrity during recovery (numerical types)""" typecodes = ['int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64', 'float32', 'float64', 'complex64', 'complex128'] for name in ('float16', 'float96', 'float128', 'complex192', 'complex256'): atomname = name.capitalize() + 'Atom' if hasattr(tables, atomname): typecodes.append(name) for typecode in typecodes: a = numpy.array(self.tupleInt, typecode) self.write_read(a) b = numpy.array(self.tupleInt, typecode) self.write_read_out_arg(b) c = numpy.array(self.tupleInt, typecode) self.write_read_atom_shape_args(c) def test03_types_nc(self): """Data integrity during recovery (non-contiguous numerical types)""" typecodes = ['int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64', 'float32', 'float64', 'complex64', 'complex128', ] for name in ('float16', 'float96', 'float128', 'complex192', 'complex256'): atomname = name.capitalize() + 'Atom' if hasattr(tables, atomname): typecodes.append(name) for typecode in typecodes: a = numpy.array(self.tupleInt, typecode) if a.ndim == 0: b1 = a.copy() b2 = a.copy() b3 = a.copy() else: b1 = a[::2] b2 = a[::2] b3 = a[::2] # Ensure that this array is non-contiguous if len(b1) > 1: self.assertEqual(b1.flags.contiguous, False) if len(b2) > 1: self.assertEqual(b2.flags.contiguous, False) if len(b3) > 1: self.assertEqual(b3.flags.contiguous, False) self.write_read(b1) self.write_read_out_arg(b2) self.write_read_atom_shape_args(b3) class Basic0DOneTestCase(BasicTestCase): # Scalar case title = "Rank-0 case 1" tupleInt = 3 tupleChar = b"3" endiancheck = True class Basic0DTwoTestCase(BasicTestCase): # Scalar case title = "Rank-0 case 2" tupleInt = 33 tupleChar = b"33" endiancheck = True class Basic1DZeroTestCase(BasicTestCase): # This test case is not supported by PyTables (HDF5 limitations) # 1D case title = "Rank-1 case 0" tupleInt = () tupleChar = () endiancheck = False class Basic1DOneTestCase(BasicTestCase): # 1D case title = "Rank-1 case 1" tupleInt = (3,) tupleChar = (b"a",) endiancheck = True class Basic1DTwoTestCase(BasicTestCase): # 1D case title = "Rank-1 case 2" tupleInt = (3, 4) tupleChar = (b"aaa",) endiancheck = True class Basic1DThreeTestCase(BasicTestCase): # 1D case title = "Rank-1 case 3" tupleInt = (3, 4, 5) tupleChar = (b"aaa", b"bbb",) endiancheck = True class Basic2DOneTestCase(BasicTestCase): # 2D case title = "Rank-2 case 1" tupleInt = numpy.array(numpy.arange((4)*2)) tupleInt.shape = (4,)2 tupleChar = numpy.array(["abc"]32, dtype="S3") tupleChar.shape = (3,)2 endiancheck = True class Basic2DTwoTestCase(BasicTestCase): # 2D case, with a multidimensional dtype title = "Rank-2 case 2" tupleInt = numpy.array(numpy.arange((4)), dtype=(numpy.int_, (4,))) tupleChar = numpy.array(["abc"]3, dtype=("S3", (3,))) endiancheck = True class Basic10DTestCase(BasicTestCase): # 10D case title = "Rank-10 test" tupleInt = numpy.array(numpy.arange((2)10)) tupleInt.shape = (2,)10 tupleChar = numpy.array( ["abc"]210, dtype="S3") tupleChar.shape = (2,)10 endiancheck = True class Basic32DTestCase(BasicTestCase): # 32D case (maximum) title = "Rank-32 test" tupleInt = numpy.array((32,)) tupleInt.shape = (1,)32 tupleChar = numpy.array(["121"], dtype="S3") tupleChar.shape = (1,)32 class ReadOutArgumentTests(common.TempFileMixin, TestCase): def setUp(self): super(ReadOutArgumentTests, self).setUp() self.size = 1000 def create_array(self): array = numpy.arange(self.size, dtype='f8') disk_array = self.h5file.create_array('/', 'array', array) return array, disk_array def test_read_entire_array(self): array, disk_array = self.create_array() out_buffer = numpy.empty((self.size, ), 'f8') disk_array.read(out=out_buffer) numpy.testing.assert_equal(out_buffer, array) def test_read_contiguous_slice1(self): array, disk_array = self.create_array() out_buffer = numpy.arange(self.size, dtype='f8') out_buffer = numpy.random.permutation(out_buffer) out_buffer_orig = out_buffer.copy() start = self.size // 2 disk_array.read(start=start, stop=self.size, out=out_buffer[start:]) numpy.testing.assert_equal(out_buffer[start:], array[start:]) numpy.testing.assert_equal(out_buffer[:start], out_buffer_orig[:start]) def test_read_contiguous_slice2(self): array, disk_array = self.create_array() out_buffer = numpy.arange(self.size, dtype='f8') out_buffer = numpy.random.permutation(out_buffer) out_buffer_orig = out_buffer.copy() start = self.size // 4 stop = self.size - start disk_array.read(start=start, stop=stop, out=out_buffer[start:stop]) numpy.testing.assert_equal(out_buffer[start:stop], array[start:stop]) numpy.testing.assert_equal(out_buffer[:start], out_buffer_orig[:start]) numpy.testing.assert_equal(out_buffer[stop:], out_buffer_orig[stop:]) def test_read_non_contiguous_slice_contiguous_buffer(self): array, disk_array = self.create_array() out_buffer = numpy.empty((self.size // 2, ), dtype='f8') disk_array.read(start=0, stop=self.size, step=2, out=out_buffer) numpy.testing.assert_equal(out_buffer, array[0:self.size:2]) def test_read_non_contiguous_buffer(self): array, disk_array = self.create_array() out_buffer = numpy.empty((self.size, ), 'f8') out_buffer_slice = out_buffer[0:self.size:2] # once Python 2.6 support is dropped, this could change # to assertRaisesRegexp to check exception type and message at once self.assertRaises(ValueError, disk_array.read, 0, self.size, 2, out_buffer_slice) try: disk_array.read(0, self.size, 2, out_buffer_slice) except ValueError as exc: self.assertEqual('output array not C contiguous', str(exc)) def test_buffer_too_small(self): array, disk_array = self.create_array() out_buffer = numpy.empty((self.size // 2, ), 'f8') self.assertRaises(ValueError, disk_array.read, 0, self.size, 1, out_buffer) try: disk_array.read(0, self.size, 1, out_buffer) except ValueError as exc: self.assertTrue('output array size invalid, got' in str(exc)) def test_buffer_too_large(self): array, disk_array = self.create_array() out_buffer = numpy.empty((self.size + 1, ), 'f8') self.assertRaises(ValueError, disk_array.read, 0, self.size, 1, out_buffer) try: disk_array.read(0, self.size, 1, out_buffer) except ValueError as exc: self.assertTrue('output array size invalid, got' in str(exc)) class SizeOnDiskInMemoryPropertyTestCase(common.TempFileMixin, TestCase): def setUp(self): super(SizeOnDiskInMemoryPropertyTestCase, self).setUp() self.array_size = (10, 10) self.array = self.h5file.create_array( '/', 'somearray', numpy.zeros(self.array_size, 'i4')) def test_all_zeros(self): self.assertEqual(self.array.size_on_disk, 10 * 10 * 4) self.assertEqual(self.array.size_in_memory, 10 * 10 * 4) class UnalignedAndComplexTestCase(common.TempFileMixin, TestCase): """Basic test for all the supported typecodes present in numpy. Most of them are included on PyTables. """ def setUp(self): super(UnalignedAndComplexTestCase, self).setUp() self.root = self.h5file.root def write_read(self, testArray): if common.verbose: print('\n', '-=' * 30) print("\nRunning test for array with type '%s'" % testArray.dtype.type) # Create the array under root and name 'somearray' a = testArray if self.endiancheck: byteorder = {"little": "big", "big": "little"}[sys.byteorder] else: byteorder = sys.byteorder self.h5file.create_array(self.root, 'somearray', a, "Some array", byteorder=byteorder) if self.reopen: self._reopen() self.root = self.h5file.root # Read the saved array b = self.root.somearray.read() # Get an array to be compared in the correct byteorder c = a.newbyteorder(byteorder) # Compare them. They should be equal. if not allequal(c, b) and common.verbose: print("Write and read arrays differ!") print("Array written:", a) print("Array written shape:", a.shape) print("Array written itemsize:", a.itemsize) print("Array written type:", a.dtype.type) print("Array read:", b) print("Array read shape:", b.shape) print("Array read itemsize:", b.itemsize) print("Array read type:", b.dtype.type) # Check strictly the array equality self.assertEqual(a.shape, b.shape) self.assertEqual(a.shape, self.root.somearray.shape) if a.dtype.byteorder != "\|": self.assertEqual(a.dtype, b.dtype) self.assertEqual(a.dtype, self.root.somearray.atom.dtype) self.assertEqual(byteorders[b.dtype.byteorder], sys.byteorder) self.assertEqual(self.root.somearray.byteorder, byteorder) self.assertTrue(allequal(c, b)) def test01_signedShort_unaligned(self): """Checking an unaligned signed short integer array""" r = numpy.rec.array(b'a'200, formats='i1,f4,i2', shape=10) a = r["f2"] # Ensure that this array is non-aligned self.assertEqual(a.flags.aligned, False) self.assertEqual(a.dtype.type, numpy.int16) self.write_read(a) def test02_float_unaligned(self): """Checking an unaligned single precision array""" r = numpy.rec.array(b'a'200, formats='i1,f4,i2', shape=10) a = r["f1"] # Ensure that this array is non-aligned self.assertEqual(a.flags.aligned, 0) self.assertEqual(a.dtype.type, numpy.float32) self.write_read(a) def test03_byte_offset(self): """Checking an offsetted byte array""" r = numpy.arange(100, dtype=numpy.int8) r.shape = (10, 10) a = r[2] self.write_read(a) def test04_short_offset(self): """Checking an offsetted unsigned short int precision array""" r = numpy.arange(100, dtype=numpy.uint32) r.shape = (10, 10) a = r[2] self.write_read(a) def test05_int_offset(self): """Checking an offsetted integer array""" r = numpy.arange(100, dtype=numpy.int32) r.shape = (10, 10) a = r[2] self.write_read(a) def test06_longlongint_offset(self): """Checking an offsetted long long integer array""" r = numpy.arange(100, dtype=numpy.int64) r.shape = (10, 10) a = r[2] self.write_read(a) def test07_float_offset(self): """Checking an offsetted single precision array""" r = numpy.arange(100, dtype=numpy.float32) r.shape = (10, 10) a = r[2] self.write_read(a) def test08_double_offset(self): """Checking an offsetted double precision array""" r = numpy.arange(100, dtype=numpy.float64) r.shape = (10, 10) a = r[2] self.write_read(a) def test09_float_offset_unaligned(self): """Checking an unaligned and offsetted single precision array""" r = numpy.rec.array(b'a'200, formats='i1,3f4,i2', shape=10) a = r["f1"][3] # Ensure that this array is non-aligned self.assertEqual(a.flags.aligned, False) self.assertEqual(a.dtype.type, numpy.float32) self.write_read(a) def test10_double_offset_unaligned(self): """Checking an unaligned and offsetted double precision array""" r = numpy.rec.array(b'a'400, formats='i1,3f8,i2', shape=10) a = r["f1"][3] # Ensure that this array is non-aligned self.assertEqual(a.flags.aligned, False) self.assertEqual(a.dtype.type, numpy.float64) self.write_read(a) def test11_int_byteorder(self): """Checking setting data with different byteorder in a range (integer)""" # Save an array with the reversed byteorder on it a = numpy.arange(25, dtype=numpy.int32).reshape(5, 5) a = a.byteswap() a = a.newbyteorder() array = self.h5file.create_array( self.h5file.root, 'array', a, "byteorder (int)") # Read a subarray (got an array with the machine byteorder) b = array[2:4, 3:5] b = b.byteswap() b = b.newbyteorder() # Set this subarray back to the array array[2:4, 3:5] = b b = b.byteswap() b = b.newbyteorder() # Set this subarray back to the array array[2:4, 3:5] = b # Check that the array is back in the correct byteorder c = array[...] if common.verbose: print("byteorder of array on disk-->", array.byteorder) print("byteorder of subarray-->", b.dtype.byteorder) print("subarray-->", b) print("retrieved array-->", c) self.assertTrue(allequal(a, c)) def test12_float_byteorder(self): """Checking setting data with different byteorder in a range (float)""" # Save an array with the reversed byteorder on it a = numpy.arange(25, dtype=numpy.float64).reshape(5, 5) a = a.byteswap() a = a.newbyteorder() array = self.h5file.create_array( self.h5file.root, 'array', a, "byteorder (float)") # Read a subarray (got an array with the machine byteorder) b = array[2:4, 3:5] b = b.byteswap() b = b.newbyteorder() # Set this subarray back to the array array[2:4, 3:5] = b b = b.byteswap() b = b.newbyteorder() # Set this subarray back to the array array[2:4, 3:5] = b # Check that the array is back in the correct byteorder c = array[...] if common.verbose: print("byteorder of array on disk-->", array.byteorder) print("byteorder of subarray-->", b.dtype.byteorder) print("subarray-->", b) print("retrieved array-->", c) self.assertTrue(allequal(a, c)) class ComplexNotReopenNotEndianTestCase(UnalignedAndComplexTestCase): endiancheck = False reopen = False class ComplexReopenNotEndianTestCase(UnalignedAndComplexTestCase): endiancheck = False reopen = True class ComplexNotReopenEndianTestCase(UnalignedAndComplexTestCase): endiancheck = True reopen = False class ComplexReopenEndianTestCase(UnalignedAndComplexTestCase): endiancheck = True reopen = True class GroupsArrayTestCase(common.TempFileMixin, TestCase): """This test class checks combinations of arrays with groups.""" def test00_iterativeGroups(self): """Checking combinations of arrays with groups.""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test00_iterativeGroups..." % self.__class__.__name__) # Get the root group group = self.h5file.root # Set the type codes to test # The typecodes below does expose an ambiguity that is reported in: # http://projects.scipy.org/scipy/numpy/ticket/283 and # http://projects.scipy.org/scipy/numpy/ticket/290 typecodes = ['b', 'B', 'h', 'H', 'i', 'I', 'l', 'L', 'q', 'f', 'd', 'F', 'D'] if hasattr(tables, 'Float16Atom'): typecodes.append('e') if hasattr(tables, 'Float96Atom') or hasattr(tables, 'Float128Atom'): typecodes.append('g') if (hasattr(tables, 'Complex192Atom') or hasattr(tables, 'Complex256Atom')): typecodes.append('G') for i, typecode in enumerate(typecodes): a = numpy.ones((3,), typecode) dsetname = 'array_' + typecode if common.verbose: print("Creating dataset:", group._g_join(dsetname)) self.h5file.create_array(group, dsetname, a, "Large array") group = self.h5file.create_group(group, 'group' + str(i)) # Reopen the file self._reopen() # Get the root group group = self.h5file.root # Get the metadata on the previosly saved arrays for i in range(len(typecodes)): # Create an array for later comparison a = numpy.ones((3,), typecodes[i]) # Get the dset object hanging from group dset = getattr(group, 'array_' + typecodes[i]) # Get the actual array b = dset.read() if common.verbose: print("Info from dataset:", dset._v_pathname) print(" shape ==>", dset.shape, end=' ') print(" type ==> %s" % dset.atom.dtype) print("Array b read from file. Shape: ==>", b.shape, end=' ') print(". Type ==> %s" % b.dtype) self.assertEqual(a.shape, b.shape) self.assertEqual(a.dtype, b.dtype) self.assertTrue(allequal(a, b)) # Iterate over the next group group = getattr(group, 'group' + str(i)) def test01_largeRankArrays(self): """Checking creation of large rank arrays (0 < rank <= 32) It also uses arrays ranks which ranges until maxrank. """ # maximum level of recursivity (deepest group level) achieved: # maxrank = 32 (for a effective maximum rank of 32) # This limit is due to HDF5 library limitations. minrank = 1 maxrank = 32 if common.verbose: print('\n', '-=' * 30) print("Running %s.test01_largeRankArrays..." % self.__class__.__name__) print("Maximum rank for tested arrays:", maxrank) group = self.h5file.root if common.verbose: print("Rank array writing progress: ", end=' ') for rank in range(minrank, maxrank + 1): # Create an array of integers, with incrementally bigger ranges a = numpy.ones((1,) * rank, numpy.int32) if common.verbose: print("%3d," % (rank), end=' ') self.h5file.create_array(group, "array", a, "Rank: %s" % rank) group = self.h5file.create_group(group, 'group' + str(rank)) # Reopen the file self._reopen() group = self.h5file.root if common.verbose: print() print("Rank array reading progress: ") # Get the metadata on the previosly saved arrays for rank in range(minrank, maxrank + 1): # Create an array for later comparison a = numpy.ones((1,) * rank, numpy.int32) # Get the actual array b = group.array.read() if common.verbose: print("%3d," % (rank), end=' ') if common.verbose and not allequal(a, b): print("Info from dataset:", group.array._v_pathname) print(" Shape: ==>", group.array.shape, end=' ') print(" typecode ==> %c" % group.array.typecode) print("Array b read from file. Shape: ==>", b.shape, end=' ') print(". Type ==> %c" % b.dtype) self.assertEqual(a.shape, b.shape) self.assertEqual(a.dtype, b.dtype) self.assertTrue(allequal(a, b)) # print(self.h5file) # Iterate over the next group group = self.h5file.get_node(group, 'group' + str(rank)) if common.verbose: print() # This flush the stdout buffer class CopyTestCase(common.TempFileMixin, TestCase): def test01_copy(self): """Checking Array.copy() method.""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test01_copy..." % self.__class__.__name__) # Create an Array arr = numpy.array([[456, 2], [3, 457]], dtype='int16') array1 = self.h5file.create_array( self.h5file.root, 'array1', arr, "title array1") # Copy to another Array array2 = array1.copy('/', 'array2') if self.close: if common.verbose: print("(closing file version)") self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.array2 if common.verbose: print("array1-->", array1.read()) print("array2-->", array2.read()) # print("dirs-->", dir(array1), dir(array2)) print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Check that all the elements are equal self.assertTrue(allequal(array1.read(), array2.read())) # Assert other properties in array self.assertEqual(array1.nrows, array2.nrows) self.assertEqual(array1.flavor, array2.flavor) self.assertEqual(array1.atom.dtype, array2.atom.dtype) self.assertEqual(array1.title, array2.title) def test02_copy(self): """Checking Array.copy() method (where specified)""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test02_copy..." % self.__class__.__name__) # Create an Array arr = numpy.array([[456, 2], [3, 457]], dtype='int16') array1 = self.h5file.create_array( self.h5file.root, 'array1', arr, "title array1") # Copy to another Array group1 = self.h5file.create_group("/", "group1") array2 = array1.copy(group1, 'array2') if self.close: if common.verbose: print("(closing file version)") self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.group1.array2 if common.verbose: print("array1-->", array1.read()) print("array2-->", array2.read()) # print("dirs-->", dir(array1), dir(array2)) print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Check that all the elements are equal self.assertTrue(allequal(array1.read(), array2.read())) # Assert other properties in array self.assertEqual(array1.nrows, array2.nrows) self.assertEqual(array1.flavor, array2.flavor) self.assertEqual(array1.atom.dtype, array2.atom.dtype) self.assertEqual(array1.title, array2.title) def test03_copy(self): """Checking Array.copy() method (checking title copying)""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test04_copy..." % self.__class__.__name__) # Create an Array arr = numpy.array([[456, 2], [3, 457]], dtype='int16') array1 = self.h5file.create_array( self.h5file.root, 'array1', arr, "title array1") # Append some user attrs array1.attrs.attr1 = "attr1" array1.attrs.attr2 = 2 # Copy it to another Array array2 = array1.copy('/', 'array2', title="title array2") if self.close: if common.verbose: print("(closing file version)") self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.array2 # Assert user attributes if common.verbose: print("title of destination array-->", array2.title) self.assertEqual(array2.title, "title array2") def test04_copy(self): """Checking Array.copy() method (user attributes copied)""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test05_copy..." % self.__class__.__name__) # Create an Array arr = numpy.array([[456, 2], [3, 457]], dtype='int16') array1 = self.h5file.create_array( self.h5file.root, 'array1', arr, "title array1") # Append some user attrs array1.attrs.attr1 = "attr1" array1.attrs.attr2 = 2 # Copy it to another Array array2 = array1.copy('/', 'array2', copyuserattrs=1) if self.close: if common.verbose: print("(closing file version)") self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.array2 if common.verbose: print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Assert user attributes self.assertEqual(array2.attrs.attr1, "attr1") self.assertEqual(array2.attrs.attr2, 2) def test04b_copy(self): """Checking Array.copy() method (user attributes not copied)""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test05b_copy..." % self.__class__.__name__) # Create an Array arr = numpy.array([[456, 2], [3, 457]], dtype='int16') array1 = self.h5file.create_array( self.h5file.root, 'array1', arr, "title array1") # Append some user attrs array1.attrs.attr1 = "attr1" array1.attrs.attr2 = 2 # Copy it to another Array array2 = array1.copy('/', 'array2', copyuserattrs=0) if self.close: if common.verbose: print("(closing file version)") self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.array2 if common.verbose: print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Assert user attributes self.assertEqual(hasattr(array2.attrs, "attr1"), 0) self.assertEqual(hasattr(array2.attrs, "attr2"), 0) class CloseCopyTestCase(CopyTestCase): close = 1 class OpenCopyTestCase(CopyTestCase): close = 0 class CopyIndexTestCase(common.TempFileMixin, TestCase): def test01_index(self): """Checking Array.copy() method with indexes.""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test01_index..." % self.__class__.__name__) # Create a numpy r = numpy.arange(200, dtype='int32') r.shape = (100, 2) # Save it in a array: array1 = self.h5file.create_array( self.h5file.root, 'array1', r, "title array1") # Copy to another array array2 = array1.copy("/", 'array2', start=self.start, stop=self.stop, step=self.step) if common.verbose: print("array1-->", array1.read()) print("array2-->", array2.read()) print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Check that all the elements are equal r2 = r[self.start:self.stop:self.step] self.assertTrue(allequal(r2, array2.read())) # Assert the number of rows in array if common.verbose: print("nrows in array2-->", array2.nrows) print("and it should be-->", r2.shape[0]) self.assertEqual(r2.shape[0], array2.nrows) def test02_indexclosef(self): """Checking Array.copy() method with indexes (close file version)""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test02_indexclosef..." % self.__class__.__name__) # Create a numpy r = numpy.arange(200, dtype='int32') r.shape = (100, 2) # Save it in a array: array1 = self.h5file.create_array( self.h5file.root, 'array1', r, "title array1") # Copy to another array array2 = array1.copy("/", 'array2', start=self.start, stop=self.stop, step=self.step) # Close and reopen the file self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.array2 if common.verbose: print("array1-->", array1.read()) print("array2-->", array2.read()) print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Check that all the elements are equal r2 = r[self.start:self.stop:self.step] self.assertTrue(allequal(r2, array2.read())) # Assert the number of rows in array if common.verbose: print("nrows in array2-->", array2.nrows) print("and it should be-->", r2.shape[0]) self.assertEqual(r2.shape[0], array2.nrows) class CopyIndex1TestCase(CopyIndexTestCase): start = 0 stop = 7 step = 1 class CopyIndex2TestCase(CopyIndexTestCase): start = 0 stop = -1 step = 1 class CopyIndex3TestCase(CopyIndexTestCase): start = 1 stop = 7 step = 1 class CopyIndex4TestCase(CopyIndexTestCase): start = 0 stop = 6 step = 1 class CopyIndex5TestCase(CopyIndexTestCase): start = 3 stop = 7 step = 1 class CopyIndex6TestCase(CopyIndexTestCase): start = 3 stop = 6 step = 2 class CopyIndex7TestCase(CopyIndexTestCase): start = 0 stop = 7 step = 10 class CopyIndex8TestCase(CopyIndexTestCase): start = 6 stop = -1 # Negative values means starting from the end step = 1 class CopyIndex9TestCase(CopyIndexTestCase): start = 3 stop = 4 step = 1 class CopyIndex10TestCase(CopyIndexTestCase): start = 3 stop = 4 step = 2 class CopyIndex11TestCase(CopyIndexTestCase): start = -3 stop = -1 step = 2 class CopyIndex12TestCase(CopyIndexTestCase): start = -1 # Should point to the last element stop = None # None should mean the last element (including it) step = 1 class GetItemTestCase(common.TempFileMixin, TestCase): def test00_single(self): """Single element access (character types)""" # Create the array under root and name 'somearray' a = self.charList arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original first element:", a[0], type(a[0])) print("Read first element:", arr[0], type(arr[0])) self.assertTrue(allequal(a[0], arr[0])) self.assertEqual(type(a[0]), type(arr[0])) def test01_single(self): """Single element access (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalList arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original first element:", a[0], type(a[0])) print("Read first element:", arr[0], type(arr[0])) self.assertEqual(a[0], arr[0]) self.assertEqual(type(a[0]), type(arr[0])) def test02_range(self): """Range element access (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original elements:", a[1:4]) print("Read elements:", arr[1:4]) self.assertTrue(allequal(a[1:4], arr[1:4])) def test03_range(self): """Range element access (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original elements:", a[1:4]) print("Read elements:", arr[1:4]) self.assertTrue(allequal(a[1:4], arr[1:4])) def test04_range(self): """Range element access, strided (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original elements:", a[1:4:2]) print("Read elements:", arr[1:4:2]) self.assertTrue(allequal(a[1:4:2], arr[1:4:2])) def test05_range(self): """Range element access, strided (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original elements:", a[1:4:2]) print("Read elements:", arr[1:4:2]) self.assertTrue(allequal(a[1:4:2], arr[1:4:2])) def test06_negativeIndex(self): """Negative Index element access (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original last element:", a[-1]) print("Read last element:", arr[-1]) self.assertTrue(allequal(a[-1], arr[-1])) def test07_negativeIndex(self): """Negative Index element access (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original before last element:", a[-2]) print("Read before last element:", arr[-2]) if isinstance(a[-2], numpy.ndarray): self.assertTrue(allequal(a[-2], arr[-2])) else: self.assertEqual(a[-2], arr[-2]) def test08_negativeRange(self): """Negative range element access (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original last elements:", a[-4:-1]) print("Read last elements:", arr[-4:-1]) self.assertTrue(allequal(a[-4:-1], arr[-4:-1])) def test09_negativeRange(self): """Negative range element access (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original last elements:", a[-4:-1]) print("Read last elements:", arr[-4:-1]) self.assertTrue(allequal(a[-4:-1], arr[-4:-1])) class GI1NATestCase(GetItemTestCase, TestCase): title = "Rank-1 case 1" numericalList = numpy.array([3]) numericalListME = numpy.array([3, 2, 1, 0, 4, 5, 6]) charList = numpy.array(["3"], 'S') charListME = numpy.array( ["321", "221", "121", "021", "421", "521", "621"], 'S') class GI1NAOpenTestCase(GI1NATestCase): close = 0 class GI1NACloseTestCase(GI1NATestCase): close = 1 class GI2NATestCase(GetItemTestCase): # A more complex example title = "Rank-1,2 case 2" numericalList = numpy.array([3, 4]) numericalListME = numpy.array([[3, 2, 1, 0, 4, 5, 6], [2, 1, 0, 4, 5, 6, 7], [4, 3, 2, 1, 0, 4, 5], [3, 2, 1, 0, 4, 5, 6], [3, 2, 1, 0, 4, 5, 6]]) charList = numpy.array(["a", "b"], 'S') charListME = numpy.array( [["321", "221", "121", "021", "421", "521", "621"], ["21", "21", "11", "02", "42", "21", "61"], ["31", "21", "12", "21", "41", "51", "621"], ["321", "221", "121", "021", "421", "521", "621"], ["3241", "2321", "13216", "0621", "4421", "5421", "a621"], ["a321", "s221", "d121", "g021", "b421", "5vvv21", "6zxzxs21"]], 'S') class GI2NAOpenTestCase(GI2NATestCase): close = 0 class GI2NACloseTestCase(GI2NATestCase): close = 1 class SetItemTestCase(common.TempFileMixin, TestCase): def test00_single(self): """Single element update (character types)""" # Create the array under root and name 'somearray' a = self.charList arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify a single element of a and arr: a[0] = b"b" arr[0] = b"b" # Get and compare an element if common.verbose: print("Original first element:", a[0]) print("Read first element:", arr[0]) self.assertTrue(allequal(a[0], arr[0])) def test01_single(self): """Single element update (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalList arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: a[0] = 333 arr[0] = 333 # Get and compare an element if common.verbose: print("Original first element:", a[0]) print("Read first element:", arr[0]) self.assertEqual(a[0], arr[0]) def test02_range(self): """Range element update (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: a[1:3] = b"xXx" arr[1:3] = b"xXx" # Get and compare an element if common.verbose: print("Original elements:", a[1:4]) print("Read elements:", arr[1:4]) self.assertTrue(allequal(a[1:4], arr[1:4])) def test03_range(self): """Range element update (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = slice(1, 3, None) rng = numpy.arange(a[s].size)2 + 3 rng.shape = a[s].shape a[s] = rng arr[s] = rng # Get and compare an element if common.verbose: print("Original elements:", a[1:4]) print("Read elements:", arr[1:4]) self.assertTrue(allequal(a[1:4], arr[1:4])) def test04_range(self): """Range element update, strided (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = slice(1, 4, 2) a[s] = b"xXx" arr[s] = b"xXx" # Get and compare an element if common.verbose: print("Original elements:", a[1:4:2]) print("Read elements:", arr[1:4:2]) self.assertTrue(allequal(a[1:4:2], arr[1:4:2])) def test05_range(self): """Range element update, strided (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = slice(1, 4, 2) rng = numpy.arange(a[s].size)2 + 3 rng.shape = a[s].shape a[s] = rng arr[s] = rng # Get and compare an element if common.verbose: print("Original elements:", a[1:4:2]) print("Read elements:", arr[1:4:2]) self.assertTrue(allequal(a[1:4:2], arr[1:4:2])) def test06_negativeIndex(self): """Negative Index element update (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = -1 a[s] = b"xXx" arr[s] = b"xXx" # Get and compare an element if common.verbose: print("Original last element:", a[-1]) print("Read last element:", arr[-1]) self.assertTrue(allequal(a[-1], arr[-1])) def test07_negativeIndex(self): """Negative Index element update (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = -2 a[s] = a[s]2 + 3 arr[s] = arr[s]2 + 3 # Get and compare an element if common.verbose: print("Original before last element:", a[-2]) print("Read before last element:", arr[-2]) if isinstance(a[-2], numpy.ndarray): self.assertTrue(allequal(a[-2], arr[-2])) else: self.assertEqual(a[-2], arr[-2]) def test08_negativeRange(self): """Negative range element update (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = slice(-4, -1, None) a[s] = b"xXx" arr[s] = b"xXx" # Get and compare an element if common.verbose: print("Original last elements:", a[-4:-1]) print("Read last elements:", arr[-4:-1]) self.assertTrue(allequal(a[-4:-1], arr[-4:-1])) def test09_negativeRange(self): """Negative range element update (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = slice(-3, -1, None) rng = numpy.arange(a[s].size)2 + 3 rng.shape = a[s].shape a[s] = rng arr[s] = rng # Get and compare an element if common.verbose: print("Original last elements:", a[-4:-1]) print("Read last elements:", arr[-4:-1]) self.assertTrue(allequal(a[-4:-1], arr[-4:-1])) def test10_outOfRange(self): """Out of range update (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of arr that are out of range: s = slice(1, a.shape[0]+1, None) s2 = slice(1, 1000, None) rng = numpy.arange(a[s].size)2 + 3 rng.shape = a[s].shape a[s] = rng rng2 = numpy.arange(a[s2].size)*2 + 3 rng2.shape = a[s2].shape arr[s2] = rng2 # Get and compare an element if common.verbose: print("Original last elements:", a[-4:-1]) print("Read last elements:", arr[-4:-1]) self.assertTrue(allequal(a[-4:-1], arr[-4:-1])) class SI1NATestCase(SetItemTestCase, TestCase): title = "Rank-1 case 1" numericalList = numpy.array([3]) numericalListME = numpy.array([3, 2, 1, 0, 4, 5, 6]) charList = numpy.array(["3"], 'S') charListME = numpy.array( ["321", "221", "121", "021", "421", "521", "621"], 'S') class SI1NAOpenTestCase(SI1NATestCase): close = 0 class SI1NACloseTestCase(SI1NATestCase): close = 1 class SI2NATestCase(SetItemTestCase): # A more complex example title = "Rank-1,2 case 2" numericalList =	numpy.array([3, 4])	numpy.array
# coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. r"""Script for running experiments. Example to run locally: python experiments.py --output_dir=may19_3d --hidden_state_dim=3 \ --min_seq_len=100 --max_seq_len=2000 --num_sampled_seq_len=20 \ --num_systems=100 --num_repeat=100 \ --cluster_center_dist_lower_bound=0.1 --hide_inputs=true The outputs will show up in output_dir may19_3d. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import logging import os # pylint: disable=g-bad-import-order from absl import app from absl import flags import matplotlib matplotlib.use('Agg') from matplotlib import pylab # pylint: disable=g-import-not-at-top import numpy as np import pandas as pd import seaborn as sns import six import sklearn import tqdm import arma import clustering import lds sns.set(style='whitegrid') FLAGS = flags.FLAGS # Flags for IO and plotting. flags.DEFINE_string('output_dir', None, 'Output filepath.') flags.DEFINE_boolean( 'load_results', False, 'Whether to skip experiments ' 'and only plot existing results from output_dir.') flags.DEFINE_boolean( 'plot_clusters', False, 'Whether to visualize each ' 'experiment run and plot clustering results.') # Flags for generating simulated clusters of LDSs. flags.DEFINE_boolean('generate_diagonalizable_only', False, 'Whether to only ' 'generate diagonalizable LDSs.') flags.DEFINE_integer('num_clusters', 2, 'Number of clusters in experiments.') flags.DEFINE_integer('num_systems', 100, 'Number of dynamical systems to cluster.') flags.DEFINE_integer('hidden_state_dim', 2, 'Hidden state dim in experiments.') flags.DEFINE_integer('input_dim', 1, 'Input dim in experiments.') flags.DEFINE_boolean( 'hide_inputs', True, 'Whether the inputs are observable ' 'to the clustering algorithm.') flags.DEFINE_spaceseplist( 'cluster_center_eigvalues', None, 'Optional List of lists of eigenvalues ' 'for each cluster. The outer list is space separated, and the inner list ' 'is comma separated. E.g. `0.9,0.1 0.5,0.1`. When null, generate random ' 'clusters centers by drawing eigenvalues uniformly from [-1, 1].') flags.DEFINE_float( 'cluster_center_dist_lower_bound', 0.2, 'Desired distance lower bound ' 'between cluster centers. Only applicable when cluster_center_eigvalues ' 'is None. Generate cluster centers until distance >= this val.') flags.DEFINE_float('cluster_radius', 0.05, 'Radius of each dynamical system cluster.') flags.DEFINE_integer('random_seed', 0, 'Random seed.') flags.DEFINE_integer('num_repeat', 1, 'Number of repeated runs for each fixed seq len.') # Flags for output sequences from LDSs. flags.DEFINE_integer('min_seq_len', 10, 'Min seq len in experiments.') flags.DEFINE_integer('max_seq_len', 1000, 'Max seq len in experiments.') flags.DEFINE_integer( 'num_sampled_seq_len', 10, 'Number of sampled seq len ' 'values in between min and max seq len.') flags.DEFINE_float('input_mean', 0.0, 'Input mean.') flags.DEFINE_float('input_stddev', 1.0, 'Input stddev.') flags.DEFINE_float('output_noise_stddev', 0.01, 'Output noise stddev.') flags.DEFINE_float('init_state_mean', 0.0, 'Init state mean.') flags.DEFINE_float('init_state_stddev', 0.0, 'Init state stddev.') # Flags for hparams in clustering algorithms. flags.DEFINE_integer('guessed_hidden_dim', 0, 'Assumed hidden dim. If 0, use true hidden dim.') flags.DEFINE_integer( 'guessed_num_clusters', 0, 'Desired number of clusters. If 0, find best number ' 'adaptively from maximizing kmeans objective score.') flags.DEFINE_integer( 'LDS_GIBBS_num_update_samples', 100, 'Number of update ' 'samples used for fitting LDS in pylds package.') flags.DEFINE_integer('spectral_filtering_num_filters', 25, 'Number of filters ' 'used in spectral filtering method.') flags.DEFINE_float('spectral_filtering_learning_rate', 0.0001, 'Learning rate ' 'in spectral filtering method.') # Flags for whether to include certain baselines. flags.DEFINE_boolean( 'include_LDS_MLE', False, 'Whether to include MLE ' 'estimation for LDS in the experiments. Could be slow.') flags.DEFINE_boolean( 'include_tslearn', True, 'Whether to include time series ' 'clustering methods from the tslearn package in the ' 'experiments.') flags.DEFINE_boolean( 'include_tslearn_slow', False, 'Whether to include time ' 'series clustering methods from the tslearn package ' 'that are slow: DTW and GAK.') flags.DEFINE_boolean('include_LDS_GIBBS', True, 'Whether to include the ' 'Gibbs sampling method for LDS.') flags.DEFINE_boolean('include_ARMA_MLE', False, 'Whether to include the ' 'MLE method for ARMA.') def create_model_fns(hdim): """Util function to create model fns to fit model params to sequences. Args: hdim: Guessed hidden dimension for model fitting. Returns: A dictionary mapping method names to model_fns. Each model_fn takes output seq and input seq, and returns fitted model params. """ model_fns = collections.OrderedDict() # Using raw outputs. # model_fns['raw_output'] = lambda o, i: o # pylint: disable=g-long-lambda # Pure AR. model_fns['AR'] = lambda o, i: arma.fit_ar(o, i, hdim) # Iterated regression without regularization and with regularization. model_fns['ARMA_OLS'] = lambda o, i: arma.fit_arma_iter(o, i, hdim) model_fns['ARMA_RLS'] = lambda o, i: arma.fit_arma_iter( o, i, hdim, l2_reg=0.01) # Fit AR model and cluster based on AR param roots. # model_fns['AR_roots'] = lambda o, i: arma.get_eig_from_arparams( # arma.fit_ar(o, i, hdim)) # Fit ARMA model and cluster based on AR param roots. # model_fns['ARMA_OLS_roots'] = lambda o, i: arma.get_eig_from_arparams( # arma.fit_arma_iter(o, i, hdim)) # model_fns['ARMA_RLS_roots_0.01'] = lambda o, i: arma.get_eig_from_arparams( # arma.fit_arma_iter(o, i, hdim, l2_reg=0.01)) if FLAGS.include_LDS_GIBBS: model_fns['LDS_GIBBS'] = lambda o, i: lds.fit_lds_gibbs( o, i, hdim, num_update_samples=FLAGS.LDS_GIBBS_num_update_samples) if FLAGS.include_ARMA_MLE: model_fns['ARMA_MLE'] = lambda o, i: arma.fit_arma_mle(o, i, hdim) if FLAGS.include_LDS_MLE: model_fns['LDS_MLE'] = lambda o, i: lds.fit_lds_mle(o, i, hdim) return model_fns def _compose_model_fn(model_fn): if FLAGS.hide_inputs: return lambda seq: model_fn(seq.outputs, None) return lambda seq: model_fn(seq.outputs, seq.inputs) def _create_pca_model_fn(pca_model): return lambda o, _: pca_model.transform(o.flatten()).flatten() # pylint: disable=g-doc-args def get_results(cluster_center_eigvalues, cluster_center_dist_lower_bound, hidden_state_dim, input_dim, guessed_hidden_dim, num_clusters, guessed_num_clusters, min_seq_len, max_seq_len, num_sampled_seq_len, num_repeat, num_systems, cluster_radius, input_mean, input_stddev, output_noise_stddev, init_state_mean=0.0, init_state_stddev=0.0, generate_diagonalizable_only=False, random_seed=0, results_path=None): """Get results for varying sequence lengths. Args: cluster_center_eigvalues: List of lists of eigenvalues for each cluster. E.g. [[0.9,0.1], [0.5,0.1], [0.2,0.2], or None. If None, eigenvalues will be generated from uniform(-1,1) with respect to cluster_center_dist_lower_bound. cluster_center_dist_lower_bound: Desired distance lower bound between clusters. When generating cluster centers, try repeatedly until distance is greater than cluster_center_dist_lower_bound. hidden_state_dim: True hidden state dim. input_dim: The input dim. guessed_hidden_dim: Assumed hidden dim. If 0, use true hidden dim. num_clusters: True number of clusters. guessed_num_clusters: Desired number of clusters. If 0, use true number. min_seq_len: Min seq len in experiments. max_seq_len: Max seq len in experiments. num_sampled_seq_len: Number of sampled seq len values in between min and max seq len. num_repeat: Number of repeated experiments for each seq_len. num_systems: Number of dynamical system in each clustering experiments. cluster_radius: Expected distance of generated systems from cluster centers. input_mean: Scalar or 1D array of length hidden state dim. input_stddev: Scalar of 1D array of length hidden state dim. output_noise_stddev: Scalar. init_state_mean: Scalar or 1D array of length hidden state dim. init_state_stddev: Scalar of 1D array of length hidden state dim. random_seed: Random seed, integer. Returns: A pandas DataFrame with columns `method`, `seq_len`, `t_secs`, `failed_ratio`, and columns for clustering metrics such as `adj_mutual_info` and `v_measure`. The same method and seq_len will appear in num_repeat many rows. """ if cluster_center_eigvalues is not None: if len(cluster_center_eigvalues) <= 1: raise ValueError('Need at least two cluster centers.') cluster_center_eigvalues = np.array(cluster_center_eigvalues) if cluster_center_eigvalues.shape != (num_clusters, hidden_state_dim): raise ValueError( 'Cluter center eig has shape %s, expected (%d, %d).' % (str(cluster_center_eigvalues.shape), num_clusters, hidden_state_dim))	np.random.seed(random_seed)	numpy.random.seed
import numpy as np from pyfmmlib2d import FMM from pyfmmlib2d.periodized.real_laplace import periodized_laplace_fmm from pyfmmlib2d.utilities.random import float_random, complex_random import matplotlib as mpl import matplotlib.pyplot as plt from pyfmmlib2d import RFMM import time plt.ion() ################################################################################ # Periodic Laplace FMM # tested against FFT based grid solve; compared outside of support of forces # the grid has been spaced so a grid node comes within ~10^{-12} of a check pt # should work in all corners and sides n_grid = 150 # get uniform grid on [0, 2π] grid_v, grid_h = np.linspace(0, 2np.pi, n_grid, endpoint=False, retstep=True) grid_xv = grid_v + 3.381898123243e-02 grid_yv = grid_v + 1e-12 grid_x, grid_y = np.meshgrid(grid_xv, grid_yv, indexing='ij') grid = np.row_stack([grid_x.ravel(), grid_y.ravel()]) k = np.fft.fftfreq(n_grid, grid_h/(2np.pi)) kx, ky = np.meshgrid(k, k, indexing='ij') k2 = kxkx + kyky k2[0,0] = np.Inf ilap = -1.0/k2 # put a test pulse at location in center k = 30 for center_x in (0.0, np.pi, 2np.pi): for center_y in (0.0, np.pi, 2np.pi): print('\nCenter x: {:0.2f}'.format(center_x)) print( 'Center y: {:0.2f}'.format(center_y)) def get_shift(xs, ys): lax = center_x+0.2 + xs lay = center_y+0.2 + ys lbx = center_x-0.2 + xs lby = center_y-0.2 + ys d2a = (grid_x-lax)2 + (grid_y-lay)2 d2b = (grid_x-lbx)2 + (grid_y-lby)2 fa = np.exp(-kd2a) fb = -np.exp(-kd2b) return fa + fb f = np.zeros_like(grid_x) shift_vec = [-2np.pi, 0.0, 2np.pi] for xs in shift_vec: for ys in shift_vec: f += get_shift(xs, ys) # solve Poisson problem on grid ua = np.fft.ifft2(ilapnp.fft.fft2(f)).real # get charge qw = grid_h2 charge = f.ravel() qw / (2np.pi) # evaluate FMM st1 = time.time() pfmm = periodized_laplace_fmm(p=16, N=4) st2 = time.time() out = pfmm(grid, charge=charge, compute_source_potential=True) periodized_time_with_setup = time.time() - st1 periodized_time_without_setup = time.time() - st2 ue = out['self']['u'].reshape([n_grid, n_grid]) # compare to raw FMM speed st = time.time() _ = RFMM(grid, grid, charge=charge, compute_source_potential=True) raw_time = time.time() - st # now ignore everything well outside of the support of f r = 2 bad = np.zeros(grid_x.shape, dtype=bool) for xs in shift_vec: for ys in shift_vec: d2 = (grid_x-center_x-xs)2 + (grid_y-center_y-ys)*2 bad = np.logical_or(bad, d2<r) good = ~bad # get error with adjusting mean ue -= np.mean(ue) error = np.abs(ue-ua)/np.abs(ua[good]).max() me =	np.ma.array(error, mask=bad)	numpy.ma.array
import numpy as np import scipy.optimize as optimization import matplotlib.pyplot as plt try: from submm_python_routines.KIDs import calibrate except: from KIDs import calibrate from numba import jit # to get working on python 2 I had to downgrade llvmlite pip install llvmlite==0.31.0 # module for fitting resonances curves for kinetic inductance detectors. # written by <NAME> 12/21/16 # for example see test_fit.py in this directory # To Do # I think the error analysis on the fit_nonlinear_iq_with_err probably needs some work # add in step by step fitting i.e. first amplitude normalizaiton, then cabel delay, then i0,q0 subtraction, then phase rotation, then the rest of the fit. # need to have fit option that just specifies tau becuase that never really changes for your cryostat #Change log #JDW 2017-08-17 added in a keyword/function to allow for gain varation "amp_var" to be taken out before fitting #JDW 2017-08-30 added in fitting for magnitude fitting of resonators i.e. not in iq space #JDW 2018-03-05 added more clever function for guessing x0 for fits #JDW 2018-08-23 added more clever guessing for resonators with large phi into guess seperate functions J=np.exp(2jnp.pi/3) Jc=1/J @jit(nopython=True) def cardan(a,b,c,d): ''' analytical root finding fast: using numba looks like x10 speed up returns only the largest real root ''' u=np.empty(2,np.complex128) z0=b/3/a a2,b2 = aa,bb p=-b2/3/a2 +c/a q=(b/27(2b2/a2-9c/a)+d)/a D=-4ppp-27qq r=np.sqrt(-D/27+0j) u=((-q-r)/2)(1/3.)#0.33333333333333333333333 v=((-q+r)/2)(1/3.)#0.33333333333333333333333 w=uv w0=np.abs(w+p/3) w1=np.abs(wJ+p/3) w2=np.abs(wJc+p/3) if w0<w1: if w2<w0 : v=Jc elif w2<w1 : v=Jc else: v=J roots = np.asarray((u+v-z0, uJ+vJc-z0,uJc+vJ-z0)) #print(roots) where_real = np.where(np.abs(np.imag(roots)) < 1e-15) #if len(where_real)>1: print(len(where_real)) #print(D) if D>0: return np.max(np.real(roots)) # three real roots else: return np.real(roots[np.argsort(np.abs(np.imag(roots)))][0]) #one real root get the value that has smallest imaginary component #return np.max(np.real(roots[where_real])) #return np.asarray((u+v-z0, uJ+vJc-z0,uJc+vJ-z0)) # function to descript the magnitude S21 of a non linear resonator @jit(nopython=True) def nonlinear_mag(x,fr,Qr,amp,phi,a,b0,b1,flin): ''' # x is the frequeciesn your iq sweep covers # fr is the center frequency of the resonator # Qr is the quality factor of the resonator # amp is Qr/Qc # phi is a rotation paramter for an impedance mismatch between the resonaotor and the readout system # a is the non-linearity paramter bifurcation occurs at a = 0.77 # b0 DC level of s21 away from resonator # b1 Frequency dependant gain varation # flin is probably the frequency of the resonator when a = 0 # # This is based of fitting code from MUSIC # The idea is we are producing a model that is described by the equation below # the frist two terms in the large parentasis and all other terms are farmilar to me # but I am not sure where the last term comes from though it does seem to be important for fitting # # / (j phi) (j phi) \ 2 #\|S21\|^2 = (b0+b1 x_lin) \|1 -ampe^ +amp(e^ -1) \|^ # \| ------------ ---- \| # \ (1+ 2jy) 2 / # # where the nonlineaity of y is described by the following eqution taken from Response of superconducting microresonators # with nonlinear kinetic inductance # yg = y+ a/(1+y^2) where yg = Qrxg and xg = (f-fr)/fr # ''' xlin = (x - flin)/flin xg = (x-fr)/fr yg = Qrxg y = np.zeros(x.shape[0]) #find the roots of the y equation above for i in range(0,x.shape[0]): # 4y^3+ -4ygy^2+ y -(yg+a) #roots = np.roots((4.0,-4.0yg[i],1.0,-(yg[i]+a))) #roots = cardan(4.0,-4.0yg[i],1.0,-(yg[i]+a)) #print(roots) #roots = np.roots((16.,-16.yg[i],8.,-8.yg[i]+4ayg[i]/Qr-4a,1.,-yg[i]+ayg[i]/Qr-a+a2/Qr)) #more accurate version that doesn't seem to change the fit at al # only care about real roots #where_real = np.where(np.imag(roots) == 0) #where_real = np.where(np.abs(np.imag(roots)) < 1e-10) #analytic version has some floating point error accumulation y[i] = cardan(4.0,-4.0yg[i],1.0,-(yg[i]+a))#np.max(np.real(roots[where_real])) z = (b0 +b1xlin)np.abs(1.0 - ampnp.exp(1.0jphi)/ (1.0 +2.01.0jy) + amp/2.(np.exp(1.0jphi) -1.0))*2 return z @jit(nopython=True) def linear_mag(x,fr,Qr,amp,phi,b0): ''' # simplier version for quicker fitting when applicable # x is the frequeciesn your iq sweep covers # fr is the center frequency of the resonator # Qr is the quality factor of the resonator # amp is Qr/Qc # phi is a rotation paramter for an impedance mismatch between the resonaotor and the readout system # b0 DC level of s21 away from resonator # # This is based of fitting code from MUSIC # The idea is we are producing a model that is described by the equation below # the frist two terms in the large parentasis and all other terms are farmilar to me # but I am not sure where the last term comes from though it does seem to be important for fitting # # / (j phi) (j phi) \ 2 #\|S21\|^2 = (b0) \|1 -ampe^ +amp(e^ -1) \|^ # \| ------------ ---- \| # \ (1+ 2jxg) 2 / # # no y just xg # with no nonlinear kinetic inductance ''' if not np.isscalar(fr): #vectorize x = np.reshape(x,(x.shape[0],1,1,1,1,1)) xg = (x-fr)/fr z = (b0)np.abs(1.0 - ampnp.exp(1.0jphi)/ (1.0 +2.01.0jxgQr) + amp/2.(np.exp(1.0jphi) -1.0))*2 return z # function to describe the i q loop of a nonlinear resonator @jit(nopython=True) def nonlinear_iq(x,fr,Qr,amp,phi,a,i0,q0,tau,f0): ''' # x is the frequeciesn your iq sweep covers # fr is the center frequency of the resonator # Qr is the quality factor of the resonator # amp is Qr/Qc # phi is a rotation paramter for an impedance mismatch between the resonaotor and the readou system # a is the non-linearity paramter bifurcation occurs at a = 0.77 # i0 # q0 these are constants that describes an overall phase rotation of the iq loop + a DC gain offset # tau cabel delay # f0 is all the center frequency, not sure why we include this as a secondary paramter should be the same as fr # # This is based of fitting code from MUSIC # # The idea is we are producing a model that is described by the equation below # the frist two terms in the large parentasis and all other terms are farmilar to me # but I am not sure where the last term comes from though it does seem to be important for fitting # # (-j 2 pi deltaf tau) / (j phi) (j phi) \ # (i0+jq0)e^ \|1 -ampe^ +amp(e^ -1) \| # \| ------------ ---- \| # \ (1+ 2jy) 2 / # # where the nonlineaity of y is described by the following eqution taken from Response of superconducting microresonators # with nonlinear kinetic inductance # yg = y+ a/(1+y^2) where yg = Qrxg and xg = (f-fr)/fr # ''' deltaf = (x - f0) xg = (x-fr)/fr yg = Qrxg y = np.zeros(x.shape[0]) #find the roots of the y equation above for i in range(0,x.shape[0]): # 4y^3+ -4ygy^2+ y -(yg+a) #roots = np.roots((4.0,-4.0yg[i],1.0,-(yg[i]+a))) #roots = np.roots((16.,-16.yg[i],8.,-8.yg[i]+4ayg[i]/Qr-4a,1.,-yg[i]+ayg[i]/Qr-a+a*2/Qr)) #more accurate version that doesn't seem to change the fit at al # only care about real roots #where_real = np.where(np.imag(roots) == 0) #y[i] = np.max(np.real(roots[where_real])) y[i] = cardan(4.0,-4.0yg[i],1.0,-(yg[i]+a)) z = (i0 +1.jq0) np.exp(-1.0j* 2* np.pi deltaftau) * (1.0 - ampnp.exp(1.0jphi)/ (1.0 +2.01.0jy) + amp/2.(np.exp(1.0jphi) -1.0)) return z def nonlinear_iq_for_fitter(x,fr,Qr,amp,phi,a,i0,q0,tau,f0,*keywords): ''' when using a fitter that can't handel complex number one needs to return both the real and imaginary components seperatly ''' if ('tau' in keywords): use_given_tau = True tau = keywords['tau'] print("hello") else: use_given_tau = False deltaf = (x - f0) xg = (x-fr)/fr yg = Qrxg y = np.zeros(x.shape[0]) for i in range(0,x.shape[0]): #roots = np.roots((4.0,-4.0yg[i],1.0,-(yg[i]+a))) #where_real = np.where(np.imag(roots) == 0) #y[i] = np.max(np.real(roots[where_real])) y[i] = cardan(4.0,-4.0yg[i],1.0,-(yg[i]+a)) z = (i0 +1.jq0) np.exp(-1.0j* 2* np.pi deltaftau) * (1.0 - ampnp.exp(1.0jphi)/ (1.0 +2.01.0jy) + amp/2.(np.exp(1.0jphi) -1.0)) real_z = np.real(z) imag_z = np.imag(z) return np.hstack((real_z,imag_z)) def brute_force_linear_mag_fit(x,z,ranges,n_grid_points,error = None, plot = False,keywords): ''' x frequencies Hz z complex or abs of s21 ranges is the ranges for each parameter i.e. np.asarray(([f_low,Qr_low,amp_low,phi_low,b0_low],[f_high,Qr_high,amp_high,phi_high,b0_high])) n_grid_points how finely to sample each parameter space. this can be very slow for n>10 an increase by a factor of 2 will take 25 times longer to marginalize over you must minimize over the unwanted axies of sum_dev i.e for fr np.min(np.min(np.min(np.min(fit['sum_dev'],axis = 4),axis = 3),axis = 2),axis = 1) ''' if error is None: error = np.ones(len(x)) fs = np.linspace(ranges[0][0],ranges[1][0],n_grid_points) Qrs = np.linspace(ranges[0][1],ranges[1][1],n_grid_points) amps = np.linspace(ranges[0][2],ranges[1][2],n_grid_points) phis = np.linspace(ranges[0][3],ranges[1][3],n_grid_points) b0s = np.linspace(ranges[0][4],ranges[1][4],n_grid_points) evaluated_ranges = np.vstack((fs,Qrs,amps,phis,b0s)) a,b,c,d,e = np.meshgrid(fs,Qrs,amps,phis,b0s,indexing = "ij") #always index ij evaluated = linear_mag(x,a,b,c,d,e) data_values = np.reshape(np.abs(z)2,(abs(z).shape[0],1,1,1,1,1)) error = np.reshape(error,(abs(z).shape[0],1,1,1,1,1)) sum_dev = np.sum(((np.sqrt(evaluated)-np.sqrt(data_values))2/error*2),axis = 0) # comparing in magnitude space rather than magnitude squared min_index = np.where(sum_dev == np.min(sum_dev)) index1 = min_index[0][0] index2 = min_index[1][0] index3 = min_index[2][0] index4 = min_index[3][0] index5 = min_index[4][0] fit_values = np.asarray((fs[index1],Qrs[index2],amps[index3],phis[index4],b0s[index5])) fit_values_names = ('f0','Qr','amp','phi','b0') fit_result = linear_mag(x,fs[index1],Qrs[index2],amps[index3],phis[index4],b0s[index5]) marginalized_1d = np.zeros((5,n_grid_points)) marginalized_1d[0,:] = np.min(np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 2),axis = 1) marginalized_1d[1,:] = np.min(np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 2),axis = 0) marginalized_1d[2,:] = np.min(np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 1),axis = 0) marginalized_1d[3,:] = np.min(np.min(np.min(np.min(sum_dev,axis = 4),axis = 2),axis = 1),axis = 0) marginalized_1d[4,:] = np.min(np.min(np.min(np.min(sum_dev,axis = 3),axis = 2),axis = 1),axis = 0) marginalized_2d = np.zeros((5,5,n_grid_points,n_grid_points)) #0 _ #1 x _ #2 x x _ #3 x x x _ #4 x x x x _ # 0 1 2 3 4 marginalized_2d[0,1,:] = marginalized_2d[1,0,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 2) marginalized_2d[2,0,:] = marginalized_2d[0,2,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 1) marginalized_2d[2,1,:] = marginalized_2d[1,2,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 0) marginalized_2d[3,0,:] = marginalized_2d[0,3,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 2),axis = 1) marginalized_2d[3,1,:] = marginalized_2d[1,3,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 2),axis = 0) marginalized_2d[3,2,:] = marginalized_2d[2,3,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 1),axis = 0) marginalized_2d[4,0,:] = marginalized_2d[0,4,:] = np.min(np.min(np.min(sum_dev,axis = 3),axis = 2),axis = 1) marginalized_2d[4,1,:] = marginalized_2d[1,4,:] = np.min(np.min(np.min(sum_dev,axis = 3),axis = 2),axis = 0) marginalized_2d[4,2,:] = marginalized_2d[2,4,:] = np.min(np.min(np.min(sum_dev,axis = 3),axis = 1),axis = 0) marginalized_2d[4,3,:] = marginalized_2d[3,4,:] = np.min(np.min(np.min(sum_dev,axis = 2),axis = 1),axis = 0) if plot: levels = [2.3,4.61] #delta chi squared two parameters 68 90 % confidence fig_fit = plt.figure(-1) axs = fig_fit.subplots(5, 5) for i in range(0,5): # y starting from top for j in range(0,5): #x starting from left if i > j: #plt.subplot(5,5,i+1+5j) #axs[i, j].set_aspect('equal', 'box') extent = [evaluated_ranges[j,0],evaluated_ranges[j,n_grid_points-1],evaluated_ranges[i,0],evaluated_ranges[i,n_grid_points-1]] axs[i,j].imshow(marginalized_2d[i,j,:]-np.min(sum_dev),extent =extent,origin = 'lower', cmap = 'jet') axs[i,j].contour(evaluated_ranges[j],evaluated_ranges[i],marginalized_2d[i,j,:]-np.min(sum_dev),levels = levels,colors = 'white') axs[i,j].set_ylim(evaluated_ranges[i,0],evaluated_ranges[i,n_grid_points-1]) axs[i,j].set_xlim(evaluated_ranges[j,0],evaluated_ranges[j,n_grid_points-1]) axs[i,j].set_aspect((evaluated_ranges[j,0]-evaluated_ranges[j,n_grid_points-1])/(evaluated_ranges[i,0]-evaluated_ranges[i,n_grid_points-1])) if j == 0: axs[i, j].set_ylabel(fit_values_names[i]) if i == 4: axs[i, j].set_xlabel("\n"+fit_values_names[j]) if i<4: axs[i,j].get_xaxis().set_ticks([]) if j>0: axs[i,j].get_yaxis().set_ticks([]) elif i < j: fig_fit.delaxes(axs[i,j]) for i in range(0,5): #axes.subplot(5,5,i+1+5i) axs[i,i].plot(evaluated_ranges[i,:],marginalized_1d[i,:]-np.min(sum_dev)) axs[i,i].plot(evaluated_ranges[i,:],np.ones(len(evaluated_ranges[i,:]))1.,color = 'k') axs[i,i].plot(evaluated_ranges[i,:],np.ones(len(evaluated_ranges[i,:]))2.7,color = 'k') axs[i,i].yaxis.set_label_position("right") axs[i,i].yaxis.tick_right() axs[i,i].xaxis.set_label_position("top") axs[i,i].xaxis.tick_top() axs[i,i].set_xlabel(fit_values_names[i]) #axs[0,0].set_ylabel(fit_values_names[0]) #axs[4,4].set_xlabel(fit_values_names[4]) axs[4,4].xaxis.set_label_position("bottom") axs[4,4].xaxis.tick_bottom() #make a dictionary to return fit_dict = {'fit_values': fit_values,'fit_values_names':fit_values_names, 'sum_dev': sum_dev, 'fit_result': fit_result,'marginalized_2d':marginalized_2d,'marginalized_1d':marginalized_1d,'evaluated_ranges':evaluated_ranges}#, 'x0':x0, 'z':z} return fit_dict # function for fitting an iq sweep with the above equation def fit_nonlinear_iq(x,z,keywords): ''' # keywards are # bounds ---- which is a 2d tuple of low the high values to bound the problem by # x0 --- intial guess for the fit this can be very important becuase because least square space over all the parameter is comple # amp_norm --- do a normalization for variable amplitude. usefull when tranfer function of the cryostat is not flat # tau forces tau to specific value # tau_guess fixes the guess for tau without have to specifiy all of x0 ''' if ('tau' in keywords): use_given_tau = True tau = keywords['tau'] else: use_given_tau = False if ('bounds' in keywords): bounds = keywords['bounds'] else: #define default bounds print("default bounds used") bounds = ([np.min(x),50,.01,-np.pi,0,-np.inf,-np.inf,0,np.min(x)],[np.max(x),200000,1,np.pi,5,np.inf,np.inf,110*-6,np.max(x)]) if ('x0' in keywords): x0 = keywords['x0'] else: #define default intial guess print("default initial guess used") #fr_guess = x[np.argmin(np.abs(z))] #x0 = [fr_guess,10000.,0.5,0,0,np.mean(np.real(z)),np.mean(np.imag(z)),310-7,fr_guess] x0 = guess_x0_iq_nonlinear(x,z,verbose = True) print(x0) if ('fr_guess' in keywords): x0[0] = keywords['fr_guess'] if ('tau_guess' in keywords): x0[7] = keywords['tau_guess'] #Amplitude normalization? do_amp_norm = 0 if ('amp_norm' in keywords): amp_norm = keywords['amp_norm'] if amp_norm == True: do_amp_norm = 1 elif amp_norm == False: do_amp_norm = 0 else: print("please specify amp_norm as True or False") if do_amp_norm == 1: z = amplitude_normalization(x,z) z_stacked = np.hstack((np.real(z),np.imag(z))) if use_given_tau == True: del bounds[0][7] del bounds[1][7] del x0[7] fit = optimization.curve_fit(lambda x_lamb,a,b,c,d,e,f,g,h: nonlinear_iq_for_fitter(x_lamb,a,b,c,d,e,f,g,tau,h), x, z_stacked,x0,bounds = bounds) fit_result = nonlinear_iq(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],tau,fit[0][7]) x0_result = nonlinear_iq(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],tau,x0[7]) else: fit = optimization.curve_fit(nonlinear_iq_for_fitter, x, z_stacked,x0,bounds = bounds) fit_result = nonlinear_iq(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7],fit[0][8]) x0_result = nonlinear_iq(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7],x0[8]) #make a dictionary to return fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z} return fit_dict def fit_nonlinear_iq_sep(fine_x,fine_z,gain_x,gain_z,keywords): ''' # same as above funciton but takes fine and gain scans seperatly # keywards are # bounds ---- which is a 2d tuple of low the high values to bound the problem by # x0 --- intial guess for the fit this can be very important becuase because least square space over all the parameter is comple # amp_norm --- do a normalization for variable amplitude. usefull when tranfer function of the cryostat is not flat ''' if ('bounds' in keywords): bounds = keywords['bounds'] else: #define default bounds print("default bounds used") bounds = ([np.min(fine_x),500.,.01,-np.pi,0,-np.inf,-np.inf,110-9,np.min(fine_x)],[np.max(fine_x),1000000,1,np.pi,5,np.inf,np.inf,110*-6,np.max(fine_x)]) if ('x0' in keywords): x0 = keywords['x0'] else: #define default intial guess print("default initial guess used") #fr_guess = x[np.argmin(np.abs(z))] #x0 = [fr_guess,10000.,0.5,0,0,np.mean(np.real(z)),np.mean(np.imag(z)),310-7,fr_guess] x0 = guess_x0_iq_nonlinear_sep(fine_x,fine_z,gain_x,gain_z) #print(x0) #Amplitude normalization? do_amp_norm = 0 if ('amp_norm' in keywords): amp_norm = keywords['amp_norm'] if amp_norm == True: do_amp_norm = 1 elif amp_norm == False: do_amp_norm = 0 else: print("please specify amp_norm as True or False") if (('fine_z_err' in keywords) & ('gain_z_err' in keywords)): use_err = True fine_z_err = keywords['fine_z_err'] gain_z_err = keywords['gain_z_err'] else: use_err = False x = np.hstack((fine_x,gain_x)) z = np.hstack((fine_z,gain_z)) if use_err: z_err = np.hstack((fine_z_err,gain_z_err)) if do_amp_norm == 1: z = amplitude_normalization(x,z) z_stacked = np.hstack((np.real(z),np.imag(z))) if use_err: z_err_stacked = np.hstack((np.real(z_err),np.imag(z_err))) fit = optimization.curve_fit(nonlinear_iq_for_fitter, x, z_stacked,x0,sigma = z_err_stacked,bounds = bounds) else: fit = optimization.curve_fit(nonlinear_iq_for_fitter, x, z_stacked,x0,bounds = bounds) fit_result = nonlinear_iq(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7],fit[0][8]) x0_result = nonlinear_iq(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7],x0[8]) if use_err: #only do it for fine data #red_chi_sqr = np.sum(z_stacked-np.hstack((np.real(fit_result),np.imag(fit_result))))2/z_err_stacked2)/(len(z_stacked)-8.) #only do it for fine data red_chi_sqr = np.sum((np.hstack((np.real(fine_z),np.imag(fine_z)))-np.hstack((np.real(fit_result[0:len(fine_z)]),np.imag(fit_result[0:len(fine_z)]))))2/np.hstack((np.real(fine_z_err),np.imag(fine_z_err)))*2)/(len(fine_z)2.-8.) #make a dictionary to return if use_err: fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z,'fit_freqs':x,'red_chi_sqr':red_chi_sqr} else: fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z,'fit_freqs':x} return fit_dict # same function but double fits so that it can get error and a proper covariance matrix out def fit_nonlinear_iq_with_err(x,z,*keywords): ''' # keywards are # bounds ---- which is a 2d tuple of low the high values to bound the problem by # x0 --- intial guess for the fit this can be very important becuase because least square space over all the parameter is comple # amp_norm --- do a normalization for variable amplitude. usefull when tranfer function of the cryostat is not flat ''' if ('bounds' in keywords): bounds = keywords['bounds'] else: #define default bounds print("default bounds used") bounds = ([np.min(x),2000,.01,-np.pi,0,-5,-5,110*-9,np.min(x)],[np.max(x),200000,1,np.pi,5,5,5,110-6,np.max(x)]) if ('x0' in keywords): x0 = keywords['x0'] else: #define default intial guess print("default initial guess used") fr_guess = x[np.argmin(np.abs(z))] x0 = guess_x0_iq_nonlinear(x,z) #Amplitude normalization? do_amp_norm = 0 if ('amp_norm' in keywords): amp_norm = keywords['amp_norm'] if amp_norm == True: do_amp_norm = 1 elif amp_norm == False: do_amp_norm = 0 else: print("please specify amp_norm as True or False") if do_amp_norm == 1: z = amplitude_normalization(x,z) z_stacked = np.hstack((np.real(z),np.imag(z))) fit = optimization.curve_fit(nonlinear_iq_for_fitter, x, z_stacked,x0,bounds = bounds) fit_result = nonlinear_iq(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7],fit[0][8]) fit_result_stacked = nonlinear_iq_for_fitter(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7],fit[0][8]) x0_result = nonlinear_iq(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7],x0[8]) # get error var = np.sum((z_stacked-fit_result_stacked)2)/(z_stacked.shape[0] - 1) err = np.ones(z_stacked.shape[0])np.sqrt(var) # refit fit = optimization.curve_fit(nonlinear_iq_for_fitter, x, z_stacked,x0,err,bounds = bounds) fit_result = nonlinear_iq(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7],fit[0][8]) x0_result = nonlinear_iq(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7],x0[8]) #make a dictionary to return fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z} return fit_dict # function for fitting an iq sweep with the above equation def fit_nonlinear_mag(x,z,keywords): ''' # keywards are # bounds ---- which is a 2d tuple of low the high values to bound the problem by # x0 --- intial guess for the fit this can be very important becuase because least square space over all the parameter is comple # amp_norm --- do a normalization for variable amplitude. usefull when tranfer function of the cryostat is not flat ''' if ('bounds' in keywords): bounds = keywords['bounds'] else: #define default bounds print("default bounds used") bounds = ([np.min(x),100,.01,-np.pi,0,-np.inf,-np.inf,np.min(x)],[np.max(x),200000,1,np.pi,5,np.inf,np.inf,np.max(x)]) if ('x0' in keywords): x0 = keywords['x0'] else: #define default intial guess print("default initial guess used") fr_guess = x[np.argmin(np.abs(z))] #x0 = [fr_guess,10000.,0.5,0,0,np.abs(z[0])2,np.abs(z[0])2,fr_guess] x0 = guess_x0_mag_nonlinear(x,z,verbose = True) fit = optimization.curve_fit(nonlinear_mag, x, np.abs(z)2 ,x0,bounds = bounds) fit_result = nonlinear_mag(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7]) x0_result = nonlinear_mag(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7]) #make a dictionary to return fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z} return fit_dict def fit_nonlinear_mag_sep(fine_x,fine_z,gain_x,gain_z,keywords): ''' # same as above but fine and gain scans are provided seperatly # keywards are # bounds ---- which is a 2d tuple of low the high values to bound the problem by # x0 --- intial guess for the fit this can be very important becuase because least square space over all the parameter is comple # amp_norm --- do a normalization for variable amplitude. usefull when tranfer function of the cryostat is not flat ''' if ('bounds' in keywords): bounds = keywords['bounds'] else: #define default bounds print("default bounds used") bounds = ([np.min(fine_x),100,.01,-np.pi,0,-np.inf,-np.inf,np.min(fine_x)],[np.max(fine_x),1000000,100,np.pi,5,np.inf,np.inf,np.max(fine_x)]) if ('x0' in keywords): x0 = keywords['x0'] else: #define default intial guess print("default initial guess used") x0 = guess_x0_mag_nonlinear_sep(fine_x,fine_z,gain_x,gain_z) if (('fine_z_err' in keywords) & ('gain_z_err' in keywords)): use_err = True fine_z_err = keywords['fine_z_err'] gain_z_err = keywords['gain_z_err'] else: use_err = False #stack the scans for curvefit x = np.hstack((fine_x,gain_x)) z = np.hstack((fine_z,gain_z)) if use_err: z_err = np.hstack((fine_z_err,gain_z_err)) z_err = np.sqrt(4np.real(z_err)*2np.real(z)*2+4np.imag(z_err)*2np.imag(z)2) #propogation of errors left out cross term fit = optimization.curve_fit(nonlinear_mag, x, np.abs(z)2 ,x0,sigma = z_err,bounds = bounds) else: fit = optimization.curve_fit(nonlinear_mag, x, np.abs(z)2 ,x0,bounds = bounds) fit_result = nonlinear_mag(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7]) x0_result = nonlinear_mag(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7]) #compute reduced chi squared print(len(z)) if use_err: #red_chi_sqr = np.sum((np.abs(z)2-fit_result)2/z_err2)/(len(z)-7.) # only use fine scan for reduced chi squared. red_chi_sqr = np.sum((np.abs(fine_z)2-fit_result[0:len(fine_z)])2/z_err[0:len(fine_z)]*2)/(len(fine_z)-7.) #make a dictionary to return if use_err: fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z,'fit_freqs':x,'red_chi_sqr':red_chi_sqr} else: fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z,'fit_freqs':x} return fit_dict def amplitude_normalization(x,z): ''' # normalize the amplitude varation requires a gain scan #flag frequencies to use in amplitude normaliztion ''' index_use = np.where(np.abs(x-np.median(x))>100000) #100kHz away from resonator poly = np.polyfit(x[index_use],np.abs(z[index_use]),2) poly_func = np.poly1d(poly) normalized_data = z/poly_func(x)np.median(np.abs(z[index_use])) return normalized_data def amplitude_normalization_sep(gain_x,gain_z,fine_x,fine_z,stream_x,stream_z): ''' # normalize the amplitude varation requires a gain scan # uses gain scan to normalize does not use fine scan #flag frequencies to use in amplitude normaliztion ''' index_use = np.where(np.abs(gain_x-np.median(gain_x))>100000) #100kHz away from resonator poly = np.polyfit(gain_x[index_use],np.abs(gain_z[index_use]),2) poly_func = np.poly1d(poly) poly_data = poly_func(gain_x) normalized_gain = gain_z/poly_datanp.median(np.abs(gain_z[index_use])) normalized_fine = fine_z/poly_func(fine_x)np.median(np.abs(gain_z[index_use])) normalized_stream = stream_z/poly_func(stream_x)np.median(np.abs(gain_z[index_use])) amp_norm_dict = {'normalized_gain':normalized_gain, 'normalized_fine':normalized_fine, 'normalized_stream':normalized_stream, 'poly_data':poly_data} return amp_norm_dict def guess_x0_iq_nonlinear(x,z,verbose = False): ''' # this is lest robust than guess_x0_iq_nonlinear_sep # below. it is recommended to use that instead #make sure data is sorted from low to high frequency ''' sort_index = np.argsort(x) x = x[sort_index] z = z[sort_index] #extract just fine data df = np.abs(x-np.roll(x,1)) fine_df = np.min(df[np.where(df != 0)]) fine_z_index = np.where(df<fine_df1.1) fine_z = z[fine_z_index] fine_x = x[fine_z_index] #extract the gain scan gain_z_index = np.where(df>fine_df1.1) gain_z = z[gain_z_index] gain_x = x[gain_z_index] gain_phase = np.arctan2(np.real(gain_z),np.imag(gain_z)) #guess f0 fr_guess_index = np.argmin(np.abs(z)) #fr_guess = x[fr_guess_index] fr_guess_index_fine = np.argmin(np.abs(fine_z)) # below breaks if there is not a right and left side in the fine scan if fr_guess_index_fine == 0: fr_guess_index_fine = len(fine_x)//2 elif fr_guess_index_fine == (len(fine_x)-1): fr_guess_index_fine = len(fine_x)//2 fr_guess = fine_x[fr_guess_index_fine] #guess Q mag_max = np.max(np.abs(fine_z)2) mag_min = np.min(np.abs(fine_z)2) mag_3dB = (mag_max+mag_min)/2. half_distance = np.abs(fine_z)2-mag_3dB right = half_distance[fr_guess_index_fine:-1] left = half_distance[0:fr_guess_index_fine] right_index = np.argmin(np.abs(right))+fr_guess_index_fine left_index = np.argmin(np.abs(left)) Q_guess_Hz = fine_x[right_index]-fine_x[left_index] Q_guess = fr_guess/Q_guess_Hz #guess amp d = np.max(20np.log10(np.abs(z)))-np.min(20np.log10(np.abs(z))) amp_guess = 0.0037848547850284574+0.11096782437821565d-0.0055208783469291173d2+0.00013900471000261687d*3+-1.3994861426891861e-06d*4#polynomial fit to amp verus depth #guess impedance rotation phi phi_guess = 0 #guess non-linearity parameter #might be able to guess this by ratioing the distance between min and max distance between iq points in fine sweep a_guess = 0 #i0 and iq guess if np.max(np.abs(fine_z))==np.max(np.abs(z)): #if the resonator has an impedance mismatch rotation that makes the fine greater that the cabel delay i0_guess = np.real(fine_z[np.argmax(np.abs(fine_z))]) q0_guess = np.imag(fine_z[np.argmax(np.abs(fine_z))]) else: i0_guess = (np.real(fine_z[0])+np.real(fine_z[-1]))/2. q0_guess = (np.imag(fine_z[0])+np.imag(fine_z[-1]))/2. #cabel delay guess tau #y = mx +b #m = (y2 - y1)/(x2-x1) #b = y-mx if len(gain_z)>1: #is there a gain scan? m = (gain_phase - np.roll(gain_phase,1))/(gain_x-np.roll(gain_x,1)) b = gain_phase -mgain_x m_best = np.median(m[~np.isnan(m)]) tau_guess = m_best/(2np.pi) else: tau_guess = 310-9 if verbose == True: print("fr guess = %.2f MHz" %(fr_guess/106)) print("Q guess = %.2f kHz, %.1f" % ((Q_guess_Hz/103),Q_guess)) print("amp guess = %.2f" %amp_guess) print("i0 guess = %.2f" %i0_guess) print("q0 guess = %.2f" %q0_guess) print("tau guess = %.2f x 10^-7" %(tau_guess/10-7)) x0 = [fr_guess,Q_guess,amp_guess,phi_guess,a_guess,i0_guess,q0_guess,tau_guess,fr_guess] return x0 def guess_x0_mag_nonlinear(x,z,verbose = False): ''' # this is lest robust than guess_x0_mag_nonlinear_sep #below it is recommended to use that instead #make sure data is sorted from low to high frequency ''' sort_index = np.argsort(x) x = x[sort_index] z = z[sort_index] #extract just fine data #this will probably break if there is no fine scan df = np.abs(x-np.roll(x,1)) fine_df = np.min(df[np.where(df != 0)]) fine_z_index = np.where(df<fine_df1.1) fine_z = z[fine_z_index] fine_x = x[fine_z_index] #extract the gain scan gain_z_index = np.where(df>fine_df1.1) gain_z = z[gain_z_index] gain_x = x[gain_z_index] gain_phase = np.arctan2(np.real(gain_z),np.imag(gain_z)) #guess f0 fr_guess_index = np.argmin(np.abs(z)) #fr_guess = x[fr_guess_index] fr_guess_index_fine = np.argmin(np.abs(fine_z)) if fr_guess_index_fine == 0: fr_guess_index_fine = len(fine_x)//2 elif fr_guess_index_fine == (len(fine_x)-1): fr_guess_index_fine = len(fine_x)//2 fr_guess = fine_x[fr_guess_index_fine] #guess Q mag_max = np.max(np.abs(fine_z)2) mag_min = np.min(np.abs(fine_z)2) mag_3dB = (mag_max+mag_min)/2. half_distance = np.abs(fine_z)*2-mag_3dB right = half_distance[fr_guess_index_fine:-1] left = half_distance[0:fr_guess_index_fine] right_index = np.argmin(np.abs(right))+fr_guess_index_fine left_index = np.argmin(np.abs(left)) Q_guess_Hz = fine_x[right_index]-fine_x[left_index] Q_guess = fr_guess/Q_guess_Hz #guess amp d = np.max(20np.log10(np.abs(z)))-np.min(20np.log10(np.abs(z))) amp_guess = 0.0037848547850284574+0.11096782437821565d-0.0055208783469291173d2+0.00013900471000261687d*3+-1.3994861426891861e-06d4#polynomial fit to amp verus depth #guess impedance rotation phi phi_guess = 0 #guess non-linearity parameter #might be able to guess this by ratioing the distance between min and max distance between iq points in fine sweep a_guess = 0 #b0 and b1 guess if len(gain_z)>1: xlin = (gain_x - fr_guess)/fr_guess b1_guess = (np.abs(gain_z)[-1]2-np.abs(gain_z)[0]2)/(xlin[-1]-xlin[0]) else: xlin = (fine_x - fr_guess)/fr_guess b1_guess = (np.abs(fine_z)[-1]2-np.abs(fine_z)[0]2)/(xlin[-1]-xlin[0]) b0_guess = np.median(np.abs(gain_z)2) if verbose == True: print("fr guess = %.2f MHz" %(fr_guess/106)) print("Q guess = %.2f kHz, %.1f" % ((Q_guess_Hz/103),Q_guess)) print("amp guess = %.2f" %amp_guess) print("phi guess = %.2f" %phi_guess) print("b0 guess = %.2f" %b0_guess) print("b1 guess = %.2f" %b1_guess) x0 = [fr_guess,Q_guess,amp_guess,phi_guess,a_guess,b0_guess,b1_guess,fr_guess] return x0 def guess_x0_iq_nonlinear_sep(fine_x,fine_z,gain_x,gain_z,verbose = False): ''' # this is the same as guess_x0_iq_nonlinear except that it takes # takes the fine scan and the gain scan as seperate variables # this runs into less issues when trying to sort out what part of # data is fine and what part is gain for the guessing #make sure data is sorted from low to high frequency ''' #gain phase gain_phase = np.arctan2(np.real(gain_z),np.imag(gain_z)) #guess f0 fr_guess_index = np.argmin(np.abs(fine_z)) # below breaks if there is not a right and left side in the fine scan if fr_guess_index == 0: fr_guess_index = len(fine_x)//2 elif fr_guess_index == (len(fine_x)-1): fr_guess_index = len(fine_x)//2 fr_guess = fine_x[fr_guess_index] #guess Q mag_max = np.max(np.abs(fine_z)2) mag_min = np.min(np.abs(fine_z)2) mag_3dB = (mag_max+mag_min)/2. half_distance = np.abs(fine_z)*2-mag_3dB right = half_distance[fr_guess_index:-1] left = half_distance[0:fr_guess_index] right_index = np.argmin(np.abs(right))+fr_guess_index left_index = np.argmin(np.abs(left)) Q_guess_Hz = fine_x[right_index]-fine_x[left_index] Q_guess = fr_guess/Q_guess_Hz #guess amp d = np.max(20np.log10(np.abs(gain_z)))-np.min(20np.log10(np.abs(fine_z))) amp_guess = 0.0037848547850284574+0.11096782437821565d-0.0055208783469291173d2+0.00013900471000261687d*3+-1.3994861426891861e-06d*4#polynomial fit to amp verus depth #guess impedance rotation phi #phi_guess = 0 #guess impedance rotation phi #fit a circle to the iq loop xc, yc, R, residu = calibrate.leastsq_circle(np.real(fine_z),np.imag(fine_z)) #compute angle between (off_res,off_res),(0,0) and (off_ress,off_res),(xc,yc) of the the fitted circle off_res_i,off_res_q = (np.real(fine_z[0])+np.real(fine_z[-1]))/2.,(np.imag(fine_z[0])+np.imag(fine_z[-1]))/2. x1, y1, = -off_res_i,-off_res_q x2, y2 = xc-off_res_i,yc-off_res_q dot = x1x2 + y1y2 # dot product det = x1y2 - y1x2 # determinant angle = np.arctan2(det, dot) phi_guess = angle # if phi is large better re guess f0 # f0 should be the farthers from the off res point if (np.abs(phi_guess)>0.3): dist1 = np.sqrt((np.real(fine_z[0])-np.real(fine_z))2+(np.imag(fine_z[0])-np.imag(fine_z))2) dist2 = np.sqrt((np.real(fine_z[-1])-np.real(fine_z))2+(np.imag(fine_z[-1])-np.imag(fine_z))2) fr_guess_index = np.argmax((dist1+dist2)) fr_guess = fine_x[fr_guess_index] #also fix the Q gues fine_z_derot = (fine_z-(off_res_i+1.joff_res_q))np.exp(1j(-phi_guess))+(off_res_i+1.joff_res_q) #fr_guess_index = np.argmin(np.abs(fine_z_derot)) #fr_guess = fine_x[fr_guess_index] mag_max = np.max(np.abs(fine_z_derot)2) mag_min = np.min(np.abs(fine_z_derot)2) mag_3dB = (mag_max+mag_min)/2. half_distance = np.abs(fine_z_derot)2-mag_3dB right = half_distance[np.argmin(np.abs(fine_z_derot)):-1] left = half_distance[0:np.argmin(np.abs(fine_z_derot))] right_index = np.argmin(np.abs(right))+np.argmin(np.abs(fine_z_derot)) left_index = np.argmin(np.abs(left)) Q_guess_Hz = fine_x[right_index]-fine_x[left_index] Q_guess = fr_guess/Q_guess_Hz #also fix amp guess d = np.max(20np.log10(np.abs(gain_z)))-np.min(20np.log10(np.abs(fine_z_derot))) amp_guess = 0.0037848547850284574+0.11096782437821565d-0.0055208783469291173d2+0.00013900471000261687d*3+-1.3994861426891861e-06d*4 #guess non-linearity parameter #might be able to guess this by ratioing the distance between min and max distance between iq points in fine sweep a_guess = 0 #i0 and iq guess if np.max(np.abs(fine_z))>np.max(np.abs(gain_z)): #if the resonator has an impedance mismatch rotation that makes the fine greater that the cabel delay i0_guess = np.real(fine_z[np.argmax(np.abs(fine_z))]) q0_guess = np.imag(fine_z[np.argmax(np.abs(fine_z))]) else: i0_guess = (np.real(fine_z[0])+np.real(fine_z[-1]))/2. q0_guess = (np.imag(fine_z[0])+np.imag(fine_z[-1]))/2. #cabel delay guess tau #y = mx +b #m = (y2 - y1)/(x2-x1) #b = y-mx m = (gain_phase - np.roll(gain_phase,1))/(gain_x-np.roll(gain_x,1)) b = gain_phase -mgain_x m_best = np.median(m[~np.isnan(m)]) tau_guess = m_best/(2np.pi) if verbose == True: print("fr guess = %.3f MHz" %(fr_guess/106)) print("Q guess = %.2f kHz, %.1f" % ((Q_guess_Hz/103),Q_guess)) print("amp guess = %.2f" %amp_guess) print("phi guess = %.2f" %phi_guess) print("i0 guess = %.2f" %i0_guess) print("q0 guess = %.2f" %q0_guess) print("tau guess = %.2f x 10^-7" %(tau_guess/10-7)) x0 = [fr_guess,Q_guess,amp_guess,phi_guess,a_guess,i0_guess,q0_guess,tau_guess,fr_guess] return x0 def guess_x0_mag_nonlinear_sep(fine_x,fine_z,gain_x,gain_z,verbose = False): ''' # this is the same as guess_x0_mag_nonlinear except that it takes # takes the fine scan and the gain scan as seperate variables # this runs into less issues when trying to sort out what part of # data is fine and what part is gain for the guessing #make sure data is sorted from low to high frequency ''' #phase of gain gain_phase = np.arctan2(np.real(gain_z),np.imag(gain_z)) #guess f0 fr_guess_index = np.argmin(np.abs(fine_z)) #protect against guessing the first or last data points if fr_guess_index == 0: fr_guess_index = len(fine_x)//2 elif fr_guess_index == (len(fine_x)-1): fr_guess_index = len(fine_x)//2 fr_guess = fine_x[fr_guess_index] #guess Q mag_max = np.max(np.abs(fine_z)2) mag_min = np.min(np.abs(fine_z)2) mag_3dB = (mag_max+mag_min)/2. half_distance = np.abs(fine_z)2-mag_3dB right = half_distance[fr_guess_index:-1] left = half_distance[0:fr_guess_index] right_index = np.argmin(np.abs(right))+fr_guess_index left_index = np.argmin(np.abs(left)) Q_guess_Hz = fine_x[right_index]-fine_x[left_index] Q_guess = fr_guess/Q_guess_Hz #guess amp d = np.max(20np.log10(np.abs(gain_z)))-np.min(20np.log10(np.abs(fine_z))) amp_guess = 0.0037848547850284574+0.11096782437821565d-0.0055208783469291173d2+0.00013900471000261687d*3+-1.3994861426891861e-06d*4 #polynomial fit to amp verus depth calculated emperically #guess impedance rotation phi #fit a circle to the iq loop xc, yc, R, residu = calibrate.leastsq_circle(np.real(fine_z),np.imag(fine_z)) #compute angle between (off_res,off_res),(0,0) and (off_ress,off_res),(xc,yc) of the the fitted circle off_res_i,off_res_q = (np.real(fine_z[0])+np.real(fine_z[-1]))/2.,(np.imag(fine_z[0])+np.imag(fine_z[-1]))/2. x1, y1, = -off_res_i,-off_res_q x2, y2 = xc-off_res_i,yc-off_res_q dot = x1x2 + y1y2 # dot product det = x1y2 - y1*x2 # determinant angle =	np.arctan2(det, dot)	numpy.arctan2
import sys, traceback import numpy as np import torch from dynamical_system.modulation import Modulator from imitation_learning.model import BinClassifier from rapidly_exploring_random_tree.rrt import RRT class Controller(): ''' This is an abstract super-class for all controllers. At the very least, get_trajectory() and log_prior() need to be implemented. ''' def __init__(self): pass def get_trajectory(self, env, kernel): ''' given an environment (i.e. a specific task instantiation), roll out the controller with randomness specified in the kernel and return the trajectory of shape T x 2. ''' raise NotImplementedError def log_prior(self, kernel): ''' return the prior probability of the controller kernel, for stochastic controllers. return 0 for deterministic controllers. ''' raise NotImplementedError class DSController(Controller): def __init__(self): super().__init__() self.modulator = Modulator() self.warned = False def get_trajectory(self, env, kernel=None): assert env.oob_termination is False, 'early termination needs to be disabled' assert env.time_limit >= 500, 'time limit should be greater than or equal to 500' if env.enable_lidar is True and not self.warned: print('WARNING: Not turning off lidar on env could be significantly slower') self.warned = True try: self.modulator.set_arena(env.arena) epsilon = sys.float_info.epsilon done = False s = env.s()[:2] traj = [s] while not done: d = self.modulator.modulate(s) d = d / max([0.3, d[0] + epsilon, d[1] + epsilon]) s, _, done, _ = env.step(d) s = s[:2] traj.append(s) traj = np.array(traj) return traj except KeyboardInterrupt: raise except: traceback.print_exc() return None def log_prior(self, kernel): return 0 class ILController(Controller): def __init__(self, model_fn='imitation_learning/data_and_model/best.pt', device=None): super().__init__() if device is None: device = ['cpu', 'cuda'][torch.cuda.is_available()] model_data = torch.load(model_fn) self.MEAN = model_data['MEAN'] self.STD = model_data['STD'] self.N_BINS = model_data['N_BINS'] self.policy = BinClassifier(18, self.N_BINS) model_data = torch.load(model_fn, map_location='cpu') self.policy.load_state_dict(model_data['model']) self.BIN_RES = 2 * np.pi / self.N_BINS self.BIN_LOW = - np.pi self.device = device self.policy.to(self.device) def get_trajectory(self, env, kernel=None): try: traj = [] with torch.no_grad(): done = False s = env.s() traj.append(s[:2]) while not done: s_scaled = torch.tensor((s - self.MEAN) / self.STD).float().to(self.device) bin_idx = self.policy(s_scaled).cpu().numpy().argmax() angle = bin_idx * self.BIN_RES - self.BIN_LOW dx =	np.cos(angle)	numpy.cos
import numpy as np import netCDF4 import torch from torch.utils.data import Dataset class iciData(Dataset): """ Pytorch dataset for the ICI training data. """ def __init__(self, path, inChannels, target, T_rec = None, batch_size = None): """ Create instance of the dataset from a given file path. Args: path: Path to the NetCDF4 containing the data. batch_size: If positive, data is provided in batches of this size """ super().__init__() self.batch_size = batch_size self.file = netCDF4.Dataset(path, mode = "r") TB = self.file.variables["TB"][:] channels = self.file.variables["channels"][:] self.surface = self.file.variables["cases"][:] self.channels = inChannels idx = [] for i in range(len(inChannels)): idx.append(np.argwhere(channels == inChannels[i])[0][0]) self.index = idx self.itarget = np.argwhere(np.array(channels) == target)[0] C = [] for i in range(len(inChannels)): ic = self.index[i] C.append(TB[1, :, ic]) self.x = np.float32(np.stack(C, axis = 1)) #store mean and std to normalise data x_noise = self.add_noise(self.x, self.index) self.std = np.std(x_noise, axis = 0) self.mean = np.mean(x_noise, axis = 0) self.y = np.float32(TB[0, :, self.itarget[0]]) self.y_noise = self.add_noise(self.y, self.itarget) self.x = self.x.data self.y = self.y.data self.y_noise = self.y_noise.data def __len__(self): """ The number of entries in the training data. This is part of the pytorch interface for datasets. Return: int: The number of samples in the data set """ if self.batch_size is None: return self.x.shape[0] else: return int(np.ceil(self.x.shape[0] / self.batch_size)) def __getitem__(self, i): """ Return element from the dataset. This is part of the pytorch interface for datasets. Args: i: The index of the sample to return """ if (i == 0): indices = np.random.permutation(self.x.shape[0]) self.x = self.x[indices, :] self.y = self.y[indices] if self.batch_size is None: return (torch.tensor(self.x[[i], :]), torch.tensor(self.y[[i]])) else: i_start = self.batch_size * i i_end = self.batch_size * (i + 1) x = self.x[i_start : i_end, :].copy() x_noise = np.float32(self.add_noise(x, self.index)) x_norm = np.float32(self.normalise(x_noise)) return (torch.tensor(x_norm), torch.tensor(self.y[i_start : i_end])) def add_noise(self, x, index): """ Gaussian noise is added to every measurement before used for training again. Args: the input TB in one batch of size (batch_size x number of channels) Returns: input TB with noise """ nedt = np.array([0.8, 0.8, 0.8, #183Ghz 0.7, 0.7, #243Ghz 1.2, 1.3, 1.5, #325Ghz 1.4, 1.6, 2.0, #448Ghz 1.6, 1.6]) #664Ghz nedt_subset = nedt[index] size_TB = int(x.size/len(nedt_subset)) x_noise = x.copy() if len(index) > 1: for ic in range(len(self.index)): noise = np.random.normal(0, nedt_subset[ic], size_TB) x_noise[:, ic] += noise else: noise =	np.random.normal(0, nedt_subset, size_TB)	numpy.random.normal
""" @file setup.py """ import numpy as np import copy # DOT_assignment from DOT_assignment import assignments from DOT_assignment import controls from DOT_assignment import dynamics from DOT_assignment import engine from DOT_assignment import linear_models_2D from DOT_assignment import linear_models_3D from DOT_assignment import run from DOT_assignment import distributions def setup_simulation(sim_profile): """ Returns dictionary of controls, dynamics, decision-making policy, and initial state parameters Input: Standard python dict containing descriptors outlining simulation requirements Output: Standard python dict containing controls, dynamics, assignment, etc. data structures """ x0 = None stationary_states = None agent_model = sim_profile["agent_model"] agent_control_policy = sim_profile["agent_control_policy"] agent_formation = sim_profile["agent_formation"] target_formation = sim_profile["target_formation"] assignment_policy = sim_profile["assignment_policy"] assignment_epoch = sim_profile["assignment_epoch"] nagents = sim_profile["nagents"] ntargets = sim_profile["ntargets"] collisions = sim_profile["collisions"] collision_tol = sim_profile["collision_tol"] dim = sim_profile["dim"] dt = sim_profile["dt"] maxtime = sim_profile["maxtime"] initial_conditions = sim_profile['initial_conditions'] if initial_conditions == None: initial_formation_params = { 'nagents': nagents, 'agent_model': agent_model, 'agent_swarm_formation': agent_formation, 'ntargets': ntargets, 'target_swarm_formation': target_formation } initial_conditions = generate_initial_conditions(dim, initial_formation_params) x0 = ic[0] targets = ic[1] else: x0 = initial_conditions[0] targets = initial_conditions[1] sim = {} parameters = ['agent_model', 'dx', 'du', 'A', 'B', 'agent_dyn', 'agent_pol', 'asst_pol', 'x0'] sim.fromkeys(parameters) ##### Dynamic Model ##### if dim == 2: if agent_model == "Double_Integrator": A, B, C, D, dx, du, statespace = linear_models_2D.double_integrator_2D() ### runner sim_runner = run.run_identical_doubleint_2D if agent_model == "Linearized_Quadcopter": A, B, C, D, dx, du, statespace = linear_models_2D.quadcopter_2D() ### runner sim_runner = run.run_identical_linearized_quadcopter_2D if dim == 3: if agent_model == "Double_Integrator": A, B, C, D, dx, du, statespace = linear_models_3D.double_integrator_3D() ### runner sim_runner = run.run_identical_doubleint_3D if agent_model == "Linearized_Quadcopter": A, B, C, D, dx, du, statespace = linear_models_3D.quadcopter_3D() ### runner sim_runner = run.run_identical_linearized_quadcopter_3D Q = np.eye(dx) R = np.eye(du) # TODO - remove # DEBUG control terms Q2 = None Q3 = None ###################### if dim == 2: if agent_model == 'Double_Integrator': Q2 = copy.deepcopy(Q) Q2[2,2] = 0.0 Q2[3,3] = 0.0 Q3 = copy.deepcopy(Q) Q3[0, 0] = 100 Q3[1, 1] = 100 Q3[2,2] = 0.0 Q3[3,3] = 0.0 if agent_model == 'Linearized_Quadcopter': Q3 = copy.deepcopy(Q) Q3[0, 0] = 100 Q3[1, 1] = 100 Q3[2,2] = 100 Q3[3,3] = 100 Q3[4,4] = 0.0 Q3[5,5] = 0.0 Q3[6, 6] = 0.0 Q3[7, 7] = 0.0 if dim == 3: if agent_model == 'Double_Integrator': Q2 = copy.deepcopy(Q) Q2[3,3] = 0.0 Q2[4,4] = 0.0 Q2[5,5] = 0.0 Q3 = copy.deepcopy(Q) Q3[0, 0] = 1000 Q3[1, 1] = 1000 Q3[2, 2] = 1000 Q3[3,3] = 0.0 Q3[4,4] = 0.0 Q3[5,5] = 0.0 if agent_model == 'Linearized_Quadcopter': Q3 = copy.deepcopy(Q) Q3[0, 0] = 1000 Q3[1, 1] = 1000 Q3[2, 2] = 1000 Q3[3,3] = 1000 Q3[4,4] = 1000 Q3[5,5] = 1000 Q3[6,6] = 0.0 Q3[7,7] = 0.0 Q3[8,8] = 0.0 Q3[9, 9] = 0.0 Q3[10, 10] = 0.0 Q3[11, 11] = 0.0 ###################### ### Agent control law if agent_control_policy == "LQR": poltrack = [controls.LinearFeedbackConstTracker(A, B, Q, R, t) for t in targets] ### Agent Dynamics ltidyn = dynamics.LTIDyn(A, B) ### Assignment Policy if assignment_policy == 'AssignmentCustom': apol = assignments.AssignmentCustom(nagents, ntargets) if assignment_policy == 'AssignmentEMD': apol = assignments.AssignmentEMD(nagents, ntargets) ### CONSTRUCT SIMULATION DICTIONARY sim['agent_control_policy'] = agent_control_policy sim['agent_model'] = agent_model sim['agent_formation'] = agent_formation sim['target_formation'] = target_formation sim['collisions'] = collisions sim['collision_tol'] = collision_tol sim['dt'] = dt sim['maxtime'] = maxtime sim['dx'] = dx sim['du'] = du sim['statespace'] = statespace sim['x0'] = x0 sim['agent_dyn'] = ltidyn sim['agent_pol'] = poltrack sim['asst_pol'] = apol sim['asst_epoch'] = assignment_epoch sim['nagents'] = nagents sim['ntargets'] = ntargets sim['runner'] = sim_runner return sim def generate_distribution(dim, space, num_particles, distribution): """ Returns discrete distribution of states (ie. X,Y,Z positions) Input: - dim: dimension - space: range of values that distribution can take - num_particles: number of particles within the distribution - distribution: name of distribution Output: - states: vector consisting of n-dimensional states corresponding to a desired distribution """ if distribution == 'uniform_distribution': states = np.random.uniform(-space, space, (num_particles,dim)) elif distribution == 'circle': radius = space states = [distributions.circle(dim, radius, num_particles, t) for t in range(num_particles)] # circle elif distribution == 'fibonacci_sphere': radius = space states = [distributions.fibonacci_sphere(radius, num_particles, t) for t in range(num_particles)] # sphere return states # TODO breakdown into more functions def generate_initial_conditions(dim, initial_formation_params): """ Returns initial states for agents, targets, and target terminal locations """ x0 = None cities = None nagents = initial_formation_params['nagents'] agent_model = initial_formation_params['agent_model'] agent_swarm_formation = initial_formation_params['agent_swarm_formation'] ntargets = initial_formation_params['ntargets'] target_swarm_formation = initial_formation_params['target_swarm_formation'] r = 100 # agent position distribution (ie. x, y, z state components) x0p = generate_distribution(dim, r, nagents, agent_swarm_formation) # target position distribution x02p = generate_distribution(dim, r, ntargets, target_swarm_formation) # TODO Place these into separate function # populate the rest of the agent/target state components given the dynamics models if dim == 2: ###### DOUBLE_INTEGRATOR ###### if agent_model == "Double_Integrator": A, B, C, D, dx, du, statespace = linear_models_2D.double_integrator_2D() ### Initial conditions # populate agent state x0 = np.zeros((nagents, dx)) # NOTE user-defined how the intial state is constructed vel_range = 500 for ii, tt in enumerate(x0): x0[ii] = np.array([x0p[ii][0], x0p[ii][1], np.random.uniform(-vel_range, vel_range, 1)[0], np.random.uniform(-vel_range, vel_range, 1)[0]]) x0 = x0.flatten() # populate target state rot_x02p = np.random.uniform(-2np.pi, 2np.pi, (ntargets,dim)) # position spread vel_range = 50 rot_vel_range = 25 x02 = np.zeros((ntargets, dx)) for ii, tt in enumerate(x02): x02[ii] = np.array([ x02p[ii][0], x02p[ii][1], 0, 0]) targets = x02.flatten() x0 = np.hstack((x0, targets)) ###### LINEARIZED_QUADCOPTER ###### if agent_model == "Linearized_Quadcopter": A, B, C, D, dx, du, statespace = linear_models_2D.quadcopter_2D() # Agents # populate agent state rot_x0p = np.random.uniform(-2np.pi, 2np.pi, (nagents,dim)) # position spread vel_range = 500 rot_vel_range = 25 x0 =	np.zeros((nagents, dx))	numpy.zeros
""" 1次元離散フーリエ変換 """ import numpy as np from scipy.fftpack import fft, ifft import matplotlib.pyplot as plt # 時系列のサンプルデータの作成 N = 120 # データ数 T=10 # サンプリング幅 del_t= T/N # サンプリング間隔 del_w=2np.pi/T # 離散フーリエ変換の振動数の間隔 # # 離散点　生成 xs = np.arange(0,T-del_t,del_t) w=np.arange(2np.pi/T, 2np.piN/T, del_w) # f1,f2=3,4 f=	np.sin(2np.pif1*xs)	numpy.sin
# coding=utf-8 # Copyright 2021 The TensorFlow Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Common corruptions to images. Define 15+4 common image corruptions: Gaussian noise, shot noise, impulse_noise, defocus blur, frosted glass blur, zoom blur, fog, brightness, contrast, elastic, pixelate, jpeg compression, frost, snow, and motion blur. 4 extra corruptions: gaussian blur, saturate, spatter, and speckle noise. """ import io import subprocess import tempfile import numpy as np import tensorflow as tf import tensorflow_datasets.public_api as tfds # To be populated by download_manager FROST_FILENAMES = [] def _imagemagick_bin(): return 'imagemagick' # pylint: disable=unreachable # /////////////// Corruption Helpers /////////////// def around_and_astype(x): """Round a numpy array, and convert to uint8. Args: x: numpy array. Returns: numpy array with dtype uint8. """ return np.around(x).astype(np.uint8) def disk(radius, alias_blur=0.1, dtype=np.float32): """Generating a Gaussian blurring kernel with disk shape. Generating a Gaussian blurring kernel with disk shape using cv2 API. Args: radius: integer, radius of blurring kernel. alias_blur: float, standard deviation of Gaussian blurring. dtype: data type of kernel Returns: cv2 object of the Gaussian blurring kernel. """ if radius <= 8: length = np.arange(-8, 8 + 1) ksize = (3, 3) else: length = np.arange(-radius, radius + 1) ksize = (5, 5) x_axis, y_axis = np.meshgrid(length, length) aliased_disk = np.array((x_axis2 + y_axis2) <= radius*2, dtype=dtype) aliased_disk /= np.sum(aliased_disk) # supersample disk to antialias return tfds.core.lazy_imports.cv2.GaussianBlur( aliased_disk, ksize=ksize, sigmaX=alias_blur) def clipped_zoom(img, zoom_factor): """Zoom image with clipping. Zoom the central part of the image and clip extra pixels. Args: img: numpy array, uncorrupted image. zoom_factor: numpy array, a sequence of float numbers for zoom factor. Returns: numpy array, zoomed image after clipping. """ h = img.shape[0] ch = int(np.ceil(h / float(zoom_factor))) top_h = (h - ch) // 2 w = img.shape[1] cw = int(np.ceil(w / float(zoom_factor))) top_w = (w - cw) // 2 img = tfds.core.lazy_imports.scipy.ndimage.zoom( img[top_h:top_h + ch, top_w:top_w + cw], (zoom_factor, zoom_factor, 1), order=1) # trim off any extra pixels trim_top_h = (img.shape[0] - h) // 2 trim_top_w = (img.shape[1] - w) // 2 return img[trim_top_h:trim_top_h + h, trim_top_w:trim_top_w + w] def plasma_fractal(mapsize=512, wibbledecay=3): """Generate a heightmap using diamond-square algorithm. Modification of the algorithm in https://github.com/FLHerne/mapgen/blob/master/diamondsquare.py Args: mapsize: side length of the heightmap, must be a power of two. wibbledecay: integer, decay factor. Returns: numpy 2d array, side length 'mapsize', of floats in [0,255]. """ if mapsize & (mapsize - 1) != 0: raise ValueError('mapsize must be a power of two.') maparray = np.empty((mapsize, mapsize), dtype=np.float_) maparray[0, 0] = 0 stepsize = mapsize wibble = 100 def wibbledmean(array): return array / 4 + wibble np.random.uniform(-wibble, wibble, array.shape) def fillsquares(): """For each square, calculate middle value as mean of points + wibble.""" cornerref = maparray[0:mapsize:stepsize, 0:mapsize:stepsize] squareaccum = cornerref + np.roll(cornerref, shift=-1, axis=0) squareaccum += np.roll(squareaccum, shift=-1, axis=1) maparray[stepsize // 2:mapsize:stepsize, stepsize // 2:mapsize:stepsize] = wibbledmean(squareaccum) def filldiamonds(): """For each diamond, calculate middle value as meanof points + wibble.""" mapsize = maparray.shape[0] drgrid = maparray[stepsize // 2:mapsize:stepsize, stepsize // 2:mapsize:stepsize] ulgrid = maparray[0:mapsize:stepsize, 0:mapsize:stepsize] ldrsum = drgrid + np.roll(drgrid, 1, axis=0) lulsum = ulgrid + np.roll(ulgrid, -1, axis=1) ltsum = ldrsum + lulsum maparray[0:mapsize:stepsize, stepsize // 2:mapsize:stepsize] = wibbledmean(ltsum) tdrsum = drgrid + np.roll(drgrid, 1, axis=1) tulsum = ulgrid + np.roll(ulgrid, -1, axis=0) ttsum = tdrsum + tulsum maparray[stepsize // 2:mapsize:stepsize, 0:mapsize:stepsize] = wibbledmean(ttsum) while stepsize >= 2: fillsquares() filldiamonds() stepsize //= 2 wibble /= wibbledecay maparray -= maparray.min() return maparray / maparray.max() # /////////////// End Corruption Helpers /////////////// # /////////////// Corruptions /////////////// def gaussian_noise(x, severity=1): """Gaussian noise corruption to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Added Gaussian noise. """ c = [.08, .12, 0.18, 0.26, 0.38][severity - 1] x = np.array(x) / 255. x_clip = np.clip(x + np.random.normal(size=x.shape, scale=c), 0, 1) * 255 return around_and_astype(x_clip) def shot_noise(x, severity=1): """Shot noise corruption to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Added shot noise. """ c = [60, 25, 12, 5, 3][severity - 1] x = np.array(x) / 255. x_clip = np.clip(np.random.poisson(x * c) / float(c), 0, 1) * 255 return around_and_astype(x_clip) def impulse_noise(x, severity=1): """Impulse noise corruption to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Added impulse noise. """ c = [.03, .06, .09, 0.17, 0.27][severity - 1] x = tfds.core.lazy_imports.skimage.util.random_noise( np.array(x) / 255., mode='s&p', amount=c) x_clip = np.clip(x, 0, 1) * 255 return around_and_astype(x_clip) def defocus_blur(x, severity=1): """Defocus blurring to images. Apply defocus blurring to images using Gaussian kernel. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied defocus blur. """ c = [(3, 0.1), (4, 0.5), (6, 0.5), (8, 0.5), (10, 0.5)][severity - 1] x = np.array(x) / 255. kernel = disk(radius=c[0], alias_blur=c[1]) channels = [] for d in range(3): channels.append(tfds.core.lazy_imports.cv2.filter2D(x[:, :, d], -1, kernel)) channels = np.array(channels).transpose((1, 2, 0)) # 3x224x224 -> 224x224x3 x_clip = np.clip(channels, 0, 1) * 255 return around_and_astype(x_clip) def glass_blur(x, severity=1): """Frosted glass blurring to images. Apply frosted glass blurring to images by shuffling pixels locally. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied frosted glass blur. """ # sigma, max_delta, iterations c = [(0.7, 1, 2), (0.9, 2, 1), (1, 2, 3), (1.1, 3, 2), (1.5, 4, 2)][severity - 1] x = np.uint8( tfds.core.lazy_imports.skimage.filters.gaussian( np.array(x) / 255., sigma=c[0], multichannel=True) * 255) # locally shuffle pixels for _ in range(c[2]): for h in range(x.shape[0] - c[1], c[1], -1): for w in range(x.shape[1] - c[1], c[1], -1): dx, dy = np.random.randint(-c[1], c[1], size=(2,)) h_prime, w_prime = h + dy, w + dx # swap x[h, w], x[h_prime, w_prime] = x[h_prime, w_prime], x[h, w] x_clip = np.clip( tfds.core.lazy_imports.skimage.filters.gaussian( x / 255., sigma=c[0], multichannel=True), 0, 1) x_clip = 255 return around_and_astype(x_clip) def zoom_blur(x, severity=1): """Zoom blurring to images. Applying zoom blurring to images by zooming the central part of the images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied zoom blur. """ c = [ np.arange(1, 1.11, 0.01), np.arange(1, 1.16, 0.01), np.arange(1, 1.21, 0.02), np.arange(1, 1.26, 0.02), np.arange(1, 1.31, 0.03) ][severity - 1] x = (np.array(x) / 255.).astype(np.float32) out = np.zeros_like(x) for zoom_factor in c: out += clipped_zoom(x, zoom_factor) x = (x + out) / (len(c) + 1) x_clip = np.clip(x, 0, 1) 255 return around_and_astype(x_clip) def fog(x, severity=1): """Fog corruption to images. Adding fog to images. Fog is generated by diamond-square algorithm. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Added fog. """ c = [(1.5, 2), (2., 2), (2.5, 1.7), (2.5, 1.5), (3., 1.4)][severity - 1] x = np.array(x) / 255. max_val = x.max() mapsize = 512 shape = x.shape max_length = max(shape[0], shape[1]) if max_length > mapsize: mapsize = 2*int(np.ceil(np.log2(float(max_length)))) tmp = plasma_fractal(mapsize=mapsize, wibbledecay=c[1]) tmp = tmp[:x.shape[0], :x.shape[1]] tmp = tmp[..., np.newaxis] x += c[0] tmp x_clip = np.clip(x * max_val / (max_val + c[0]), 0, 1) * 255 return around_and_astype(x_clip) def brightness(x, severity=1): """Change brightness of images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Changed brightness. """ c = [.1, .2, .3, .4, .5][severity - 1] x = np.array(x) / 255. x = tfds.core.lazy_imports.skimage.color.rgb2hsv(x) x[:, :, 2] = np.clip(x[:, :, 2] + c, 0, 1) x = tfds.core.lazy_imports.skimage.color.hsv2rgb(x) x_clip = np.clip(x, 0, 1) * 255 return around_and_astype(x_clip) def contrast(x, severity=1): """Change contrast of images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Changed contrast. """ c = [0.4, .3, .2, .1, .05][severity - 1] x = np.array(x) / 255. means = np.mean(x, axis=(0, 1), keepdims=True) x_clip = np.clip((x - means) * c + means, 0, 1) * 255 return around_and_astype(x_clip) def elastic_transform(x, severity=1): """Conduct elastic transform to images. Elastic transform is performed on small patches of the images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied elastic transform. """ c = [(244 * 2, 244 * 0.7, 244 * 0.1), (244 * 2, 244 * 0.08, 244 * 0.2), (244 * 0.05, 244 * 0.01, 244 * 0.02), (244 * 0.07, 244 * 0.01, 244 * 0.02), (244 * 0.12, 244 * 0.01, 244 * 0.02)][severity - 1] image = np.array(x, dtype=np.float32) / 255. shape = image.shape shape_size = shape[:2] # random affine center_square = np.float32(shape_size) // 2 square_size = min(shape_size) // 3 pts1 = np.float32([ center_square + square_size, [center_square[0] + square_size, center_square[1] - square_size], center_square - square_size ]) pts2 = pts1 + np.random.uniform( -c[2], c[2], size=pts1.shape).astype(np.float32) affine_trans = tfds.core.lazy_imports.cv2.getAffineTransform(pts1, pts2) image = tfds.core.lazy_imports.cv2.warpAffine( image, affine_trans, shape_size[::-1], borderMode=tfds.core.lazy_imports.cv2.BORDER_REFLECT_101) dx = (tfds.core.lazy_imports.skimage.filters.gaussian( np.random.uniform(-1, 1, size=shape[:2]), c[1], mode='reflect', truncate=3) * c[0]).astype(np.float32) dy = (tfds.core.lazy_imports.skimage.filters.gaussian( np.random.uniform(-1, 1, size=shape[:2]), c[1], mode='reflect', truncate=3) * c[0]).astype(np.float32) dx, dy = dx[..., np.newaxis], dy[..., np.newaxis] x, y, z = np.meshgrid( np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2])) indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1)) x_clip = np.clip( tfds.core.lazy_imports.scipy.ndimage.interpolation.map_coordinates( image, indices, order=1, mode='reflect').reshape(shape), 0, 1) * 255 return around_and_astype(x_clip) def pixelate(x, severity=1): """Pixelate images. Conduct pixelating corruptions to images by first shrinking the images and then resizing to original size. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied pixelating corruption. """ c = [0.6, 0.5, 0.4, 0.3, 0.25][severity - 1] shape = x.shape x = tfds.core.lazy_imports.PIL_Image.fromarray(x.astype(np.uint8)) x = x.resize((int(shape[1] * c), int(shape[0] * c))) x = x.resize((shape[1], shape[0])) return np.asarray(x) def jpeg_compression(x, severity=1): """Conduct jpeg compression to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied jpeg compression. """ c = [25, 18, 15, 10, 7][severity - 1] x = tfds.core.lazy_imports.PIL_Image.fromarray(x.astype(np.uint8)) output = io.BytesIO() x.save(output, 'JPEG', quality=c) output.seek(0) x = tfds.core.lazy_imports.PIL_Image.open(output) return np.asarray(x) def frost(x, severity=1): """Apply frost to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied frost. """ c = [(1, 0.4), (0.8, 0.6), (0.7, 0.7), (0.65, 0.7), (0.6, 0.75)][severity - 1] filename = FROST_FILENAMES[np.random.randint(5)] with tempfile.NamedTemporaryFile() as im_frost: tf.io.gfile.copy(filename, im_frost.name, overwrite=True) frost_img = tfds.core.lazy_imports.cv2.imread(im_frost.name) # randomly crop and convert to rgb x_start, y_start = np.random.randint( 0, frost_img.shape[0] - 224), np.random.randint(0, frost_img.shape[1] - 224) frost_img = frost_img[x_start:x_start + 224, y_start:y_start + 224][..., [2, 1, 0]] x = np.clip(c[0] * np.array(x) + c[1] * frost_img, 0, 255) return around_and_astype(x) def snow(x, severity=1): """Apply snow to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied snow. """ cv2 = tfds.core.lazy_imports.cv2 PIL_Image = tfds.core.lazy_imports.PIL_Image # pylint: disable=invalid-name c = [(0.1, 0.3, 3, 0.5, 10, 4, 0.8), (0.2, 0.3, 2, 0.5, 12, 4, 0.7), (0.55, 0.3, 4, 0.9, 12, 8, 0.7), (0.55, 0.3, 4.5, 0.85, 12, 8, 0.65), (0.55, 0.3, 2.5, 0.85, 12, 12, 0.55)][severity - 1] x = np.array(x, dtype=np.float32) / 255. snow_layer = np.random.normal( size=x.shape[:2], loc=c[0], scale=c[1]) # [:2] for monochrome snow_layer = clipped_zoom(snow_layer[..., np.newaxis], c[2]) snow_layer[snow_layer < c[3]] = 0 snow_layer = PIL_Image.fromarray( (np.clip(snow_layer.squeeze(), 0, 1) * 255).astype(np.uint8), mode='L') with tempfile.NamedTemporaryFile() as im_input: with tempfile.NamedTemporaryFile() as im_output: snow_layer.save(im_input.name, format='PNG') convert_bin = _imagemagick_bin() radius = c[4] sigma = c[5] angle = np.random.uniform(-135, -45) subprocess.check_output([ convert_bin, '-motion-blur', '{}x{}+{}'.format(radius, sigma, angle), im_input.name, im_output.name ]) with open(im_output.name, 'rb') as f: output = f.read() snow_layer = cv2.imdecode( np.frombuffer(output, np.uint8), cv2.IMREAD_UNCHANGED) / 255. snow_layer = snow_layer[..., np.newaxis] x = c[6] * x + (1 - c[6]) * np.maximum( x, cv2.cvtColor(x, cv2.COLOR_RGB2GRAY).reshape(224, 224, 1) * 1.5 + 0.5) x = np.clip(x + snow_layer + np.rot90(snow_layer, k=2), 0, 1) * 255 return around_and_astype(x) def motion_blur(x, severity=1): """Apply motion blur to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied motion blur. """ c = [(10, 3), (15, 5), (15, 8), (15, 12), (20, 15)][severity - 1] x = tfds.core.lazy_imports.PIL_Image.fromarray(x.astype(np.uint8)) with tempfile.NamedTemporaryFile() as im_input: with tempfile.NamedTemporaryFile() as im_output: x.save(im_input.name, format='PNG') convert_bin = _imagemagick_bin() radius = c[0] sigma = c[1] angle = np.random.uniform(-45, -45) subprocess.check_output([ convert_bin, '-motion-blur', '{}x{}+{}'.format(radius, sigma, angle), im_input.name, im_output.name ]) with open(im_output.name, 'rb') as f: output = f.read() x = tfds.core.lazy_imports.cv2.imdecode( np.frombuffer(output, np.uint8), tfds.core.lazy_imports.cv2.IMREAD_UNCHANGED) if x.shape != (224, 224): x = np.clip(x[..., [2, 1, 0]], 0, 255) # BGR to RGB else: # greyscale to RGB x = np.clip(np.array([x, x, x]).transpose((1, 2, 0)), 0, 255) return around_and_astype(x) # /////////////// Extra Corruptions /////////////// def gaussian_blur(x, severity=1): """Apply gaussian blur to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied gaussian blur. """ c = [1, 2, 3, 4, 6][severity - 1] x = tfds.core.lazy_imports.skimage.filters.gaussian( np.array(x) / 255., sigma=c, multichannel=True) x = np.clip(x, 0, 1) * 255 return around_and_astype(x) def saturate(x, severity=1): """Increase saturation of images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied saturation. """ c = [(0.3, 0), (0.1, 0), (2, 0), (5, 0.1), (20, 0.2)][severity - 1] x = np.array(x) / 255. x = tfds.core.lazy_imports.skimage.color.rgb2hsv(x) x[:, :, 1] = np.clip(x[:, :, 1] * c[0] + c[1], 0, 1) x = tfds.core.lazy_imports.skimage.color.hsv2rgb(x) x = np.clip(x, 0, 1) * 255 return around_and_astype(x) def spatter(x, severity=1): """Apply spatter to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied spatter. """ cv2 = tfds.core.lazy_imports.cv2 skimage = tfds.core.lazy_imports.skimage c = [(0.65, 0.3, 4, 0.69, 0.6, 0), (0.65, 0.3, 3, 0.68, 0.6, 0), (0.65, 0.3, 2, 0.68, 0.5, 0), (0.65, 0.3, 1, 0.65, 1.5, 1), (0.67, 0.4, 1, 0.65, 1.5, 1)][severity - 1] x = np.array(x, dtype=np.float32) / 255. liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1]) liquid_layer = skimage.filters.gaussian(liquid_layer, sigma=c[2]) liquid_layer[liquid_layer < c[3]] = 0 if c[5] == 0: liquid_layer = (liquid_layer * 255).astype(np.uint8) dist = 255 - cv2.Canny(liquid_layer, 50, 150) dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5) _, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC) dist = cv2.blur(dist, (3, 3)).astype(np.uint8) dist = cv2.equalizeHist(dist) # ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32) # ker -= np.mean(ker) ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]]) dist = cv2.filter2D(dist, cv2.CVX_8U, ker) dist = cv2.blur(dist, (3, 3)).astype(np.float32) m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA) m /= np.max(m, axis=(0, 1)) m = c[4] # water is pale turqouise color = np.concatenate( (175 / 255. np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1])), axis=2) color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA) x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA) x = cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255 else: m =	np.where(liquid_layer > c[3], 1, 0)	numpy.where
from keras.models import Sequential from keras.layers import Conv2D, Conv2DTranspose, Input, BatchNormalization, PReLU from keras.callbacks import ModelCheckpoint, Callback, TensorBoard from keras.optimizers import SGD, Adam import numpy as np import math import os import random from os import listdir, makedirs from os.path import isfile, join, exists from PIL import Image import os.path, sys sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir)) from s3sync import S3SyncCallback def model(scale = 2): d = 56 s = 12 m = 4 c = 3 SRCNN = Sequential() SRCNN.add(Conv2D(nb_filter=d, nb_row=5, nb_col=5, init='glorot_uniform', border_mode='same', bias=True, input_shape=(100, 100, 3))) SRCNN.add(PReLU(shared_axes=[1, 2])) SRCNN.add(Conv2D(nb_filter=s, nb_row=1, nb_col=1, init='glorot_uniform', border_mode='same', bias=True)) SRCNN.add(PReLU(shared_axes=[1, 2])) for i in range(m): SRCNN.add(Conv2D(nb_filter=s, nb_row=3, nb_col=3, init='glorot_uniform', border_mode='same', bias=True)) SRCNN.add(PReLU(shared_axes=[1, 2])) SRCNN.add(Conv2D(nb_filter=d, nb_row=1, nb_col=1, init='glorot_uniform', border_mode='same', bias=True)) SRCNN.add(PReLU(shared_axes=[1, 2])) SRCNN.add(Conv2DTranspose(filters=3, kernel_size=(9,9), strides=(scale, scale), init='glorot_uniform', border_mode='same', bias=True)) adam = Adam(lr=0.0003) SRCNN.compile(optimizer=adam, loss='mean_squared_error', metrics=['mean_squared_error']) return SRCNN class MyDataGenerator(object): def flow_from_directory(self, input_dir, label_dir, batch_size=32): images = [] labels = [] while True: files = listdir(input_dir) random.shuffle(files) for f in files: images.append(self.load_image(input_dir, f)) labels.append(self.load_image(label_dir, f)) if len(images) == batch_size: x_inputs =	np.asarray(images)	numpy.asarray
"""Testing for K-means""" import re import sys import numpy as np from scipy import sparse as sp from threadpoolctl import threadpool_limits import pytest from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils.fixes import _astype_copy_false from sklearn.base import clone from sklearn.exceptions import ConvergenceWarning from sklearn.utils.extmath import row_norms from sklearn.metrics import pairwise_distances_argmin from sklearn.metrics.cluster import v_measure_score from sklearn.cluster import KMeans, k_means from sklearn.cluster import MiniBatchKMeans from sklearn.cluster._kmeans import _labels_inertia from sklearn.cluster._kmeans import _mini_batch_step from sklearn.cluster._k_means_fast import _relocate_empty_clusters_dense from sklearn.cluster._k_means_fast import _relocate_empty_clusters_sparse from sklearn.cluster._k_means_fast import _euclidean_dense_dense_wrapper from sklearn.cluster._k_means_fast import _euclidean_sparse_dense_wrapper from sklearn.cluster._k_means_fast import _inertia_dense from sklearn.cluster._k_means_fast import _inertia_sparse from sklearn.datasets import make_blobs from io import StringIO from sklearn.metrics.cluster import homogeneity_score # non centered, sparse centers to check the centers = np.array([ [0.0, 5.0, 0.0, 0.0, 0.0], [1.0, 1.0, 4.0, 0.0, 0.0], [1.0, 0.0, 0.0, 5.0, 1.0], ]) n_samples = 100 n_clusters, n_features = centers.shape X, true_labels = make_blobs(n_samples=n_samples, centers=centers, cluster_std=1., random_state=42) X_csr = sp.csr_matrix(X) @pytest.mark.parametrize("array_constr", [np.array, sp.csr_matrix], ids=["dense", "sparse"]) @pytest.mark.parametrize("algo", ["full", "elkan"]) @pytest.mark.parametrize("dtype", [np.float32, np.float64]) def test_kmeans_results(array_constr, algo, dtype): # Checks that KMeans works as intended on toy dataset by comparing with # expected results computed by hand. X = array_constr([[0, 0], [0.5, 0], [0.5, 1], [1, 1]], dtype=dtype) sample_weight = [3, 1, 1, 3] init_centers = np.array([[0, 0], [1, 1]], dtype=dtype) expected_labels = [0, 0, 1, 1] expected_inertia = 0.375 expected_centers = np.array([[0.125, 0], [0.875, 1]], dtype=dtype) expected_n_iter = 2 kmeans = KMeans(n_clusters=2, n_init=1, init=init_centers, algorithm=algo) kmeans.fit(X, sample_weight=sample_weight) assert_array_equal(kmeans.labels_, expected_labels) assert_allclose(kmeans.inertia_, expected_inertia) assert_allclose(kmeans.cluster_centers_, expected_centers) assert kmeans.n_iter_ == expected_n_iter @pytest.mark.parametrize("array_constr", [np.array, sp.csr_matrix], ids=['dense', 'sparse']) @pytest.mark.parametrize("algo", ['full', 'elkan']) def test_relocated_clusters(array_constr, algo): # check that empty clusters are relocated as expected X = array_constr([[0, 0], [0.5, 0], [0.5, 1], [1, 1]]) # second center too far from others points will be empty at first iter init_centers = np.array([[0.5, 0.5], [3, 3]]) expected_labels = [0, 0, 1, 1] expected_inertia = 0.25 expected_centers = [[0.25, 0], [0.75, 1]] expected_n_iter = 3 kmeans = KMeans(n_clusters=2, n_init=1, init=init_centers, algorithm=algo) kmeans.fit(X) assert_array_equal(kmeans.labels_, expected_labels) assert_almost_equal(kmeans.inertia_, expected_inertia) assert_array_almost_equal(kmeans.cluster_centers_, expected_centers) assert kmeans.n_iter_ == expected_n_iter @pytest.mark.parametrize("representation", ["dense", "sparse"]) def test_relocate_empty_clusters(representation): # test for the _relocate_empty_clusters_(dense/sparse) helpers # Synthetic dataset with 3 obvious clusters of different sizes X = np.array( [-10., -9.5, -9, -8.5, -8, -1, 1, 9, 9.5, 10]).reshape(-1, 1) if representation == "sparse": X = sp.csr_matrix(X) sample_weight = np.full(shape=10, fill_value=1.) # centers all initialized to the first point of X centers_old = np.array([-10., -10, -10]).reshape(-1, 1) # With this initialization, all points will be assigned to the first center # At this point a center in centers_new is the weighted sum of the points # it contains if it's not empty, otherwise it is the same as before. centers_new = np.array([-16.5, -10, -10]).reshape(-1, 1) weight_in_clusters = np.array([10., 0, 0]) labels = np.zeros(10, dtype=np.int32) if representation == "dense": _relocate_empty_clusters_dense(X, sample_weight, centers_old, centers_new, weight_in_clusters, labels) else: _relocate_empty_clusters_sparse(X.data, X.indices, X.indptr, sample_weight, centers_old, centers_new, weight_in_clusters, labels) # The relocation scheme will take the 2 points farthest from the center and # assign them to the 2 empty clusters, i.e. points at 10 and at 9.9. The # first center will be updated to contain the other 8 points. assert_array_equal(weight_in_clusters, [8, 1, 1]) assert_allclose(centers_new, [[-36], [10], [9.5]]) @pytest.mark.parametrize("distribution", ["normal", "blobs"]) @pytest.mark.parametrize("array_constr", [np.array, sp.csr_matrix], ids=["dense", "sparse"]) @pytest.mark.parametrize("tol", [1e-2, 1e-8, 1e-100, 0]) def test_kmeans_elkan_results(distribution, array_constr, tol): # Check that results are identical between lloyd and elkan algorithms rnd = np.random.RandomState(0) if distribution == 'normal': X = rnd.normal(size=(5000, 10)) else: X, _ = make_blobs(random_state=rnd) km_full = KMeans(algorithm='full', n_clusters=5, random_state=0, n_init=1, tol=tol) km_elkan = KMeans(algorithm='elkan', n_clusters=5, random_state=0, n_init=1, tol=tol) km_full.fit(X) km_elkan.fit(X) assert_allclose(km_elkan.cluster_centers_, km_full.cluster_centers_) assert_array_equal(km_elkan.labels_, km_full.labels_) assert km_elkan.n_iter_ == km_full.n_iter_ assert km_elkan.inertia_ == pytest.approx(km_full.inertia_, rel=1e-6) @pytest.mark.parametrize('algorithm', ['full', 'elkan']) def test_kmeans_convergence(algorithm): # Check that KMeans stops when convergence is reached when tol=0. (#16075) rnd = np.random.RandomState(0) X = rnd.normal(size=(5000, 10)) max_iter = 300 km = KMeans(algorithm=algorithm, n_clusters=5, random_state=0, n_init=1, tol=0, max_iter=max_iter).fit(X) assert km.n_iter_ < max_iter @pytest.mark.parametrize('distribution', ['normal', 'blobs']) def test_elkan_results_sparse(distribution): # check that results are identical between lloyd and elkan algorithms # with sparse input rnd = np.random.RandomState(0) if distribution == 'normal': X = sp.random(100, 100, density=0.1, format='csr', random_state=rnd) X.data = rnd.randn(len(X.data)) else: X, _ = make_blobs(n_samples=100, n_features=100, random_state=rnd) X = sp.csr_matrix(X) km_full = KMeans(algorithm='full', n_clusters=5, random_state=0, n_init=1) km_elkan = KMeans(algorithm='elkan', n_clusters=5, random_state=0, n_init=1) km_full.fit(X) km_elkan.fit(X) assert_allclose(km_elkan.cluster_centers_, km_full.cluster_centers_) assert_allclose(km_elkan.labels_, km_full.labels_) def test_labels_assignment_and_inertia(): # pure numpy implementation as easily auditable reference gold # implementation rng = np.random.RandomState(42) noisy_centers = centers + rng.normal(size=centers.shape) labels_gold = np.full(n_samples, -1, dtype=np.int) mindist = np.empty(n_samples) mindist.fill(np.infty) for center_id in range(n_clusters): dist = np.sum((X - noisy_centers[center_id]) 2, axis=1) labels_gold[dist < mindist] = center_id mindist = np.minimum(dist, mindist) inertia_gold = mindist.sum() assert (mindist >= 0.0).all() assert (labels_gold != -1).all() sample_weight = None # perform label assignment using the dense array input x_squared_norms = (X 2).sum(axis=1) labels_array, inertia_array = _labels_inertia( X, sample_weight, x_squared_norms, noisy_centers) assert_array_almost_equal(inertia_array, inertia_gold) assert_array_equal(labels_array, labels_gold) # perform label assignment using the sparse CSR input x_squared_norms_from_csr = row_norms(X_csr, squared=True) labels_csr, inertia_csr = _labels_inertia( X_csr, sample_weight, x_squared_norms_from_csr, noisy_centers) assert_array_almost_equal(inertia_csr, inertia_gold) assert_array_equal(labels_csr, labels_gold) def test_minibatch_update_consistency(): # Check that dense and sparse minibatch update give the same results rng = np.random.RandomState(42) old_centers = centers + rng.normal(size=centers.shape) new_centers = old_centers.copy() new_centers_csr = old_centers.copy() weight_sums = np.zeros(new_centers.shape[0], dtype=np.double) weight_sums_csr = np.zeros(new_centers.shape[0], dtype=np.double) x_squared_norms = (X 2).sum(axis=1) x_squared_norms_csr = row_norms(X_csr, squared=True) buffer = np.zeros(centers.shape[1], dtype=np.double) buffer_csr = np.zeros(centers.shape[1], dtype=np.double) # extract a small minibatch X_mb = X[:10] X_mb_csr = X_csr[:10] x_mb_squared_norms = x_squared_norms[:10] x_mb_squared_norms_csr = x_squared_norms_csr[:10] sample_weight_mb = np.ones(X_mb.shape[0], dtype=np.double) # step 1: compute the dense minibatch update old_inertia, incremental_diff = _mini_batch_step( X_mb, sample_weight_mb, x_mb_squared_norms, new_centers, weight_sums, buffer, 1, None, random_reassign=False) assert old_inertia > 0.0 # compute the new inertia on the same batch to check that it decreased labels, new_inertia = _labels_inertia( X_mb, sample_weight_mb, x_mb_squared_norms, new_centers) assert new_inertia > 0.0 assert new_inertia < old_inertia # check that the incremental difference computation is matching the # final observed value effective_diff = np.sum((new_centers - old_centers) 2) assert_almost_equal(incremental_diff, effective_diff) # step 2: compute the sparse minibatch update old_inertia_csr, incremental_diff_csr = _mini_batch_step( X_mb_csr, sample_weight_mb, x_mb_squared_norms_csr, new_centers_csr, weight_sums_csr, buffer_csr, 1, None, random_reassign=False) assert old_inertia_csr > 0.0 # compute the new inertia on the same batch to check that it decreased labels_csr, new_inertia_csr = _labels_inertia( X_mb_csr, sample_weight_mb, x_mb_squared_norms_csr, new_centers_csr) assert new_inertia_csr > 0.0 assert new_inertia_csr < old_inertia_csr # check that the incremental difference computation is matching the # final observed value effective_diff = np.sum((new_centers_csr - old_centers) ** 2) assert_almost_equal(incremental_diff_csr, effective_diff) # step 3: check that sparse and dense updates lead to the same results assert_array_equal(labels, labels_csr) assert_array_almost_equal(new_centers, new_centers_csr) assert_almost_equal(incremental_diff, incremental_diff_csr) assert_almost_equal(old_inertia, old_inertia_csr) assert_almost_equal(new_inertia, new_inertia_csr) def _check_fitted_model(km): # check that the number of clusters centers and distinct labels match # the expectation centers = km.cluster_centers_ assert centers.shape == (n_clusters, n_features) labels = km.labels_ assert np.unique(labels).shape[0] == n_clusters # check that the labels assignment are perfect (up to a permutation) assert v_measure_score(true_labels, labels) == 1.0 assert km.inertia_ > 0.0 # check error on dataset being too small assert_raise_message(ValueError, "n_samples=1 should be >= n_clusters=%d" % km.n_clusters, km.fit, [[0., 1.]]) def test_k_means_new_centers(): # Explore the part of the code where a new center is reassigned X = np.array([[0, 0, 1, 1], [0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0]]) labels = [0, 1, 2, 1, 1, 2] bad_centers = np.array([[+0, 1, 0, 0], [.2, 0, .2, .2], [+0, 0, 0, 0]]) km = KMeans(n_clusters=3, init=bad_centers, n_init=1, max_iter=10, random_state=1) for this_X in (X, sp.coo_matrix(X)): km.fit(this_X) this_labels = km.labels_ # Reorder the labels so that the first instance is in cluster 0, # the second in cluster 1, ... this_labels = np.unique(this_labels, return_index=True)[1][this_labels] np.testing.assert_array_equal(this_labels, labels) @pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse']) @pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()]) def test_k_means_init(data, init): km = KMeans(init=init, n_clusters=n_clusters, random_state=42, n_init=1) km.fit(data) _check_fitted_model(km) @pytest.mark.parametrize("init", ["random", "k-means++", centers, lambda X, k, random_state: centers], ids=["random", "k-means++", "ndarray", "callable"]) def test_minibatch_kmeans_partial_fit_init(init): # Check MiniBatchKMeans init with partial_fit km = MiniBatchKMeans(init=init, n_clusters=n_clusters, random_state=0) for i in range(100): # "random" init requires many batches to recover the true labels. km.partial_fit(X) _check_fitted_model(km) def test_k_means_fortran_aligned_data(): # Check the KMeans will work well, even if X is a fortran-aligned data. X = np.asfortranarray([[0, 0], [0, 1], [0, 1]]) centers = np.array([[0, 0], [0, 1]]) labels = np.array([0, 1, 1]) km = KMeans(n_init=1, init=centers, random_state=42, n_clusters=2) km.fit(X) assert_array_almost_equal(km.cluster_centers_, centers) assert_array_equal(km.labels_, labels) @pytest.mark.parametrize('algo', ['full', 'elkan']) @pytest.mark.parametrize('dtype', [np.float32, np.float64]) @pytest.mark.parametrize('constructor', [np.asarray, sp.csr_matrix]) @pytest.mark.parametrize('seed, max_iter, tol', [ (0, 2, 1e-7), # strict non-convergence (1, 2, 1e-1), # loose non-convergence (3, 300, 1e-7), # strict convergence (4, 300, 1e-1), # loose convergence ]) def test_k_means_fit_predict(algo, dtype, constructor, seed, max_iter, tol): # check that fit.predict gives same result as fit_predict # There's a very small chance of failure with elkan on unstructured dataset # because predict method uses fast euclidean distances computation which # may cause small numerical instabilities. # NB: This test is largely redundant with respect to test_predict and # test_predict_equal_labels. This test has the added effect of # testing idempotence of the fittng procesdure which appears to # be where it fails on some MacOS setups. if sys.platform == "darwin": pytest.xfail( "Known failures on MacOS, See " "https://github.com/scikit-learn/scikit-learn/issues/12644") rng = np.random.RandomState(seed) X = make_blobs(n_samples=1000, n_features=10, centers=10, random_state=rng)[0].astype(dtype, copy=False) X = constructor(X) kmeans = KMeans(algorithm=algo, n_clusters=10, random_state=seed, tol=tol, max_iter=max_iter) labels_1 = kmeans.fit(X).predict(X) labels_2 = kmeans.fit_predict(X) # Due to randomness in the order in which chunks of data are processed when # using more than one thread, the absolute values of the labels can be # different between the 2 strategies but they should correspond to the same # clustering. assert v_measure_score(labels_1, labels_2) == 1 def test_minibatch_kmeans_verbose(): # Check verbose mode of MiniBatchKMeans for better coverage. km = MiniBatchKMeans(n_clusters=n_clusters, random_state=42, verbose=1) old_stdout = sys.stdout sys.stdout = StringIO() try: km.fit(X) finally: sys.stdout = old_stdout @pytest.mark.parametrize("algorithm", ["full", "elkan"]) @pytest.mark.parametrize("tol", [1e-2, 0]) def test_kmeans_verbose(algorithm, tol, capsys): # Check verbose mode of KMeans for better coverage. X = np.random.RandomState(0).normal(size=(5000, 10)) KMeans(algorithm=algorithm, n_clusters=n_clusters, random_state=42, init="random", n_init=1, tol=tol, verbose=1).fit(X) captured = capsys.readouterr() assert re.search(r"Initialization complete", captured.out) assert re.search(r"Iteration [0-9]+, inertia", captured.out) if tol == 0: assert re.search(r"strict convergence", captured.out) else: assert re.search(r"center shift .* within tolerance", captured.out) def test_minibatch_kmeans_warning_init_size(): # Check that a warning is raised when init_size is smaller than n_clusters with pytest.warns(RuntimeWarning, match=r"init_size.* should be larger than n_clusters"): MiniBatchKMeans(init_size=10, n_clusters=20).fit(X) def test_minibatch_k_means_init_multiple_runs_with_explicit_centers(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=10) assert_warns(RuntimeWarning, mb_k_means.fit, X) @pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse']) @pytest.mark.parametrize('init', ["random", 'k-means++', centers.copy()]) def test_minibatch_k_means_init(data, init): mb_k_means = MiniBatchKMeans(init=init, n_clusters=n_clusters, random_state=42, n_init=10) mb_k_means.fit(data) _check_fitted_model(mb_k_means) def test_minibatch_sensible_reassign_fit(): # check if identical initial clusters are reassigned # also a regression test for when there are more desired reassignments than # samples. zeroed_X, true_labels = make_blobs(n_samples=100, centers=5, cluster_std=1., random_state=42) zeroed_X[::2, :] = 0 mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=10, random_state=42, init="random") mb_k_means.fit(zeroed_X) # there should not be too many exact zero cluster centers assert mb_k_means.cluster_centers_.any(axis=1).sum() > 10 # do the same with batch-size > X.shape[0] (regression test) mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=201, random_state=42, init="random") mb_k_means.fit(zeroed_X) # there should not be too many exact zero cluster centers assert mb_k_means.cluster_centers_.any(axis=1).sum() > 10 def test_minibatch_sensible_reassign_partial_fit(): zeroed_X, true_labels = make_blobs(n_samples=n_samples, centers=5, cluster_std=1., random_state=42) zeroed_X[::2, :] = 0 mb_k_means = MiniBatchKMeans(n_clusters=20, random_state=42, init="random") for i in range(100): mb_k_means.partial_fit(zeroed_X) # there should not be too many exact zero cluster centers assert mb_k_means.cluster_centers_.any(axis=1).sum() > 10 def test_minibatch_reassign(): # Give a perfect initialization, but a large reassignment_ratio, # as a result all the centers should be reassigned and the model # should no longer be good sample_weight = np.ones(X.shape[0], dtype=X.dtype) for this_X in (X, X_csr): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, random_state=42) mb_k_means.fit(this_X) score_before = mb_k_means.score(this_X) try: old_stdout = sys.stdout sys.stdout = StringIO() # Turn on verbosity to smoke test the display code _mini_batch_step(this_X, sample_weight, (X 2).sum(axis=1), mb_k_means.cluster_centers_, mb_k_means.counts_, np.zeros(X.shape[1], np.double), False, distances=np.zeros(X.shape[0]), random_reassign=True, random_state=42, reassignment_ratio=1, verbose=True) finally: sys.stdout = old_stdout assert score_before > mb_k_means.score(this_X) # Give a perfect initialization, with a small reassignment_ratio, # no center should be reassigned for this_X in (X, X_csr): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, init=centers.copy(), random_state=42, n_init=1) mb_k_means.fit(this_X) clusters_before = mb_k_means.cluster_centers_ # Turn on verbosity to smoke test the display code _mini_batch_step(this_X, sample_weight, (X 2).sum(axis=1), mb_k_means.cluster_centers_, mb_k_means.counts_, np.zeros(X.shape[1], np.double), False, distances=np.zeros(X.shape[0]), random_reassign=True, random_state=42, reassignment_ratio=1e-15) assert_array_almost_equal(clusters_before, mb_k_means.cluster_centers_) def test_minibatch_with_many_reassignments(): # Test for the case that the number of clusters to reassign is bigger # than the batch_size n_samples = 550 rnd = np.random.RandomState(42) X = rnd.uniform(size=(n_samples, 10)) # Check that the fit works if n_clusters is bigger than the batch_size. # Run the test with 550 clusters and 550 samples, because it turned out # that this values ensure that the number of clusters to reassign # is always bigger than the batch_size n_clusters = 550 MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, init_size=n_samples, random_state=42).fit(X) def test_sparse_mb_k_means_callable_init(): def test_init(X, k, random_state): return centers mb_k_means = MiniBatchKMeans(n_clusters=3, init=test_init, random_state=42).fit(X_csr) _check_fitted_model(mb_k_means) def test_mini_batch_k_means_random_init_partial_fit(): km = MiniBatchKMeans(n_clusters=n_clusters, init="random", random_state=42) # use the partial_fit API for online learning for X_minibatch in np.array_split(X, 10): km.partial_fit(X_minibatch) # compute the labeling on the complete dataset labels = km.predict(X) assert v_measure_score(true_labels, labels) == 1.0 def test_minibatch_kmeans_default_init_size(): # Check the internal _init_size attribute of MiniBatchKMeans # default init size should be 3 * batch_size km = MiniBatchKMeans(n_clusters=10, batch_size=5, n_init=1).fit(X) assert km._init_size == 15 # if 3 * batch size < n_clusters, it should then be 3 * n_clusters km = MiniBatchKMeans(n_clusters=10, batch_size=1, n_init=1).fit(X) assert km._init_size == 30 # it should not be larger than n_samples km = MiniBatchKMeans(n_clusters=10, batch_size=5, n_init=1, init_size=n_samples + 1).fit(X) assert km._init_size == n_samples def test_minibatch_tol(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=10, random_state=42, tol=.01).fit(X) _check_fitted_model(mb_k_means) def test_minibatch_set_init_size(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, init_size=666, random_state=42, n_init=1).fit(X) assert mb_k_means.init_size == 666 assert mb_k_means.init_size_ == n_samples _check_fitted_model(mb_k_means) def test_k_means_copyx(): # Check if copy_x=False returns nearly equal X after de-centering. my_X = X.copy() km = KMeans(copy_x=False, n_clusters=n_clusters, random_state=42) km.fit(my_X) _check_fitted_model(km) # check if my_X is centered assert_array_almost_equal(my_X, X) def test_k_means_non_collapsed(): # Check k_means with a bad initialization does not yield a singleton # Starting with bad centers that are quickly ignored should not # result in a repositioning of the centers to the center of mass that # would lead to collapsed centers which in turns make the clustering # dependent of the numerical unstabilities. my_X = np.array([[1.1, 1.1], [0.9, 1.1], [1.1, 0.9], [0.9, 1.1]]) array_init = np.array([[1.0, 1.0], [5.0, 5.0], [-5.0, -5.0]]) km = KMeans(init=array_init, n_clusters=3, random_state=42, n_init=1) km.fit(my_X) # centers must not been collapsed assert len(np.unique(km.labels_)) == 3 centers = km.cluster_centers_ assert np.linalg.norm(centers[0] - centers[1]) >= 0.1 assert np.linalg.norm(centers[0] - centers[2]) >= 0.1 assert np.linalg.norm(centers[1] - centers[2]) >= 0.1 @pytest.mark.parametrize('algo', ['full', 'elkan']) def test_score(algo): # Check that fitting k-means with multiple inits gives better score km1 = KMeans(n_clusters=n_clusters, max_iter=1, random_state=42, n_init=1, algorithm=algo) s1 = km1.fit(X).score(X) km2 = KMeans(n_clusters=n_clusters, max_iter=10, random_state=42, n_init=1, algorithm=algo) s2 = km2.fit(X).score(X) assert s2 > s1 @pytest.mark.parametrize('Estimator', [KMeans, MiniBatchKMeans]) @pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse']) @pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()]) def test_predict(Estimator, data, init): k_means = Estimator(n_clusters=n_clusters, init=init, n_init=10, random_state=0).fit(data) # sanity check: re-predict labeling for training set samples assert_array_equal(k_means.predict(data), k_means.labels_) # sanity check: predict centroid labels pred = k_means.predict(k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # re-predict labels for training set using fit_predict pred = k_means.fit_predict(data) assert_array_equal(pred, k_means.labels_) @pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()]) def test_predict_minibatch_dense_sparse(init): # check that models trained on sparse input also works for dense input at # predict time mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init=init, n_init=10, random_state=0).fit(X_csr) assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_int_input(): X_list = [[0, 0], [10, 10], [12, 9], [-1, 1], [2, 0], [8, 10]] for dtype in [np.int32, np.int64]: X_int = np.array(X_list, dtype=dtype) X_int_csr = sp.csr_matrix(X_int) init_int = X_int[:2] fitted_models = [ KMeans(n_clusters=2).fit(X_int), KMeans(n_clusters=2, init=init_int, n_init=1).fit(X_int), # mini batch kmeans is very unstable on such a small dataset hence # we use many inits MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_int), MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit( X_int_csr), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int, n_init=1).fit(X_int), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int, n_init=1).fit(X_int_csr), ] for km in fitted_models: assert km.cluster_centers_.dtype == np.float64 expected_labels = [0, 1, 1, 0, 0, 1] scores = np.array([v_measure_score(expected_labels, km.labels_) for km in fitted_models]) assert_array_almost_equal(scores, np.ones(scores.shape[0])) def test_transform(): km = KMeans(n_clusters=n_clusters) km.fit(X) X_new = km.transform(km.cluster_centers_) for c in range(n_clusters): assert X_new[c, c] == 0 for c2 in range(n_clusters): if c != c2: assert X_new[c, c2] > 0 def test_fit_transform(): X1 = KMeans(n_clusters=3, random_state=51).fit(X).transform(X) X2 = KMeans(n_clusters=3, random_state=51).fit_transform(X) assert_array_almost_equal(X1, X2) @pytest.mark.parametrize('algo', ['full', 'elkan']) def test_predict_equal_labels(algo): km = KMeans(random_state=13, n_init=1, max_iter=1, algorithm=algo) km.fit(X) assert_array_equal(km.predict(X), km.labels_) def test_full_vs_elkan(): km1 = KMeans(algorithm='full', random_state=13).fit(X) km2 = KMeans(algorithm='elkan', random_state=13).fit(X) assert homogeneity_score( km1.predict(X), km2.predict(X) ) == pytest.approx(1.0) def test_n_init(): # Check that increasing the number of init increases the quality n_runs = 5 n_init_range = [1, 5, 10] inertia = np.zeros((len(n_init_range), n_runs)) for i, n_init in enumerate(n_init_range): for j in range(n_runs): km = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, random_state=j).fit(X) inertia[i, j] = km.inertia_ inertia = inertia.mean(axis=1) failure_msg = ("Inertia %r should be decreasing" " when n_init is increasing.") % list(inertia) for i in range(len(n_init_range) - 1): assert inertia[i] >= inertia[i + 1], failure_msg def test_k_means_function(): # test calling the k_means function directly # catch output old_stdout = sys.stdout sys.stdout = StringIO() try: cluster_centers, labels, inertia = k_means(X, n_clusters=n_clusters, sample_weight=None, verbose=True) finally: sys.stdout = old_stdout centers = cluster_centers assert centers.shape == (n_clusters, n_features) labels = labels assert np.unique(labels).shape[0] == n_clusters # check that the labels assignment are perfect (up to a permutation) assert v_measure_score(true_labels, labels) == 1.0 assert inertia > 0.0 # check warning when centers are passed assert_warns(RuntimeWarning, k_means, X, n_clusters=n_clusters, sample_weight=None, init=centers) def test_x_squared_norms_init_centroids(): # Test that x_squared_norms can be None in _init_centroids from sklearn.cluster._kmeans import _init_centroids X_norms = np.sum(X2, axis=1) precompute = _init_centroids( X, 3, "k-means++", random_state=0, x_squared_norms=X_norms) assert_array_almost_equal( precompute, _init_centroids(X, 3, "k-means++", random_state=0)) @pytest.mark.parametrize("data", [X, X_csr], ids=["dense", "sparse"]) @pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans]) def test_float_precision(Estimator, data): # Check that the results are the same for single and double precision. km = Estimator(n_init=1, random_state=0) inertia = {} Xt = {} centers = {} labels = {} for dtype in [np.float64, np.float32]: X = data.astype(dtype, _astype_copy_false(data)) km.fit(X) inertia[dtype] = km.inertia_ Xt[dtype] = km.transform(X) centers[dtype] = km.cluster_centers_ labels[dtype] = km.labels_ # dtype of cluster centers has to be the dtype of the input data assert km.cluster_centers_.dtype == dtype # same with partial_fit if Estimator is MiniBatchKMeans: km.partial_fit(X[0:3]) assert km.cluster_centers_.dtype == dtype # compare arrays with low precision since the difference between 32 and # 64 bit comes from an accumulation of rounding errors. assert_allclose(inertia[np.float32], inertia[np.float64], rtol=1e-5) assert_allclose(Xt[np.float32], Xt[np.float64], rtol=1e-5) assert_allclose(centers[np.float32], centers[np.float64], rtol=1e-5) assert_array_equal(labels[np.float32], labels[np.float64]) def test_k_means_init_centers(): # This test is used to check KMeans won't mutate the user provided input # array silently even if input data and init centers have the same type X_small = np.array([[1.1, 1.1], [-7.5, -7.5], [-1.1, -1.1], [7.5, 7.5]]) init_centers = np.array([[0.0, 0.0], [5.0, 5.0], [-5.0, -5.0]]) for dtype in [np.int32, np.int64, np.float32, np.float64]: X_test = dtype(X_small) init_centers_test = dtype(init_centers) assert_array_equal(init_centers, init_centers_test) km = KMeans(init=init_centers_test, n_clusters=3, n_init=1) km.fit(X_test) assert np.may_share_memory(km.cluster_centers_, init_centers) is False @pytest.mark.parametrize("data", [X, X_csr], ids=["dense", "sparse"]) def test_k_means_init_fitted_centers(data): # Get a local optimum centers = KMeans(n_clusters=3).fit(X).cluster_centers_ # Fit starting from a local optimum shouldn't change the solution new_centers = KMeans(n_clusters=3, init=centers, n_init=1).fit(X).cluster_centers_ assert_array_almost_equal(centers, new_centers) def test_less_centers_than_unique_points(): X = np.asarray([[0, 0], [0, 1], [1, 0], [1, 0]]) # last point is duplicated km = KMeans(n_clusters=4).fit(X) # only three distinct points, so only three clusters # can have points assigned to them assert set(km.labels_) == set(range(3)) # k_means should warn that fewer labels than cluster # centers have been used msg = ("Number of distinct clusters (3) found smaller than " "n_clusters (4). Possibly due to duplicate points in X.") assert_warns_message(ConvergenceWarning, msg, k_means, X, sample_weight=None, n_clusters=4) def _sort_centers(centers): return	np.sort(centers, axis=0)	numpy.sort
# -- coding: utf-8 -- """ Created on Thu Jun 11 10:17:34 2020 compare the features calculated by kubios and calculated by our own algorithm @author: skjerns """ import os import matplotlib.pyplot as plt import numpy as np from tqdm import tqdm import config as cfg from sleep import SleepSet import matplotlib.pyplot as plt import scipy import pandas as pd from scipy.ndimage import median_filter, convolve from scipy.ndimage.filters import gaussian_filter1d os.makedirs(os.path.join(cfg.documents, 'reports', 'feat_comparison'), exist_ok=True) ss = SleepSet(cfg.folder_unisens) p = ss[16] p.reset() matfile = dict(p.feats.get_data()) kubios = matfile['TimeVar'] def best_corr(kubios, feat1): """ we correlate all features of kubios with this feature this is a somewhat sound way to check whether our feature has the best correlation with what is actually calculated """ df = pd.DataFrame(columns=['Name', 'corr', 'data']) for feat2_name, feat2 in kubios.items(): if abs(len(feat2)-len(feat1))>10: continue if np.isnan(feat2).all(): continue min_len = min(len(feat1), len(feat2)) mean = np.nan_to_num(np.nanmean(feat2)) feat2 =	np.nan_to_num(feat2[:min_len], nan=mean)	numpy.nan_to_num
import numpy as np import pytest from continuum.scenarios import Rotations from tests.test_classorder import InMemoryDatasetTest from continuum.datasets import MNIST, CIFAR100 @pytest.fixture def numpy_data(): nb_classes = 6 nb_data = 100 x_train = [] y_train = [] for i in range(nb_classes): x_train.append(np.ones((nb_data, 4, 4, 3), dtype=np.uint8) * i) y_train.append(	np.ones(nb_data)	numpy.ones
""" Potential field transformations, like upward continuation and derivatives. .. note:: Most, if not all, functions here required gridded data. Transformations * :func:`~fatiando.gravmag.transform.upcontinue`: Upward continuation of gridded potential field data on a level surface. * :func:`~fatiando.gravmag.transform.reduce_to_pole`: Reduce the total field magnetic anomaly to the pole. * :func:`~fatiando.gravmag.transform.tga`: Calculate the amplitude of the total gradient (also called the analytic signal) * :func:`~fatiando.gravmag.transform.tilt`: Calculates the tilt angle * :func:`~fatiando.gravmag.transform.power_density_spectra`: Calculates the Power Density Spectra of a gridded potential field data. * :func:`~fatiando.gravmag.transform.radial_average`: Calculates the the radial average of a Power Density Spectra using concentring rings. Derivatives * :func:`~fatiando.gravmag.transform.derivx`: Calculate the n-th order derivative of a potential field in the x-direction (North-South) * :func:`~fatiando.gravmag.transform.derivy`: Calculate the n-th order derivative of a potential field in the y-direction (East-West) * :func:`~fatiando.gravmag.transform.derivz`: Calculate the n-th order derivative of a potential field in the z-direction ---- """ from __future__ import division, absolute_import import warnings import numpy from .. import utils def reduce_to_pole(x, y, data, shape, inc, dec, sinc, sdec): r""" Reduce total field magnetic anomaly data to the pole. The reduction to the pole if a phase transformation that can be applied to total field magnetic anomaly data. It "simulates" how the data would be if both the Geomagnetic field and the magnetization of the source were vertical (:math:`90^\circ` inclination) (Blakely, 1996). This functions performs the reduction in the frequency domain (using the FFT). The transform filter is (in the frequency domain): .. math:: RTP(k_x, k_y) = \frac{\|k\|}{ a_1 k_x^2 + a_2 k_y^2 + a_3 k_x k_y + i\|k\|(b_1 k_x + b_2 k_y)} in which :math:`k_x` and :math:`k_y` are the wave-numbers in the x and y directions and .. math:: \|k\| = \sqrt{k_x^2 + k_y^2} \\ a_1 = m_z f_z - m_x f_x \\ a_2 = m_z f_z - m_y f_y \\ a_3 = -m_y f_x - m_x f_y \\ b_1 = m_x f_z + m_z f_x \\ b_2 = m_y f_z + m_z f_y :math:`\mathbf{m} = (m_x, m_y, m_z)` is the unit-vector of the total magnetization of the source and :math:`\mathbf{f} = (f_x, f_y, f_z)` is the unit-vector of the Geomagnetic field. .. note:: Requires gridded data. .. warning:: The magnetization direction of the anomaly source is crucial to the reduction-to-the-pole. *Wrong values of sinc* and sdec will lead to a wrong reduction.** Parameters: * x, y : 1d-arrays The x, y, z coordinates of each data point. * data : 1d-array The total field anomaly data at each point. * shape : tuple = (nx, ny) The shape of the data grid * inc, dec : floats The inclination and declination of the inducing Geomagnetic field * sinc, sdec : floats The inclination and declination of the total magnetization of the anomaly source. The total magnetization is the vector sum of the induced and remanent magnetization. If there is only induced magnetization, use the inc and dec of the Geomagnetic field. Returns: * rtp : 1d-array The data reduced to the pole. References: Blakely, <NAME>. (1996), Potential Theory in Gravity and Magnetic Applications, Cambridge University Press. """ fx, fy, fz = utils.ang2vec(1, inc, dec) if sinc is None or sdec is None: mx, my, mz = fx, fy, fz else: mx, my, mz = utils.ang2vec(1, sinc, sdec) kx, ky = [k for k in _fftfreqs(x, y, shape, shape)] kz_sqr = kx2 + ky2 a1 = mzfz - mxfx a2 = mzfz - myfy a3 = -myfx - mxfy b1 = mxfz + mzfx b2 = myfz + mzfy # The division gives a RuntimeWarning because of the zero frequency term. # This suppresses the warning. with numpy.errstate(divide='ignore', invalid='ignore'): rtp = (kz_sqr)/(a1kx2 + a2ky*2 + a3kxky + 1jnumpy.sqrt(kz_sqr)(b1kx + b2ky)) rtp[0, 0] = 0 ft_pole = rtpnumpy.fft.fft2(numpy.reshape(data, shape)) return numpy.real(numpy.fft.ifft2(ft_pole)).ravel() def upcontinue(x, y, data, shape, height): r""" Upward continuation of potential field data. Calculates the continuation through the Fast Fourier Transform in the wavenumber domain (Blakely, 1996): .. math:: F\{h_{up}\} = F\{h\} e^{-\Delta z \|k\|} and then transformed back to the space domain. :math:`h_{up}` is the upward continue data, :math:`\Delta z` is the height increase, :math:`F` denotes the Fourier Transform, and :math:`\|k\|` is the wavenumber modulus. .. note:: Requires gridded data. .. note:: x, y, z and height should be in meters. .. note:: It is not possible to get the FFT of a masked grid. The default :func:`fatiando.gridder.interp` call using minimum curvature will not be suitable. Use ``extrapolate=True`` or ``algorithm='nearest'`` to get an unmasked grid. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * height : float The height increase (delta z) in meters. Returns: * cont : array The upward continued data References: <NAME>. (1996), Potential Theory in Gravity and Magnetic Applications, Cambridge University Press. """ assert x.shape == y.shape, \ "x and y arrays must have same shape" if height <= 0: warnings.warn("Using 'height' <= 0 means downward continuation, " + "which is known to be unstable.") nx, ny = shape # Pad the array with the edge values to avoid instability padded, padx, pady = _pad_data(data, shape) kx, ky = _fftfreqs(x, y, shape, padded.shape) kz = numpy.sqrt(kx2 + ky2) upcont_ft = numpy.fft.fft2(padded)numpy.exp(-heightkz) cont = numpy.real(numpy.fft.ifft2(upcont_ft)) # Remove padding cont = cont[padx: padx + nx, pady: pady + ny].ravel() return cont def _upcontinue_space(x, y, data, shape, height): """ Upward continuation using the space-domain formula. DEPRECATED. Use the better implementation using FFT. Kept here for historical reasons. """ nx, ny = shape dx = (x.max() - x.min())/(nx - 1) dy = (y.max() - y.min())/(ny - 1) area = dxdy deltaz_sqr = (height)2 cont = numpy.zeros_like(data) for i, j, g in zip(x, y, data): cont += garea((x - i)2 + (y - j)2 + deltaz_sqr)(-1.5) cont = abs(height)/(2numpy.pi) return cont def tga(x, y, data, shape, method='fd'): r""" Calculate the total gradient amplitude (TGA). This the same as the `3D analytic signal` of Roest et al. (1992), but we prefer the newer, more descriptive nomenclature suggested by Reid (2012). The TGA is defined as the amplitude of the gradient vector of a potential field :math:`T` (e.g. the magnetic total field anomaly): .. math:: TGA = \sqrt{ \left(\frac{\partial T}{\partial x}\right)^2 + \left(\frac{\partial T}{\partial y}\right)^2 + \left(\frac{\partial T}{\partial z}\right)^2 } .. note:: Requires gridded data. .. warning:: If the data is not in SI units, the derivatives will be in strange units and so will the total gradient amplitude! I strongly recommend converting the data to SI before* calculating the TGA is you need the gradient in Eotvos (use one of the unit conversion functions of :mod:`fatiando.utils`). Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * method : string The method used to calculate the horizontal derivatives. Options are: ``'fd'`` for finite-difference (more stable) or ``'fft'`` for the Fast Fourier Transform. The z derivative is always calculated by FFT. Returns: * tga : 1D-array The amplitude of the total gradient References: <NAME>. (2012), Forgotten truths, myths and sacred cows of Potential Fields Geophysics - II, in SEG Technical Program Expanded Abstracts 2012, pp. 1-3, Society of Exploration Geophysicists. <NAME>., <NAME>, and <NAME> (1992), Magnetic interpretation using the 3-D analytic signal, GEOPHYSICS, 57(1), 116-125, doi:10.1190/1.1443174. """ dx = derivx(x, y, data, shape, method=method) dy = derivy(x, y, data, shape, method=method) dz = derivz(x, y, data, shape) res = numpy.sqrt(dx 2 + dy 2 + dz ** 2) return res def tilt(x, y, data, shape, xderiv=None, yderiv=None, zderiv=None): r""" Calculates the potential field tilt, as defined by Miller and Singh (1994) .. math:: tilt(f) = tan^{-1}\left( \frac{ \frac{\partial T}{\partial z}}{ \sqrt{\frac{\partial T}{\partial x}^2 + \frac{\partial T}{\partial y}^2}} \right) When used on magnetic total field anomaly data, works best if the data is reduced to the pole. It's useful to plot the zero contour line of the tilt to represent possible outlines of the source bodies. Use matplotlib's ``pyplot.contour`` or ``pyplot.tricontour`` for this. .. note:: Requires gridded data if ``xderiv``, ``yderiv`` and ``zderiv`` are not given. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid. Ignored if xderiv, yderiv and zderiv are given. * xderiv : 1D-array or None Optional. Values of the derivative in the x direction. If ``None``, will calculated using the default options of :func:`~fatiando.gravmag.transform.derivx` * yderiv : 1D-array or None Optional. Values of the derivative in the y direction. If ``None``, will calculated using the default options of :func:`~fatiando.gravmag.transform.derivy` * zderiv : 1D-array or None Optional. Values of the derivative in the z direction. If ``None``, will calculated using the default options of :func:`~fatiando.gravmag.transform.derivz` Returns: * tilt : 1D-array The tilt angle of the total field in radians. References: Miller, <NAME>, and <NAME>. 1994. "Potential Field Tilt --- a New Concept for Location of Potential Field Sources." Journal of Applied Geophysics 32 (2--3): 213-17. doi:10.1016/0926-9851(94)90022-1. """ if xderiv is None: xderiv = derivx(x, y, data, shape) if yderiv is None: yderiv = derivy(x, y, data, shape) if zderiv is None: zderiv = derivz(x, y, data, shape) horiz_deriv = numpy.sqrt(xderiv2 + yderiv2) tilt = numpy.arctan2(zderiv, horiz_deriv) return tilt def derivx(x, y, data, shape, order=1, method='fd'): """ Calculate the derivative of a potential field in the x direction. .. note:: Requires gridded data. .. warning:: If the data is not in SI units, the derivative will be in strange units! I strongly recommend converting the data to SI before calculating the derivative (use one of the unit conversion functions of :mod:`fatiando.utils`). This way the derivative will be in SI units and can be easily converted to what unit you want. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * order : int The order of the derivative * method : string The method used to calculate the derivatives. Options are: ``'fd'`` for central finite-differences (more stable) or ``'fft'`` for the Fast Fourier Transform. Returns: * deriv : 1D-array The derivative """ nx, ny = shape assert method in ['fft', 'fd'], \ 'Invalid method "{}".'.format(method) if method == 'fft': # Pad the array with the edge values to avoid instability padded, padx, pady = _pad_data(data, shape) kx, _ = _fftfreqs(x, y, shape, padded.shape) deriv_ft = numpy.fft.fft2(padded)(kx1j)*order deriv_pad = numpy.real(numpy.fft.ifft2(deriv_ft)) # Remove padding from derivative deriv = deriv_pad[padx: padx + nx, pady: pady + ny] elif method == 'fd': datamat = data.reshape(shape) dx = (x.max() - x.min())/(nx - 1) deriv = numpy.empty_like(datamat) deriv[1:-1, :] = (datamat[2:, :] - datamat[:-2, :])/(2dx) deriv[0, :] = deriv[1, :] deriv[-1, :] = deriv[-2, :] if order > 1: deriv = derivx(x, y, deriv, shape, order=order - 1, method='fd') return deriv.ravel() def derivy(x, y, data, shape, order=1, method='fd'): """ Calculate the derivative of a potential field in the y direction. .. note:: Requires gridded data. .. warning:: If the data is not in SI units, the derivative will be in strange units! I strongly recommend converting the data to SI before calculating the derivative (use one of the unit conversion functions of :mod:`fatiando.utils`). This way the derivative will be in SI units and can be easily converted to what unit you want. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * order : int The order of the derivative * method : string The method used to calculate the derivatives. Options are: ``'fd'`` for central finite-differences (more stable) or ``'fft'`` for the Fast Fourier Transform. Returns: * deriv : 1D-array The derivative """ nx, ny = shape assert method in ['fft', 'fd'], \ 'Invalid method "{}".'.format(method) if method == 'fft': # Pad the array with the edge values to avoid instability padded, padx, pady = _pad_data(data, shape) _, ky = _fftfreqs(x, y, shape, padded.shape) deriv_ft = numpy.fft.fft2(padded)(ky1j)*order deriv_pad = numpy.real(numpy.fft.ifft2(deriv_ft)) # Remove padding from derivative deriv = deriv_pad[padx: padx + nx, pady: pady + ny] elif method == 'fd': datamat = data.reshape(shape) dy = (y.max() - y.min())/(ny - 1) deriv = numpy.empty_like(datamat) deriv[:, 1:-1] = (datamat[:, 2:] - datamat[:, :-2])/(2dy) deriv[:, 0] = deriv[:, 1] deriv[:, -1] = deriv[:, -2] if order > 1: deriv = derivy(x, y, deriv, shape, order=order - 1, method='fd') return deriv.ravel() def derivz(x, y, data, shape, order=1, method='fft'): """ Calculate the derivative of a potential field in the z direction. .. note:: Requires gridded data. .. warning:: If the data is not in SI units, the derivative will be in strange units! I strongly recommend converting the data to SI before calculating the derivative (use one of the unit conversion functions of :mod:`fatiando.utils`). This way the derivative will be in SI units and can be easily converted to what unit you want. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * order : int The order of the derivative * method : string The method used to calculate the derivatives. Options are: ``'fft'`` for the Fast Fourier Transform. Returns: * deriv : 1D-array The derivative """ assert method == 'fft', \ "Invalid method '{}'".format(method) nx, ny = shape # Pad the array with the edge values to avoid instability padded, padx, pady = _pad_data(data, shape) kx, ky = _fftfreqs(x, y, shape, padded.shape) deriv_ft = numpy.fft.fft2(padded)numpy.sqrt(kx2 + ky2)order deriv = numpy.real(numpy.fft.ifft2(deriv_ft)) # Remove padding from derivative return deriv[padx: padx + nx, pady: pady + ny].ravel() def power_density_spectra(x, y, data, shape): r""" Calculates the Power Density Spectra of a 2D gridded potential field through the FFT: .. math:: \Phi_{\Delta T}(k_x, k_y) = \| F\left{\Delta T \right}(k_x, k_y) \|^2 .. note:: Requires gridded data. .. note:: x, y, z and height should be in meters. Parameters: x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid Returns: * kx, ky : 2D-arrays The wavenumbers of each Power Density Spectra point * pds : 2D-array The Power Density Spectra of the data """ kx, ky = _fftfreqs(x, y, shape, shape) pds = abs(numpy.fft.fft2(numpy.reshape(data, shape)))*2 return kx, ky, pds def radial_average_spectrum(kx, ky, pds, max_radius=None, ring_width=None): r""" Calculates the average of the Power Density Spectra points that falls inside concentric rings built around the origin of the wavenumber coordinate system with constant width. The width of the rings and the inner radius of the biggest ring can be changed by setting the optional parameters ring_width and max_radius, respectively. .. note:: To calculate the radially averaged power density spectra use the outputs of the function power_density_spectra as input of this one. Parameters: kx, ky : 2D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * max_radius : float (optional) Inner radius of the biggest ring. By default it's set as the minimum of kx.max() and ky.max(). Making it smaller leaves points outside of the averaging, and making it bigger includes points nearer to the boundaries. * ring_width : float (optional) Width of the rings. By default it's set as the largest value of :math:`\Delta k_x` and :math:`\Delta k_y`, being them the equidistances of the kx and ky arrays. Making it bigger gives more populated averages, and making it smaller lowers the ammount of points per ring (use it carefully). Returns: * k_radial : 1D-array Wavenumbers of each Radially Averaged Power Spectrum point. Also, the inner radius of the rings. * pds_radial : 1D array Radially Averaged Power Spectrum """ nx, ny = pds.shape if max_radius is None: max_radius = min(kx.max(), ky.max()) if ring_width is None: ring_width = max(kx[1, 0], ky[0, 1]) k = numpy.sqrt(kx2 + ky2) pds_radial = [] k_radial = [] radius_i = -1 while True: radius_i += 1 if radius_iring_width > max_radius: break else: if radius_i == 0: inside = k <= 0.5ring_width else: inside = numpy.logical_and(k > (radius_i - 0.5)ring_width, k <= (radius_i + 0.5)ring_width) pds_radial.append(pds[inside].mean()) k_radial.append(radius_iring_width) return numpy.array(k_radial), numpy.array(pds_radial) def _pad_data(data, shape): n = _nextpow2(numpy.max(shape)) nx, ny = shape padx = (n - nx)//2 pady = (n - ny)//2 padded = numpy.pad(data.reshape(shape), ((padx, padx), (pady, pady)), mode='edge') return padded, padx, pady def _nextpow2(i): buf = numpy.ceil(numpy.log(i)/numpy.log(2)) return int(2buf) def _fftfreqs(x, y, shape, padshape): """ Get two 2D-arrays with the wave numbers in the x and y directions. """ nx, ny = shape dx = (x.max() - x.min())/(nx - 1) fx = 2numpy.pinumpy.fft.fftfreq(padshape[0], dx) dy = (y.max() - y.min())/(ny - 1) fy = 2numpy.pi*numpy.fft.fftfreq(padshape[1], dy) return	numpy.meshgrid(fy, fx)	numpy.meshgrid
""" April 2018 Simulates the trajectory implementing a CZ gate. June 2018 Included noise in the simulation. July 2018 Added distortions to simulation. September 2018 Added flux noise as a quasi-static component with Gaussian distribution """ import time import numpy as np import qutip as qtp from pycqed.measurement import detector_functions as det from scipy.interpolate import interp1d from pycqed.measurement.waveform_control_CC import waveforms_flux as wfl import scipy import matplotlib.pyplot as plt import logging #np.set_printoptions(threshold=np.inf) # operators b = qtp.tensor(qtp.destroy(3), qtp.qeye(3)) # LSB is static qubit a = qtp.tensor(qtp.qeye(3), qtp.destroy(3)) n_q0 = a.dag() * a n_q1 = b.dag() * b H_coupling = (a.dag() + a) * (b + b.dag()) H_c = n_q0 scalefactor=1 # scalefactor not used anymore # Hamiltonian def coupled_transmons_hamiltonian(w_q0, w_q1, alpha_q0, alpha_q1, J, w_bus): """ Hamiltonian of two coupled anharmonic transmons. Because the intention is to tune one qubit into resonance with the other, the number of levels is limited. q1 -> static qubit, 3-levels q0 -> fluxing qubit, 3-levels intended avoided crossing: 11 <-> 02 (q1 is the first (left) qubit and q0 the second (right) one) N.B. the frequency of q0 is expected to be larger than that of q1 w_q0 > w_q1 and the anharmonicities alpha negative """ raise NotImplementedError("Old way of handling the hamiltonian H_0. Use calc_hamiltonian") eps=0 delta_q1=w_q1-w_bus delta_q0_interactionpoint=(w_q1-alpha_q0)-w_bus delta_q0=(w_q0+eps)-w_bus J_new = J / ((delta_q1+delta_q0_interactionpoint)/(delta_q1delta_q0_interactionpoint)) (delta_q1+delta_q0)/(delta_q1delta_q0) H_0 = w_q0 n_q0 + w_q1 * n_q1 + \ 1/2alpha_q0(a.dag()a.dag()aa) + 1/2alpha_q1(b.dag()b.dag()bb) +\ J_new * (a.dag() + a) * (b + b.dag()) return H_0 def hamiltonian_timedependent(H_0,eps,w_bus): raise NotImplementedError("Old way of handling the hamiltonian time-dependent. Use calc_hamiltonian") w_q0=np.real(H_0[1,1]) w_q1=np.real(H_0[3,3]) alpha_q0=np.real(H_0[2,2])-2w_q0 J=np.real(H_0[1,3]) delta_q1=w_q1-w_bus delta_q0_sweetspot=(w_q0)-w_bus delta_q0=(w_q0+eps)-w_bus J_new = J / ((delta_q1+delta_q0_sweetspot)/(delta_q1delta_q0_sweetspot)) * (delta_q1+delta_q0)/(delta_q1delta_q0) return H_0+epsH_c+(J_new-J)H_coupling # target in the case with no noise # note that the Hilbert space is H_q1 /otimes H_q0 # so the ordering of basis states below is 00,01,02,10,11,12,20,21,22 U_target = qtp.Qobj([[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, -1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]], type='oper', dims=[[3, 3], [3, 3]]) #U_target._type = 'oper' U_target_diffdims = qtp.Qobj([[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, -1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]], type='oper', dims=[[9], [9]]) # otherwise average_gate_fidelity doesn't work # if there is noise the target is the corresponding superoperator U_super_target = qtp.to_super(U_target) ''' remember that qutip uses the Liouville (matrix) representation for superoperators, with column stacking. This means that rho_{xy,x'y'}=rho[3x+y,3x'+y'] rho_{xy,x'y'}=operator_to_vector(rho)[3x+y+27x'+9y'] where xy is the row and x'y' is the column ''' def plot(x_plot_vec,y_plot_vec,title='No title',xlabel='No xlabel',ylabel='No ylabel',legend_labels=list(),yscale='linear'): # tool for plotting # x_plot_vec and y_plot_vec should be passed as either lists or np.array if isinstance(y_plot_vec,list): y_length=len(y_plot_vec) else: y_length=np.size(y_plot_vec) if legend_labels==[]: legend_labels=np.arange(y_length) for i in range(y_length): if isinstance(y_plot_vec[i],list): y_plot_vec[i]=np.array(y_plot_vec[i]) if isinstance(legend_labels[i],int): legend_labels[i]=str(legend_labels[i]) if len(x_plot_vec)==1: if isinstance(x_plot_vec[0],list): x_plot_vec[0]=np.array(x_plot_vec[0]) plt.plot(x_plot_vec[0], y_plot_vec[i], label=legend_labels[i]) else: if isinstance(x_plot_vec[i],list): x_plot_vec[i]=np.array(x_plot_vec[i]) plt.plot(x_plot_vec[i], y_plot_vec[i], label=legend_labels[i]) plt.legend() plt.title(title) plt.xlabel(xlabel) plt.ylabel(ylabel) plt.yscale(yscale) plt.show() def jump_operators(T1_q0,T1_q1): c_ops=[] if T1_q0 != 0: c_ops.append(np.sqrt(1/T1_q0)a) if T1_q1 != 0: c_ops.append(np.sqrt(1/T1_q1)b) return c_ops def c_ops_amplitudedependent(T1_q0,T1_q1,Tphi01_q0_vec,Tphi01_q1): # case where the pure decoherence for qubit q0 is time dependent, or better pulse-amplitude dependent c_ops=[] if T1_q0 != 0: c_ops.append(np.sqrt(1/T1_q0)a) if T1_q1 != 0: c_ops.append(np.sqrt(1/T1_q1)b) if Tphi01_q1 != 0: # we automatically put also the decoherence for 12 and 02 sigmaZinqutrit = qtp.Qobj([[1,0,0], [0,-1,0], [0,0,0]]) collapse=qtp.tensor(sigmaZinqutrit,qtp.qeye(3)) c_ops.append(collapsenp.sqrt(1/(2Tphi01_q1))) Tphi12_q1=Tphi01_q1 sigmaZinqutrit = qtp.Qobj([[0,0,0], [0,1,0], [0,0,-1]]) collapse=qtp.tensor(sigmaZinqutrit,qtp.qeye(3)) c_ops.append(collapsenp.sqrt(1/(2Tphi12_q1))) Tphi02_q1=Tphi01_q1/2 sigmaZinqutrit = qtp.Qobj([[1,0,0], [0,0,0], [0,0,-1]]) collapse=qtp.tensor(sigmaZinqutrit,qtp.qeye(3)) c_ops.append(collapsenp.sqrt(1/(2Tphi02_q1))) if Tphi01_q0_vec != []: # we automatically put also the decoherence for 12 and 02 sigmaZinqutrit = qtp.Qobj([[1,0,0], [0,-1,0], [0,0,0]]) collapse=qtp.tensor(qtp.qeye(3),sigmaZinqutrit) c_ops.append([collapse,np.sqrt(1/(2Tphi01_q0_vec))]) Tphi12_q0_vec=Tphi01_q0_vec sigmaZinqutrit = qtp.Qobj([[0,0,0], [0,1,0], [0,0,-1]]) collapse=qtp.tensor(qtp.qeye(3),sigmaZinqutrit) c_ops.append([collapse,np.sqrt(1/(2Tphi12_q0_vec))]) Tphi02_q0_vec=Tphi01_q0_vec/2 sigmaZinqutrit = qtp.Qobj([[1,0,0], [0,0,0], [0,0,-1]]) collapse=qtp.tensor(qtp.qeye(3),sigmaZinqutrit) c_ops.append([collapse,np.sqrt(1/(2Tphi02_q0_vec))]) return c_ops def rotating_frame_transformation_propagator(U, t: float, w_q0: float=0, w_q1: float =0): """ Transforms the frame of the unitary according to U' = U_{RF}U NOTE: remember that this is how the time evolution operator changes from one picture to another with U_{RF} = e^{-i w_q0 a^dag a t } otimes e^{-i w_q1 b^dag b t } (method for the case where we are simply rotating away the two qubit frequencies) Args: U (QObj): Unitary to be transformed t (float): time at which to transform w_q0 (float): freq of frame for q0 w_q1 (float): freq of frame for q1 """ logging.warning('Recommended to use rotating_frame_transformation_new passing the hamiltonian as an argument.') U_RF = (1jw_q0n_q0t).expm() (1jw_q1n_q1t).expm() if U.type=='super': U_RF=qtp.to_super(U_RF) U_prime = U_RF U """ U_RF only on one side because that's the operator that satisfies the Schroedinger equation in the interaction picture. """ return U_prime def rotating_frame_transformation_propagator_new(U, t: float, H): """ Transforms the frame of the unitary according to U' = U_{RF}U NOTE: remember that this is how the time evolution operator changes from one picture to another Args: U (QObj): Unitary to be transformed t (float): time at which to transform H (QObj): hamiltonian to be rotated away """ U_RF = (1jHt).expm() if U.type=='super': U_RF=qtp.to_super(U_RF) U_prime = U_RF U """ U_RF only on one side because that's the operator that satisfies the Schroedinger equation in the interaction picture. """ return U_prime def rotating_frame_transformation_operators(operator, t: float, H): """ Transforms the frame of an operator (hamiltonian, or jump operator) according to O' = U_{RF}OU_{RF}^dag Args: operator (QObj): operator to be transformed t (float): time at which to transform H (QObj): hamiltonian to be rotated away """ U_RF = (1jHt).expm() return U_RF * H * U_RF.dag() def correct_reference(U,w_q1,w_q0,t): # w_qi should be a frequency (not including the 2pi factor). Moreover they and t should be in the same scale. # this functions should be used just to make sanity checks. phase_to_correct_q1 = w_q1(2np.pi)t phase_to_correct_q0 = w_q0(2np.pi)t Ucorrection = qtp.Qobj([[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, np.exp(1jphase_to_correct_q0), 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, np.exp(1jphase_to_correct_q1), 0, 0, 0, 0, 0], [0, 0, 0, 0, np.exp(1j(phase_to_correct_q0+phase_to_correct_q1)), 0, 0, 0, 0], [0, 0, 0, 0, 0, np.exp(1jphase_to_correct_q1), 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, np.exp(1jphase_to_correct_q0), 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]], type='oper', dims=[[3, 3], [3, 3]]) if U.type=='oper': return UcorrectionU elif U.type=='super': return qtp.to_super(Ucorrection)U def phases_from_superoperator(U): """ Returns the phases from the unitary or superoperator U """ if U.type=='oper': phi_00 = np.rad2deg(np.angle(U[0, 0])) # expected to equal 0 because of our # choice for the energy, not because of rotating frame. But not guaranteed including the coupling phi_01 = np.rad2deg(np.angle(U[1, 1])) phi_10 = np.rad2deg(np.angle(U[3, 3])) phi_11 = np.rad2deg(np.angle(U[4, 4])) phi_02 = np.rad2deg(np.angle(U[2, 2])) # used only for avgatefid_superoperator_phasecorrected phi_20 = np.rad2deg(np.angle(U[6, 6])) # used only for avgatefid_superoperator_phasecorrected elif U.type=='super': phi_00 = 0 # we set it to 0 arbitrarily but it is indeed not knowable phi_01 = np.rad2deg(np.angle(U[1, 1])) # actually phi_01-phi_00 etc phi_10 = np.rad2deg(np.angle(U[3, 3])) phi_11 = np.rad2deg(np.angle(U[4, 4])) phi_02 = np.rad2deg(np.angle(U[2, 2])) phi_20 = np.rad2deg(np.angle(U[6, 6])) phi_cond = (phi_11 - phi_01 - phi_10 + phi_00) % 360 # still the right formula independently from phi_00 return phi_00, phi_01, phi_10, phi_11, phi_02, phi_20, phi_cond def pro_avfid_superoperator_compsubspace(U,L1): """ Average process (gate) fidelity in the qubit computational subspace for two qutrits. Leakage has to be taken into account, see Woods & Gambetta. The function assumes that the computational subspace (:= the 4 energy levels chosen as the two qubits) is given by the standard basis \|0> /otimes \|0>, \|0> /otimes \|1>, \|1> /otimes \|0>, \|1> /otimes \|1>. If this is not the case, one need to change the basis to that one, before calling this function. """ if U.type=='oper': inner = U.dag()U_target part_idx = [0, 1, 3, 4] # only computational subspace ptrace = 0 for i in part_idx: ptrace += inner[i, i] dim = 4 # 2 qubits comp subspace return np.real(((np.abs(ptrace))2+dim(1-L1))/(dim(dim+1))) elif U.type=='super': kraus_form = qtp.to_kraus(U) dim=4 # 2 qubits in the computational subspace part_idx = [0, 1, 3, 4] # only computational subspace psum=0 for A_k in kraus_form: ptrace = 0 inner = U_target_diffdims.dag()A_k # otherwise dimension mismatch for i in part_idx: ptrace += inner[i, i] psum += (np.abs(ptrace))*2 return np.real((dim(1-L1) + psum) / (dim(dim + 1))) def pro_avfid_superoperator_compsubspace_phasecorrected(U,L1,phases): """ Average process (gate) fidelity in the qubit computational subspace for two qutrits Leakage has to be taken into account, see Woods & Gambetta The phase is corrected with Z rotations considering both transmons as qubits. The correction is done perfectly. The function assumes that the computational subspace (:= the 4 energy levels chosen as the two qubits) is given by the standard basis \|0> /otimes \|0>, \|0> /otimes \|1>, \|1> /otimes \|0>, \|1> /otimes \|1>. If this is not the case, one need to change the basis to that one, before calling this function. """ Ucorrection = qtp.Qobj([[np.exp(-1jnp.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0, 0], [0, np.exp(-1jnp.deg2rad(phases[1])), 0, 0, 0, 0, 0, 0, 0], [0, 0, np.exp(-1jnp.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0], [0, 0, 0, np.exp(-1jnp.deg2rad(phases[2])), 0, 0, 0, 0, 0], [0, 0, 0, 0, np.exp(-1jnp.deg2rad(phases[3]-phases[-1])), 0, 0, 0, 0], [0, 0, 0, 0, 0, np.exp(-1jnp.deg2rad(phases[2])), 0, 0, 0], [0, 0, 0, 0, 0, 0, np.exp(-1jnp.deg2rad(phases[0])), 0, 0], [0, 0, 0, 0, 0, 0, 0, np.exp(-1jnp.deg2rad(phases[1])), 0], [0, 0, 0, 0, 0, 0, 0, 0, np.exp(-1jnp.deg2rad(phases[0]))]], type='oper', dims=[[3, 3], [3, 3]]) if U.type=='oper': U=UcorrectionU inner = U.dag()U_target part_idx = [0, 1, 3, 4] # only computational subspace ptrace = 0 for i in part_idx: ptrace += inner[i, i] dim = 4 # 2 qubits comp subspace return np.real(((np.abs(ptrace))*2+dim(1-L1))/(dim(dim+1))) elif U.type=='super': U=qtp.to_super(Ucorrection)U kraus_form = qtp.to_kraus(U) dim=4 # 2 qubits in the computational subspace part_idx = [0, 1, 3, 4] # only computational subspace psum=0 for A_k in kraus_form: ptrace = 0 inner = U_target_diffdims.dag()A_k # otherwise dimension mismatch for i in part_idx: ptrace += inner[i, i] psum += (np.abs(ptrace))2 return np.real((dim(1-L1) + psum) / (dim(dim + 1))) def pro_avfid_superoperator_compsubspace_phasecorrected_onlystaticqubit(U,L1,phases): """ Average process (gate) fidelity in the qubit computational subspace for two qutrits Leakage has to be taken into account, see Woods & Gambetta The phase is corrected with Z rotations considering both transmons as qubits. The correction is done perfectly. The function assumes that the computational subspace (:= the 4 energy levels chosen as the two qubits) is given by the standard basis \|0> /otimes \|0>, \|0> /otimes \|1>, \|1> /otimes \|0>, \|1> /otimes \|1>. If this is not the case, one need to change the basis to that one, before calling this function. """ Ucorrection = qtp.Qobj([[np.exp(-1jnp.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0, 0], [0, np.exp(-1jnp.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0], [0, 0, np.exp(-1j	np.deg2rad(phases[0])	numpy.deg2rad
""" isicarchive.jitfunc This module provides JIT (numba) helper functions and doesn't have to be imported from outside the main package functionality (isicapi). Functions --------- conv_kernel Generate convolution smoothing kernel image_mix Mix two images (RGB and/or gray scale, alpha parameter supported) image_resample_u1 Cheap (!) image resampling for uint8 images image_resample_f4 Cheap (!) image resampling for float32 images superpixel_contour Extract superpixel contour superpixel_decode Converts an RGB superpixel image to a 2D superpixel index array superpixel_map Decodes a superpixel (index) array into a 2D mapping array superpixel_outline_dir Extract SVG path directions from binary mask of outline superpixel_path Extract superpixel path svg_coord_list Generate SVG-path-suitable list of directions from coordinates list svg_path_from_list Generate SVG-path-suitable list of directions from v/h list """ __version__ = '0.4.8' from typing import Optional, Tuple import numba from numba import jit, prange import numpy # convolution (smoothing) kernel @jit('f4[:](f4)', nopython=True) def conv_kernel(fwhm:numpy.float32 = 2.0) -> numpy.ndarray: """ Generate convolution smoothing kernel Parameters ---------- fwhm : numpy scalar float32 Gaussian kernel size in FWHM (full-width at half-maximum) Returns ------- kernel : ndarray Gaussian smoothing kernel (numpy.float32) """ if fwhm <= 0.29: return numpy.asarray([0,1,0]).astype(numpy.float32) fwhm = fwhm / numpy.sqrt(8.0 * numpy.log(2.0)) if fwhm < 2.0: md = numpy.trunc(0.5 + 6.0 * fwhm) else: md = numpy.trunc(0.5 + 6.0 * numpy.log2(fwhm) * fwhm) k = numpy.exp(-((numpy.arange(-md,md+1.0,1.0) ** 2) / (2.0 * fwhm * fwhm))) k = k[k >= 0.00000001] return (k / numpy.sum(k)).astype(numpy.float32) # image convolution (cheap!) @jit('f4[:,:](f4[:,:],f4[:])', nopython=True) def image_conv_float( data:numpy.ndarray, kernel:numpy.ndarray, ) -> numpy.ndarray: """ Two-dimensional image convolution with kernel vector (staggered) Parameters ---------- data : ndarray Image data (must be 2D numpy.float32!) kernel : ndarray Kernel vector (must be numpy.float32!) Returns ------- conv_data : ndarray Convolved data array """ if (kernel.size) == 1: kernel = conv_kernel(kernel[0]) if (kernel.size % 2) != 1: raise ValueError('Parameter kernel must have odd length of elements.') s = numpy.sum(kernel) if s <= 0.0: raise ValueError('Parameter kernel must have a positive sum.') if s < 0.999999 or s > 1.000001: kernel = kernel / s ds0 = data.shape[0] ds1 = data.shape[1] kh = kernel.size // 2 temp = numpy.zeros(data.size, dtype=numpy.float32).reshape(data.shape) tempv = numpy.zeros(ds0, dtype=numpy.float32) for c in prange(ds0): #pylint: disable=not-an-iterable col = temp[c,:] colv = 0.0 for k in range(kernel.size): dc = c + k - kh if dc < 0 or dc >= ds0: continue colv += kernel[k] col += kernel[k] * data[dc,:] temp[c,:] = col tempv[c] = colv temp = numpy.true_divide(temp, tempv.reshape((ds0,1,))) out = numpy.zeros(data.size, dtype=numpy.float32).reshape(data.shape) tempv = numpy.zeros(ds1, dtype=numpy.float32) for c in prange(ds1): #pylint: disable=not-an-iterable col = out[:,c] colv = 0.0 for k in range(kernel.size): dc = c + k - kh if dc < 0 or dc >= ds1: continue colv += kernel[k] col += kernel[k] * temp[:,dc] out[:,c] = col tempv[c] = colv return numpy.true_divide(out, tempv.reshape((1,ds1,))) # image mixing @jit('u1[:,:](u1[:,:],u1[:,:],optional(f4[:]))', nopython=True) def image_mix( i1:numpy.ndarray, i2:numpy.ndarray, a2:numpy.ndarray = None, ) -> numpy.ndarray: """ Mix two images with optional alpha channel Parameters ---------- i1, i2 : ndarray Image vectors array (second dimension is RGB color!) a2 : ndarray Optional alpha (opacity) array for second image Returns ------- mixed : ndarray Mixed image vectors array """ ishape = i1.shape i2shape = i2.shape oi = numpy.zeros(i1.size, dtype=numpy.uint8).reshape(ishape) num_pix = ishape[0] if i2shape[0] != num_pix: raise ValueError('Images mismatch in number of pixels') if (not a2 is None) and (a2.size != num_pix): raise ValueError('Alpha mismatch in number of pixels') if ishape[1] == 1: if i2shape[1] == 1: if a2 is None: for p in prange(num_pix): #pylint: disable=not-an-iterable oi[p,0] = max(i1[p,0], i2[p,0]) else: o = numpy.float32(1.0) for p in prange(num_pix): #pylint: disable=not-an-iterable a = a2[p] ia = o - a oi[p,0] = round( ia * numpy.float32(i1[p,0]) + a * numpy.float32(i2[p,0])) elif i2shape[1] != 3: raise ValueError('i2 not a valid image array') else: th = numpy.float32(1.0) / numpy.float32(3) if a2 is None: for p in prange(num_pix): #pylint: disable=not-an-iterable i2m = round(th * ( numpy.float32(i2[p,0]) + numpy.float32(i2[p,1]) + numpy.float32(i2[p,2]))) oi[p,0] = max(i1[p,0], i2m) else: o = numpy.float32(1.0) for p in prange(num_pix): #pylint: disable=not-an-iterable a = a2[p] ia = o - a i2m = th * ( numpy.float32(i2[p,0]) + numpy.float32(i2[p,1]) + numpy.float32(i2[p,2])) oi[p,0] = round(ia * numpy.float32(i1[p,0]) + a * i2m) elif ishape[1] != 3: raise ValueError('i1 not a valid image array') else: if i2shape[1] == 1: if a2 is None: for p in prange(num_pix): #pylint: disable=not-an-iterable oi[p,0] = max(i1[p,0], i2[p,0]) oi[p,1] = max(i1[p,1], i2[p,0]) oi[p,2] = max(i1[p,2], i2[p,0]) else: o = numpy.float32(1.0) for p in prange(num_pix): #pylint: disable=not-an-iterable a = a2[p] ia = o - a i2ap = a * numpy.float32(i2[p,0]) oi[p,0] = round(ia * numpy.float32(i1[p,0]) + i2ap) oi[p,1] = round(ia * numpy.float32(i1[p,1]) + i2ap) oi[p,2] = round(ia * numpy.float32(i1[p,2]) + i2ap) elif i2shape[1] != 3: raise ValueError('i2 not a valid image array') else: if a2 is None: for p in prange(num_pix): #pylint: disable=not-an-iterable oi[p,0] = max(i1[p,0], i2[p,0]) oi[p,1] = max(i1[p,1], i2[p,1]) oi[p,2] = max(i1[p,2], i2[p,2]) else: o = numpy.float32(1.0) for p in prange(num_pix): #pylint: disable=not-an-iterable a = a2[p] ia = o - a oi[p,0] = round( ia * numpy.float32(i1[p,0]) + a * numpy.float32(i2[p,0])) oi[p,1] = round( ia * numpy.float32(i1[p,1]) + a * numpy.float32(i2[p,1])) oi[p,2] = round( ia * numpy.float32(i1[p,2]) + a * numpy.float32(i2[p,2])) return oi # image resampling (cheap!) @jit('u1[:,:,:](u1[:,:,:],i4,i4)', nopython=True) def image_resample_u1(image:numpy.ndarray, d0:numpy.int, d1:numpy.int) -> numpy.ndarray: """ Cheap (!) image resampling for uint8 images Parameters ---------- image : ndarray Image array d0, d1 : int Target image size in first and second dimension Returns ------- res : ndarray Resampled image array """ im_shape = image.shape f0 = numpy.float(im_shape[0]) / numpy.float(d0) f1 = numpy.float(im_shape[1]) / numpy.float(d1) temp = numpy.zeros(im_shape[0] * d1 * im_shape[2], dtype=numpy.uint8).reshape( (numpy.int64(im_shape[0]),numpy.int64(d1),numpy.int64(im_shape[2]),)) for c in prange(d1): #pylint: disable=not-an-iterable ffrom = f1 * numpy.float(c) + 0.5 fto = ffrom + f1 - 1.0 ifrom = numpy.int64(	numpy.trunc(ffrom)	numpy.trunc
import utm as UTM import unittest import numpy as np class UTMTestCase(unittest.TestCase): def assert_utm_equal(self, a, b): self.assertTrue(np.allclose(a[0], b[0])) self.assertTrue(np.allclose(a[1], b[1])) self.assertEqual(a[2], b[2]) self.assertEqual(a[3].upper(), b[3].upper()) def assert_latlon_equal(self, a, b): self.assertTrue(np.allclose(a[0], b[0], rtol=1e-4, atol=1e-4)) self.assertTrue(np.allclose(a[1], b[1], rtol=1e-4, atol=1e-4)) class KnownValues(UTMTestCase): known_values = [ # Aachen, Germany ( (50.77535, 6.08389), (294409, 5628898, 32, 'U'), {'northern': True}, ), # New York, USA ( (40.71435, -74.00597), (583960, 4507523, 18, 'T'), {'northern': True}, ), # Wellington, New Zealand ( (-41.28646, 174.77624), (313784, 5427057, 60, 'G'), {'northern': False}, ), # Capetown, South Africa ( (-33.92487, 18.42406), (261878, 6243186, 34, 'H'), {'northern': False}, ), # Mendoza, Argentina ( (-32.89018, -68.84405), (514586, 6360877, 19, 'h'), {'northern': False}, ), # Fairbanks, Alaska, USA ( (64.83778, -147.71639), (466013, 7190568, 6, 'W'), {'northern': True}, ), # <NAME>, Scotland, UK ( (56.79680, -5.00601), (377486, 6296562, 30, 'V'), {'northern': True}, ), # Latitude 84 ( (84, -5.00601), (476594, 9328501, 30, 'X'), {'northern': True}, ), ] def test_from_latlon(self): lats = np.array([0.0, 3.0, 6.0]) lons = np.array([0.0, 1.0, 3.4]) result = UTM.from_latlon(lats, lons) self.assert_utm_equal((np.array([166021.44317933032, 277707.83075574087, 544268.12794623]), np.array([0.0, 331796.29167519242, 663220.7198366751]), 31, 'N'), result) for latlon, utm, _ in self.known_values: result = UTM.from_latlon([np.array([x]) for x in latlon]) self.assert_utm_equal(utm, result) def test_to_latlon(self): result = UTM.to_latlon(np.array([166021.44317933032, 277707.83075574087, 544268.12794623]), np.array([0.0, 331796.29167519242, 663220.7198366751]), 31, 'N') self.assert_latlon_equal((np.array([0.0, 3.0, 6.0]), np.array([0.0, 1.0, 3.4])), result) for latlon, utm, utm_kw in self.known_values: utm = [np.array([x]) for x in utm[:2]] + list(utm[2:]) result = UTM.to_latlon(utm) self.assert_latlon_equal(latlon, result) class BadInput(UTMTestCase): def test_from_latlon_range_checks(self): '''from_latlon should fail with out-of-bounds input''' self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(-100), np.array(0)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(-80.1), np.array(0)) for i in range(-8000, 8400): UTM.from_latlon(np.array(i / 100.0), np.array(0)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(84.1), np.array(0)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(100), np.array(0)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(0), np.array(-300)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(0), np.array(-180.1)) for i in range(-18000, 18000): UTM.from_latlon(np.array(0), np.array(i / 100.0)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(0), np.array(180.1)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(0), np.array(300)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(-100), np.array(-300)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(100), np.array(-300)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(-100), np.array(300)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(100), np.array(300)) # test forcing zone ranges # NYC should be zone 18T self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(40.71435), np.array(-74.00597), 70, 'T') self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(40.71435), np.array(-74.00597), 18, 'A') def test_to_latlon_range_checks(self): '''to_latlon should fail with out-of-bounds input''' # test easting range self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(0), np.array(5000000), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(99999), np.array(5000000), 32, 'U') for i in range(100000, 999999, 1000): UTM.to_latlon(np.array(i), np.array(5000000), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(1000000), np.array(5000000), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(100000000000), np.array(5000000), 32, 'U') # test northing range self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(-100000), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(-1), 32, 'U') for i in range(10, 10000000, 1000): UTM.to_latlon(np.array(500000), np.array(i), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(10000001), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(50000000), 32, 'U') # test zone numbers self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 0, 'U') for i in range(1, 60): UTM.to_latlon(np.array(500000), np.array(5000000), i, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 61, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 1000, 'U') # test zone letters self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'A') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'B') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'I') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'O') for i in range(ord('C'), ord('X')): i = chr(i) if i != 'I' and i != 'O': UTM.to_latlon(np.array(500000), np.array(5000000), 32, i) self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'Y') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'Z') class Zone32V(unittest.TestCase): def assert_zone_equal(self, result, expected_number, expected_letter): self.assertEqual(result[2], expected_number) self.assertEqual(result[3].upper(), expected_letter.upper()) def test_inside(self): self.assert_zone_equal(UTM.from_latlon( np.array(56), np.array(3)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(56), np.array(6)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(56), np.array(9)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(56), np.array(11.999999)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(60), np.array(3)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(60), np.array(6)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(60), np.array(9)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(60), np.array(11.999999)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(63.999999), np.array(3)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(63.999999), np.array(6)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(63.999999), np.array(9)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(63.999999),	np.array(11.999999)	numpy.array
import numpy as np import mc.util import matplotlib.pyplot as plt import matplotlib as mpl from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm class Likelihood: def __init__(self, true_tx_power=0.8, true_min_power=1e-5, true_path_loss_exp=3, n=10) -> None: self.true_tx_power = true_tx_power self.true_min_power = true_min_power self.true_path_loss_exp = true_path_loss_exp self.tx_power_array = np.linspace(true_tx_power/2., true_tx_power2., num=n) self.min_power_array = np.linspace(true_min_power / 2., true_min_power 2., num=n) self.path_loss_exp_array = np.linspace(true_path_loss_exp / 2., true_path_loss_exp * 2., num=n) self.i_max = None self.i_closest = None self.likelihood = None # non-blocking plots plt.ion() self.tx_power_grid_1st, self.min_power_grid_2nd = np.meshgrid(self.tx_power_array, self.min_power_array) _, self.path_loss_exp_grid_2nd = np.meshgrid(self.tx_power_array, self.path_loss_exp_array) self.min_power_grid_1st, _ = np.meshgrid(self.min_power_array, self.path_loss_exp_array) self.plots_z_value = 10000 # to be run from, at least, mc.pmc.PopulationMonteCarlo.step def explore(self, pf, observations): log_tx_power_array, log_min_power_array, log_path_loss_exp_array =\ np.log(self.tx_power_array), np.log(self.min_power_array), np.log(self.path_loss_exp_array) self.likelihood = np.empty((len(self.tx_power_array), len(self.min_power_array), len(self.path_loss_exp_array))) for i_log_tx_power, log_tx_power in enumerate(log_tx_power_array): print('log_tx_power = {}'.format(log_tx_power)) for i_log_min_power, log_min_power in enumerate(log_min_power_array): for i_log_path_loss_exp, log_path_loss_exp in enumerate(log_path_loss_exp_array): self.likelihood[i_log_tx_power, i_log_min_power, i_log_path_loss_exp] =\ mc.util.loglikelihood(pf, observations, log_tx_power, log_min_power, log_path_loss_exp) print(self.likelihood) # this is a tuple self.i_max = np.unravel_index(self.likelihood.argmax(), self.likelihood.shape) print('maximum likelihood = {} at transmiter power = {}, min power = {}, path loss exponent = {} ({})'.format( self.likelihood[self.i_max], self.tx_power_array[self.i_max[0]], self.min_power_array[self.i_max[1]], self.path_loss_exp_array[self.i_max[2]], self.i_max)) likelihood_at_true = mc.util.loglikelihood( pf, observations, np.log(self.true_tx_power), np.log(self.true_min_power), np.log(self.true_path_loss_exp)) print('likelihood at true parameters: {}'.format(likelihood_at_true)) self.i_closest = ( np.abs(self.tx_power_array - self.true_tx_power).argmin(), np.abs(self.min_power_array - self.true_min_power).argmin(), np.abs(self.path_loss_exp_array - self.true_path_loss_exp).argmin()) print('closest point to ground truth is transmiter power = {}, min power = {}, path loss exponent = {}, likelihood {} ({})'.format( self.tx_power_array[self.i_closest[0]], self.min_power_array[self.i_closest[1]], self.path_loss_exp_array[self.i_closest[2]], self.likelihood[self.i_closest], self.i_closest)) def plot3d(self, x, y, Z): fig = plt.figure() ax = fig.gca(projection='3d') X, Y =	np.meshgrid(x, y)	numpy.meshgrid
# -- coding: utf-8 -- import sys import os import toml import librosa import librosa.display import matplotlib.pyplot as plt import soundfile as sf import numpy as np from tqdm import tqdm from joblib import Parallel, delayed import paddle from paddle.io import DataLoader from paddle.signal import stft, istft from visualdl import LogWriter sys.path.append("./") from FullBandNet.model import FullBandNet from dataset.dataset import DNS_Dataset from audio.feature import is_clipped from audio.mask import decompress_cIRM from audio.metrics import STOI, WB_PESQ from audio.utils import prepare_empty_path plt.switch_backend("agg") class Inferencer: def __init__(self, model, test_iter, config): # get checkpoints path self.checkpoints_path = os.path.join(os.path.dirname(__file__), "checkpoints") # get output path self.output_path = os.path.join(os.path.dirname(__file__), "enhanced") # get logs path self.logs_path = os.path.join(os.path.dirname(__file__), "logs", "inference") prepare_empty_path([self.output_path, self.logs_path]) # set iter self.test_iter = test_iter # get model self.model = model self.load_checkpoint() # get dataset args self.sr = config["dataset"]["sr"] self.n_fft = config["dataset"]["n_fft"] self.win_len = config["dataset"]["win_len"] self.hop_len = config["dataset"]["hop_len"] self.window = paddle.to_tensor(np.hanning(self.win_len), dtype=paddle.float32) # get inference args self.audio_visual_samples = config["inference"]["audio_visual_samples"] # config logs self.writer = LogWriter(logdir=self.logs_path, max_queue=5, flush_secs=60) self.writer_text_enh_clipped_step = 1 self.writer.add_text( tag="config", text_string=f"<pre \n{toml.dumps(config)} \n</pre>", step=1, ) def load_checkpoint(self): best_model_path = os.path.join(self.checkpoints_path, "best_model.tar") assert os.path.exists(best_model_path) checkpoint = paddle.load(best_model_path) self.epoch = checkpoint["epoch"] self.model.set_state_dict(checkpoint["model"]) print(f"Loading model checkpoint (epoch == {self.epoch})...") def check_clipped(self, enh, enh_file): if is_clipped(enh): self.writer.add_text( tag="enh_clipped", text_string=enh_file, step=self.writer_text_enh_clipped_step, ) self.writer_text_enh_clipped_step += 1 def audio_visualization(self, noisy, clean, enh, name): self.writer.add_audio("audio/noisy", noisy, 1, sample_rate=self.sr) self.writer.add_audio("audio/clean", clean, 1, sample_rate=self.sr) self.writer.add_audio("audio/enh", enh, 1, sample_rate=self.sr) # Visualize the spectrogram of noisy speech, clean speech, and enhanced speech noisy_mag, _ = librosa.magphase(librosa.stft(noisy, n_fft=320, hop_length=160, win_length=320)) clean_mag, _ = librosa.magphase(librosa.stft(clean, n_fft=320, hop_length=160, win_length=320)) enh_mag, _ = librosa.magphase(librosa.stft(enh, n_fft=320, hop_length=160, win_length=320)) fig, axes = plt.subplots(3, 1, figsize=(6, 6)) for k, mag in enumerate([noisy_mag, clean_mag, enh_mag]): axes[k].set_title( f"mean: {np.mean(mag):.3f}, " f"std: {np.std(mag):.3f}, " f"max: {np.max(mag):.3f}, " f"min: {np.min(mag):.3f}" ) librosa.display.specshow(librosa.amplitude_to_db(mag), cmap="magma", y_axis="linear", ax=axes[k], sr=16000) plt.tight_layout() self.writer.add_figure(f"spec/{name}", fig, 1) def metrics_visualization(self, noisy_list, clean_list, enh_list, n_jobs=8): noisy_stoi_score = Parallel(n_jobs=n_jobs)( delayed(STOI)(noisy, clean) for noisy, clean in tqdm(zip(noisy_list, clean_list)) ) enh_stoi_score = Parallel(n_jobs=n_jobs)( delayed(STOI)(noisy, clean) for noisy, clean in tqdm(zip(enh_list, clean_list)) ) noisy_stoi_score_mean = np.mean(noisy_stoi_score) enh_stoi_score_mean = np.mean(enh_stoi_score) self.writer.add_scalar("STOI/test/noisy", noisy_stoi_score_mean, 1) self.writer.add_scalar("STOI/test/enh", enh_stoi_score_mean, 1) noisy_wb_pesq_score = Parallel(n_jobs=n_jobs)( delayed(WB_PESQ)(noisy, clean) for noisy, clean in tqdm(zip(noisy_list, clean_list)) ) enh_wb_pesq_score = Parallel(n_jobs=n_jobs)( delayed(WB_PESQ)(noisy, clean) for noisy, clean in tqdm(zip(enh_list, clean_list)) ) noisy_wb_pesq_score_mean =	np.mean(noisy_wb_pesq_score)	numpy.mean
import numpy as np import os import mxnet as mx import gluonnlp as nlp def prepare_dataset(filename, allow_pickle=False): return nlp.data.NumpyDataset(filename[0], allow_pickle=allow_pickle) def prepare_bucket_sampler(dataset, batch_size, shuffle=False, num_buckets=1): lengths = dataset.transform(lambda x: len(x)) sampler = nlp.data.FixedBucketSampler(lengths, batch_size=batch_size, num_buckets=num_buckets, ratio=0, shuffle=shuffle) return sampler def test_dataset_loader(): num_files = 5 for i in range(num_files): np.save(os.path.join('tests', 'data', 'part_{}.npy'.format(i)),	np.random.uniform(size=(100, 20))	numpy.random.uniform

from hsi_toolkit.util import img_det from hsi_toolkit.dev.dim_reduction import mnf import numpy as np def mtmf_statistic(hsi_img,tgt_sig, mask = None): """ Mixture Tuned Matched Filter Infeasibility Statistic Inputs: hsi_image - n_row x n_col x n_band hyperspectral image tgt_sig - target signature (n_band x 1 - column vector) mask - binary image limiting detector operation to pixels where mask is true if not present or empty, no mask restrictions are used Outputs: mtmf_out - MTMF infeasibility statistic alpha - matched filter output 8/12/2012 - <NAME> - <EMAIL> 12/2018 - Python Implementation by Yutai Zhou """ if tgt_sig.ndim == 1: tgt_sig = tgt_sig[:, np.newaxis] mnf_img, n_dim, mnf_vecs, mnf_eigvals, mnf_mu = mnf(hsi_img,1); # tgt_sig = tgt_sig[:n_dim,0][:,np.newaxis] s = mnf_vecs @ (tgt_sig - mnf_mu) mtmf_out, kwargsout = img_det(mtmf_helper, mnf_img, s, mnf_eigvals = mnf_eigvals) return mtmf_out, kwargsout['alpha'] def mtmf_helper(hsi_data, tgt_sig, kwargs): mnf_eigvals = kwargs['mnf_eigvals'] n_band, n_pixel = hsi_data.shape z = hsi_data s = tgt_sig sts = s.T @ s alpha = np.zeros(n_pixel) mtmf_data = np.zeros(n_pixel) ev =

np.sqrt(mnf_eigvals)

numpy.sqrt

""" ******************************************************************************** 2D Robot Localization - Benchmark ******************************************************************************** Goals of this script: - implement different UKFs on the 2D robot localization example. - design the Extended Kalman Filter (EKF) and the Invariant Extended Kalman Filter (IEKF) :cite:`barrauInvariant2017`. - compare the different algorithms with Monte-Carlo simulations. *We assume the reader is already familiar with the considered problem described in the tutorial.* We previously designed an UKF with a standard uncertainty representation. An advantage of the versatility of the UKF is to speed up implementation, tests, and comparision of algorithms with different uncertainty representations. Indeed, for the given problem, three different UKFs emerge, defined respectively as: 1) The state is embedded in :math:`SO(2) \\times \mathbb{R}^2`, where: * the retraction :math:`\\varphi(.,.)` is the :math:`SO(2)` exponential for orientation and the vector addition for position. * the inverse retraction :math:`\\varphi^{-1}(.,.)` is the :math:`SO(2)` logarithm for orientation and the vector subtraction for position. 2) The state is embedded in :math:`SE(2)` with left multiplication, i.e. - the retraction :math:`\\varphi(.,.)` is the :math:`SE(2)` exponential, where the state multiplies on the left the uncertainty :math:`\\boldsymbol{\\xi}`. - the inverse retraction :math:`\\varphi^{-1}(.,.)` is the :math:`SE(2)` logarithm. - this left UKF on :math:`SE(2)` corresponds to the Invariant Extended Kalman Filter (IEKF) recommended in :cite:`barrauInvariant2017`. 3) The state is embedded in :math:`SE(2)` with right multiplication, i.e. - the retraction :math:`\\varphi(.,.)` is the :math:`SE(2)` exponential, where the state multiplies on the right the uncertainty :math:`\\boldsymbol{\\xi}`. - the inverse retraction :math:`\\varphi^{-1}(.,.)` is the :math:`SE(2)` logarithm. We tests the filters on simulation with strong initial heading error. """ ################################################################################ # Import # ============================================================================== from ukfm import SO2, UKF, EKF from ukfm import LOCALIZATION as MODEL import ukfm import numpy as np import matplotlib ukfm.utils.set_matplotlib_config() ################################################################################ # We compare the filters on a large number of Monte-Carlo runs. # Monte-Carlo runs N_mc = 100 ################################################################################ # Simulation Setting # ============================================================================== # We set the simulation as in :cite:`barrauInvariant2017`, section IV. The robot # drives along a 10 m diameter circle for 40 seconds with high rate odometer # measurements (100 Hz) and low rate GPS measurements (1 Hz). The vehicle gets # moderate angular velocity uncertainty and highly precise linear velocity. The # initial values of the heading error is very strong, **45° standard # deviation**, while the initial position is known. # sequence time (s) T = 40 # odometry frequency (Hz) odo_freq = 100 # create the model model = MODEL(T, odo_freq) # odometry noise standard deviation odo_std = np.array([0.01, # speed (v/m) 0.01, # speed (v/m) 1 / 180 * np.pi]) # angular speed (rad/s) # GPS frequency (Hz) gps_freq = 1 # GPS noise standard deviation (m) gps_std = 1 # radius of the circle trajectory (m) radius = 5 # initial heading error standard deviation theta0_std = 45/180*np.pi ################################################################################ # Filter Design # ============================================================================== # The UKFs are compared to an Extended Kalman FIlter (EKF) and an Invariant EKF # (IEKF). The EKF has the same uncertainty representation as the UKF with the # retraction on :math:`SO(2) \times \mathbb{R}^2`, whereas the IEKF has the same # uncertainty representation as the UKF with the left retraction on # :math:`SE(2)`. # propagation noise covariance matrix Q = np.diag(odo_std**2) # measurement noise covariance matrix R = gps_std**2*np.eye(2) # initial covariance matrix P0 = np.zeros((3, 3)) # we take into account initial heading error P0[0, 0] = theta0_std ** 2 # sigma point parameter alpha = np.array([1e-3, 1e-3, 1e-3]) ################################################################################ # We set error variables before launching Monte-Carlo simulations. As we have # five similar methods, the code is redundant. ukf_err = np.zeros((N_mc, model.N, 3)) left_ukf_err = np.zeros_like(ukf_err) right_ukf_err = np.zeros_like(ukf_err) iekf_err = np.zeros_like(ukf_err) ekf_err = np.zeros_like(ukf_err) ################################################################################ # We record Normalized Estimation Error Squared (NEES) for consistency # evaluation (see Results). ukf_nees =

np.zeros((N_mc, model.N, 2))

numpy.zeros

# -*- coding: utf-8 -*- """ ############################################################################### # # autoPACK Authors: <NAME>, <NAME>, <NAME>, <NAME> # Based on COFFEE Script developed by <NAME> between 2005 and 2010 # with assistance from <NAME> in 2009 and periodic input # from Arthur Olson's Molecular Graphics Lab # # BaseGrid.py Authors: <NAME> & <NAME> with editing/enhancement from Ludovic Autin # # Translation to Python initiated March 1, 2010 by <NAME> with <NAME> # # Class restructuring and organization: Michel Sanner # # Copyright: <NAME> ©2010 # # This file "BaseGrid.py" is part of autoPACK, cellPACK. # # autoPACK is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # autoPACK is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with autoPACK (See "CopyingGNUGPL" in the installation. # If not, see <http://www.gnu.org/licenses/>. # # ############################################################################### @author: <NAME>, <NAME>, & <NAME> """ import logging import numpy from scipy import spatial import math from math import ceil, floor from random import randrange import cellpack.autopack as autopack from cellpack.autopack.ldSequence import cHaltonSequence3 from cellpack.mgl_tools.RAPID import RAPIDlib from cellpack.mgl_tools.bhtree import bhtreelib # Kevin Grid point class class gridPoint: def __init__(self, i, globalC, isPolyhedron): self.index = int(i) self.isOutside = None self.minDistance = ( 99999 # Only store a number here if within certain distance from polyhedron ) self.representsPolyhedron = isPolyhedron self.closeFaces = [] self.closestFaceIndex = 0 self.testedEndpoint = None self.allDistances = ( [] ) # Stores a tuple list of distances to all points. (point,distance) = (5,2.5) self.globalCoord = numpy.array( globalC ) # Stores the global coordinate associated with this point class BaseGrid: """ The Grid class ========================== This class handle the use of grid to control the packing. The grid keep information of 3d positions, distances, freePoints and inside/surface points from organelles. NOTE : thi class could be completely replace if openvdb is wrapped to python. """ @staticmethod def reorder_free_points(pt, freePoints, nbFreePoints): # TODO: move this to env class, ing shouldn't aware of the whole grid # Swap the newly inside point value with the value of the last free point # Point will no longer be considered "free" because it will be beyond the range of # nbFreePoints. The value of the point itself is the history of it's original index # so any future swaps will still result in the correct index being move into the range # of nbFreePoints nbFreePoints -= 1 vKill = freePoints[pt] vLastFree = freePoints[nbFreePoints] freePoints[vKill] = vLastFree freePoints[vLastFree] = vKill # Turn on these printlines if there is a problem with incorrect points showing in display points # self.log.debug("*************pt = masterGridPointValue = %d", pt) # self.log.debug("nbFreePointAfter = %d", nbFreePoints) # self.log.debug("vKill = %d", vKill) # self.log.debug("vLastFree = %d", vLastFree) # self.log.debug("freePoints[vKill] = %d", freePoints[vKill]) # self.log.debug("freePoints[vLastFree] = %d", freePoints[vLastFree]) # self.log.debug("pt = masterGridPointValue = %d", pt) # self.log.debug("freePoints[nbFreePoints-1] = %d", freePoints[nbFreePoints]) # self.log.debug("freePoints[pt] = %d", freePoints[pt]) # freePoints will now have all the available indices between 0 and nbFreePoints in # freePoints[nbFreePoints:] won't necessarily be the indices of inside points return freePoints, nbFreePoints @staticmethod def updateDistances( insidePoints, newDistPoints, freePoints, nbFreePoints, distance, ): # self.log.info( # "*************updating Distances %d %d", nbFreePoints, len(insidePoints) # ) # TODO: move this to env class, ing shouldn't aware of the whole grid # t1 = time() # distChanges = {} for pt, dist in list(insidePoints.items()): try: freePoints, nbFreePoints = BaseGrid.reorder_free_points( pt, freePoints, nbFreePoints ) except Exception: print(pt, "not in freeePoints********************************") pass distance[pt] = dist # self.log.debug("update free points loop %d", time() - t1) # t2 = time() for pt, dist in list(newDistPoints.items()): if pt not in insidePoints: distance[pt] = dist # self.log.debug("update distance loop %d", time() - t2) return nbFreePoints def __init__( self, boundingBox=([0, 0, 0], [0.1, 0.1, 0.1]), space=1, setup=True, lookup=0 ): self.log = logging.getLogger("grid") self.log.propagate = False # a grid is attached to an environnement self.boundingBox = boundingBox # this list provides the id of the component this grid points belongs # to. The id is an integer where 0 is the Histological Volume, and +i is # the surface of compartment i and -i is the interior of compartment i # in the list self. compartments self.gridPtId = [] # will be a list of indices into 3D of compartment # of points that have not yet been used by the fill algorithm # entries are removed from this list as grid points are used up # during hte fill. This list is used to pick points randomly during # the fill self.freePoints = [] self.nbFreePoints = 0 # this list evolves in parallel with self.freePoints and provides # the distance to the closest surface (either an already placed # object (or an compartment surface NOT IMPLEMENTED) self.distToClosestSurf = [] self.distToClosestSurf_store = [] self.diag = self.getDiagonal() self.gridSpacing = space # * 1.1547#cubic grid with a diagonal spacing equal to that smallest packing radius self.nbGridPoints = None self.nbSurfacePoints = 0 self.gridVolume = 0 # will be the total number of grid points # list of (x,y,z) for each grid point (x index moving fastest) self.masterGridPositions = [] self._x = None self._y = None self._z = None # this are specific for each compartment self.aInteriorGrids = [] self.aSurfaceGrids = [] # bhtree self.surfPtsBht = None self.ijkPtIndice = [] self.filename = None # used for storing before fill so no need rebuild self.result_filename = None # used after fill to store result self.tree = None self.tree_free = None self.encapsulatingGrid = 0 self.lookup = lookup self.center = None self.backup = None if setup: self.setup(boundingBox, space) # use np.roll to have periodic condition # what about collision ? def setup(self, boundingBox, space): # TODO : verify the gridSpacing calculation / setup after reading the recipe if space == 0: space = 20 self.gridSpacing = space # * 1.1547 self.boundingBox = boundingBox if self.lookup == 0: self.create3DPointLookupCover() elif self.lookup == 1: self.create3DPointLookup() elif self.lookup == 2: self.create3DPointLookup_loop() nx, ny, nz = self.nbGridPoints self.ijkPtIndice = self.cartesian([range(nx), range(ny), range(nz)]) self.getDiagonal() self.nbSurfacePoints = 0 self.log.info("SETUP BASE GRID %d %d", self.gridVolume, self.gridSpacing) self.gridPtId = numpy.zeros(self.gridVolume, "i") # [0]*nbPoints # self.distToClosestSurf = [self.diag]*self.gridVolume#surface point too? self.distToClosestSurf = ( numpy.ones(self.gridVolume) * self.diag ) # (self.distToClosestSurf) self.freePoints = list(range(self.gridVolume)) self.nbFreePoints = len(self.freePoints) self.log.info( "Lookup: %d, bounding box: %r, gridSpacing %r, length gridPtId %r", self.lookup, boundingBox, self.gridSpacing, len(self.gridPtId), ) self.setupBoundaryPeriodicity() return self.gridSpacing def reset( self, ): # reset the distToClosestSurf and the freePoints # boundingBox should be the same otherwise why keeping the grid # self.gridPtId = numpy.zeros(self.gridVolume,'i') # self.distToClosestSurf = numpy.ones(self.gridVolume)*self.diag#(self.distToClosestSurf) self.log.info("reset Grid distance to closest surface and freePoints") self.distToClosestSurf = ( numpy.array(self.distToClosestSurf[:]) * 0.0 ) + self.diag # self.distToClosestSurf[:] = self.diag # numpy.array([self.diag]*len(self.distToClosestSurf))#surface point too? self.freePoints = list(range(len(self.freePoints))) self.nbFreePoints = len(self.freePoints) def removeFreePoint(self, pti): tmp = self.freePoints[self.nbFreePoints] # last one self.freePoints[self.nbFreePoints] = pti self.freePoints[pti] = tmp self.nbFreePoints -= 1 def getDiagonal(self, boundingBox=None): if boundingBox is None: boundingBox = self.boundingBox self.diag = numpy.sqrt( (numpy.array(boundingBox[0]) - numpy.array(boundingBox[1])) ** 2 ).sum() return self.diag def create3DPointLookup_loop(self, boundingBox=None): """ Fill the orthogonal bounding box described by two global corners with an array of points spaces pGridSpacing apart.: """ if boundingBox is None: boundingBox = self.boundingBox xl, yl, zl = boundingBox[0] self.gridVolume, self.nbGridPoints = self.computeGridNumberOfPoint( boundingBox, self.gridSpacing ) nx, ny, nz = self.nbGridPoints pointArrayRaw = numpy.zeros((nx * ny * nz, 3), "f") self.ijkPtIndice = numpy.zeros((nx * ny * nz, 3), "i") # this is unused space = self.gridSpacing # Vector for lower left broken into real of only the z coord. i = 0 padding = space / 2.0 for zi in range(nz): for yi in range(ny): for xi in range(nx): x = xl + xi * space + padding y = yl + yi * space + padding z = zl + zi * space + padding pointArrayRaw[i] = (x, y, z) self.ijkPtIndice[i] = (xi, yi, zi) i += 1 self.log.info("grid spacing %d", space) self.masterGridPositions = pointArrayRaw def create3DPointLookup(self, boundingBox=None): """ Fill the orthogonal bounding box described by two global corners with an array of points spaces pGridSpacing apart. Optimized version using numpy broadcasting """ if boundingBox is None: boundingBox = self.boundingBox space = self.gridSpacing # we want the diagonal of the voxel, not the diagonal of the plane, so the second 1.1547 is was incorrect environmentBoxEqualFillBox = False self.log.info("Using create3DPointLookup") if environmentBoxEqualFillBox: # environment.environmentBoxEqualFillBox: self._x = x = numpy.arange( boundingBox[0][0], boundingBox[1][0], space ) # *1.1547) gridspacing is already multiplied by 1.1547 self._y = y = numpy.arange( boundingBox[0][1], boundingBox[1][1], space ) # *1.1547) self._z = z = numpy.arange( boundingBox[0][2], boundingBox[1][2], space ) # *1.1547) else: self._x = x = numpy.arange( boundingBox[0][0] - space, boundingBox[1][0] + space, space ) # *1.1547) gridspacing is already multiplied by 1.1547 self._y = y = numpy.arange( boundingBox[0][1] - space, boundingBox[1][1] + space, space ) # *1.1547) self._z = z = numpy.arange( boundingBox[0][2] - space, boundingBox[1][2] + space, space ) # *1.1547) nx = len( x ) # sizes must be +1 or the right, top, and back edges don't get any points using this numpy.arange method ny = len(y) nz = len(z) # Dec 5 2013, we need to confirm that the getPointsInBox function is also using +1, or potential neighbors will be missed # This used to be fine, but it may have changed? self.nbGridPoints = [nx, ny, nz] self.gridVolume = nx * ny * nz self.ijkPtIndice = numpy.ndindex(nx, ny, nz) # this is 60% faster than the for loop # self.masterGridPositions = numpy.array(list(numpy.broadcast(*numpy.ix_(x, y, z)))) # self.masterGridPositions = numpy.vstack(numpy.meshgrid(x,y,z)).reshape(3,-1).T self.masterGridPositions = ( numpy.vstack(numpy.meshgrid(x, y, z, copy=False)).reshape(3, -1).T ) # this ay be faster but don't know the implication # from http://stackoverflow.com/questions/18253210/creating-a-numpy-array-of-3d-coordinates-from-three-1d-arrays def create3DPointLookupCover(self, boundingBox=None): """ Fill the orthogonal bounding box described by two global corners with an array of points spaces pGridSpacing apart. Optimized version using numpy broadcasting """ if boundingBox is None: boundingBox = self.boundingBox space = self.gridSpacing S = numpy.array(boundingBox[1]) - numpy.array(boundingBox[0]) NX, NY, NZ = numpy.around(S / (self.gridSpacing)) # / 1.1547)) if NX == 0: NX = 1 if NY == 0: NY = 1 if NZ == 0: NZ = 1 self.log.info("using create3DPointLookupCover") # we want the diagonal of the voxel, not the diagonal of the plane, so the second 1.1547 is was incorrect environmentBoxEqualFillBox = True # np.linspace(2.0, 3.0, num=5) if environmentBoxEqualFillBox: # environment.environmentBoxEqualFillBox: self._x = x = numpy.linspace( boundingBox[0][0], boundingBox[1][0], int(NX) ) # *1.1547) gridspacing is already multiplied by 1.1547 self._y = y = numpy.linspace( boundingBox[0][1], boundingBox[1][1], int(NY) ) # *1.1547) self._z = z = numpy.linspace( boundingBox[0][2], boundingBox[1][2], int(NZ) ) # *1.1547) else: self._x = x = numpy.arange( boundingBox[0][0], boundingBox[1][0] + space, space ) # *1.1547) gridspacing is already multiplied by 1.1547 self._y = y = numpy.arange( boundingBox[0][1], boundingBox[1][1] + space, space ) # *1.1547) self._z = z = numpy.arange( boundingBox[0][2], boundingBox[1][2] + space, space ) # *1.1547) xyz = numpy.meshgrid(x, y, z, copy=False) nx = len( x ) # sizes must be +1 or the right, top, and back edges don't get any points using this numpy.arange method ny = len(y) nz = len(z) self.gridSpacing = (x[1] - x[0]) * 1.1547 # ? should I multiply here ? self.nbGridPoints = [nx, ny, nz] self.gridVolume = nx * ny * nz self.ijkPtIndice = numpy.ndindex(nx, ny, nz) self.masterGridPositions = numpy.vstack(xyz).reshape(3, -1).T # self.masterGridPositions = numpy.vstack(numpy.meshgrid(x,y,z,copy=False)).reshape(3,-1).T def getPointCompartmentId(self, point, ray=1): # check if point inside on of the compartments # surface point ? n_comp = len(self.histoVol.compartments) if n_comp: for i in range(n_comp): inside = self.checkPointInside_rapid( self.histoVol.compartments[i], point, self.histoVol.grid.diag, ray=ray, ) if inside: return -(i + 1) # comp=comp-1 # the point is not inside , is it on the surface ? ie distance to surface < X? for i in range(n_comp): distance, nb = self.histoVol.compartments[i].OGsrfPtsBht.query(point) if distance < 10.0: return i + 1 return 0 def getClosestGridPoint(self, pt3d): if self.tree is None: self.tree = spatial.cKDTree(self.masterGridPositions, leafsize=10) distance, nb = self.tree.query(pt3d) # len of ingr posed so far return distance, nb def getClosestFreeGridPoint( self, pt3d, compId=None, updateTree=True, ball=0.0, distance=0.0 ): free_indices = self.freePoints[: self.nbFreePoints] arr = numpy.array(self.masterGridPositions[free_indices]) indices = numpy.nonzero(numpy.equal(self.gridPtId[free_indices], compId)) distances = self.distToClosestSurf[free_indices] if not len(indices): return None tree_free = spatial.cKDTree(arr[indices], leafsize=10) arr = arr[indices] # arr of free indice in compartments res = tree_free.query_ball_point(pt3d, ball) # max distance if not len(res): return None all_distances = distances[res] all_pts = arr[res] ind = numpy.nonzero( numpy.logical_and( numpy.greater_equal(all_distances, distance), numpy.less(all_distances, distance * 1.5), ) )[0] if not len(ind): return None # should pick closest ? targetPoint = all_pts[ ind[randrange(len(ind))] ] # randomly pick free surface point at given distance return targetPoint free_indices = self.freePoints[: self.nbFreePoints] arr = numpy.array(self.masterGridPositions[free_indices]) if self.tree_free is None or updateTree: if compId is not None: arr = numpy.array(self.masterGridPositions[free_indices]) indices = numpy.nonzero( numpy.equal(self.gridPtId[free_indices], compId) ) self.tree_free = spatial.cKDTree(arr[indices], leafsize=10) arr = arr[indices] else: self.tree_free = spatial.cKDTree( self.masterGridPositions[: self.nbFreePoints], leafsize=10 ) if distance != 0.0: res = self.tree_free.query_ball_point(pt3d, distance) # return 0, res, arr else: res = self.tree_free.query(pt3d) # len of ingr posed so far return res, arr def cartesian(self, arrays, out=None): """ #http://stackoverflow.com/questions/1208118/using-numpy-to-build-an-array-of-all-combinations-of-two-arrays Generate a cartesian product of input arrays. Parameters ---------- arrays : list of array-like 1-D arrays to form the cartesian product of. out : ndarray Array to place the cartesian product in. Returns ------- out : ndarray 2-D array of shape (M, len(arrays)) containing cartesian products formed of input arrays. Examples -------- >>> cartesian(([1, 2, 3], [4, 5], [6, 7])) array([[1, 4, 6], [1, 4, 7], [1, 5, 6], [1, 5, 7], [2, 4, 6], [2, 4, 7], [2, 5, 6], [2, 5, 7], [3, 4, 6], [3, 4, 7], [3, 5, 6], [3, 5, 7]]) """ arrays = [numpy.asarray(x) for x in arrays] dtype = arrays[0].dtype n = numpy.prod([x.size for x in arrays]) if out is None: out = numpy.zeros([n, len(arrays)], dtype=dtype) m = int(n / arrays[0].size) out[:, 0] = numpy.repeat(arrays[0], m) if arrays[1:]: self.cartesian(arrays[1:], out=out[0:m, 1:]) for j in range(1, arrays[0].size): out[j * m : (j + 1) * m, 1:] = out[0:m, 1:] return out def getPointFrom3D(self, pt3d): """ get point number from 3d coordinates """ x, y, z = pt3d # Continuous 3D point to be discretized spacing1 = ( 1.0 / self.gridSpacing ) # Grid spacing = diagonal of the voxel determined by smallest packing radius ( NX, NY, NZ, ) = ( self.nbGridPoints ) # vector = [length, height, depth] of grid, units = gridPoints OX, OY, OZ = self.boundingBox[0] # origin of fill grid # Algebra gives nearest gridPoint ID to pt3D i = min(NX - 1, max(0, round((x - OX) * spacing1))) j = min(NY - 1, max(0, round((y - OY) * spacing1))) k = min(NZ - 1, max(0, round((z - OZ) * spacing1))) return int(k * NX * NY + j * NX + i) def getIJK(self, ptInd): """ get i,j,k (3d) indices from u (1d) only work for grid point, not compartments points """ if ptInd > self.nbGridPoints[0] * self.nbGridPoints[1] * self.nbGridPoints[2]: return [0, 0, 0] return self.ijkPtIndice[ptInd] def setupBoundaryPeriodicity(self): # we create a dictionary for the adjacent cell of the current grid. self.sizeXYZ = numpy.array(self.boundingBox[1]) - numpy.array( self.boundingBox[0] ) self.periodic_table = {} self.periodic_table["left"] = ( numpy.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) * self.sizeXYZ ) self.periodic_table["right"] = ( numpy.array([[-1, 0, 0], [0, -1, 0], [0, 0, -1]]) * self.sizeXYZ ) def getPositionPeridocity(self, pt3d, jitter, cutoff): tr = [] if autopack.biasedPeriodicity: biased = numpy.array(autopack.biasedPeriodicity) else: biased = numpy.array(jitter) if not autopack.testPeriodicity: return tr ox, oy, oz = self.boundingBox[0] ex, ey, ez = self.boundingBox[1] px, py, pz = pt3d p_xyz = [0, 0, 0] # can I use rapid and find the collision ? # distance plane X dist_origin_x = px - ox dist_edge_x = ex - px dx = 0 if dist_origin_x < dist_edge_x: dx = dist_origin_x # 1 p_xyz[0] = 1 else: dx = dist_edge_x # -1 p_xyz[0] = -1 if dx < cutoff and dx != 0: pass else: p_xyz[0] = 0 # distance plane Y doy = py - oy dey = ey - py dy = 0 if doy < dey: dy = doy # 1 p_xyz[1] = 1 else: dy = dey # -1 p_xyz[1] = -1 if dy < cutoff and dy != 0.0: pass else: p_xyz[1] = 0 # distance plane Z doz = pz - oz dez = ez - pz dz = 0 if doz < dez: dz = doz # 1 p_xyz[2] = 1 else: dz = dez # -1 p_xyz[2] = -1 if dz < cutoff and dz != 0: pass else: p_xyz[2] = 0 p_xyz = numpy.array(p_xyz) * biased # for 2D we need 3 corner tiles # for 3D we need 7 corner tiles corner = numpy.zeros((4, 3)) indices_non_zero = numpy.nonzero(p_xyz)[0] for i in indices_non_zero: # i is the axis that is close to the point tr.append(pt3d + (self.periodic_table["left"][i] * p_xyz[i])) # 0,1,2 corner[0] += self.periodic_table["left"][i] * p_xyz[i] # 1 # the corner are # X+Y+Z corner[0] # X+Y+0 corner[1] # X+0+Z corner[2] # 0+Y+Z corner[3] if len(indices_non_zero) == 2: # two axis cross-> three pos tr.append(pt3d + corner[0]) if len(indices_non_zero) == 3: # in a corner need total 7 pos, never happen in 2D corner[1] = ( self.periodic_table["left"][0] * p_xyz[0] + self.periodic_table["left"][1] * p_xyz[1] ) corner[2] = ( self.periodic_table["left"][0] * p_xyz[0] + self.periodic_table["left"][2] * p_xyz[2] ) corner[3] = ( self.periodic_table["left"][1] * p_xyz[1] + self.periodic_table["left"][2] * p_xyz[2] ) for i in range(4): # 4+1=5 tr.append(pt3d + corner[i]) return tr def checkPointInside(self, pt3d, dist=None, jitter=[1, 1, 1], bb=None): """ Check if the given 3d points is inside the grid """ if bb is None: bb = self.boundingBox origin = numpy.array(bb[0]) edge = numpy.array(bb[1]) for i in range(len(edge)): if edge[i] < self.gridSpacing: edge[i] = self.gridSpacing packing_location = numpy.array(pt3d) # *jitter test1 = packing_location < origin test2 = packing_location > edge if True in test1 or True in test2: # outside return False else: if dist is not None: # distance to closest wall d1 = (packing_location - origin) * jitter s1 = min(x for x in d1[d1 != 0] if x != 0) d2 = (edge - packing_location) * jitter s2 = min(x for x in d2[d2 != 0] if x != 0) if s1 <= dist or s2 <= dist: self.log.info("s1 s2 smaller than dist %d %d %d", s1, s2, dist) return False return True def getCenter(self): """ Get the center of the grid """ if self.center is None: self.center = [0.0, 0.0, 0.0] for i in range(3): self.center[i] = (self.boundingBox[0][i] + self.boundingBox[1][i]) / 2.0 return self.center def getRadius(self): """ Get the radius the grid """ d = numpy.array(self.boundingBox[0]) - numpy.array(self.boundingBox[1]) s = numpy.sum(d * d) return math.sqrt(s) def getPointsInSphere(self, pt, radius): if self.tree is None: self.tree = spatial.cKDTree(self.masterGridPositions, leafsize=10) # add surface points ptIndices = self.tree.query_ball_point(pt, radius) # , n_jobs=-1) return ptIndices def getPointsInCubeFillBB(self, bb, pt, radius, addSP=True, info=False): """ Return all grid points indices inside the given bounding box. NOTE : need to fix with grid build with numpy arrange """ spacing1 = 1.0 / self.gridSpacing NX, NY, NZ = self.nbGridPoints OX, OY, OZ = self.boundingBox[ 0 ] # origin of fill grid-> bottom lef corner not origin ox, oy, oz = bb[0] ex, ey, ez = bb[1] i0 = int(max(0, floor((ox - OX) * spacing1))) i1 = int(min(NX, int((ex - OX) * spacing1)) + 1) j0 = int(max(0, floor((oy - OY) * spacing1))) j1 = int(min(NY, int((ey - OY) * spacing1)) + 1) k0 = int(max(0, floor((oz - OZ) * spacing1))) k1 = int(min(NZ, int((ez - OZ) * spacing1)) + 1) i0 = int(min(NX - 1, max(0, round((ox - OX) * spacing1)))) j0 = int(min(NY - 1, max(0, round((oy - OY) * spacing1)))) k0 = int(min(NZ - 1, max(0, round((oz - OZ) * spacing1)))) i1 = int(min(NX, max(0, round((ex - OX) * spacing1)))) j1 = int(min(NY, max(0, round((ey - OY) * spacing1)))) k1 = int(min(NZ, max(0, round((ez - OZ) * spacing1)))) if NZ == 1: k0 = 0 k1 = 1 elif NY == 1: j0 = 0 j1 = 1 elif NX == 1: i0 = 0 i1 = 1 ptIndices = [] pid = numpy.mgrid[i0:i1, j0:j1, k0:k1] ijk = numpy.vstack(pid).reshape(3, -1).T # in case 2D, meaning one of the dimension is 1 if NZ == 1: ptIndices = [p[2] + p[1] + NX * p[0] for p in ijk] elif NY == 1: ptIndices = [p[2] + p[1] + NX * p[0] for p in ijk] elif NX == 1: ptIndices = [p[2] + NY * p[1] + p[0] for p in ijk] else: 0.02451198 # add surface points if addSP and self.nbSurfacePoints != 0: result = numpy.zeros((self.nbSurfacePoints,), "i") nb = self.surfPtsBht.closePoints(tuple(pt), radius, result) # nb = self.surfPtsBht.query(tuple(pt),k=self.nbSurfacePoints) ptIndices.extend( list(map(lambda x, length=self.gridVolume: x + length, result[:nb])) ) return ptIndices def test_points_in_bb(self, bb, pt): # given a bounding box, does the point is contains in it origin = numpy.array(bb[0]) E = numpy.array(bb[1]) P = numpy.array(pt) # *jitter test1 = P < origin test2 = P > E inside = False if True in test1 or True in test2: # outside inside = False return inside def getPointsInCube(self, bb, pt, radius, addSP=True, info=False): """ Return all grid points indicesinside the given bounding box. """ spacing1 = 1.0 / self.gridSpacing NX, NY, NZ = self.nbGridPoints OX, OY, OZ = self.boundingBox[ 0 ] # origin of Pack grid-> bottom lef corner not origin ox, oy, oz = bb[0] ex, ey, ez = bb[1] i0 = int(max(0, floor((ox - OX) * spacing1))) i1 = int(min(NX, int((ex - OX) * spacing1) + 1)) j0 = int(max(0, floor((oy - OY) * spacing1))) j1 = int(min(NY, int((ey - OY) * spacing1) + 1)) k0 = int(max(0, floor((oz - OZ) * spacing1))) k1 = int(min(NZ, int((ez - OZ) * spacing1) + 1)) zPlaneLength = NX * NY ptIndices = [] for z in range(int(k0), int(k1)): offz = z * zPlaneLength for y in range(int(j0), int(j1)): off = y * NX + offz for x in range(int(i0), int(i1)): ptIndices.append(x + off) # add surface points if addSP and self.nbSurfacePoints != 0: result = numpy.zeros((self.nbSurfacePoints,), "i") nb = self.surfPtsBht.closePoints(tuple(pt), radius, result) dimx, dimy, dimz = self.nbGridPoints ptIndices.extend( list(map(lambda x, length=self.gridVolume: x + length, result[:nb])) ) return ptIndices def computeGridNumberOfPoint(self, boundingBox, space): """ Return the grid size : total number of point and number of point per axes """ xl, yl, zl = boundingBox[0] xr, yr, zr = boundingBox[1] encapsulatingGrid = self.encapsulatingGrid # Graham Added on Oct17 to allow for truly 2D grid for test fills... may break everything! nx = int(ceil((xr - xl) / space)) + encapsulatingGrid ny = int(ceil((yr - yl) / space)) + encapsulatingGrid nz = int(ceil((zr - zl) / space)) + encapsulatingGrid # nx = nx if (nx == 1) else nx-1 # ny = ny if (ny == 1) else ny-1 # nz = nz if (nz == 1) else nz-1 return nx * ny * nz, (nx, ny, nz) def set_surfPtsBht(self, verts): self.surfPtsBht = None if verts is not None and len(verts): self.surfPtsBht = bhtreelib.BHtree(verts, None, 10) self.nbSurfacePoints = len(verts) def set_surfPtscht(self, verts): self.surfPtsBht = None if verts is not None and len(verts): self.surfPtsBht = spatial.cKDTree(verts, leafsize=10) self.nbSurfacePoints = len(verts) def computeExteriorVolume(self, compartments=None, space=None, fbox_bb=None): # compute exterior volume, totalVolume without compartments volume unitVol = self.gridSpacing ** 3 totalVolume = self.gridVolume * unitVol if fbox_bb is not None: V, nbG = self.computeGridNumberOfPoint(fbox_bb, space) totalVolume = V * unitVol if compartments is not None: for o in compartments: # totalVolume -= o.surfaceVolume totalVolume -= o.interiorVolume return totalVolume def computeVolume(self, space=None, fbox_bb=None): # compute exterior volume, totalVolume without compartments volume unitVol = self.gridSpacing ** 3 totalVolume = self.gridVolume * unitVol if fbox_bb is not None: V, nbG = self.computeGridNumberOfPoint(fbox_bb, space) totalVolume = V * unitVol return totalVolume def create_rapid_model(self): self.rapid_model = RAPIDlib.RAPID_model() # need triangle and vertices self.rapid_model.addTriangles( numpy.array(self.vertices, "f"), numpy.array(self.faces, "i") ) def get_rapid_model(self): if self.rapid_model is None: self.create_rapid_model() return self.rapid_model def one_rapid_ray(self, pt1, pt2, diag): # return number of triangle /triangle contact helper = autopack.helper rm = self.get_rapid_model() v1 = numpy.array(pt1) direction = helper.unit_vector(pt2 - pt1) * diag v2 = v1 + direction if sum(v1) == 0.0: v3 = v2 + numpy.array([0.0, 1.0, 0.0]) else: v3 = v2 + helper.unit_vector(numpy.cross(v1, v2)) f = [0, 1, 2] ray_model = RAPIDlib.RAPID_model() ray_model.addTriangles( numpy.array([v1, v2, v3], "f"), numpy.array( [f], "i", ), ) RAPIDlib.RAPID_Collide_scaled( numpy.identity(3), numpy.array([0.0, 0.0, 0.0], "f"), 1.0, rm,

numpy.identity(3)

numpy.identity

# Copyright (c) 2016,2017 MetPy Developers. # Distributed under the terms of the BSD 3-Clause License. # SPDX-License-Identifier: BSD-3-Clause """Contains a collection of generally useful calculation tools.""" import functools import warnings import numpy as np import numpy.ma as ma from scipy.spatial import cKDTree from . import height_to_pressure_std, pressure_to_height_std from ..package_tools import Exporter from ..units import check_units, units exporter = Exporter(globals()) @exporter.export def resample_nn_1d(a, centers): """Return one-dimensional nearest-neighbor indexes based on user-specified centers. Parameters ---------- a : array-like 1-dimensional array of numeric values from which to extract indexes of nearest-neighbors centers : array-like 1-dimensional array of numeric values representing a subset of values to approximate Returns ------- An array of indexes representing values closest to given array values """ ix = [] for center in centers: index = (np.abs(a - center)).argmin() if index not in ix: ix.append(index) return ix @exporter.export def nearest_intersection_idx(a, b): """Determine the index of the point just before two lines with common x values. Parameters ---------- a : array-like 1-dimensional array of y-values for line 1 b : array-like 1-dimensional array of y-values for line 2 Returns ------- An array of indexes representing the index of the values just before the intersection(s) of the two lines. """ # Difference in the two y-value sets difference = a - b # Determine the point just before the intersection of the lines # Will return multiple points for multiple intersections sign_change_idx, = np.nonzero(np.diff(np.sign(difference))) return sign_change_idx @exporter.export @units.wraps(('=A', '=B'), ('=A', '=B', '=B')) def find_intersections(x, a, b, direction='all'): """Calculate the best estimate of intersection. Calculates the best estimates of the intersection of two y-value data sets that share a common x-value set. Parameters ---------- x : array-like 1-dimensional array of numeric x-values a : array-like 1-dimensional array of y-values for line 1 b : array-like 1-dimensional array of y-values for line 2 direction : string, optional specifies direction of crossing. 'all', 'increasing' (a becoming greater than b), or 'decreasing' (b becoming greater than a). Defaults to 'all'. Returns ------- A tuple (x, y) of array-like with the x and y coordinates of the intersections of the lines. """ # Find the index of the points just before the intersection(s) nearest_idx = nearest_intersection_idx(a, b) next_idx = nearest_idx + 1 # Determine the sign of the change sign_change = np.sign(a[next_idx] - b[next_idx]) # x-values around each intersection _, x0 = _next_non_masked_element(x, nearest_idx) _, x1 = _next_non_masked_element(x, next_idx) # y-values around each intersection for the first line _, a0 = _next_non_masked_element(a, nearest_idx) _, a1 = _next_non_masked_element(a, next_idx) # y-values around each intersection for the second line _, b0 = _next_non_masked_element(b, nearest_idx) _, b1 = _next_non_masked_element(b, next_idx) # Calculate the x-intersection. This comes from finding the equations of the two lines, # one through (x0, a0) and (x1, a1) and the other through (x0, b0) and (x1, b1), # finding their intersection, and reducing with a bunch of algebra. delta_y0 = a0 - b0 delta_y1 = a1 - b1 intersect_x = (delta_y1 * x0 - delta_y0 * x1) / (delta_y1 - delta_y0) # Calculate the y-intersection of the lines. Just plug the x above into the equation # for the line through the a points. One could solve for y like x above, but this # causes weirder unit behavior and seems a little less good numerically. intersect_y = ((intersect_x - x0) / (x1 - x0)) * (a1 - a0) + a0 # If there's no intersections, return if len(intersect_x) == 0: return intersect_x, intersect_y # Check for duplicates duplicate_mask = (np.ediff1d(intersect_x, to_end=1) != 0) # Make a mask based on the direction of sign change desired if direction == 'increasing': mask = sign_change > 0 elif direction == 'decreasing': mask = sign_change < 0 elif direction == 'all': return intersect_x[duplicate_mask], intersect_y[duplicate_mask] else: raise ValueError('Unknown option for direction: {0}'.format(str(direction))) return intersect_x[mask & duplicate_mask], intersect_y[mask & duplicate_mask] @exporter.export def interpolate_nans(x, y, kind='linear'): """Interpolate NaN values in y. Interpolate NaN values in the y dimension. Works with unsorted x values. Parameters ---------- x : array-like 1-dimensional array of numeric x-values y : array-like 1-dimensional array of numeric y-values kind : string specifies the kind of interpolation x coordinate - 'linear' or 'log', optional. Defaults to 'linear'. Returns ------- An array of the y coordinate data with NaN values interpolated. """ x_sort_args = np.argsort(x) x = x[x_sort_args] y = y[x_sort_args] nans = np.isnan(y) if kind == 'linear': y[nans] = np.interp(x[nans], x[~nans], y[~nans]) elif kind == 'log': y[nans] = np.interp(np.log(x[nans]), np.log(x[~nans]), y[~nans]) else: raise ValueError('Unknown option for kind: {0}'.format(str(kind))) return y[x_sort_args] def _next_non_masked_element(a, idx): """Return the next non masked element of a masked array. If an array is masked, return the next non-masked element (if the given index is masked). If no other unmasked points are after the given masked point, returns none. Parameters ---------- a : array-like 1-dimensional array of numeric values idx : integer index of requested element Returns ------- Index of next non-masked element and next non-masked element """ try: next_idx = idx + a[idx:].mask.argmin() if ma.is_masked(a[next_idx]): return None, None else: return next_idx, a[next_idx] except (AttributeError, TypeError, IndexError): return idx, a[idx] def delete_masked_points(*arrs): """Delete masked points from arrays. Takes arrays and removes masked points to help with calculations and plotting. Parameters ---------- arrs : one or more array-like source arrays Returns ------- arrs : one or more array-like arrays with masked elements removed """ if any(hasattr(a, 'mask') for a in arrs): keep = ~functools.reduce(np.logical_or, (np.ma.getmaskarray(a) for a in arrs)) return tuple(ma.asarray(a[keep]) for a in arrs) else: return arrs @exporter.export def reduce_point_density(points, radius, priority=None): r"""Return a mask to reduce the density of points in irregularly-spaced data. This function is used to down-sample a collection of scattered points (e.g. surface data), returning a mask that can be used to select the points from one or more arrays (e.g. arrays of temperature and dew point). The points selected can be controlled by providing an array of ``priority`` values (e.g. rainfall totals to ensure that stations with higher precipitation remain in the mask). Parameters ---------- points : (N, K) array-like N locations of the points in K dimensional space radius : float minimum radius allowed between points priority : (N, K) array-like, optional If given, this should have the same shape as ``points``; these values will be used to control selection priority for points. Returns ------- (N,) array-like of boolean values indicating whether points should be kept. This can be used directly to index numpy arrays to return only the desired points. Examples -------- >>> metpy.calc.reduce_point_density(np.array([1, 2, 3]), 1.) array([ True, False, True], dtype=bool) >>> metpy.calc.reduce_point_density(np.array([1, 2, 3]), 1., ... priority=np.array([0.1, 0.9, 0.3])) array([False, True, False], dtype=bool) """ # Handle 1D input if points.ndim < 2: points = points.reshape(-1, 1) # Make a kd-tree to speed searching of data. tree = cKDTree(points) # Need to use sorted indices rather than sorting the position # so that the keep mask matches *original* order. if priority is not None: # Need to sort the locations in decreasing priority. sorted_indices = np.argsort(priority)[::-1] else: # Take advantage of iterator nature of range here to avoid making big lists sorted_indices = range(len(points)) # Keep all points initially keep = np.ones(len(points), dtype=np.bool) # Loop over all the potential points for ind in sorted_indices: # Only proceed if we haven't already excluded this point if keep[ind]: # Find the neighbors and eliminate them neighbors = tree.query_ball_point(points[ind], radius) keep[neighbors] = False # We just removed ourselves, so undo that keep[ind] = True return keep def _get_bound_pressure_height(pressure, bound, heights=None, interpolate=True): """Calculate the bounding pressure and height in a layer. Given pressure, optional heights, and a bound, return either the closest pressure/height or interpolated pressure/height. If no heights are provided, a standard atmosphere is assumed. Parameters ---------- pressure : `pint.Quantity` Atmospheric pressures bound : `pint.Quantity` Bound to retrieve (in pressure or height) heights : `pint.Quantity`, optional Atmospheric heights associated with the pressure levels. Defaults to using heights calculated from ``pressure`` assuming a standard atmosphere. interpolate : boolean, optional Interpolate the bound or return the nearest. Defaults to True. Returns ------- `pint.Quantity` The bound pressure and height. """ # Bound is given in pressure if bound.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}: # If the bound is in the pressure data, we know the pressure bound exactly if bound in pressure: bound_pressure = bound # If we have heights, we know the exact height value, otherwise return standard # atmosphere height for the pressure if heights is not None: bound_height = heights[pressure == bound_pressure] else: bound_height = pressure_to_height_std(bound_pressure) # If bound is not in the data, return the nearest or interpolated values else: if interpolate: bound_pressure = bound # Use the user specified bound if heights is not None: # Interpolate heights from the height data bound_height = log_interp(bound_pressure, pressure, heights) else: # If not heights given, use the standard atmosphere bound_height = pressure_to_height_std(bound_pressure) else: # No interpolation, find the closest values idx = (np.abs(pressure - bound)).argmin() bound_pressure = pressure[idx] if heights is not None: bound_height = heights[idx] else: bound_height = pressure_to_height_std(bound_pressure) # Bound is given in height elif bound.dimensionality == {'[length]': 1.0}: # If there is height data, see if we have the bound or need to interpolate/find nearest if heights is not None: if bound in heights: # Bound is in the height data bound_height = bound bound_pressure = pressure[heights == bound] else: # Bound is not in the data if interpolate: bound_height = bound # Need to cast back to the input type since interp (up to at least numpy # 1.13 always returns float64. This can cause upstream users problems, # resulting in something like np.append() to upcast. bound_pressure = np.interp(np.atleast_1d(bound), heights, pressure).astype(bound.dtype) * pressure.units else: idx = (np.abs(heights - bound)).argmin() bound_pressure = pressure[idx] bound_height = heights[idx] else: # Don't have heights, so assume a standard atmosphere bound_height = bound bound_pressure = height_to_pressure_std(bound) # If interpolation is on, this is all we need, if not, we need to go back and # find the pressure closest to this and refigure the bounds if not interpolate: idx = (np.abs(pressure - bound_pressure)).argmin() bound_pressure = pressure[idx] bound_height = pressure_to_height_std(bound_pressure) # Bound has invalid units else: raise ValueError('Bound must be specified in units of length or pressure.') # If the bound is out of the range of the data, we shouldn't extrapolate if (bound_pressure < np.min(pressure)) or (bound_pressure > np.max(pressure)): raise ValueError('Specified bound is outside pressure range.') if heights is not None: if (bound_height > np.max(heights)) or (bound_height < np.min(heights)): raise ValueError('Specified bound is outside height range.') return bound_pressure, bound_height @exporter.export @check_units('[length]') def get_layer_heights(heights, depth, *args, **kwargs): """Return an atmospheric layer from upper air data with the requested bottom and depth. This function will subset an upper air dataset to contain only the specified layer using the heights only. Parameters ---------- heights : array-like Atmospheric heights depth : `pint.Quantity` The thickness of the layer *args : array-like Atmospheric variable(s) measured at the given pressures bottom : `pint.Quantity`, optional The bottom of the layer interpolate : bool, optional Interpolate the top and bottom points if they are not in the given data. Defaults to True. with_agl : bool, optional Returns the heights as above ground level by subtracting the minimum height in the provided heights. Defaults to False. Returns ------- `pint.Quantity, pint.Quantity` The height and data variables of the layer """ bottom = kwargs.pop('bottom', None) interpolate = kwargs.pop('interpolate', True) with_agl = kwargs.pop('with_agl', False) # Make sure pressure and datavars are the same length for datavar in args: if len(heights) != len(datavar): raise ValueError('Height and data variables must have the same length.') # If we want things in AGL, subtract the minimum height from all height values if with_agl: sfc_height = np.min(heights) heights -= sfc_height # If the bottom is not specified, make it the surface if bottom is None: bottom = heights[0] # Make heights and arguments base units heights = heights.to_base_units() bottom = bottom.to_base_units() # Calculate the top of the layer top = bottom + depth ret = [] # returned data variables in layer # Ensure heights are sorted in ascending order sort_inds = np.argsort(heights) heights = heights[sort_inds] # Mask based on top and bottom inds = (heights >= bottom) & (heights <= top) heights_interp = heights[inds] # Interpolate heights at bounds if necessary and sort if interpolate: # If we don't have the bottom or top requested, append them if top not in heights_interp: heights_interp = np.sort(np.append(heights_interp, top)) * heights.units if bottom not in heights_interp: heights_interp = np.sort(np.append(heights_interp, bottom)) * heights.units ret.append(heights_interp) for datavar in args: # Ensure that things are sorted in ascending order datavar = datavar[sort_inds] if interpolate: # Interpolate for the possibly missing bottom/top values datavar_interp = interp(heights_interp, heights, datavar) datavar = datavar_interp else: datavar = datavar[inds] ret.append(datavar) return ret @exporter.export @check_units('[pressure]') def get_layer(pressure, *args, **kwargs): r"""Return an atmospheric layer from upper air data with the requested bottom and depth. This function will subset an upper air dataset to contain only the specified layer. The bottom of the layer can be specified with a pressure or height above the surface pressure. The bottom defaults to the surface pressure. The depth of the layer can be specified in terms of pressure or height above the bottom of the layer. If the top and bottom of the layer are not in the data, they are interpolated by default. Parameters ---------- pressure : array-like Atmospheric pressure profile *args : array-like Atmospheric variable(s) measured at the given pressures heights: array-like, optional Atmospheric heights corresponding to the given pressures. Defaults to using heights calculated from ``p`` assuming a standard atmosphere. bottom : `pint.Quantity`, optional The bottom of the layer as a pressure or height above the surface pressure. Defaults to the lowest pressure or height given. depth : `pint.Quantity`, optional The thickness of the layer as a pressure or height above the bottom of the layer. Defaults to 100 hPa. interpolate : bool, optional Interpolate the top and bottom points if they are not in the given data. Defaults to True. Returns ------- `pint.Quantity, pint.Quantity` The pressure and data variables of the layer """ # Pop off keyword arguments heights = kwargs.pop('heights', None) bottom = kwargs.pop('bottom', None) depth = kwargs.pop('depth', 100 * units.hPa) interpolate = kwargs.pop('interpolate', True) # If we get the depth kwarg, but it's None, set it to the default as well if depth is None: depth = 100 * units.hPa # Make sure pressure and datavars are the same length for datavar in args: if len(pressure) != len(datavar): raise ValueError('Pressure and data variables must have the same length.') # If the bottom is not specified, make it the surface pressure if bottom is None: bottom = pressure[0] bottom_pressure, bottom_height = _get_bound_pressure_height(pressure, bottom, heights=heights, interpolate=interpolate) # Calculate the top if whatever units depth is in if depth.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}: top = bottom_pressure - depth elif depth.dimensionality == {'[length]': 1}: top = bottom_height + depth else: raise ValueError('Depth must be specified in units of length or pressure') top_pressure, _ = _get_bound_pressure_height(pressure, top, heights=heights, interpolate=interpolate) ret = [] # returned data variables in layer # Ensure pressures are sorted in ascending order sort_inds = np.argsort(pressure) pressure = pressure[sort_inds] # Mask based on top and bottom pressure inds = (pressure <= bottom_pressure) & (pressure >= top_pressure) p_interp = pressure[inds] # Interpolate pressures at bounds if necessary and sort if interpolate: # If we don't have the bottom or top requested, append them if top_pressure not in p_interp: p_interp = np.sort(np.append(p_interp, top_pressure)) * pressure.units if bottom_pressure not in p_interp: p_interp = np.sort(np.append(p_interp, bottom_pressure)) * pressure.units ret.append(p_interp[::-1]) for datavar in args: # Ensure that things are sorted in ascending order datavar = datavar[sort_inds] if interpolate: # Interpolate for the possibly missing bottom/top values datavar_interp = log_interp(p_interp, pressure, datavar) datavar = datavar_interp else: datavar = datavar[inds] ret.append(datavar[::-1]) return ret @exporter.export @units.wraps(None, ('=A', '=A')) def interp(x, xp, *args, **kwargs): r"""Interpolates data with any shape over a specified axis. Interpolation over a specified axis for arrays of any shape. Parameters ---------- x : array-like 1-D array of desired interpolated values. xp : array-like The x-coordinates of the data points. args : array-like The data to be interpolated. Can be multiple arguments, all must be the same shape as xp. axis : int, optional The axis to interpolate over. Defaults to 0. fill_value: float, optional Specify handling of interpolation points out of data bounds. If None, will return ValueError if points are out of bounds. Defaults to nan. Returns ------- array-like Interpolated values for each point with coordinates sorted in ascending order. Examples -------- >>> x = np.array([1., 2., 3., 4.]) >>> y = np.array([1., 2., 3., 4.]) >>> x_interp = np.array([2.5, 3.5]) >>> metpy.calc.interp(x_interp, x, y) array([ 2.5, 3.5]) Notes ----- xp and args must be the same shape. """ # Pull out keyword args fill_value = kwargs.pop('fill_value', np.nan) axis = kwargs.pop('axis', 0) # Make x an array x = np.asanyarray(x).reshape(-1) # Save number of dimensions in xp ndim = xp.ndim # Sort input data sort_args =

np.argsort(xp, axis=axis)

numpy.argsort

from __future__ import absolute_import from __future__ import print_function import pytest import os import numpy as np from numpy.testing import assert_allclose from keras import backend as K import keras from keras.models import Sequential from keras.layers import Dense, Activation from keras.utils import np_utils from keras.utils.test_utils import get_test_data from keras.models import model_from_json, model_from_yaml from keras import losses from keras.engine.training_utils import make_batches input_dim = 16 num_hidden = 8 num_classes = 4 batch_size = 32 epochs = 1 @pytest.fixture def in_tmpdir(tmpdir): """Runs a function in a temporary directory. Checks that the directory is empty afterwards. """ with tmpdir.as_cwd(): yield None assert not tmpdir.listdir() def test_sequential_pop(): model = Sequential() model.add(Dense(num_hidden, input_dim=input_dim)) model.add(Dense(num_classes)) model.compile(loss='mse', optimizer='sgd') x = np.random.random((batch_size, input_dim)) y =

np.random.random((batch_size, num_classes))

numpy.random.random

import numpy as np from gmmmc.gmm import GMM from gmmmc.proposals.proposals import Proposal import pdb import logging class GaussianStepMeansProposal(Proposal): """Gaussian Proposal distribution for means of a GMM""" def __init__(self, step_sizes=(0.001,)): """ Gaussian proposal distribution for the means. The multivariate Gaussian is centered at the means of the current state in the Markov Chain and has covariance given by step_sizes. Multiple step sizes can be specified. The proposal algorithm will take these steps in the sequence specified in step_sizes. Parameters ---------- step_sizes : 1-D array_like Iterable containing the sequence of step sizes (covariances of the Gaussian proposal distribution" """ super(GaussianStepMeansProposal, self).__init__() self.step_sizes = step_sizes self.count_accepted = np.zeros((len(step_sizes),)) self.count_illegal = np.zeros((len(step_sizes),)) self.count_proposed = np.zeros((len(step_sizes),)) def propose(self, X, gmm, target, n_jobs=1): """ Propose a new set of GMM means. Parameters ---------- X : 2-D array like of shape (n_samples, n_features) The observed data or evidence. gmm : GMM object The current state (set of gmm parameters) in the Markov Chain target : GMMPosteriorTarget object The target distribution to be found. n_jobs : int Number of cpu cores to use in the calculation of log probabilities. Returns ------- : GMM A new GMM object initialised with new mean parameters. """ new_means = np.array(gmm.means) beta = target.beta prior = target.prior steps = [np.random.multivariate_normal(np.zeros(gmm.n_features), step_size * np.eye(gmm.n_features), size=gmm.n_mixtures) for step_size in self.step_sizes] # calculation of prior probabilities of only the means, since only means will change log_priors = np.array([prior.means_prior.log_prob_single(gmm.means[mixture], mixture) for mixture in xrange(gmm.n_mixtures)]) log_prob_priors = np.sum(log_priors) previous_prob = beta * gmm.log_likelihood(X, n_jobs) + np.sum(log_priors) for i, step in enumerate(steps): for mixture in xrange(gmm.n_mixtures): self.count_proposed[i] += 1 # propose new means new_mixture_means = gmm.means[mixture] + step[mixture] # try out the new means proposed_means = np.array(new_means) proposed_means[mixture] = new_mixture_means proposed_gmm = GMM(proposed_means, np.array(gmm.covars), np.array(gmm.weights)) # calculate new prior new_log_prob_mixture = prior.means_prior.log_prob_single(new_mixture_means, mixture) new_log_prob_priors = log_prob_priors - log_priors[mixture] + new_log_prob_mixture # priors proposed_prob = beta * proposed_gmm.log_likelihood(X, n_jobs) + new_log_prob_priors # ratio ratio = proposed_prob - previous_prob if ratio > 0 or ratio > np.log(np.random.uniform()): # accept proposal new_means = proposed_means previous_prob = proposed_prob # update prior probability calculation log_prob_priors = new_log_prob_priors log_priors[mixture] = new_log_prob_mixture self.count_accepted[i] += 1 return GMM(new_means, np.array(gmm.covars), np.array(gmm.weights)) class GaussianStepCovarProposal(Proposal): def __init__(self, step_sizes=(0.001,)): """ Gaussian proposal function for the covariances of the GMM. Parameters ---------- step_sizes : array_like Array of covariance values for the Gaussian proposal. """ super(GaussianStepCovarProposal, self).__init__() self.step_sizes = step_sizes self.count_accepted = np.zeros((len(step_sizes),)) self.count_illegal = np.zeros((len(step_sizes),)) self.count_proposed = np.zeros((len(step_sizes),)) def propose(self, X, gmm, target, n_jobs=1): """ Propose a new set of GMM covariances (diagonal only). Parameters ---------- X : 2-D array like of shape (n_samples, n_features) The observed data or evidence. gmm : GMM object The current state (set of gmm parameters) in the Markov Chain target : GMMPosteriorTarget object The target distribution to be found. n_jobs : int Number of cpu cores to use in the calculation of log probabilities. Returns ------- : GMM A new GMM object initialised with new covariance parameters. """ new_covars = np.array(gmm.covars) beta = target.beta prior = target.prior previous_prob = beta * gmm.log_likelihood(X, n_jobs) + prior.log_prob(gmm) steps = [np.random.multivariate_normal(np.zeros(gmm.n_features), step_size * np.eye(gmm.n_features), size=gmm.n_mixtures) for step_size in self.step_sizes] log_priors = np.array([prior.means_prior.log_prob_single(gmm.means[mixture], mixture) for mixture in xrange(gmm.n_mixtures)]) log_prob_priors = np.sum(log_priors) for i, step in enumerate(steps): for mixture in xrange(gmm.n_mixtures): self.count_proposed[i] += 1 # propose new covars new_mixture_covars = gmm.covars[mixture] + step[mixture] if (new_mixture_covars > 0).all(): # check covariances are valid # try out the new covars proposed_covars = np.array(new_covars) proposed_covars[mixture] = new_mixture_covars proposed_gmm = GMM(np.array(gmm.means), proposed_covars, np.array(gmm.weights)) # calculate desired distribution new_log_prob_mixture = prior.covars_prior.log_prob_single(new_mixture_covars, mixture) new_log_prob_priors = log_prob_priors - log_priors[mixture] + new_log_prob_mixture proposed_prob = beta * proposed_gmm.log_likelihood(X, n_jobs) + new_log_prob_priors # ratio ratio = proposed_prob - previous_prob if ratio > 0 or ratio > np.log(np.random.uniform()): # accept proposal new_covars = proposed_covars previous_prob = proposed_prob log_prob_priors = new_log_prob_priors log_priors[mixture] = new_log_prob_mixture self.count_accepted[i] += 1 else: self.count_illegal[i] += 1 return GMM(np.array(gmm.means), np.array(new_covars), np.array(gmm.weights)) class GaussianStepWeightsProposal(Proposal): def __init__(self, n_mixtures, step_sizes=(0.001,), threshold=0.001): """ Gaussian proposal function for the weights of a GMM. Parameters ---------- n_mixtures step_sizes Notes ---------- The proposal function works by projecting the weight vector w onto the simplex defined by w_1 + w_2 + ..... w_n = 1 , 0<=w_i<=1. The change of basis matrix is found by finding n-1 vectors lying on the plane and using gramm schmidt to get an orthonormal basis. A Gaussian proposal function in (n-1)-d space is used to find the next point on the simplex. """ super(GaussianStepWeightsProposal, self).__init__() self.step_sizes = step_sizes self.n_mixtures = n_mixtures self.count_accepted = np.zeros((len(step_sizes),)) self.count_illegal = np.zeros((len(step_sizes),)) self.count_proposed = np.zeros((len(step_sizes),)) self.threshold = threshold if n_mixtures > 1: # get change of basis matrix mapping n dim coodinates to n-1 dim coordinates on simplex # x1 + x2 + x3 ..... =1 points = np.random.dirichlet([1 for i in xrange(n_mixtures)], size=n_mixtures - 1) points = points.T self.plane_origin = np.ones((n_mixtures)) / float(n_mixtures) # get vectors parallel to plane from its center (1/n,1/n,....) parallel = points - np.ones(points.shape) / float(n_mixtures) # do gramm schmidt to get mutually orthonormal vectors (basis) self.e, _ = np.linalg.qr(parallel) def transformSimplex(self, weights): """ Project weight vector onto the normal simplex. Parameters ---------- weights : array_like of shape (n_mixtures,) vector of weights for each gaussian component Returns ------- : array_like of shape (n_mixtures-1,) vector of weights projected onto the simplex plane """ # project onto the simplex return np.dot(self.e.T, weights - self.plane_origin) def invTransformSimplex(self, simplex_coords): """ Transforms a point on the simplex to the original vector space. Parameters ---------- simplex_coords : array_like of shape (n_mixtures - 1,) Coordinates of a weight vector on the simplex. Returns ------- : array_like of shape(n_mixtures,) vector of weights. """ return self.plane_origin + np.dot(self.e, simplex_coords) def propose(self, X, gmm, target, n_jobs=1): """ Propose a new set of weight vectors. Parameters ---------- X : 2-D array like of shape (n_samples, n_features) The observed data or evidence. gmm : GMM object The current state (set of gmm parameters) in the Markov Chain target : GMMPosteriorTarget object The target distribution to be found. n_jobs : int Number of cpu cores to use in the calculation of log probabilities. Returns ------- : GMM A new GMM object initialised with new covariance parameters. """ accepted = False cur_gmm = gmm if gmm.n_mixtures > 1: for i, step_size in enumerate(self.step_sizes): self.count_proposed[i] += 1 current_weights_transformed = self.transformSimplex(cur_gmm.weights) proposed_weights_transformed = np.random.multivariate_normal(current_weights_transformed, np.eye(self.n_mixtures - 1) * step_size) proposed_weights = self.invTransformSimplex(proposed_weights_transformed) if np.logical_and(0 <= proposed_weights, proposed_weights <= 1).all()\ and np.isclose(np.sum(proposed_weights), 1.0) and (proposed_weights>self.threshold).all(): previous_prob = target.log_prob(X, cur_gmm, n_jobs) proposed_gmm = GMM(np.array(cur_gmm.means), np.array(cur_gmm.covars), proposed_weights) proposed_prob = target.log_prob(X, proposed_gmm, n_jobs) ratio = proposed_prob - previous_prob if ratio > 0 or ratio > np.log(np.random.uniform()): # accept proposal self.count_accepted[i] += 1 accepted = True cur_gmm = proposed_gmm else: self.count_illegal[i] += 1 if accepted is True: return cur_gmm else: return GMM(np.array(gmm.means), np.array(gmm.covars), np.array(gmm.weights)) class GaussianTuningStepMeansProposal(Proposal): """Gaussian Proposal distribution for means of a GMM""" def __init__(self, step_sizes=(0.001,), limit=200): """ Gaussian proposal distribution for the means. The multivariate Gaussian is centered at the means of the current state in the Markov Chain and has covariance given by step_sizes. Multiple step sizes can be specified. The proposal algorithm will take these steps in the sequence specified in step_sizes. Parameters ---------- step_sizes : 1-D array_like Iterable containing the sequence of step sizes (covariances of the Gaussian proposal distribution" """ super(GaussianTuningStepMeansProposal, self).__init__() self.limit = limit self.count_steps = 0 self.count_acceptance_bucket = np.zeros((len(step_sizes),)) self.record = [] self.step_sizes = step_sizes self.count_accepted = np.zeros((len(step_sizes),)) self.count_illegal = np.zeros((len(step_sizes),)) self.count_proposed = np.zeros((len(step_sizes),)) def propose(self, X, gmm, target, n_jobs=1): """ Propose a new set of GMM means. Parameters ---------- X : 2-D array like of shape (n_samples, n_features) The observed data or evidence. gmm : GMM object The current state (set of gmm parameters) in the Markov Chain target : GMMPosteriorTarget object The target distribution to be found. n_jobs : int Number of cpu cores to use in the calculation of log probabilities. Returns ------- : GMM A new GMM object initialised with new mean parameters. """ new_means = np.array(gmm.means) beta = target.beta prior = target.prior steps = [np.random.multivariate_normal(np.zeros(gmm.n_features), step_size * np.eye(gmm.n_features), size=gmm.n_mixtures) for step_size in self.step_sizes] # calculation of prior probabilities of only the means, since only means will change log_priors = np.array([prior.means_prior.log_prob_single(gmm.means[mixture], mixture) for mixture in xrange(gmm.n_mixtures)]) log_prob_priors =

np.sum(log_priors)

numpy.sum

from private.symbol import TSymbol, NTSymbol, BondSymbol from private.utils import _node_match, _node_match_prod_rule, _edge_match, masked_softmax from private.hypergraph import Hypergraph, common_node_list from collections import Counter from copy import deepcopy from functools import partial from abc import ABCMeta, abstractmethod from networkx.algorithms.isomorphism import GraphMatcher from typing import List, Dict, Tuple import networkx as nx import numpy as np import torch import os import random import warnings DEBUG = False class GraphGrammarBase(metaclass=ABCMeta): @abstractmethod def learn(self): pass @abstractmethod def sample(self): pass class ProductionRule(object): """ A class of a production rule Attributes ---------- lhs : Hypergraph or None the left hand side of the production rule. if None, the rule is a starting rule. rhs : Hypergraph the right hand side of the production rule. """ def __init__(self, lhs, rhs): self.lhs = lhs self.rhs = rhs @property def is_start_rule(self) -> bool: return self.lhs.num_nodes == 0 @property def is_gluing(self) -> bool: if self.lhs: return len(self.lhs.get_all_NT_edges()) > 1 return False @property def is_ending(self) -> bool: return len(self.rhs.get_all_NT_edges()) == 0 @property def ext_node(self) -> Dict[int, str]: """ return a dict of external nodes """ if self.is_start_rule: return {} else: ext_node_dict = {} for each_node in self.lhs.nodes: ext_node_dict[self.lhs.node_attr(each_node)["ext_id"]] = each_node return ext_node_dict @property def lhs_nt_symbol(self) -> NTSymbol: if self.is_start_rule: return NTSymbol(degree=0, is_aromatic=False, bond_symbol_list=[]) else: # return self.lhs.edge_attr(list(self.lhs.edges)[0])['symbol'] return [self.lhs.edge_attr(edge)['symbol'] for edge in list(self.lhs.edges)] def rhs_adj_mat(self, node_edge_list): ''' return the adjacency matrix of rhs of the production rule ''' return nx.adjacency_matrix(self.rhs.hg, node_edge_list) def draw(self, file_path=None): return self.rhs.draw(file_path) def is_same(self, prod_rule, ignore_order=False): """ judge whether this production rule is the same as the input one, `prod_rule` Parameters ---------- prod_rule : ProductionRule production rule to be compared Returns ------- is_same : bool isomap : dict isomorphism of nodes and hyperedges. ex) {'bond_42': 'bond_37', 'bond_2': 'bond_1', 'e36': 'e11', 'e16': 'e12', 'e25': 'e18', 'bond_40': 'bond_38', 'e26': 'e21', 'bond_41': 'bond_39'}. key comes from `prod_rule`, value comes from `self`. """ if self.is_start_rule: if not prod_rule.is_start_rule: return False, {} else: if prod_rule.is_start_rule: return False, {} else: if prod_rule.lhs.num_nodes != self.lhs.num_nodes: return False, {} if prod_rule.rhs.num_nodes != self.rhs.num_nodes: return False, {} if prod_rule.rhs.num_edges != self.rhs.num_edges: return False, {} subhg_bond_symbol_counter \ = Counter([prod_rule.rhs.node_attr(each_node)['symbol'] \ for each_node in prod_rule.rhs.nodes]) each_bond_symbol_counter \ = Counter([self.rhs.node_attr(each_node)['symbol'] \ for each_node in self.rhs.nodes]) if subhg_bond_symbol_counter != each_bond_symbol_counter: return False, {} subhg_atom_symbol_counter \ = Counter([prod_rule.rhs.edge_attr(each_edge)['symbol'] \ for each_edge in prod_rule.rhs.edges]) each_atom_symbol_counter \ = Counter([self.rhs.edge_attr(each_edge)['symbol'] \ for each_edge in self.rhs.edges]) if subhg_atom_symbol_counter != each_atom_symbol_counter: return False, {} gm = GraphMatcher(prod_rule.rhs.hg, self.rhs.hg, partial(_node_match_prod_rule, ignore_order=ignore_order), partial(_edge_match, ignore_order=ignore_order)) try: return True, next(gm.isomorphisms_iter()) except StopIteration: return False, {} def graph_rule_applied_to(self, input_hg, selected_edge=None, selected_iso_mapping=None, vis=False): """ augment `hg` by replacing `edge` with `self.rhs`. Parameters ---------- hg : Hypergraph edge : str `edge` must belong to `hg` Returns ------- hg : Hypergraph resultant hypergraph nt_edge_list : list list of non-terminal edges """ hg = deepcopy(input_hg) nt_edge_dict = {} if self.is_start_rule: node_idx = hg.num_nodes # hg = Hypergraph() node_map_rhs = {} # node id in rhs -> node id in hg, where rhs is augmented. for num_idx, each_node in enumerate(self.rhs.nodes): hg.add_node(f"bond_{num_idx+node_idx}", attr_dict=self.rhs.node_attr(each_node)) node_map_rhs[each_node] = f"bond_{num_idx+node_idx}" for each_edge in self.rhs.edges: node_list = [] for each_node in self.rhs.nodes_in_edge(each_edge): node_list.append(node_map_rhs[each_node]) if isinstance(self.rhs.nodes_in_edge(each_edge), set): node_list = set(node_list) edge_id = hg.add_edge( node_list, attr_dict=self.rhs.edge_attr(each_edge)) if "nt_idx" in hg.edge_attr(edge_id): nt_edge_dict[hg.edge_attr(edge_id)["nt_idx"]] = edge_id nt_edge_list = [nt_edge_dict[key] for key in range(len(nt_edge_dict))] return hg, nt_edge_list, True else: hg_NT_edges = hg.get_all_NT_edges() lhs_NT_edges = self.lhs.get_all_NT_edges() match_flag = [] lhs_hg_matched_edge_dict = {} assert len(lhs_NT_edges) == 1 # print("NTs:") # print([edge.edges for edge in hg_NT_edges]) # print([edge.edge_attr(list(edge.edges)[0])['symbol'].symbol for edge in hg_NT_edges]) # eq = lhs_NT_edges[0] == hg_NT_edges[0] for lhs_edge in lhs_NT_edges: if lhs_edge in hg_NT_edges: match_flag.append(True) assert len(lhs_edge.edges) == 1 for hg_NT_edge in hg_NT_edges: if hg_NT_edge == lhs_edge: if list(lhs_edge.edges)[0] not in lhs_hg_matched_edge_dict.keys(): lhs_hg_matched_edge_dict[list(lhs_edge.edges)[0]] = [hg_NT_edge] else: lhs_hg_matched_edge_dict[list(lhs_edge.edges)[0]].append(hg_NT_edge) else: match_flag.append(False) if not all(match_flag): return hg, [], False # for edge in hg.edges: # print("edge: {} -> nodes: {}".format(edge, hg.nodes_in_edge(edge))) # order of nodes that belong to the non-terminal edge in hg nt_order_dict = {} # hg_node -> order ("bond_17" : 1) nt_order_dict_inv = {} # order -> hg_node to_rm_edges = [] assert len(lhs_NT_edges) == 1 for _i, edge_lhs in enumerate(lhs_NT_edges): edges_hg = lhs_hg_matched_edge_dict[list(edge_lhs.edges)[0]] edges_name_to_hg = {list(edge_hg.edges)[0]:edge_hg for edge_hg in edges_hg} edges_cand = edges_name_to_hg.keys() # filter out those removed edges in previous iterations edges = [_edge for _edge in edges_cand if _edge not in to_rm_edges] # if it is empty, meaning that there is only actually one NT in hg that matches multiple NTs in the lhs, the rule should be abandoned if len(edges) == 0: return hg, [], False # TODO add options edge = selected_edge if edge == None: edge = np.random.choice(edges, 1)[0] # print('selected edge:', edge) # edge_hg = edges_hg[edges_cand.index(edge)] edge_hg = edges_name_to_hg[edge] iso_mappings = edge_hg.find_isomorphism_mapping(edge_lhs, vis) # From edge_hg to edge_lhs # print("mapping:", iso_mapping) if selected_iso_mapping is None: iso_mapping =

np.random.choice(iso_mappings, 1)

numpy.random.choice

#!/usr/bin/env python # -*- coding:utf-8 -*- from collections import Iterable from enum import Enum import os import pkg_resources import sys import pandas as pd import numpy as np import scipy as sp from clonesig import mixin_init_parameters try: rows, columns = os.popen('stty size', 'r').read().split() pd.set_option('display.width', int(columns)) pd.set_option('display.max_columns', 200) except: print("running on server, otherwise please investigate") # this is the threshold to consider eigenvalues null to get an approximation # of the degree of freedom of the cosine distance matrix for signatures EV_DOF_THRESHOLD = 0.5 def _get_projected(L, R, x): x[x < L] = L[x < L] x[x > R] = R[x > R] return x def log_binomial_coeff(n, k): return (sp.special.gammaln(n+1) - (sp.special.gammaln(k+1) + sp.special.gammaln(n-k+1))) def _beta_binomial_logpmf(x, n, phi, tau): alpha = phi / tau beta = 1/tau - alpha return log_binomial_coeff(n, x)\ + sp.special.betaln(x + alpha, n - x + beta)\ - sp.special.betaln(alpha, beta) def beta_binomial_pmf(x, n, phi, tau): return np.exp(_beta_binomial_logpmf(x, n, phi, tau)) class Estimator(mixin_init_parameters.MixinInitParameters): def __init__(self, T, B, C_normal, C_tumor_tot, C_tumor_minor, D, p, J, maxiter=10000, pi=None, phi=None, xi=None, rho=None, tau=None, verbose=False, inputMU=None, nu=None, save_trace=False): """ est = Estimator(...) est.init_params(...) est.fit(...) """ self.T = T self.B = B self.C_normal = C_normal self.C_tumor_tot = C_tumor_tot self.C_tumor_minor = C_tumor_minor self.C_tumor_major = (self.C_tumor_tot - self.C_tumor_minor).astype(int) self.C_est_tumor_mut = C_tumor_minor.copy() # this is a choice self.C_est_tumor_mut[self.C_est_tumor_mut == 0] = \ self.C_tumor_tot[self.C_est_tumor_mut == 0] # alternative could be # self.C_est_tumor_mut[self.C_est_tumor_mut==0] = 1 self.Mmax = max(self.C_tumor_major) self.D = D self.p = p self.J = J self.N = len(B) if inputMU is None: self.mu_matrix = self.default_mu() else: self.mu_matrix = inputMU self.mu_matrix = self._remove_zeros_distrib(self.mu_matrix) self.mu = np.moveaxis(np.tile(self.mu_matrix[:, self.T.astype(int)], (self.J, self.Mmax, 1, 1)), [0, 1, 2, 3], [1, 3, 2, 0]) self.L = self.mu.shape[2] self.Fs = list() self.maxiter = maxiter self.init_params(pi, phi, xi, rho, tau, spasePi=False) self.init_nu_param(nu) self.verbose = verbose ev, _ = np.linalg.eig(1-sp.spatial.distance.squareform(sp.spatial.distance.pdist(self.mu_matrix, 'cosine'))) self.dof = sum(ev > EV_DOF_THRESHOLD) self.save_trace = save_trace def init_nu_param(self, nu=None): if nu is None: self.nu = np.ones((self.N, self.Mmax)) * \ mixin_init_parameters.ZERO_PADDING for i in range(self.N): self.nu[i, :self.C_tumor_major[i]] = 1 / self.C_tumor_major[i] # self.nu[i, min(int(np.round(max(1, self.B[i]/self.D[i] * (self.p*self.C_tumor_tot[i] + (1-self.p) * self.C_normal[i]) / self.p))), self.C_tumor_major[i]) - 1] = 1 else: self.nu = nu # make sure there are no null values self.nu[self.nu == 0] = mixin_init_parameters.ZERO_PADDING self.nu = self.nu / self.nu.sum(axis=1)[:, np.newaxis] # get log(nu) in the right dimension (N * J * L * Mmax) for computation self.lognu_sig = np.moveaxis(np.tile(np.log(self.nu), [self.J, self.L, 1, 1]), [0, 1, 2], [1, 2, 0]) self.Mask = (self.nu > mixin_init_parameters.ZERO_PADDING * 10).astype(bool) self.Mask_sig = np.moveaxis(np.tile(self.Mask, [self.J, self.L, 1, 1]), [0, 1, 2], [1, 2, 0]) pre_eta = np.repeat(np.arange(1, self.Mmax+1).reshape([-1, 1]), self.N, axis=1).T self.eta = self.p * pre_eta / ((1 - self.p) * np.repeat(self.C_normal.reshape([-1, 1]), self.Mmax, axis=1) + self.p * np.repeat(self.C_tumor_tot.reshape([-1, 1]), self.Mmax, axis=1)) self.eta[~self.Mask] = 1 self.qun, self.vmnu, self.rnus = self.get_responsabilities @property def get_theta(self): return np.concatenate((self.xi.flatten(), self.pi.flatten(), self.phi.flatten(), [self.tau])) def get_log_xi_sig(self, xi): """ get log(xi) in the right dimension (N * J * L * Mmax) for computation """ return np.moveaxis(np.tile(np.log(xi).reshape(-1, 1).T, [self.N, self.L, self.Mmax, 1]), [1, 2, 3], [2, 3, 1]) def get_log_bb_sig(self, phi, tau): """ computes the logbinomial probability of Bn|Dn for all point, in all clones in the right dimension (N * J * L * Mmax) for computation """ phi_un_bar = np.rollaxis(np.repeat([self.eta], self.J, axis=0), 0, 2) \ * np.tile(phi.reshape(-1, 1), [self.N, 1, self.Mmax]) log_bb = _beta_binomial_logpmf( np.rollaxis(np.tile(self.B.reshape(-1, 1), [self.J, 1, self.Mmax]), 1, 0), np.rollaxis(np.tile(self.D.reshape(-1, 1), [self.J, 1, self.Mmax]), 1, 0), phi_un_bar, tau) log_bb_sig = np.rollaxis(np.repeat([log_bb], self.L, axis=0), 0, 3) return log_bb_sig def get_log_pi_sig(self, pi): """ get log(pi) in the right dimension (N * J * L * Mmax) for computation """ return np.moveaxis(np.tile(np.log(pi), (self.N, self.Mmax, 1, 1)), [1, 2, 3], [3, 1, 2]) def compute_F(self, qun, rnus, vmnu, new_xi, new_pi, tau, phi, nu): q = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, tau, phi, nu) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) big_qun_sig = np.moveaxis(np.repeat([big_qun], self.L, axis=0), [0, 1, 2], [2, 0, 1]) big_rnus_m = np.moveaxis(np.repeat([rnus], self.Mmax, axis=0), [0, 1, 2, 3], [3, 0, 1, 2]) big_vmn_sig = np.moveaxis(np.repeat([vmnu], self.L, axis=0), [0, 1, 2], [2, 0, 1]) joint_dist = self._remove_zeros_joint( big_qun_sig * big_rnus_m * big_vmn_sig) # old version - (joint_dist * np.log(joint_dist)).sum() h = - (joint_dist * np.log(joint_dist)).sum() return q - h def compute_Q(self, qun, rnus, vmnu, new_xi, new_pi, tau, phi, nu): log_xi_sig = self.get_log_xi_sig(new_xi) log_mu = np.log(self.mu) log_pi = self.get_log_pi_sig(new_pi) combiln = log_binomial_coeff(self.D, self.B) bin_coeff = np.moveaxis(np.tile(combiln, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) phi_un_bar = np.repeat(self.eta.reshape(self.N, -1, self.Mmax), self.J, axis=1) *\ np.swapaxes(np.tile(phi, (self.N, self.Mmax, 1)), 1, 2) big_b = np.moveaxis(np.tile(self.B, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) big_d = np.moveaxis(np.tile(self.D, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) term1 = sp.special.loggamma(big_b + phi_un_bar / tau) term2 = sp.special.loggamma((1 - phi_un_bar) / tau + big_d - big_b) term3 = sp.special.loggamma(np.ones((self.N, self.J, self.Mmax)) / tau) term4 = sp.special.loggamma(np.ones((self.N, self.J, self.Mmax)) / tau + big_d) term5 = sp.special.loggamma(phi_un_bar / tau) term6 = sp.special.loggamma((1 - phi_un_bar) / tau) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) big_qun_sig = np.moveaxis(np.repeat([big_qun], self.L, axis=0), [0, 1, 2], [2, 0, 1]) big_rnus_m = np.moveaxis(np.repeat([rnus], self.Mmax, axis=0), [0, 1, 2, 3], [3, 0, 1, 2]) big_vmn_sig = np.moveaxis(np.repeat([vmnu], self.L, axis=0), [0, 1, 2], [2, 0, 1]) Q = (big_qun * vmnu * (bin_coeff + term1 + term2 + term3 - term4 - term5 - term6)).sum() + \ (big_qun_sig * big_rnus_m * big_vmn_sig * (log_xi_sig + log_mu + log_pi + self.lognu_sig)).sum() return -Q def compute_alternative_Q(self, qun, rnus, vmnu, new_xi, new_pi, tau, phi, nu): """ this function implements another way to compute Q on can then test self.compute_Q(qun, rnus, new_xi, new_pi, tau, phi) ==\ self.compute_alternative_Q(qun, rnus, new_xi, new_pi, tau, phi) """ log_xi_sig = self.get_log_xi_sig(new_xi) log_bb_sig = self.get_log_bb_sig(phi, tau) log_pi = self.get_log_pi_sig(new_pi) log_mu = np.log(self.mu) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) big_qun_sig = np.moveaxis(np.repeat([big_qun], self.L, axis=0), [0, 1, 2], [2, 0, 1]) big_rnus_m = np.moveaxis(np.repeat([rnus], self.Mmax, axis=0), [0, 1, 2, 3], [3, 0, 1, 2]) big_vmn_sig = np.moveaxis(np.repeat([vmnu], self.L, axis=0), [0, 1, 2], [2, 0, 1]) Q = (big_qun_sig * big_rnus_m * big_vmn_sig * (log_xi_sig + log_mu + log_pi + log_bb_sig + self.lognu_sig) * self.Mask_sig).sum() return -Q def compute_dQ(self, qun, vmnu, tau, phi): # convention : tau and then phi dQ = np.zeros(self.J + 1) # general stuff big_eta = np.repeat(self.eta.reshape(self.N, -1, self.Mmax), self.J, axis=1) phi_un_bar = big_eta *\ np.swapaxes(np.tile(phi, (self.N, self.Mmax, 1)), 1, 2) big_b = np.moveaxis(np.tile(self.B, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) big_d = np.moveaxis(np.tile(self.D, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) # compute dQ/d\tau term1 = sp.special.psi(big_b + phi_un_bar / tau) term2 = sp.special.psi((1 - phi_un_bar) / tau + big_d - big_b) term3 = sp.special.psi(np.ones((self.N, self.J, self.Mmax)) / tau) term4 = sp.special.psi(np.ones((self.N, self.J, self.Mmax)) / tau + big_d) term5 = sp.special.psi(phi_un_bar / tau) term6 = sp.special.psi((1 - phi_un_bar) / tau) dQ[0] = (big_qun * vmnu / (tau**2) * (- phi_un_bar * term1 - (1 - phi_un_bar) * term2 - term3 + term4 + phi_un_bar * term5 + (1 - phi_un_bar) * term6)).sum() # compute dQ/f\phi_u factor = big_qun * vmnu * big_eta / tau dQ[1:] = (factor * (term1 - term2 - term5 + term6)).sum(axis=0).sum(axis=1) return -dQ def compute_alternative_dQ(self, qun, vmnu, tau, phi): """ this function implements another way to compute dQ on can then test self.compute_dQ(qun, rnus, new_xi, new_pi, tau, phi)[1:] == \ self.compute_alternative_dQ(qun, rnus, new_xi, new_pi, tau, phi)[1:] # only implemented for dQ/d\phi as \tau=1/\tau, so not the same """ dQ = np.zeros(self.J + 1) big_eta = np.repeat(self.eta.reshape(self.N, -1, self.Mmax), self.J, axis=1) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) for mut in range(self.N): bn = np.moveaxis(np.tile(np.arange(self.B[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) dn = np.moveaxis(np.tile(np.arange(self.D[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) phi_big1 = np.swapaxes(np.tile(phi, (int(self.B[mut]), self.Mmax, 1)), 1, 2) to_add_phi_1 = (big_eta[mut, :, :] / (big_eta[mut, :, :] * phi_big1 + bn * tau)).sum(axis=0) bndn = np.moveaxis(np.tile(np.arange(self.D[mut] - self.B[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) phi_big2 = np.swapaxes(np.tile(phi, (int(self.D[mut] - self.B[mut]), self.Mmax, 1)), 1, 2) to_add_phi_2 = (-big_eta[mut, :, :] / (1 - phi_big2 * big_eta[mut, :, :] + bndn * tau))\ .sum(axis=0) dQ[1:] += (big_qun[mut, :, :] * vmnu[mut, :, :] * (to_add_phi_1 + to_add_phi_2)).sum(axis=1) to_add_tau_1 = (bn / (big_eta[mut, :, :] * phi_big1 + bn * tau))\ .sum(axis=0) to_add_tau_2 = (bndn / (1 - phi_big2 * big_eta[mut, :, :] + bndn * tau))\ .sum(axis=0) to_add_tau_3 = (dn / (1 + dn * tau)).sum(axis=0) dQ[0] += (big_qun[mut, :, :] * vmnu[mut, :, :] * (to_add_tau_1 + to_add_tau_2 - to_add_tau_3)).sum() return -dQ def compute_dQ2(self, qun, vmnu, tau, phi): dQ2 = np.zeros((self.J + 1, self.J + 1)) big_eta = np.repeat(self.eta.reshape(self.N, -1, self.Mmax), self.J, axis=1) phi_un_bar = big_eta *\ np.swapaxes(np.tile(phi, (self.N, self.Mmax, 1)), 1, 2) big_b = np.moveaxis(np.tile(self.B, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) big_d = np.moveaxis(np.tile(self.D, (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) term1_0 = sp.special.psi(big_b + phi_un_bar / tau) term2_0 = sp.special.psi((1 - phi_un_bar) / tau + big_d - big_b) term3_0 = sp.special.psi(np.ones((self.N, self.J, self.Mmax)) / tau) term4_0 = sp.special.psi(np.ones((self.N, self.J, self.Mmax)) / tau + big_d) term5_0 = sp.special.psi(phi_un_bar / tau) term6_0 = sp.special.psi((1 - phi_un_bar) / tau) term1_1 = sp.special.polygamma(1, big_b + phi_un_bar / tau) term2_1 = sp.special.polygamma(1, (1 - phi_un_bar) / tau + big_d - big_b) term3_1 = sp.special.polygamma(1, np.ones((self.N, self.J, self.Mmax)) / tau) term4_1 = sp.special.polygamma(1, np.ones((self.N, self.J, self.Mmax)) / tau + big_d) term5_1 = sp.special.polygamma(1, phi_un_bar / tau) term6_1 = sp.special.polygamma(1, (1 - phi_un_bar) / tau) u_prime_v = (2 / (tau**3)) * (phi_un_bar * term1_0 + (1 - phi_un_bar) * term2_0 + term3_0 - term4_0 - phi_un_bar * term5_0 - (1 - phi_un_bar) * term6_0) v_prime_u = (phi_un_bar**2 / tau**4 * term1_1 + (1 - phi_un_bar)**2 / tau**4 * term2_1 + term3_1 / tau**4 - term4_1 / tau**4 - phi_un_bar**2 / tau**4 * term5_1 - (1 - phi_un_bar)**2 / tau**4 * term6_1) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) dQ2[0] = (big_qun * vmnu * (u_prime_v + v_prime_u)).sum() factor = big_qun * vmnu * big_eta / tau**2 dQ2[0, 1:] = dQ2[1:, 0] = (factor * (- term1_0 - phi_un_bar / tau * term1_1 + term2_0 + (1 - phi_un_bar) / tau * term2_1 + term5_0 + phi_un_bar / tau * term5_1 - term6_0 - (1 - phi_un_bar) / tau * term6_1)).sum(axis=0).sum(axis=1) dQ2[np.arange(1, self.J+1), np.arange(1, self.J+1)] = \ (big_qun * vmnu * big_eta**2 / tau**2 * (term1_1 + term2_1 - term5_1 - term6_1)).sum(axis=0).sum(axis=1) return -dQ2 def compute_alternative_dQ2(self, qun, vmnu, tau, phi): """ this function implements another way to compute dQ on can then test self.compute_dQ(qun, rnus, new_xi, new_pi, tau, phi)[1:] == \ elf.compute_alternative_dQ(qun, rnus, new_xi, new_pi, tau, phi)[1:] # only implemented for dQ/d\phi as \tau=1/\tau, so not the same """ dQ2 = np.zeros((self.J + 1, self.J + 1)) big_eta = np.repeat(self.eta.reshape(self.N, -1, self.Mmax), self.J, axis=1) big_qun = np.moveaxis(np.repeat([qun], self.Mmax, axis=0), [0, 1, 2], [2, 0, 1]) for mut in range(self.N): bn = np.moveaxis(np.tile(np.arange(self.B[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) dn = np.moveaxis(np.tile(np.arange(self.D[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) phi_big1 = np.swapaxes(np.tile(phi, (int(self.B[mut]), self.Mmax, 1)), 1, 2) to_add_phi_1 = (big_eta[mut, :, :]**2 / (big_eta[mut, :, :] * phi_big1 + bn * tau)**2).sum(axis=0) bndn = np.moveaxis(np.tile(np.arange(self.D[mut] - self.B[mut]), (self.J, self.Mmax, 1)), [0, 1, 2], [1, 2, 0]) phi_big2 = np.swapaxes(np.tile(phi, (int(self.D[mut] - self.B[mut]), self.Mmax, 1)), 1, 2) to_add_phi_2 = (big_eta[mut, :, :]**2 / (1 - phi_big2 * big_eta[mut, :, :] + bndn * tau)**2).sum(axis=0) dQ2[np.arange(1, self.J+1), np.arange(1, self.J+1)] += (big_qun[mut, :, :] * vmnu[mut, :, :] * (- to_add_phi_1 - to_add_phi_2)).sum(axis=1) to_add_tau_1 = (bn**2 / ((big_eta[mut, :, :] * phi_big1 + bn * tau))**2).sum(axis=0) to_add_tau_2 = (bndn**2 / ((1 - phi_big2 * big_eta[mut, :, :] + bndn * tau))**2).sum(axis=0) to_add_tau_3 = (dn**2 / ((1 + dn * tau))**2).sum(axis=0) dQ2[0, 0] += (big_qun[mut, :, :] * vmnu[mut, :, :] * (- to_add_tau_1 - to_add_tau_2 + to_add_tau_3)).sum() to_add_phi_tau_1 = (big_eta[mut, :, :] * bn / ((big_eta[mut, :, :] * phi_big1 + bn * tau)**2)).sum(axis=0) to_add_phi_tau_2 = (big_eta[mut, :, :] * bndn / ((1 - phi_big2 * self.eta[mut] + bndn * tau)**2)).sum(axis=0) dQ2[1:, 0] += (big_qun[mut, :, :] * vmnu[mut, :, :] * (- to_add_phi_tau_1 + to_add_phi_tau_2)).sum(axis=1) dQ2[0, 1:] += (big_qun[mut, :, :] * vmnu[mut, :, :] * (- to_add_phi_tau_1 + to_add_phi_tau_2)).sum(axis=1) return -dQ2 @staticmethod def _remove_zeros_joint(joint): pre_joint = joint + mixin_init_parameters.ZERO_PADDING * (joint == 0) joint_norm = pre_joint / pre_joint.sum(axis=3).sum(axis=2).sum(axis=1)[:, np.newaxis, np.newaxis, np.newaxis] return joint_norm @staticmethod def _get_binded_variables(L, R, x, dQ, epsilon): B_left = (x <= L + epsilon) & (dQ > 0) B_right = (x >= R - epsilon) & (dQ < 0) return B_left | B_right @staticmethod def _compute_right_term(x, x_new, B, dQ, dQ2, L, R): pre_lam = np.linalg.inv(dQ2).dot(dQ)[~B] left_part = np.sum((dQ[~B]).dot(pre_lam)) right_part = np.sum(((dQ[B]).dot((x - x_new)[B]))) return left_part, right_part @property def get_responsabilities(self): # compute qun log_xi_sig = self.get_log_xi_sig(self.xi) log_bb_sig = self.get_log_bb_sig(self.phi, self.tau) log_pi = self.get_log_pi_sig(self.pi) pre_altfinal = np.exp(log_xi_sig + log_bb_sig + log_pi + np.log(self.mu) + self.lognu_sig) pre_altfinal[pre_altfinal == 0] = 2 * sys.float_info.min final = pre_altfinal * self.Mask_sig pre_qun = final.sum(axis=3).sum(axis=2) row_sums = pre_qun.sum(axis=1) qun = pre_qun / row_sums[:, np.newaxis] # compute vmnu pre_vmnu = final.sum(axis=2) row_sums = pre_vmnu.sum(axis=2) vmnu = pre_vmnu / np.expand_dims(row_sums, axis=2) # compute rnus pre_rnus = np.exp(log_pi[:, :, :, 0] + np.log(self.mu)[:, :, :, 0]) row_sums = pre_rnus.sum(axis=2) rnus = pre_rnus / row_sums[:, :, np.newaxis] return qun, vmnu, rnus def fit(self, epsilon_em=None, epsilon_newton=10**-6, epsilon_box=10**-6, beta=0.5, sigma=0.25): if epsilon_em is None: epsilon_em = 10**-5 * self.J * self.L new_theta = self.get_theta * 10000 em = 0 while (np.sqrt(np.sum((new_theta-self.get_theta)**2)) > epsilon_em) and (em < self.maxiter): if self.verbose: print(em, self.xi, self.phi, self.tau, np.sqrt(np.sum((new_theta - self.get_theta)**2))) em = em + 1 new_theta = self.get_theta ########### # E-phase # ########### qun, vmnu, rnus = self.get_responsabilities ########### # M-phase # ########### new_xi = qun.sum(axis=0) / self.N pre_new_pi = (rnus*np.rollaxis(np.repeat([qun], self.L, axis=0), 0, 3)).sum(axis=0)/np.rollaxis(np.repeat([qun], self.L, axis=0), 0, 3).sum(axis=0) new_pi = self._remove_zeros_distrib(pre_new_pi) L = np.zeros(self.J + 1) + 1e-5 R = np.ones(self.J + 1) - 1e-5 R[0] = 0.5 # newton method x_0 = np.concatenate(([self.tau], self.phi)) x = x_0 currentQ = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, x[0], x[1:], self.nu) newt = 0 while True: if self.verbose: print('newt', newt, x, currentQ) newt = newt + 1 dQ2 = self.compute_dQ2(qun, vmnu, x[0], x[1:]) dQ = self.compute_dQ(qun, vmnu, x[0], x[1:]) if (np.sum(dQ) == 0) & (em == 1): break # get epsilon tmp_new_x = _get_projected(L, R, x - dQ) eps_k = min(epsilon_box, np.sqrt(np.sum((x - tmp_new_x)**2))) # get I^{sharp} (binded variables) B = self._get_binded_variables(L, R, x, dQ, eps_k) # get D dQ2[:, B] = dQ2[B, :] = 0 dQ2[B, B] = 1 # get alpha k m = 1 alpha = 1 x_new = _get_projected(L, R, x - alpha * np.linalg.inv(dQ2).dot(dQ)) new_Q = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, x_new[0], x_new[1:], self.nu) left_part, right_part = self._compute_right_term(x, x_new, B, dQ, dQ2, L, R) # deal with cases where the hessian in indefinite! if left_part < 0: eigenvalues = np.linalg.eigvals(dQ2) if min(eigenvalues) < 0: to_add_eig = np.abs(min(eigenvalues)) + np.finfo(np.float32).eps dQ2 = dQ2 + to_add_eig * np.identity(len(x)) x_new = _get_projected(L, R, x - alpha * np.linalg.inv(dQ2).dot(dQ)) new_Q = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, x_new[0], x_new[1:], self.nu) left_part, right_part = \ self._compute_right_term(x, x_new, B, dQ, dQ2, L, R) # stopping criterion if left_part + right_part < epsilon_newton: break # line search right_term = sigma * (beta * alpha * left_part + right_part) if self.verbose: print('linesearch_before', m, currentQ, new_Q, right_term, x, x_new) while not ((currentQ - new_Q) >= right_term): if self.verbose: print('linesearch', m, currentQ, new_Q, right_term, x, x_new) m += 1 alpha = beta * alpha x_new = _get_projected(L, R, x - alpha * np.linalg.inv(dQ2).dot(dQ)) new_Q = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, x_new[0], x_new[1:], self.nu) left_part, right_part = self._compute_right_term(x, x_new, B, dQ, dQ2, L, R) right_term = sigma * (beta * alpha * left_part + right_part) x = x_new currentQ = self.compute_Q(qun, rnus, vmnu, new_xi, new_pi, x[0], x[1:], self.nu) new_tau = x[0] new_phi = x[1:] self.xi = new_xi self.pi = new_pi self.tau = new_tau self.phi = new_phi self.qun = qun self.rnus = rnus self.vmnu = vmnu currentQ = self.compute_F(qun, rnus, vmnu, new_xi, new_pi, x[0], x[1:], self.nu) if self.save_trace: self.Fs.append(currentQ) self.Fs.append(currentQ) @property def get_k(self): return self.J * (self.L - 1 + 2) @property def get_k_cn(self): """ k is the number of parameters of the model using this function to test several values wrt data. True value is self.J * (self.L - 1 + 2) (self.L - 1) because of 1 degree of liberty in pi 2 for phi and xi -1 because xi lacks a degree of freedom 1 on top for tau/rho np.mean(self.C_tumor_major) to account for the extra degree of freedom of the model due to fitting the copy number. """ return self.J * (self.L - 1 + 2) * np.mean(self.C_tumor_major) @property def get_k_dof_cn(self): """ same as get_k_cn but with the degree of freedom of the input signature matrix instead of the number of signatures. """ return self.J * (self.dof - 1 + 2) * np.mean(self.C_tumor_major) def get_bic(self, dof=False, cn=False): if not cn: k = self.get_k else: if dof: k = self.get_k_dof_cn else: k = self.get_k_cn return - k * np.log(self.N) / 2 + self.get_loglikelihood def get_bic_heuristics(self, dof=True, factor=0.042, cn=False): """ the factor is valid for dof=True (O.O65 for a subset, O.O34 for 47 signatures) otherwise, we advise factor around 0.015 for the 47 signatures or around 0.040 for a subset of signatures. """ if not cn: k = self.get_k else: if dof: k = self.get_k_dof_cn else: k = self.get_k_cn return - factor * k *

np.log(self.N)

numpy.log

# This module has been generated automatically from space group information # obtained from the Computational Crystallography Toolbox # """ Space groups This module contains a list of all the 230 space groups that can occur in a crystal. The variable space_groups contains a dictionary that maps space group numbers and space group names to the corresponding space group objects. .. moduleauthor:: <NAME> <<EMAIL>> """ #----------------------------------------------------------------------------- # Copyright (C) 2013 The Mosaic Development Team # # Distributed under the terms of the BSD License. The full license is in # the file LICENSE.txt, distributed as part of this software. #----------------------------------------------------------------------------- import numpy as N class SpaceGroup(object): """ Space group All possible space group objects are created in this module. Other modules should access these objects through the dictionary space_groups rather than create their own space group objects. """ def __init__(self, number, symbol, transformations): """ :param number: the number assigned to the space group by international convention :type number: int :param symbol: the Hermann-Mauguin space-group symbol as used in PDB and mmCIF files :type symbol: str :param transformations: a list of space group transformations, each consisting of a tuple of three integer arrays (rot, tn, td), where rot is the rotation matrix and tn/td are the numerator and denominator of the translation vector. The transformations are defined in fractional coordinates. :type transformations: list """ self.number = number self.symbol = symbol self.transformations = transformations self.transposed_rotations = N.array([N.transpose(t[0]) for t in transformations]) self.phase_factors = N.exp(N.array([(-2j*N.pi*t[1])/t[2] for t in transformations])) def __repr__(self): return "SpaceGroup(%d, %s)" % (self.number, repr(self.symbol)) def __len__(self): """ :return: the number of space group transformations :rtype: int """ return len(self.transformations) def symmetryEquivalentMillerIndices(self, hkl): """ :param hkl: a set of Miller indices :type hkl: Scientific.N.array_type :return: a tuple (miller_indices, phase_factor) of two arrays of length equal to the number of space group transformations. miller_indices contains the Miller indices of each reflection equivalent by symmetry to the reflection hkl (including hkl itself as the first element). phase_factor contains the phase factors that must be applied to the structure factor of reflection hkl to obtain the structure factor of the symmetry equivalent reflection. :rtype: tuple """ hkls = N.dot(self.transposed_rotations, hkl) p = N.multiply.reduce(self.phase_factors**hkl, -1) return hkls, p space_groups = {} transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(1, 'P 1', transformations) space_groups[1] = sg space_groups['P 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(2, 'P -1', transformations) space_groups[2] = sg space_groups['P -1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(3, 'P 1 2 1', transformations) space_groups[3] = sg space_groups['P 1 2 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(4, 'P 1 21 1', transformations) space_groups[4] = sg space_groups['P 1 21 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(5, 'C 1 2 1', transformations) space_groups[5] = sg space_groups['C 1 2 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(6, 'P 1 m 1', transformations) space_groups[6] = sg space_groups['P 1 m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(7, 'P 1 c 1', transformations) space_groups[7] = sg space_groups['P 1 c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(8, 'C 1 m 1', transformations) space_groups[8] = sg space_groups['C 1 m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(9, 'C 1 c 1', transformations) space_groups[9] = sg space_groups['C 1 c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(10, 'P 1 2/m 1', transformations) space_groups[10] = sg space_groups['P 1 2/m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(11, 'P 1 21/m 1', transformations) space_groups[11] = sg space_groups['P 1 21/m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(12, 'C 1 2/m 1', transformations) space_groups[12] = sg space_groups['C 1 2/m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(13, 'P 1 2/c 1', transformations) space_groups[13] = sg space_groups['P 1 2/c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(14, 'P 1 21/c 1', transformations) space_groups[14] = sg space_groups['P 1 21/c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(15, 'C 1 2/c 1', transformations) space_groups[15] = sg space_groups['C 1 2/c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(16, 'P 2 2 2', transformations) space_groups[16] = sg space_groups['P 2 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(17, 'P 2 2 21', transformations) space_groups[17] = sg space_groups['P 2 2 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(18, 'P 21 21 2', transformations) space_groups[18] = sg space_groups['P 21 21 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(19, 'P 21 21 21', transformations) space_groups[19] = sg space_groups['P 21 21 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(20, 'C 2 2 21', transformations) space_groups[20] = sg space_groups['C 2 2 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(21, 'C 2 2 2', transformations) space_groups[21] = sg space_groups['C 2 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(22, 'F 2 2 2', transformations) space_groups[22] = sg space_groups['F 2 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(23, 'I 2 2 2', transformations) space_groups[23] = sg space_groups['I 2 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(24, 'I 21 21 21', transformations) space_groups[24] = sg space_groups['I 21 21 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(25, 'P m m 2', transformations) space_groups[25] = sg space_groups['P m m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(26, 'P m c 21', transformations) space_groups[26] = sg space_groups['P m c 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(27, 'P c c 2', transformations) space_groups[27] = sg space_groups['P c c 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(28, 'P m a 2', transformations) space_groups[28] = sg space_groups['P m a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(29, 'P c a 21', transformations) space_groups[29] = sg space_groups['P c a 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(30, 'P n c 2', transformations) space_groups[30] = sg space_groups['P n c 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(31, 'P m n 21', transformations) space_groups[31] = sg space_groups['P m n 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(32, 'P b a 2', transformations) space_groups[32] = sg space_groups['P b a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(33, 'P n a 21', transformations) space_groups[33] = sg space_groups['P n a 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(34, 'P n n 2', transformations) space_groups[34] = sg space_groups['P n n 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(35, 'C m m 2', transformations) space_groups[35] = sg space_groups['C m m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(36, 'C m c 21', transformations) space_groups[36] = sg space_groups['C m c 21'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(37, 'C c c 2', transformations) space_groups[37] = sg space_groups['C c c 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(38, 'A m m 2', transformations) space_groups[38] = sg space_groups['A m m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(39, 'A b m 2', transformations) space_groups[39] = sg space_groups['A b m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(40, 'A m a 2', transformations) space_groups[40] = sg space_groups['A m a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(41, 'A b a 2', transformations) space_groups[41] = sg space_groups['A b a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(42, 'F m m 2', transformations) space_groups[42] = sg space_groups['F m m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,1,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,1,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(43, 'F d d 2', transformations) space_groups[43] = sg space_groups['F d d 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(44, 'I m m 2', transformations) space_groups[44] = sg space_groups['I m m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(45, 'I b a 2', transformations) space_groups[45] = sg space_groups['I b a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(46, 'I m a 2', transformations) space_groups[46] = sg space_groups['I m a 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(47, 'P m m m', transformations) space_groups[47] = sg space_groups['P m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(48, 'P n n n :2', transformations) space_groups[48] = sg space_groups['P n n n :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(49, 'P c c m', transformations) space_groups[49] = sg space_groups['P c c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(50, 'P b a n :2', transformations) space_groups[50] = sg space_groups['P b a n :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(51, 'P m m a', transformations) space_groups[51] = sg space_groups['P m m a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(52, 'P n n a', transformations) space_groups[52] = sg space_groups['P n n a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(53, 'P m n a', transformations) space_groups[53] = sg space_groups['P m n a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(54, 'P c c a', transformations) space_groups[54] = sg space_groups['P c c a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(55, 'P b a m', transformations) space_groups[55] = sg space_groups['P b a m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(56, 'P c c n', transformations) space_groups[56] = sg space_groups['P c c n'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(57, 'P b c m', transformations) space_groups[57] = sg space_groups['P b c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(58, 'P n n m', transformations) space_groups[58] = sg space_groups['P n n m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(59, 'P m m n :2', transformations) space_groups[59] = sg space_groups['P m m n :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(60, 'P b c n', transformations) space_groups[60] = sg space_groups['P b c n'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(61, 'P b c a', transformations) space_groups[61] = sg space_groups['P b c a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(62, 'P n m a', transformations) space_groups[62] = sg space_groups['P n m a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(63, 'C m c m', transformations) space_groups[63] = sg space_groups['C m c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(64, 'C m c a', transformations) space_groups[64] = sg space_groups['C m c a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(65, 'C m m m', transformations) space_groups[65] = sg space_groups['C m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(66, 'C c c m', transformations) space_groups[66] = sg space_groups['C c c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(67, 'C m m a', transformations) space_groups[67] = sg space_groups['C m m a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(68, 'C c c a :2', transformations) space_groups[68] = sg space_groups['C c c a :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(69, 'F m m m', transformations) space_groups[69] = sg space_groups['F m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([4,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([4,4,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([4,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([4,4,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,3,3]) trans_den = N.array([1,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([4,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,1]) trans_den = N.array([4,4,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,1,1]) trans_den = N.array([4,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,1,1]) trans_den = N.array([4,4,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([2,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,0,3]) trans_den = N.array([4,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,1,1]) trans_den = N.array([4,4,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,-1,1]) trans_den = N.array([2,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([4,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,-1,1]) trans_den = N.array([4,4,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,3,1]) trans_den = N.array([2,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,1,1]) trans_den = N.array([4,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,0]) trans_den = N.array([4,4,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([2,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([4,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([4,4,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(70, 'F d d d :2', transformations) space_groups[70] = sg space_groups['F d d d :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(71, 'I m m m', transformations) space_groups[71] = sg space_groups['I m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(72, 'I b a m', transformations) space_groups[72] = sg space_groups['I b a m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(73, 'I b c a', transformations) space_groups[73] = sg space_groups['I b c a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(74, 'I m m a', transformations) space_groups[74] = sg space_groups['I m m a'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(75, 'P 4', transformations) space_groups[75] = sg space_groups['P 4'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(76, 'P 41', transformations) space_groups[76] = sg space_groups['P 41'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(77, 'P 42', transformations) space_groups[77] = sg space_groups['P 42'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(78, 'P 43', transformations) space_groups[78] = sg space_groups['P 43'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(79, 'I 4', transformations) space_groups[79] = sg space_groups['I 4'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(80, 'I 41', transformations) space_groups[80] = sg space_groups['I 41'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(81, 'P -4', transformations) space_groups[81] = sg space_groups['P -4'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(82, 'I -4', transformations) space_groups[82] = sg space_groups['I -4'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(83, 'P 4/m', transformations) space_groups[83] = sg space_groups['P 4/m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(84, 'P 42/m', transformations) space_groups[84] = sg space_groups['P 42/m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(85, 'P 4/n :2', transformations) space_groups[85] = sg space_groups['P 4/n :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(86, 'P 42/n :2', transformations) space_groups[86] = sg space_groups['P 42/n :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(87, 'I 4/m', transformations) space_groups[87] = sg space_groups['I 4/m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-3,-3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,5,5]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,-1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(88, 'I 41/a :2', transformations) space_groups[88] = sg space_groups['I 41/a :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(89, 'P 4 2 2', transformations) space_groups[89] = sg space_groups['P 4 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(90, 'P 4 21 2', transformations) space_groups[90] = sg space_groups['P 4 21 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(91, 'P 41 2 2', transformations) space_groups[91] = sg space_groups['P 41 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(92, 'P 41 21 2', transformations) space_groups[92] = sg space_groups['P 41 21 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(93, 'P 42 2 2', transformations) space_groups[93] = sg space_groups['P 42 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(94, 'P 42 21 2', transformations) space_groups[94] = sg space_groups['P 42 21 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,3]) trans_den = N.array([1,1,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(95, 'P 43 2 2', transformations) space_groups[95] = sg space_groups['P 43 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([2,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(96, 'P 43 21 2', transformations) space_groups[96] = sg space_groups['P 43 21 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(97, 'I 4 2 2', transformations) space_groups[97] = sg space_groups['I 4 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(98, 'I 41 2 2', transformations) space_groups[98] = sg space_groups['I 41 2 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(99, 'P 4 m m', transformations) space_groups[99] = sg space_groups['P 4 m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(100, 'P 4 b m', transformations) space_groups[100] = sg space_groups['P 4 b m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(101, 'P 42 c m', transformations) space_groups[101] = sg space_groups['P 42 c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(102, 'P 42 n m', transformations) space_groups[102] = sg space_groups['P 42 n m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(103, 'P 4 c c', transformations) space_groups[103] = sg space_groups['P 4 c c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(104, 'P 4 n c', transformations) space_groups[104] = sg space_groups['P 4 n c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(105, 'P 42 m c', transformations) space_groups[105] = sg space_groups['P 42 m c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(106, 'P 42 b c', transformations) space_groups[106] = sg space_groups['P 42 b c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(107, 'I 4 m m', transformations) space_groups[107] = sg space_groups['I 4 m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(108, 'I 4 c m', transformations) space_groups[108] = sg space_groups['I 4 c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(109, 'I 41 m d', transformations) space_groups[109] = sg space_groups['I 41 m d'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(110, 'I 41 c d', transformations) space_groups[110] = sg space_groups['I 41 c d'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(111, 'P -4 2 m', transformations) space_groups[111] = sg space_groups['P -4 2 m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(112, 'P -4 2 c', transformations) space_groups[112] = sg space_groups['P -4 2 c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(113, 'P -4 21 m', transformations) space_groups[113] = sg space_groups['P -4 21 m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(114, 'P -4 21 c', transformations) space_groups[114] = sg space_groups['P -4 21 c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(115, 'P -4 m 2', transformations) space_groups[115] = sg space_groups['P -4 m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(116, 'P -4 c 2', transformations) space_groups[116] = sg space_groups['P -4 c 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(117, 'P -4 b 2', transformations) space_groups[117] = sg space_groups['P -4 b 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(118, 'P -4 n 2', transformations) space_groups[118] = sg space_groups['P -4 n 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(119, 'I -4 m 2', transformations) space_groups[119] = sg space_groups['I -4 m 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(120, 'I -4 c 2', transformations) space_groups[120] = sg space_groups['I -4 c 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(121, 'I -4 2 m', transformations) space_groups[121] = sg space_groups['I -4 2 m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,3]) trans_den = N.array([2,1,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,5]) trans_den = N.array([1,2,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(122, 'I -4 2 d', transformations) space_groups[122] = sg space_groups['I -4 2 d'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(123, 'P 4/m m m', transformations) space_groups[123] = sg space_groups['P 4/m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(124, 'P 4/m c c', transformations) space_groups[124] = sg space_groups['P 4/m c c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(125, 'P 4/n b m :2', transformations) space_groups[125] = sg space_groups['P 4/n b m :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(126, 'P 4/n n c :2', transformations) space_groups[126] = sg space_groups['P 4/n n c :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(127, 'P 4/m b m', transformations) space_groups[127] = sg space_groups['P 4/m b m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(128, 'P 4/m n c', transformations) space_groups[128] = sg space_groups['P 4/m n c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(129, 'P 4/n m m :2', transformations) space_groups[129] = sg space_groups['P 4/n m m :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(130, 'P 4/n c c :2', transformations) space_groups[130] = sg space_groups['P 4/n c c :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(131, 'P 42/m m c', transformations) space_groups[131] = sg space_groups['P 42/m m c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(132, 'P 42/m c m', transformations) space_groups[132] = sg space_groups['P 42/m c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(133, 'P 42/n b c :2', transformations) space_groups[133] = sg space_groups['P 42/n b c :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(134, 'P 42/n n m :2', transformations) space_groups[134] = sg space_groups['P 42/n n m :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(135, 'P 42/m b c', transformations) space_groups[135] = sg space_groups['P 42/m b c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(136, 'P 42/m n m', transformations) space_groups[136] = sg space_groups['P 42/m n m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(137, 'P 42/n m c :2', transformations) space_groups[137] = sg space_groups['P 42/n m c :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,-1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(138, 'P 42/n c m :2', transformations) space_groups[138] = sg space_groups['P 42/n c m :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(139, 'I 4/m m m', transformations) space_groups[139] = sg space_groups['I 4/m m m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(140, 'I 4/m c m', transformations) space_groups[140] = sg space_groups['I 4/m c m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,3,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-3,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-3,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,5,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,5]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,5,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,3,5]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,-1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,-1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(141, 'I 41/a m d :2', transformations) space_groups[141] = sg space_groups['I 41/a m d :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,3,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-3,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,0,0]) trans_den = N.array([2,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,-1,0]) trans_den = N.array([1,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-3,-3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([-1,-1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,5,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([3,3,5]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,5,5]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([3,3,3]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,-1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,-1,-1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([4,4,4]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(142, 'I 41/a c d :2', transformations) space_groups[142] = sg space_groups['I 41/a c d :2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(143, 'P 3', transformations) space_groups[143] = sg space_groups['P 3'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(144, 'P 31', transformations) space_groups[144] = sg space_groups['P 31'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(145, 'P 32', transformations) space_groups[145] = sg space_groups['P 32'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(146, 'R 3 :H', transformations) space_groups[146] = sg space_groups['R 3 :H'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(147, 'P -3', transformations) space_groups[147] = sg space_groups['P -3'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(148, 'R -3 :H', transformations) space_groups[148] = sg space_groups['R -3 :H'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(149, 'P 3 1 2', transformations) space_groups[149] = sg space_groups['P 3 1 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(150, 'P 3 2 1', transformations) space_groups[150] = sg space_groups['P 3 2 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(151, 'P 31 1 2', transformations) space_groups[151] = sg space_groups['P 31 1 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(152, 'P 31 2 1', transformations) space_groups[152] = sg space_groups['P 31 2 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(153, 'P 32 1 2', transformations) space_groups[153] = sg space_groups['P 32 1 2'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(154, 'P 32 2 1', transformations) space_groups[154] = sg space_groups['P 32 2 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(155, 'R 3 2 :H', transformations) space_groups[155] = sg space_groups['R 3 2 :H'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(156, 'P 3 m 1', transformations) space_groups[156] = sg space_groups['P 3 m 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(157, 'P 3 1 m', transformations) space_groups[157] = sg space_groups['P 3 1 m'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(158, 'P 3 c 1', transformations) space_groups[158] = sg space_groups['P 3 c 1'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,-1,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,-1,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(159, 'P 3 1 c', transformations) space_groups[159] = sg space_groups['P 3 1 c'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,2]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den = N.array([3,3,3]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,1,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,1]) trans_den =

N.array([3,3,3])

numpy.array

import numpy as np from sklearn.metrics import accuracy_score, f1_score class Particle: def __init__(self, resource, feature_num, k): self.resource = resource self.k = k # features to be selected if k is not None: self.feature_selected = np.random.choice(feature_num, self.k, replace=False).tolist() self.feature_selected.sort() self.position = indices2binary(self.feature_selected, feature_num) else: self.position = np.random.randint(0, 2, size=feature_num) self.feature_selected = binary2indices(self.position) self.performance = None """ evaluate the fitness/metrics of a particle """ def evaluate(self, x, y, clf, metrics="accuracy"): x_train, x_valid = x y_train, y_valid = y indices = self.feature_selected clf.fit(x_train[:, indices], y_train) y_pred = clf.predict(x_valid[:, indices]) if metrics == "accuracy": self.performance = accuracy_score(y_valid, y_pred) if metrics == "f1": self.performance = f1_score(y_valid, y_pred) def mutation(self, global_best, sample_best): # d = len(self.position) # c1 = 0.5 # c2 = 1.0 - c1 # r1 = np.random.rand(d) # r2 = np.random.rand(d) # # # update the position # v = c1 * r1 * (global_best.position - self.position) + \ # c2 * r2 * (sample_best[self.resource].position - self.position) # s = 1 / (1 + np.exp(-v)) # self.position = np.array([1 if si >= 0.5 else 0 for si in s]) # self.feature_selected = binary2indices(self.position) d = len(self.position) m = int(d * 0.1 / 2) for _ in range(m): bit1, bit2 = np.random.choice(d, 2, replace=False) if self.position[bit1] + self.position[bit2] == 1: self.position[bit1] = 1 - self.position[bit1] self.position[bit2] = 1 - self.position[bit2] def __lt__(self, other): return self.performance < other.performance def __le__(self, other): return self.performance <= other.performance def __gt__(self, other): return self.performance > other.performance def __ge__(self, other): return self.performance >= other.performance def __str__(self): return "resource = " + str(self.resource) + ", performance = " + str(self.performance) def crossover(chromosome1, chromosome2, m): assert len(chromosome1) == len(chromosome2) chromosome1 = list(chromosome1) chromosome2 = list(chromosome2) cutting_points = np.random.choice(len(chromosome1), m, replace=False).tolist() cutting_points.append(len(chromosome1)) cutting_points.sort() result = [] index = 0 flag = 1 for point in cutting_points: if flag > 0: result = result + chromosome1[index:point] index = point flag = -1 elif flag < 0: result = result + chromosome2[index:point] index = point flag = 1 return np.array(result, dtype=int) def mutation_ga(chromosome, pm, k): n1 = sum(chromosome) n0 = len(chromosome) - n1 p1 = pm p0 = pm * n1 / n0 for i, g in enumerate(chromosome): r = np.random.rand() if g == 1 and r < p1: chromosome[i] = 0 elif g == 0 and r < p0: chromosome[i] = 1 if k is not None: # ensure the number of the selected features to be same selected_num = sum(chromosome) if selected_num == k: return if selected_num > k: one_index = [index for index in range(len(chromosome)) if chromosome[index] == 1] remove_index = np.random.choice(one_index, selected_num - k, replace=False) chromosome[remove_index] = 0 if selected_num < k: zero_index = [index for index in range(len(chromosome)) if chromosome[index] == 0] remove_index = np.random.choice(zero_index, k - selected_num) chromosome[remove_index] = 1 def binary2indices(binary): indices = [] for i, bit in enumerate(binary): if bit == 1: indices.append(i) return indices def indices2binary(indices, feature_num): binary = [0] * feature_num for indice in indices: binary[indice] = 1 return np.array(binary, dtype=int) def local_mutation(particle): position = list(particle.position) d = len(position) m = int(d * 0.1 / 2) for _ in range(m): bit1, bit2 = np.random.choice(d, 2, replace=False) if position[bit1] + position[bit2] == 1: position[bit1] = 1 - position[bit1] position[bit2] = 1 - position[bit2] p = Particle(particle.resource, d, particle.k) p.position =

np.array(position)

numpy.array

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ model ocean waves with kayak sitting on top. Created on Sat Sep 4 15:59:49 2021 @author: jimlux """ import numpy as np from matplotlib import cm import matplotlib.pyplot as plt Earthg = 9.8 """ define swells period in seconds height in feet (because that's what the reports give) direction is "from" in degrees """ swells = [{"period":5,"height":.9,"direction":250}, {"period":13,"height":.8,"direction":210}, {"period":16,"height":.9,"direction":215}] nswells = len(swells) """ calculate wavelength (lambda) and speed and convert height to meters, and unipolar and convert direction to radians""" for swell in swells: swell["speed"] = Earthg/(2. * np.pi) *swell["period"] swell["wavelength"] = swell["speed"] *swell["period"] swell["height"] = swell["height"]/3.28/2 swell["direction"] = (90-swell["direction"])*np.pi/180. gridspacing = 2.0 gridsize = 400 xrange = np.arange(0,gridsize*gridspacing,gridspacing) yrange = np.arange(0,gridsize*gridspacing,gridspacing) X,Y = np.meshgrid(xrange,yrange) phases = np.zeros((gridsize,gridsize,nswells)) for idx,swell in enumerate(swells): swellcos = np.cos(swell["direction"]) swellsin = np.sin(swell["direction"]) wavelength = swell["wavelength"] print(wavelength) """ there's got to be a better way to do this """ dswell1 = X*swellcos + Y*swellsin phases[:,:,idx]=np.pi*2.0 * dswell1/wavelength """for i in range(gridsize): for j in range(gridsize): dx = i*gridspacing dy = j*gridspacing dswell = dx * swellcos + dy*swellsin phases[i,j,idx]=np.pi * 2 * dswell/wavelength """ #fig,ax = plt.subplots(subplot_kw={"projection": "3d"}) #plt.figure() #plt.title("swell #%d"%idx) #surf = ax.plot_surface(xrange, yrange, phases[:,:,idx], cmap=cm.coolwarm, # linewidth=0, antialiased=False) #seaheight = np.cos(phases[:,:,idx]) #img = plt.imshow(seaheight) makefigs = True for t in

np.arange(0,60,step=1)

numpy.arange

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2021/2/19 10:38 # @Author : xiaorun # @Site : # @File : stereo.py # @Software: PyCharm import cv2 import numpy as np #####################左右相机外参################################################# ###########左相机世界坐标与图像坐标 object_3d_points_L = np.array(([-1300, 1200, 0], [1400, 1360, 0], [500, 2100, 0], [-700, 2400, 0], [-300,3000,0], [1300,3100,0], [-800,3350,0], [-1200,4260,0], [100,4650,0], [1350,4920,0]),dtype=np.double) object_2d_point_L= np.array(([535, 662], [1038, 594], [839, 494], [641, 471], [694,403], [909,383], [619,373], [567,304], [718,273], [847,250]),dtype=np.double) ##########左相机世界图像坐标 ###########右相机世界坐标与图像坐标 object_3d_points_R= np.array(([-1300, 1200, 0], [1400, 1360, 0], [500, 2100, 0], [-700, 2400, 0], [-300,3000,0], [1300,3100,0], [-800,3350,0], [-1200,4260,0], [100,4650,0], [1350,4920,0]),dtype=np.double) object_2d_point_R= np.array(([271, 619], [792, 693], [629, 504], [449, 457], [529, 397], [758,400], [470,361], [451,289], [612,270], [756,259]),dtype=np.double) ##########右相机世界图像坐标 ######################################################################## #################左右相机内参############################################### camera_matrix_L = np.array(([548.2646, 0, 666.9979], [0, 549.5398, 494.3196], [0, 0, 1.0]), dtype=np.double) dist_coefs_L = np.array([0.0119, -0.0018, 0, 0], dtype=np.double) rotM_L=np.array(([ 0.99647804, -0.0835722, 0.00687046], [-0.06215961, -0.7911815, -0.60841435], [ 0.05628231, 0.60584447, -0.79358981]),dtype=np.double) tvec_L=np.array(([ 734.59639027], [ 1704.06440486], [ 2086.22518369]),dtype=np.double) RT_leftcamera=np.hstack((rotM_L,tvec_L)) m_left=np.matmul(camera_matrix_L ,RT_leftcamera) camera_matrix_R=np.array(([554.9156,0,635.8487], [0,555.5995,509.9433], [0,0,1.0]),dtype=np.double) dist_coefs_R=np.array([0.0256,-0.0133,0,0],dtype=np.double) rotM_R=np.array(([ 0.99353492, 0.1123029, 0.01662595], [ 0.10227696, -0.82186833, -0.56042115], [-0.04927258, 0.55849843, -0.82804089]),dtype=np.double) tvec_R=np.array(([ -773.49701673], [ 1739.81170979], [ 2156.35612044]),dtype=np.double) RT_rightcamera=np.hstack((rotM_R,tvec_R)) #右相机M矩阵 # [u1] |X| [u2] |X| # Z*[v1| = Ml*|Y| Z*|v2| = Mr*|Y| # [ 1] |Z| [ 1] |Z| # |1| |1| m_right=

np.matmul(camera_matrix_R ,RT_rightcamera)

numpy.matmul

import numpy as np import os import re import requests import sys import time from netCDF4 import Dataset import pandas as pd from bs4 import BeautifulSoup from tqdm import tqdm # setup constants used to access the data from the different M2M interfaces BASE_URL = 'https://ooinet.oceanobservatories.org/api/m2m/' # base M2M URL SENSOR_URL = '12576/sensor/inv/' # Sensor Information # setup access credentials AUTH = ['OOIAPI-853A3LA6QI3L62', '<KEY>'] def M2M_Call(uframe_dataset_name, start_date, end_date): options = '?beginDT=' + start_date + '&endDT=' + end_date + '&format=application/netcdf' r = requests.get(BASE_URL + SENSOR_URL + uframe_dataset_name + options, auth=(AUTH[0], AUTH[1])) if r.status_code == requests.codes.ok: data = r.json() else: return None # wait until the request is completed print('Waiting for OOINet to process and prepare data request, this may take up to 20 minutes') url = [url for url in data['allURLs'] if re.match(r'.*async_results.*', url)][0] check_complete = url + '/status.txt' with tqdm(total=400, desc='Waiting') as bar: for i in range(400): r = requests.get(check_complete) bar.update(1) if r.status_code == requests.codes.ok: bar.n = 400 bar.last_print_n = 400 bar.refresh() print('\nrequest completed in %f minutes.' % elapsed) break else: time.sleep(3) elapsed = (i * 3) / 60 return data def M2M_Files(data, tag=''): """ Use a regex tag combined with the results of the M2M data request to collect the data from the THREDDS catalog. Collected data is gathered into an xarray dataset for further processing. :param data: JSON object returned from M2M data request with details on where the data is to be found for download :param tag: regex tag to use in discriminating the data files, so we only collect the correct ones :return: the collected data as an xarray dataset """ # Create a list of the files from the request above using a simple regex as a tag to discriminate the files url = [url for url in data['allURLs'] if re.match(r'.*thredds.*', url)][0] files = list_files(url, tag) return files def list_files(url, tag=''): """ Function to create a list of the NetCDF data files in the THREDDS catalog created by a request to the M2M system. :param url: URL to user's THREDDS catalog specific to a data request :param tag: regex pattern used to distinguish files of interest :return: list of files in the catalog with the URL path set relative to the catalog """ page = requests.get(url).text soup = BeautifulSoup(page, 'html.parser') pattern = re.compile(tag) return [node.get('href') for node in soup.find_all('a', text=pattern)] def M2M_Data(nclist,variables): thredds = 'https://opendap.oceanobservatories.org/thredds/dodsC/ooi/' #nclist is going to contain more than one url eventually for jj in range(len(nclist)): url=nclist[jj] url=url[25:] dap_url = thredds + url + '#fillmismatch' openFile = Dataset(dap_url,'r') for ii in range(len(variables)): dum = openFile.variables[variables[ii].name] variables[ii].data = np.append(variables[ii].data, dum[:].data) tmp = variables[0].data/60/60/24 time_converted = pd.to_datetime(tmp, unit='D', origin=pd.Timestamp('1900-01-01')) return variables, time_converted class var(object): def __init__(self): """A Class that generically holds data with a variable name and the units as attributes""" self.name = '' self.data = np.array([]) self.units = '' def __repr__(self): return_str = "name: " + self.name + '\n' return_str += "units: " + self.units + '\n' return_str += "data: size: " + str(self.data.shape) return return_str class structtype(object): def __init__(self): """ A class that imitates a Matlab structure type """ self._data = [] def __getitem__(self, index): """implement index behavior in the struct""" if index == len(self._data): self._data.append(var()) return self._data[index] def __len__(self): return len(self._data) def M2M_URLs(platform_name,node,instrument_class,method): var_list = structtype() #MOPAK if platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #METBK elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' #FLORT elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/06-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/06-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' #FDCHP elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/08-FDCHPA000/telemetered/fdchp_a_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #DOSTA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' #ADCP elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/01-ADCPTA000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/01-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/01-ADCPTA000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/01-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/04-ADCPTM000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/04-ADCPTM000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/04-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/04-ADCPSJ000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' #ZPLSC elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #WAVSS elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' #VELPT elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' #PCO2W elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' #PHSEN elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' #SPKIR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' #PRESF elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/02-PRESFA000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/02-PRESFA000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/02-PRESFB000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/02-PRESFC000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' #CTDBP elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/06-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/06-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/03-CTDBPE000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' #VEL3D elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' #VEL3DK elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' #PCO2A elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' #PARAD elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' #OPTAA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/01-OPTAAC000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #NUTNR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' ## #MOPAK elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/SBD17/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/SBD17/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #METBK elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' #FLORT elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/SBD17/06-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/SBD17/06-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' #FDCHP elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/08-FDCHPA000/recovered_host/fdchp_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #DOSTA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD37/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD37/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD37/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD37/03-DOSTAD000/recovered_host/dosta_abcdjm_ctdbp_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' #ADCP elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID26/01-ADCPTA000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID26/01-ADCPTC000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID26/01-ADCPTA000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID26/01-ADCPTC000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD35/04-ADCPTM000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD35/04-ADCPTM000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD35/04-ADCPTC000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD35/04-ADCPSJ000/recovered_host/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' #WAVSS elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_statistics_recovered' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_statistics_recovered' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_statistics_recovered' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_statistics_recovered' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' #VELPT elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/SBD17/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': #uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/recovered_host/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' #PCO2W elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' #PHSEN elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' #SPKIR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' #PRESF elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD35/02-PRESFA000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD35/02-PRESFA000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD35/02-PRESFB000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD35/02-PRESFC000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' #CTDBP elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD37/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/SBD17/06-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD37/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/SBD17/06-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD37/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD37/03-CTDBPE000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' #VEL3D elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD35/01-VEL3DD000/recovered_host/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD35/01-VEL3DD000/recovered_host/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD35/01-VEL3DD000/recovered_host/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD35/01-VEL3DD000/recovered_host/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' #PCO2A elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' #OPTAA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD37/01-OPTAAC000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #NUTNR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/RID16/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/RID16/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD37/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/SBD17/06-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD37/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/SBD17/06-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD37/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD37/03-CTDBPE000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CE09OSPM/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/RID26/01-ADCPTA000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/RID26/01-ADCPTC000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/RID26/01-ADCPTA000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/RID26/01-ADCPTC000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/04-ADCPTM000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/04-ADCPTM000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD35/04-ADCPTC000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD35/04-ADCPSJ000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/SBD17/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/SBD11/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/SBD11/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/SBD17/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/SBD11/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/SBD11/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CE09OSPM/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/01-VEL3DD000/recovered_inst/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/01-VEL3DD000/recovered_inst/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD35/01-VEL3DD000/recovered_inst/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD35/01-VEL3DD000/recovered_inst/vel3d_cd_dcl_velocity_data_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/02-PRESFA000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/02-PRESFA000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD35/02-PRESFB000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD35/02-PRESFC000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CE09OSPM/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'RecoveredInst': uframe_dataset_name = 'CE02SHSM/SBD12/08-FDCHPA000/recovered_inst/fdchp_a_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/SBD17/06-FLORTD000/recovered_inst/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/SBD17/06-FLORTD000/recovered_inst/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CE09OSPM/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CE09OSPM/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/RID16/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/RID16/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD37/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD37/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE07SHSM/MFD37/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE09OSSM/MFD37/03-DOSTAD000/recovered_inst/dosta_abcdjm_ctdbp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[3].name = 'ctdbp_seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredInst': uframe_dataset_name = 'CE01ISSM/MFD35/04-ADCPTM000/recovered_inst/adcpt_m_instrument_log9_recovered' var_list[0].name = 'time' var_list[1].name = 'significant_wave_height' var_list[2].name = 'peak_wave_period' var_list[3].name = 'peak_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'seconds' var_list[3].units = 'degrees' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredInst': uframe_dataset_name = 'CE06ISSM/MFD35/04-ADCPTM000/recovered_inst/adcpt_m_instrument_log9_recovered' var_list[0].name = 'time' var_list[1].name = 'significant_wave_height' var_list[2].name = 'peak_wave_period' var_list[3].name = 'peak_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'seconds' var_list[3].units = 'degrees' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'CTD' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/06-CTDBPN106/streamed/ctdbp_no_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_no_seawater_pressure' var_list[5].name = 'ctdbp_no_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'CTD' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/06-CTDBPO108/streamed/ctdbp_no_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_no_seawater_pressure' var_list[5].name = 'ctdbp_no_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'DOSTA' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/06-CTDBPN106/streamed/ctdbp_no_sample' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'DOSTA' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/06-CTDBPO108/streamed/ctdbp_no_sample' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'ctd_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'PHSEN' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/10-PHSEND103/streamed/phsen_data_record' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'PHSEN' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/10-PHSEND107/streamed/phsen_data_record' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'PCO2W' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/09-PCO2WB103/streamed/pco2w_b_sami_data_record' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'PCO2W' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/09-PCO2WB104/streamed/pco2w_b_sami_data_record' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'ADCP' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/05-ADCPTB104/streamed/adcp_velocity_beam' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'ADCP' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/05-ADCPSI103/streamed/adcp_velocity_beam' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'VEL3D' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/07-VEL3DC108/streamed/vel3d_cd_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'VEL3D' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/07-VEL3DC107/streamed/vel3d_cd_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE02SHBP' and node == 'BEP' and instrument_class == 'OPTAA' and method == 'Streamed': uframe_dataset_name = 'CE02SHBP/LJ01D/08-OPTAAD106/streamed/optaa_sample' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSBP' and node == 'BEP' and instrument_class == 'OPTAA' and method == 'Streamed': uframe_dataset_name = 'CE04OSBP/LJ01C/08-OPTAAC104/streamed/optaa_sample' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #CSPP Data below elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/08-FLORTJ000/telemetered/flort_dj_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/08-FLORTJ000/recovered_cspp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/08-FLORTJ000/telemetered/flort_dj_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/08-FLORTJ000/recovered_cspp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/02-DOSTAJ000/telemetered/dosta_abcdjm_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/02-DOSTAJ000/recovered_cspp/dosta_abcdjm_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/02-DOSTAJ000/telemetered/dosta_abcdjm_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/02-DOSTAJ000/recovered_cspp/dosta_abcdjm_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/09-CTDPFJ000/telemetered/ctdpf_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/09-CTDPFJ000/recovered_cspp/ctdpf_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/09-CTDPFJ000/telemetered/ctdpf_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/09-CTDPFJ000/recovered_cspp/ctdpf_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/10-PARADJ000/telemetered/parad_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/10-PARADJ000/recovered_cspp/parad_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/10-PARADJ000/telemetered/parad_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/10-PARADJ000/recovered_cspp/parad_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'NUTNR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/06-NUTNRJ000/recovered_cspp/nutnr_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'salinity_corrected_nitrate' var_list[2].name = 'nitrate_concentration' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'NUTNR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/06-NUTNRJ000/recovered_cspp/nutnr_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'salinity_corrected_nitrate' var_list[2].name = 'nitrate_concentration' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/07-SPKIRJ000/telemetered/spkir_abj_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/07-SPKIRJ000/recovered_cspp/spkir_abj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/07-SPKIRJ000/telemetered/spkir_abj_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/07-SPKIRJ000/recovered_cspp/spkir_abj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSP/SP001/05-VELPTJ000/telemetered/velpt_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/05-VELPTJ000/recovered_cspp/velpt_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSP/SP001/05-VELPTJ000/telemetered/velpt_j_cspp_instrument' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/05-VELPTJ000/recovered_cspp/velpt_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE01ISSP' and node == 'PROFILER' and instrument_class == 'OPTAA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE01ISSP/SP001/04-OPTAAJ000/recovered_cspp/optaa_dj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' elif platform_name == 'CE06ISSP' and node == 'PROFILER' and instrument_class == 'OPTAA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE06ISSP/SP001/04-OPTAAJ000/recovered_cspp/optaa_dj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/07-FLORTJ000/recovered_cspp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/07-FLORTJ000/recovered_cspp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/01-DOSTAJ000/recovered_cspp/dosta_abcdjm_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/01-DOSTAJ000/recovered_cspp/dosta_abcdjm_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[4].name = 'optode_temperature' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'umol/L' var_list[4].units = 'degC' var_list[5].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/08-CTDPFJ000/recovered_cspp/ctdpf_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/08-CTDPFJ000/recovered_cspp/ctdpf_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temperature' var_list[2].name = 'salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/09-PARADJ000/recovered_cspp/parad_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/09-PARADJ000/recovered_cspp/parad_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_j_par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'NUTNR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/05-NUTNRJ000/recovered_cspp/nutnr_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'salinity_corrected_nitrate' var_list[2].name = 'nitrate_concentration' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'NUTNR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/05-NUTNRJ000/recovered_cspp/nutnr_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'salinity_corrected_nitrate' var_list[2].name = 'nitrate_concentration' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/06-SPKIRJ000/recovered_cspp/spkir_abj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/06-SPKIRJ000/recovered_cspp/spkir_abj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/02-VELPTJ000/recovered_cspp/velpt_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/02-VELPTJ000/recovered_cspp/velpt_j_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'velpt_j_eastward_velocity' var_list[2].name = 'velpt_j_northward_velocity' var_list[3].name = 'velpt_j_upward_velocity' var_list[4].name = 'heading' var_list[5].name = 'roll' var_list[6].name = 'pitch' var_list[7].name = 'temperature' var_list[8].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'degrees' var_list[5].units = 'degrees' var_list[6].units = 'degrees' var_list[7].units = 'degC' var_list[8].units = 'dbar' elif platform_name == 'CE02SHSP' and node == 'PROFILER' and instrument_class == 'OPTAA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE02SHSP/SP001/04-OPTAAJ000/recovered_cspp/optaa_dj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' elif platform_name == 'CE07SHSP' and node == 'PROFILER' and instrument_class == 'OPTAA' and method == 'RecoveredCSPP': uframe_dataset_name = 'CE07SHSP/SP001/04-OPTAAJ000/recovered_cspp/optaa_dj_cspp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL386/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL386/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL384/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL384/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL383/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL383/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL382/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL382/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL381/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL381/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL327/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL327/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL326/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL326/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL320/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL320/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL319/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL319/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL312/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL312/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL311/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL311/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL247/05-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL247/05-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL386/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL386/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL384/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL384/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL383/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL383/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL382/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL382/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL381/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL381/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL327/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL327/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL326/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL326/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL320/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL320/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL319/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL319/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL312/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL312/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL311/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL311/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL247/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL247/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL386/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL386/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL384/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL384/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL383/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL383/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL382/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL382/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL381/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL381/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL327/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL327/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL326/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL326/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL320/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL320/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL319/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL319/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL312/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL312/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL311/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL311/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL247/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL247/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL386/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL386/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL384/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL384/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL383/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL383/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL382/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL382/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL381/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL381/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL327/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL327/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL326/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL326/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL320/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL320/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL319/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL319/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL312/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL312/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL311/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL311/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE05MOAS/GL247/01-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL247/01-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CEGL386' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL386/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL384' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL384/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL383' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL383/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL382' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL382/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL381' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL381/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL327' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL327/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL326' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL326/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL320' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL320/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL319' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL319/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL312' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL312/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL311' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL311/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CEGL247' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CE05MOAS/GL247/03-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_mean_directional' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_mean_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_mean_directional' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_mean_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_mean_directional' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_mean_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_mean_directional' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_mean_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_non_directional' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_non_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_non_directional' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_non_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_non_directional' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_non_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_non_directional' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_non_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_motion' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_motion_recovered' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_motion' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_motion_recovered' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_motion' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_motion_recovered' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_motion' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_motion_recovered' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_fourier' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_fourier_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_fourier' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_fourier_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_fourier' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_fourier_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_fourier' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_fourier_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/2A-CTDPFA107/streamed/ctdpf_sbe43_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'seawater_pressure' var_list[5].name = 'seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSPD/DP01B/01-CTDPFL105/recovered_inst/dpc_ctd_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'dpc_ctd_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CE04OSPD/DP01B/01-CTDPFL105/recovered_wfp/dpc_ctd_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'dpc_ctd_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/2A-CTDPFA107/streamed/ctdpf_sbe43_sample' var_list[0].name = 'time' var_list[1].name = 'corrected_dissolved_oxygen' var_list[2].name = 'seawater_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSPD/DP01B/06-DOSTAD105/recovered_inst/dpc_optode_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CE04OSPD/DP01B/06-DOSTAD105/recovered_wfp/dpc_optode_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/3A-FLORTD104/streamed/flort_d_data_record' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSPD/DP01B/04-FLNTUA103/recovered_inst/dpc_flnturtd_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'flntu_x_mmp_cds_fluorometric_chlorophyll_a' var_list[2].name = 'flntu_x_mmp_cds_total_volume_scattering_coefficient ' var_list[3].name = 'flntu_x_mmp_cds_bback_total' var_list[4].name = 'flcdr_x_mmp_cds_fluorometric_cdom' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'ug/L' var_list[2].units = 'm-1 sr-1' var_list[3].units = 'm-1' var_list[4].units = 'ppb' var_list[5].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CE04OSPD/DP01B/03-FLCDRA103/recovered_wfp/dpc_flcdrtd_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'flntu_x_mmp_cds_fluorometric_chlorophyll_a' var_list[2].name = 'flntu_x_mmp_cds_total_volume_scattering_coefficient ' var_list[3].name = 'flntu_x_mmp_cds_bback_total' var_list[4].name = 'flcdr_x_mmp_cds_fluorometric_cdom' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'ug/L' var_list[2].units = 'm-1 sr-1' var_list[3].units = 'm-1' var_list[4].units = 'ppb' var_list[5].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'PHSEN' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/2B-PHSENA108/streamed/phsen_data_record' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'ph_seawater' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/3C-PARADA102/streamed/parad_sa_sample' var_list[0].name = 'time' var_list[1].name = 'par_counts_output' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'SPKIR' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/3D-SPKIRA102/streamed/spkir_data_record' var_list[0].name = 'time' var_list[1].name = 'spkir_downwelling_vector' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' var_list[2].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'NUTNR' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/4A-NUTNRA102/streamed/nutnr_a_sample' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' var_list[3].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'PCO2W' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/4F-PCO2WA102/streamed/pco2w_a_sami_data_record' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' var_list[3].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PROFILER' and instrument_class == 'VELPT' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/SF01B/4B-VELPTD106/streamed/velpt_velocity_data' var_list[0].name = 'time' var_list[1].name = 'velpt_d_eastward_velocity' var_list[2].name = 'velpt_d_northward_velocity' var_list[3].name = 'velpt_d_upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[9].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' var_list[9].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredInst': uframe_dataset_name = 'CE04OSPD/DP01B/02-VEL3DA105/recovered_inst/dpc_acm_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_a_eastward_velocity' var_list[2].name = 'vel3d_a_northward_velocity' var_list[3].name = 'vel3d_a_upward_velocity_ascending' var_list[4].name = 'vel3d_a_upward_velocity_descending' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'm/s' var_list[5].units = 'dbar' elif platform_name == 'CE04OSPD' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CE04OSPD/DP01B/02-VEL3DA105/recovered_wfp/dpc_acm_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'vel3d_a_eastward_velocity' var_list[2].name = 'vel3d_a_northward_velocity' var_list[3].name = 'vel3d_a_upward_velocity_ascending' var_list[4].name = 'vel3d_a_upward_velocity_descending' var_list[5].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'm/s' var_list[5].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PLATFORM200M' and instrument_class == 'CTD' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/PC01B/4A-CTDPFA109/streamed/ctdpf_optode_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'seawater_pressure' var_list[5].name = 'seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSPS' and node == 'PLATFORM200M' and instrument_class == 'DOSTA' and method == 'Streamed': #uframe_dataset_name = 'CE04OSPS/PC01B/4A-DOSTAD109/streamed/ctdpf_optode_sample' uframe_dataset_name = 'CE04OSPS/PC01B/4A-CTDPFA109/streamed/ctdpf_optode_sample' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'seawater_pressure' #also use this for the '4A-DOSTAD109/streamed/ctdpf_optode_sample' stream var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'dbar' elif platform_name == 'CE04OSPS' and node == 'PLATFORM200M' and instrument_class == 'PHSEN' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/PC01B/4B-PHSENA106/streamed/phsen_data_record' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSPS' and node == 'PLATFORM200M' and instrument_class == 'PCO2W' and method == 'Streamed': uframe_dataset_name = 'CE04OSPS/PC01B/4D-PCO2WA105/streamed/pco2w_a_sami_data_record' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' #Coastal Pioneer CSM Data Streams elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK2' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK2' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' #WAVSS elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_statistics_recovered' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_mean_directional' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_MeanDir' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_mean_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'mean_direction' var_list[2].name = 'number_bands' var_list[3].name = 'initial_frequency' var_list[4].name = 'frequency_spacing' var_list[5].name = 'psd_mean_directional' var_list[6].name = 'mean_direction_array' var_list[7].name = 'directional_spread_array' var_list[8].name = 'spread_direction' var_list[9].name = 'wavss_a_directional_frequency' var_list[10].name = 'wavss_a_corrected_mean_wave_direction' var_list[11].name = 'wavss_a_corrected_directional_wave_direction' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degrees' var_list[2].units = '1' var_list[3].units = 'Hz' var_list[4].units = 'Hz' var_list[5].units = 'm2 Hz-1' var_list[6].units = 'degrees' var_list[7].units = 'degrees' var_list[8].units = 'degrees' var_list[9].units = 'Hz' var_list[10].units = 'deg' var_list[11].units = 'deg' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_non_directional' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_NonDir' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_non_directional_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'psd_non_directional' var_list[5].name = 'wavss_a_non_directional_frequency' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = 'm2 Hz-1' var_list[5].units = 'Hz' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_motion' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Motion' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_motion_recovered' var_list[0].name = 'time' var_list[1].name = 'number_time_samples' var_list[2].name = 'initial_time' var_list[3].name = 'time_spacing' var_list[4].name = 'solution_found' var_list[5].name = 'heave_offset_array' var_list[6].name = 'north_offset_array' var_list[7].name = 'east_offset_array' var_list[8].name = 'wavss_a_buoymotion_time' var_list[9].name = 'wavss_a_magcor_buoymotion_x' var_list[10].name = 'wavss_a_magcor_buoymotion_y' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'sec' var_list[3].units = 'sec' var_list[4].units = '1' var_list[5].units = 'm' var_list[6].units = 'm' var_list[7].units = 'm' var_list[8].units = 'seconds since 1900-01-01' var_list[9].units = 'm' var_list[10].units = 'm' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_fourier' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'WAVSS_Fourier' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/05-WAVSSA000/recovered_host/wavss_a_dcl_fourier_recovered' var_list[0].name = 'time' var_list[1].name = 'number_bands' var_list[2].name = 'initial_frequency' var_list[3].name = 'frequency_spacing' var_list[4].name = 'number_directional_bands' var_list[5].name = 'initial_directional_frequency' var_list[6].name = 'directional_frequency_spacing' var_list[7].name = 'fourier_coefficient_2d_array' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = '1' var_list[2].units = 'Hz' var_list[3].units = 'Hz' var_list[4].units = '1' var_list[5].units = 'Hz' var_list[6].units = 'Hz' var_list[7].units = '1' #PCO2A elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' #PCO2A elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/SBD12/04-PCO2AA000/recovered_host/pco2a_a_dcl_instrument_water_recovered' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' #FDCHP elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/SBD12/08-FDCHPA000/recovered_inst/fdchp_a_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/08-FDCHPA000/telemetered/fdchp_a_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/08-FDCHPA000/recovered_host/fdchp_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/SBD11/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'METBK1-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/SBD11/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK2-hr' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD12/06-METBKA000/telemetered/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'METBK2-hr' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD12/06-METBKA000/recovered_host/metbk_hourly' var_list[0].name = 'met_timeflx' var_list[1].name = 'met_rainrte' var_list[2].name = 'met_buoyfls' var_list[3].name = 'met_buoyflx' var_list[4].name = 'met_frshflx' var_list[5].name = 'met_heatflx' var_list[6].name = 'met_latnflx' var_list[7].name = 'met_mommflx' var_list[8].name = 'met_netlirr' var_list[9].name = 'met_rainflx' var_list[10].name = 'met_sensflx' var_list[11].name = 'met_sphum2m' var_list[12].name = 'met_stablty' var_list[13].name = 'met_tempa2m' var_list[14].name = 'met_tempskn' var_list[15].name = 'met_wind10m' var_list[16].name = 'met_netsirr_hourly' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'mm/hr' var_list[2].units = 'W/m2' var_list[3].units = 'W/m2' var_list[4].units = 'mm/hr' var_list[5].units = 'W/m2' var_list[6].units = 'W/m2' var_list[7].units = 'N/m2' var_list[8].units = 'W/m2' var_list[9].units = 'W/m2' var_list[10].units = 'W/m2' var_list[11].units = 'g/kg' var_list[12].units = 'unitless' var_list[13].units = 'degC' var_list[14].units = 'degC' var_list[15].units = 'm/s' var_list[16].units = 'W/m2' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID27/03-CTDBPC000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/RID27/03-CTDBPC000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD37/03-CTDBPE000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD37/03-CTDBPE000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD37/03-CTDBPE000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD37/03-CTDBPD000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD37/03-CTDBPD000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD37/03-CTDBPD000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD37/03-CTDBPD000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD37/03-CTDBPD000/recovered_host/ctdbp_cdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD37/03-CTDBPD000/recovered_inst/ctdbp_cdef_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdbp_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdbp_seawater_pressure' var_list[5].name = 'ctdbp_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID27/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD37/01-OPTAAD000/recovered_host/optaa_dj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/RID26/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID26/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID27/02-FLORTD000/recovered_host/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID26/08-SPKIRB000/recovered_host/spkir_abj_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID27/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID26/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/RID26/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD35/06-PHSEND000/recovered_host/phsen_abcdef_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD35/06-PHSEND000/recovered_inst/phsen_abcdef_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD35/05-PCO2WB000/recovered_inst/pco2w_abc_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD35/05-PCO2WB000/recovered_host/pco2w_abc_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD35/02-PRESFB000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD35/02-PRESFB000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD35/02-PRESFB000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD35/02-PRESFB000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD35/02-PRESFB000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD35/02-PRESFB000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD35/02-PRESFC000/recovered_host/presf_abc_dcl_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD35/02-PRESFC000/recovered_inst/presf_abc_tide_measurement_recovered' var_list[0].name = 'time' var_list[1].name = 'presf_tide_pressure' var_list[2].name = 'presf_tide_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD35/02-PRESFC000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD35/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD35/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD35/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD35/04-VELPTA000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD35/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD35/04-VELPTA000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD35/04-VELPTB000/recovered_inst/velpt_ab_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD35/04-VELPTB000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'VELPT' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD35/04-VELPTB000/recovered_host/velpt_ab_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD37/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD37/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD37/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD37/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD37/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD37/04-DOSTAD000/recovered_host/dosta_abcdjm_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD37/07-ZPLSCC000/recovered_inst/zplsc_echogram_data' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/MFD35/01-ADCPTF000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP01CNSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/MFD35/01-ADCPTF000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/MFD35/01-ADCPTF000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP03ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISSM/MFD35/01-ADCPTF000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/MFD35/01-ADCPSJ000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP04OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/MFD35/01-ADCPSJ000/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' #Coastal Pioneer WireFollowing Profilers (WFP elif platform_name == 'CP04OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/SBS11/02-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSPM/SBS11/02-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP04OSPM/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP04OSPM/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP04OSPM/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP04OSPM/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP04OSPM/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP01CNPM/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP01CNPM/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP01CNPM/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP01CNPM/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP01CNPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP01CNPM/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCI/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCI/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCI/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCI/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCI/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCI/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCO/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCO/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCO/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCO/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCO/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMCO/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUI/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUI/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUI/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUI/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUI/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUI/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUO/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUO/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUO/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUO/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUO/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP02PMUO/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP': uframe_dataset_name = 'CP03ISPM/WFP01/04-FLORTK000/recovered_wfp/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP': uframe_dataset_name = 'CP03ISPM/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP': uframe_dataset_name = 'CP03ISPM/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP03ISPM/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP': uframe_dataset_name = 'CP03ISPM/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' elif platform_name == 'CP04OSPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSPM/RII01/02-ADCPSL010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP04OSPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSPM/RII01/02-ADCPSL010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP04OSPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/RII01/02-ADCPSL010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP01CNPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNPM/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP01CNPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNPM/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP01CNPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP02PMCI/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCI/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP02PMCO/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCO/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMCO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP02PMUI/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUI/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP02PMUO/RII01/02-ADCPSL010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUO/RII01/02-ADCPSL010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP02PMUO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/RII01/02-ADCPSL010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP03ISPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst': uframe_dataset_name = 'CP03ISPM/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP03ISPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISPM/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CP03ISPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'adcps_jln_heading' var_list[3].name = 'adcps_jln_pitch' var_list[4].name = 'adcps_jln_roll' var_list[5].name = 'adcps_jln_eastward_seawater_velocity2' var_list[6].name = 'adcps_jln_northward_seawater_velocity2' var_list[7].name = 'adcps_jln_upward_seawater_velocity2' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'cdegree' var_list[3].units = 'cdegree' var_list[4].units = 'cdegree' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL336/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL336/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL336/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL336/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL336/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL336/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL336/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL336/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL336/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL388/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL388/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL388/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL388/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL388/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL388/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL388/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL388/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL388/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL335/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL335/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL335/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL335/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL335/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL335/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL335/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL335/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL335/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL339/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL339/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL339/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL339/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL339/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL339/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL339/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL339/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL339/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL340/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL340/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL340/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL340/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL340/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL340/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL340/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL340/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL340/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL374/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL374/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL374/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL374/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL374/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL374/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL374/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL374/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL374/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL375/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL375/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL375/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL375/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL375/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL375/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL375/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL375/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL375/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL376/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL376/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL376/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL376/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL376/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL376/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL376/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL376/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL376/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL379/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL379/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL379/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL379/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL379/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL379/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL379/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL379/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL379/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL380/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL380/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL380/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL380/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL380/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL380/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL380/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL380/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL380/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL387/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL387/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL387/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL387/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL387/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL387/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL387/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL387/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL387/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL389/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL389/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL389/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL389/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL389/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL389/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL389/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL389/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL389/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL514/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL514/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_water_temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'sci_seawater_density' var_list[4].name = 'sci_water_pressure_dbar' var_list[5].name = 'sci_water_cond' var_list[6].name = 'lat' var_list[7].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' var_list[6].units = 'degree_north' var_list[7].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL514/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL514/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'sci_oxy4_oxygen' var_list[2].name = 'sci_abs_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[4].name = 'lat' var_list[5].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/kg' var_list[3].units = 'dbar' var_list[4].units = 'degree_north' var_list[5].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL514/02-FLORTM000/telemetered/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL514/02-FLORTM000/recovered_host/flort_m_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'sci_flbbcd_chlor_units' var_list[3].name = 'sci_flbbcd_cdom_units' var_list[4].name = 'sci_flbbcd_bb_units' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[7].name = 'lat' var_list[8].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' var_list[7].units = 'degree_north' var_list[8].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CP05MOAS/GL514/05-PARADM000/telemetered/parad_m_glider_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL514/05-PARADM000/recovered_host/parad_m_glider_recovered' var_list[0].name = 'time' var_list[1].name = 'parad_m_par' var_list[2].name = 'int_ctd_pressure' var_list[3].name = 'lat' var_list[4].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' var_list[3].units = 'degree_north' var_list[4].units = 'degree_east' elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost': uframe_dataset_name = 'CP05MOAS/GL514/01-ADCPAM000/recovered_host/adcp_velocity_glider' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[8].name = 'int_ctd_pressure' var_list[9].name = 'lat' var_list[10].name = 'lon' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' var_list[8].units = 'dbar' var_list[9].units = 'degree_north' var_list[10].units = 'degree_east' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP03ISSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP01CNPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP01CNPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCI/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCI/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMCO/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMCO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMCO/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUI/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUI/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP02PMUO/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP02PMUO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP02PMUO/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP03ISPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP03ISPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP03ISPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CP04OSPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CP04OSSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CP04OSSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CP04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CP04OSSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CP01CNSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredHost': uframe_dataset_name = 'CP01CNSM/RID26/07-NUTNRB000/recovered_host/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CP01CNSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'RecoveredInst': uframe_dataset_name = 'CP01CNSM/RID26/07-NUTNRB000/recovered_inst/suna_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data =

np.array([])

numpy.array

from functools import reduce from math import exp, isclose, log, pi from os import makedirs, path import matplotlib.pyplot as plt import numpy as np from scipy import special working_dir = path.dirname(path.abspath(__file__)) makedirs(path.join(working_dir, 'plots'), exist_ok=True) try: data = np.load(path.join(working_dir, 'data.npy')) except FileNotFoundError: data = np.load(path.join(working_dir, 'task4.npy')) def hist(x_array, n_bins, continuous=True, normalize=True): min_val = x_array.min() max_val = x_array.max() count = np.zeros(int(n_bins)) for x in x_array: bin_number = int((n_bins - 1) * ((x - min_val) / (max_val - min_val))) count[bin_number] += 1 # normalize the distribution if normalize: count /= x_array.shape[0] if continuous: count /= ((max_val - min_val) / n_bins) return count, np.linspace(min_val, max_val, num=n_bins) num_bins = 100 counts, bins = hist(data, num_bins, continuous=False, normalize=False) plt.bar(bins, counts, width=0.5, align='edge', color='gray') plt.xlabel('x') plt.ylabel(r'$P\left(x\right)$') plt.savefig(path.join(working_dir, 'plots/hist.eps'), bbox_inches='tight') plt.close() counts, bins = hist(data, num_bins, continuous=False, normalize=True) plt.bar(bins, counts, width=0.5, align='edge', color='gray') plt.xlabel('x') plt.ylabel(r'$P\left(x\right)$') plt.savefig( path.join(working_dir, 'plots/hist_normalized.eps'), bbox_inches='tight' ) def poisson_likelihood(x, lambda_): n = x.shape[0] lambda_x = reduce( lambda y, z: y * z, (lambda_ ** x).tolist() ) x_factorial = reduce( lambda y, z: y * z, special.factorial(x, exact=True).tolist() ) return exp(- lambda_ * n) * lambda_x / x_factorial def poisson_log_likelihood(x, lambda_): n = x.shape[0] log_lambda_x = log(lambda_) * np.sum(x) log_x_factorial = np.sum(np.log(special.factorial(x, exact=True))) return (- lambda_ * n) + log_lambda_x - log_x_factorial # Poisson MLE lambda_hat = np.mean(data) def gaussian_likelihood(x, mu, var): n = x.shape[0] normalization_factor = (2. * pi * var) ** (-.5 * n) x_minus_mu_squared = np.sum((x - mu) ** 2) return normalization_factor * exp(- x_minus_mu_squared / (2. * var)) def gaussian_log_likelihood(x, mu, var): n = x.shape[0] log_normalization_factor = (-.5 * n) * log(2. * pi * var) x_minus_mu_squared = np.sum((x - mu) ** 2) return log_normalization_factor - x_minus_mu_squared / (2. * var) # Gaussian MLE mu_hat = np.mean(data) var_hat = np.var(data) assert isclose( gaussian_log_likelihood(data, mu_hat, var_hat), -40287.57, abs_tol=1e-2 ) assert np.equal( exp(gaussian_log_likelihood(data, mu_hat, var_hat)), gaussian_likelihood(data, mu_hat, var_hat) ) print('Poisson') print('Lambda (MLE): \t\t', lambda_hat) try: print('Likelihood: \t\t', poisson_likelihood(data, lambda_hat)) except OverflowError: print('Likelihood: \t\t', exp(poisson_log_likelihood(data, lambda_hat))) print('Log-Likelihood: \t', poisson_log_likelihood(data, lambda_hat)) print() print('Gaussian') print('Mu (MLE): \t\t', mu_hat) print('Var (MLE): \t\t', var_hat) print('Likelihood: \t\t', gaussian_likelihood(data, mu_hat, var_hat)) print('Log-Likelihood: \t', gaussian_log_likelihood(data, mu_hat, var_hat)) # plot gaussian distribution data_range = np.arange(0, np.max(data), step=1) gaussian_densities = list( map( lambda x: gaussian_likelihood(

np.array([x])

numpy.array

""" Plot anomalies for Arctic and Antarctic sea ice extents of the current year from Sea Ice Index 3 (NSIDC). Total anomaly (global) is also shown. Website : ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/daily/data/ Author : <NAME> Date : 5 September 2016 """ ### Import modules import numpy as np import urllib.request import urllib as UL import datetime import matplotlib.pyplot as plt ### Directory and time directoryfigure = './Figures/' now = datetime.datetime.now() currentmn = str(now.month) currentdy = str(now.day) currentyr = str(now.year) currenttime = currentmn + '_' + currentdy + '_' + currentyr currentdoy = now.timetuple().tm_yday ### Load url url = 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/daily/data/' \ 'N_seaice_extent_daily_v3.0.csv' ### Read Arctic file raw_data = UL.request.urlopen(url) dataset = np.genfromtxt(raw_data, skip_header=2,delimiter=',', usecols=[0,1,2,3,4]) print('\nCompleted: Read sea ice data!') ### Set missing data to nan dataset[np.where(dataset==-9999)] = np.nan ### Variables year = dataset[:,0] month = dataset[:,1] day = dataset[:,2] iceAR = dataset[:,3] missing = dataset[:,4] ### Find current year yr2018 = np.where(year == 2018)[0] iceAR18 = iceAR[yr2018] ### Ice unit Conversion icevalAR = iceAR18 * 1e6 ########################################################################### ########################################################################### ########################################################################### ### Reads in 1981-2010 means ### Load url url2 = 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/daily/data/' \ 'N_seaice_extent_climatology_1981-2010_v3.0.csv' ### Read file raw_data2 = UL.request.urlopen(url2) dataset2 = np.genfromtxt(raw_data2, skip_header=2,delimiter=',', usecols=[0,1,2]) ### Create variables doy = dataset2[:,0] meaniceAR = dataset2[:,1] * 1e6 std = dataset2[:,2] ### Anomalies currentanomAR = icevalAR-meaniceAR[:currentdoy-1] ########################################################################### ########################################################################### ########################################################################### ### Antarctic file ### Load url url = 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/south/daily/data/' \ 'S_seaice_extent_daily_v3.0.csv' ### Read file raw_data = UL.request.urlopen(url) dataset = np.genfromtxt(raw_data, skip_header=2,delimiter=',', usecols=[0,1,2,3,4]) print('\nCompleted: Read sea ice data!') ### Set missing data to nan dataset[np.where(dataset==-9999)] = np.nan ### Variables year = dataset[:,0] month = dataset[:,1] day = dataset[:,2] iceAA = dataset[:,3] missing = dataset[:,4] ### Find current year yr2018 = np.where(year == 2018)[0] iceAA18 = iceAA[yr2018] ### Ice Conversion icevalAA = iceAA18 * 1e6 ########################################################################### ########################################################################### ########################################################################### ### Reads in 1981-2010 means ### Load url url2 = 'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/south/daily/data/' \ 'S_seaice_extent_climatology_1981-2010_v3.0.csv' ### Read file raw_data2 = UL.request.urlopen(url2) dataset2 = np.genfromtxt(raw_data2, skip_header=2,delimiter=',', usecols=[0,1,2]) ### Create variables doy = dataset2[:,0] meaniceAA = dataset2[:,1] * 1e6 ### Anomalies currentanomAA = icevalAA-meaniceAA[:currentdoy-1] ########################################################################### ########################################################################### ########################################################################### ### Total Anomaly totalanom = (currentanomAR + currentanomAA) / 1e6 currentanomAR = currentanomAR/1e6 currentanomAA = currentanomAA/1e6 print('Completed script!') ########################################################################### ########################################################################### ########################################################################### ### Create plot plt.rc('text',usetex=True) plt.rc('font',**{'family':'sans-serif','sans-serif':['Avant Garde']}) plt.rc('savefig',facecolor='black') plt.rc('axes',edgecolor='white') plt.rc('xtick',color='white') plt.rc('ytick',color='white') plt.rc('axes',labelcolor='white') plt.rc('axes',facecolor='black') fig = plt.figure() ax = plt.subplot(111) xlabels = [r'Jan',r'Feb',r'Mar',r'Apr',r'May',r'Jun',r'Jul', r'Aug',r'Sep',r'Oct',r'Nov',r'Dec',r'Jan'] plt.xticks(

np.arange(0,366,30.4)

numpy.arange

import numpy as np from scipy.optimize import curve_fit import logging class stfmrSpectraFitting: def __init__(self, hArray, vArray, frequency, inputFileName): self.fieldArray = hArray self.amplitudeArray = vArray #self.fieldArray, self.amplitudeArray = self.assignFieldAndAmpArray(hArray, vArray, minRange, maxRange) self.deltaH = self.findDh(self.fieldArray, self.amplitudeArray) #Units: T self.Hres = self.fieldArray[int(len(self.fieldArray)/2)] #Units: T self.frequency = frequency #Units : Hz self.V0 = (self.amplitudeArray[0] + self.amplitudeArray[-1])/2 self.V1 = 0 self.Vsym = np.amax(self.amplitudeArray) + np.amin(self.amplitudeArray) - 2*self.V0 self.Vas = np.amax(self.amplitudeArray) -

np.amin(self.amplitudeArray)

numpy.amin

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import division import unittest import numpy as np from singa import loss from singa import tensor class TestLoss(unittest.TestCase): def setUp(self): self.x_np = np.asarray([[0.9, 0.2, 0.1], [0.1, 0.4, 0.5], [0.2, 0.4, 0.4]], dtype=np.float32) self.y_np = np.asarray([[1, 0, 1], [0, 1, 1], [1, 0, 0]], dtype=np.float32) self.x = tensor.from_numpy(self.x_np) self.y = tensor.from_numpy(self.y_np) def test_sigmoid_cross_entropy(self): sig = loss.SigmoidCrossEntropy() l1 = sig.forward(True, self.x, self.y) sig.backward() l2 = sig.evaluate(True, self.x, self.y) p = 1.0 / (1 + np.exp(-self.x_np)) l = - (self.y_np * np.log(p) + (1 - self.y_np) * np.log(1 - p)) self.assertAlmostEqual(l1.l1(), l2) self.assertAlmostEqual(l1.l1(),

np.average(l)

numpy.average

"""A collection of core functions.""" import logging import os from collections import defaultdict from typing import Union import numpy as np import xarray as xr from scipy.spatial.distance import pdist, squareform logger = logging.getLogger(os.path.basename(__file__)) def area_weighted_mean(data_array: 'xr.DataArray') -> 'xr.DataArray': """Calculate area mean weighted by the latitude. Returns a data array consisting of N values, where N == number of ensemble members. """ weights_lat = np.cos(np.radians(data_array.lat)) means = data_array.weighted(weights_lat).mean(dim=['lat', 'lon']) return means def distance_matrix(values: 'np.ndarray', weights: 'np.ndarray' = None) -> 'np.ndarray': """Calculate the pairwise distance between model members. Takes a dataset with ensemble member/lon/lat. Flattens lon/lat into a single dimension. Calculates the distance between every ensemble member. If weights are passed, they should have the same shape as values. Returns 2D NxN array, where N == number of ensemble members. """ n_members = values.shape[0] values = values.reshape(n_members, -1) # pdist does not work with NaN not_nan = np.where(np.all(np.isfinite(values), axis=0))[0] values = values[:, not_nan] if weights is not None: # Reshape weights to match values array weights = weights.reshape(n_members, -1) weights = weights[:, not_nan] weights = weights[0] # Weights are equal along first dim d_matrix = squareform(pdist(values, metric='euclidean', w=weights)) return d_matrix def calculate_model_distances( data_array: 'xr.DataArray', dimension: str = 'model_ensemble_reference') -> 'xr.DataArray': """Calculate pair-wise distances between all values in data_array. Distances are calculated as the area weighted euclidean distance between each pair of models in data_array. Returned is a square matrix with where the number of elements along each edge equals the number of ensemble members. Parameters ---------- data_array : array_like, shape (N,...) Array of (2 dimensional) model fields. dimension : string Name of the newly created reference dimension (default: 'model_ensemble_reference'. Must not be equal to the existing model dimension ('model_ensemble')! Returns ------- distances : array_like, shape (N, N) Symmetric matrix of pairwise model distances. """ assert dimension != 'model_ensemble', f'{dimension} != "model_ensemble"' weights = np.cos(np.radians(data_array.lat)) weights, _ = xr.broadcast(weights, data_array) diff = xr.apply_ufunc( distance_matrix, data_array, weights, input_core_dims=[['model_ensemble', 'lat', 'lon'], ['model_ensemble', 'lat', 'lon']], output_core_dims=[[dimension, 'model_ensemble']], ) diff.name = f'd{data_array.name}' diff.attrs['variable_group'] = data_array.name diff.attrs["units"] = data_array.units diff[dimension] = diff.model_ensemble.values return diff def compute_overall_mean(dataset: 'xr.Dataset', weights: dict) -> 'xr.DataArray': """Normalize all variables in a dataset and return their weighted mean. Relative weights for each variable group are passed via the recipe. """ normalized = dataset / dataset.median() weights_selected = xr.DataArray( [weights[variable_group] for variable_group in dataset], coords={'variable_group': list(dataset)}, dims='variable_group') overall_mean = normalized.to_array( dim='variable_group').weighted(weights_selected).mean('variable_group') overall_mean.name = 'overall_mean' overall_mean.attrs['variable_group'] = 'overall_mean' overall_mean.attrs['units'] = '1' return overall_mean def combine_ensemble_members( dataset: Union['xr.DataArray', None], dimensions: Union[str, list] = 'model_ensemble', ) -> (Union['xr.DataArray', None], dict): """Combine ensemble members of the same model. Parameters ---------- dataset : None or data_array, shape (N,) or (N, N) A vector containing model-observations distances or a matrix containing model-model distances. dimensions : string or list of up to two strings Spezifies the dimensions along which ensemble members are combined. Returns ------- dataset : None or data_array, shape (M,), (M, L) with M, L <= N data_array where ensemble members along the given dimensions are combined by averaging. groups : dict of form {string: list} Dictionary mapping the combined model names (keys) to the original ensemble member names (values). """ if isinstance(dimensions, str): dimensions = [dimensions] assert len( dimensions) <= 2, 'dimensions can contain a maximum of two strings' if dataset is None: return None, {} groups = defaultdict(list) models = [] for name in dataset['model_ensemble'].values: model = name.split('_')[0] groups[model].append(name) models.append(model) for dimension in dimensions: if dimension in dataset.dims: model = xr.DataArray(models, dims=dimension) dataset = dataset.groupby(model).mean(keep_attrs=True).rename( {'group': dimension}) if len(dimensions) == 2: # need to set the diagonal elements back to zero after averaging dataset.values[np.diag_indices(dataset['model_ensemble'].size)] = 0 return dataset, groups def calculate_weights_data( performance: Union['np.array', None], independence: Union['np.array', None], performance_sigma: Union[float, None], independence_sigma: Union[float, None]) -> 'np.array': """Calculate normalized weights for each model N. Parameters ---------- performance : array_like, shape (N,) or None Array specifying the model performance. None is mutually exclusive with independence being None. Single values in performance can be nan, then they will be excluded from the independence calculation as well (used for the perfect model test). independence : array_like, shape (N, N) or None Array specifying the model independence. None is mutually exclusive with performance being None. performance_sigma : float or None Sigma value defining the form of the weighting function for the performance. Can be one only if performance is also None. independence_sigma : float or None Sigma value defining the form of the weighting function for the independence. Can be one only if independence is also None. Returns ------- weights : ndarray, shape (N,) """ numerator = 1 not_nan = True denominator = 1 if performance is not None: numerator = np.exp(-((performance / performance_sigma)**2)) # nans in the performance vector indicate models to be excluded not_nan = np.isfinite(performance) if independence is not None: # don't consider nan models for independence of other models! exp = np.exp(-((independence[:, not_nan] / independence_sigma)**2)) # Note diagonal = exp(0) = 1, thus this is equal to 1 + sum(i!=j) denominator = exp.sum(axis=1) weights = numerator / denominator weights /= weights.sum(where=not_nan) return weights def calculate_weights( performance: Union['xr.DataArray', None], independence: Union['xr.DataArray', None], performance_sigma: Union[float, None], independence_sigma: Union[float, None]) -> 'xr.DataArray': """Xarray wrapper for calculate_weights_data.""" performance_core_dims = [] if performance is None else ['model_ensemble'] independence_core_dims = [] if independence is None else [ 'model_ensemble', 'model_ensemble_reference' ] weights = xr.apply_ufunc( calculate_weights_data, performance, independence, performance_sigma, independence_sigma, input_core_dims=[ performance_core_dims, independence_core_dims, [], [] ], output_core_dims=[['model_ensemble']], vectorize=True, ) weights.name = 'weight' weights.attrs['variable_group'] = 'weight' # used in barplot weights.attrs['units'] = '1' return weights def weighted_quantile(values: list, quantiles: list, weights: list = None) -> 'np.array': """Calculate weighted quantiles. Analogous to np.quantile, but supports weights. Based on: https://stackoverflow.com/a/29677616/6012085 Parameters ---------- values: array_like List of input values. quantiles: array_like List of quantiles between 0.0 and 1.0. weights: array_like List with same length as `values` containing the weights. Returns ------- np.array Numpy array with computed quantiles. """ values = np.array(values) quantiles = np.array(quantiles) if weights is None: weights = np.ones(len(values)) weights = np.array(weights) # remove nans not_nan = np.where((np.isfinite(values) &

np.isfinite(weights)

numpy.isfinite

import random import numpy as np import cv2 import matplotlib.pyplot as plt # from matplotlib import pyplot as plt import scipy from scipy import signal from PIL import Image from scipy.ndimage import median_filter # 由于卷积核的大小一般是奇数，因此这里假设卷积核是奇数的 ''' #################### 图像处理的基本函数 #################### ''' # 图像加框 def addBoundary(img, kernel): ''' 给图像添加边界 :param img: 输入图像 :param kernel:卷积核 :return: 加边界后的图像 ''' kernel_size = kernel.shape[0] addLine = (int)((kernel_size - 1) / 2) img_ = cv2.copyMakeBorder(img, addLine, addLine, addLine, addLine, cv2.BORDER_CONSTANT, value=0); return img_ def convolve1(img, kernel, filter_type, mode='same'): ''' 单通道图像与卷积核的卷积，主要用于灰度图 :param img: 输入单通道图像矩阵 :param kernel: 卷积核 :param model: medium,gauss,mean, 即选择中值滤波、高斯滤波、还是均值滤波，其他滤波方式以后添加 :return: 卷积后的图像 ''' if mode == 'same': img_ = addBoundary(img, kernel) kernel_height = kernel.shape[0] kernel_width = kernel.shape[1] # 横向卷积、纵向卷积的次数 conv_height = img_.shape[0] - kernel_height + 1 conv_width = img_.shape[1] - kernel_width + 1 # 卷积结果存储在conv中 conv = np.zeros((conv_height, conv_width), dtype='uint8') for i in range(conv_height): for j in range(conv_width): conv[i][j] = wise_element_sum(img_[i:i + kernel_height, j:j + kernel_width], kernel, filter_type) return conv def wise_element_sum(img, kernel, filter_type): ''' 对于某一次卷积结果的取值 :param img: 输入的图片片段矩阵 :param kernel: 卷积核 :param modle: medium,gauss,mean, 即选择中值滤波、高斯滤波、还是均值滤波，其他滤波方式以后添加 :return: 返回该像素值 ''' if filter_type == 'medium_Filter': temp = img * kernel list = [] for i in range(temp.shape[0]): for j in range(temp.shape[1]): list.append(temp[i][j]) list.sort() if list[int(len(list) / 2)] > 255: return 255 elif list[int(len(list) / 2)] < 0: return 0 else: return list[int(len(list) / 2)] # 均值、高斯滤波等 else: result = (img * kernel).sum() if result < 0: return 0 elif result > 255: return 255 else: return result def convolve(img, kernel, filter_type, mode='same'): ''' 三通道卷积，主要用于彩色图 :param img: 输入图像矩阵 :param kernel: 卷积核 :param mode: medium,gauss,mean, 即选择中值滤波、高斯滤波、还是均值滤波，其他滤波方式以后添加 :return: 卷积后的图像矩阵 ''' R = np.mat(img[:, :, 0]) G = np.mat(img[:, :, 1]) B = np.mat(img[:, :, 2]) conv_B = convolve1(img[:, :, 0], kernel, filter_type, mode) conv_G = convolve1(img[:, :, 1], kernel, filter_type, mode) conv_R = convolve1(img[:, :, 2], kernel, filter_type, mode) conv_img = np.dstack([conv_B, conv_G, conv_R]) return conv_img ''' ############################################ 噪声函数脉冲噪声：add_PulseNoise(img, SNR) 椒盐噪声：add_Salt_PepperNoise(img, SNR) 高斯噪声：add_Gauss_Noise(img, mean, sigma) ############################################# ''' # 添加脉冲噪声 def add_PulseNoise(img, SNR): ''' 给图像添加脉冲噪声 :param img: 输入图像 :param SNR: 信噪比，决定添加多少噪声 :return: 添加噪声后的图像 ''' rows, cols, dims = img.shape # 创建与图像大小一样的矩阵 R =

np.mat(img[:, :, 0])

numpy.mat

#!/usr/bin/env python # -*- coding: utf-8 -*- # Evaluation config: line 489-497 for mitigated embedding, p822-824 for original embedding import json, codecs, time, re, os import gensim import logging import numpy as np import pandas as pd from gensim.models import word2vec, FastText from gensim.test.utils import datapath from sklearn.decomposition import PCA from sklearn import svm, metrics from sklearn.metrics import accuracy_score, roc_auc_score, precision_score, recall_score, confusion_matrix from collections import OrderedDict, defaultdict from copy import deepcopy # for visualizing import matplotlib.pyplot as plt from sklearn.manifold import TSNE import random import config, mitigating_stereotypes, base_words from config import SOURCE_DIR start_time = time.time() logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) # Basic constants DEFAULT_ARGUMENTS_W2V = dict(workers=4, sg=1, size=300, window=5, min_count=5, sample=10^-4, negative=5, seed=1, iter=2) DEFAULT_ARGUMENTS_FT = dict(**DEFAULT_ARGUMENTS_W2V, min_n=3, max_n=6) SVM_Cs = [10] ### UCI setting ### SMALL_UCI_NUM = 32561 def load_professions(fname): with codecs.open(fname, 'r', encoding='utf-8', errors='ignore') as f: professions = json.load(f) print('Loaded professions\n' + 'Format:\n' + 'word,\n' + 'definitional female -1.0 -> definitional male 1.0\n' + 'stereotypical female -1.0 -> stereotypical male 1.0') return professions sensitive_pair, neutral_word_list = config.load_analogy_pair(SOURCE_DIR + 'minority_groups.txt') def load_UCI(): X_train, y_train, X_test, y_test = [], [], [], [] with codecs.open(SOURCE_DIR + 'UCI_adult_dataset.txt', 'r', encoding='utf-8', errors='ignore') as f: for i, line in enumerate(re.split('[\r\n]+', f.read())): line = re.sub(r' ', '', line) tokens = re.split(r',', line) if len(tokens) == 15: X_train.append(tokens[:-1]) y_train.append(tokens[-1]) with codecs.open(SOURCE_DIR + 'UCI_adult_test.txt', 'r', encoding='utf-8', errors='ignore') as f: for i, line in enumerate(re.split('[\r\n]+', f.read())): if i == 0: continue line = re.sub(r' ', '', line) tokens = re.split(r',', line) if len(tokens) == 15: X_test.append(tokens[:-1]) y_test.append(tokens[-1]) print("### UCI train set statistics ###") UCI_stats_by_gender(X_train[:SMALL_UCI_NUM], y_train[:SMALL_UCI_NUM]) print("### UCI test set statistics ###") UCI_stats_by_gender(X_test, y_test) return (X_train, y_train), (X_test, y_test) def word2rep(_X_train, _y_train, model): X_train, y_train = [], [] avg_fnl_weight = np.array([int(e[2]) for e in _X_train]).mean() avg_hpw_weight = np.array([int(e[12]) for e in _X_train]).mean() for X, y in zip(_X_train, _y_train): tmp_X = np.array([]) for token in X: if not re.search(r'[a-zA-Z\-?]+', token): #tmp_X = np.append(tmp_X, np.array([float(token)/10000])) tmp_X = np.append(tmp_X, np.array([float(token)*avg_hpw_weight/avg_fnl_weight])) tmp_X = np.append(tmp_X, np.zeros(np.shape(model.syn0[1])[0] - 1)) elif not config.CONSIDER_GENDER and (token == 'Male' or token == 'Female'): continue elif token in model.vocab: tmp_X = np.append(tmp_X, model[token]) # compound with '-': only select first vocab without oov for regulating sizes of all X elif re.search(r'-', token): add_tokens = re.split(r'-', token) i = 1 for add_token in add_tokens: if add_token in model.vocab: tmp_X = np.append(tmp_X, model[add_token]) i = 0 break else: continue if i: tmp_X = np.append(tmp_X, np.zeros(np.shape(model.syn0[1]), dtype=float)) else: tmp_X = np.append(tmp_X, np.zeros(np.shape(model.syn0[1]), dtype=float)) if np.shape(tmp_X)[0] > 0: X_train.append(tmp_X) if re.search(r'>', y): y_train.append(1) else: y_train.append(0) return np.array(X_train), np.array(y_train) def identify_index_by_gender(X, y): stats_dict = {} stats_dict['Male'] = [] stats_dict['Female'] = [] for i, (tokens, y) in enumerate(zip(X, y)): stats_dict[tokens[9]].append(i) return np.array(stats_dict['Male']), np.array(stats_dict['Female']) def identify_index_by_race(X, y): stats_dict = {} stats_dict['Amer-Indian-Eskimo'] = [] stats_dict['Asian-Pac-Islander'] = [] stats_dict['Black'] = [] stats_dict['White'] = [] stats_dict['Other'] = [] for i, (tokens, y) in enumerate(zip(X, y)): stats_dict[tokens[8]].append(i) return np.array(stats_dict['Amer-Indian-Eskimo']), np.array(stats_dict['Asian-Pac-Islander']), \ np.array(stats_dict['Black']), np.array(stats_dict['White']), np.array(stats_dict['Other']) def UCI_stats_by_gender(X, y): stats_dict = {} stats_dict['Male'] = [0, 0] stats_dict['Female'] = [0, 0] for tokens, label in zip(X, y): stats_dict[tokens[9]][1 if re.search(r'>', label) else 0] += 1 print("<=50K Male:Female = {:.3f} / {:.3f} ({} / {})".format(stats_dict['Male'][0] / (stats_dict['Male'][0] + stats_dict['Female'][0]), stats_dict['Female'][0] / (stats_dict['Male'][0] + stats_dict['Female'][0]), stats_dict['Male'][0], stats_dict['Female'][0])) print(" >50K Male:Female = {:.3f} / {:.3f} ({} / {})".format(stats_dict['Male'][1] / (stats_dict['Male'][1] + stats_dict['Female'][1]), stats_dict['Female'][1] / (stats_dict['Male'][1] + stats_dict['Female'][1]), stats_dict['Male'][1], stats_dict['Female'][1])) return 0 def print_result(y_test, pred, test_male_index, test_female_index): acc, auc, pre, rec = accuracy_score(y_test, pred), roc_auc_score(y_test, pred), \ precision_score(y_test, pred, average=None), recall_score(y_test, pred, average=None) cnf_matrix = confusion_matrix(y_test, pred) male_cnf_matrix = confusion_matrix(y_test[test_male_index], pred[test_male_index]) female_cnf_matrix = confusion_matrix(y_test[test_female_index], pred[test_female_index]) print(acc, auc, pre, rec) print("<=50K Male:Female = {:.3f} / {:.3f} ({} / {})".format(np.sum(male_cnf_matrix, axis=0)[0] / np.sum(cnf_matrix, axis=0)[0], np.sum(female_cnf_matrix, axis=0)[0] / np.sum(cnf_matrix, axis=0)[0], np.sum(male_cnf_matrix, axis=0)[0], np.sum(female_cnf_matrix, axis=0)[0])) print(" >50K Male:Female = {:.3f} / {:.3f} ({} / {})".format(np.sum(male_cnf_matrix, axis=0)[1] / np.sum(cnf_matrix, axis=0)[1], np.sum(female_cnf_matrix, axis=0)[1] / np.sum(cnf_matrix, axis=0)[1], np.sum(male_cnf_matrix, axis=0)[1], np.sum(female_cnf_matrix, axis=0)[1])) fpr, fnr = print_cnf_matrix(cnf_matrix) male_fpr, male_fnr = print_cnf_matrix(male_cnf_matrix) female_fpr, female_fnr = print_cnf_matrix(female_cnf_matrix) print("fpr_bias_ratio: {:.2f}, fnr_bias_ratio: {:.2f}".format(male_fpr / female_fpr, male_fnr / female_fnr)) print('-' * 30) return fpr, fnr def print_cnf_matrix(cnf_matrix, normalize=True): print(cnf_matrix) fpr, fnr = 0, 0 if normalize: cnf_matrix = cnf_matrix.astype('float') / cnf_matrix.sum(axis=1)[:, np.newaxis] print(cnf_matrix) fpr = cnf_matrix[0, 1] fnr = cnf_matrix[1, 0] return fpr, fnr # means predicted:X, target:y def find_optimal_cutoff(predicted, target): fpr, tpr, threshold = metrics.roc_curve(target, predicted) i = np.arange(len(tpr)) roc = pd.DataFrame({'tf' : pd.Series(tpr-(1-fpr), index=i), 'threshold': pd.Series(threshold, index=i)}) roc_t = roc.ix[(roc.tf-0).abs().argsort()[:1]] return list(roc_t['threshold']).pop() class W2vModel(object): def __init__(self, vocab_limit=None): """ :param is_selected_gender_vocab: 'True' means selected_gender_vocab is prepared. :param remove_oov: remove words not in w2v.model vocab. """ # embedding models self.w2v_fname = config.WORD_EMBEDDING_NAME self.w2v_model = self.load_w2v_model(self.w2v_fname, vocab_limit) if not vocab_limit: self.w2v_model.init_sims() # for using wv.syn0norm def load_w2v_model(self, fname, vocab_limit): try: try: print('Loading W2v Model... in {0:.2f} seconds'.format(time.time() - start_time)) w2v_model = word2vec.Word2Vec.load(fname) if vocab_limit: # it uses KeyedVector class (Word2vec.wv). Do not point wv. tmp_w2v = gensim.models.KeyedVectors(vector_size=300) tmp_w2v.index2word = w2v_model.wv.index2word[:vocab_limit] tmp_w2v.vocab = {w: w2v_model.wv.vocab[w] for w in tmp_w2v.index2word} # check if the order of keyedvector is broken for i, w in enumerate(tmp_w2v.index2word): if tmp_w2v.vocab[w].index != i: print(w, tmp_w2v.vocab[w].index, i) tmp_w2v.syn0 = w2v_model.wv.syn0[:vocab_limit, :] w2v_model.wv.vocab = {} w2v_model.wv.index2word = [] w2v_model.wv.syn0 = np.zeros((10, 300)) print(tmp_w2v) return tmp_w2v print(w2v_model) except Exception as e: w2v_model = word2vec.Word2VecKeyedVectors.load_word2vec_format(fname, binary=False) if vocab_limit: # it uses KeyedVector class (Word2vec.wv). Do not point wv. tmp_w2v = gensim.models.KeyedVectors(vector_size=300) tmp_w2v.index2word = w2v_model.index2word[:vocab_limit] tmp_w2v.vocab = {w: w2v_model.vocab[w] for w in tmp_w2v.index2word} # check if the order of keyedvector is broken for i, w in enumerate(tmp_w2v.index2word): if tmp_w2v.vocab[w].index != i: print(w, tmp_w2v.vocab[w].index, i) tmp_w2v.syn0 = w2v_model.syn0[:vocab_limit, :] w2v_model.vocab = {} w2v_model.index2word = [] w2v_model.syn0 = np.zeros((10, 300)) print(tmp_w2v) print('Success to load W2v Model... in {0:.2f} seconds'.format(time.time() - start_time)) return tmp_w2v print(w2v_model) print('Success to load W2v Model... in {0:.2f} seconds'.format(time.time() - start_time)) return w2v_model except Exception as e: print('No existed model. Training W2v Model... in {0:.2f} seconds'.format(time.time() - start_time)) texts = '' if config.MODEL_NAME == 'wiki': texts = config.WikiCorpus() elif config.MODEL_NAME == 'reddit': texts = config.RedditCorpus() else: print("please select corpus for training model.") exit(1) print('training w2v with {} corpus ... in {:.2f} seconds'.format(config.MODEL_NAME, config.whattime())) w2v_model = word2vec.Word2Vec(texts, **DEFAULT_ARGUMENTS_W2V) # init_sims: reduce memory but cannot continue training (because original vectors are removed.) w2v_model.init_sims(replace=True) #w2v_model.save(fname) # save model self.w2v_model.save_word2vec_format(fname, binary=False) print('Success to load W2v Model... in {0:.2f} seconds'.format(time.time() - start_time)) return w2v_model.wv def test_intrinsic(self): try: self.w2v_model.wv.accuracy(SOURCE_DIR+'questions-words.txt', restrict_vocab=300000) """ analogy_score, result_list = self.w2v_model.wv.evaluate_word_analogies(datapath('questions-words.txt')) print("score: {:.2f}".format(analogy_score)) for result_dict in result_list: print("{}: True {} / False {}".format(result_dict['section'], result_dict['correct'][:3], result_dict['incorrect'][:3])) """ except Exception as e: self.w2v_model.accuracy(SOURCE_DIR + 'questions-words.txt', restrict_vocab=300000) try: similarities = self.w2v_model.wv.evaluate_word_pairs(datapath('wordsim353.tsv'), restrict_vocab=300000) except Exception as e: similarities = self.w2v_model.evaluate_word_pairs(datapath('wordsim353.tsv'), restrict_vocab=300000) def test_UCI(self, uci_dataset, small_train=True): (_X_train, _y_train), (_X_test, _y_test) = uci_dataset test_male_index, test_female_index = identify_index_by_gender(_X_test, _y_test) # test_amer_index, test_asian_index, test_black_index, test_white_index, test_other_index = identify_index_by_race(_X_test, _y_test) (X_train, y_train), (X_test, y_test) = word2rep(_X_train, _y_train, self.w2v_model), word2rep(_X_test, _y_test, self.w2v_model) assert len(X_train) == len(y_train) assert len(X_test) == len(y_test) print("num of tests / num of labels: {} {} / {} {} in {:.2f} sec".format( len(X_train), len(X_test), len(set(y_train)), len(set(y_test)), time.time() - start_time)) for c in SVM_Cs: clf = svm.SVC(C=c) if small_train: clf.fit(X_train[:SMALL_UCI_NUM], y_train[:SMALL_UCI_NUM]) else: clf.fit(X_train, y_train) pred = clf.predict(X_test) if not os.path.exists(SOURCE_DIR + 'pred_UCI'): os.makedirs(SOURCE_DIR + 'pred_UCI') with codecs.open(SOURCE_DIR + 'pred_UCI/w2v_' + config.MODEL_NAME + str(c) + '_pred.txt', 'w', encoding='utf-8', errors='ignore') as f: for tokens, label in zip(_X_test, pred): f.write('\t'.join(tokens) + '\t' + str(label) + '\n') print_result(y_test, pred, test_male_index, test_female_index) return 0 def test_analogy(self): for w1, w2 in sensitive_pair: for word in neutral_word_list: try: print('{}:{} = {}:{}'.format( w1, w2, word, self.w2v_model.most_similar(positive=[w2, word], negative=[w1], topn=10))) except Exception as e: continue def save(self, fname): self.w2v_model.save_word2vec_format(fname, binary=False) def save_vocab(self): """ Setting 4: remove noun particle / foreign words / digit and gender_specific suffix / prefix. After that, only remain the data between upper and lower cut off based on frequency. :return: """ with codecs.open(SOURCE_DIR + '{}_vocabs.txt'.format(config.MODEL_NAME), "w", encoding='utf-8', errors='ignore') as write_file: tmp_vocab = OrderedDict() for word, vocab_obj in sorted(self.w2v_model.wv.vocab.items(), key=lambda item: -item[1].count): if re.search(r'^[a-zA-Z][a-zA-Z0-9]{0,}$', word): tmp_vocab[word] = vocab_obj write_file.write('{0}\t{1}\n'.format(word, vocab_obj.count)) print("Success to save wiki vocabulary.") self.w2v_vocab = tmp_vocab def get_keyedvectors(self): return self.w2v_model class FtModel(object): def __init__(self): """ :param is_selected_gender_vocab: 'True' means selected_gender_vocab is prepared. :param remove_oov: remove words not in w2v.model vocab. """ # embedding models self.ft_fname = config.MODEL_DIR + 'ft_{0}_sg_300_neg5_it2.model'.format(config.MODEL_NAME) self.ft_model = self.load_ft_model(self.ft_fname) def load_ft_model(self, fname): """ class FastText(sentences=None, sg=0, hs=0, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, word_ngrams=1, sample=0.001, seed=1, workers=3, min_alpha=0.0001, negative=5, cbow_mean=1, hashfxn=hash, iter=5, null_word=0, min_n=3, max_n=6, sorted_vocab=1, bucket=2000000, trim_rule=None, batch_words=MAX_WORDS_IN_BATCH) min_n : int Min length of char ngrams to be used for training word representations. max_n : int Max length of char ngrams to be used for training word representations. Set max_n to be lesser than min_n to avoid char ngrams being used. word_ngrams : int {1,0} If 1, uses enriches word vectors with subword(ngrams) information. If 0, this is equivalent to word2vec. bucket : int Character ngrams are hashed into a fixed number of buckets, in order to limit the memory usage of the model. This option specifies the number of buckets used by the model. """ print('Loading Fasttext Model... in {0:.2f} seconds'.format(time.time() - start_time)) try: fasttext_model = FastText.load(fname) print(fasttext_model) except IOError: print('No existed model. Training Ft Model... in {0:.2f} seconds'.format(time.time() - start_time)) texts = config.WikiCorpus() fasttext_model = FastText(texts, **DEFAULT_ARGUMENTS_FT) fasttext_model.save(fname) print('Success to load Fasttext Model... in {0:.2f} seconds'.format(time.time() - start_time)) return fasttext_model def test(self): self.ft_model.wv.accuracy(SOURCE_DIR + 'questions-words.txt') similarities = self.ft_model.wv.evaluate_word_pairs(datapath('wordsim353.tsv')) # print(similarities) class MyModel(object): def __init__(self, threshold=None, space_order=[1, 1]): """ :param is_selected_gender_vocab: 'True' means selected_gender_vocab is prepared. :param remove_oov: remove words not in w2v.model vocab. """ # embedding models self.my_fname = config.MITIGATED_EMBEDDING_NAME self.my_model = self.load_w2v_model(self.my_fname) self.init_modulate = np.shape(self.my_model.syn0)[1] self._modulate_vector_linalg(dim=1, dim2=1) self.threshold = threshold self.space_order = space_order self.modulated_number = 0 def load_w2v_model(self, fname, arranged_savfile=True): try: print('Loading My Model... in {0:.2f} seconds'.format(time.time() - start_time)) if not arranged_savfile: w2v_model = gensim.models.KeyedVectors.load(fname) wi = {w: i for i, w in enumerate(w2v_model.index2word)} w2v_model.vocab = {word: config.Vocab(count=count, index=wi[word]) for word, count in w2v_model.vocab.items()} w2v_model.save_word2vec_format(fname, binary=False) my_model = word2vec.Word2VecKeyedVectors.load_word2vec_format(fname, binary=False) #my_model = word2vec.Word2Vec.load(fname + 'w2vf') print(my_model) except IOError: print('No existed model. Training My Model... in {0:.2f} seconds'.format(time.time() - start_time)) print("constructing") exit() print('Success to load My Model... in {0:.2f} seconds'.format(time.time() - start_time)) return my_model def _modulate_vector_linalg(self, dim=1, dim2=1): self.my_model.syn0[:, :dim + dim2] = self.my_model.syn0[:, :dim + dim2] / self.init_modulate def modulate_sentiment(self, dim=1, dim2=1, intensity=1): assert len(self.space_order) < 3, "please set space_order with type 'list' (e.g. [1, 1])." if self.threshold and self.space_order[1] == 1: # modulate sentiment only for entity words self.my_model.syn0[:, :dim] = np.multiply(self.my_model.syn0[:, :dim], np.where(self.my_model.syn0[:, dim:dim + dim2] >= (self.threshold / self.init_modulate), intensity, 1)) elif self.threshold and self.space_order[1] == -1: # modulate sentiment only for entity words self.my_model.syn0[:, :dim] = np.multiply(self.my_model.syn0[:, :dim], np.where(self.my_model.syn0[:, dim:dim + dim2] <= -(self.threshold / self.init_modulate), intensity, 1)) else: # modulate sentiment for entire words self.my_model.syn0[:, :dim] = self.my_model.syn0[:, :dim] * intensity self.my_model.syn0norm = (self.my_model.syn0 / np.sqrt((self.my_model.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) self.modulated_number += intensity*1 # self.my_model.init_sims(replace=True) # it makes syn0 and vectors to be also normalized (same as syn0norm and vectors_norm) def modulate_all(self, dim=1, dim2=1, intensity=1): if intensity < 1: assert len(self.space_order) < 3, "please set space_order with type 'list' (e.g. [1, 1])." self.my_model.syn0[:, :dim+dim2] = self.my_model.syn0[:, :dim+dim2] * intensity # self.my_model.init_sims(replace=True) # it makes syn0 and vectors to be also normalized (same as syn0norm and vectors_norm) self.my_model.syn0norm = ( self.my_model.syn0 / np.sqrt((self.my_model.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) def test(self, uci_dataset, intensity_order=1): for i, intensity in enumerate([1, 10]): #, 10, 10]): if i == 0 and intensity_order < 0: continue print("Model with intensity 10^{}, threshold {}".format(i*intensity_order, self.threshold)) self.modulate_sentiment(intensity=intensity**intensity_order) #self.test_analogy() self.test_UCI(uci_dataset) self.test_intrinsic() #self.show_vocab_tsnescatterplot() #self.show_topn_embedding() print("Model with intensity 0, threshold {}".format(self.threshold)) self.modulate_sentiment(intensity=0) #self.test_analogy() self.test_UCI(uci_dataset) self.test_intrinsic() def test_intrinsic(self): self.my_model.accuracy(SOURCE_DIR + 'questions-words.txt', restrict_vocab=300000) similarities = self.my_model.evaluate_word_pairs(datapath('wordsim353.tsv'), restrict_vocab=300000) print(similarities) def test_analogy(self): for w1, w2 in sensitive_pair: for word in neutral_word_list: try: print('{}:{} = {}:{}'.format( w1, w2, word, self.my_model.most_similar(positive=[w2, word], negative=[w1], topn=10))) except Exception as e: continue def test_UCI(self, uci_dataset, small_train=True): (_X_train, _y_train), (_X_test, _y_test) = uci_dataset test_male_index, test_female_index = identify_index_by_gender(_X_test, _y_test) (X_train, y_train), (X_test, y_test) = word2rep(_X_train, _y_train, self.my_model), word2rep(_X_test, _y_test, self.my_model) assert len(X_train) == len(y_train) assert len(X_test) == len(y_test) print("num of tests / num of labels: {} {} / {} {} in {:.2f} sec".format( len(X_train), len(X_test), len(set(y_train)), len(set(y_test)), time.time() - start_time)) for c in SVM_Cs: clf = svm.SVC(C=c) if small_train: clf.fit(X_train[:SMALL_UCI_NUM], y_train[:SMALL_UCI_NUM]) else: clf.fit(X_train, y_train) pred = clf.predict(X_test) with codecs.open(SOURCE_DIR + 'pred_UCI\\my' + str(self.modulated_number) + '_' + config.MODEL_NAME + str(c) + '_pred.txt', 'w', encoding='utf-8', errors='ignore') as f: for tokens, label in zip(_X_test, pred): f.write('\t'.join(tokens) + '\t' + str(label) + '\n') print_result(y_test, pred, test_male_index, test_female_index) return 0 def show_topn_affect(self, dim=1, dim2=1, topn=50): sort_index_sum = np.ndarray.flatten(self.my_model.vectors[:, :dim]).argsort() sort_index = np.prod(self.my_model.vectors[:, :dim+dim2], axis=1).argsort() cond = np.ndarray.flatten(self.my_model.vectors[sort_index, dim:dim+dim2]) >= ( self.threshold / self.init_modulate) if self.space_order[1] == 1 else \ np.ndarray.flatten(self.my_model.vectors[sort_index, dim:dim+dim2]) <= -( self.threshold / self.init_modulate) print("< top {} positive stereotypes >".format(topn)) if self.space_order[0] * self.space_order[1] == 1: for index in sort_index[cond][:-1-topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) else: for index in sort_index[cond][:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) print("< top {} negative stereotypes >".format(topn)) if self.space_order[0] * self.space_order[1] == 1: for index in sort_index[cond][:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) else: for index in sort_index[cond][:-1-topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) def show_vocab_tsnescatterplot(self, dim=1, dim2=1, shown_word=60, top=False): sort_index = np.prod(self.my_model.vectors[:, :dim + dim2], axis=1).argsort() cond = np.ndarray.flatten(self.my_model.vectors[sort_index, dim:dim + dim2]) >= ( self.threshold / self.init_modulate) if self.space_order[1] == 1 else \ np.ndarray.flatten(self.my_model.vectors[sort_index, dim:dim + dim2]) <= -( self.threshold / self.init_modulate) # get random words # close_words = model.similar_by_word(word) if top: entity_words = list(sort_index[cond][::self.space_order[1]])[:int(shown_word / 2)] notity_words = list(sort_index[np.logical_not(cond)][::-self.space_order[1]])[:int(shown_word / 2)] else: entity_words = random.sample(list(sort_index[cond]), int(shown_word / 2)) notity_words = random.sample(list(sort_index[np.logical_not(cond)]), int(shown_word / 2)) # add the vector for each of the closest words to the array arr, word_labels = np.empty((0, 300), dtype='f'), [] for index in entity_words + notity_words: wrd_vector = self.my_model.syn0norm[index] word_labels.append(self.my_model.index2word[index]) arr = np.append(arr, np.array([wrd_vector]), axis=0) # find tsne coords for 1 dimensions tsne = TSNE(n_components=1, random_state=0) np.set_printoptions(suppress=True) x_coords = arr[:, 1] y_coords = arr[:, 0] # display scatter plot plt.scatter(x_coords, y_coords) for label, x, y in zip(word_labels, x_coords, y_coords): plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points') plt.xlim(x_coords.min() + 0.05, x_coords.max() + 0.05) plt.ylim(y_coords.min() + 0.05, y_coords.max() + 0.05) plt.show() def show_topn_embedding(self, dim=1, dim2=1, topn=30): sort_index_sent = np.sum(self.my_model.vectors[:, :dim], axis=1).argsort() if self.space_order[0] == -1: print("< top {} positive stereotypes >".format(topn)) for index in sort_index_sent[:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) print("< top {} negative stereotypes >".format(topn)) for index in sort_index_sent[:-1-topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) else: print("< top {} positive stereotypes >".format(topn)) for index in sort_index_sent[:-1-topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) print("< top {} negative stereotypes >".format(topn)) for index in sort_index_sent[:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) sort_index_sent = np.sum(self.my_model.vectors[:, dim:dim+dim2], axis=1).argsort() if self.space_order[1] == -1: print("< top {} entity stereotypes >".format(topn)) for index in sort_index_sent[:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) print("< top {} notity stereotypes >".format(topn)) for index in sort_index_sent[:-1-topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim+dim2]) else: print("< top {} entity stereotypes >".format(topn)) for index in sort_index_sent[:-1 - topn:-1]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim + dim2]) print("< top {} notity stereotypes >".format(topn)) for index in sort_index_sent[:topn]: print(self.my_model.index2word[index], self.my_model.vectors[index][:dim + dim2]) class DebiasModel(object): def __init__(self, bias_model, same_env=True): """ :param is_selected_gender_vocab: 'True' means selected_gender_vocab is prepared. :param remove_oov: remove words not in w2v.model vocab. """ # embedding models print("same_env: {}".format(same_env)) if same_env: self.model = self.debias_we_same_env(bias_model) else: self.model = self.debias_we(bias_model) def debias_we(self, E): print('Loading Debias Model... in {0:.2f} seconds'.format(time.time() - start_time)) with open(SOURCE_DIR + 'definitional_pairs.json', "r") as f: definitional = json.load(f) with open(SOURCE_DIR + 'equalize_pairs.json', "r") as f: equalize = json.load(f) with open(SOURCE_DIR + 'gender_specific_seed.json', "r") as f: gender_specific_words = json.load(f) tmp_w2v = gensim.models.KeyedVectors(vector_size=300) tmp_w2v.index2word = E.index2word tmp_w2v.vocab = E.vocab tmp_w2v.syn0 = E.syn0 tmp_w2v.syn0norm = (E.syn0 / np.sqrt((E.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) gender_direction = self.doPCA(definitional, tmp_w2v).components_[0] specific_set = set(gender_specific_words) for i, w in enumerate(tmp_w2v.index2word): if w not in specific_set: tmp_w2v.syn0[i] = self.drop(tmp_w2v.syn0[i], gender_direction) tmp_w2v.syn0norm = (tmp_w2v.syn0 / np.sqrt((tmp_w2v.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) candidates = {x for e1, e2 in equalize for x in [(e1.lower(), e2.lower()), (e1.title(), e2.title()), (e1.upper(), e2.upper())]} print(candidates) for (a, b) in candidates: if (a in tmp_w2v.index2word and b in tmp_w2v.index2word): y = self.drop((tmp_w2v[a] + tmp_w2v[b]) / 2, gender_direction) z = np.sqrt(1 - np.linalg.norm(y) ** 2) if (tmp_w2v[a] - tmp_w2v[b]).dot(gender_direction) < 0: z = -z tmp_w2v.syn0[tmp_w2v.vocab[a].index] = z * gender_direction + y tmp_w2v.syn0[tmp_w2v.vocab[b].index] = -z * gender_direction + y tmp_w2v.syn0norm = (tmp_w2v.syn0 / np.sqrt((tmp_w2v.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) print('Success to load Debias Model... in {0:.2f} seconds'.format(time.time() - start_time)) return tmp_w2v def debias_we_same_env(self, E, random_sent_pair=False): print('Loading Debias (same env.) Model... in {0:.2f} seconds'.format(time.time() - start_time)) print('example: {} \n {}'.format(np.array(E['Male']), np.array(E['Female']))) lexicon = config.load_sent_lexicon() lexicon2, lexicon2_vocab = config.load_entity_lexicon() num = int(config.BASE_WORD_NUM) if random_sent_pair: positive_seeds, negative_seeds = mitigating_stereotypes.generate_random_seeds(lexicon, num=num) else: positive_seeds, negative_seeds = base_words.sent_seeds(10) print(positive_seeds, negative_seeds) entity_seeds, notity_seeds = mitigating_stereotypes.generate_random_seeds(lexicon2, num=num) definitional = zip(positive_seeds, negative_seeds) random_pos_seeds, random_neg_seeds = mitigating_stereotypes.generate_random_seeds(lexicon, num=num) equalize = zip(random_pos_seeds, random_neg_seeds) #notity_specific_words = notity_seeds notity_specific_words = [item[0] for item in lexicon2.items() if item[1] == -1] tmp_w2v = gensim.models.KeyedVectors(vector_size=300) tmp_w2v.index2word = E.index2word tmp_w2v.vocab = E.vocab tmp_w2v.syn0 = E.syn0 tmp_w2v.syn0norm = (E.syn0 / np.sqrt((E.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) gender_direction = self.doPCA(definitional, tmp_w2v).components_[0] specific_set = set(notity_specific_words) for i, w in enumerate(tmp_w2v.index2word): if w not in specific_set: tmp_w2v.syn0[i] = self.drop(tmp_w2v.syn0[i], gender_direction) tmp_w2v.syn0norm = (tmp_w2v.syn0 / np.sqrt((tmp_w2v.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) candidates = {x for e1, e2 in equalize for x in [(e1.lower(), e2.lower()), (e1.title(), e2.title()), (e1.upper(), e2.upper())]} print(candidates) for (a, b) in candidates: if (a in tmp_w2v.index2word and b in tmp_w2v.index2word): y = self.drop((tmp_w2v[a] + tmp_w2v[b]) / 2, gender_direction) z = np.sqrt(1 - np.linalg.norm(y) ** 2) if (tmp_w2v[a] - tmp_w2v[b]).dot(gender_direction) < 0: z = -z tmp_w2v.syn0[tmp_w2v.vocab[a].index] = z * gender_direction + y tmp_w2v.syn0[tmp_w2v.vocab[b].index] = -z * gender_direction + y tmp_w2v.syn0norm = (tmp_w2v.syn0 / np.sqrt((tmp_w2v.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(float) print('Success to load Debias (same env.) Model... in {0:.2f} seconds'.format(time.time() - start_time)) print('example: {} \n {}'.format(np.array(E['Male']),

np.array(E['Female'])

numpy.array

import numpy as np import pandas as pd import seaborn as sns import pickle import warnings from tqdm import tqdm import os from collections import defaultdict import matplotlib.pyplot as plt from sklearn.linear_model import LogisticRegression pd.set_option('display.max_columns', 500) warnings.filterwarnings('ignore') def sigmoid(x): return 1 / (1 + np.exp(-x)) def cal_accuracy(y_pred, y_true): y_pred = np.where(y_pred >= 0.5, 1, 0) return (y_pred == y_true).mean() def l2_logistic_regression(x, y, lr, lbd, dev_x, dev_y): w = np.zeros(x.shape[1]) epsilon = 1e-8 min_loss = float('inf') best_train_acc = 0 best_dev_acc = 0 train_acc_history = [] dev_acc_history = [] for i in tqdm(range(10000)): gradient = ((x.multiply(y - sigmoid(x.dot(w)), axis=0)).mean()) w = w + lr * gradient w[1:] = w[1:] - lr * lbd * w[1:] train_acc = cal_accuracy(sigmoid(x.dot(w)), y) dev_acc = cal_accuracy(sigmoid(dev_x.dot(w)), dev_y) train_acc_history.append(train_acc) dev_acc_history.append(dev_acc) best_train_acc = max(best_train_acc, train_acc) best_dev_acc = max(best_dev_acc, dev_acc) loss = ((-1 * y * np.log(sigmoid(x.dot(w)))) - ((np.ones(x.shape[0]) - y) * np.log( np.ones(x.shape[0]) - sigmoid(x.dot(w))))).mean() + lbd * np.sum(np.power(w[1:], 2)) # print("iter={}, loss={}, train_acc={}, dev_acc={}".format(i + 1, loss, train_acc, dev_acc)) min_loss = min(min_loss, loss) if np.linalg.norm(gradient) <= epsilon: # print("lr={}, min_loss={}".format(lr, min_loss)) break loss = ((-1 * y * np.log(sigmoid(x.dot(w)))) - ((np.ones(x.shape[0]) - y) * np.log( np.ones(x.shape[0]) - sigmoid(x.dot(w))))).mean() + lbd * np.sum(np.power(w[1:], 2)) train_acc = cal_accuracy(sigmoid(x.dot(w)), y) dev_acc = cal_accuracy(sigmoid(dev_x.dot(w)), dev_y) print("lr={}, lambda={}, min_loss={}, best_train_acc={}, best_dev_acc={}, loss={}, train_acc={}, dev_acc={}".format( lr, lbd, min_loss, best_train_acc, best_dev_acc, loss, train_acc, dev_acc)) return w, train_acc_history, dev_acc_history def l1_logistic_regression(x, y, lr, lbd, dev_x, dev_y): w = np.zeros(x.shape[1]) epsilon = 1e-8 min_loss = float('inf') best_train_acc = 0 best_dev_acc = 0 train_acc_history = [] dev_acc_history = [] for i in tqdm(range(10000)): gradient = ((x.multiply(y - sigmoid(x.dot(w)), axis=0)).mean()) w = w + lr * gradient w[1:] = np.sign(w[1:]) * np.maximum(np.abs(w[1:]) - (lr * lbd), np.zeros(w[1:].shape)) train_acc = cal_accuracy(sigmoid(x.dot(w)), y) dev_acc = cal_accuracy(sigmoid(dev_x.dot(w)), dev_y) train_acc_history.append(train_acc) dev_acc_history.append(dev_acc) best_train_acc = max(best_train_acc, train_acc) best_dev_acc = max(best_dev_acc, dev_acc) loss = ((-1 * y * np.log(sigmoid(x.dot(w)))) - ((np.ones(x.shape[0]) - y) * np.log( np.ones(x.shape[0]) - sigmoid(x.dot(w))))).mean() + lbd * np.sum(np.abs(w[1:])) # print("iter={}, loss={}, train_acc={}, dev_acc={}".format(i + 1, loss, train_acc, dev_acc)) min_loss = min(min_loss, loss) if np.linalg.norm(gradient) <= epsilon: # print("lr={}, min_loss={}".format(lr, min_loss)) break loss = ((-1 * y * np.log(sigmoid(x.dot(w)))) - ((

np.ones(x.shape[0])

numpy.ones

import os import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np from common.utils import compute_std_of_mean SAVE_ROOT = '../../figs_sigcomm22' plt.style.use('seaborn-deep') plt.rcParams['font.family'] = 'Arial' # plt.rcParams['font.size'] = 42 # plt.rcParams['axes.labelsize'] = 42 # plt.rcParams['legend.fontsize'] = 42 # plt.rcParams['figure.figsize'] = (11, 9) plt.rcParams['svg.fonttype'] = 'none' HATCHES = ['/', '\\', 'x', 'o', '.', 'O', '-', '*', '+'] WIDTH = 0.3 bbr_reward, bbr_tput, bbr_tail_lat, bbr_loss = 192.81, 32.94, 368.93, 0.03 copa_reward, copa_tput, copa_tail_lat, copa_loss = 183.89, 25.70, 265.02, 0.01 cubic_reward, cubic_tput, cubic_tail_lat, cubic_loss = -19.16, 33.99, 802.69, 0.02 vivace_reward, vivace_tput, vivace_tail_lat, vivace_loss = -547.01, 21.71, 947.25, 0.13 vivace_latency_reward, vivace_latency_tput, vivace_latency_tail_lat, vivace_latency_loss = -548.64, 21.84, 1010.43, 0.13 vivace_loss_reward, vivace_loss_tput, vivace_loss_tail_lat, vivace_loss_loss = -825.15, 28.89, 1125.94, 0.26 genet_reward = 223.88 genet_reward_err = 8.05 genet_tput, genet_tail_lat, genet_loss = 31.77, 183.75, 0.02 udr1_reward = 136.81 udr1_reward_err = 23.61 udr1_tput, udr1_tail_lat, udr1_loss = 23.16, 204.23, 0.03 udr2_reward = 158.48 udr2_reward_err = 17.71 udr2_tput, udr2_tail_lat, udr2_loss = 23.09, 185.58, 0.02 udr3_reward = 159.34 udr3_reward_err = 22.83 udr3_tput, udr3_tail_lat, udr3_loss = 22.72, 179.06, 0.02 real_reward = 191.61 real_reward_err = 3.88 # 26.39 250.47 0.02 cl1_reward = 143.86 cl1_reward_err = 7.64 # 22.53 206.07 0.02 cl2_reward = 177.97 cl2_reward_err = 4.55 # 23.17 204.86 0.01 udr3_real_5percent_ethernet_rewards = [177.2, 209.8, 95.2] udr3_real_10percent_ethernet_rewards = [139, 175, 173] udr3_real_20percent_ethernet_rewards = [133, 125, 151] udr3_real_50percent_ethernet_rewards = [162, 124, 78] column_wid = 0.7 capsize_wid = 8 eline_wid = 2 def generalization_test_ethernet(): plt.rcParams['font.size'] = 36 plt.rcParams['axes.labelsize'] = 36 plt.rcParams['axes.titlesize'] = 36 plt.rcParams['legend.fontsize'] = 36 fig, ax = plt.subplots(figsize=(9, 5)) # plt.bar([1, 2], [bbr_reward, cubic_reward], hatch=HATCHES[:2]) bars = ax.bar([1, 2, 3, 4], [udr1_reward, udr2_reward, udr3_reward, real_reward], yerr=[udr1_reward_err, udr2_reward_err, udr3_reward_err, real_reward_err], color='C0', width=column_wid, error_kw=dict( lw=eline_wid, capsize=capsize_wid)) # bars = ax.bar([1, 2, 3, 4], # [udr1_reward, udr2_reward, udr3_reward, real_reward], # color=None, edgecolor='white') for bar, pat in zip(bars, HATCHES): bar.set_hatch(pat) ax.bar([5], [genet_reward], yerr=[genet_reward_err], capsize=8, width=column_wid, color='C2', error_kw=dict( lw=eline_wid, capsize=capsize_wid)) # plt.title('Ethernet') ax.set_xticks([1, 2, 3, 4, 5]) ax.set_xticklabels(['RL1', 'RL2', 'RL3', 'RL-real', 'Genet'], rotation=20) ax.set_ylabel('Test reward') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.tick_params( axis='x', # changes apply to the x-axis which='both', # both major and minor ticks are affected bottom=False, # ticks along the bottom edge are off top=False) # ticks along the top edge are off # plt.tight_layout() svg_file = os.path.join(SAVE_ROOT, 'evaluation_generalization_test_ethernet.svg') pdf_file = os.path.join(SAVE_ROOT, 'evaluation_generalization_test_ethernet.pdf') fig.savefig(svg_file, bbox_inches='tight') os.system("inkscape {} --export-pdf={}".format(svg_file, pdf_file)) os.system("pdfcrop --margins 1 {} {}".format(pdf_file, pdf_file)) def asymptotic_real(): plt.rcParams['font.size'] = 34 plt.rcParams['axes.labelsize'] = 34 plt.rcParams['axes.titlesize'] = 34 plt.rcParams['legend.fontsize'] = 34 fig, ax = plt.subplots(figsize=(9.5, 5)) bbr_reward = 192.81 #32.94 368.93 0.03 udr_real_synthetic_reward = 171.16 # 23.67 194.00 0.02 udr_real_synthetic_reward_err = 24.22 genet_real_synthetic_reward = 239.39 # 30.93 208.04 0.02 genet_real_synthetic_reward_err = 7.34 cubic_reward = 97.16 # 33.99 802.69 0.02 # plt.bar([1, 2], [bbr_reward, cubic_reward]) # plt.bar([3.5, 4.5,], ax.bar([1, 2.2, 3.4, 4.6, 5.8], [udr_real_synthetic_reward, # 1% np.mean(udr3_real_10percent_ethernet_rewards),

np.mean(udr3_real_20percent_ethernet_rewards)

numpy.mean

# -*- coding: utf-8 -* """ --------------------- SOFTWARE DESCRIPTION: --------------------- Written October 2018 -- <NAME> Typeset in Python 3 This python class is specifically made for the spectroscopic data reduction of the Shelyak eShel spectrograph which is installed at the Hertzsprung SONG node telescope at Tenrife, Spain. The software is originally built from structures of the 'SONGWriter' which is SONG's spectroscopic data reduction pipeline, and by others is inspired by the data reduction pipeline 'FIESTools' of the FIES spectrograph at the NOT on La Palma. """ # Numpy: import numpy as np # Astropy: from astropy.io import fits from astropy.time import Time from astropy.coordinates import SkyCoord, EarthLocation # PyAstronomy: import PyAstronomy as pyas # SciPy: import scipy import scipy.constants import scipy.io import scipy.ndimage from scipy.ndimage import median_filter # Matplotlib: import matplotlib.pyplot as plt from matplotlib import gridspec from tikzplotlib import save as tikz_save # Others: import math, sys, time, glob, pylab, heapq import bottleneck from skimage import feature as skfeature # Error of propagation (nominal_value, std_dev): import uncertainties.unumpy as up from uncertainties import ufloat def val(x): return up.nominal_values(x) def err(x): return up.std_devs(x) # Own functions: import Plot_Tools as pt # Global settings for out-print to terminal (allow more digits and nice coloum ordering): np.set_printoptions(suppress=True, formatter={'float_kind':'{:7.5f}'.format}, linewidth=100) ############################################################################################################ # DEFINE CLASS # ############################################################################################################ class BlueSONG(object): # INITILIZE THE CLASS: def __init__(self, path, img_name): #------------------------------- # DEFINE GLOBAL VARIABLES (DGV): #------------------------------- # USER DEFINED VARIABLES: self.img_name = img_name # Name of image files self.path = path # Directory path to data self.path_img = '/home/nicholas/Dropbox/thesis/latex/pictures/' self.path_blues = '/home/nicholas/Dropbox/Software/Python/blues/' self.cross_cut = [50, 500] # Cut of spectral region in cross dispersion self.orders = [1, 2] self.n_orders = len(self.orders) # File handling: self.img_files = np.sort(glob.glob('{}{}*'.format(self.path, self.img_name))) self.hdul = np.array([fits.open(str(files)) for files in self.img_files]) # Extract headers and sepearte files: self.img_type = [self.hdul[i][0].header['IMAGETYP'] for i in range(len(self.img_files))] self.BF_dex = np.where(np.array(self.img_type)=='bias')[0] self.DF_dex = np.where(np.array(self.img_type)=='dark')[0] self.FF_dex = np.where(np.array(self.img_type)=='flat')[0] self.TA_dex = np.where(np.array(self.img_type)=='thar')[0] self.SF_dex = np.where(np.array(self.img_type)=='star')[0] # Open header of object: header = self.hdul[self.SF_dex[0]][0].header # Observation information: self.datetime = header['DATE-OBS'] # Date and time of observation (string) self.date = self.datetime[:10] # Date of observation (string) self.jdate = header['JD-DATE'] # Julian date (float) self.altitude = header['OBJ-ALT'] # [deg] Initial altitude of target during obs (float) self.seeing = header['SEEING2'] # [arcsec] Running mean seeing on slit guiders (float) # Target information: self.target = header['OBJECT'] # Name of target (string) self.ra = header['OBJ-RA'] # Object Right Accension (string) self.dec = header['OBJ-DEC'] # Object Declination (string) self.magnitude = header['OBJ-MAG'] # Magnitude of object (float) # Dimension constants and arrays: self.len_disp = header['NAXIS1'] # [pixel] Height of image (int) self.len_cross = header['NAXIS2'] # [pixel] Width of image (int) self.cen_disp = int(self.len_disp/2) # [pixel] Center position of disp (int) self.cen_cross = int(self.len_cross/2) # [pixel] Center position of cross (int) self.disp = np.arange(self.len_disp) # [pixel] Integers spanning disp (array) # eShel and CCD setup constants: self.res_power = 10000 # Resolving power self.gain = 0.27 # [e-/ADU] Gain at -10 degC self.ron = 20 # [e-] Read-out-noise self.pixel_size= header['PIXSIZE1'] # [micro m] # HK survey constants: self.V = 3901.000 # [Å] V quasi-continuum center self.K = 3933.664 # [Å] Ca II K line self.H = 3968.470 # [Å] Ca II H line self.R = 4001.000 # [Å] R quasi-continuum center self.bands = [self.V, self.K, self.H, self.R] self.VR_bandpass = 20.0 # [Å] Bandpass of V and R continuums self.HK_bandpass = 1.09 # [Å] Bandpass of K and H lines ###################################################################################################### # CALIBRATION # ###################################################################################################### def image_reduction(self, redo=0, plot=0): """ This routine takes data path and loads all image files given in the directory. It combines the bias, dark, flat, and ThAr frames and make master frames used for the image reduction of the science frames. The routine checks if master calibrations frames already exists: (1) if they do it terminates, (2) if not it continues the image reduction. All calibration frames are saved with an extensions of the date, and science frames with the extension of the date and time of observation. ---------------------------- INPUT : ---------------------------- path (string): Path to data plot (integer): Plot flag activated by 1 ---------------------------- OUTPUT : ---------------------------- BF_XX-XX-XX (fits): Master bias DF_XX-XX-XX (fits): Master dark FF_XX-XX-XX (fits): Master flat TA_XX-XX-XX (fits): Master flat SF_XX-XX-XXTXX:XX:XX (fits): Science frame(s): Bias and dark frame calibrated light frames. """ #------------------------------------------ # TEST IF CALIBRATION IMAGES ALREADY EXIST: #------------------------------------------ try: BF = fits.open('{}BF_{}.fits'.format(self.path, self.date))[0].data DF = fits.open('{}DF_{}.fits'.format(self.path, self.date))[0].data FF = fits.open('{}FF_{}.fits'.format(self.path, self.date))[0].data TA = fits.open('{}TA_{}.fits'.format(self.path, self.date))[0].data SF = fits.open('{}SF_{}.fits'.format(self.path, self.datetime))[0].data except IOError: BF = [] #------------------------- # ELSE USE AVAILABLE DATA: #------------------------- if BF==[] or redo==1: # Find all calibration images: BF_i = np.array([fits.getdata(str(self.img_files[i])) for i in self.BF_dex]) DF_i = np.array([fits.getdata(str(self.img_files[i])) for i in self.DF_dex]) FF_i = np.array([fits.getdata(str(self.img_files[i])) for i in self.FF_dex]) TA_i = np.array([fits.getdata(str(self.img_files[i])) for i in self.TA_dex]) SF_i = np.array([fits.getdata(str(self.img_files[i])) for i in self.SF_dex]) # Exposure times: DF_exptimes = [self.hdul[self.DF_dex[i]][0].header['EXPTIME'] for i in range(len(self.DF_dex))] FF_exptimes = [self.hdul[self.FF_dex[i]][0].header['EXPTIME'] for i in range(len(self.FF_dex))] TA_exptimes = [self.hdul[self.TA_dex[i]][0].header['EXPTIME'] for i in range(len(self.TA_dex))] SF_exptimes = [self.hdul[self.SF_dex[i]][0].header['EXPTIME'] for i in range(len(self.SF_dex))] # Test if exposure times are the same: if int(np.sum(np.diff(DF_exptimes))) is not 0: print('ERROR: Dark exposure times are different!'); sys.exit() if int(np.sum(np.diff(FF_exptimes))) is not 0: print('ERROR: Flat exposure times are different!'); sys.exit() if int(np.sum(np.diff(TA_exptimes))) is not 0: print('ERROR: ThAr exposure times are different!'); sys.exit() #--------------------- # PERFORM CALIBRATION: #--------------------- # Make master bias: BF = np.median(BF_i, axis=0) # Make scaled master dark: DF_current = np.median(DF_i - BF, axis=0) DF_FF = (FF_exptimes[0]/DF_exptimes[0]) * DF_current DF_TA = (TA_exptimes[0]/DF_exptimes[0]) * DF_current DF = (SF_exptimes[0]/DF_exptimes[0]) * DF_current # Make master flat: FF = np.median(FF_i - BF - DF_FF, axis=0) # Make master ThAr: TA = np.median(TA_i - BF - DF_TA, axis=0) # Calibrate science frames: SF = (SF_i - BF - DF)#/(FF/np.max(FF)) #-------------------- # SAVE MASTER FRAMES: #-------------------- # Find hdulists: BF_hdul = self.hdul[self.BF_dex[0]][0].header DF_hdul = self.hdul[self.DF_dex[0]][0].header FF_hdul = self.hdul[self.FF_dex[0]][0].header TA_hdul = self.hdul[self.TA_dex[0]][0].header # Save master calibration images: fits.writeto('{}BF_{}.fits'.format(self.path, self.date), BF, BF_hdul, overwrite=True) fits.writeto('{}DF_{}.fits'.format(self.path, self.date), DF, DF_hdul, overwrite=True) fits.writeto('{}FF_{}.fits'.format(self.path, self.date), FF, FF_hdul, overwrite=True) fits.writeto('{}TA_{}.fits'.format(self.path, self.date), TA, TA_hdul, overwrite=True) # Save calibrated science frames one by one: for i in range(len(self.SF_dex)): SF_hdul = self.hdul[self.SF_dex[i]][0].header header = self.hdul[self.SF_dex[i]][0].header['DATE-OBS'] fits.writeto('{}SF_{}.fits'.format(self.path, header), SF[0], SF_hdul, overwrite=True) # Only use first image if routine is running furter: SF = SF[0] #----------------------------- # LOAD RV AMPLITUDE OF OBJECT: #----------------------------- file_object = glob.glob('{}SF*'.format(self.path)) hdul_object = fits.open(str(file_object[0])) self.rv_amp = hdul_object[0].header['OBJ-RV'] # [km/s] CDS RV amplitude (float) #----------------------------------------------------------- if plot==1: pt.plot_image_reduction(BF, DF, FF, TA, SF) #----------------------------------------------------------- # Select spectral region of interest: self.BF = BF; self.DF = DF; self.FF = FF; self.TA = TA self.F_calib = FF[self.cross_cut[0]:self.cross_cut[1], :].T self.T_calib = TA[self.cross_cut[0]:self.cross_cut[1], :].T self.S_calib = SF[self.cross_cut[0]:self.cross_cut[1], :].T self.noise = np.sqrt(np.mean(BF**2)) #----------------------------------------------------------- return self.S_calib, self.F_calib, self.T_calib ######################################################################################################## # FIND ORDERS # ######################################################################################################## def trace_orders(self, data=None, smooth_win=10, exclude_border=10, min_order_width=40, \ threshold_abs=0, disp_gap_tol=5, num_orders=5, num_peaks=10, plot=0): """ This function find the orders in an eshel spectrum by tracing the maximum light distribution along each order. First the function finds a center order position and use this as a reference. Next the function finds the ridges of the specified number of order 'num_orders' using the skfeature package. Lastely, each order is the discribed by a 5 order polynomial and returned as output. ---------------------------- INPUT : ---------------------------- data (array): A single image smooth_win (float): Smooth value to enhance orders exclude_border (float): Border edges that should be exluded order_min_width (float): Minimum distance to locate where the orders are threshold_abs (float): Threshold used to locate peaks with skfeature disp_gap_tol (float): Tolerance for how big a gap there may be num_orders (float): User specified number of orders the program should find num_peaks (float): Number of peaks found for each bin ---------------------------- OUTPUT : ---------------------------- order_traces (dict): Orders within 'order x' and corresponding array with polynomials """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if data==None: data = self.F_calib #---------------------------------- # FIND CENTRAL REFERENCE POSITIONS: #---------------------------------- # Central position interval ref_int = [self.cen_disp-5, self.cen_disp+6] ref_cen_pos = self.find_ref_cen_pos(data, ref_int, smooth_win, exclude_border, min_order_width,\ threshold_abs, num_orders, plot) #------------------------ # TRACE THE ORDER RIDGES: #------------------------ ridge_pos_cross, ridge_pos_disp = self.find_order_ridges(data, smooth_win, exclude_border,\ min_order_width, threshold_abs, num_peaks) #------------------------------------ # FILL IN DATA INTO THE FOUND RIDGES: #------------------------------------ # Make dict placeholders: order_traced = {} order_trace = {} for i, order_pos in enumerate(np.sort(ref_cen_pos)[::-1]): # Here "order_pos" is the cross dispersion center value. order_pos[0] simply chooses one # value and not the increasing list within the loop. # Using ridges trace each order in each direction: min_order_width = 10 order_trace_cross, order_trace_disp = self.find_order_outliers(self.cen_disp, order_pos[0],\ ridge_pos_disp, ridge_pos_cross,\ min_order_width, disp_gap_tol) # Fit ridges with polynomial: poly_coefs = np.polyfit(order_trace_disp, order_trace_cross, 5) order_traced['order_{}'.format(i)] = poly_coefs order_trace['order_{}'.format(i)] = [order_trace_disp, order_trace_cross] #----------------------------------------------------------------------------- if plot==1: pt.plot_trace_order(ridge_pos_disp, ridge_pos_cross, order_trace, order_traced, \ order_trace_disp, self.cen_disp, ref_cen_pos) #----------------------------------------------------------------------------- self.ref_cen_pos = ref_cen_pos self.trace = order_traced #----------------------------------------------------------------------------- return order_traced def find_ref_cen_pos(self, data, ref_int, smooth_win, exclude_border, min_distance, threshold_abs, \ num_peaks, plot): """ This function finds the center order position used as a reference. """ # Collapse in disp direction to reduce cosmic ray contamination: # (FIXME done to make this robust against cosmics - maybe it is not needed) center_rows_median = np.median(data[ref_int[0]:ref_int[1], :], axis=0) # Smooth cross_dispersion direction to prepare for the peak-detection algorithm: center_row_median_convolved = bottleneck.move_sum(center_rows_median.astype(np.float), \ smooth_win, min_count=1) # Find orders using a peak detection function from scikit-image: order_centres = skfeature.peak_local_max(center_row_median_convolved, \ exclude_border=exclude_border,\ min_distance=min_distance, threshold_rel=0,\ threshold_abs=threshold_abs, num_peaks=num_peaks) # Peaks detected minus the smooth window applied (simply due to the moving sum of bottleneck): ref_cen_pos = order_centres - int(smooth_win/2) #------------------------------------------------------------------------------ if plot==1: pt.plot_find_ref_cen_pos(center_rows_median, center_row_median_convolved, \ self.len_cross, smooth_win, ref_cen_pos) #------------------------------------------------------------------------------ return ref_cen_pos def find_order_ridges(self, data, smooth_win, exclude_border, min_distance, threshold_abs, num_peaks): """ This function finds the ridge of each order. It does so by making a slice in cross dispersion and colvolve that with a smooth filter such as the "bottleneck.move_sum". It then finds the local max for each slice and saves the position """ # Placeholders: ridge_indices_disp = [] ridge_indices_cross = [] # Loop over the dispersion length (i) and the cross order row: for i, crossorder in enumerate(data): # Collapse in dispersion axis: # TODO should smoothing be handled separately? top_hat_conv = bottleneck.move_sum(crossorder.astype(np.float), smooth_win, min_count=1) # Again find the peaks as done in "find_ref_cen_pos": peaks = skfeature.peak_local_max(top_hat_conv, exclude_border=exclude_border,\ min_distance=min_distance, threshold_rel=0,\ threshold_abs=threshold_abs, indices=True, num_peaks=num_peaks) # Convert peaks to a list covering the ridges: peaks -= int(smooth_win/2) ridge_indices_cross = np.append(ridge_indices_cross, peaks) ridge_indices_disp = np.append(ridge_indices_disp, np.ones(peaks.shape[0]) * i) #----------------------------------------------------- return ridge_indices_cross, ridge_indices_disp def find_order_outliers(self, cen_disp, ref_cen_cross, all_orders_x, all_orders_y, order_width,\ disp_gap_tol): """ This utility takes the found reference positions in cross dispersion and the traced ridges and locate all the outliers defined by 'order_width' threshold. If center_row is not an integer this will fail! """ # To simplify the code we make some abbreviations: x = np.unique(all_orders_x) y_last = ref_cen_cross x_last = x[cen_disp] cross_gap_tol = int(order_width/2.) # Placeholders for outliers: cross = [] disp = [] # Outliers on the left side of cen_disp: for xi in x[cen_disp:]: index_xi = all_orders_x == xi orders_y = all_orders_y[index_xi] min_dist_index = np.argmin(np.abs(orders_y - y_last)) new_y_pos = orders_y[min_dist_index] if (np.abs(new_y_pos - y_last) < cross_gap_tol) & (np.abs(xi - x_last) < disp_gap_tol): cross.append(new_y_pos) y_last = cross[-1] disp.append(xi) x_last = disp[-1] y_last = ref_cen_cross x_last = x[cen_disp] # Outliers on the right side of cen_disp: for xi in x[cen_disp-1::-1]: index_xi = all_orders_x == xi orders_y = all_orders_y[index_xi] min_dist_index = np.argmin(np.abs(orders_y - y_last)) new_y_pos = orders_y[min_dist_index] if (np.abs(new_y_pos - y_last) < cross_gap_tol) & (np.abs(xi - x_last) < disp_gap_tol): cross.append(new_y_pos) y_last = cross[-1] disp.append(xi) x_last = disp[-1] index = np.argsort(disp) #--------------------------------------------------- return np.array(cross)[index], np.array(disp)[index] ######################################################################################################## # INTER-ORDER MASK # ######################################################################################################## def inter_order_mask(self, data=None, order_traces=None, order_width=None, \ low_nudge=0, high_nudge=0, plot=0): """ This function is used to determine the background flux which will be used to correct for scattered light, wignetting, etc. The function looks at the flux level in between the orders ("inter-order") and make and return a mask with ones for which is inter-order and zero elsewhere. The function uses the result from the previos subdroutine "traced orders". ---------------------------- INPUT : ---------------------------- order_width (dict) : Traced orders found from the function 'trace' order_traces (int, float): Width of inter-order mask low_nudge (int, float): Number of pixels used below the traced orders high_nudge (int, float): Number of pixels used above the traced orders plot (int, float): Plot result if you like ---------------------------- OUTPUT : ---------------------------- inter_order_mask (dict) : Orders within 'order x' and corresponding array with polynomials """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if data ==None: data = self.F_calib if order_traces==None: order_traces = self.trace if order_width ==None: order_width = self.find_optimal_width(plot=plot) # FUNCTION CALL! # Check if the inter-order width is odd integer: inter_order_width = int(order_width * 4/3) if inter_order_width % 2 == 0: inter_order_width = inter_order_width - 1 # Constants and placeholders: inter_order_mask = data * 0 + 1 # Initial image mask of ones disp = np.arange(self.len_disp) # Number pixel interval in dispersion order_no = sorted(order_traces.keys()) # Orders numbers (string) cross_order_center = [] #----------------------- # FIND ALL INTER-ORDERS: #----------------------- # First loop through each order: for order in order_no: # Get the coefficients from the trace function: coefs = order_traces[order] cross_order_position = np.polyval(coefs, disp) # Polyfit to each order cross_order_center = np.append(cross_order_center, cross_order_position[int(self.len_disp/2)]) # Each inter order is found: for disp_i in range(self.len_disp): lower_order_edge =int(np.round(cross_order_position[disp_i]-inter_order_width/2-low_nudge)) upper_order_edge =int(np.round(cross_order_position[disp_i]+inter_order_width/2+high_nudge)) inter_order_mask[int(disp_i), lower_order_edge:upper_order_edge] = 0 # Distance/size of each inter order: inter_order_size = cross_order_center[1:] - cross_order_center[:-1] - inter_order_width \ - low_nudge - high_nudge #----------------------- # REMOVE 'GHOST' ORDERS: #----------------------- # Predict inter_order_size: xx = np.arange(len(cross_order_center)-1) inter_order_size_fit = np.polyfit(xx, inter_order_size, 2) size_before = np.polyval(inter_order_size_fit, -1) size_after = np.polyval(inter_order_size_fit, len(cross_order_center)) # Remove 'ghost orders' before first order: coefs = order_traces[order_no[0]] cross_order_position = np.polyval(coefs, disp) for disp_i in range(self.len_disp): lower_inter_order_edge = np.round(cross_order_position[disp_i] - inter_order_width/2 \ - low_nudge - size_before).astype(int) # Remove orders below edges: if lower_inter_order_edge < 0: lower_inter_order_edge = 0 inter_order_mask[disp_i, :lower_inter_order_edge] = 0 # Remove 'ghost orders' after last order: coefs = order_traces[order_no[-1]] cross_order_position = np.polyval(coefs, disp) for disp_i in range(self.len_disp): upper_inter_order_edge = np.round(cross_order_position[disp_i] + inter_order_width/2 \ + high_nudge + size_after).astype(int) # Remove orders above edges: if upper_inter_order_edge > self.len_cross+50: upper_inter_order_edge = 0 inter_order_mask[disp_i, upper_inter_order_edge:] = 0 #-------------------------------------------------------------- if plot==1: pt.plot_inter_order_mask(data, inter_order_mask) #-------------------------------------------------------------- self.inter_order_width = inter_order_width self.inter_order_mask = inter_order_mask #-------------------------------------------------------------- return self.inter_order_mask ######################################################################################################## # BACKGROUND IMAGE # ######################################################################################################## def background(self, image, inter_order_mask=None, order_ref=None, \ poly_order_y=2, poly_order_x=4, filter_size=5, plot=0): """ This function estimates the background flux of scattered light and subtract it. It uses the inter_order_mask to perform this removal. ---------------------------- INPUT : ---------------------------- mask (2d array): Background mask with ones and zeros poly_order_x (int, float): Order of polynomy to fits background flux in dispersion poly_order_y (int, float): Order of polynomy to fits background flux in cross dispersion nsteps (int, float): Number of steps orderdef (int, float): ---------------------------- OUTPUT : ---------------------------- background_image (2d array): """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if inter_order_mask==None: inter_order_mask = self.inter_order_mask #---------------------------- # CONSTANTS AND PLACEHOLDERS: #---------------------------- # Create a background image: (ysize, xsize) = image.shape background_image = np.zeros((ysize, xsize), dtype=np.float64) # Data size in arange: xx = np.arange(xsize, dtype=np.float64) yy = np.arange(ysize, dtype=np.float64) # Array to withhold fitted y values: xfitarr = np.zeros((len(yy), xsize), dtype=np.float64) # Step size and range: yvals = np.arange(len(yy)) # Constants: ycount = 0 niter = 0 sigma_limit = 3 # For plots: s_disp = [500, 1500, int(yvals[-1])] # Slices in disp s_cros = [50, 200, int(xx[-1])] # Slices in cross #---------------------------- # FIT IN Y-DIRECTION (CROSS): #---------------------------- for i in yvals: # Cut out slice in cross dispersion with width determined by 'filter_size': ymin_ind = np.max([i - filter_size, 0]) ymax_ind = np.min([i + filter_size, ysize-1]) y_slice = image[ymin_ind:ymax_ind, :] # Collapse in dispersion to a single cross row: y_mean = np.mean(y_slice, axis=0) # Indices/image of inter-order mask in cross row: y_image = np.where(inter_order_mask[i, :] == 1)[0] # Perform fitting with sigma-clipping: while 1: # Make polynomial fit: coefs = np.polyfit(y_image, y_mean[y_image], poly_order_y) xfit = np.polyval(coefs, y_image) # Find sigma: sigma = (y_mean[y_image] - xfit) / np.std(y_mean[y_image] - xfit) rejected = np.extract(sigma > sigma_limit, y_image) y_image = np.extract(sigma < sigma_limit, y_image) # Loop until all image are within sigma or niter is reached: niter = niter + 1 if niter == 5 or rejected.size == 0: break # Final polynomial fit: xfit = np.polyval(coefs, xx) # fitted line xfitarr[ycount, :] = xfit # Array with fit constants for each slice ycount = ycount + 1 # Save values for plotting: if i==s_disp[0]: yi0, ym0, yfit0 = y_image, y_mean[y_image], xfit if i==s_disp[1]: yi1, ym1, yfit1 = y_image, y_mean[y_image], xfit if i==s_disp[2]: yi2, ym2, yfit2 = y_image, y_mean[y_image], xfit #--------------------------- # FIT IN X-DIRECTION (DISP): #--------------------------- goodind = np.arange(len(yy)) for i in np.arange(xsize): # Perform fitting with sigma-clipping: while 1: # Make polynomial fit: coefs = np.polyfit(yvals.take(goodind), xfitarr[goodind, i], poly_order_x) yfit = np.polyval(coefs, yvals[goodind]) # Find sigma: sigma = (xfitarr[goodind, i] - yfit) / np.std(xfitarr[goodind, i] - yfit) rejected = np.extract(sigma > sigma_limit, goodind) goodind = np.extract(sigma < sigma_limit, goodind) # Loop until all image are within sigma: niter = niter + 1 if niter == 3 or rejected.size == 0 or goodind.size == 0: break # In case the image quality is higher than sigma_limit (poor quality image): if goodind.size == 0: print("Error: no points left when y-fitting the background") coefs = np.polyfit(xfitarr[:, i]) # Final background image is constructed: background_image[:, i] = np.polyval(coefs, yy) # Save values for plotting: if i==s_cros[0]: xi0, xm0, xfit0 = yvals[goodind], xfitarr[goodind, i], background_image[:,i] if i==s_cros[1]: xi1, xm1, xfit1 = yvals[goodind], xfitarr[goodind, i], background_image[:,i] if i==s_cros[2]: xi2, xm2, xfit2 = yvals[goodind], xfitarr[goodind, i], background_image[:,i] #--------------------- # SUBTRACT BACKGROUND: #--------------------- corrected_image = image - background_image #-------------------------------------------------------------- if plot is 1: pt.plot_background_fits(s_disp, s_cros, poly_order_y, poly_order_x, \ xx, yi0, yi1, yi2, ym0, ym1, ym2, yfit0, yfit1, yfit2, \ yy, xi0, xi1, xi2, xm0, xm1, xm2, xfit0, xfit1, xfit2) pt.plot_background(background_image) #-------------------------------------------------------------- return corrected_image, background_image ######################################################################################################## # EXTRACT SPECTRUM # ######################################################################################################## def spectral_extraction(self, S, F, T, trace=None, order_width=None, plot=0): """ This function uses the 'order_width' estimated earlier and first cut the spectrum in question using the utility 'cut_out_order'. All order of relevance is cut out, and a simple-sum over the spatial profile is used to get the 1D spectrum. Next clear cosmic hits are removed using the utility 'locate_outliers' and finally the normalized flat blaze from each order is used to de-blaze each spectral order. ---------------------------- INPUT : ---------------------------- S (2d array): Stellar spectrum F (2d array): Flat spectrum T (2d array): ThAr arc spectrum trace (bib): subfunction with poly-fits to all the orders order_width (int): Spatial order width for cutting out the order ---------------------------- OUTPUT : ---------------------------- s_deblaze (1d array): De-blazed 1D stellar spectral orders T_orders (2d array): ThAr arc image orders """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if trace ==None: trace = self.trace if order_width==None: order_width = self.order_width #---------------------------------------- # FIRST ITERATION WITH LINEAR EXTRACTION: #---------------------------------------- # Make sure that the order width is a odd number: if order_width % 2 == 0: order_width = order_width - 1 # Cut out orders with spatial size (order number is coundt bottom-up): S_orders = [self.cut_out_order(S, np.polyval(trace['order_{}'.format(self.orders[i])], self.disp), \ order_width) for i in range(self.n_orders)] F_orders = [self.cut_out_order(F, np.polyval(trace['order_{}'.format(self.orders[i])], self.disp), \ order_width) for i in range(self.n_orders)] T_orders = [self.cut_out_order(T, np.polyval(trace['order_{}'.format(self.orders[i])], self.disp), \ order_width) for i in range(self.n_orders)] # Linear extraction object and blaze: s_orders = [S_orders[i].sum(axis=1) for i in range(self.n_orders)] f_orders = [F_orders[i].sum(axis=1) for i in range(self.n_orders)] #------------------------------ # HEREAFTER OPTIMAL EXTRACTION: #------------------------------ # Initial variance image: #V = V0 + np.abs(S_calib, axis=0)/Q # # Cut out orders with spatial size 5*FWHM: # S = self.cut_out_order(np.polyval(trace['order_2'], self.disp), S_calib) # # Find extracted spectrum: # s = np.zeros(np.shape(s)) # for i in range(self.len_disp): # # Variance image: # V = self.V0 + np.abs(s*P+S_sky, axis=0)/self.Q # # Linear image: # s[i] = np.sum((P*S_sky/V)/(P**2/V), axis=1) #-------------------------------------------------------------- #if plot==1: pt.plot_optimal_extraction(S_orders[1][:, 900:950].T) #-------------------------------------------------------------- self.S_orders = S_orders; self.F_orders = F_orders; self.T_orders = T_orders self.s_orders = s_orders; self.f_orders = f_orders #-------------------------------------------------------------- return [self.S_orders, self.F_orders, self.T_orders] def cut_out_order(self, image, traced_order, cross_order_width=21): """ This utility takes the polynomial describtion 'traced_order' and the relevant order and a spectrum, and cuts out the spectrum in total dispersion length and 'cross_order_width' pixels in cross dispersion around this spectral order. It the returns the bandpass image 'order_cut' and positions which can be used for an easy way of flotting the result. 'cross_order_width' needs to be odd number. """ # Conatant and placeholders: half_width = int(cross_order_width/2.) order_cut = np.zeros((self.len_disp, cross_order_width)) cross_order_positions = np.zeros((self.len_disp, cross_order_width)) # This loop cuts out the order: for d in np.arange(self.len_disp): position = traced_order[d] rounded_position = int(np.round(position)) # Fill in the columns of the order: cp = image[d, rounded_position - half_width:rounded_position + half_width + 1] order_cut[d,:] = cp # Fill in the cross order position: x = np.arange(-half_width, half_width + 1) + rounded_position cross_order_positions[d, :] = x #-------------------------------------------------------------- return order_cut ######################################################################################################## # WAVELENGTH CALIBRATION # ######################################################################################################## def wavelength_calib(self, T_orders=None, poly_order=3, plot=0): """ This utility performs the wavelength calibration. This is done first using the Ca II lines as a initial reference of the wavelength scale. Thus, the utility 'calcium_lines_identifier' finds the peaks values of the H and K lines, then 'peak_finder' finds all ThAr lines in the order above a certain threshold, and lastly a FIES ThAr atlas is used to set the real wavelength scale of the order, which is returned as output. The terminology here is that (p) is pixel wavelength, (l) is wavelenght in Å, and (x) is the spatial direction. ---------------------------- INPUT : ---------------------------- s_orders (1d array): Stellar spectrum for each order T_orders (2d array): Extracted ThAr image of each order poly_order (int): Order of polynomial function for fitting wavelength relation ---------------------------- OUTPUT : ---------------------------- [l0, l1] (1d arrays): New wavelength scale for each order """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if T_orders==None: T_orders = self.T_orders #------------------------- # FIND OBSERVED ARC LINES: #------------------------- # Identify lines from different sigma levels: COF, radii = np.zeros(self.n_orders), np.zeros(self.n_orders) for i in range(self.n_orders): COF_i, radii_i, _ = self.peak_finder(T_orders[i], sigma=0.5, plot=0) COF = [COF[i], COF_i] radii = [radii[i], radii_i] # Only keep disp values: l_cof0 = COF[0][:,0] l_cof1 = COF[1][:,0] #--------------------------------------------------- # ITER 1: USE KNOWN FIES LINES AS INITIAL REFERENCE: TODO! automate the first iteration #--------------------------------------------------- # Cacium lines: l_ca = [self.K, self.H] # Load FIES arc atlas: l_fies0 = [3868.5284, 3873.8224, 3916.4176, 3925.7188, 3928.6233, 3948.9789] l_fies1 = [3925.7188, 3928.6233, 3948.9789, 3950.3951, 3979.3559, 4013.8566] # Pixel coordinats to known identified FIES peaks: l_pix0 = np.array([1091, 1190, 2025, 2217, 2279, 2723]) l_pix1 = np.array([ 900, 952, 1326, 1352, 1910, 2624]) # Find COF lines closest to known pixel coordinates: l_cof0_ini = [min(l_cof0, key=lambda x:abs(x-l_pix0[i])) for i in range(len(l_pix0))] l_cof1_ini = [min(l_cof1, key=lambda x:abs(x-l_pix1[i])) for i in range(len(l_pix1))] # Find wavelenght relations: r0_1 = self.find_arc_scale(self.disp, l_cof0_ini, l_fies0, poly_order, l_cof0, \ param=['1. ITER: 58', T_orders[0], COF[0], radii[0]], plot=plot) r1_1 = self.find_arc_scale(self.disp, l_cof1_ini, l_fies1, poly_order, l_cof1, \ param=['1. ITER: 57', T_orders[1], COF[1], radii[1]], plot=plot) l0_ini, l_cof0_ini, res0_ini, l_cof0_all = r0_1[0], r0_1[1], r0_1[2], r0_1[3] l1_ini, l_cof1_ini, res1_ini, l_cof1_all = r1_1[0], r1_1[1], r1_1[2], r1_1[3] #-------------------------------------- # ITER 2: CALIBRATE WITH PHOTRON ATLAS: #-------------------------------------- # Load Photron arc atlas (http://iraf.noao.edu/specatlas/thar_photron/thar_photron.html): thar_atlas_phot = np.loadtxt('dependencies/thar_atlas_photron.txt') l_atlas_phot = thar_atlas_phot[:,2] # Find wavelenght relations: r0_2 = self.find_arc_scale(l0_ini, l_cof0_all, l_atlas_phot, poly_order, threshold=0.02, \ param=['2. ITER: 58', T_orders[0], COF[0], radii[0]], plot=plot) r1_2 = self.find_arc_scale(l1_ini, l_cof1_all, l_atlas_phot, poly_order, threshold=0.02, \ param=['2. ITER: 57', T_orders[1], COF[1], radii[1]], plot=plot) l0, l_cof0, std0 = r0_2[0], r0_2[1], r0_2[2] l1, l_cof1, std1 = r1_2[0], r1_2[1], r1_2[2] #-------------------------------------------------------------- if plot==1: pt.plot_arc_check([l0, l1], T_orders, l_ca, 'FINAL RESULT') #-------------------------------------------------------------- self.l_orders = [l0, l1] self.sigma_w = [std0, std1] #-------------------------------------------------------------- return self.l_orders def find_arc_scale(self, l_scale, l_obs, l_atlas, poly_order, l_all=None, \ threshold=None, param=None, plot=None): """ This utility takes observed and atlas arc lines and find a wavelength solution given a threshold for comparing when the lines are close enough to be identified as a match. Thus, this utility works only if given a fair initial wavelength solution for the observed lines. If no threshold is given it will be assumed that the this is the initial step going from pixel to wavelenght space, where the exact coordinate matches are known. """ if threshold is not None: # Find atlas lines closest to observed lines (COF lines): l_atlas_match = [min(l_atlas, key=lambda x:abs(x-l_obs[i])) for i in range(len(l_obs))] # Find value difference between matched lines and keep only if diff < 1 Å: dex_goodin = np.where(abs(l_obs - l_atlas_match) < threshold)[0] l_atlas_good = [l_atlas_match[dex_goodin[i]] for i in range(len(dex_goodin))] l_obs_good = [l_obs[dex_goodin[i]] for i in range(len(dex_goodin))] else: l_obs_good = l_obs.copy() l_atlas_good = l_atlas.copy() # Find new pixel-wavelength relation: coefs = np.polyfit(l_obs_good, l_atlas_good, poly_order) # Copy solution to scale and observed lines: ipoly = np.arange(poly_order+1)[::-1] l_scale_new = np.sum([coefs[i]*l_scale**ipoly[i] for i in range(poly_order+1)], axis=0) l_obs_new = np.sum([coefs[i]*l_obs**ipoly[i] for i in range(poly_order+1)], axis=0) if l_all is not None: l_all_new = np.sum([coefs[i]*l_all**ipoly[i] for i in range(poly_order+1)], axis=0) else: l_all_new = None # Calculate fit parameters: poly = np.poly1d(coefs) xp = np.linspace(min(l_obs_good), max(l_obs_good), 1e3) residuals = poly(l_obs_good)-l_atlas_good chi2r = 1-residuals/(len(l_obs_good)-poly_order+1) sigma = np.std(residuals) #-------------------------------------------------------------- if plot is 1: # Activate only illustration and save: #pt.plot_arc_illustration(l_obs, l_atlas, l_obs_good, l_atlas_good, l_scale, param) pt.plot_arc_fit(l_obs_good, l_atlas_good, coefs, poly_order, residuals, chi2r, sigma, param[0]) pt.plot_arc_scale(l_obs, l_atlas, l_obs_good, l_atlas_good, l_scale, param) #-------------------------------------------------------------- return l_scale_new, l_obs_new, sigma, l_all_new ######################################################################################################## # DE-BLAZING # ######################################################################################################## def deblazing(self, F_orders=None, l_orders=None, f_orders=None, s_orders=None, plot=0): """ This spectral order. ---------------------------- INPUT : ---------------------------- T (2d array): ThAr arc spectrum trace (bib): subfunction with poly-fits to all the orders order_width (int): Spatial order width for cutting out the order ---------------------------- OUTPUT : ---------------------------- s_deblaze (1d array): De-blazed 1D stellar spectral orders T_orders (2d array): ThAr arc image orders """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if F_orders==None: F_orders = self.F_orders if l_orders==None: l_orders = self.l_orders if f_orders==None: f_orders = self.f_orders if s_orders==None: s_orders = self.s_orders #---------------------- # CORRECT FOR COMSMICS: TODO! IF using optimal extraction this can be fixed at once #---------------------- # Do not work for arc images ask it is a peak detection algorithm: f_lincor = [self.locate_outliers(f_orders[i], convolve_step=3, cutoff=2e-2, plot=0) \ for i in range(self.n_orders)] #---------------------------- # CORRECT FOR BLAZE FUNCTION: #---------------------------- # Perform blaze correction: s_deblaz = [(s_orders[i]/f_lincor[i]) for i in range(self.n_orders)] #----------------------------------- # SCALE FLAT BALZE TO STELLAR BALZE: #----------------------------------- # Find maximum of each blaze: dex_blaze_max = [np.nanargmax(self.f_orders[i]) for i in range(self.n_orders)] # Remove all cosmics from spectra to be used only for scaling: s_coscor = [self.locate_outliers(s_orders[i], convolve_step=3, cutoff=1e-1, plot=0) \ for i in range(self.n_orders)] # With cosmics removed now scale to maximum difference: continuum_cor = 0.85 # Correction factor only valid for solar type stars dif_max = [continuum_cor*np.max(self.f_orders[i])/np.max(s_coscor[i]) for i in range(self.n_orders)] #-------------------------------------------------------------- if plot==1: pt.plot_blaze(s_orders, f_orders, f_lincor, dif_max) pt.plot_deblaze(s_deblaz) #-------------------------------------------------------------- self.f_orders = f_lincor; self.s_deblaz = s_deblaz self.dif_max = dif_max #-------------------------------------------------------------- return self.f_orders, self.s_deblaz def norm_blaze_function(self, F_order): #TODO! this is not used but may be useful in future """ This utility finds the blaze function which recides from the fact that an échelle spectrum is bowed along the dispersion and thus... To find the blaze function here the order is collapsed in cross dispersion to a one dimentional array using a simple sum. The 'normalized_order' also gives an estimate of scatter within the order. """ # Use simple sum to collapse order: f_blaze = np.sum(F_order, axis=1) # Normalize the spectrum: F_norm_order = np.zeros(F_order.shape) for i in range(F_order.shape[1]): F_norm_order[:, i] = F_order[:, i] / f_blaze #-------------------------------------------------------------- return F_norm_order #F_norm = [self.norm_blaze_function(F_orders[i]) for i in range(self.n_orders)] #S_deblaze = [self.S_orders[i]/F_norm[i] for i in range(self.n_orders)] #s_deblaz_norm = [S_deblaze[i].sum(axis=1) for i in range(self.n_orders)] ##################################################################################################### # SCRUNCH, MERGE, AND CLIP # ##################################################################################################### def scrunch_and_merge(self, l_orders=None, s_deblaz=None, plot=0): """ This function estimates ---------------------------- INPUT : ---------------------------- mask (2d array): Background mask with ones and zeros ---------------------------- OUTPUT : ---------------------------- background_image (2d array): """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if l_orders==None: l_orders = self.l_orders if s_deblaz==None: s_deblaz = self.s_deblaz #---------------- # SCRUNCH ORDERS: #---------------- # Prepare uniform wavelength grid: dl = np.median([np.median(np.diff(l_orders[i])) for i in range(self.n_orders)]) dl_orders = [np.arange(l_orders[i][0], l_orders[i][-1]+dl, dl) for i in range(self.n_orders)] # Linear interpolate to uniform grid: s_grids = [scipy.interpolate.griddata(l_orders[i], s_deblaz[i], dl_orders[i], method='nearest') \ for i in range(self.n_orders)] #-------------- # MERGE ORDERS: #-------------- # Find index of merge boarders: dex0_min = np.where(dl_orders[0].astype(int)==3880)[0][0] dex0_max = np.where(dl_orders[0].astype(int)==3915)[0][0] dex1_min = np.where(dl_orders[1].astype(int)==3915)[0][0] # Merge wavelength axis: l_merge0 = dl_orders[0][dex0_min:dex0_max] l_merge1 = dl_orders[1][dex1_min::] l_merge = np.append(l_merge0, l_merge1) # Merge flux axis: s_merge0 = s_grids[0][dex0_min:dex0_max] s_merge1 = s_grids[1][dex1_min::] s_merge = np.append(s_merge0, s_merge1) #-------------------------------------------------------------- if plot is 1: pt.plot_merge(s_merge, l_merge, [self.H, self.K]) #-------------------------------------------------------------- self.s = s_merge; self.l = l_merge #-------------------------------------------------------------- return self.s, self.l ##################################################################################################### # RV CORRECTION # ##################################################################################################### def rv_correction(self, s=None, l=None, plot=0): """ This function is used to create a transit model for the Cross-Correlation (CC). To create the model the subrutine called 'model' is used. To perform the CC the CC coefficient is also needed and this is calculated in the subroutine 'cc_coefficients'. For the precision needed here future test of RV correction is needed. For now Astropy's find the projected RV component including the stellar motion, the baryocentric motion, and Earth's heliocentric and rotational velocities. ---------------------------- INPUT : ---------------------------- mask (2d array): ---------------------------- OUTPUT : ---------------------------- background_image (2d array): """ # Check if program parameters is defined: if s==None: s = self.s if l==None: l = self.l #--------------- # RV CORRECTION: #--------------- # Use astropy for Barycentric: obstime = Time(self.datetime) target = SkyCoord.from_name(self.target) song = EarthLocation.of_site('roque de los muchachos') # Closest observatory to Teide rv_baryc = target.radial_velocity_correction(obstime=obstime, location=song).to('km/s').value # Use baryocentric + stellar RV amplitude as RV correction: rv_shift = self.rv_amp - rv_baryc - self.rv_amp * rv_baryc / (scipy.constants.c/1e3) # Use standard equation for RV shift (dl/l = v/c) calculated in [km/s]: c = scipy.constants.c/1e3 delta_lambda = rv_shift / c * l # Perform wavelenght shift: l = l - delta_lambda # Calculate this for approx results: delta_l3950 = rv_shift / (scipy.constants.c/1e3) * 3950 delta_p3950 = delta_l3950 / np.diff(l)[0] #--------------------------- # HANDLING IDL SUN SPECTRUM: #--------------------------- # # Save IDL format to python: # import scipy.io # s_sun = scipy.io.readsav('{}sun_reference/ARCTURUS.IDL'.format(self.path_blues)) # np.savetxt('{}sun_reference/sun_python.txt'.format(self.path_blues), s_sun['atlas']) # # Import sun spectrum and save smaller spectral domain: # sun = np.loadtxt('{}/sun_reference/sun_python.txt'.format(self.path_blues)) # l_sun = np.array([sun[i][0] for i in range(len(sun))]) # s_sun = np.array([sun[i][2] for i in range(len(sun))]) # plt.figure() # plt.plot(l_sun, s_sun, 'k-', linewidth=0.1) # plt.show() # # Find index of merge boarders: # borders = [3880, 4020] # i_min = np.where(l_sun.astype(int)==borders[0])[0][0] # i_max = np.where(l_sun.astype(int)==borders[1])[0][0] # # Merge wavelength axis and save data: # l_sun = l_sun[i_min:i_max] # s_sun = s_sun[i_min:i_max] # np.savetxt('{}sun_reference/sun.txt'.format(self.path_blues), np.vstack([l_sun, s_sun]).T) #------------------------- # TRANSFORM AND NORMALIZE: #------------------------- # # Load sun data: # sun = np.loadtxt('{}/sun_reference/sun.txt'.format(self.path_blues)) # l_sun = sun[:,0] # s_sun = sun[:,1] # # Tranform to regular grid: # dl_sun = np.diff(l_sun)[0] # l_gsun = np.arange(l_sun[0], l_sun[-1], dl_sun) # # Interpolate: # s_gsun = scipy.interpolate.griddata(l_sun, s_sun, l_gsun, method='cubic') # Sun # s_gobs = scipy.interpolate.griddata(l, s, l_gsun, method='nearest') # Star observed # # Flux normalize signals: # s_gobs = s_gobs/np.max(s_gobs) # # Inverted and Normalized signal MUST be used: # x = (1-s_gobs) - np.mean(1-s_gobs) # y = (1-s_gsun) - np.mean(1-s_gsun) # #x = self.convolve(x, 'median', 1000) # #y = self.convolve(y, 'median', 1000) # print(np.diff(l_gsun)) # print(len(l_gsun)) # l_gsun_new = np.roll(l_gsun, 200) # plt.figure() # plt.plot(l_gsun, s_gsun, 'r-') # plt.plot(l_gsun_new, s_gsun, 'b-') # plt.plot(l_gsun, s_gobs, 'k-') # plt.show() #sys.exit() #--------------------------- # PERFORM CROSS CORRELATION: #--------------------------- # # Prepare indices for spectrum shift: # dx = 200 # dy = np.arange(-dx, dx) # # Perform Cross-correlation: # cc = np.zeros(len(dy)) # for i in dy: # y = np.roll(y, -i) # r_cc = self.cc_coefficient(x, y) # cc[i] = r_cc # # Find peaks maximum: # peaks_dex, _ = scipy.signal.find_peaks(cc) # dy_peaks = dy[peaks_dex] # cc_peaks = cc[peaks_dex] # # Choose only resonable RV shifts (<200 km/s) and good cc (>0.8): # peaks_good_dex = (dy_peaks>-200) * (dy_peaks<200) * (cc_peaks>0.8) # dy_peaks = dy_peaks[peaks_good_dex] # cc_peaks = cc_peaks[peaks_good_dex] # Find max peak corresponding to RV: # cc_max_dex = np.argmax(cc_peaks) # dy_max = dy_peaks[cc_max_dex] # cc_max = cc_peaks[cc_max_dex] # plt.figure() # plt.plot(dy, cc, 'k-') # plt.plot(dy_peaks, cc_peaks, 'r+') #plt.title('RV shift = {} km/s'.format(rv_mean)) #plt.axvline(max(cc), color='r') #plt.plot(l_sun, y, 'k-') #plt.plot(ll, x, 'b-') #plt.xlim(-200, 200) # plt.show() #-------------------------------------------------------------- #if plot is 1: pt.plot_merge(s_merge, l_merge, [self.H, self.K]) #-------------------------------------------------------------- self.delta_v_baryc = rv_baryc self.delta_v = rv_shift self.delta_l = delta_l3950 self.delta_p = delta_p3950 self.l = l #-------------------------------------------------------------- return self.l def cc_coefficient(self, x, y): """ This function find the cross-correlation coefficienten between two datasets. Here x is the data have an offset and y is the data that is cross correlated for every small step. """ cor = np.sum( (x-np.mean(x)) * (y-np.mean(y)) ) norm = np.sqrt( np.sum((x-np.mean(x))**2) * np.sum((x-np.mean(x))**2) ) r = cor/norm #-------------------------------------------------------------- return r ######################################################################################################## # CONTINUUM NORMALIZATION # ######################################################################################################## def continuum_norm(self, l=None, s=None, rv_amp=0, plot=0): """ This function estimates ---------------------------- INPUT : ---------------------------- mask (2d array): Background mask with ones and zeros - uses rv corrected data only! ---------------------------- OUTPUT : ---------------------------- background_image (2d array): """ #------------------------------ # CHECK FOR PROGRAM PARAMETERS: #------------------------------ if l==None: l = self.l if s==None: s = self.s #---------------------------- # NORMALIZE WITH POINTS ONLY: #---------------------------- # Pseudo-continuum points used for the SSS included in the Ca H & K order: ps_min, ps_max = 3912, 4000 # Find central index for wavelength: dex_min = np.where(l.astype(int)==ps_min)[0][0] dex_max = np.where(l.astype(int)==ps_max)[0][0] # Values for points used to linear relation: l_point = [l[dex_min], l[dex_max]] s_point = [s[dex_max], s[dex_max]] # Find linear relation: coefs_point = np.polyfit(l_point, s_point, 0) poly_point = np.poly1d(coefs_point) s_norm_point = s/poly_point(l) #----------------------------- # NORMALIZE WITH HIGHEST PEAK: #----------------------------- # Find max peak around pseudo peaks: # (This function needs a initial guess for the RV shift!) dex_peak_min = self.find_peak_in_noise(s, dex_min, plot=0) dex_peak_max = self.find_peak_in_noise(s, dex_max, plot=0) # Values for fit: l_peak = [l[dex_peak_min], l[dex_peak_max]] s_peak = [s[dex_peak_min], s[dex_peak_max]] # Find linear relation: coefs_peak = np.polyfit(l_peak, s_peak, 1) poly_peak = np.poly1d(coefs_peak) s_norm_peak = s/poly_peak(l) #------------------------------ # NORMALIZE WITH MEAN BANDPASS: #------------------------------ # Another methods is to use pseudo-continuum bandpass': bold_band_min = (l > ps_min-0.5)*(l < ps_min+0.5) bold_band_max = (l > ps_max-0.5)*(l < ps_max+0.5) # Find meadian bandpass values: l_mean = [np.median(l[bold_band_min]), np.median(l[bold_band_max])] s_mean = [np.median(s[bold_band_min]), np.median(s[bold_band_max])] # Find linear relation: coefs_mean = np.polyfit(l_mean, s_mean, 1) poly_mean = np.poly1d(coefs_mean) s_norm_mean = s/poly_mean(l) #-------------------------------------------------------------- if plot is 1: pt.plot_continuum_norm_all(l, s, [l_point, l_peak, l_mean],[s_point, s_peak, s_mean], \ [s_norm_point, s_norm_peak, s_norm_mean], \ [poly_point, poly_peak, poly_mean], [self.K, self.H]) #-------------------------------------------------------------- self.l = l self.s = [s_norm_peak, s_norm_point, s_norm_mean] #-------------------------------------------------------------- return self.l, self.s ######################################################################################################## # S-INDEX # ######################################################################################################## def eshel_sindex(self, S, F, trace=None, order_width=None, l_orders=None, f_orders=None, \ l=None, s=None, plot=0): """ This function estimates ---------------------------- INPUT : ---------------------------- mask (2d array): Background mask with ones and zeros ---------------------------- OUTPUT : ---------------------------- background_image (2d array): """ #-------------------------- # CHECK PROGRAM PARAMETERS: #-------------------------- if trace==None: trace = self.trace if order_width==None: order_width = self.order_width if l_orders==None: l_orders = self.l_orders if f_orders==None: f_orders = self.f_orders if l ==None: l = self.l if s ==None: s = self.s #------------------ # FIND UNCERTAINTY: #------------------ self.uncertainty(S, F, trace, order_width, l_orders, f_orders, l, s[0], plot=1) #--------------------------------------------- # FIND FLUX AND UNCERTAINTY FOR EACH BANDPASS: #-------------------------------------------- result0 = self.find_bandpass_fluxes(l, s[0]) result1 = self.find_bandpass_fluxes(l, s[1]) result2 = self.find_bandpass_fluxes(l, s[2]) results = [result0, result1, result2] #-------------- # FIND S INDEX: #-------------- sindices = [self.sindex(results[i], l, save=i) for i in range(len(results))] # Find fractional difference between each continuum method: sindex_diff12 = abs(sindices[0] - sindices[1]) / sindices[0] * 100 sindex_diff13 = abs(sindices[0] - sindices[2]) / sindices[0] * 100 print(sindex_diff12) print(sindex_diff13) #-------------------------------------------------------------- if plot is 1: self.sindex = sindices self.results() #-------------------------------------------------------------- return def uncertainty(self, F, S, trace, order_width, l_orders, f_orders, l, s, plot=0): """ This utility estimates the mean flux-uncertainty in a given bandpass using S/N ratio. """ #------------------------------------- # S/N RATIO OF ALONG EACH ORDER BLAZE: #------------------------------------- # Calculate mean sky background from background subtracted image: self.f_flux_sky, _ = self.mean_background(F, trace, plot=0) self.s_flux_sky, _ = self.mean_background(S, trace, plot=0) # Scale flat orders to that of the stellar orders: f_flux_obj = f_orders s_flux_obj = [f_flux_obj[i]/self.dif_max[i] for i in range(self.n_orders)] # Convolve data for smoothing: f_conv_obj = [self.convolve(f_flux_obj[i], 'mean', 10) for i in range(self.n_orders)] s_conv_obj = [self.convolve(s_flux_obj[i], 'mean', 10) for i in range(self.n_orders)] # Find SNR along the orders: f_snr_orders = np.zeros(self.n_orders) s_snr_orders = np.zeros(self.n_orders) for i in range(self.n_orders): fsnr = [self.signal_to_noise(f_conv_obj[i][j], order_width, self.f_flux_sky) \ for j in range(self.len_disp)] ssnr = [self.signal_to_noise(s_conv_obj[i][j], order_width, self.s_flux_sky) \ for j in range(self.len_disp)] f_snr_orders = [f_snr_orders[i], fsnr] s_snr_orders = [s_snr_orders[i], ssnr] # Find SNR peak maxima: self.f_snr_max = [max(f_snr_orders[i]) for i in range(self.n_orders)] self.s_snr_max = [max(s_snr_orders[i]) for i in range(self.n_orders)] #---------------------------- # UNCERTAINTY FROM S/N RATIO: #---------------------------- # Min and max indices for each bandpass: l_min = [self.V-self.VR_bandpass, self.K-self.HK_bandpass, \ self.H-self.HK_bandpass, self.R-self.VR_bandpass] l_max = [self.V+self.VR_bandpass, self.K+self.HK_bandpass, \ self.H+self.HK_bandpass, self.R+self.VR_bandpass] # Order nr to loop over: j = [0, 1, 1, 1] # Find mean S/N and uncertainty in each bandpass: self.f_snr_X = np.zeros(4) self.s_snr_X = np.zeros(4) self.sigma_f_snr = np.zeros(4) self.sigma_s_snr = np.zeros(4) self.std_f = np.zeros(4) for i in range(4): # Find min and max bandpass pixel indices: X_pix_min = np.where(min(l_orders[j[i]], key=lambda x:abs(x-(l_min[i])))==l_orders[j[i]])[0][0] X_pix_max = np.where(min(l_orders[j[i]], key=lambda x:abs(x-(l_max[i])))==l_orders[j[i]])[0][0] # Find number of pixels used in bandpass: n_pix_X = len(range(X_pix_min, X_pix_max)) # Estimate flux uncertainty from each bandpass: f_snr_X = np.sum(f_snr_orders[j[i]][X_pix_min:X_pix_max]) / n_pix_X s_snr_X = np.sum(s_snr_orders[j[i]][X_pix_min:X_pix_max]) / n_pix_X # Estimate S/N and uncertainties: self.sigma_f_snr[i] = 1/f_snr_X self.sigma_s_snr[i] = 1/s_snr_X self.f_snr_X[i] = f_snr_X self.s_snr_X[i] = s_snr_X # Estimate flat scatter: f = f_orders[j[i]]/np.max(f_orders[j[i]]) f_std0 = self.convolve(f, 'std', 2) f_std = self.convolve(f_std0, 'mean', 100) self.std_f[i] = np.mean(f_std[X_pix_min:X_pix_max]) # plt.figure() # plt.plot(l_orders[j[i]], f_std0, 'k-', linewidth=1.0, label=r'$\sigma_i$') # plt.plot(l_orders[j[i]], f_std, 'r-', linewidth=1.2, label=r'$\sigma_i/\mu_i$') # plt.show() # Uncertainty internally from spectrum: s_mea = self.convolve(s, 'mean', 2) s_dif = s/s_mea - 1 s_std0 = self.convolve(s, 'std', 2) s_std = self.convolve(s_std0, 'mean', 100) #---------------------------- # ALL UNCERTAINTY CONSIDERED: #---------------------------- # Shot Noise from flat blaze: self.sigma_f = np.sum(self.std_f) # Three lines from order #57 and one from order #58 is used: self.sigma_w = ( 1/4*self.sigma_w[0] + 3/4*self.sigma_w[1] )/2 # Find bandpass indices: _, _, bands = self.find_bandpass_fluxes(l, s, plot) V_indices, R_indices = bands[0], bands[1] K1_indices, H1_indices = bands[2], bands[3] Km_indices, Hm_indices = bands[4], bands[5] K2_indices, H2_indices = bands[6], bands[7] # Translate into flux uncertainty from each bandpass: self.std_V = np.sum(s_std[V_indices]) / len(V_indices) # Continuum bands self.std_R = np.sum(s_std[R_indices]) / len(R_indices) self.std_K1 = np.sum(s_std[K1_indices]) / len(K1_indices) # Used for 1.09 Å square band fluxes self.std_H1 = np.sum(s_std[H1_indices]) / len(H1_indices) self.std_Km = np.sum(s_std[Km_indices]) / len(Km_indices) # Used for mean fluxes per wavelength self.std_Hm = np.sum(s_std[Hm_indices]) / len(Hm_indices) self.std_K2 = np.sum(s_std[K2_indices]) / len(K2_indices) # Used for triangular integrated fluxes self.std_H2 = np.sum(s_std[H2_indices]) / len(H2_indices) self.sigma_bands = [s_std[K2_indices], s_std[H2_indices]] # Used for triangular norm fluxes # Combined uncertainties to be used for error propagation: x = self.sigma_w + self.sigma_f self.sigma_V = self.std_V + x self.sigma_R = self.std_R + x self.sigma_K1 = self.std_K1 + x; self.sigma_Km = self.std_Km + x; self.sigma_K2 = self.std_K2 + x self.sigma_H1 = self.std_H1 + x; self.sigma_Hm = self.std_Hm + x; self.sigma_H2 = self.std_H2 + x #-------------------------------------------------------------- if plot is 1: pt.plot_sindex_scatter(l, s_dif, s_std0, s_std, self.bands) #-------------------------------------------------------------- return s_std def find_bandpass_fluxes(self, l, s, plot=0): """ This utility simply find the bandpass indices and fluxes given the bandpass widths. """ # Shortwrite parameters: HK = self.HK_bandpass VR = self.VR_bandpass #------------------------------------------------ # FIND INITIAL INDICES NEEDED FOR ALL BANDPASSES: #------------------------------------------------ # Find central bandpass index: V_dex_cen = (np.abs(l-(self.V))).argmin() K_dex_cen = (np.abs(l-(self.K))).argmin() H_dex_cen = (np.abs(l-(self.H))).argmin() R_dex_cen = (np.abs(l-(self.R))).argmin() # Find wavelength indices for each bandpass: V_dex = (np.abs(l-(self.V+VR/2))).argmin() - (np.abs(l-(self.V-VR/2))).argmin() R_dex = (np.abs(l-(self.R+VR/2))).argmin() - (np.abs(l-(self.R-VR/2))).argmin() # Find wavelength indices for 1.09 Å bandpass: K1_dex = (np.abs(l-(self.K+HK/2))).argmin() - (np.abs(l-(self.K-HK/2))).argmin() H1_dex = (np.abs(l-(self.H+HK/2))).argmin() - (np.abs(l-(self.H-HK/2))).argmin() # Find wavelength indices for 2 x 1.09 Å lower widths: K2_dex = (np.abs(l-(self.K+HK))).argmin() - (np.abs(l-(self.K-HK))).argmin() H2_dex = (np.abs(l-(self.H+HK))).argmin() - (np.abs(l-(self.H-HK))).argmin() # Select only even bandswidths: for VR_dex_i in [V_dex, R_dex]: if VR_dex_i % 2 == 0: VR_dex = int(VR_dex_i / 2) else: VR_dex = int((VR_dex_i - 1) / 2) for HK1_dex_i in [K1_dex, H1_dex]: if HK1_dex_i % 2 == 0: HK1_dex = int(HK1_dex_i / 2) else: HK1_dex = int((HK1_dex_i - 1) / 2) for HK2_dex_i in [K2_dex, H2_dex]: if HK2_dex_i % 2 == 0: HK2_dex = int(HK2_dex_i / 2) else: HK2_dex = int((HK2_dex_i - 1) / 2) # Find square bandpass indices: V_indices = np.arange(V_dex_cen-VR_dex, V_dex_cen+VR_dex) R_indices = np.arange(R_dex_cen-VR_dex, R_dex_cen+VR_dex) # Covert to a full-range indices: K1_indices = np.arange(K_dex_cen-HK1_dex, K_dex_cen+HK1_dex) H1_indices = np.arange(H_dex_cen-HK1_dex, H_dex_cen+HK1_dex) # Covert to a full-range indices: K2_indices = np.arange(K_dex_cen-HK2_dex, K_dex_cen+HK2_dex+1) H2_indices = np.arange(H_dex_cen-HK2_dex, H_dex_cen+HK2_dex+1) #-------------------------------------- # DEFINE TRIANGULAR H AND K BANDPASSES: #-------------------------------------- # Split out index ranges: k1_indices = np.arange(K_dex_cen-HK2_dex, K_dex_cen) k2_indices = np.arange(K_dex_cen, K_dex_cen+HK2_dex+1) h1_indices = np.arange(H_dex_cen-HK2_dex, H_dex_cen) h2_indices = np.arange(H_dex_cen, H_dex_cen+HK2_dex+1) # Compute linear relations on either side triangle: coefs_k1 = np.polyfit([K_dex_cen-HK2_dex, K_dex_cen], [0, 1], 1); poly_k1 = np.poly1d(coefs_k1) coefs_k2 = np.polyfit([K_dex_cen, K_dex_cen+HK2_dex], [1, 0], 1); poly_k2 = np.poly1d(coefs_k2) coefs_h1 = np.polyfit([H_dex_cen-HK2_dex, H_dex_cen], [0, 1], 1); poly_h1 = np.poly1d(coefs_h1) coefs_h2 = np.polyfit([H_dex_cen, H_dex_cen+HK2_dex], [1, 0], 1); poly_h2 = np.poly1d(coefs_h2) # Find count values for each line: s_tri_k1 = poly_k1(k1_indices) s_tri_k2 = poly_k2(k2_indices) s_tri_h1 = poly_h1(h1_indices) s_tri_h2 = poly_h2(h2_indices) # Combine triangular count values: s_tri_K = np.append(s_tri_k1, s_tri_k2) s_tri_H = np.append(s_tri_h1, s_tri_h2) # Define finer regular grid: l_k1_grid = np.linspace(l[k1_indices[0]], l[k1_indices[-1]], 1e4) l_k2_grid = np.linspace(l[k2_indices[0]], l[k2_indices[-1]], 1e4) l_h1_grid = np.linspace(l[h1_indices[0]], l[h1_indices[-1]], 1e4) l_h2_grid = np.linspace(l[h2_indices[0]], l[h2_indices[-1]], 1e4) # Interpolate triangular data function: s_tri_k1_grid = scipy.interpolate.griddata(l[k1_indices], s_tri_k1, l_k1_grid, method='linear') s_tri_k2_grid = scipy.interpolate.griddata(l[k2_indices], s_tri_k2, l_k2_grid, method='linear') s_tri_h1_grid = scipy.interpolate.griddata(l[h1_indices], s_tri_h1, l_h1_grid, method='linear') s_tri_h2_grid = scipy.interpolate.griddata(l[h2_indices], s_tri_h2, l_h2_grid, method='linear') # Interpolate spectral data: s_spc_k1_grid = scipy.interpolate.griddata(l[k1_indices], s[k1_indices], l_k1_grid, method='linear') s_spc_k2_grid = scipy.interpolate.griddata(l[k2_indices], s[k2_indices], l_k2_grid, method='linear') s_spc_h1_grid = scipy.interpolate.griddata(l[h1_indices], s[h1_indices], l_h1_grid, method='linear') s_spc_h2_grid = scipy.interpolate.griddata(l[h2_indices], s[h2_indices], l_h2_grid, method='linear') # Find wavelength value of intersection: dex_k1_inter = np.where(np.abs(s_spc_k1_grid - s_tri_k1_grid) < 5e-4)[0][0] dex_k2_inter = np.where(np.abs(s_spc_k2_grid - s_tri_k2_grid) < 5e-4)[0][-1] dex_h1_inter = np.where(np.abs(s_spc_h1_grid - s_tri_h1_grid) < 5e-4)[0][0] dex_h2_inter = np.where(np.abs(s_spc_h2_grid - s_tri_h2_grid) < 5e-4)[0][-1] # Find coordinates of intersection points: l_k1_inter, s_k1_inter = l_k1_grid[dex_k1_inter], s_tri_k1_grid[dex_k1_inter] l_k2_inter, s_k2_inter = l_k2_grid[dex_k2_inter], s_tri_k2_grid[dex_k2_inter] l_h1_inter, s_h1_inter = l_h1_grid[dex_h1_inter], s_tri_h1_grid[dex_h1_inter] l_h2_inter, s_h2_inter = l_h2_grid[dex_h2_inter], s_tri_h2_grid[dex_h2_inter] # Find closest wavelength index to intersection point: dex_spc_k1_inter = (np.abs(l_k1_inter - l[k1_indices])).argmin() dex_spc_k2_inter = (np.abs(l_k2_inter - l[k2_indices])).argmin() dex_spc_h1_inter = (np.abs(l_h1_inter - l[h1_indices])).argmin() dex_spc_h2_inter = (np.abs(l_h2_inter - l[h2_indices])).argmin() #------------------ # FIND FLUX VALUES: #------------------ # Find fluxes in each continuum passbands: V_fluxes = s[V_indices] R_fluxes = s[R_indices] # Find final count values for retangular 1.09Å filter: K1_fluxes = s[K1_indices] H1_fluxes = s[H1_indices] # Find final count values for mean filter: Km_indices = np.arange(k1_indices[dex_spc_k1_inter], k2_indices[dex_spc_k2_inter]) Hm_indices = np.arange(h1_indices[dex_spc_h1_inter], h2_indices[dex_spc_h2_inter]) Km_fluxes = s[Km_indices] Hm_fluxes = s[Hm_indices] # Find final count values for triangular filter: k1_fluxes = s_tri_k1[:dex_spc_k1_inter].tolist() h1_fluxes = s_tri_h1[:dex_spc_h1_inter].tolist() k2_fluxes = s_tri_k2[dex_spc_k2_inter:].tolist() h2_fluxes = s_tri_h2[dex_spc_h2_inter:].tolist() K2_fluxes = np.array(k1_fluxes + Km_fluxes.tolist() + k2_fluxes) H2_fluxes = np.array(h1_fluxes + Hm_fluxes.tolist() + h2_fluxes) # Construct coordinate array for precise polygon-area calculation: Kp_wave = np.array([l[K2_indices[0]], l_k1_inter] + l[Km_indices].tolist() + \ [l_k2_inter, l[K2_indices[-1]] ]) Hp_wave = np.array([l[H2_indices[0]], l_h1_inter] + l[Hm_indices].tolist() + \ [l_h2_inter, l[H2_indices[-1]] ]) Kp_fluxes = np.array([0, s_k1_inter] + Km_fluxes.tolist() + [s_k2_inter, 0]) Hp_fluxes = np.array([0, s_h1_inter] + Hm_fluxes.tolist() + [s_h2_inter, 0]) Kp_coors = np.array([Kp_wave, Kp_fluxes]) Hp_coors = np.array([Hp_wave, Hp_fluxes]) # Combine to return: band_fluxes = [V_fluxes, R_fluxes, K1_fluxes, H1_fluxes, Km_fluxes, Hm_fluxes, \ K2_fluxes, H2_fluxes, Kp_coors, Hp_coors] tri_function = [s_tri_K, s_tri_H] band_indices = [V_indices, R_indices, K1_indices, H1_indices, Km_indices, Hm_indices, \ K2_indices, H2_indices, K_dex_cen, H_dex_cen] #-------------------------------------------------------------- if plot is 1: pt.plot_sindex_bands(l, s, s_tri_K, s_tri_H, K2_indices, H2_indices, K2_fluxes, H2_fluxes, \ l_k1_inter, l_k2_inter, l_h1_inter, l_h2_inter, \ s_k1_inter, s_k2_inter, s_h1_inter, s_h2_inter, \ Kp_wave, Hp_wave, Kp_fluxes, Hp_fluxes, Km_indices, Hm_indices, \ self.K, self.H, K1_indices, H1_indices) pt.plot_sindex_fluxes(l, s, band_indices, band_fluxes, self.bands) #-------------------------------------------------------------- return band_fluxes, tri_function, band_indices def sindex(self, flux_results, l, save=None): """ This utility is a general function to calculate the S index in (1) the standard way and (2) using mean bandpass fluxes if 'dex_meanband' is available. """ # Unpack flux results: bandfluxes, tri_func, bandindices = flux_results[0], flux_results[1], flux_results[2] # Find V an R bandpass fluxes: val_V = np.sum(bandfluxes[0]); V = ufloat(val_V, val_V*self.sigma_V) val_R = np.sum(bandfluxes[1]); R = ufloat(val_R, val_R*self.sigma_R) #-------------------------------------------- # FIND S INDEX FROM 1.09 Å INTEGRATED FLUXES: #-------------------------------------------- val_K1 = np.sum(bandfluxes[2]); K1 = ufloat(val_K1, val_K1*self.sigma_K1) val_H1 = np.sum(bandfluxes[3]); H1 = ufloat(val_H1, val_H1*self.sigma_H1) # Calculate S index: sindex_HK1 = 8 * (H1 + K1)/(R + V) *2.4 #-------------------------------------------- # FIND S INDEX FROM MEAN FLUX PER WAVELENGTH: #-------------------------------------------- val_Vm = np.mean(bandfluxes[0]); Vm = ufloat(val_Vm, val_Vm*self.sigma_V) val_Rm = np.mean(bandfluxes[1]); Rm = ufloat(val_Rm, val_Rm*self.sigma_R) val_Km = np.mean(bandfluxes[6]); Km = ufloat(val_Km, val_Km*self.sigma_Km) val_Hm = np.mean(bandfluxes[7]); Hm = ufloat(val_Hm, val_Hm*self.sigma_Hm) # val_Km = np.mean(bandfluxes[4]); Km = ufloat(val_Km, val_Km*self.sigma_Km) # val_Hm = np.mean(bandfluxes[5]); Hm = ufloat(val_Hm, val_Hm*self.sigma_Hm) # Calculate S index: sindex_HKm = 8 * (Hm + Km)/(Rm + Vm) * self.HK_bandpass/self.VR_bandpass * 2.4 #------------------------------------------------------ # FIND S INDEX FROM TRIANGULAR BANDSPASS NORMALIZATION: #------------------------------------------------------ val_Kn = np.sum(bandfluxes[6] * tri_func[0]) val_Hn = np.sum(bandfluxes[7] * tri_func[1]) sigma_Kn = np.sum(self.sigma_bands[0] * tri_func[0]) sigma_Hn = np.sum(self.sigma_bands[1] * tri_func[1]) Kn = ufloat(val_Kn, val_Kn*sigma_Kn) Hn = ufloat(val_Hn, val_Hn*sigma_Hn) # Calculate S index: sindex_HKn = 8 * (Hn + Kn)/(R + V) *2.4 #------------------------------------------------ # FIND S INDEX FROM TRIANGULAR INTEGRATED FLUXES: #------------------------------------------------ val_K2 = np.sum(bandfluxes[6]); K2 = ufloat(val_K2, val_K2*self.sigma_K2) val_H2 = np.sum(bandfluxes[7]); H2 = ufloat(val_H2, val_H2*self.sigma_H2) # Calculate S index: sindex_HK2 = 8 * (H2 + K2)/(R + V) #---------------------------------------------- # FIND S INDEX FROM INCLOSED POLYGON FLUX AREA: #---------------------------------------------- # Unpack, reverse, add starting point: l_V = l[bandindices[0]].tolist(); lr_V = l_V[::-1]; lr_V = [l_V[0]] + [l_V[-1]] + lr_V l_R = l[bandindices[1]].tolist(); lr_R = l_R[::-1]; lr_R = [l_R[0]] + [l_R[-1]] + lr_R l_K = bandfluxes[8][0].tolist(); lr_K = l_K[::-1]; lr_K = [l_K[0]] + lr_K l_H = bandfluxes[9][0].tolist(); lr_H = l_H[::-1]; lr_H = [l_H[0]] + lr_H s_V = bandfluxes[0].tolist(); sr_V = s_V[::-1]; sr_V = [0] + [0] + sr_V s_R = bandfluxes[1].tolist(); sr_R = s_R[::-1]; sr_R = [0] + [0] + sr_R s_K = bandfluxes[8][1].tolist(); sr_K = s_K[::-1]; sr_K = [0] + sr_K s_H = bandfluxes[9][1].tolist(); sr_H = s_H[::-1]; sr_H = [0] + sr_H # list for calculation: val_Vp = self.polygon_area(np.array([lr_V, sr_V]).T) val_Kp = self.polygon_area(np.array([lr_K, sr_K]).T) val_Hp = self.polygon_area(np.array([lr_H, sr_H]).T) val_Rp = self.polygon_area(np.array([lr_R, sr_R]).T) # plt.figure() # plt.plot(lr_V, sr_V, 'b-') # plt.plot(lr_V, sr_V, 'r*') # plt.show() # Combine with uncertainty: Vp = ufloat(val_Vp, val_Vp*self.sigma_V) Kp = ufloat(val_Kp, val_Kp*self.sigma_K2) Hp = ufloat(val_Hp, val_Hp*self.sigma_H2) Rp = ufloat(val_Rp, val_Rp*self.sigma_R) # Calculate S index: sindex_HKp = 8 * (Hp + Kp)/(Rp + Vp) #-------------------------------------------------------------- if save is 0: self.s1 = ['1:', val_V, val_K1, val_H1, val_R, sindex_HK1] self.sn = ['n:', val_V, val_Kn, val_Hn, val_R, sindex_HKn] self.sm = ['m:', val_Vm, val_Km, val_Hm, val_Rm, sindex_HKm] self.s2 = ['2:', val_V, val_K2, val_H2, val_R, sindex_HK2] self.sp = ['p:', val_Vp, val_Kp, val_Hp, val_Rp, sindex_HKp] return np.array([sindex_HK1, sindex_HKn, sindex_HKm, sindex_HK2, sindex_HKp]) def polygon_area(self, xy, plot=0): """ This utility takes coordinates (x, y) ordered in an array and calculates the polygon area enclosed. The coordinates needs to be ordered in a counter-clock-wise manner since the circuference using Green's theorem is used to equate the polygon area. """ l = len(xy) s = 0.0 for i in range(l): j = (i+1)%l # keep index in [0,l) s += (xy[j,0] - xy[i,0])*(xy[j,1] + xy[i,1]) #-------------------------------------------------------------- return -0.5*s def results(self): print('################################################') print(' {} - {} '.format(self.target, self.date)) print('################################################') head_SF = self.hdul[self.SF_dex[0]][0].header head_FF = self.hdul[self.FF_dex[0]][0].header print('Magnitude = {}, Seeing = {}'.format(self.magnitude, self.seeing)) print('Exptime flat: t = {} s'.format(head_SF['EXPTIME'])) print('Exptime star: t = {} s'.format(head_FF['EXPTIME'])) print('------------------------------------------------') print(' CCD NOISE PROPERTIES ') print('------------------------------------------------') BF_mean, BF_std = np.mean(self.BF), np.std(self.BF) DF_mean, DF_std = np.mean(self.DF), np.std(self.DF) print('Bias master : mean = {:.4g}, std = {:.4g}'.format(BF_mean, BF_std)) print('Dark current: mean = {:.4g}, std = {:.4g}'.format(DF_mean, DF_std)) print('GAIN = {:.3g} e-/ADU'.format(self.gain)) print('RON = {:.3g} ADU'.format(BF_std)) print('VAR = {:.3g} ADU (=<RON^2>)'.format(BF_std**2)) print('------------------------------------------------') print(' BACKGROUND SKY & SCATTER ') print('------------------------------------------------') print('Flat mean background counts: {:.1f}'.format(self.f_flux_sky)) print('Star mean background counts: {:.1f}'.format(self.s_flux_sky)) print('------------------------------------------------') print(' RV CORRECTION ') print('------------------------------------------------') print('Barycentric RV correction: {:.2f} km/s'.format(self.delta_v_baryc)) print('Star motion RV Correction: {:.2f} km/s'.format(self.rv_amp)) print('Correction in velocity : {:.2f} km/s'.format(self.delta_v)) print('Correction in wavelength : {:.2f} Å'.format(self.delta_l)) print('Correction in pixelspace : {:.2f}'.format(self.delta_p)) print('------------------------------------------------') print(' SNR & UNCERTAINTIES ') print('------------------------------------------------') f_snr_max, s_snr_max = self.f_snr_max, self.s_snr_max print('S/N in order #57: {:.1f} (flat), {:.1f} (star)'.format(f_snr_max[1], s_snr_max[1])) print('S/N in order #58: {:.1f} (flat), {:.1f} (star)'.format(f_snr_max[0], s_snr_max[0])) print('------------------------------------------------') snx = [self.s_snr_X[0], self.s_snr_X[1], self.s_snr_X[2], self.s_snr_X[3]] snr =[self.sigma_s_snr[0]*100,self.sigma_s_snr[1]*100,self.sigma_s_snr[2]*100,self.sigma_s_snr[3]*100] std = [self.sigma_V*100, self.sigma_K1*100, self.sigma_H1*100, self.sigma_R*100] print('Bandpass : V K H R | Total') print('S/N : {:.3g} {:.3g} {:.3g} {:.3g} |'.format(snx[0], snx[1], snx[2], snx[3])) print('sigma(S/N): {:.3g}% {:.3g}% {:.3g}% {:.3g}% | {:.1f}%'.format(snr[0], snr[1], snr[2],\ snr[3], np.sum(snr))) print('sigma(std): {:.3g}% {:.3g}% {:.3g}% {:.3g}% | {:.1f}%'.format(std[0], std[1], std[2],\ std[3], np.sum(std))) print('sigma(wav): | {:.2f}%'.format(self.sigma_w*100)) print('sigma(fla): | {:.2f}%'.format(self.sigma_f*100)) print('------------------------------------------------') print(' S INDEX ') print('------------------------------------------------') print(self.s1) print(self.sn) print(self.sm) print(self.s2) print(self.sp) print('------------------------------------------------') ######################################################################################################## # OPTIMAL WIDTHS # ######################################################################################################## def find_optimal_width(self, image=None, trace=None, plot=0): """ This utility takes most preferably a reduced flat image and the polynomial describtion traced, and first cut out a bandpass defined by disp_lenght and cross_width. Looping through increasing spatials widths the S/N ratio is found for each, and the spatial width asigned to the highest S/N ratio is optimal for linear extraction. To return the results in terms of FWHM a Gauss function is fitted to the spatial width of maximum flux. """ # Check if 'image' and 'trace' is defined: if image==None: image = self.F_calib if trace==None: trace = self.trace # Cut out order: widths = np.arange(1, 40) order = self.cut_out_order(image, np.polyval(trace['order_2'], self.disp), widths[-1]) # Find maximum of blaze function: blaze = order.sum(axis=1) blaze_max = np.max(blaze) index_max = np.nanargmax(blaze) # Find mean sky background along disp direction used for S/N ratio: flux_inter, _ = self.mean_background(image, trace, plot=0) # Loop over spatial widths: snr = np.zeros(len(widths)) for w in widths: order_w = order[index_max, widths[-1]-1-w:widths[-1]-1+w] flux_order = np.sum(order_w) snr[w-1] = self.signal_to_noise(flux_order, len(order_w), flux_order) # Find highest S/N ratio optimal order width: index_max_snr = np.argmax(snr) optimal_order_width = widths[index_max_snr] # Find residual inter-order width: order_distance = int(((self.ref_cen_pos[1] - self.ref_cen_pos[2]) + \ (self.ref_cen_pos[2] - self.ref_cen_pos[3]))/2) #optimal_inter_order_width = int(order_distance - 2.5*optimal_order_width) #-------------------------------------------------------------- if plot is 1: pt.plot_optimal_width(widths, order, blaze_max, index_max, flux_inter, snr, optimal_order_width) #-------------------------------------------------------------- self.order_width = optimal_order_width #-------------------------------------------------------------- return self.order_width def mean_background(self, image, trace, plot=0): """ This utility use 'trace' and 'cut_out_order' to select the pixel sky-background in a bandpass on both sides of the order of interest. In spatial direction on each side the median pixel value is found, and lastly the mean value of each side is then computed. Returned is a 1D spectrum describing the background (e.g. used by the 'signal_to_noise' utility). """ # Find midpoint of inter orders: midpoint_below = (self.ref_cen_pos[1] - self.ref_cen_pos[2])/2 midpoint_above = (self.ref_cen_pos[2] - self.ref_cen_pos[3])/2 # Move fit to the midpoint of inter orders: yfit_below = np.polyval(trace['order_1'], self.disp) + np.ones(len(self.disp))*midpoint_below yfit_above = np.polyval(trace['order_2'], self.disp) + np.ones(len(self.disp))*midpoint_above yfit_order = np.polyval(trace['order_2'], self.disp) + np.ones(len(self.disp)) # Set cross width for background cut to half the distance between orders: # (here the position of the order is a limitation) cross_order_width = math.floor(yfit_below[0])*2 - 1 # (else if order are moved up use) #cross_order_width = int((self.ref_cen_pos[1] - self.ref_cen_pos[2])[0]/2 - 1) # Cut out stellar background on both sides: back_below = self.cut_out_order(image, yfit_below, cross_order_width) back_above = self.cut_out_order(image, yfit_above, cross_order_width) # Sum order to 1D spectrum and mean them: l_sky = (np.median(back_below, axis=1) + np.median(back_above, axis=1))/2. flux_sky_mean = abs(l_sky.mean()) #-----------------------------------------------------------: if plot is 1: pt.plot_sky_background(image, self.disp, yfit_below, yfit_above, yfit_order, l_sky) #-------------------------------------------------------------- return flux_sky_mean, l_sky def signal_to_noise(self, flux_star, n_pix_star, flux_sky): """ This function calculates the S/N ratio using the 1D spectrum of the object and sky-background. Purely by statistics with and increasing number of pixel used to define the object 'n_pix_object', the S/N ratio will decrease. The noise sources describing a CCD are the 'gain' (e-/ADU) and 'ron', read-out-noise (e-). """ # See Schroeder (1999) p. 317 or Bradt (2004) p. 163: signal = flux_star*self.gain noise = np.sqrt(flux_star*self.gain + flux_sky*self.gain*n_pix_star + self.ron*n_pix_star) #-------------------------------------------------------------- return signal / noise ######################################################################################################## # GENERAL UTILITIES SPECIALIZED TO THIS SOFTWARE # ######################################################################################################## def blue_moves(self, path, plot=0): """ This routine measures the drift of the spectrum over time by using ThAr lines in the same order as the Ca II H & K lines. (Fun fact: the software name comes from 'Blue Moves' which is the eleventh studio album release by <NAME>, released in October 1976. """ # Load all files from same folder: img_files = np.sort(glob.glob('{}{}*'.format(path, self.img_name))) hdu = np.array([fits.open(str(files)) for files in img_files]) n = len(img_files) # Find time scaling to utc time and Julian Date time = [hdu[i][0].header['JD-DATE'] for i in range(n)] # Loop through all ThAr images: move_x = np.zeros(n) move_y = np.zeros(n) sigma_x = np.zeros(n-1) sigma_y = np.zeros(n-1) for i in range(n): # Open and close one image at a time: with fits.open(str(img_files[i])) as hdu_i: # Select focused spectral region: T_i = hdu_i[0].data[300:480, 420:2270].T # UTILITY CALL: Locate coordinates of lines: COF_i, _, _ = self.peak_finder(T_i, sigma=5, plot=0) # UTILITY CALL: Remove lines too close to borders: COF_i, N_lines = self.image_border(T_i, COF_i) # UTILITY CALL: Only use same lines each time: if i==0: #COF_0, _, _ = self.peak_finder(T_i, sigma=5, plot=0) COF_0 = COF_i if i is not 0: indices0, indices1 = self.match_coordinates(COF_0, COF_i, threshold=5, plot=1) # Find scatter of the drift for each line: if i > 1: diff_x = COF_i[indices1,0] - x diff_y = COF_i[indices1,1] - y sigma_x[i-1] = np.std(diff_x) sigma_y[i-1] = np.std(diff_y) # Find coordinates (x and y needs to be after if < 1 statement): x = COF_i[indices1,0] y = COF_i[indices1,1] move_x[i] = x.mean() move_y[i] = y.mean() # Print to bash: pt.compilation(i, n, 'Blue Moves') print # Convert to relative changes: move_x = move_x[1::] - move_x[1::].mean() move_y = move_y[1::] - move_y[1::].mean() time = time[1::] #----------------------------------------------------------- if plot is 1: np.savetxt('{}bluemoves.txt'.format(self.path), np.vstack([time, move_y, sigma_y]).T) pt.plot_rv_stability(time, move_y, sigma_y) #----------------------------------------------------------- return def image_border(self, image, pixel_coor, border_edge=20): """ This utility takes an array of pixel coordinates and finds coordinates that is closer than 20 pixels to the image 'border_edge'. These coordinates are then removed from the array and a new array, 'new_pixel_coor', is returned together with the new (lower) number of coordinates 'N_coor'. """ # Unpack pixel coordinates: x = pixel_coor[:,0] y = pixel_coor[:,1] # Check if stellar coordinates are too close to borders: i_x1 = np.where(x < border_edge)[0] i_y1 = np.where(y < border_edge)[0] i_x2 = np.where(x > np.shape(image)[0]-border_edge)[0] i_y2 = np.where(y > np.shape(image)[1]-border_edge)[0] i_xy = np.hstack([i_x1, i_x2, i_y1, i_y2]) # Discard these coordinates: x_new = np.delete(x, i_xy) y_new = np.delete(y, i_xy) N_coor = len(x) #----------------------------------------------------------- return np.array([x_new, y_new]).T, N_coor ######################################################################################################## # GENERAL STRUCTURAL ALGORITHMS # ######################################################################################################## def peak_finder(self, pixel_array, min_pix=7, sigma=1, plot=0): """ This utility takes a pixel array and use the 'scipy.ndimage' package to find local maxima within an image. These are determined upon the number of standard deviations, 'sigma', and a minimum of pixels, 'min_pix', a structure should be considered. From the returned structure the same package determines the Center Of Flux ('COF') in coordinate space (x, y), and the circular 'radius' for each, together with the number of local maximum structures, 'N_struct', detected within the pixel_array. """ # FIND CENTER OF FLUX FOR STARS ABOVE THRESHOLD: # Define threshold as a number of standard deviations above the mean: threshold = np.mean(pixel_array) + sigma*np.std(pixel_array) # Find all pixels above the threshold: above_threshold = np.where(pixel_array > threshold, 1, 0) # Label the structures (where starmap = 1 that are adjacent to others): labels, N_structs = scipy.ndimage.label(above_threshold, structure = np.ones((3,3))) # Sum the number of elements in each structure: sums = scipy.ndimage.sum(above_threshold, labels, range(1,N_structs+1)) # Choose only structures with more than min_pix elements (+1 for index mismatch): structs = np.where(sums > min_pix)[0] + 1 # Define starmap as 0 where there are no stars and 1 where there are stars: struct_map = np.zeros(np.shape(pixel_array)) for struct in structs: struct_map = struct_map + np.where(labels == struct, 1, 0) # Label all the structures again: labels, N_structs = scipy.ndimage.label(struct_map, structure = np.ones((3,3))) # Find the center of flux of all the structures found above threshold: COF = scipy.ndimage.center_of_mass(pixel_array, labels, range(1, N_structs+1)) # Estimate the radius of the structures in pixels: radius = np.sqrt(sums[structs-1]/np.pi) # From tuple to array: COF = np.asarray(COF) #-------------------------------------------------------------- if plot is 1: # NEEDS ACTIVATION FROM SOURCE plt.figure() plt.imshow(pt.linear(pixel_array.T), cmap='Blues', origin='lower') plt.scatter(COF[:,0], COF[:,1], s=radius*12, facecolors='none', edgecolors='r', marker='s') plt.show() #-------------------------------------------------------------- return COF, radius, N_structs def find_peak_in_noise(self, s, peak_coor, plot=0): """ This utility identifies the """ # Define limits for peak search: limits = [int(peak_coor-25), int(peak_coor+25)] # Different conditions: conv = self.res_power * 1e-3 # Smooth-filter scale linear with resolving power width = self.len_disp/10 # Width scales likewise with the pixel scale # Find all peaks: peaks_all_dex, _ = scipy.signal.find_peaks(s) peaks_all_val = s[peaks_all_dex] # Find all approx peaks from convolved spectrum: s_conv = self.convolve(s, 'median', int(conv)) s_conv = self.convolve(s_conv, 'mean', int(conv)) peaks_conv_dex, _ = scipy.signal.find_peaks(s_conv) peaks_conv_val = s_conv[peaks_conv_dex] # Select peaks inside disp limits range: ndarray = (peaks_conv_dex > limits[0]) * (peaks_conv_dex < limits[1]) peaks_limit_dex = peaks_conv_dex[ndarray] peaks_limit_val = peaks_conv_val[ndarray] # Find x highest peaks from convolved spectrum: peak_conv_dex = heapq.nlargest(1, np.arange(len(peaks_limit_val)), key=peaks_limit_val.__getitem__)[0] peak_conv_pix = peaks_limit_dex[peak_conv_dex] # Make bold array around peak with dobbelt the width of conv: peak_dex = (peaks_all_dex>peak_conv_pix-conv)*(peaks_all_dex<peak_conv_pix+conv) # Select peak: peak = peaks_all_dex[peak_dex][np.argmax(peaks_all_val[peak_dex])] #-------------------------------------------------------------- if plot is 1: pt.plot_arc_peak(s, s_conv, peaks_limit_dex, peaks_limit_val, peaks_all_dex, \ peaks_all_val, peak, limits) #-------------------------------------------------------------- return peak def convolve(self, data0, filtertype, n): """ This function can be used to correct for slow trends using e.g. a "moving mean" filter. The utility takes the flatten data, a string with the desired filter, and n number of points is should smooth the data with. Compared to the bottleneck package this function do not leave a offset. """ # Constants: data = data0.copy() # Avoid overwritting data: data_new = np.zeros(len(data)) # To pick up new data nzero = np.zeros(2*n+1) # optimization constant # Available filters: if filtertype=='mean': moving_filter = np.mean if filtertype=='median': moving_filter = np.median if filtertype=='sum': moving_filter = np.sum if filtertype=='std': moving_filter = np.std # Interval: d[n, 1+n, ... , N-1, N-n] for i in range(len(data)-2*n): data_new[n+i] = moving_filter(data[range((n+i)-n, (n+i)+n+1)]) for i in range(n): # Interval: d[-n, -(n-1), ... , n-1, n] - Low end of data low = nzero low[range(n-i)] = data[0]*np.ones(n-i) low[-(n+1+i):] = data[range(0, n+1+i)] data_new[i] = moving_filter(low) # Interval: d[N-n, N-(n-1), ... , N+(n-1), N+n] - High end of data high = nzero high[range(n+1+i)] = data[range(len(data)-(n+i+1), len(data))] high[-(n-i):] = data[-1]*np.ones(n-i) data_new[len(data)-1-i] = moving_filter(high) #-------------------------------------------------------------- return data_new def match_coordinates(self, array1, array2, threshold=10, plot=0): """ This function match two set of coordinates. This is done by a purely geometrical technique and looking at the histogram. It finds the minimum distance from i'th array1 star to every other array2 star. Here indices is the rows with all the indices of matching. To select only common coordinates, then use 'indices2'. """ # Placeholders: value_min = np.zeros(len(array1)) index_min = {} # FIND MINIMUM DISTANCE WITH PYTHAGOREAN GEOMETRY: for i in range(len(array1)): d =

np.sqrt( (array2[:,0]-array1[i,0])**2 + (array2[:,1]-array1[i,1])**2 )

numpy.sqrt

# Copyright (c) 2015-2018 by the parties listed in the AUTHORS file. # All rights reserved. Use of this source code is governed by # a BSD-style license that can be found in the LICENSE file. # from memory_profiler import profile import os from toast_planck.reproc_modules.destripe_tools import (fast_hit_binning, fast_binning) import scipy.signal from toast import qarray as qa from toast.mpi import MPI from toast_planck.preproc_modules import MapSampler, flagged_running_average import astropy.io.fits as pf import numpy as np import toast.fod as tf import toast.timing as timing class OpNoiseEstim(): def __init__( self, signal=None, flags=None, detmask=1, commonmask=3, out=None, maskfile=None, mapfile=None, rimo=None, pol=True, nbin_psd=1000, lagmax=100000, stationary_period=86400., nosingle=False, no_spin_harmonics=False, calibrate_signal_estimate=False, nsum=10, naverage=100): self._signal = signal self._flags = flags self._detmask = detmask self._commonmask = commonmask self._out = out self._maskfile = maskfile self._mapfile = mapfile if rimo is None: raise RuntimeError('OpNoiseEstim: You must provide a RIMO') self._rimo = rimo self._pol = pol self._nbin_psd = nbin_psd self._lagmax = lagmax self._stationary_period = stationary_period self._nosingle = nosingle self._no_spin_harmonics = no_spin_harmonics self._calibrate_signal_estimate = calibrate_signal_estimate # Parameters for downsampling the data self._nsum = nsum self._naverage = naverage def exec(self, data): cworld = data.comm.comm_world rank = cworld.Get_rank() masksampler = None if self._maskfile: masksampler = MapSampler(self._maskfile, comm=cworld) mapsampler = None if self._mapfile: mapsampler = MapSampler(self._mapfile, comm=cworld, pol=True) for obs in data.obs: tod = obs['tod'] local_intervals = tod.local_intervals(obs['intervals']) dets = tod.local_dets ndet = len(dets) timestamps = tod.local_timestamps() commonflags = tod.local_common_flags() commonflags = (commonflags & self._commonmask != 0) fsample = self.subtract_signal( tod, cworld, rank, masksampler, mapsampler, local_intervals) # Extend the gap between intervals to prevent sample pairs # that cross the gap. intervals = obs['intervals'] gap_min = np.int(self._lagmax) + 1 # Downsampled data requires longer gaps gap_min_nsum = np.int(self._lagmax * self._nsum) + 1 offset, nsamp = tod.local_samples gapflags = np.zeros_like(commonflags) gapflags_nsum = np.zeros_like(commonflags) for ival1, ival2 in zip(intervals[:-1], intervals[1:]): gap_start = ival1.last + 1 gap_stop = max(gap_start + gap_min, ival2.first) gap_stop_nsum = max(gap_start + gap_min_nsum, ival2.first) if gap_start < offset + nsamp and gap_stop > offset: gap_start = max(0, gap_start - offset) gap_stop = min(offset + nsamp, gap_stop - offset) gapflags[gap_start:gap_stop] = True gap_stop_nsum = min(offset + nsamp, gap_stop_nsum - offset) gapflags_nsum[gap_start:gap_stop_nsum] = True for idet1 in range(ndet): for idet2 in range(idet1, ndet): det1 = dets[idet1] det2 = dets[idet2] if det1 == det2 and self._nosingle: continue signal1 = tod.local_signal(det1) flags1 = tod.local_flags(det1, name=self._flags) flags = (flags1 & self._detmask != 0) signal2 = None flags2 = None if det1 != det2: signal2 = tod.local_signal(det2) flags2 = tod.local_flags(det2, name=self._flags) flags[flags2 & self._detmask != 0] = True flags[commonflags] = True self.process_noise_estimate( signal1, signal2, flags, gapflags, gapflags_nsum, timestamps, fsample, cworld, rank, 'noise', det1, det2, local_intervals) return def subtract_signal(self, tod, cworld, rank, masksampler, mapsampler, local_intervals): """ Subtract a signal estimate from the TOD and update the flags for noise estimation. """ start_signal_subtract = MPI.Wtime() for det in tod.local_dets: if rank == 0: print('Subtracting signal for {}'.format(det), flush=True) tod.cache.report() fsample = self._rimo[det].fsample epsilon = self._rimo[det].epsilon eta = (1 - epsilon) / (1 + epsilon) signal = tod.local_signal(det, name=self._signal) flags = tod.local_flags(det, name=self._flags) flags &= self._detmask for ival in local_intervals: ind = slice(ival.first, ival.last + 1) sig = signal[ind] flg = flags[ind] quat = tod.local_pointing(det)[ind] if self._pol: theta, phi, psi = qa.to_angles(quat) iw = np.ones_like(theta) qw = eta * np.cos(2 * psi) uw = eta * np.sin(2 * psi) iquw = np.column_stack([iw, qw, uw]) else: theta, phi = qa.to_position(quat) if masksampler is not None: maskflg = masksampler.at(theta, phi) < 0.5 flg[maskflg] |= 255 if mapsampler is not None: if self._pol: bg = mapsampler.atpol(theta, phi, iquw) else: bg = mapsampler.at(theta, phi) if self._calibrate_signal_estimate: good = flg == 0 ngood = np.sum(good) if ngood > 1: templates = np.vstack([np.ones(ngood), bg[good]]) invcov = np.dot(templates, templates.T) cov = np.linalg.inv(invcov) proj = np.dot(templates, sig[good]) coeff = np.dot(cov, proj) bg = coeff[0] + coeff[1] * bg sig -= bg cworld.barrier() stop_signal_subtract = MPI.Wtime() if rank == 0: print('TOD signal-subtracted in {:.2f} s'.format( stop_signal_subtract - start_signal_subtract), flush=True) return fsample def decimate(self, x, flg, gapflg, local_intervals): # Low-pass filter with running average, then downsample xx = x.copy() flags = flg.copy() for ival in local_intervals: ind = slice(ival.first, ival.last + 1) xx[ind], flags[ind] = flagged_running_average( x[ind], flg[ind], self._naverage, return_flags=True) return xx[::self._nsum].copy(), (flags + gapflg)[::self._nsum].copy() """ def highpass(self, x, flg): # Flagged real-space high pass filter xx = x.copy() j = 0 while j < x.size and flg[j]: j += 1 alpha = .999 for i in range(j+1, x.size): if flg[i]: xx[i] = x[j] else: xx[i] = alpha*(xx[j] + x[i] - x[j]) j = i xx /= alpha return xx """ def log_bin(self, freq, nbin=100, fmin=None, fmax=None): if np.any(freq == 0): raise Exception('Logarithmic binning should not include ' 'zero frequency') if fmin is None: fmin = np.amin(freq) if fmax is None: fmax = np.amax(freq) bins = np.logspace(np.log(fmin), np.log(fmax), num=nbin + 1, endpoint=True, base=np.e) bins[-1] *= 1.01 # Widen the last bin not to have a bin with one entry locs = np.digitize(freq, bins).astype(np.int32) hits = np.zeros(nbin + 2, dtype=np.int32) fast_hit_binning(locs, hits) return locs, hits def bin_psds(self, my_psds, fmin=None, fmax=None): my_binned_psds = [] my_times = [] binfreq0 = None for i in range(len(my_psds)): t0, _, freq, psd = my_psds[i] good = freq != 0 if self._no_spin_harmonics: # Discard the bins containing spin harmonics and their # immediate neighbors for i0 in range(1, 3): f0 = i0 / 60. for i in range(1, 30): fmask = f0 * i imin = np.argmin(np.abs(freq - fmask)) if i == 1: # The first bin has a wider neighborhood good[imin - 2:imin + 3] = False else: good[imin - 1:imin + 2] = False if self._nbin_psd is not None: locs, hits = self.log_bin(freq[good], nbin=self._nbin_psd, fmin=fmin, fmax=fmax) binfreq = np.zeros(hits.size) fast_binning(freq[good], locs, binfreq) binfreq = binfreq[hits != 0] / hits[hits != 0] else: binfreq = freq hits = np.ones(len(binfreq)) if binfreq0 is None: binfreq0 = binfreq else: if np.any(binfreq != binfreq0): raise Exception('Binned PSD frequencies change') if self._nbin_psd is not None: binpsd = np.zeros(hits.size) fast_binning(psd[good], locs, binpsd) binpsd = binpsd[hits != 0] / hits[hits != 0] else: binpsd = psd my_times.append(t0) my_binned_psds.append(binpsd) return my_binned_psds, my_times, binfreq0 def discard_spin_harmonics(self, binfreq, all_psds): ind = binfreq != 0 for i0 in range(1, 3): f0 = i0 / 60. for i in range(1, 10): fmask = f0 * i imin = np.argmin(np.abs(binfreq - fmask)) if i == 1: ind[imin - 1:imin + 2] = False else: ind[imin] = False binfreq = binfreq[ind] all_psds = all_psds[:, ind] return binfreq, all_psds def discard_outliers(self, binfreq, all_psds, all_times): all_psds = list(all_psds) all_times = list(all_times) nrow, ncol = np.shape(all_psds) # Discard empty PSDs i = 1 nbad = 0 all_psds = list(all_psds) all_times = list(all_times) while i < nrow: p = all_psds[i] if np.all(p == 0) or np.any(np.isnan(p)): del all_psds[i] del all_times[i] nrow -= 1 nbad += 1 else: i += 1 if nbad > 0: print('Discarded {} empty or NaN psds'.format(nbad), flush=True) # Throw away outlier PSDs by comparing the PSDs in specific bins if nrow > 10: all_good = np.isfinite(np.sum(all_psds, 1)) for col in range(ncol - 1): if binfreq[col] < .001: continue # Local outliers psdvalues = np.array([x[col] for x in all_psds]) smooth_values = scipy.signal.medfilt(psdvalues, 11) good = np.ones(psdvalues.size, dtype=np.bool) good[psdvalues == 0] = False for i in range(10): # Local test diff = np.zeros(psdvalues.size) diff[good] = np.log(psdvalues[good]) - \ np.log(smooth_values[good]) sdev =

np.std(diff[good])

numpy.std

from __future__ import absolute_import from __future__ import division from __future__ import print_function import nibabel as nib from dltk.io.preprocessing import whitening,normalise_zero_one from sklearn import preprocessing import matplotlib.pyplot as plt import matplotlib.image as mpimg import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import os import matplotlib.pyplot as plt import numpy as np import fnmatch import AD_Constants as constants import requests import sys from imgaug import augmenters as iaa from numpy import random import time import re import tensorflow as tf from tensorflow.python.keras.api.keras.preprocessing.image import ImageDataGenerator,img_to_array, load_img,array_to_img class Dataset_Import(): def __init__(self): self.main_directory=constants.main_image_directory self.training_part=constants.training_frac self.i = 0 self.auto_shuffling_state=False self.trainer_shuffling_state = False self.train_ad_fnames = None self.train_mci_fnames = None self.train_nc_fnames = None self.source=constants.source self.target=constants.target self.img_shape_tuple=constants.img_shape_tuple self.img_channel=constants.img_channel self.shu_control_state=False self.data_augmentation=constants.augment_data self.image_group=constants.chosen_epi_format self.strict_match=constants.strict_match self.pic_index =constants.pic_index self.train_dir =constants.train_dir self.validation_dir =constants.validation_dir self.set_checker=0 self.classify_group=constants.classify_group # Directory with our training AD dataset self.train_ad_dir =constants.train_ad_dir # Directory with our training MCI dataset self.train_mci_dir = constants.train_mci_dir # Directory with our training NC dataset self.train_nc_dir =constants.train_nc_dir # Directory with our validation AD dataset self.validation_ad_dir = constants.validation_ad_dir # Directory with our validation MCI dataset self.validation_mci_dir =constants.validation_mci_dir # Directory with our validation NC dataset self.validation_nc_dir =constants.validation_nc_dir self.nrows =constants.nrows self.ncols = constants.ncols def statistics(self): #read_directory_file print('total training AD Data:', self.readNiiFiles(self.train_ad_dir,augment_data=True,gan_train=True), end="\n") # print('total training MCI Data:', len(self.readNiiFiles(self.train_mci_dir,augment_data=False)), end="\n") # print('total training NC Data:', len(self.readNiiFiles(self.train_nc_dir,augment_data=True)), end="\n") # print('total validation AD Data:', len(self.readNiiFiles(self.validation_ad_dir,augment_data=True)), end="\n") # print('total validation MCI Data:', len(self.readNiiFiles(self.validation_mci_dir,augment_data=False)), end="\n") # print('total validation NC Data:', len(self.readNiiFiles(self.validation_nc_dir,augment_data=False)), end="\n") #print('total main validation AD Data:', len(self.readNiiFiles(constants.main_validation_ad_dir, augment_data=False,checker=9)), end="\n") #print('total main validation MCI Data:', len(self.readNiiFiles(constants.main_validation_mci_dir, augment_data=False,checker=9)), end="\n") #print('total main validation NC Data:', len(self.readNiiFiles(constants.main_validation_nc_dir, augment_data=False,checker=9)), end="\n") def readNiiFiles(self,original_dir,augment_data=False,checker=8,gan_train=False): # creating destinations folders counter = 0 group_data = [] ad_class =original_dir.split(os.sep)[-1] domain_class = original_dir.split(os.sep)[-2] if domain_class =='train': source = '1.5T' elif domain_class =='validation': source='3.0T' elif domain_class == 'model_validate': source='1.5T' checker_file="" for root, dir, f_names in os.walk(original_dir): for f in f_names: if f.lower().find(".nii") > -1: sourcefile = os.path.join(root, f) if sourcefile.find("skull_workflow") <= -1 : for fgroup in self.image_group: if fgroup in sourcefile: if sourcefile.split(os.sep)[checker] != checker_file: checker_file = sourcefile.split(os.sep)[checker] try: label =self.get_nii_group(ad_class) source_label =self.get_nii_source(source) if gan_train == False : group_data.append([sourcefile, label, source_label]) if augment_data == True: for l in range(constants.augment_factor): aug_source_file = sourcefile.replace('.nii',str(l+1)+'_aug.nii') group_data.append([aug_source_file, label, source_label]) #print("data" ,[sourcefile, label, source_label]) except (IOError, OSError) as e: print(e.errno) sys.exit(2) return group_data def readNiiFiles_2(self,original_dir,augment_data=False,gan_train=False,augment_factor=constants.augment_factor,limit_number=0): # creating destinations folders group_data = [] ad_class =original_dir.split(os.sep)[-1] domain_class = original_dir.split(os.sep)[-2] augment_counter=0 augment_pause=False augment_limit=constants.max_class_value-limit_number if domain_class =='train': source = '1.5T' elif domain_class =='validation': source='3.0T' elif domain_class == 'model_validate': source='1.5T' else: source='1.5T' for root, dir, f_names in os.walk(original_dir): for f in f_names: if f.lower().find(".nii") > -1: sourcefile = os.path.join(root, f) #check=sourcefile.split(os.sep)[9] # check.lower().find("localizer") < 0 if True: try: label =self.get_nii_group(ad_class) source_label =self.get_nii_source(source) if gan_train == False : group_data.append([sourcefile, label, source_label]) if augment_data == True: if augment_pause==False: aug_source_file = sourcefile.replace('.nii',str(1)+'_aug.nii') group_data.append([aug_source_file, label, source_label]) augment_counter =augment_counter+1 if augment_counter==limit_number: augment_pause=True #print("data" ,[sourcefile, label, source_label]) except (IOError, OSError) as e: print(e.errno) sys.exit(2) augment_pause=False augment_counter=0 return group_data def read_directory_file(self,original_dir,dx_group,source=None): group_data = [] try: if original_dir is not None: for file in os.listdir(original_dir): if os.path.isdir(os.path.join(original_dir,file)): filepath = os.path.join(original_dir, file) for sub_file in os.listdir(filepath): if os.path.isdir(os.path.join(filepath,sub_file)): filepath2 = os.path.join(filepath, sub_file) for sub_file2 in os.listdir(filepath2): decision_check=None if self.strict_match : decision_check= sub_file2.strip() in self.image_group #str(self.image_group) == str(sub_file.strip()) if decision_check==True : modal_groupings_source=os.path.join(filepath2 ,sub_file2) files_in_time = len(os.listdir(modal_groupings_source)) if files_in_time > 1: time_file=max(os.listdir(modal_groupings_source)) else: time_file=os.listdir(modal_groupings_source)[0] time_grouping_source=os.path.join(modal_groupings_source ,time_file) files_in_time=len(os.listdir(modal_groupings_source)) for image_file in os.listdir(time_grouping_source): image_grouping_source = os.path.join(time_grouping_source, image_file) if os.path.isdir(os.path.join(image_grouping_source)): if os.path.isdir(os.path.join(image_grouping_source, "skull_workflow")): image_grouping_source=os.path.join(image_grouping_source,"skull_workflow") if os.path.isdir(os.path.join(image_grouping_source,"BSE")): image_grouping_source = os.path.join(image_grouping_source,"BSE") for image_file in os.listdir(image_grouping_source): pattern="*.bse.nii.gz" if fnmatch.fnmatch(image_file,pattern): image_grouping_source_file=os.path.join(image_grouping_source,image_file) label=self.get_nii_group(dx_group) source_label=self.get_nii_source(source) group_data.append([image_grouping_source_file,label,source_label]) image_grouping_source="" image_grouping_source_file="" except OSError as e: print('Error: %s' % e) return group_data def try_replace(self): st='/HDD/public/data/ADNI_NEW_DATASET/train/MCI/016_S_11493_aug.nii' #st.replace('_aug.nii', '.nii') if st.find("_aug.nii") > -1: st = re.sub("\d_aug.nii",'.nii',st) return st def convert_nii_to_image_data(self,nii_path): if nii_path.find("_aug.nii") > -1: nii_path = re.sub("\d_aug.nii",'.nii',nii_path) image_load = nib.load(nii_path, mmap=False) img_data = self.dataAugment(np.asarray(image_load.get_data()[:,:,:,0])) else: image_load = nib.load(nii_path, mmap=False) #use_shape=image_load img_data = np.asarray(image_load.get_data()[:,:,:,0]) shape = img_data.shape #print(shape) #img_data = img_data[65:shape[0]-65,30:shape[1]-30, int(shape[2]/2)-40:int(shape[2]/2)+40] #img_data = img_data[45:105, 40:170, 55:115] #img_data = img_data[int(shape[2] / 2) - 65:int(shape[2] / 2) + 65,int(shape[2] / 2) - 70:int(shape[2] / 2) + 50, int(shape[2] / 2) - 40:int(shape[2] / 2) + 40] #img_data = img_data[0:90, 0:90,:] img_data = img_data.astype(np.float32) img_data -= np.mean(img_data) img_data/=np.maximum(np.std(img_data),10**(-5)) img_data = np.pad(img_data, pad_width=((int(np.ceil(1.0 * (constants.img_shape_tuple[0] - shape[0]) / 2)), int(np.floor(1.0 * (constants.img_shape_tuple[0] - shape[0]) / 2))), ( int(np.ceil(1.0 * (constants.img_shape_tuple[1] - shape[1]) / 2)), int(np.floor(1.0 * (constants.img_shape_tuple[1] - shape[1]) / 2))), (int(np.ceil(1.0 * (constants.img_shape_tuple[2] - shape[2]) / 2)), int(np.floor(1.0 * (constants.img_shape_tuple[2] - shape[2]) / 2)))), mode="constant", constant_values=(0, 0)) #print("shape",img_data.shape) # img_to_array # seg=iaa.Sequential([ # iaa.BilateralBlur(d=(3,10), sigma_color=(10,255), sigma_space=(10,255)) # ]) # # #print(np.resize(img_data,constants.img_bilateral_filter).shape) # # filter_bilateral=seg.augment_image(np.resize(np.uint8(img_data),constants.img_bilateral_filter)) #determine_shape = np.resize(img_data, self.img_shape_tuple) #print("data",normalise_zero_one(determine_shape+ np.random.normal(0, 0.05, determine_shape.shape))) normalise_zero_one(img_data) return img_data #normalise_zero_one( #determine_shape*(1./255) def convert_nii_to_image_dataK(self,nii_path): if nii_path.find("_aug.nii") > -1: nii_path = re.sub("\d_aug.nii",'.nii',nii_path) image_load = nib.load(nii_path, mmap=False) img_data = self.dataAugment(np.asarray(image_load.get_data())) else: image_load = nib.load(nii_path, mmap=False) img_data = np.asarray(image_load.get_data()) K_SHAPE=(self.img_shape_tuple[0],self.img_shape_tuple[1],self.img_shape_tuple[2],self.img_channel) #img_to_array determine_shape = np.resize(img_data,K_SHAPE) #print("data",normalise_zero_one(determine_shape+ np.random.normal(0, 0.05, determine_shape.shape))) return normalise_zero_one(determine_shape) def convert_nii_to_image_real(self,nii_path): if nii_path.find("_aug.nii") > -1: nii_path = re.sub("\d_aug.nii",'.nii',nii_path) image_load = nib.load(nii_path, mmap=False) img_data = np.asarray(image_load.get_data()) else: image_load = nib.load(nii_path, mmap=False) img_data = np.asarray(image_load.get_data()) #img_to_array determine_shape = np.resize(img_data, self.img_shape_tuple) #print("data",normalise_zero_one(determine_shape+ np.random.normal(0, 0.05, determine_shape.shape))) return normalise_zero_one(determine_shape) #normalise_zero_one( #determine_shape*(1./255) def one_hot_encode(self,vec): ''' For use to one-hot encode the 3- possible labels ''' n_classes=len(constants.classify_group) return np.eye(n_classes)[vec] def one_hot_encode_d(self,vec): ''' For use to one-hot encode the 3- possible labels ''' n_classes=2 return np.eye(n_classes)[vec] def get_nii_group(self,nii_path): img_label=nii_path if img_label=="AD": label=1 elif img_label=="NC": label=2 elif img_label=="MCI": label=0 return label def get_nii_source(self, source_target): source_label = source_target if source_label == "1.5T": source_label = 0 elif source_label == "3.0T": source_label = 1 return source_label def all_source_data(self,augment_data=False): if "AD" in constants.classify_group: all_ad_train = self.readNiiFiles_2(self.train_ad_dir,augment_data=augment_data,limit_number=264) if "MCI" in constants.classify_group: all_mci_train = self.readNiiFiles_2(self.train_mci_dir,augment_data=augment_data,limit_number=320) if "NC" in constants.classify_group: all_nc_train = self.readNiiFiles_2(self.train_nc_dir,augment_data=augment_data,limit_number=320) data_feed=[] for groups in constants.classify_group: if groups=="AD": data_feed=data_feed+all_ad_train elif groups=="MCI": data_feed = data_feed + all_mci_train elif groups=="NC": data_feed=data_feed + all_nc_train all_source = [img_path for i, img_path in enumerate(data_feed)] all_source = np.array(all_source) self.set_random_seed(random.random_integers(1200)) return self.shuffle(all_source) def all_source_gan(self, augment_data=False): if "AD" in constants.classify_group: all_ad_train = self.readNiiFiles_2(self.train_ad_dir, augment_data=False,gan_train=False) if "MCI" in constants.classify_group: all_mci_train = self.readNiiFiles_2(self.train_mci_dir, augment_data=False,gan_train=False) if "NC" in constants.classify_group: all_nc_train = self.readNiiFiles_2(self.train_nc_dir, augment_data=False,gan_train=False) data_feed = [] for groups in constants.classify_group: if groups == "AD": data_feed = data_feed + all_ad_train elif groups == "MCI": data_feed = data_feed + all_mci_train elif groups == "NC": data_feed = data_feed + all_nc_train all_source = [img_path for i, img_path in enumerate(data_feed)] all_source = np.array(all_source) self.set_random_seed(random.random_integers(1200)) return self.shuffle(all_source) def all_main_validate(self, augment_data=False): if "AD" in constants.classify_group: all_ad_val = self.readNiiFiles_2(constants.main_validation_ad_dir, augment_data=augment_data) if "MCI" in constants.classify_group: all_mci_val = self.readNiiFiles_2(constants.main_validation_mci_dir, augment_data=augment_data) if "NC" in constants.classify_group: all_nc_val = self.readNiiFiles_2(constants.main_validation_nc_dir, augment_data=augment_data) data_feed = [] for groups in constants.classify_group: if groups == "AD": data_feed = data_feed + all_ad_val elif groups == "MCI": data_feed = data_feed + all_mci_val elif groups == "NC": data_feed = data_feed + all_nc_val all_val = [img_path for i, img_path in enumerate(data_feed)] all_val = np.array(all_val) self.set_random_seed(

random.random_integers(1200)

numpy.random.random_integers

import pytest pytest.importorskip('numpy') import numpy as np import pytest import dask.array as da from dask.array.utils import assert_eq def test_linspace(): darr = da.linspace(6, 49, chunks=5) nparr = np.linspace(6, 49) assert_eq(darr, nparr) darr = da.linspace(1.4, 4.9, chunks=5, num=13) nparr = np.linspace(1.4, 4.9, num=13) assert_eq(darr, nparr) darr = da.linspace(6, 49, chunks=5, dtype=float) nparr = np.linspace(6, 49, dtype=float) assert_eq(darr, nparr) darr = da.linspace(1.4, 4.9, chunks=5, num=13, dtype=int) nparr = np.linspace(1.4, 4.9, num=13, dtype=int) assert_eq(darr, nparr) assert (sorted(da.linspace(1.4, 4.9, chunks=5, num=13).dask) == sorted(da.linspace(1.4, 4.9, chunks=5, num=13).dask)) assert (sorted(da.linspace(6, 49, chunks=5, dtype=float).dask) == sorted(da.linspace(6, 49, chunks=5, dtype=float).dask)) def test_arange(): darr = da.arange(77, chunks=13) nparr = np.arange(77) assert_eq(darr, nparr) darr = da.arange(2, 13, chunks=5) nparr = np.arange(2, 13) assert_eq(darr, nparr) darr = da.arange(4, 21, 9, chunks=13) nparr = np.arange(4, 21, 9) assert_eq(darr, nparr) # negative steps darr = da.arange(53, 5, -3, chunks=5) nparr = np.arange(53, 5, -3) assert_eq(darr, nparr) darr = da.arange(77, chunks=13, dtype=float) nparr = np.arange(77, dtype=float) assert_eq(darr, nparr) darr = da.arange(2, 13, chunks=5, dtype=int) nparr = np.arange(2, 13, dtype=int) assert_eq(darr, nparr) assert (sorted(da.arange(2, 13, chunks=5).dask) == sorted(da.arange(2, 13, chunks=5).dask)) assert (sorted(da.arange(77, chunks=13, dtype=float).dask) == sorted(da.arange(77, chunks=13, dtype=float).dask)) # 0 size output darr = da.arange(0, 1, -0.5, chunks=20) nparr = np.arange(0, 1, -0.5) assert_eq(darr, nparr) darr = da.arange(0, -1, 0.5, chunks=20) nparr = np.arange(0, -1, 0.5) assert_eq(darr, nparr) def test_arange_has_dtype(): assert da.arange(5, chunks=2).dtype ==

np.arange(5)

numpy.arange

import glob import subprocess import numpy as np import dynapack.LSwrite as LSw from fnmatch import filter as fltr import matplotlib.pyplot as plt """ Functions for post-processing of LS-DYNA files """ def readrcforc( Inputfilename): """ Function to read rcforc files from LS-DYNA Run as rc = readrcforc('rcforc') Plot as plt.plot(rc[contactID,forcedir,:]) """ ncontacts=0 contactnames = [] f = open(Inputfilename,'r') for i in range(0,5): dummy = f.readline() # Read to find number of contacts temp=0 while temp>-1: line = f.readline() if 'END' in line: temp = -5 line = f.readline() else: ncontacts = ncontacts + 1 s=' ' seq = line.strip().split()[1:] contactnames.append(s.join(seq)) # Create 2d list for data storage rcforc= [[] for i in range(ncontacts)] # Read reaction forces for each contact temp=0 while temp>-1: for i in range(0,ncontacts*2): line = f.readline() if 'master' in line: #print line contactID = int(float(line.strip().split()[1])) time = float(line.strip().split()[3]) Fx = float(line.strip().split()[5]) Fy = float(line.strip().split()[7]) Fz = float(line.strip().split()[9]) Mx = float(line.strip().split()[13]) My = float(line.strip().split()[15]) Mz = float(line.strip().split()[17]) Fres = np.sqrt(Fx**2 + Fy**2 + Fz**2) rcforc[contactID-1].append([Fx, Fy, Fz, Mx, My, Mz, time, Fres]) if len(line)==0: temp=-5 f.close() # Sort into better array def column(matrix, i): #Extract column from array return [row[i] for row in matrix] def rclen(rc): # Find length of rcforc file rclengths = np.zeros(len(rcforc)) for i in range(0,len(rcforc)): rclengths[i] = len(column(rcforc[i],0)) return int(np.max(rclengths)) temp = np.zeros((ncontacts, 8, rclen(rcforc))) for i in range(0,ncontacts): if len(rcforc[i])>0: for j in range(0,8): temp[i,j,:] = column(rcforc[i],j) return temp def readnodout( Inputfilename, stringname = ''): """ Script to read nodout-files from LS-DYNA Run as nodout = readnodout('nodout') nodoutID = readnodout('nodout', 'stringname') Plot as plt.plot(nodout[:,nodeID,variableID]) Variables : 0 : time 1 : nodal_point 2 : x-disp 3 : y-disp 4 : z-disp 5 : x-vel 6 : y-vel 7 : z-vel 8 : x-accl 9 : y-accl 10 : z-accl 11 : x-coor 12 : y-coor 13 : z-coor """ import numpy as np def chunks(l, n): """Yield successive n-sized chunks from l.""" for i in range(0, len(l), n): yield l[i:i + n] # Read file to get node index heading f = open(Inputfilename,'r') temp = 0 val = [] while temp==0: line = f.readline() if '{BEGIN LEGEND}' in line: temp=2 line = f.readline() # heading while temp==2: line = f.readline() if '{END LEGEND}' in line: temp=3 else: val.append(line.strip()) elif 'n o d a l p r i n t o u t f o r' in line: # abort if no names temp=1 # Read file to get number of elements f = open(Inputfilename,'r') temp = 0 nodes = [] while temp==0: line = f.readline() if 'nodal point' in line: temp=2 nnode = 0 while temp==2: line = f.readline() if len(line.split()) == 0: temp=3 else: nnode += 1 nodes.append(int(line.split()[0])) nodes = np.array(nodes) f.close() # Find match between element ID and index selval = [] if len(val) == nnode: # Then all elements have title for ID, v in enumerate(val): if stringname in v: selval.append(ID) else: for ID, v in enumerate(val): # Then we find matching node index if stringname in v: selval.append(np.where(nodes == int(v.split()[0]))[0][0]) if stringname == '': values = [] # Read data f = open(Inputfilename,'r') temp = 0 while temp==0: line = f.readline() if 'n o d a l' in line: # Then we have a new time step time = line.split()[-2] vval = [] for i in range(0,2): dummy = f.readline() for i in range(0,nnode): line = f.readline() nodeID = line.split()[0] templist = list(chunks(line[10:],12))[:-1] templist = [float(i) for i in np.concatenate(([float(time), nodeID], templist))] vval.append(templist) teststop = 0 for i in range(0,5): dummy = f.readline() teststop += len(dummy) if teststop == 0: temp=3 #end of file values.append(vval) if len(line) == 0: temp=3 f.close() if stringname == '': #then return nodout values ret =

np.array(values)

numpy.array

""" sparse tables ============= Might look like this: level 1 level 2 level 3 columns columns columns idx a b c d e f idx g h i j k l idx m n o p ___ _ _ _ _ _ _ ___ _ _ _ _ _ _ |_0_|_|_|_|_|_|_||_0_|_|_|_|_|_|_| |_1_|_|_|_|_|_|_| |_2_|_|_|_|_|_|_| ___ _ _ _ _ _ _ |_3_|_|_|_|_|_|_||_3_|_|_|_|_|_|_| |_4_|_|_|_|_|_|_||_4_|_|_|_|_|_|_| ___ _ _ _ _ |_5_|_|_|_|_|_|_||_5_|_|_|_|_|_|_||_5_|_|_|_|_| |_6_|_|_|_|_|_|_| |_7_|_|_|_|_|_|_| |_8_|_|_|_|_|_|_| ___ _ _ _ _ _ _ |_9_|_|_|_|_|_|_||_9_|_|_|_|_|_|_| |10_|_|_|_|_|_|_||10_|_|_|_|_|_|_| |11_|_|_|_|_|_|_| ___ _ _ _ _ _ _ ___ _ _ _ _ |12_|_|_|_|_|_|_||12_|_|_|_|_|_|_||12_|_|_|_|_| |13_|_|_|_|_|_|_| ___ _ _ _ _ _ _ |14_|_|_|_|_|_|_||14_|_|_|_|_|_|_| Can be represented in memory like this: level 1 level 2 level 3 columns columns columns idx a b c d e f idx g h i j k l idx m n o p ___ _ _ _ _ _ _ ___ _ _ _ _ _ _ ___ _ _ _ _ |_0_|_|_|_|_|_|_| |_0_|_|_|_|_|_|_| |_5_|_|_|_|_| |_1_|_|_|_|_|_|_| |_3_|_|_|_|_|_|_| |12_|_|_|_|_| |_2_|_|_|_|_|_|_| |_4_|_|_|_|_|_|_| |_3_|_|_|_|_|_|_| |_9_|_|_|_|_|_|_| |_4_|_|_|_|_|_|_| |10_|_|_|_|_|_|_| |_6_|_|_|_|_|_|_| |12_|_|_|_|_|_|_| |_7_|_|_|_|_|_|_| |14_|_|_|_|_|_|_| |_8_|_|_|_|_|_|_| |_9_|_|_|_|_|_|_| |10_|_|_|_|_|_|_| |11_|_|_|_|_|_|_| |12_|_|_|_|_|_|_| |13_|_|_|_|_|_|_| |14_|_|_|_|_|_|_| Written to tape-archive table.tar |_ level_1/idx |_ level_1/column_a |_ level_1/column_b |_ level_1/column_c |_ level_1/column_d |_ level_1/column_e |_ level_1/column_f |_ level_2/idx |_ level_2/column_g |_ level_2/column_h |_ level_2/column_i |_ level_2/column_j |_ level_2/column_k |_ level_2/column_l |_ level_3/idx |_ level_3/column_m |_ level_3/column_n |_ level_3/column_o |_ level_3/column_p """ import pandas as pd import numpy as np import tarfile import io import shutil import tempfile import os IDX = "idx" IDX_DTYPE = "<u8" LEVEL_COLUMN_DELIMITER = "/" FILEAME_TEMPLATE = "{:s}" + LEVEL_COLUMN_DELIMITER + "{:s}.{:s}" DTYPES = [ "<u1", "<u2", "<u4", "<u8", "<i1", "<i2", "<i4", "<i8", "<f2", "<f4", "<f8", ] # logical operations # ================== def intersection(list_of_lists_of_indices): """ Returns the common indices among the lists of indices. Example ------- [4, 5, 6] = intersection([[1,2,3,4,5,6], [3,4,5,6,7,8], [4,5,6,7,8,9,10]]) """ inter = list_of_lists_of_indices[0] for i in range(len(list_of_lists_of_indices)): inter = np.intersect1d(inter, list_of_lists_of_indices[i]) return inter def cut_level_on_indices(level, indices, column_keys=None): """ Returns a level (recarray) only containing the row-indices in 'indices'. Parameters ---------- level : recarray A level in a sparse table. indices : list The row-indices to be written to the output-level. column_keys : list of strings (None) When specified, only these columns will be in the output-level. """ if column_keys is None: column_keys = list(level.dtype.names) column_keys.append(IDX) _part = {} for column_key in column_keys: _part[column_key] = level[column_key] part_df = pd.DataFrame(_part) del _part common_df = pd.merge( part_df, pd.DataFrame(dict_to_recarray({IDX: indices})), on=IDX, how="inner", ) del part_df return common_df.to_records(index=False) def cut_table_on_indices(table, common_indices, level_keys=None): """ Returns table but only with the rows listed in common_indices. Parameters ---------- table : dict of recarrays. The sparse numeric table. common_indices : list of indices The row-indices to cut on. Only row-indices in this list will go in the output-table. level_keys : list of strings (None) When provided, the output-table will only contain these levels. """ if level_keys is None: level_keys = list(table.keys()) out = {} for level_key in level_keys: out[level_key] = cut_level_on_indices( level=table[level_key], indices=common_indices, ) return out def sort_table_on_common_indices(table, common_indices): """ Returns a table with all row-indices ordered same as common_indices. table : dict of recarrays. The table. But must be rectangular, i.e. not sparse. common_indices : list of indices The row-indices to sort by. """ common_order_args =

np.argsort(common_indices)

numpy.argsort

""" Produce calibrated light curves. ``SDTlcurve`` is a script that, given a list of cross scans from different sources, is able to recognize calibrators and use them to convert the observed counts into a density flux value in Jy. """ import os import sys import glob import re import warnings import traceback import configparser import copy import numpy as np from astropy import log import astropy.units as u from scipy.optimize import curve_fit from astropy.table import Table, Column from .scan import Scan, list_scans from .read_config import read_config, sample_config_file, get_config_file from .fit import fit_baseline_plus_bell from .io import mkdir_p from .utils import standard_string, standard_byte, compare_strings from .utils import HAS_STATSM, calculate_moments, scantype try: import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec HAS_MPL = True except ImportError: HAS_MPL = False CALIBRATOR_CONFIG = None __all__ = ["CalibratorTable", "read_calibrator_config"] def _constant(x, p): return p FLUX_QUANTITIES = {"Jy/beam": "Flux", "Jy/pixel": "Flux Integral", "Jy/sr": "Flux Integral"} def _get_flux_quantity(map_unit): try: return FLUX_QUANTITIES[map_unit] except Exception: raise ValueError("Incorrect map_unit for flux conversion. Use one " "of {}".format(list(FLUX_QUANTITIES.keys()))) def read_calibrator_config(): """Read the configuration of calibrators in data/calibrators. Returns ------- configs : dict Dictionary containing the configuration for each calibrator. Each key is the name of a calibrator. Each entry is another dictionary, in one of the following formats: 1) {'Kind' : 'FreqList', 'Frequencies' : [...], 'Bandwidths' : [...], 'Fluxes' : [...], 'Flux Errors' : [...]} where 'Frequencies' is the list of observing frequencies in GHz, 'Bandwidths' is the list of bandwidths in GHz, 'Fluxes' is the list of flux densities in Jy from the literature and 'Flux Errors' are the uncertainties on those fluxes. 2) {'Kind' : 'CoeffTable', 'CoeffTable': {'coeffs' : 'time, a0, a0e, a1, a1e, a2, a2e, a3, a3e\n2010.0,0 ...}} where the 'coeffs' key contains a dictionary with the table of coefficients a la Perley & Butler ApJS 204, 19 (2013), as a comma-separated string. See Also -------- srttools.calibration.flux_function Examples -------- >>> calibs = read_calibrator_config() # doctest: +ELLIPSIS INFO... >>> calibs['DummyCal']['Kind'] 'CoeffTable' >>> 'coeffs' in calibs['DummyCal']['CoeffTable'] True """ flux_re = re.compile(r'^Flux') curdir = os.path.dirname(__file__) calibdir = os.path.join(curdir, 'data', 'calibrators') calibrator_file_list = glob.glob(os.path.join(calibdir, '*.ini')) configs = {} for cfile in calibrator_file_list: cparser = configparser.ConfigParser() cparser.read(cfile) log.info(f"Reading {cfile}") if 'CoeffTable' not in list(cparser.sections()): configs[cparser.get("Info", "Name")] = {"Kind": "FreqList", "Frequencies": [], "Bandwidths": [], "Fluxes": [], "Flux Errors": []} for section in cparser.sections(): if not flux_re.match(section): continue configs[cparser.get("Info", "Name")]["Frequencies"].append( float(cparser.get(section, "freq"))) configs[cparser.get("Info", "Name")]["Bandwidths"].append( float(cparser.get(section, "bwidth"))) configs[cparser.get("Info", "Name")]["Fluxes"].append( float(cparser.get(section, "flux"))) configs[cparser.get("Info", "Name")]["Flux Errors"].append( float(cparser.get(section, "eflux"))) else: configs[cparser.get("Info", "Name")] = \ {"CoeffTable": dict(cparser.items("CoeffTable")), "Kind": "CoeffTable"} return configs def _get_calibrator_flux(calibrator, frequency, bandwidth=1, time=0): global CALIBRATOR_CONFIG log.info(f"Getting calibrator flux from {calibrator}") if CALIBRATOR_CONFIG is None: CALIBRATOR_CONFIG = read_calibrator_config() calibrators = CALIBRATOR_CONFIG.keys() for cal in calibrators: if cal == calibrator: calibrator = cal break else: return None, None conf = CALIBRATOR_CONFIG[calibrator] # find closest value among frequencies if conf["Kind"] == "FreqList": idx = (np.abs(np.array(conf["Frequencies"]) - frequency)).argmin() return conf["Fluxes"][idx], \ conf["Flux Errors"][idx] elif conf["Kind"] == "CoeffTable": return _calc_flux_from_coeffs(conf, frequency, bandwidth, time) def _treat_scan(scan_path, plot=False, **kwargs): scandir, sname = os.path.split(scan_path) if plot and HAS_MPL: outdir = os.path.splitext(sname)[0] + "_scanfit" outdir = os.path.join(scandir, outdir) mkdir_p(outdir) try: # For now, use nosave. HDF5 doesn't store meta, essential for # this scan = Scan(scan_path, norefilt=True, nosave=True, **kwargs) except KeyError as e: log.warning( "Missing key. Bad file? {}: {}".format(sname, str(e)) ) return False, None except Exception as e: log.warning( "Error while processing {}: {}".format(sname, str(e)) ) log.warning(traceback.format_exc()) return False, None feeds = np.arange(scan['ra'].shape[1]) chans = scan.chan_columns() chan_nums = np.arange(len(chans)) F, N = np.meshgrid(feeds, chan_nums) F = F.flatten() N = N.flatten() rows = [] for feed, nch in zip(F, N): channel = chans[nch] ras = np.degrees(scan['ra'][:, feed]) decs = np.degrees(scan['dec'][:, feed]) els = np.degrees(scan['el'][:, feed]) azs = np.degrees(scan['az'][:, feed]) time = np.mean(scan['time'][:]) el = np.mean(els) az = np.mean(azs) source = scan.meta['SOURCE'] pnt_ra = np.degrees(scan.meta['RA']) pnt_dec = np.degrees(scan.meta['Dec']) frequency = scan[channel].meta['frequency'] bandwidth = scan[channel].meta['bandwidth'] temperature = scan[channel + '-Temp'] y = scan[channel] # Fit for gain curves x, _ = scantype(ras, decs, els, azs) temperature_model, _ = \ fit_baseline_plus_bell(x, temperature, kind='gauss') source_temperature = temperature_model['Bell'].amplitude.value # Fit RA and/or Dec x, scan_type = scantype(ras, decs) model, fit_info = fit_baseline_plus_bell(x, y, kind='gauss') try: uncert = fit_info['param_cov'].diagonal() ** 0.5 except Exception: message = fit_info['message'] warnings.warn( "Fit failed in scan {s}: {m}".format(s=sname, m=message)) continue bell = model['Bell'] baseline = model['Baseline'] # pars = model.parameters pnames = model.param_names counts = model.amplitude_1.value backsub = y - baseline(x) moments = calculate_moments(backsub) skewness = moments['skewness'] kurtosis = moments['kurtosis'] if scan_type.startswith("RA"): fit_ra = bell.mean.value fit_width = bell.stddev.value * np.cos(np.radians(pnt_dec)) fit_dec = None ra_err = fit_ra * u.degree - pnt_ra dec_err = None fit_mean = fit_ra fit_label = 'RA' pnt = pnt_ra elif scan_type.startswith("Dec"): fit_ra = None fit_dec = bell.mean.value fit_width = bell.stddev.value dec_err = fit_dec * u.degree - pnt_dec ra_err = None fit_mean = fit_dec fit_label = 'Dec' pnt = pnt_dec else: raise ValueError("Unknown scan type") index = pnames.index("amplitude_1") counts_err = uncert[index] index = pnames.index("stddev_1") width_err = uncert[index] flux_density, flux_density_err = 0, 0 flux_over_counts, flux_over_counts_err = 0, 0 calculated_flux, calculated_flux_err = 0, 0 new_row = [scandir, sname, scan_type, source, channel, feed, time, frequency, bandwidth, counts, counts_err, fit_width, width_err, flux_density, flux_density_err, el, az, source_temperature, flux_over_counts, flux_over_counts_err, flux_over_counts, flux_over_counts_err, calculated_flux, calculated_flux_err, pnt_ra, pnt_dec, fit_ra, fit_dec, ra_err, dec_err, skewness, kurtosis] rows.append(new_row) if plot and HAS_MPL: fig = plt.figure("Fit information") gs = GridSpec(2, 1, height_ratios=(3, 1)) ax0 = plt.subplot(gs[0]) ax1 = plt.subplot(gs[1], sharex=ax0) ax0.plot(x, y, label="Data") ax0.plot(x, bell(x), label="Fit: Amp: {}, Wid: {}".format(counts, fit_width)) ax1.plot(x, y - bell(x)) ax0.axvline(fit_mean, label=fit_label + " Fit", ls="-") ax0.axvline(pnt.to(u.deg).value, label=fit_label + " Pnt", ls="--") ax0.set_xlim([min(x), max(x)]) ax1.set_xlabel(fit_label) ax0.set_ylabel("Counts") ax1.set_ylabel("Residual (cts)") ax0.legend() ax1.legend() plt.savefig(os.path.join(outdir, "Feed{}_chan{}.png".format(feed, nch))) plt.close(fig) fig = plt.figure("Fit information - temperature") gs = GridSpec(2, 1, height_ratios=(3, 1)) ax0 = plt.subplot(gs[0]) ax1 = plt.subplot(gs[1], sharex=ax0) ax0.plot(x, temperature, label="Data") ax0.plot(x, temperature_model['Bell'](x), label="Fit") ax1.plot(x, temperature - temperature_model['Bell'](x)) ax0.axvline(pnt.to(u.deg).value, label=fit_label + " Pnt", ls="--") ax0.set_xlim([min(x), max(x)]) ax1.set_xlabel(fit_label) ax0.set_ylabel("Counts") ax1.set_ylabel("Residual (cts)") plt.legend() plt.savefig(os.path.join(outdir, "Feed{}_chan{}_temp.png".format(feed, nch))) plt.close(fig) return True, rows class CalibratorTable(Table): """Table composed of fitted and tabulated fluxes.""" def __init__(self, *args, **kwargs): """Initialize the object.""" Table.__init__(self, *args, **kwargs) self.calibration_coeffs = {} self.calibration_uncerts = {} self.calibration = {} names = ["Dir", "File", "Scan Type", "Source", "Chan", "Feed", "Time", "Frequency", "Bandwidth", "Counts", "Counts Err", "Width", "Width Err", "Flux", "Flux Err", "Elevation", "Azimuth", "Source_temperature", "Flux/Counts", "Flux/Counts Err", "Flux Integral/Counts", "Flux Integral/Counts Err", "Calculated Flux", "Calculated Flux Err", "RA", "Dec", "Fit RA", "Fit Dec", "RA err", "Dec err", "Skewness", "Kurtosis"] dtype = ['S200', 'S200', 'S200', 'S200', 'S200', np.int, np.double, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float, np.float] for n, d in zip(names, dtype): if n not in self.keys(): self.add_column(Column(name=n, dtype=d)) def from_scans(self, scan_list=None, debug=False, freqsplat=None, config_file=None, nofilt=False, plot=False): """Load source table from a list of scans. For each scan, a fit is performed. Since we are assuming point-like sources here, the fit is a Gaussian plus a slope. The centroid, width and amplitude of the fit fill out new rows of the CalibratorTable ('Fit RA' or 'Fit Dec', 'Width' and 'Counts' respectively). Parameters ---------- scan_list : list of str List of files containing cross scans to be fitted config_file : str File containing the configuration (list of directories etc.) Other parameters ---------------- debug : bool Throw debug information freqsplat : str List of frequencies to be merged into one. See :func:`srttools.scan.interpret_frequency_range` nofilt : bool Do not filter the noisy channels of the scan. See :class:`srttools.scan.clean_scan_using_variability` plot : bool Plot diagnostic plots? Default False, True if debug is True. Returns ------- retval : bool True if at least one scan was correctly processed See Also -------- srttools.scan.interpret_frequency_range """ if debug is True: plot = True if scan_list is None: if config_file is None: config_file = get_config_file() config = read_config(config_file) scan_list = \ list_scans(config['datadir'], config['list_of_directories']) + \ list_scans(config['datadir'], config['calibrator_directories']) scan_list.sort() nscan = len(scan_list) out_retval = False for i_s, s in enumerate(scan_list): log.info('{}/{}: Loading {}'.format(i_s + 1, nscan, s)) retval, rows = _treat_scan(s, plot=plot, debug=debug, freqsplat=freqsplat, nofilt=nofilt) if retval: out_retval = True for r in rows: self.add_row(r) return out_retval def write(self, fname, *args, **kwargs): """Same as Table.write, but adds path information for HDF5.""" if fname.endswith('.hdf5'): super(CalibratorTable, self).write(fname, *args, **kwargs) else: super(CalibratorTable, self).write(fname, *args, **kwargs) def check_not_empty(self): """Check that table is not empty. Returns ------- good : bool True if all checks pass, False otherwise. """ if len(self["Flux/Counts"]) == 0: warnings.warn("The calibrator table is empty!") return False return True def check_up_to_date(self): """Check that the calibration information is up to date. Returns ------- good : bool True if all checks pass, False otherwise. """ if not self.check_not_empty(): return False if np.any(self["Flux/Counts"] == 0): warnings.warn("The calibrator table needs an update!") self.update() return True def update(self): """Update the calibration information. Execute ``get_fluxes``, ``calibrate`` and ``compute_conversion_function`` """ if not self.check_not_empty(): return self.get_fluxes() self.calibrate() self.compute_conversion_function() def get_fluxes(self): """Get the tabulated flux of the source, if listed as calibrators. Updates the table. """ if not self.check_not_empty(): return for it, t in enumerate(self['Time']): source = self['Source'][it] frequency = self['Frequency'][it] / 1000 bandwidth = self['Bandwidth'][it] / 1000 flux, eflux = \ _get_calibrator_flux(source, frequency, bandwidth, time=t) self['Flux'][it] = flux self['Flux Err'][it] = eflux def calibrate(self): """Calculate the calibration constants. The following conversion functions are calculated for each tabulated cross scan belonging to a calibrator: + 'Flux/Counts' and 'Flux/Counts Err': Tabulated flux density divided by the _height_ of the fitted Gaussian. This is used, e.g. to calibrate images in Jy/beam, as it calibrates the local amplitude to the flux density + 'Flux Integral/Counts' and 'Flux Integral/Counts Err': Tabulated flux density divided by the _volume_ of the 2D Gaussian corresponding to the fitted cross scans, assuming a symmetrical beam (which is generally not the case, but a good approximation). This is used, e.g., to perform the calibration in Jy/pixel: Each pixel will be normalized to the expected total flux in the corresponding pixel area See Also -------- srttools.calibration.CalibratorTable.from_scans """ if not self.check_not_empty(): return flux = self['Flux'] * u.Jy eflux = self['Flux Err'] * u.Jy counts = self['Counts'] * u.ct ecounts = self['Counts Err'] * u.ct width = np.radians(self['Width']) * u.radian ewidth = np.radians(self['Width Err']) * u.radian # Volume in a beam: For a 2-d Gaussian with amplitude A and sigmas sx # and sy, this is 2 pi A sx sy. total = 2 * np.pi * counts * width ** 2 etotal = 2 * np.pi * ecounts * width ** 2 flux_integral_over_counts = flux / total flux_integral_over_counts_err = \ (etotal / total + eflux / flux + 2 * ewidth / width) * flux_integral_over_counts flux_over_counts = flux / counts flux_over_counts_err = \ (ecounts / counts + eflux / flux) * flux_over_counts self['Flux/Counts'][:] = \ flux_over_counts.to(u.Jy / u.ct).value self['Flux/Counts Err'][:] = \ flux_over_counts_err.to(u.Jy / u.ct).value self['Flux Integral/Counts'][:] = \ flux_integral_over_counts.to(u.Jy / u.ct / u.steradian).value self['Flux Integral/Counts Err'][:] = \ flux_integral_over_counts_err.to(u.Jy / u.ct / u.steradian).value def compute_conversion_function(self, map_unit="Jy/beam", good_mask=None): """Compute the conversion between Jy and counts. Try to get a meaningful second-degree polynomial fit over elevation. Revert to the rough function :func:`Jy_over_counts_rough` in case ``statsmodels`` is not installed. In this latter case, only the baseline value is given for flux conversion and error. These values are saved in the ``calibration_coeffs`` and ``calibration_uncerts`` attributes of ``CalibratorTable``, and a dictionary called ``calibration`` is also created. For each channel, this dictionary contains either None or an object. This object is the output of a ``fit`` procedure in ``statsmodels``. The method object.predict(X) returns the calibration corresponding to elevation X. """ if not HAS_STATSM: channels = list(set(self["Chan"])) for channel in channels: fc, fce = self.Jy_over_counts_rough(channel=channel, map_unit=map_unit, good_mask=None) self.calibration_coeffs[channel] = [fc, 0, 0] self.calibration_uncerts[channel] = \ [fce, 0, 0] self.calibration[channel] = None return else: import statsmodels.api as sm if good_mask is None: good_mask = self['Flux'] > 0 flux_quantity = _get_flux_quantity(map_unit) channels = list(set(self["Chan"])) for channel in channels: good_chans = (self["Chan"] == channel) & good_mask f_c_ratio = self[flux_quantity + "/Counts"][good_chans] f_c_ratio_err = self[flux_quantity + "/Counts Err"][good_chans] elvs = np.radians(self["Elevation"][good_chans]) good_fc = (f_c_ratio == f_c_ratio) & (f_c_ratio > 0) good_fce = (f_c_ratio_err == f_c_ratio_err) & (f_c_ratio_err >= 0) good = good_fc & good_fce x_to_fit = np.array(elvs[good]) y_to_fit = np.array(f_c_ratio[good]) ye_to_fit = np.array(f_c_ratio_err[good]) order = np.argsort(x_to_fit) x_to_fit = x_to_fit[order] y_to_fit = y_to_fit[order] ye_to_fit = ye_to_fit[order] X = np.column_stack((np.ones(len(x_to_fit)), x_to_fit)) # X = np.c_[np.ones(len(x_to_fit)), X] # X = sm.add_constant(X) model = sm.RLM(y_to_fit, X, missing='drop') results = model.fit() self.calibration_coeffs[channel] = results.params self.calibration_uncerts[channel] = \ results.cov_params().diagonal()**0.5 self.calibration[channel] = results def Jy_over_counts(self, channel=None, elevation=None, map_unit="Jy/beam", good_mask=None): """Compute the Jy/Counts conversion corresponding to a given map unit. Parameters ---------- channel : str Channel name (e.g. 'Feed0_RCP', 'Feed0_LCP' etc.) elevation : float or array-like The elevation or a list of elevations map_unit : str A valid unit for the calibrated map (See the keys of FLUX_QUANTITIES) good_mask : array of bools, default None This mask can be used to specify the valid entries of the table. If None, the mask is set to an array of True values Returns ------- fc : float or array-like One conversion value for each elevation fce : float or array-like the uncertainties corresponding to each ``fc`` """ rough = False if not HAS_STATSM: rough = True if good_mask is None: good_mask = self['Flux'] > 0 flux_quantity = _get_flux_quantity(map_unit) if channel not in self.calibration.keys(): self.compute_conversion_function(map_unit, good_mask=good_mask) if elevation is None or rough is True or channel is None: elevation = np.array(elevation) fc, fce = self.Jy_over_counts_rough(channel=channel, map_unit=map_unit, good_mask=good_mask) if elevation.size > 1: fc = np.zeros_like(elevation) + fc fce = np.zeros_like(elevation) + fce return fc, fce X = np.column_stack((np.ones(np.array(elevation).size), np.array(elevation))) fc = self.calibration[channel].predict(X) goodch = self["Chan"] == channel good = good_mask & goodch fce = np.sqrt(np.mean( self[flux_quantity + "/Counts Err"][good]**2)) + np.zeros_like(fc) if len(fc) == 1: fc, fce = fc[0], fce[0] return fc, fce def Jy_over_counts_rough(self, channel=None, map_unit="Jy/beam", good_mask=None): """Get the conversion from counts to Jy. Other parameters ---------------- channel : str Name of the data channel map_unit : str A valid unit for the calibrated map (See the keys of FLUX_QUANTITIES) good_mask : array of bools, default None This mask can be used to specify the valid entries of the table. If None, the mask is set to an array of True values Returns ------- fc : float flux density /count ratio fce : float uncertainty on ``fc`` """ self.check_up_to_date() flux_quantity = _get_flux_quantity(map_unit) if good_mask is None: good_mask = self['Flux'] > 0 good_chans = np.ones(len(self["Time"]), dtype=bool) if channel is not None: good_chans = self['Chan'] == channel good_chans = good_chans & good_mask f_c_ratio = self[flux_quantity + "/Counts"][good_chans] f_c_ratio_err = self[flux_quantity + "/Counts Err"][good_chans] times = self["Time"][good_chans] good_fc = (f_c_ratio == f_c_ratio) & (f_c_ratio > 0) good_fce = (f_c_ratio_err == f_c_ratio_err) & (f_c_ratio_err >= 0) good = good_fc & good_fce x_to_fit = np.array(times[good]) y_to_fit = np.array(f_c_ratio[good]) ye_to_fit = np.array(f_c_ratio_err[good]) p = [np.median(y_to_fit)] pcov = np.array([[np.median(ye_to_fit)**2]]) first = True print(x_to_fit, y_to_fit, ye_to_fit) while 1: bad = np.abs((y_to_fit - _constant(x_to_fit, p)) / ye_to_fit) > 5 if not np.any(bad) and not first: break if len(x_to_fit[bad]) > len(x_to_fit) - 5: warnings.warn("Calibration fit is shaky") break xbad = x_to_fit[bad] ybad = y_to_fit[bad] for xb, yb in zip(xbad, ybad): log.warning("Outliers: {}, {}".format(xb, yb)) good = np.logical_not(bad) x_to_fit = x_to_fit[good] y_to_fit = y_to_fit[good] ye_to_fit = ye_to_fit[good] p, pcov = curve_fit(_constant, x_to_fit, y_to_fit, sigma=ye_to_fit, p0=p) first = False fc = p[0] fce = np.sqrt(pcov[0, 0]) return fc, fce def calculate_src_flux(self, channel=None, map_unit="Jy/beam", source=None): """Calculate source flux and error, pointing by pointing. Uses the conversion factors calculated from the tabulated fluxes for all sources but the current, and the fitted Gaussian amplitude for the current source. Updates the calibrator table and returns the average flux Parameters ---------- channel : str or list of str Data channel map_unit : str Units in the map (default Jy/beam) source : str Source name. Must match one of the sources in the table. Default Returns ------- mean_flux : array of floats Array with as many channels as the input ones mean_flux_err : array of floats Uncertainties corresponding to mean_flux """ if source is None: good_source = np.ones_like(self['Flux'], dtype=bool) else: good_source = self['Source'] == source non_source = np.logical_not(good_source) if channel is None: channels = [s for s in set(self['Chan'])] else: channels = [channel] mean_flux = [] mean_flux_err = [] for channel in channels: good_chan = self['Chan'] == channel good = good_source & good_chan elevation = np.radians(self['Elevation'][good]) fc, fce = self.Jy_over_counts(channel=channel, elevation=elevation, map_unit=map_unit, good_mask=non_source) calculated_flux = copy.deepcopy(self['Calculated Flux']) calculated_flux_err = copy.deepcopy(self['Calculated Flux Err']) counts = np.array(self['Counts']) counts_err = np.array(self['Counts Err']) calculated_flux[good] = counts[good] * fc calculated_flux_err[good] = \ (counts_err[good] / counts[good] + fce / fc) * \ calculated_flux[good] self['Calculated Flux'][:] = calculated_flux self['Calculated Flux Err'][:] = calculated_flux_err mean_flux.append(np.mean(calculated_flux[good])) mean_flux_err.append( np.sqrt(np.mean(calculated_flux_err[good] ** 2))) return mean_flux, mean_flux_err def check_consistency(self, channel=None, epsilon=0.05): """Check the consistency of calculated and fitted flux densities. For each source in the ``srttools``' calibrator list, use ``calculate_src_flux`` to calculate the source flux ignoring the tabulated value, and compare the calculated and tabulated values. Returns ------- retval : bool True if, for all calibrators, the tabulated and calculated values of the flux are consistent. False otherwise. """ is_cal = (~np.isnan(self['Flux']))&(self['Flux'] > 0) calibrators = list(set(self['Source'][is_cal])) for cal in calibrators: self.calculate_src_flux(channel=channel, source=cal) if channel is None: good_chan = np.ones_like(self['Chan'], dtype=bool) else: good_chan = self['Chan'] == channel calc_fluxes = self['Calculated Flux'][is_cal & good_chan] biblio_fluxes = self['Flux'][is_cal & good_chan] names = self['Source'][is_cal & good_chan] times = self['Time'][is_cal & good_chan] consistent = \ np.abs(biblio_fluxes - calc_fluxes) < epsilon * biblio_fluxes for n, t, b, c, cons, in zip( names, times, biblio_fluxes, calc_fluxes, consistent): if not cons: warnings.warn("{}, MJD {}: Expected {}, " "measured {}".format(n, t, b, c)) return consistent def beam_width(self, channel=None): """Calculate the (weighted) mean beam width, in radians. Checks for invalid (nan and such) values. """ goodch = np.ones(len(self), dtype=bool) if channel is not None: goodch = self['Chan'] == channel allwidths = self[goodch]['Width'] allwidth_errs = self[goodch]['Width Err'] good = (allwidth_errs > 0) & (allwidth_errs == allwidth_errs) allwidths = allwidths[good] allwidth_errs = allwidth_errs[good] # Weighted mean width =

np.sum(allwidths/allwidth_errs)

numpy.sum

# General Packages from math import atan2, degrees from datetime import datetime from pathlib import Path import time import pprint import numpy as np import pandas as pd import pickle # Plotting import matplotlib.pyplot as plt import matplotlib.ticker as mtick from matplotlib.dates import date2num import seaborn as sns # Scaling from sklearn.preprocessing import StandardScaler settings = { # # audit settings 'data_name': 'credit', 'method_name': 'logreg', 'normalize_data': True, 'force_rational_actions': False, # # script flags 'audit_recourse': True, 'plot_audits': True, 'print_flag': True, 'save_flag': True, 'randomseed': 2338, # # placeholders 'method_suffixes': [''], 'audit_suffixes': [''], } # Paths repo_dir = Path(__file__).absolute().parent.parent paper_dir = repo_dir / 'paper/' # directory containing paper related info data_dir = paper_dir / 'data/' # directory containing data files results_dir = paper_dir / 'results/' # directory containing results # create directories that don't exist for d in [data_dir, results_dir]: d.mkdir(exist_ok = True) # Formatting Options np.set_printoptions(precision = 4, suppress = False) pd.set_option('display.max_columns', 30) pd.options.mode.chained_assignment = None pp = pprint.PrettyPrinter(indent = 4) # Plotting Settings sns.set(style="white", palette="muted", color_codes = True) plt.rcParams['font.size'] = 20 plt.rcParams['axes.labelsize'] = 24 plt.rcParams['axes.spines.top'] = False plt.rcParams['axes.spines.right'] = False plt.rcParams['xtick.labelsize'] = 20 plt.rcParams['ytick.labelsize'] = 20 plt.rc('legend', fontsize = 20) # file names output_dir = results_dir / settings['data_name'] output_dir.mkdir(exist_ok = True) if settings['normalize_data']: settings['method_suffixes'].append('normalized') if settings['force_rational_actions']: settings['audit_suffixes'].append('rational') # set file header settings['dataset_file'] = '%s/%s_processed.csv' % (data_dir, settings['data_name']) settings['file_header'] = '%s/%s_%s%s' % (output_dir, settings['data_name'], settings['method_name'], '_'.join(settings['method_suffixes'])) settings['audit_file_header'] = '%s%s' % (settings['file_header'], '_'.join(settings['audit_suffixes'])) settings['model_file'] = '%s_models.pkl' % settings['file_header'] settings['audit_file'] = '%s_audit_results.pkl' % settings['audit_file_header'] # Recourse Objects from recourse.action_set import ActionSet from recourse.builder import RecourseBuilder from recourse.auditor import RecourseAuditor from recourse.flipset import Flipset ### Helper Functions for Experimental Script def load_data(): """Helper function to load in data, and output that and optionally a scaler object: Output: data: dict with the following fields outcome_name: Name of the outcome variable (inferred as the first column.) variable_names: A list of names indicating input columns. X: The input features for our model. y: The column of the dataframe indicating our outcome variable. scaler: The sklearn StandardScaler used to normalize the dataset, if we wish to scale. X_scaled: Scaled version of X, if we wish to scale X_train: The training set: set to the whole dataset if not scaled. Set to X_scaled if we do scale. scaler: Object used to scale data. If "scale" is set to None, then this is returned as None. """ # data set data_df = pd.read_csv(settings['dataset_file']) data = { 'outcome_name': data_df.columns[0], 'variable_names': data_df.columns[1:].tolist(), 'X': data_df.iloc[:, 1:], 'y': data_df.iloc[:, 0] } scaler = None data['X_train'] = data['X'] data['scaler'] = None if settings['normalize_data']: from sklearn.preprocessing import StandardScaler scaler = StandardScaler(copy=True, with_mean=True, with_std=True) data['X_scaled'] = pd.DataFrame(scaler.fit_transform(data['X'].to_numpy(dtype=float), data['y'].values), columns=data['X'].columns) data['X_train'] = data['X_scaled'] data['scaler'] = scaler return data, scaler def undo_coefficient_scaling(clf = None, coefficients = None, intercept = 0.0, scaler = None): """ given coefficients and data for scaled data, returns coefficients and intercept for unnormalized data w = w_scaled / sigma b = b_scaled - (w_scaled / sigma).dot(mu) = b_scaled - w.dot(mu) :param sklearn linear classifier :param coefficients: vector of coefficients :param intercept: scalar for the intercept function :param scaler: sklearn.Scaler or :return: coefficients and intercept for unnormalized data """ if coefficients is None: assert clf is not None assert intercept == 0.0 assert hasattr(clf, 'coef_') coefficients = clf.coef_ intercept = clf.intercept_ if hasattr(clf, 'intercept_') else 0.0 if scaler is None: w = np.array(coefficients) b = float(intercept) else: isinstance(scaler, StandardScaler) x_shift = np.array(scaler.mean_) x_scale = np.sqrt(scaler.var_) w = coefficients / x_scale b = intercept -

np.dot(w, x_shift)

numpy.dot

import zipfile import os import PIL.Image from typing import List, Tuple, Dict import numpy as np from deep500.utils.download import real_download, unrar from deep500.lv2.dataset import FileListDataset from deep500.utils.onnx_interop.losses import SoftmaxCrossEntropy # Optionally import PyAV try: import av except (ImportError, ModuleNotFoundError) as ex: av = None def ucf101_shape(): return (101, None, 3, 240, 320) def ucf101_loss(): return SoftmaxCrossEntropy def download_ucf101_and_get_file_paths(folder='', split='01'): """ Download ucf101 from University of Central Florida The archive contains the videos of different action classes :return: paths to different files """ base_url = "https://www.crcv.ucf.edu/data/UCF101/" filenames = [('ucf101', 'UCF101.rar'), ('ucf101_split','UCF101TrainTestSplits-RecognitionTask.zip')] sub_folder = '/ucf101' local_files = real_download(base_url, filenames, sub_folder, output_dir=folder) files = unrar(local_files['ucf101']) zip = zipfile.ZipFile(local_files['ucf101_split']) path = os.path.dirname(os.path.abspath(local_files['ucf101']))+'/UCF-101/' train_files = [] with zip.open('ucfTrainTestlist/trainlist{}.txt'.format(split)) as file_split: for line in file_split: file = path + bytes.decode(line.split()[0]) if file in files: train_files.append(file) test_files = [] with zip.open('ucfTrainTestlist/testlist{}.txt'.format(split)) as file_split: for line in file_split: file = path + bytes.decode(line.strip()) if file in files: test_files.append(file) label_list = {} with zip.open('ucfTrainTestlist/classInd.txt') as labels: for line in labels: line = bytes.decode(line.strip()) label = line.split()[1] idx = int(line.split()[0]) - 1 label_list[label] = idx return train_files, test_files, label_list ucf101_mean = (0.39607886, 0.37930175, 0.351559) ucf101_std = (0.28261574, 0.27613039, 0.28061599) class ucf101_loader(): def __init__(self, normalize=True, max_length=1777, skip_frames=10): if av is None: raise ImportError('Cannot load ucf101 videos without PyAV. Please see ' 'https://github.com/mikeboers/PyAV for installation instructions.') self.normalize = normalize self.max_length = max_length self.skip_frames = skip_frames def _video_loader(self, video_path): container = av.open(video_path) container.streams.video[0].thread_type = 'AUTO' _data = [frame.to_ndarray(format='rgb24') for frame in container.decode(video=0)] if _data[0].shape != (240, 320, 3): _data = [np.array(PIL.Image.fromarray(img, 'RGB').resize((320,240))) for img in _data] _data = np.asarray(_data, dtype=np.float32) if self.normalize: for ch in range(3): _data[:,:,:,ch] -= ucf101_mean[ch] _data[:,:,:,ch] /= ucf101_std[ch] return _data def __call__(self, data_path): #load multiple videos if type(data_path) is np.ndarray: data = [self._video_loader(path) for path in data_path] max_frames = max([x.shape[0] for x in data]) data = [np.pad(x, ((max_frames-x.shape[0],0), (0,0), (0,0), (0,0)), 'constant') for x in data] data = np.vstack([

np.expand_dims(x, axis=0)

numpy.expand_dims

#!/home/daniel/anaconda3/bin/python # -*- coding: utf-8 -*- """ ================================================ main_extract_trt ================================================ This program extracts individual TRT cell data from the original files and puts it in a separate file for each cell """ # Author: fvj # License: BSD 3 clause import datetime import argparse import atexit import os import numpy as np from pyrad.io import get_trtfile_list, read_trt_data, write_trt_cell_data print(__doc__) def main(): """ """ # parse the arguments parser = argparse.ArgumentParser( description='Entry to Pyrad processing framework') # positional arguments parser.add_argument( 'start_times', type=str, help=('Start times of the data to process. Format YYYYMMDDhhmmss.' + 'Coma separated')) parser.add_argument( 'end_times', type=str, help=('End times of the data to process. Format YYYYMMDDhhmmss.' + 'Coma separated')) # keyword arguments parser.add_argument( '--raw_trtbase', type=str, default='/store/msrad/radar/rad4alp/TRT/', help='name of folder containing the TRT cell data') parser.add_argument( '--proc_trtbase', type=str, default='/store/msrad/radar/trt/', help='name of folder containing the TRT cell data') parser.add_argument( '--nsteps_min', type=int, default=3, help=('Minimum number of time steps to consider the TRT cell ' + 'worth processing')) args = parser.parse_args() print("====== TRT cell extraction started: %s" % datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")) atexit.register(_print_end_msg, "====== TRT cell extraction finished: ") start_time_list = args.start_times.split(',') end_time_list = args.end_times.split(',') for i, start_time_str in enumerate(start_time_list): end_time_str = end_time_list[i] starttime = datetime.datetime.strptime(start_time_str, '%Y%m%d%H%M%S') endtime = datetime.datetime.strptime(end_time_str, '%Y%m%d%H%M%S') data_input_path = ( args.raw_trtbase+starttime.strftime('%y%j/TRTC%y%j/')) data_output_path = ( args.proc_trtbase+starttime.strftime('%Y-%m-%d')+'/TRTC_cell/') if not os.path.isdir(data_output_path): os.makedirs(data_output_path) flist = get_trtfile_list(data_input_path, starttime, endtime) if flist is None: continue traj_ID =

np.array([], dtype=int)

numpy.array

import numpy as np import pandas as pd import itertools import sys import math import Constant class Parameter: USER_NUM_CONST = 40 CAPACITY_CONST = 10 def __init__(self, seed): np.random.seed(seed) self.USER_NUM = Parameter.USER_NUM_CONST self.CAPACITY = Parameter.CAPACITY_CONST df_server = pd.read_csv("../../network/kanto.csv") self.SERVER_NUM = len(df_server) def create_input(self): self.e_u = list(itertools.product(range(self.USER_NUM), range(self.SERVER_NUM))) self.e_s = list(itertools.combinations(list(range(0, self.SERVER_NUM)), 2)) self.m_s = np.full(self.SERVER_NUM, self.CAPACITY) df_server = pd.read_csv("../../network/kanto.csv") self.d_st = self.get_d_st(df_server) self.d_us = self.get_d_us(df_server) def get_d_st(self, df): d_st = [] for link in self.e_s: city_1, city_2 = link[0], link[1] x_1, y_1 = df.iloc[city_1]["latitude"], df.iloc[city_1]["longitude"] x_2, y_2 = df.iloc[city_2]["latitude"], df.iloc[city_2]["longitude"] d_st.append(Parameter.get_distance(x_1, y_1, x_2, y_2)) return np.array(d_st) def get_lower_and_upper(df, col): return df[col].min() - 0.3, df[col].max() + 0.3 def get_d_us(self, df_server): # range lati_lower, lati_upper = Parameter.get_lower_and_upper(df_server, "latitude") longi_lower, longi_upper = Parameter.get_lower_and_upper(df_server, "longitude") # create users location lati_array = (lati_upper - lati_lower) *

np.random.rand(self.USER_NUM)

numpy.random.rand

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Feb 18 12:05:56 2018 @author: fabrizio """ import os import os.path import numpy as np import sys from prody import execDSSP from Amino import Amino as aminoacido from Protein import Protein as proteina from DSSPData import DSSPData as DSSP from keras.utils import to_categorical class PdbParser: def __init__(self): #default folders with pdbs and (saved) DSSPs: self.pdbdir='data/cullpdb/pdbs/' self.dsspdir='data/cullpdb/dssp/' if not os.path.exists(self.dsspdir): os.makedirs(self.dsspdir) def convertSomePDBtoDSSP(self,quantity): #converts a given number of pdb to DSSP for root, dirs, files in os.walk(self.pdbdir): for filename in files: quantity-=1 if(quantity<0): break print(quantity,' )',filename,' converted to dssp') # here uses from prody import execDSSP execDSSP(self.pdbdir+filename,outputdir=self.dsspdir) break def convertiSinglePDBtoDSSP(self,file): # con estensione if(os.path.isfile(self.pdbdir+file)): execDSSP(self.pdbdir+file,outputdir=self.dsspdir) else: print("File "+self.pdbdir+file+" not found..\n") def getDSSPInfo(self,file): # for debug if(os.path.isfile(self.dsspdir+file)): print('-'*7,'DSSP ',file,'-'*7) dsspData = DSSP() dsspData.parseDSSP(self.dsspdir+file) dsspACC = np.array(dsspData.getACC()) print('ACC: ',dsspACC) print(dsspACC.shape) getAAs = np.array(dsspData.getAAs()) print('ACC: ',getAAs) print(getAAs.shape) if(dsspACC.shape[0]>700): return 0 else: return 1 else: print('dssp not found..searching in pdbs folder') self.convertiSinglePDBtoDSSP(file.replace('dssp','pdb')) if(os.path.isfile(self.dsspdir+file)): self.getDSSPInfo(file) else: print('file not found: ',file) def getAllDSSPInfo(self,c=8000): count=0 for root, dirs, files in os.walk(self.dsspdir): for filename in files: #count += self.getDSSPInfo(filename) dssps = self.extractSSfromDSSP(filename) print([x[1] for x in dssps]) count +=1 if(count>=c): break break print('count: ',count) def convertDSSPtoSample(self,file): if(os.path.isfile(self.dsspdir+file)): #print('-'*7,'CONVERTING DSSP ',file,' TO SAMPLE','-'*7) primaryArray=['A', 'C', 'E', 'D', 'G', 'F', 'I', 'H', 'K', 'M', 'L', 'N', 'Q', 'P', 'S', 'R', 'T', 'W', 'V', 'Y', 'X'] secondaryArray=['L', 'B', 'E', 'G', 'I', 'H', 'S', 'T'] dssp = self.extractSSfromDSSP(file) dsspData = DSSP() dsspData.parseDSSP(self.dsspdir+file) dsspPrimary = np.array(dsspData.getAAs()) dsspSecondary =

np.array([x[1] for x in dssp])

numpy.array

"""deals with data for project""" import re import json import os import sys import math import random import tarfile import numpy as np import pandas as pd from PIL import Image from filecmp import dircmp from collections import deque from Reco3D.third_party import binvox_rw from Reco3D.lib import utils, dataset from sklearn import model_selection from keras.utils import to_categorical from numpy.random import randint, permutation, shuffle from natsort import natsorted from skimage.transform import resize import boto3 def load_obj_id(obj_id): data_path, label_path = id_to_path(obj_id) return load_imgs_from_dir(data_path), np.squeeze(load_voxs_from_dir(label_path)) def id_to_path(obj_id, data_dir="./data/ShapeNetRendering/", label_dir="./data/ShapeNetVox32/"): regex = re.search("(.*)_(.*)", obj_id) ret_1 = os.path.join(data_dir, regex.group(1), regex.group(2)) ret_2 = os.path.join(label_dir, regex.group(1), regex.group(2)) return ret_1, ret_2 def resize_img(img): max_size = max(img.size) ratio = 137/max_size size = tuple([int(x*ratio) for x in img.size]) img.thumbnail(size,Image.ANTIALIAS) # loading functions def load_img(img_path): img = Image.open(img_path) if img.size != (137, 137, 137): resize_img(img) return np.array(img) def load_vox(vox_path): with open(vox_path, 'rb') as f: voxel = binvox_rw.read_as_3d_array(f).data #if False: #if np.shape(voxel) != (32, 32, 32): # voxel = resize(voxel, (32, 32, 32), anti_aliasing=True,anti_aliasing_sigma=0.01)>0 return to_categorical(voxel) def load_imgs(img_path_list): assert(isinstance(img_path_list, (list, np.ndarray))) ret = [] for p in img_path_list: ret.append(load_img(p)) return np.stack(ret) def load_voxs(vox_path_list): assert(isinstance(vox_path_list, (list, np.ndarray))) ret = [] for p in vox_path_list: ret.append(load_vox(p)) return np.stack(ret) def load_imgs_from_dir(img_dir): img_path_list = construct_file_path_list_from_dir(img_dir, [".png"]) return load_imgs(img_path_list) def load_voxs_from_dir(vox_dir): vox_path_list = construct_file_path_list_from_dir(vox_dir, [".binvox"]) return load_voxs(vox_path_list) # # dataset loading functions def load_data(data_samples): if isinstance(data_samples, str): data_samples = [data_samples] return load_imgs(data_samples) def load_label(label_samples): if isinstance(label_samples, str): label_samples = [label_samples] return np.squeeze(load_voxs(label_samples)) def load_data_Pix3D(): ''' Read Pix3D data. This dataset includes real imageas and binvox''' data_path = utils.read_params()['DIRS']['DATA_PIX3D'] data_all = sorted(construct_file_path_list_from_dir(data_path, ['_x.npy'])) label_all = sorted(construct_file_path_list_from_dir(data_path, ['_y.npy'])) return np.array(data_all), np.array(label_all) def load_random_data_Pix3D(): data, label = load_data_Pix3D() while True: i = np.random.randint(0, len(data)) data_np, label_np = np.load(data[i]), np.load(label[i]) if data_np.shape[-1] == 3: return data_np, label_np # load preprocessed data and labels def load_preprocessed_dataset(): data_preprocessed_dir = utils.read_params( )["DIRS"]["DATA_PREPROCESSED"] data_all = sorted( dataset.construct_file_path_list_from_dir(data_preprocessed_dir, ["_x.npy"])) label_all = sorted( dataset.construct_file_path_list_from_dir(data_preprocessed_dir, ["_y.npy"])) return np.array(data_all), np.array(label_all) def load_random_sample(): data, label = load_preprocessed_dataset() i = randint(0, len(data)) return np.load(data[i]), np.load(label[i]) def load_testset(model_dir): try: X_test = np.load( "{}/X_test.npy".format(model_dir)) y_test = np.load( "{}/y_test.npy".format(model_dir)) except: model_dir = os.path.dirname(model_dir) X_test = np.load( "{}/X_test.npy".format(model_dir)) y_test = np.load( "{}/y_test.npy".format(model_dir)) return X_test, y_test def shuffle_batchs(data, label, batch_size): # print(data, label, batch_size) assert(len(data) == len(label)) num_of_batches = math.ceil(len(data)/batch_size) perm = permutation(len(data)) data_batchs = np.array_split(data[perm], num_of_batches) label_batchs = np.array_split(label[perm], num_of_batches) return deque(data_batchs), deque(label_batchs) def train_val_test_split(data, label, split=0.1): # split into training and test set X_train, X_test, y_train, y_test = model_selection.train_test_split( data, label, test_size=split) # shuffled # split of validation set X_train, X_val, y_train, y_val = model_selection.train_test_split( X_train, y_train, test_size=split) # shuffled return X_train, y_train, X_val, y_val, X_test, y_test def setup_dir(): params = utils.read_params() DIR = params["DIRS"] for d in DIR.values(): if 'ikea' in d: continue utils.make_dir(d) def construct_file_path_list_from_dir(dir, file_filter): if isinstance(file_filter, str): file_filter = [file_filter] paths = [[] for _ in range(len(file_filter))] for root, _, files in os.walk(dir): for f_name in files: for i, f_substr in enumerate(file_filter): if f_substr in f_name: (paths[i]).append(root + '/' + f_name) for i, p in enumerate(paths): paths[i] = natsorted(p) if len(file_filter) == 1: return paths[0] return tuple(paths) def create_path_csv(data_dir, label_dir): print("creating path csv for {} and {}".format(data_dir, label_dir)) params = utils.read_params() common_paths = [] for dir_top, subdir_cmps in dircmp(data_dir, label_dir).subdirs.items(): for dir_bot in subdir_cmps.common_dirs: common_paths.append(os.path.join(dir_top, dir_bot)) mapping = pd.DataFrame(common_paths, columns=["common_dirs"]) mapping['data_dirs'] = mapping.apply( lambda data_row: os.path.join(data_dir, data_row.common_dirs), axis=1) mapping['label_dirs'] = mapping.apply( lambda data_row: os.path.join(label_dir, data_row.common_dirs), axis=1) table = [] for n, d, l in zip(common_paths, mapping.data_dirs, mapping.label_dirs): data_row = [os.path.dirname(n)+"_"+os.path.basename(n)] data_row += construct_file_path_list_from_dir(d, [".png"]) data_row += construct_file_path_list_from_dir(l, [".binvox"]) if not construct_file_path_list_from_dir(l, [".binvox"]): continue table.append(data_row) paths = pd.DataFrame(table) paths.to_csv("{}/paths.csv".format(params["DIRS"]["OUTPUT"])) return paths def download_from_link(link): download_folder = os.path.splitext(os.path.basename(link))[0] archive = download_folder + ".tgz" if not os.path.isfile(archive): os.system('wget -c {0}'.format(link)) os.system("tar -xvzf {0}".format(archive)) os.rename(download_folder, "data/{}".format(download_folder)) # os.system("rm -f {0}".format(archive)) def download_dataset(): LABEL_LINK = 'ftp://cs.stanford.edu/cs/cvgl/ShapeNetVox32.tgz' DATA_LINK = "ftp://cs.stanford.edu/cs/cvgl/ShapeNetRendering.tgz" if not os.path.isdir("data/ShapeNetVox32"): download_from_link(LABEL_LINK) if not os.path.isdir("data/ShapeNetRendering"): download_from_link(DATA_LINK) # download data from s3 bucket def download_from_s3_folder(s3_bucket='shapenetv1'): #s3_bucket_name = 'shapenetv1' #s3 = boto3.resource('s3') #print ("Downloading the data {} from s3 to {}".format("shapenetv1.tar", "./data")) #s3.meta.client.download_file(s3_bucket, 'data/shapenetv1.tar', './data/shapenetv1.tar') LINK = 'https://shapenetv1.s3-us-west-2.amazonaws.com/data/shapenetv1.tar' os.system('wget -c {0} -P ./data'.format(LINK)) def prepare_dataset(): archive = 'data/shapenetv1.tar' if not os.path.isfile(archive) and not os.path.isdir("data/ShapeNetVox32"): download_from_s3_folder() if not os.path.isdir("data/ShapeNetVox32") or not os.path.isdir('data/ShapeNetRendering') : os.system("tar -xvzf {0} -C ./data/".format(archive)) def preprocess_dataset(is_high_res=False): params = utils.read_params() dataset_size = params["DATASET_SIZE"] output_dir = params["DIRS"]["OUTPUT"] data_preprocessed_dir = params["DIRS"]["DATA_PREPROCESSED"] data_dir = params["DIRS"]["DATA"] if not os.path.isfile("{}/paths.csv".format(output_dir)): if is_high_res: dataset.create_path_csv( "{}/ShapeNetRendering".format(data_dir), "{}/ShapeNetVox64".format(data_dir)) else: dataset.create_path_csv( "{}/ShapeNetRendering".format(data_dir), "{}/ShapeNetVox32".format(data_dir)) path_list = pd.read_csv( "{}/paths.csv".format(output_dir), index_col=0).as_matrix() # randomly pick examples from dataset shuffle(path_list) if dataset_size <= 0 or dataset_size >= len(path_list): dataset_size = len(path_list) for i in range(dataset_size): model_name = path_list[i, 0] utils.to_npy('{}/{}_x'.format(data_preprocessed_dir, model_name), load_data(path_list[i, 1:-1])) utils.to_npy('{}/{}_y'.format(data_preprocessed_dir, model_name), load_label(path_list[i, -1])) def render_dataset(dataset_dir="ShapeNet", num_of_examples=None, render_count=24): print("[load_dataset] loading from {0}".format(dataset_dir)) pathlist_tuple = construct_file_path_list_from_dir( dataset_dir, ['.obj', '.mtl']) pathlist = pathlist_tuple[0] # DANGER, RANDOM pathlist = pathlist[:num_of_examples] if num_of_examples is not None else pathlist render_list = [] for mesh_path in pathlist: if not os.path.isfile(mesh_path): continue try: mesh_obj = trimesh.load_mesh(mesh_path) except: print("failed to load {}".format(mesh_path)) continue if isinstance(mesh_obj, list): compund_mesh = mesh_obj.pop(0) for m in mesh_obj: compund_mesh += m else: compund_mesh = mesh_obj render_dir = "./ShapeNet_Renders" renders = os.path.dirname( str.replace(mesh_path, dataset_dir, render_dir)) if os.path.isdir(renders) and os.listdir(renders) != []: render_list.append(load_imgs_from_dir(renders)) else: write_renders_to_disk(compund_mesh, renders, render_count) render_list.append(load_imgs_from_dir(renders)) return render_list def write_renders_to_disk(mesh, renders, render_count=10): print("[write_renders_to_disk] writing renders to {0} ... ".format( renders)) # FIXME: stupid but clean os.system("rm -rf {}".format(renders)) utils.make_dir(renders) scene = mesh.scene() for i in range(render_count): angle = math.radians(random.randint(15, 30)) axis = random.choice([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) rotate = trimesh.transformations.rotation_matrix( angle, axis, scene.centroid) camera_old, _geometry = scene.graph['camera'] camera_new =

np.dot(camera_old, rotate)

numpy.dot

## Copyright 2020 <NAME> ## Licensed under the Apache License, Version 2.0 (the "License"); ## you may not use this file except in compliance with the License. ## You may obtain a copy of the License at ## http://www.apache.org/licenses/LICENSE-2.0 ## Unless required by applicable law or agreed to in writing, software ## distributed under the License is distributed on an "AS IS" BASIS, ## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ## See the License for the specific language governing permissions and ## limitations under the License. ########################################################################### ########################################################################### import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import json class Validation: def __init__(self,config): self.config = config self.cut_off = config["cut_off"] self.data = {} self.result_dir = config["result_dir"] def validate(self,model,data): # model.to(device) criterion_bce = nn.BCELoss() # criterion = nn.BCEWithLogitsLoss() criterion_mse = nn.MSELoss() model.eval() correct = 0 # false_safe = 0 under_approx = 0 over_approx = 0 total = 0 metric_mse = [] metric_bce = [] for i in range(data.n_all_batches): state, safe = data.giveBatch(i) safe_model = model(state).view(-1) safe_model_max = (safe_model >= self.cut_off).type(torch.FloatTensor) metric_mse.append(criterion_mse(safe_model, safe).item()) metric_bce.append(criterion_bce(safe_model, safe).item()) total += safe.size(0) correct += (safe_model_max == safe).sum().item() under_approx += (safe_model_max < safe).sum().item() over_approx += (safe_model_max > safe).sum().item() print('\tMSE: %.4f, BCE: %.4f, Acc: %.4f, UnderApprox: %.4f, OverApprox: %.4f' % (np.mean(metric_mse), np.mean(metric_bce), correct / total, under_approx / total, over_approx / total)) self.data['full_set'] = [] self.data['full_set'].append({ 'acc': correct / total, 'under': under_approx / total, 'over': over_approx / total, 'total': total, 'correct': correct, 'mse': np.mean(metric_mse), 'bce': np.mean(metric_bce) }) def validateTest(self,model,data): criterion_bce = nn.BCELoss() criterion_mse = nn.MSELoss() model.eval() correct = 0 # false_safe = 0 under_approx = 0 over_approx = 0 total = 0 metric_mse = [] metric_bce = [] for i in range(data.n_train_batches,data.n_all_batches): state, safe = data.giveBatch(i) safe_model = model(state).view(-1) safe_model_max = (safe_model >= self.cut_off).type(torch.FloatTensor) metric_mse.append(criterion_mse(safe_model, safe).item()) metric_bce.append(criterion_bce(safe_model, safe).item()) total += safe.size(0) correct += (safe_model_max == safe).sum().item() under_approx += (safe_model_max < safe).sum().item() over_approx += (safe_model_max > safe).sum().item() print('\tMSE: %.4f, BCE: %.4f, Acc: %.4f, UnderApprox: %.4f, OverApprox: %.4f' % (np.mean(metric_mse),

np.mean(metric_bce)

numpy.mean

import os import folium # Importing explicitly the module from 'folium' - 'folium.plugins' import folium.plugins as plugins ''' *************************************** Generating Folium Base Map **************************************''' # Generating a 'Leaflet' map for the location in interest by passing through the coordinates # Calling the 'folium.folium.Map' object Site_Coord = [4.145825, 108.3035] m_folium = folium.Map(location = Site_Coord, zoom_start = 5) ''' *************************************** Adding Minimap onto Folium Base Map **************************************''' # Activating the 'folium.plugins' to include a minimap at the bottomright of the main map m_minimap_Batu_Kawan = plugins.MiniMap(toggle_display = True, width=200, height=200, zoom_level_fixed=None, minimized=True) m_folium.add_child(m_minimap_Batu_Kawan) ''' ***************************** Extracting G&P Geotechnics Project with Coordinates *******************************''' import pyexcel as pyex import numpy as np DATA = pyex.get_book(file_name = '2020_Gallery.xlsx') ''' ******************************************** 2008 **************************************************************''' # Data extraction for projects secured in year 2008 DATA_2008 = np.array(DATA.sheet_by_name('2008')) lat_2008 = np.ndarray.tolist(DATA_2008[1:,1]) lgn_2008 = np.ndarray.tolist(DATA_2008[1:,0]) pgn_2008 = np.ndarray.tolist(DATA_2008[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2008 = folium.FeatureGroup("2008 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2008, lgn_2008, pgn_2008): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2008.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='black',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2008) ''' ******************************************** 2009 **************************************************************''' # Data extraction for projects secured in year 2009 DATA_2009 = np.array(DATA.sheet_by_name('2009')) lat_2009 = np.ndarray.tolist(DATA_2009[1:,1]) lgn_2009 = np.ndarray.tolist(DATA_2009[1:,0]) pgn_2009 = np.ndarray.tolist(DATA_2009[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2009 = folium.FeatureGroup("2009 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2009, lgn_2009, pgn_2009): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2009.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='black',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2009) ''' ******************************************** 2010 **************************************************************''' # Data extraction for projects secured in year 2010 DATA_2010 = np.array(DATA.sheet_by_name('2010')) lat_2010 = np.ndarray.tolist(DATA_2010[1:,1]) lgn_2010 = np.ndarray.tolist(DATA_2010[1:,0]) pgn_2010 = np.ndarray.tolist(DATA_2010[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2010 = folium.FeatureGroup("2010 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2010, lgn_2010, pgn_2010): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2010.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='black',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2010) ''' ******************************************** 2011 **************************************************************''' # Data extraction for projects secured in year 2011 DATA_2011 = np.array(DATA.sheet_by_name('2011')) lat_2011 = np.ndarray.tolist(DATA_2011[1:,1]) lgn_2011 = np.ndarray.tolist(DATA_2011[1:,0]) pgn_2011 = np.ndarray.tolist(DATA_2011[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2011 = folium.FeatureGroup("2011 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2011, lgn_2011, pgn_2011): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2011.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='orange',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2011) ''' ******************************************** 2012 **************************************************************''' # Data extraction for projects secured in year 2012 DATA_2012 = np.array(DATA.sheet_by_name('2012')) lat_2012 = np.ndarray.tolist(DATA_2012[1:,1]) lgn_2012 = np.ndarray.tolist(DATA_2012[1:,0]) pgn_2012 = np.ndarray.tolist(DATA_2012[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2012 = folium.FeatureGroup("2012 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2012, lgn_2012, pgn_2012): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2012.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='orange',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2012) ''' ******************************************** 2013 **************************************************************''' # Data extraction for projects secured in year 2013 DATA_2013 = np.array(DATA.sheet_by_name('2013')) lat_2013 = np.ndarray.tolist(DATA_2013[1:,1]) lgn_2013 = np.ndarray.tolist(DATA_2013[1:,0]) pgn_2013 = np.ndarray.tolist(DATA_2013[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2013 = folium.FeatureGroup("2013 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2013, lgn_2013, pgn_2013): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2013.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='orange',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2013) ''' ******************************************** 2014 **************************************************************''' # Data extraction for projects secured in year 2014 DATA_2014 = np.array(DATA.sheet_by_name('2014')) lat_2014 = np.ndarray.tolist(DATA_2014[1:,1]) lgn_2014 = np.ndarray.tolist(DATA_2014[1:,0]) pgn_2014 = np.ndarray.tolist(DATA_2014[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2014 = folium.FeatureGroup("2014 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2014, lgn_2014, pgn_2014): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2014.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='orange',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2014) ''' ******************************************** 2015 **************************************************************''' # Data extraction for projects secured in year 2015 DATA_2015 = np.array(DATA.sheet_by_name('2015')) lat_2015 = np.ndarray.tolist(DATA_2015[1:,1]) lgn_2015 = np.ndarray.tolist(DATA_2015[1:,0]) pgn_2015 = np.ndarray.tolist(DATA_2015[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2015 = folium.FeatureGroup("2015 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2015, lgn_2015, pgn_2015): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2015.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='orange',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2015) ''' ******************************************** 2016 **************************************************************''' # Data extraction for projects secured in year 2016 DATA_2016 = np.array(DATA.sheet_by_name('2016')) lat_2016 = np.ndarray.tolist(DATA_2016[1:,1]) lgn_2016 = np.ndarray.tolist(DATA_2016[1:,0]) pgn_2016 = np.ndarray.tolist(DATA_2016[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2016 = folium.FeatureGroup("2016 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2016, lgn_2016, pgn_2016): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2016.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='red',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2016) ''' ******************************************** 2017 **************************************************************''' # Data extraction for projects secured in year 2017 DATA_2017 = np.array(DATA.sheet_by_name('2017')) lat_2017 = np.ndarray.tolist(DATA_2017[1:,1]) lgn_2017 = np.ndarray.tolist(DATA_2017[1:,0]) pgn_2017 = np.ndarray.tolist(DATA_2017[1:,3]) # Calling the class folium.map.FeatureGroup to group the places of interest in the LayerControl panel feature_group_2017 = folium.FeatureGroup("2017 Projects") for (lat_tooltip, long_tooltip, m_tooltip_label) in zip(lat_2017, lgn_2017, pgn_2017): tooltip_Coord = [lat_tooltip, long_tooltip] feature_group_2017.add_child(folium.Marker(location = tooltip_Coord, icon = folium.Icon(color='red',icon='info-sign'), popup = folium.Popup(m_tooltip_label, max_width=200, min_width=200))) m_folium.add_child(feature_group_2017) ''' ******************************************** 2018 **************************************************************''' # Data extraction for projects secured in year 2018 DATA_2018 = np.array(DATA.sheet_by_name('2018')) lat_2018 = np.ndarray.tolist(DATA_2018[1:,1]) lgn_2018 =

np.ndarray.tolist(DATA_2018[1:,0])

numpy.ndarray.tolist

''' Layers for NN-models (forward+backward pass). Written by <NAME> (https://github.com/SLotAbr). BSD License ''' # from multiprocessing import Process import numpy as np import pickle from tools.functions import string_softmax from tools.optimizers import AdaM as AdaM class token_embedding: def __init__(self, vocabulary_size, d_model, context_size, optim_param): self.TE_table = np.random.randn(vocabulary_size, d_model) * 1e-3 self.vocabulary_size = vocabulary_size self.d_model = d_model self.context_size = context_size self.input_field = 0 self.optim = AdaM(optim_param) def __call__(self, index_list): # form X matrix from tokens indexes # We should use 2D array for further concatenation self.input_indexes = index_list context =[[self.TE_table[j] for j in index_list]] return np.concatenate(context, axis=1) def update_weights(self, dX, dTE_linear): # dTE_linear - the second part of TE derivative # TE derivative have 2 parts - so, we'll get it by external source dTE = np.zeros((self.vocabulary_size, self.d_model)) for i in range(self.context_size): dTE[self.input_indexes[i]]+= dX[i] dTE += dTE_linear self.TE_table = self.optim.weights_update(self.TE_table, dTE) def linear(self, x): ''' using token_embeddings as linear layer with bias=0 we'll use it for finding out output token probabilities :x.shape = [context_size; d_model] :output.shape = [context_size; vocabulary_size] ''' self.input_field = x return [email protected]_table.T def linear_backward(self, dl): # returns derivatives for input signal and TE_table return [email protected]_table, (self.input_field.T@dl).T def save_weights(self, path): with open(path, 'wb') as f: pickle.dump([self.TE_table, self.optim], f) def restore_weights(self, path): with open(path, 'rb') as f: self.TE_table, self.optim = pickle.load(f) class linear: def __init__(self, hidden_units, number_of_neurons, optim_param): # mean = 0, var = 1 self.W = np.random.randn(hidden_units, number_of_neurons) * 1e-3 self.b = np.zeros(number_of_neurons) self.input_field = 0 # Memory for backpropagation self.w_optim = AdaM(optim_param) self.b_optim = AdaM(optim_param) def __call__(self, x): self.input_field = x return (x @ self.W + self.b) #np.dot(x, w) + b def backward(self, dl): dw = self.input_field.T @ dl db = dl.sum(axis=0) # Updating weights self.W = self.w_optim.weights_update(self.W, dw) self.b = self.b_optim.weights_update(self.b, db) # returns dl for previous layers return dl @ self.W.T def save_weights(self, path): with open(path, 'wb') as f: pickle.dump([self.W, self.b, self.w_optim, self.b_optim], f) def restore_weights(self, path): with open(path, 'rb') as f: self.W, self.b, self.w_optim, self.b_optim = pickle.load(f) class ReLU: def __call__(self, x): result = np.maximum(0, x) self.mask = result>0 return result def backward(self, dl): return dl * self.mask class LayerNormalization: def __init__(self, context_size): self.context_size = context_size def __call__(self, x, phase='train'): ''' I'll delete if-else construction and replace it more eficient version for evaluation phase later. There is the same construction in MH_attention_mechanism (__call__ field) ''' if phase == 'train': context_size = self.context_size else: context_size = x.shape[0] x_mean = (x.mean(axis=1).reshape(1,context_size)).T self.x_var = (x.var(axis=1).reshape(1,context_size)).T return (x-x_mean)/np.sqrt(self.x_var+1e-12) def backward(self, dl): l_mean = (dl.mean(axis=1).reshape(1,self.context_size)).T return (dl - l_mean)/np.sqrt(self.x_var+1e-12) class MH_attention_mechanism: def __init__(self, context_size, d_model, H): self.d_k = 1/np.sqrt(d_model/H) self.context_size = context_size self.H = H # matrix with 'True' values above the main diagonal # We'll use it for replacing elements in dot product of Q and K self.mask=(np.tril(np.ones((context_size, context_size)))==0) self.backward_mask=np.tril(np.ones((context_size, context_size))) def __call__(self, x, phase='train'): self.Q, self.K, self.V = np.split(x, 3, axis=1) self.Q = np.split(self.Q, self.H, axis=1) self.K = np.split(self.K, self.H, axis=1) self.V = np.split(self.V, self.H, axis=1) # When we generate text ('eval phase'), context_size always different if phase == 'train': context_size = self.context_size else: context_size = x.shape[0] # Replace it by pre-init fields for faster implementation? C = [0 for h in range(self.H)] self.S = [0 for h in range(self.H)] Z = [0 for h in range(self.H)] # https://docs.python.org/3/library/multiprocessing.html for h in range(self.H): # Attention formula C[h] = self.Q[h] @ self.K[h].T * self.d_k if phase == 'train': C[h][self.mask]=-1e12 else: # We've got different context_size during evaluation mask = (np.tril(np.ones((context_size, context_size)))==0) C[h][mask]=-1e12 self.S[h] = string_softmax(C[h], context_size) # print('softmax\'s state:\n', self.S[h]) Z[h] = self.S[h]@self.V[h] # print('Z\'s state:\n', Z[h]) return np.concatenate(Z, axis=1) def backward(self, dl): dZ =

np.split(dl, self.H, axis=1)

numpy.split

################################################################################ # UNIVERSIDADE FEDERAL DE CATALÃO (UFCAT) # <NAME>, ENG. CIVIL / PROF (UFCAT) # <NAME> ENG. CIVIL / PROF (UFCAT) # <NAME>, ENG. CIVIL (UFCAT) ################################################################################ ################################################################################ # DESCRIÇÃO ALGORITMO: # BIBLIOTECA DE DIMENSIONAMENTO DE VIGAS PRÉ-FABRICADAS E PROTENDIDAS DESENVOL- # VIDA PELO GRUPO DE PESQUISA E ESTUDOS EM ENGENHARIA (GPEE) ################################################################################ ################################################################################ # BIBLIOTECAS NATIVAS PYTHON import numpy as np ################################################################################ # BIBLIOTECAS DESENVOLVEDORES GPEE def PROP_GEOMETRICA_I(H, B_FS, B_FI, B_W, H_S, H_I, H_SI, H_II): """ Esta função determina as propriedades geométricas de uma seção I. Entrada: H | Altura da viga | m | float B_FS | Base de mesa superior da viga | m | float B_FI | Base de mesa inferior da viga | m | float B_W | Base de alma da viga | m | float H_S | Altura de mesa superior da viga | m | float H_I | Altura de mesa inferior da viga | m | float H_SI | Altura inclinada de mesa superior da viga | m | float H_II | Altura inclinada de mesa inferior da viga | m | float Saída: A_C | Área da seção transversal da viga | m² | float I_C | Inércia da viga | m^4 | float Y_SUP | Ordenada da fibra superior | m | float Y_INF | Ordenada da fibra inferior | m | float W_SUP | Modulo de resistência superior | m³ | float W_INF | Modulo de resistência inferior | m³ | float """ A_1 = B_W * H A_2 = (B_FS - B_W) * H_S A_3 = ((B_FS - B_W) * H_SI) / 2 A_4 = (B_FI - B_W) * H_I A_5 = ((B_FI - B_W) * H_I)/2 A_C = A_1 + A_2 + A_3 + A_4 + A_5 Y_CG = (A_1 * (H/2) + A_2 * (H - H_S / 2) + A_3 * (H - H_S - H_SI / 2) + A_4 * (H_I / 2) + A_5 * (H_I + H_II)) / A_C I_1 = (B_W * H**3) / 12 + A_1 * (H / 2 - Y_CG)**2 I_2 = ((B_FS - B_W)* H_S**3) / 12 + A_2 * (H - H_S/2 - Y_CG)**2 I_3 = ((B_FS - B_W)* H_SI**3) / 36 + A_3 * (H - H_S - H_SI / 3 - Y_CG)**2 I_4 = ((B_FI - B_W)* H_I**3) / 12 + A_4 * (Y_CG - H_I / 2)**2 I_5 = ((B_FI - B_W)* H_II**3) / 36 + A_5 * (Y_CG - H_I - H_II / 3)**2 I_C = I_1 + I_2 + I_3 + I_4 + I_5 Y_SUP = H - Y_CG Y_INF = Y_CG W_SUP = I_C / Y_SUP W_INF = I_C / Y_INF return A_C, I_C, Y_SUP, Y_INF, W_SUP, W_INF def PROP_GEOMETRICA_RET(B_W, H): """ Esta função determina as propriedades geométricas de uma seção retangular. Entrada: B_W | Largura da viga | m | float H | Altura da viga | m | float Saída: A_C | Área da seção transversal da viga | m² | float I_C | Inércia da viga | m^4 | float Y_SUP | Ordenada da fibra superior | m | float Y_INF | Ordenada da fibra inferior | m | float W_SUP | Modulo de resistência superior | m³ | float W_INF | Modulo de resistência inferior | m³ | float """ A_C = B_W * H I_C = (B_W * H ** 3) / 12 Y_SUP = H / 2 Y_INF = H / 2 W_SUP = I_C / Y_SUP W_INF = I_C / Y_INF return A_C, I_C, Y_SUP, Y_INF, W_SUP, W_INF def FATOR_BETA1(TEMPO, CIMENTO): """ Esta função calcula o valor de BETA_1 que representa a função de crescimento da resistência do cimento. Entrada: TEMPO | Tempo | dias | float CIMENTO | Cimento utilizado | | string | 'CP1' - Cimento portland 1 | | | 'CP2' - Cimento portland 2 | | | 'CP3' - Cimento portland 3 | | | 'CP4' - Cimento portland 4 | | | 'CP5' - Cimento portland 5 | | Saída: BETA_1 | Parâmetro de crescimento da resistência | | float """ if TEMPO < 28 : if CIMENTO == 'CP1' or CIMENTO == 'CP2': S = 0.25 elif CIMENTO == 'CP3' or CIMENTO == 'CP4': S = 0.38 elif CIMENTO == 'CP5': S = 0.20 BETA_1 = np.exp(S * (1 - (28 / TEMPO) ** 0.50)) else : BETA_1 = 1 return BETA_1 def MODULO_ELASTICIDADE_CONCRETO(AGREGADO, F_CK, F_CKJ): """ Esta função calcula os módulos de elasticidade do concreto. Entrada: AGREGADO | Tipo de agragado usado no traço do cimento | | string | 'BAS' - Agregado de Basalto | | | 'GRA' - Agregado de Granito | | | 'CAL' - Agregado de Calcário | | | 'ARE' - Agregado de Arenito | | F_CK | Resistência característica à compressão | kN/m² | float F_CKJ | Resistência característica à compressão idade J | kN/m² | float Saída: E_CIJ | Módulo de elasticidade tangente | kN/m² | float E_CSJ | Módulo de elasticidade do secante | kN/m² | float """ # Determinação do módulo tangente E_CI idade T if AGREGADO == 'BAS': ALFA_E = 1.2 elif AGREGADO == 'GRA': ALFA_E = 1.0 elif AGREGADO == 'CAL': ALFA_E = 0.9 elif AGREGADO == 'ARE': ALFA_E = 0.7 F_CK /= 1E3 if F_CK <= 50: E_CI = ALFA_E * 5600 * np.sqrt(F_CK) elif F_CK > 50: E_CI = 21.5 * (10 ** 3) * ALFA_E * (F_CK / 10 + 1.25) ** (1 / 3) ALFA_I = 0.8 + 0.2 * F_CK / 80 if ALFA_I > 1: ALFA_I = 1 # Determinação do módulo secante E_CS idade T E_CS = E_CI * ALFA_I if F_CK <= 45 : F_CK *= 1E3 E_CIJ = E_CI * (F_CKJ / F_CK) ** 0.5 elif F_CK > 45 : F_CK *= 1E3 E_CIJ = E_CI * (F_CKJ / F_CK) ** 0.3 E_CSJ = E_CIJ * ALFA_I E_CIJ *= 1E3 E_CSJ *= 1E3 return E_CIJ, E_CSJ def PROP_MATERIAL(F_CK, TEMPO, CIMENTO, AGREGADO): """ Esta função determina propriedades do concreto em uma idade TEMPO. Entrada: F_CK | Resistência característica à compressão | kN/m² | float TEMPO | Tempo | dias | float CIMENTO | Cimento utilizado | | string | 'CP1' - Cimento portland 1 | | | 'CP2' - Cimento portland 2 | | | 'CP3' - Cimento portland 3 | | | 'CP4' - Cimento portland 4 | | | 'CP5' - Cimento portland 5 | | AGREGADO | Tipo de agragado usado no traço do cimento | | string | 'BAS' - Agregado de Basalto | | | 'GRA' - Agregado de Granito | | | 'CAL' - Agregado de Calcário | | | 'ARE' - Agregado de Arenito | | Saída: F_CKJ | Resistência característica à compressão idade J | kN/m² | float F_CTMJ | Resistência média caracteristica a tração idade J | kN/m² | float F_CTKINFJ | Resistência média caracteristica a tração inf idade J | kN/m² | float F_CTKSUPJ | Resistência média caracteristica a tração sup idade J | kN/m² | float E_CIJ | Módulo de elasticidade tangente | kN/m² | float E_CSJ | Módulo de elasticidade do secante | kN/m² | float """ # Propriedades em situação de compressão F_C idade TEMPO em dias BETA_1 = FATOR_BETA1(TEMPO, CIMENTO) F_CKJ = F_CK * BETA_1 F_CKJ /= 1E3 F_CK /= 1E3 if F_CKJ < 21 : F_CKJ = 21 # Propriedades em situação de tração F_CT idade TEMPO em dias if F_CK <= 50: F_CTMJ = 0.3 * F_CKJ ** (2/3) elif F_CK > 50: F_CTMJ = 2.12 *

np.log(1 + 0.11 * F_CKJ)

numpy.log

import sys import warnings import itertools import platform import pytest from decimal import Decimal import numpy as np from numpy.core import umath from numpy.random import rand, randint, randn from numpy.testing import ( assert_, assert_equal, assert_raises, assert_raises_regex, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_warns, HAS_REFCOUNT ) class TestResize(object): def test_copies(self): A = np.array([[1, 2], [3, 4]]) Ar1 = np.array([[1, 2, 3, 4], [1, 2, 3, 4]]) assert_equal(np.resize(A, (2, 4)), Ar1) Ar2 = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) assert_equal(np.resize(A, (4, 2)), Ar2) Ar3 = np.array([[1, 2, 3], [4, 1, 2], [3, 4, 1], [2, 3, 4]]) assert_equal(np.resize(A, (4, 3)), Ar3) def test_zeroresize(self): A = np.array([[1, 2], [3, 4]]) Ar = np.resize(A, (0,)) assert_array_equal(Ar, np.array([])) assert_equal(A.dtype, Ar.dtype) Ar = np.resize(A, (0, 2)) assert_equal(Ar.shape, (0, 2)) Ar = np.resize(A, (2, 0)) assert_equal(Ar.shape, (2, 0)) def test_reshape_from_zero(self): # See also gh-6740 A = np.zeros(0, dtype=[('a', np.float32)]) Ar = np.resize(A, (2, 1)) assert_array_equal(Ar, np.zeros((2, 1), Ar.dtype)) assert_equal(A.dtype, Ar.dtype) class TestNonarrayArgs(object): # check that non-array arguments to functions wrap them in arrays def test_choose(self): choices = [[0, 1, 2], [3, 4, 5], [5, 6, 7]] tgt = [5, 1, 5] a = [2, 0, 1] out = np.choose(a, choices) assert_equal(out, tgt) def test_clip(self): arr = [-1, 5, 2, 3, 10, -4, -9] out = np.clip(arr, 2, 7) tgt = [2, 5, 2, 3, 7, 2, 2] assert_equal(out, tgt) def test_compress(self): arr = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] tgt = [[5, 6, 7, 8, 9]] out = np.compress([0, 1], arr, axis=0) assert_equal(out, tgt) def test_count_nonzero(self): arr = [[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]] tgt = np.array([2, 3]) out = np.count_nonzero(arr, axis=1) assert_equal(out, tgt) def test_cumproduct(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.all(np.cumproduct(A) == np.array([1, 2, 6, 24, 120, 720]))) def test_diagonal(self): a = [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] out = np.diagonal(a) tgt = [0, 5, 10] assert_equal(out, tgt) def test_mean(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.mean(A) == 3.5) assert_(np.all(np.mean(A, 0) == np.array([2.5, 3.5, 4.5]))) assert_(np.all(np.mean(A, 1) == np.array([2., 5.]))) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.mean([]))) assert_(w[0].category is RuntimeWarning) def test_ptp(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.ptp(a, axis=0), 15.0) def test_prod(self): arr = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] tgt = [24, 1890, 600] assert_equal(np.prod(arr, axis=-1), tgt) def test_ravel(self): a = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] tgt = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] assert_equal(np.ravel(a), tgt) def test_repeat(self): a = [1, 2, 3] tgt = [1, 1, 2, 2, 3, 3] out = np.repeat(a, 2) assert_equal(out, tgt) def test_reshape(self): arr = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] tgt = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]] assert_equal(np.reshape(arr, (2, 6)), tgt) def test_round(self): arr = [1.56, 72.54, 6.35, 3.25] tgt = [1.6, 72.5, 6.4, 3.2] assert_equal(np.around(arr, decimals=1), tgt) def test_searchsorted(self): arr = [-8, -5, -1, 3, 6, 10] out = np.searchsorted(arr, 0) assert_equal(out, 3) def test_size(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.size(A) == 6) assert_(np.size(A, 0) == 2) assert_(np.size(A, 1) == 3) def test_squeeze(self): A = [[[1, 1, 1], [2, 2, 2], [3, 3, 3]]] assert_equal(np.squeeze(A).shape, (3, 3)) assert_equal(np.squeeze(np.zeros((1, 3, 1))).shape, (3,)) assert_equal(np.squeeze(np.zeros((1, 3, 1)), axis=0).shape, (3, 1)) assert_equal(np.squeeze(np.zeros((1, 3, 1)), axis=-1).shape, (1, 3)) assert_equal(np.squeeze(np.zeros((1, 3, 1)), axis=2).shape, (1, 3)) assert_equal(np.squeeze([np.zeros((3, 1))]).shape, (3,)) assert_equal(np.squeeze([np.zeros((3, 1))], axis=0).shape, (3, 1)) assert_equal(np.squeeze([np.zeros((3, 1))], axis=2).shape, (1, 3)) assert_equal(np.squeeze([np.zeros((3, 1))], axis=-1).shape, (1, 3)) def test_std(self): A = [[1, 2, 3], [4, 5, 6]] assert_almost_equal(np.std(A), 1.707825127659933) assert_almost_equal(np.std(A, 0), np.array([1.5, 1.5, 1.5])) assert_almost_equal(np.std(A, 1), np.array([0.81649658, 0.81649658])) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.std([]))) assert_(w[0].category is RuntimeWarning) def test_swapaxes(self): tgt = [[[0, 4], [2, 6]], [[1, 5], [3, 7]]] a = [[[0, 1], [2, 3]], [[4, 5], [6, 7]]] out = np.swapaxes(a, 0, 2) assert_equal(out, tgt) def test_sum(self): m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] tgt = [[6], [15], [24]] out = np.sum(m, axis=1, keepdims=True) assert_equal(tgt, out) def test_take(self): tgt = [2, 3, 5] indices = [1, 2, 4] a = [1, 2, 3, 4, 5] out = np.take(a, indices) assert_equal(out, tgt) def test_trace(self): c = [[1, 2], [3, 4], [5, 6]] assert_equal(np.trace(c), 5) def test_transpose(self): arr = [[1, 2], [3, 4], [5, 6]] tgt = [[1, 3, 5], [2, 4, 6]] assert_equal(np.transpose(arr, (1, 0)), tgt) def test_var(self): A = [[1, 2, 3], [4, 5, 6]] assert_almost_equal(np.var(A), 2.9166666666666665) assert_almost_equal(np.var(A, 0), np.array([2.25, 2.25, 2.25])) assert_almost_equal(np.var(A, 1), np.array([0.66666667, 0.66666667])) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.var([]))) assert_(w[0].category is RuntimeWarning) B = np.array([None, 0]) B[0] = 1j assert_almost_equal(np.var(B), 0.25) class TestIsscalar(object): def test_isscalar(self): assert_(np.isscalar(3.1)) assert_(np.isscalar(np.int16(12345))) assert_(np.isscalar(False)) assert_(np.isscalar('numpy')) assert_(not np.isscalar([3.1])) assert_(not np.isscalar(None)) # PEP 3141 from fractions import Fraction assert_(np.isscalar(Fraction(5, 17))) from numbers import Number assert_(np.isscalar(Number())) class TestBoolScalar(object): def test_logical(self): f = np.False_ t = np.True_ s = "xyz" assert_((t and s) is s) assert_((f and s) is f) def test_bitwise_or(self): f = np.False_ t = np.True_ assert_((t | t) is t) assert_((f | t) is t) assert_((t | f) is t) assert_((f | f) is f) def test_bitwise_and(self): f = np.False_ t = np.True_ assert_((t & t) is t) assert_((f & t) is f) assert_((t & f) is f) assert_((f & f) is f) def test_bitwise_xor(self): f = np.False_ t = np.True_ assert_((t ^ t) is f) assert_((f ^ t) is t) assert_((t ^ f) is t) assert_((f ^ f) is f) class TestBoolArray(object): def setup(self): # offset for simd tests self.t = np.array([True] * 41, dtype=bool)[1::] self.f = np.array([False] * 41, dtype=bool)[1::] self.o = np.array([False] * 42, dtype=bool)[2::] self.nm = self.f.copy() self.im = self.t.copy() self.nm[3] = True self.nm[-2] = True self.im[3] = False self.im[-2] = False def test_all_any(self): assert_(self.t.all()) assert_(self.t.any()) assert_(not self.f.all()) assert_(not self.f.any()) assert_(self.nm.any()) assert_(self.im.any()) assert_(not self.nm.all()) assert_(not self.im.all()) # check bad element in all positions for i in range(256 - 7): d = np.array([False] * 256, dtype=bool)[7::] d[i] = True assert_(np.any(d)) e = np.array([True] * 256, dtype=bool)[7::] e[i] = False assert_(not np.all(e)) assert_array_equal(e, ~d) # big array test for blocked libc loops for i in list(range(9, 6000, 507)) + [7764, 90021, -10]: d = np.array([False] * 100043, dtype=bool) d[i] = True assert_(np.any(d), msg="%r" % i) e = np.array([True] * 100043, dtype=bool) e[i] = False assert_(not np.all(e), msg="%r" % i) def test_logical_not_abs(self): assert_array_equal(~self.t, self.f) assert_array_equal(np.abs(~self.t), self.f) assert_array_equal(np.abs(~self.f), self.t) assert_array_equal(np.abs(self.f), self.f) assert_array_equal(~np.abs(self.f), self.t) assert_array_equal(~np.abs(self.t), self.f) assert_array_equal(np.abs(~self.nm), self.im) np.logical_not(self.t, out=self.o) assert_array_equal(self.o, self.f) np.abs(self.t, out=self.o) assert_array_equal(self.o, self.t) def test_logical_and_or_xor(self): assert_array_equal(self.t | self.t, self.t) assert_array_equal(self.f | self.f, self.f) assert_array_equal(self.t | self.f, self.t) assert_array_equal(self.f | self.t, self.t) np.logical_or(self.t, self.t, out=self.o) assert_array_equal(self.o, self.t) assert_array_equal(self.t & self.t, self.t) assert_array_equal(self.f & self.f, self.f) assert_array_equal(self.t & self.f, self.f) assert_array_equal(self.f & self.t, self.f) np.logical_and(self.t, self.t, out=self.o) assert_array_equal(self.o, self.t) assert_array_equal(self.t ^ self.t, self.f) assert_array_equal(self.f ^ self.f, self.f) assert_array_equal(self.t ^ self.f, self.t) assert_array_equal(self.f ^ self.t, self.t) np.logical_xor(self.t, self.t, out=self.o) assert_array_equal(self.o, self.f) assert_array_equal(self.nm & self.t, self.nm) assert_array_equal(self.im & self.f, False) assert_array_equal(self.nm & True, self.nm) assert_array_equal(self.im & False, self.f) assert_array_equal(self.nm | self.t, self.t) assert_array_equal(self.im | self.f, self.im) assert_array_equal(self.nm | True, self.t) assert_array_equal(self.im | False, self.im) assert_array_equal(self.nm ^ self.t, self.im) assert_array_equal(self.im ^ self.f, self.im) assert_array_equal(self.nm ^ True, self.im) assert_array_equal(self.im ^ False, self.im) class TestBoolCmp(object): def setup(self): self.f = np.ones(256, dtype=np.float32) self.ef = np.ones(self.f.size, dtype=bool) self.d = np.ones(128, dtype=np.float64) self.ed = np.ones(self.d.size, dtype=bool) # generate values for all permutation of 256bit simd vectors s = 0 for i in range(32): self.f[s:s+8] = [i & 2**x for x in range(8)] self.ef[s:s+8] = [(i & 2**x) != 0 for x in range(8)] s += 8 s = 0 for i in range(16): self.d[s:s+4] = [i & 2**x for x in range(4)] self.ed[s:s+4] = [(i & 2**x) != 0 for x in range(4)] s += 4 self.nf = self.f.copy() self.nd = self.d.copy() self.nf[self.ef] = np.nan self.nd[self.ed] = np.nan self.inff = self.f.copy() self.infd = self.d.copy() self.inff[::3][self.ef[::3]] = np.inf self.infd[::3][self.ed[::3]] = np.inf self.inff[1::3][self.ef[1::3]] = -np.inf self.infd[1::3][self.ed[1::3]] = -np.inf self.inff[2::3][self.ef[2::3]] = np.nan self.infd[2::3][self.ed[2::3]] = np.nan self.efnonan = self.ef.copy() self.efnonan[2::3] = False self.ednonan = self.ed.copy() self.ednonan[2::3] = False self.signf = self.f.copy() self.signd = self.d.copy() self.signf[self.ef] *= -1. self.signd[self.ed] *= -1. self.signf[1::6][self.ef[1::6]] = -np.inf self.signd[1::6][self.ed[1::6]] = -np.inf self.signf[3::6][self.ef[3::6]] = -np.nan self.signd[3::6][self.ed[3::6]] = -np.nan self.signf[4::6][self.ef[4::6]] = -0. self.signd[4::6][self.ed[4::6]] = -0. def test_float(self): # offset for alignment test for i in range(4): assert_array_equal(self.f[i:] > 0, self.ef[i:]) assert_array_equal(self.f[i:] - 1 >= 0, self.ef[i:]) assert_array_equal(self.f[i:] == 0, ~self.ef[i:]) assert_array_equal(-self.f[i:] < 0, self.ef[i:]) assert_array_equal(-self.f[i:] + 1 <= 0, self.ef[i:]) r = self.f[i:] != 0 assert_array_equal(r, self.ef[i:]) r2 = self.f[i:] != np.zeros_like(self.f[i:]) r3 = 0 != self.f[i:] assert_array_equal(r, r2) assert_array_equal(r, r3) # check bool == 0x1 assert_array_equal(r.view(np.int8), r.astype(np.int8)) assert_array_equal(r2.view(np.int8), r2.astype(np.int8)) assert_array_equal(r3.view(np.int8), r3.astype(np.int8)) # isnan on amd64 takes the same code path assert_array_equal(np.isnan(self.nf[i:]), self.ef[i:]) assert_array_equal(np.isfinite(self.nf[i:]), ~self.ef[i:]) assert_array_equal(np.isfinite(self.inff[i:]), ~self.ef[i:]) assert_array_equal(np.isinf(self.inff[i:]), self.efnonan[i:]) assert_array_equal(np.signbit(self.signf[i:]), self.ef[i:]) def test_double(self): # offset for alignment test for i in range(2): assert_array_equal(self.d[i:] > 0, self.ed[i:]) assert_array_equal(self.d[i:] - 1 >= 0, self.ed[i:]) assert_array_equal(self.d[i:] == 0, ~self.ed[i:]) assert_array_equal(-self.d[i:] < 0, self.ed[i:]) assert_array_equal(-self.d[i:] + 1 <= 0, self.ed[i:]) r = self.d[i:] != 0 assert_array_equal(r, self.ed[i:]) r2 = self.d[i:] != np.zeros_like(self.d[i:]) r3 = 0 != self.d[i:] assert_array_equal(r, r2) assert_array_equal(r, r3) # check bool == 0x1 assert_array_equal(r.view(np.int8), r.astype(np.int8)) assert_array_equal(r2.view(np.int8), r2.astype(np.int8)) assert_array_equal(r3.view(np.int8), r3.astype(np.int8)) # isnan on amd64 takes the same code path assert_array_equal(np.isnan(self.nd[i:]), self.ed[i:]) assert_array_equal(

np.isfinite(self.nd[i:])

numpy.isfinite

#! /usr/bin/env python # Copyright 2021 <NAME> # # This file is part of WarpX. # # License: BSD-3-Clause-LBNL import os import sys import yt sys.path.insert(1, '../../../../warpx/Regression/Checksum/') import checksumAPI import numpy as np import scipy.constants as scc ## This script performs various checks for the proton boron nuclear fusion module. The simulation ## that we check is made of 5 different tests, each with different proton, boron and alpha species. ## ## The first test is performed in the proton-boron center of mass frame. It could correspond to the ## physical case of a proton beam colliding with a boron beam. The kinetic energy of the colliding ## particles depends on the cell number in the z direction and varies in the few keV to few MeV ## range. All the particles within a cell have the exact same momentum, which allows detailed ## checks of the energy of produced alpha particles. The proton and boron species have the same ## density and number of particles in this test. The number of produced alphas is much smaller than ## the initial number of protons and borons. ## ## The second test is performed in the boron rest frame. It corresponds to the physical case of a ## low density proton beam colliding with a high-density proton+boron target. The energy of the ## proton beam is varied in the few keV to few MeV range, depending on the cell number in the z ## direction. As in the previous case, all the particles within a cell have the exact same ## momentum, which allows detailed checks of the energy of produced alpha particles. In this test, ## there are 100 immobile boron and 100 immobile proton macroparticles per cell, as well as 900 ## beam proton macroparticles per cell. The density of the immobile particles is 6 orders of ## magnitude higher than the number of beam particles, which means that they have a much higher ## weight. This test is similar to the example given in section 3 of Higginson et al., ## Journal of Computation Physics, 388 439–453 (2019), which was found to be sensitive to the way ## unsampled pairs are accounted for. As before, the number of produced alphas is much smaller than ## the initial number of protons and borons. ## ## The third test corresponds to a Maxwellian plasma with a 44 keV temperature. The alpha yield is ## directly compared to the analytical fits of <NAME> and <NAME>, Nuclear Fusion, 40, 865 ## (2000) for a thermal plasma. ## ## The fourth test corresponds to a plasma with an extremely small boron density, so that all boron ## macroparticles should have disappeared by the end of the simulation, which we verify. ## ## The fifth test is exactly the same as the fourth test, except that the ## fusion_probability_threshold parameter is increased to an excessive value. Because of that, we ## severely underestimate the fusion yield and boron macroparticles remain at the end of the ## simulation, which we verify. ## ## In all simulations, we check particle number, charge, momentum and energy conservation and ## perform basic checks regarding the produced particles. When possible, we also compare the number ## of produced macroparticles, fusion yield and energy of the produced particles to theoretical ## values. ## ## Please be aware that the relative tolerances are often set empirically in this analysis script, ## so it would not be surprising that some tolerances need to be increased in the future. default_tol = 1.e-12 # Default relative tolerance ## Some physical parameters keV_to_Joule = scc.e*1e3 MeV_to_Joule = scc.e*1e6 barn_to_square_meter = 1.e-28 m_p = scc.m_p # Proton mass m_b = 10.9298*m_p # Boron 11 mass m_reduced = m_p*m_b/(m_p+m_b) m_a = 3.97369*m_p # Alpha mass m_be = 7.94748*m_p # Beryllium 8 mass Z_boron = 5. Z_proton = 1. E_Gamow = (Z_boron*Z_proton*np.pi*scc.fine_structure)**2*2.*m_reduced*scc.c**2 E_Gamow_MeV = E_Gamow/MeV_to_Joule E_Gamow_keV = E_Gamow/keV_to_Joule E_fusion = 8.59009*MeV_to_Joule # Energy released during p + B -> alpha + Be E_decay = 0.0918984*MeV_to_Joule # Energy released during Be -> 2*alpha E_fusion_total = E_fusion + E_decay # Energy released during p + B -> 3*alpha ## Some numerical parameters for this test size_x = 8 size_y = 8 size_z = 16 dV_total = size_x*size_y*size_z # Total simulation volume # Volume of a slice corresponding to a single cell in the z direction. In tests 1 and 2, all the # particles of a given species in the same slice have the exact same momentum dV_slice = size_x*size_y dt = 1./(scc.c*np.sqrt(3.)) # In test 1 and 2, the energy in cells number i (in z direction) is typically Energy_step * i**2 Energy_step = 22.*keV_to_Joule def is_close(val1, val2, rtol=default_tol, atol=0.): ## Wrapper around numpy.isclose, used to override the default tolerances. return np.isclose(val1, val2, rtol=rtol, atol=atol) def add_existing_species_to_dict(yt_ad, data_dict, species_name, prefix, suffix): data_dict[prefix+"_px_"+suffix] = yt_ad[species_name, "particle_momentum_x"].v data_dict[prefix+"_py_"+suffix] = yt_ad[species_name, "particle_momentum_y"].v data_dict[prefix+"_pz_"+suffix] = yt_ad[species_name, "particle_momentum_z"].v data_dict[prefix+"_w_"+suffix] = yt_ad[species_name, "particle_weight"].v data_dict[prefix+"_id_"+suffix] = yt_ad[species_name, "particle_id"].v data_dict[prefix+"_cpu_"+suffix] = yt_ad[species_name, "particle_cpu"].v data_dict[prefix+"_z_"+suffix] = yt_ad[species_name, "particle_position_z"].v def add_empty_species_to_dict(data_dict, species_name, prefix, suffix): data_dict[prefix+"_px_"+suffix] = np.empty(0) data_dict[prefix+"_py_"+suffix] = np.empty(0) data_dict[prefix+"_pz_"+suffix] = np.empty(0) data_dict[prefix+"_w_"+suffix] = np.empty(0) data_dict[prefix+"_id_"+suffix] = np.empty(0) data_dict[prefix+"_cpu_"+suffix] = np.empty(0) data_dict[prefix+"_z_"+suffix] = np.empty(0) def add_species_to_dict(yt_ad, data_dict, species_name, prefix, suffix): try: ## If species exist, we add its data to the dictionary add_existing_species_to_dict(yt_ad, data_dict, species_name, prefix, suffix) except yt.utilities.exceptions.YTFieldNotFound: ## If species does not exist, we avoid python crash and add empty arrays to the ## dictionnary. Currently, this happens for the boron species in test number 4, which ## entirely fuses into alphas. add_empty_species_to_dict(data_dict, species_name, prefix, suffix) def check_particle_number_conservation(data): total_w_proton_start = np.sum(data["proton_w_start"]) total_w_proton_end = np.sum(data["proton_w_end"]) total_w_boron_start = np.sum(data["boron_w_start"]) total_w_boron_end = np.sum(data["boron_w_end"]) consumed_proton = total_w_proton_start - total_w_proton_end consumed_boron = total_w_boron_start - total_w_boron_end created_alpha = np.sum(data["alpha_w_end"]) assert(consumed_proton >= 0.) assert(consumed_boron >= 0.) assert(created_alpha >= 0.) ## Check that number of consumed proton and consumed boron are equal assert_scale = max(total_w_proton_start, total_w_boron_start) assert(is_close(consumed_proton, consumed_boron, rtol = 0., atol = default_tol*assert_scale)) ## Check that number of consumed particles corresponds to number of produced alpha ## Factor 3 is here because each nuclear fusion reaction produces 3 alphas assert(is_close(total_w_proton_start, total_w_proton_end + created_alpha/3.)) assert(is_close(total_w_boron_start, total_w_boron_end + created_alpha/3.)) def compute_energy_array(data, species_name, suffix, m): ## Relativistic computation of kinetic energy for a given species psq_array = data[species_name+'_px_'+suffix]**2 + data[species_name+'_py_'+suffix]**2 + \ data[species_name+'_pz_'+suffix]**2 rest_energy = m*scc.c**2 return np.sqrt(psq_array*scc.c**2 + rest_energy**2) - rest_energy def check_energy_conservation(data): proton_energy_start = compute_energy_array(data, "proton", "start", m_p) proton_energy_end = compute_energy_array(data, "proton", "end", m_p) boron_energy_start = compute_energy_array(data, "boron", "start", m_b) boron_energy_end = compute_energy_array(data, "boron", "end", m_b) alpha_energy_end = compute_energy_array(data, "alpha", "end", m_a) total_energy_start = np.sum(proton_energy_start*data["proton_w_start"]) + \ np.sum(boron_energy_start*data["boron_w_start"]) total_energy_end = np.sum(proton_energy_end*data["proton_w_end"]) + \ np.sum(boron_energy_end*data["boron_w_end"]) + \ np.sum(alpha_energy_end*data["alpha_w_end"]) ## Factor 3 is here because each nuclear fusion reaction produces 3 alphas n_fusion_reaction = np.sum(data["alpha_w_end"])/3. assert(is_close(total_energy_end, total_energy_start + n_fusion_reaction*E_fusion_total, rtol = 1.e-8)) def check_momentum_conservation(data): proton_total_px_start = np.sum(data["proton_px_start"]*data["proton_w_start"]) proton_total_py_start = np.sum(data["proton_py_start"]*data["proton_w_start"]) proton_total_pz_start = np.sum(data["proton_pz_start"]*data["proton_w_start"]) proton_total_px_end = np.sum(data["proton_px_end"]*data["proton_w_end"]) proton_total_py_end = np.sum(data["proton_py_end"]*data["proton_w_end"]) proton_total_pz_end = np.sum(data["proton_pz_end"]*data["proton_w_end"]) boron_total_px_start = np.sum(data["boron_px_start"]*data["boron_w_start"]) boron_total_py_start = np.sum(data["boron_py_start"]*data["boron_w_start"]) boron_total_pz_start = np.sum(data["boron_pz_start"]*data["boron_w_start"]) boron_total_px_end = np.sum(data["boron_px_end"]*data["boron_w_end"]) boron_total_py_end = np.sum(data["boron_py_end"]*data["boron_w_end"]) boron_total_pz_end = np.sum(data["boron_pz_end"]*data["boron_w_end"]) alpha_total_px_end = np.sum(data["alpha_px_end"]*data["alpha_w_end"]) alpha_total_py_end = np.sum(data["alpha_py_end"]*data["alpha_w_end"]) alpha_total_pz_end = np.sum(data["alpha_pz_end"]*data["alpha_w_end"]) total_px_start = proton_total_px_start + boron_total_px_start total_py_start = proton_total_py_start + boron_total_py_start total_pz_start = proton_total_pz_start + boron_total_pz_start total_px_end = proton_total_px_end + boron_total_px_end + alpha_total_px_end total_py_end = proton_total_py_end + boron_total_py_end + alpha_total_py_end total_pz_end = proton_total_pz_end + boron_total_pz_end + alpha_total_pz_end ## Absolute tolerance is needed because sometimes the initial momentum is exactly 0 assert(is_close(total_px_start, total_px_end, atol=1.e-15)) assert(is_close(total_py_start, total_py_end, atol=1.e-15)) assert(is_close(total_pz_start, total_pz_end, atol=1.e-15)) def check_id(data): ## Check that all created particles have unique id + cpu identifier (two particles with ## different cpu can have the same id) complex_id = data["alpha_id_end"] + 1j*data["alpha_cpu_end"] assert(complex_id.shape == np.unique(complex_id).shape) def basic_product_particles_check(data): ## For each nuclear fusion reaction in the code, we create 6 alpha macroparticles. So the ## total number of alpha macroparticles must be a multiple of 6. num_alpha = data["alpha_w_end"].shape[0] assert(num_alpha%6 == 0) ## The weight of the 6 macroparticles coming from a single fusion event should be the same. ## We verify this here. assert(np.array_equal(data["alpha_w_end"][::6], data["alpha_w_end"][1::6])) assert(np.array_equal(data["alpha_w_end"][::6], data["alpha_w_end"][2::6])) assert(np.array_equal(data["alpha_w_end"][::6], data["alpha_w_end"][3::6])) assert(np.array_equal(data["alpha_w_end"][::6], data["alpha_w_end"][4::6])) assert(np.array_equal(data["alpha_w_end"][::6], data["alpha_w_end"][5::6])) ## When we create 6 macroparticles, the first has the exact same momentum as the second, the ## third has the same as the fourth and the fifth has the same as the sixth. We verify this ## here assert(np.array_equal(data["alpha_px_end"][::6], data["alpha_px_end"][1::6])) assert(np.array_equal(data["alpha_py_end"][::6], data["alpha_py_end"][1::6])) assert(np.array_equal(data["alpha_pz_end"][::6], data["alpha_pz_end"][1::6])) assert(np.array_equal(data["alpha_px_end"][2::6], data["alpha_px_end"][3::6])) assert(np.array_equal(data["alpha_py_end"][2::6], data["alpha_py_end"][3::6])) assert(np.array_equal(data["alpha_pz_end"][2::6], data["alpha_pz_end"][3::6])) assert(np.array_equal(data["alpha_px_end"][4::6], data["alpha_px_end"][5::6])) assert(np.array_equal(data["alpha_py_end"][4::6], data["alpha_py_end"][5::6])) assert(np.array_equal(data["alpha_pz_end"][4::6], data["alpha_pz_end"][5::6])) def generic_check(data): check_particle_number_conservation(data) check_energy_conservation(data) check_momentum_conservation(data) check_id(data) basic_product_particles_check(data) def check_isotropy(data, relative_tolerance): ## Checks that the alpha particles are emitted isotropically average_px_sq = np.average(data["alpha_px_end"]*data["alpha_px_end"]) average_py_sq = np.average(data["alpha_py_end"]*data["alpha_py_end"]) average_pz_sq = np.average(data["alpha_pz_end"]*data["alpha_pz_end"]) assert(is_close(average_px_sq, average_py_sq, rtol = relative_tolerance)) assert(is_close(average_px_sq, average_pz_sq, rtol = relative_tolerance)) def astrophysical_factor_lowE(E): ## E is in keV ## Returns astrophysical factor in MeV b using the low energy fit in the range E < 400 keV ## described in equation (2) of <NAME> and <NAME>, Nuclear Fusion, 40, 865 (2000) C0 = 197. C1 = 0.24 C2 = 2.31e-4 AL = 1.82e4 EL = 148. dEL = 2.35 return C0 + C1*E + C2*E**2 + AL/((E-EL)**2 + dEL**2) def astrophysical_factor_midE(E): ## E is in keV ## Returns astrophysical factor in MeV b using the mid energy fit in the range ## 400 keV < E < 642 keV described in equation (3) of <NAME> and <NAME>, ## Nuclear Fusion, 40, 865 (2000) D0 = 330. D1 = 66.1 D2 = -20.3 D5 = -1.58 E_400 = 400. E_100 = 100. E_norm = (E - E_400)/E_100 return D0 + D1*E_norm + D2*E_norm**2 + D5*E_norm**5 def astrophysical_factor_highE(E): ## E is in keV ## Returns astrophysical factor in MeV b using the high energy fit in the range ## 642 keV < E < 3500 keV described in equation (4) of <NAME> and <NAME>, ## Nuclear Fusion, 40, 865 (2000) A0 = 2.57e6 A1 = 5.67e5 A2 = 1.34e5 A3 = 5.68e5 E0 = 581.3 E1 = 1083. E2 = 2405. E3 = 3344. dE0 = 85.7 dE1 = 234. dE2 = 138. dE3 = 309. B = 4.38 return A0/((E-E0)**2 + dE0**2) + A1/((E-E1)**2 + dE1**2) + \ A2/((E-E2)**2 + dE2**2) + A3/((E-E3)**2 + dE3**2) + B def astrophysical_factor(E): ## E is in keV ## Returns astrophysical factor in MeV b using the fits described in <NAME> ## and <NAME>, Nuclear Fusion, 40, 865 (2000) conditions = [E <= 400, E <= 642, E > 642] choices = [astrophysical_factor_lowE(E), astrophysical_factor_midE(E), astrophysical_factor_highE(E)] return np.select(conditions, choices) def pb_cross_section_buck_fit(E): ## E is in MeV ## Returns cross section in b using a power law fit of the data presented in Buck et al., ## Nuclear Physics A, 398(2), 189-202 (1983) in the range E > 3.5 MeV. E_start_fit = 3.5 ## Cross section at E = E_start_fit = 3.5 MeV cross_section_start_fit = 0.2168440845211521 slope_fit = -2.661840717596765 return cross_section_start_fit*(E/E_start_fit)**slope_fit def pb_cross_section(E): ## E is in keV ## Returns cross section in b using the fits described in <NAME> and <NAME>, ## Nuclear Fusion, 40, 865 (2000) for E < 3.5 MeV and a power law fit of the data presented in ## Buck et al., Nuclear Physics A, 398(2), 189-202 (1983) for E > 3.5 MeV. E_MeV = E/1.e3 conditions = [E <= 3500, E > 3500] choices = [astrophysical_factor(E)/E_MeV * np.exp(-np.sqrt(E_Gamow_MeV / E_MeV)), pb_cross_section_buck_fit(E_MeV)] return np.select(conditions, choices) def E_com_to_p_sq_com(m1, m2, E): ## E is the total (kinetic+mass) energy of a two particle (with mass m1 and m2) system in ## its center of mass frame, in J. ## Returns the square norm of the momentum of each particle in that frame. return E**2/(4.*scc.c**2) - (m1**2 + m2**2)*scc.c**2/2. + \ scc.c**6/(4.*E**2)*((m1**2 - m2**2)**2) def compute_relative_v_com(E): ## E is the kinetic energy of proton+boron in the center of mass frame, in keV ## Returns the relative velocity between proton and boron in this frame, in m/s E_J = E*keV_to_Joule + (m_p + m_b)*scc.c**2 p_sq = E_com_to_p_sq_com(m_p, m_b, E_J) p = np.sqrt(p_sq) gamma_p = np.sqrt(1. + p_sq / (m_p*scc.c)**2) gamma_b = np.sqrt(1. + p_sq / (m_b*scc.c)**2) v_p = p/(gamma_p*m_p) v_b = p/(gamma_b*m_b) return v_p+v_b def expected_alpha_weight_com(E_com, proton_density, boron_density, dV, dt): ## Computes expected number of produced alpha particles as a function of energy E_com in the ## center of mass frame. E_com is in keV. assert(np.all(E_com>=0)) ## Case E_com == 0 is handled manually to avoid division by zero conditions = [E_com == 0, E_com > 0] ## Necessary to avoid division by 0 warning when pb_cross_section is evaluated E_com_never_zero = np.clip(E_com, 1.e-15, None) choices = [0., pb_cross_section(E_com_never_zero)*compute_relative_v_com(E_com_never_zero)] sigma_times_vrel = np.select(conditions, choices) ## Factor 3 is here because each fusion reaction produces 3 alphas return 3.*proton_density*boron_density*sigma_times_vrel*barn_to_square_meter*dV*dt def check_macroparticle_number(data, fusion_probability_target_value, num_pair_per_cell): ## Checks that the number of macroparticles is as expected for the first and second tests ## The first slice 0 < z < 1 does not contribute to alpha creation numcells = dV_total - dV_slice ## In these tests, the fusion_multiplier is so high that the fusion probability per pair is ## equal to the parameter fusion_probability_target_value fusion_probability_per_pair = fusion_probability_target_value expected_fusion_number = numcells*num_pair_per_cell*fusion_probability_per_pair ## Each fusion event produces 6 alpha macroparticles expected_macroparticle_number = 6.*expected_fusion_number std_macroparticle_number = 6.*np.sqrt(expected_fusion_number) actual_macroparticle_number = data["alpha_w_end"].shape[0] # 5 sigma test that has an intrinsic probability to fail of 1 over ~2 millions assert(is_close(actual_macroparticle_number, expected_macroparticle_number, rtol = 0., atol = 5.*std_macroparticle_number)) ## used in subsequent function return expected_fusion_number def p_sq_boron_frame_to_E_COM_frame(p_proton_sq): # Takes the proton square norm of the momentum in the boron rest frame and returns the total # kinetic energy in the center of mass frame. Everything is in SI units. # Total (kinetic + mass) energy in lab frame E_lab = np.sqrt(p_proton_sq*scc.c**2 + (m_p*scc.c**2)**2) + m_b*scc.c**2 # Use invariant E**2 - p**2c**2 of 4-momentum norm to compute energy in center of mass frame E_com = np.sqrt(E_lab**2 - p_proton_sq*scc.c**2) # Corresponding kinetic energy E_com_kin = E_com - (m_b+scc.m_p)*scc.c**2 return E_com_kin def p_sq_to_kinetic_energy(p_sq, m): ## Returns the kinetic energy of a particle as a function of its squared momentum. ## Everything is in SI units. return np.sqrt(p_sq*scc.c**2 + (m*scc.c**2)**2) - (m*scc.c**2) def compute_E_com1(data): ## Computes kinetic energy (in Joule) in the center of frame for the first test ## Square norm of the momentum of proton/boron as a function of cell number in z direction p_sq = 2.*m_reduced*(Energy_step*np.arange(size_z)**2) return p_sq_to_kinetic_energy(p_sq, m_b) + p_sq_to_kinetic_energy(p_sq, m_p) def compute_E_com2(data): ## Computes kinetic energy (in Joule) in the center of frame for the second test ## Square norm of the momentum of the proton as a function of cell number in z direction p_proton_sq = 2.*m_p*(Energy_step*np.arange(size_z)**2) return p_sq_boron_frame_to_E_COM_frame(p_proton_sq) def check_alpha_yield(data, expected_fusion_number, E_com, proton_density, boron_density): ## Checks that the fusion yield is as expected for the first and second tests. ## Proton and boron densities are in m^-3. alpha_weight_theory = expected_alpha_weight_com(E_com/keV_to_Joule, proton_density, boron_density, dV_slice, dt) alpha_weight_simulation = np.histogram(data["alpha_z_end"], bins=size_z, range=(0, size_z), weights = data["alpha_w_end"])[0] ## -1 is here because the first slice 0 < z < 1 does not contribute to alpha creation expected_fusion_number_per_slice = expected_fusion_number/(size_z-1) relative_std_alpha_weight = 1./np.sqrt(expected_fusion_number_per_slice) # 5 sigma test that has an intrinsic probability to fail of 1 over ~2 millions assert(np.all(is_close(alpha_weight_theory, alpha_weight_simulation, rtol = 5.*relative_std_alpha_weight))) def check_initial_energy1(data, E_com): ## In WarpX, the initial momentum of the alphas is computed assuming that the fusion process ## takes place in two steps: ## (1): proton + boron 11 -> alpha + beryllium 8 ## (2): beryllium 8 -> alpha + alpha ## The alpha generated in the first step (labeled alpha1) generally has a different initial ## energy distribution than the alphas generated in the second step (labeled alpha2 and ## alpha3). ## In the first test, we are in the center of mass frame. Therefore, the momentum of alpha1 is ## entirely determined by the energy in the center of mass frame, so we check in this function ## that the energy of the alpha1 macroparticles is as expected. On the other hand, the energy ## of alpha2 and alpha3 follows a continuous distribution within a given range. In this test, ## we check that this range is as expected by comparing the maximum and minimum energy of the ## obtained macroparticles to the theoretical maximum and minimum. ## Note that in the simulations, 6 macroparticles are generated during for each fusion event. ## The first and second macroparticles are alpha1 particles. The third and fourth are alpha2. ## The fifth and sixth are alpha3. energy_alpha_simulation = compute_energy_array(data, "alpha", "end", m_a) z_alpha = data["alpha_z_end"] # Loop over all slices (i.e. cells in the z direction) for slice_number in range(1, size_z): ## Kinetic energy in the lab frame before fusion E_kinetic_com_before = E_com[slice_number] ## Total (kinetic + mass) energy in the lab frame after ## proton + boron 11 -> alpha + beryllium 8 E_total_com_after = E_kinetic_com_before + E_fusion + (m_a + m_be)*scc.c**2 ## Corresponding momentum norm squared of alpha1/beryllium p_sq_after = E_com_to_p_sq_com(m_a, m_be, E_total_com_after) ## Corresponding kinetic energy for alpha1 energy_alpha1_theory = p_sq_to_kinetic_energy(p_sq_after, m_a) ## Corresponding kinetic energy for beryllium energy_beryllium_theory = p_sq_to_kinetic_energy(p_sq_after, m_be) ## Corresponding kinetic energy for alpha2 + alpha3 after beryllium decay energy_alpha2_plus_3_theory = energy_beryllium_theory + E_decay ## Compute the theoretical maximum and minimum energy of alpha2 and alpha3. This ## calculation is done nonrelativistically, by noting that the maximum (minimum) energy ## corresponds to an alpha emitted exactly in the (opposite) direction of the beryllium ## in the center of mass frame. This calculation involves solving a polynomial equation of ## order 2 in p_alpha23. max_p_alpha23 = 0.5*(np.sqrt(p_sq_after) + \ np.sqrt(4*m_a*energy_alpha2_plus_3_theory - p_sq_after)) min_p_alpha23 = 0.5*(np.sqrt(p_sq_after) - \ np.sqrt(4*m_a*energy_alpha2_plus_3_theory - p_sq_after)) max_energy_alpha23 = max_p_alpha23**2/(2.*m_a) min_energy_alpha23 = min_p_alpha23**2/(2.*m_a) ## Get the energy of all alphas in the slice energy_alpha_slice = energy_alpha_simulation[(z_alpha >= slice_number)* \ (z_alpha < (slice_number + 1))] ## Energy of alphas1 (here, first macroparticle of each fusion event) in the slice energy_alpha1_simulation = energy_alpha_slice[::6] ## Energy of alphas2 (here, third macroparticle of each fusion event) in the slice energy_alpha2_simulation = energy_alpha_slice[2::6] ## Energy of alphas3 (here, fifth macroparticle of each fusion event) in the slice energy_alpha3_simulation = energy_alpha_slice[4::6] assert(np.all(is_close(energy_alpha1_simulation, energy_alpha1_theory, rtol=5.e-8))) assert(is_close(np.amax(energy_alpha2_simulation), max_energy_alpha23, rtol=1.e-2)) assert(is_close(np.amin(energy_alpha2_simulation), min_energy_alpha23, rtol=1.e-2)) assert(is_close(np.amax(energy_alpha3_simulation), max_energy_alpha23, rtol=1.e-2)) assert(is_close(np.amin(energy_alpha3_simulation), min_energy_alpha23, rtol=1.e-2)) def check_initial_energy2(data): ## In WarpX, the initial momentum of the alphas is computed assuming that the fusion process ## takes place in two steps: ## (1): proton + boron 11 -> alpha + beryllium 8 ## (2): beryllium 8 -> alpha + alpha ## The alpha generated in the first step (labeled alpha1) generally has a different initial ## energy distribution than the alphas generated in the second step (labeled alpha2 and ## alpha3). ## In the second test, we are in the boron rest frame. In this case, the momentum of each alpha ## follows a continuous distribution within a given range. In this function, we verify that ## this range is as expected by comparing the maximum and minimum energy of the obtained ## macroparticles to the theoretical maximum and minimum. Be aware that the range for alpha1 ## is not the same as the range for alpha2 and alpha3 (typically alpha1 particles will carry ## more energy). ## Note that in the simulations, 6 macroparticles are generated during for each fusion event. ## The first and second macroparticles are alpha1 particles. The third and fourth are alpha2. ## The fifth and sixth are alpha3. energy_alpha_simulation = compute_energy_array(data, "alpha", "end", m_a) z_alpha = data["alpha_z_end"] # Loop over all slices (i.e. cells in the z direction) for slice_number in range(1, size_z): ## For simplicity, all the calculations in this functino are done nonrelativistically ## Proton kinetic energy in the lab frame before fusion E_proton_nonrelativistic = Energy_step*slice_number**2 ## Corresponding square norm of proton momentum p_proton_sq = 2.*scc.m_p*E_proton_nonrelativistic ## Kinetic energy in the lab frame after ## proton + boron 11 -> alpha + beryllium 8 E_after_fusion = E_proton_nonrelativistic + E_fusion ## Compute the theoretical maximum and minimum energy of alpha1 in the lab frame. This ## calculation is done by noting that the maximum (minimum) energy corresponds to an alpha ## emitted exactly in the (opposite) direction of the proton in the lab frame. This ## calculation involves solving a polynomial equation of order 2 in p_alpha1. max_p_alpha1 = (m_a/m_be*np.sqrt(p_proton_sq) + \ np.sqrt(-m_a/m_be*p_proton_sq + 2.*E_after_fusion*m_a*(m_a/m_be + 1.))) / \ (m_a/m_be + 1.) min_p_alpha1 = (m_a/m_be*np.sqrt(p_proton_sq) - \ np.sqrt(-m_a/m_be*p_proton_sq + 2.*E_after_fusion*m_a*(m_a/m_be + 1.))) / \ (m_a/m_be + 1.) max_energy_alpha1 = max_p_alpha1**2/(2*m_a) min_energy_alpha1 = min_p_alpha1**2/(2*m_a) ## Corresponding max/min kinetic energy of Beryllium in the lab frame max_E_beryllium = E_after_fusion - min_energy_alpha1 min_E_beryllium = E_after_fusion - max_energy_alpha1 ## Corresponding max/min momentum square of Beryllium in the lab frame max_p_sq_beryllium = 2.*m_be*max_E_beryllium min_p_sq_beryllium = 2.*m_be*min_E_beryllium ## Corresponding max/min kinetic energy in the lab frame for alpha2 + alpha3 after ## Beryllium decay max_energy_alpha2_plus_3 = max_E_beryllium + E_decay min_energy_alpha2_plus_3 = min_E_beryllium + E_decay ## Compute the theoretical maximum and minimum energy of alpha2 and alpha3 in the lab ## frame. This calculation is done by noting that the maximum (minimum) energy corresponds ## to an alpha emitted exactly in the (opposite) direction of a beryllium with energy ## max_E_beryllium (min_E_beryllium). This calculation involves solving a polynomial ## equation of order 2 in p_alpha23. max_p_alpha23 = 0.5*(np.sqrt(max_p_sq_beryllium) + \ np.sqrt(4*m_a*max_energy_alpha2_plus_3 - max_p_sq_beryllium)) min_p_alpha23 = 0.5*(np.sqrt(min_p_sq_beryllium) - \ np.sqrt(4*m_a*min_energy_alpha2_plus_3 - min_p_sq_beryllium)) max_energy_alpha23 = max_p_alpha23**2/(2*m_a) min_energy_alpha23 = min_p_alpha23**2/(2*m_a) ## Get the energy of all alphas in the slice energy_alpha_slice = energy_alpha_simulation[(z_alpha >= slice_number)* \ (z_alpha < (slice_number + 1))] ## Energy of alphas1 (here, first macroparticle of each fusion event) in the slice energy_alpha1_simulation = energy_alpha_slice[::6] ## Energy of alphas2 (here, third macroparticle of each fusion event) in the slice energy_alpha2_simulation = energy_alpha_slice[2::6] ## Energy of alphas3 (here, fifth macroparticle of each fusion event) in the slice energy_alpha3_simulation = energy_alpha_slice[4::6] assert(is_close(np.amax(energy_alpha1_simulation), max_energy_alpha1, rtol=1.e-2)) assert(is_close(np.amin(energy_alpha1_simulation), min_energy_alpha1, rtol=1.e-2)) ## Tolerance is quite high below because we don't have a lot of alphas to produce good ## statistics and an event like alpha1 emitted exactly in direction of proton & alpha2 ## emitted exactly in direction opposite to Beryllium is somewhat rare. assert(is_close(np.amax(energy_alpha2_simulation), max_energy_alpha23, rtol=2.5e-1)) assert(is_close(np.amin(energy_alpha2_simulation), min_energy_alpha23, rtol=2.5e-1)) assert(is_close(np.amax(energy_alpha3_simulation), max_energy_alpha23, rtol=2.5e-1)) assert(is_close(np.amin(energy_alpha3_simulation), min_energy_alpha23, rtol=2.5e-1)) def check_xy_isotropy(data): ## Checks that the alpha particles are emitted isotropically in x and y average_px_sq = np.average(data["alpha_px_end"]*data["alpha_px_end"]) average_py_sq =

np.average(data["alpha_py_end"]*data["alpha_py_end"])

numpy.average

"""""" # -*- coding: utf-8 -*- # date: 2021 # author: AllChooseC import os import numpy as np from tensorboardX import SummaryWriter import torch from tqdm import tqdm from utils import EarlyStopping, class_penalty class_distribution = [59.68, 8.68, 28.55, 3.08] # 2017 class_distribution = [59.22, 8.65, 28.80, 3.33] def loss_batch(model, loss_func, xb, yb, opt=None, metric=None): """Calculates the loss and metric value for a batch of data, and optionally performs gradient descent if an optimizer is provided.""" preds = model(xb) loss = loss_func(preds, yb, weight=class_penalty(class_distribution, class_penalty=0.2)) if opt is not None: loss.backward() # Compute gradients opt.step() # Update parameters opt.zero_grad() # Reset gradients metric_result = None if metric is not None: metric_result = metric(preds, yb) # Compute the metric return loss.item(), len(xb), metric_result def evaluate(model, loss_func, valid_dl, metric=None): """""" with torch.no_grad(): # Pass each batch through the model results = [loss_batch(model, loss_func, xb, yb, metric=metric) for xb, yb in tqdm(valid_dl)] losses, nums, metrics = zip(*results) # Separate losses, counts and metrics total = np.sum(nums) # Total size of the dataset avg_loss = np.sum(np.multiply(losses, nums)) / total avg_metric = None if metric is not None: avg_metric = np.sum(np.multiply(metrics, nums)) / total return avg_loss, total, avg_metric def learn_on_batch(model, loss_func, train_dl, opt=None, metric=None): """""" # Pass each batch through the model results = [loss_batch(model, loss_func, xb, yb, opt, metric=metric) for xb, yb in tqdm(train_dl)] losses, nums, metrics = zip(*results) # Separate losses, counts and metrics total = np.sum(nums) # Total size of the dataset avg_loss = np.sum(

np.multiply(losses, nums)

numpy.multiply

# This code is part of Qiskit. # # (C) Copyright IBM 2019, 2021. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """Analytical Quantum Gradient Descent (AQGD) optimizer.""" import logging from typing import Callable, Tuple, List, Dict, Union import numpy as np from qiskit.utils.validation import validate_range_exclusive_max from .optimizer import Optimizer, OptimizerSupportLevel from ..exceptions import AlgorithmError logger = logging.getLogger(__name__) class AQGD(Optimizer): """Analytic Quantum Gradient Descent (AQGD) with Epochs optimizer. Performs gradient descent optimization with a momentum term, analytic gradients, and customized step length schedule for parametrized quantum gates, i.e. Pauli Rotations. See, for example: * <NAME>, <NAME>, <NAME>, and <NAME>. (2018). Quantum circuit learning. Phys. Rev. A 98, 032309. https://arxiv.org/abs/1803.00745 * <NAME>, <NAME>, <NAME>, <NAME>, <NAME>. (2019). Evaluating analytic gradients on quantum hardware. Phys. Rev. A 99, 032331. https://arxiv.org/abs/1811.11184 for further details on analytic gradients of parametrized quantum gates. Gradients are computed "analytically" using the quantum circuit when evaluating the objective function. """ _OPTIONS = ['maxiter', 'eta', 'tol', 'disp', 'momentum', 'param_tol', 'averaging'] def __init__(self, maxiter: Union[int, List[int]] = 1000, eta: Union[float, List[float]] = 1.0, tol: float = 1e-6, # this is tol momentum: Union[float, List[float]] = 0.25, param_tol: float = 1e-6, averaging: int = 10) -> None: """ Performs Analytical Quantum Gradient Descent (AQGD) with Epochs. Args: maxiter: Maximum number of iterations (full gradient steps) eta: The coefficient of the gradient update. Increasing this value results in larger step sizes: param = previous_param - eta * deriv tol: Tolerance for change in windowed average of objective values. Convergence occurs when either objective tolerance is met OR parameter tolerance is met. momentum: Bias towards the previous gradient momentum in current update. Must be within the bounds: [0,1) param_tol: Tolerance for change in norm of parameters. averaging: Length of window over which to average objective values for objective convergence criterion Raises: AlgorithmError: If the length of ``maxiter``, `momentum``, and ``eta`` is not the same. """ super().__init__() if isinstance(maxiter, int): maxiter = [maxiter] if isinstance(eta, (int, float)): eta = [eta] if isinstance(momentum, (int, float)): momentum = [momentum] if len(maxiter) != len(eta) or len(maxiter) != len(momentum): raise AlgorithmError("AQGD input parameter length mismatch. Parameters `maxiter`, " "`eta`, and `momentum` must have the same length.") for m in momentum: validate_range_exclusive_max('momentum', m, 0, 1) self._eta = eta self._maxiter = maxiter self._momenta_coeff = momentum self._param_tol = param_tol self._tol = tol self._averaging = averaging # state self._avg_objval = None self._prev_param = None self._eval_count = 0 # function evaluations self._prev_loss = [] # type: List[float] self._prev_grad = [] # type: List[List[float]] def get_support_level(self) -> Dict[str, OptimizerSupportLevel]: """ Support level dictionary Returns: Dict[str, int]: gradient, bounds and initial point support information that is ignored/required. """ return { 'gradient': OptimizerSupportLevel.ignored, 'bounds': OptimizerSupportLevel.ignored, 'initial_point': OptimizerSupportLevel.required } def _compute_objective_fn_and_gradient(self, params: List[float], obj: Callable) -> Tuple[float, np.array]: """ Obtains the objective function value for params and the analytical quantum derivatives of the objective function with respect to each parameter. Requires 2*(number parameters) + 1 objective evaluations Args: params: Current value of the parameters to evaluate the objective function obj: Objective function of interest Returns: Tuple containing the objective value and array of gradients for the given parameter set. """ num_params = len(params) param_sets_to_eval = params + np.concatenate( (np.zeros((1, num_params)), # copy of the parameters as is np.eye(num_params) * np.pi / 2, # copy of the parameters with the positive shift -np.eye(num_params) * np.pi / 2), # copy of the parameters with the negative shift axis=0) # Evaluate, # reshaping to flatten, as expected by objective function values = np.array(obj(param_sets_to_eval.reshape(-1))) # Update number of objective function evaluations self._eval_count += 2 * num_params + 1 # return the objective function value obj_value = values[0] # return the gradient values gradient = 0.5 * (values[1:num_params + 1] - values[1 + num_params:]) return obj_value, gradient def _update(self, params: np.array, gradient: np.array, mprev: np.array, step_size: float, momentum_coeff: float) -> Tuple[List[float], List[float]]: """ Updates full parameter array based on a step that is a convex combination of the gradient and previous momentum Args: params: Current value of the parameters to evaluate the objective function at gradient: Gradient of objective wrt parameters mprev: Momentum vector for each parameter step_size: The scaling of step to take momentum_coeff: Bias towards previous momentum vector when updating current momentum/step vector Returns: Tuple of the updated parameter and momentum vectors respectively. """ # Momentum update: # Convex combination of previous momentum and current gradient estimate mnew = (1 - momentum_coeff) * gradient + momentum_coeff * mprev params -= step_size * mnew return params, mnew def _converged_objective(self, objval: float, tol: float, window_size: int) -> bool: """ Tests convergence based on the change in a moving windowed average of past objective values Args: objval: Current value of the objective function tol: tolerance below which (average) objective function change must be window_size: size of averaging window Returns: Bool indicating whether or not the optimization has converged. """ # If we haven't reached the required window length, # append the current value, but we haven't converged if len(self._prev_loss) < window_size: self._prev_loss.append(objval) return False # Update last value in list with current value self._prev_loss.append(objval) # (length now = n+1) # Calculate previous windowed average # and current windowed average of objective values prev_avg = np.mean(self._prev_loss[:window_size]) curr_avg = np.mean(self._prev_loss[1:window_size + 1]) self._avg_objval = curr_avg # Update window of objective values # (Remove earliest value) self._prev_loss.pop(0) if np.absolute(prev_avg - curr_avg) < tol: # converged logger.info("Previous obj avg: %f\nCurr obj avg: %f", prev_avg, curr_avg) return True return False def _converged_parameter(self, parameter: List[float], tol: float) -> bool: """ Tests convergence based on change in parameter Args: parameter: current parameter values tol: tolerance for change in norm of parameters Returns: Bool indicating whether or not the optimization has converged """ if self._prev_param is None: self._prev_param = np.copy(parameter) return False order = np.inf p_change =

np.linalg.norm(self._prev_param - parameter, ord=order)

numpy.linalg.norm

import os import sys file_dir = os.path.dirname(__file__) sys.path.append(file_dir) import numpy as np import torch from pytorch_resnet import ResNet43 import torch.nn.functional as F import kornia as K import torchvision from matplotlib import pyplot as plt class Attention: def __init__(self,in_shape,n_rotations,preprocess,device,lite=False): # TODO BY HAOJIE: add lite model self.device = device self.preprocess = preprocess self.n_rotations = n_rotations max_dim = np.max(in_shape[:2]) max_dim = 480 self.padding = np.zeros((3, 2), dtype=int) pad = (max_dim - np.array(in_shape[:2])) / 2 self.padding[:2] = pad.reshape(2, 1) in_shape = np.array(in_shape) in_shape += np.sum(self.padding, axis=1) in_shape = tuple(in_shape) self.in_type = in_shape[-1] # self.in_type = 6, self.outdim=1 # get the location self.model = ResNet43(self.in_type,outdim=1,include_batch_normal=False).to(self.device) # use the location as pivot to rotate the image and get the angle #self.angle_model = ResNet43(self.in_type,outdim=1,include_batch_normal=False).to(self.device) self.optim = torch.optim.Adam(self.model.parameters(),lr=1e-5) #self.pad_2 = (80,80,80,80) def forward(self,in_img,softmax=True,train=True): #print('padding',self.padding) #print('img',in_img.shape) in_data = np.pad(in_img, self.padding, mode='constant') #print('indata',in_data.shape) in_data = self.preprocess(in_data) in_shape = (1,) + in_data.shape in_data = in_data.reshape(in_shape).transpose(0, 3, 1, 2) in_data = torch.from_numpy(in_data).to(self.device) #print(in_data.size()) # rotate image pivot = torch.as_tensor([in_data.shape[-2]/2,in_data.shape[-1]/2]) pivot =pivot.to(self.device).repeat(self.n_rotations//2,1).to(torch.float32) in_data = in_data.repeat(self.n_rotations//2,1,1,1) in_data = K.geometry.rotate(in_data,torch.from_numpy(-np.linspace(0., 360., self.n_rotations, endpoint=False, dtype=np.float32))[0:18].to(self.device), mode='nearest',center=pivot) #print('indata rotate 36/2',in_data.shape) #self.imshow(in_data,size=(36,12),name='rotation') if not train: self.model.eval() with torch.no_grad(): logits = self.model(in_data) else: logits = self.model(in_data) #print('logits',logits.shape) # rotate back logits = K.geometry.rotate(logits,torch.from_numpy(np.linspace(0., 360., self.n_rotations, endpoint=False,dtype=np.float32))[0:18].to(self.device), mode='nearest',center=pivot) #print('atenion logits1',logits.shape) #self.imshow(logits) #self.imshow(logits,size=(36,12),name='rotation_back') #logits = logits[:,:,80:-80,80:-80] #print('first crop',logits.size()) c0 = self.padding[:2, 0] c1 = c0 + in_img.shape[:2] #print('crop',c0) #print('crop',c1) logits = logits[:, :, c0[0]:c1[0], c0[1]:c1[1]] #print('second crop',logits.size()) #print('attention logits',logits.shape) #self.imshow(logits) output = logits.reshape(1,-1) if softmax: output = F.softmax(output,dim=-1) output = output.reshape(logits.shape[0],logits.shape[-2],logits.shape[-1]).cpu().detach().numpy() #print('output',output.shape) output = output.transpose(1,2,0) return output def train(self,in_img,p,theta,backprop=True): self.model.train() self.optim.zero_grad() output = self.forward(in_img,softmax=False) # Get label theta = (theta + 2*np.pi)%(2*np.pi) if theta >= np.pi: theta = theta -np.pi # angle label # dgree interval: 10 theta_i = theta / (2 * np.pi / 36) # theta_i is in range [0,17] theta_i = np.int32(

np.round(theta_i)

numpy.round

import numpy as np import pandas as pd from numpy.linalg.linalg import LinAlgError from numpy.linalg import norm import matplotlib.pyplot as plt import sys from ctypes import CDLL, POINTER from ctypes import c_int, c_double # Load the library I created for extra speed mylib = CDLL("./mylib.so") # C-type corresponding to numpy 2-dimensional array (matrix) ND_POINTER_1 = np.ctypeslib.ndpointer(dtype=np.float64, ndim=1, flags="C") ND_POINTER_2 = np.ctypeslib.ndpointer(dtype=np.float64, ndim=2, flags="C") ND_POINTER_3 = np.ctypeslib.ndpointer(dtype=np.float64, ndim=3, flags="C") # define the prototypes of the functions mylib.lennard_jones_function.argtypes = [ND_POINTER_2, c_int, c_double, c_double] mylib.lennard_jones_function.restype = c_double mylib.evaluate.argtypes = [ND_POINTER_3, ND_POINTER_2, c_int, c_int] mylib.evaluate.restype = None # For Genetic Algorithms # Evaluation def evaluate_population(population, number_of_atoms): values = np.zeros(shape=(population.shape[0], 1), dtype=np.float64) mylib.evaluate(population, values, population.shape[0], number_of_atoms) x_best_index = np.argmin(values) return values, x_best_index, values.min() # Selection def roulette_wheel_selection(population, evaluations, selective_pressure): descenting_order = np.argsort(evaluations, axis=0)[::-1] population = population[descenting_order] N = evaluations.shape[0] fitness_scores = np.zeros(shape=(N, 1)) random_vector = np.random.uniform(low=0, high=1, size=(N, 1)) selected_indexs = np.zeros(shape=(N, 1), dtype=int) for i, _ in enumerate(fitness_scores): fitness_scores[i] = 2 - selective_pressure + 2 * (selective_pressure - 1) * (i - 1) / (N - 1) selection_probabilities = fitness_scores / np.sum(fitness_scores) for rn_index, random_number in enumerate(random_vector): probability_sum = 0 for sp_index, selection_probability in enumerate(selection_probabilities): probability_sum += selection_probability if random_number <= probability_sum: selected_indexs[rn_index] = sp_index break return np.squeeze(population[selected_indexs]) def tournament_selection(population, evaluations, tournament_size, dtype): N = population.shape[0] tournament_winners = np.zeros(shape=population.shape, dtype=dtype) for i in range(0, N): random_choices = np.random.choice(N, size=tournament_size, replace=False) tournament_winner_index = evaluations[random_choices].argmin() tournament_winners[i] = population[random_choices][tournament_winner_index] return tournament_winners def new_population_top_N(population, mutated_population, population_evaluations, mutated_population_evaluations): N = population.shape[0] all_population = np.stack((population, mutated_population), axis=0) all_population = all_population.reshape((2 * population.shape[0], population.shape[1])) all_evaluations = np.stack((population_evaluations, mutated_population_evaluations)) all_evaluations = all_evaluations.reshape((2 * population_evaluations.shape[0], 1)) ascending_order = np.argsort(all_evaluations, axis=0) all_evaluations = all_evaluations[ascending_order] all_evaluations = all_evaluations.reshape((all_evaluations.shape[0], 1)) all_population = all_population[ascending_order] all_population = np.squeeze(all_population) return all_population[0:N], all_evaluations[0:N] # Genetic Algorithm Binary def calculate_number_of_bits(Umin, Umax, error): length_of_space = Umax - Umin possible_numbers = 1 + length_of_space / error for n in range(1, 64): if np.power(2, n-1) < possible_numbers <= np.power(2, n): return n def calculate_base_10(binary_number): number_base_10 = 0 for i, bi in enumerate(binary_number): number_base_10 += bi * np.power(2, i) return number_base_10 def calculate_number_base_10_in_feasible_space(Umin, Umax, n_bits, number_base_10): length_of_space = Umax - Umin return Umin + number_base_10 * length_of_space / (np.power(2, n_bits) - 1) def decoder(population, Umin, Umax, number_of_atoms, dimensionality, n_bits): population_base_10 = np.zeros(shape=(population.shape[0], number_of_atoms, dimensionality)) for i, pi in enumerate(population): pi = np.array_split(pi, number_of_atoms) for j, pij in enumerate(pi): pij = np.array_split(pij, dimensionality) pij_base_10 = list() for binary_number in pij: number_base_10 = calculate_base_10(binary_number) number_base_10_fs = calculate_number_base_10_in_feasible_space(Umin, Umax, n_bits, number_base_10) pij_base_10.append(number_base_10_fs) population_base_10[i][j] = np.asarray(pij_base_10) return population_base_10 def initialize_binary_population(population_size, number_of_atoms, dimensionality, n_bits): population = np.random.randint(low=0, high=2, size=(population_size, number_of_atoms, dimensionality * n_bits)) population = population.reshape(population_size, number_of_atoms * dimensionality * n_bits) return population def crossover_binary_population(selected_population, crossover_rate, crossover_points): # crossover_rate = [0, 1] # crossover_points = m - 1, where m is the length of the dna N = selected_population.shape[0] to_crossover = np.random.uniform(low=0, high=1, size=(N, 1)) < crossover_rate to_crossover_indexes = np.where(np.any(to_crossover==True, axis=1))[0] crossover_population = np.array(selected_population) if to_crossover_indexes.shape[0] % 2 != 0: random_choice = np.random.randint(low=0, high=N) to_crossover_indexes = np.append(to_crossover_indexes, random_choice) parents = selected_population[to_crossover_indexes] children = np.zeros(shape=(parents.shape[0], parents.shape[1]), dtype=int) if parents.shape[0] == 0: return selected_population points_of_crossover = np.arange(1, selected_population.shape[1]) np.random.shuffle(points_of_crossover) points_of_crossover = points_of_crossover[:crossover_points] points_of_crossover = np.sort(points_of_crossover, axis=0) for i in range(0, parents.shape[0], 2): parent_0 = np.array_split(parents[i], points_of_crossover) parent_1 = np.array_split(parents[i + 1], points_of_crossover) child_0, child_1 = list(), list() for j in range(0, crossover_points + 1): if j % 2 == 0: child_0.append(parent_0[j]) child_1.append(parent_1[j]) else: child_0.append(parent_1[j]) child_1.append(parent_0[j]) child_0 = np.asarray(child_0, dtype=object) child_1 = np.asarray(child_1, dtype=object) children[i] = np.concatenate(child_0, axis=None) children[i + 1] = np.concatenate(child_1, axis=None) # Replace parents with their children for child_index, parent_index in enumerate(to_crossover_indexes): crossover_population[parent_index] = children[child_index] return crossover_population def mutation_binary_population(crossover_population, mutation_rate): # mutation_rate = [0, 1] mutated_population = np.array(crossover_population) for i, pi in enumerate(mutated_population): to_mutate = np.random.uniform(low=0, high=1, size=(pi.shape[0], 1)) < mutation_rate to_mutate_indexes = np.where(np.any(to_mutate==True, axis=1))[0] for j in to_mutate_indexes: pi[j] = 1 - pi[j] mutated_population[i] = pi return mutated_population def Genetic_Algorithm_Binary(Umin, Umax, number_of_atoms, selection_method): # Algorithm parameters population_size = 1000 selective_pressure = 1.3 # selective_pressure = [1, 2] tournament_size = 100 # tournament_size = [1, population_size] crossover_rate = 0.5 # crossover_rate = [0, 1] crossover_points = 6 # crossover_points = [0, m-1] mutation_rate = 0.1 # mutation_rate = [0, 1] # Do not change error = 1e-3 dimensionality = 3 # 3D space n_bits = calculate_number_of_bits(Umin, Umax, error) iteration = population_size best_iteration = iteration max_iterations = number_of_atoms * 1e+5 population = initialize_binary_population(population_size, number_of_atoms, dimensionality, n_bits) decoded_population = decoder(population, Umin, Umax, number_of_atoms, dimensionality, n_bits) population_evaluations, x_best_index, min_value = evaluate_population(decoded_population, number_of_atoms) x_best = population[x_best_index] x_best_fvalue = min_value while(True): if (max_iterations <= iteration): break iteration += population_size if(selection_method == "rw"): selected_population = roulette_wheel_selection(population, population_evaluations, selective_pressure) else: selected_population = tournament_selection(population, population_evaluations, tournament_size, int) crossover_population = crossover_binary_population(selected_population, crossover_rate, crossover_points) mutated_population = mutation_binary_population(crossover_population, mutation_rate) decoded_population = decoder(mutated_population, Umin, Umax, number_of_atoms, dimensionality, n_bits) mutated_population_evaluations, _, _ = evaluate_population(decoded_population, number_of_atoms) population, population_evaluations = new_population_top_N(population, mutated_population, population_evaluations, mutated_population_evaluations) if population_evaluations[0] < x_best_fvalue: x_best, x_best_fvalue = population[0], population_evaluations[0] best_iteration = iteration print("Iterations: %d/%d Lennard-Jones potential: %.10f!!!" % (iteration, max_iterations, x_best_fvalue)) else: print("Iterations: %d/%d Lennard-Jones potential: %.10f" % (iteration, max_iterations, x_best_fvalue)) x_best = x_best.reshape((1, x_best.size)) population = np.append(population, x_best, axis=0) decoded_population = decoder(population, Umin, Umax, number_of_atoms, dimensionality, n_bits) population_evaluations, x_best_index, x_best_fvalue = evaluate_population(decoded_population, number_of_atoms) x_best = decoded_population[x_best_index] return x_best, x_best_fvalue, decoded_population, population_evaluations, best_iteration # Genetic Algorithm Real def initialize_real_population(Umin, Umax, population_size, number_of_atoms, dimensionality): population = np.random.uniform(low=Umin, high=Umax, size=(population_size, number_of_atoms, dimensionality)) return population def crossover_real_population(selected_population, crossover_rate, delta=0.25): # crossover_rate = [0, 1] # delta > 0 N = selected_population.shape[0] to_crossover = np.random.uniform(low=0, high=1, size=(N, 1)) < crossover_rate to_crossover_indexes = np.where(np.any(to_crossover==True, axis=1))[0] crossover_population = np.array(selected_population) if to_crossover_indexes.shape[0] % 2 != 0: random_choice = np.random.randint(low=0, high=N) to_crossover_indexes = np.append(to_crossover_indexes, random_choice) parents = selected_population[to_crossover_indexes] children = np.zeros(shape=parents.shape, dtype=float) if parents.shape[0] == 0: return selected_population for i in range(0, parents.shape[0], 2): # Create a pair of children for a pair of parents for j in range(0, 2): random_vector = np.random.uniform(low=-delta, high=1+delta, size=selected_population.shape[1]) child = np.multiply(random_vector, parents[i]) + np.multiply((1 - random_vector), parents[i + 1]) children[i + j] = child # Replace parents with their children for child_index, parent_index in enumerate(to_crossover_indexes): crossover_population[parent_index] = children[child_index] return crossover_population def mutation_real_population(crossover_population, mutation_rate, Umin, Umax): # mutation_rate = [0, 1] mutated_population = np.array(crossover_population) for i, pi in enumerate(mutated_population): to_mutate = np.random.uniform(low=0, high=1, size=(pi.shape[0], 1)) < mutation_rate to_mutate_indexes = np.where(np.any(to_mutate==True, axis=1))[0] for j in to_mutate_indexes: distance_from_Umin = abs(abs(pi[j]) - abs(Umin)) distance_from_Umax = abs(abs(pi[j]) - abs(Umax)) min_distance = min(distance_from_Umin, distance_from_Umax) sigma = min_distance / 3 zj = np.random.normal(0, sigma) pi[j] = pi[j] + zj mutated_population[i] = pi return mutated_population def Genetic_Algorithm_Real(Umin, Umax, number_of_atoms, selection_method): # Algorithm parameters population_size = 1000 selective_pressure = 1.3 tournament_size = 100 crossover_rate = 0.5 mutation_rate = 0.1 # Do not change dimensionality = 3 iteration = population_size best_iteration = iteration max_iterations = number_of_atoms * 1e+5 population = initialize_real_population(Umin, Umax, population_size, number_of_atoms, dimensionality) population_evaluations, x_best_index, min_value = evaluate_population(population, number_of_atoms) # vectorize population for the alogrithm population = population.reshape(population_size, number_of_atoms * dimensionality) x_best = population[x_best_index] x_best_fvalue = min_value while(True): if (max_iterations <= iteration): break iteration += population_size if selection_method == "rw": selected_population = roulette_wheel_selection(population, population_evaluations, selective_pressure) else: selected_population = tournament_selection(population, population_evaluations, tournament_size, float) crossover_population = crossover_real_population(selected_population, crossover_rate) mutated_population = mutation_real_population(crossover_population, mutation_rate, Umin, Umax) # create mutated_population as array for evaluation array_mutated_population = mutated_population.reshape(mutated_population.shape[0], number_of_atoms, dimensionality) mutated_population_evaluations, _, _ = evaluate_population(array_mutated_population, number_of_atoms) population, population_evaluations = new_population_top_N(population, mutated_population, population_evaluations, mutated_population_evaluations) if population_evaluations[0] < x_best_fvalue: x_best, x_best_fvalue = population[0], population_evaluations[0] best_iteration = iteration print("Iterations: %d/%d Lennard-Jones potential: %.10f!!!" % (iteration, max_iterations, x_best_fvalue)) else: print("Iterations: %d/%d Lennard-Jones potential: %.10f" % (iteration, max_iterations, x_best_fvalue)) x_best = x_best.reshape(number_of_atoms, dimensionality) population = population.reshape(population.shape[0], number_of_atoms, dimensionality) return x_best, x_best_fvalue.item(), population, population_evaluations, best_iteration # Particle Swarm Optimization def initialize_velocity(swarm_size, number_of_atoms, dimensionality, max_velocity): velocity = np.random.uniform(low=-max_velocity, high=max_velocity, size=(swarm_size, number_of_atoms, dimensionality)) return velocity def create_neighborhoods(swarm_size, neighborhood_radius): # neighborhood_radius = [0, N/2] neighborhoods = list() for i in range(0, swarm_size): neighborhood_i = list() for j in range(i - neighborhood_radius, i + neighborhood_radius + 1): neighborhood_i.append(j % swarm_size) neighborhoods.append(np.asarray(neighborhood_i)) return np.asarray(neighborhoods) def update_velocity(swarm, velocity, best_positions, best_positions_evaluations, neighborhoods, c1=2.05, c2=2.05, x=0.729): rgn_0 = np.random.uniform(low=0, high=1) rgn_1 = np.random.uniform(low=0, high=1) best_neighbors = np.zeros(shape=swarm.shape) for particle_i, neighbors_i in enumerate(neighborhoods): best_neighbor_index = 0 best_neighbor_f_value = best_positions_evaluations[neighbors_i[0]] for j, neighbor_ij in enumerate(neighbors_i): neighborij_f_value = best_positions_evaluations[neighbor_ij] if neighborij_f_value < best_neighbor_f_value: best_neighbor_index = j best_neighbor_f_value = neighborij_f_value best_neighbors[particle_i] = swarm[neighbors_i[best_neighbor_index]] velocity = x * (velocity + rgn_0 * c1 * (best_positions - swarm) + rgn_1 * c2 * (best_neighbors - swarm)) return velocity def update_particles(swarm, velocity): return swarm + velocity def check_velocity_bounds(velocity, max_velocity): for i, vi in enumerate(velocity): for j, vij in enumerate(vi): if vij < -max_velocity: vij = -max_velocity elif max_velocity < vij: vij = max_velocity vi[j] = vij velocity[i] = vi return velocity def check_particles_bounds(swarm, Umin, Umax): for i, pi in enumerate(swarm): for j, pij in enumerate(pi): if pij < Umin: pij = Umin elif Umax < pij: pij = Umax pi[j] = pij swarm[i] = pi return swarm def update_best_positions(best_positions, best_positions_evaluations, swarm, swarm_evaluations): for i, _ in enumerate(swarm_evaluations): if swarm_evaluations[i] < best_positions_evaluations[i]: best_positions[i] = swarm[i] best_positions_evaluations[i] = swarm_evaluations[i] return best_positions, best_positions_evaluations def Particle_Swarm_Optimization(Umin, Umax, number_of_atoms, model): # Algorithm parameters swarm_size = 1000 alpha = 0.5 # alpha = [0, 1] # neighborhood_radius = [0, swarm_size / 2] if model == "lbest": neighborhood_radius = 5 else: neighborhood_radius = int(swarm_size / 2) # Do not change max_velocity = alpha * (Umax - Umin) iteration = swarm_size best_iteration = iteration max_iterations = number_of_atoms * 1e+5 dimensionality = 3 # Initializations swarm = initialize_real_population(Umin, Umax, swarm_size, number_of_atoms, dimensionality) velocity = initialize_velocity(swarm_size, number_of_atoms, dimensionality, max_velocity) best_positions =

np.array(swarm)

numpy.array

import numpy as np from collections import defaultdict import torch import pandas as pd from scipy.sparse import csr_matrix from scipy.sparse.csgraph import connected_components import cosypose.utils.tensor_collection as tc from cosypose.lib3d.transform_ops import invert_T, compute_transform_from_pose9d from cosypose.lib3d.camera_geometry import project_points from cosypose.lib3d.symmetric_distances import symmetric_distance_reprojected from .ransac import make_obj_infos from cosypose.utils.logging import get_logger from cosypose.utils.timer import Timer logger = get_logger(__name__) def make_view_groups(pairs_TC1C2): views = pairs_TC1C2.infos.loc[:, ['view1', 'view2']].values.T views = np.unique(views.reshape(-1)) view_df = pd.DataFrame(dict(view_id=views, view_local_id=np.arange(len(views)))) view_to_id = view_df.set_index('view_id') view1 = view_to_id.loc[pairs_TC1C2.infos.loc[:, 'view1'], 'view_local_id'].values view2 = view_to_id.loc[pairs_TC1C2.infos.loc[:, 'view2'], 'view_local_id'].values data = np.ones(len(view1)) n_views = len(views) graph = csr_matrix((data, (view1, view2)), shape=(n_views, n_views)) n_components, ids = connected_components(graph, directed=True, connection='strong') view_df['view_group'] = ids view_df = view_df.drop(columns=['view_local_id']) return view_df class SamplerError(Exception): pass class MultiviewRefinement: def __init__(self, candidates, cameras, pairs_TC1C2, mesh_db): self.device, self.dtype = candidates.device, candidates.poses.dtype self.mesh_db = mesh_db cameras = cameras.to(self.device).to(self.dtype) pairs_TC1C2 = pairs_TC1C2.to(self.device).to(self.dtype) view_ids = np.unique(candidates.infos['view_id']) keep_ids = np.logical_and( np.isin(pairs_TC1C2.infos['view1'], view_ids), np.isin(pairs_TC1C2.infos['view2'], view_ids), ) pairs_TC1C2 = pairs_TC1C2[

np.where(keep_ids)

numpy.where

import numpy as np import tensorflow as tf from tqdm import tqdm import copy from flearn.models.client import Client from flearn.utils.model_utils import Metrics from flearn.utils.tf_utils import process_grad, norm_grad, norm_grad_sparse class BaseFedarated(object): def __init__(self, params, learner, dataset): # transfer parameters to self for key, val in params.items(): setattr(self, key, val); # create worker nodes tf.reset_default_graph() self.client_model = learner(*params['model_params'], self.q, self.inner_opt, self.seed) self.clients = self.setup_clients(dataset, self.dynamic_lam, self.client_model) print('{} Clients in Total'.format(len(self.clients))) self.latest_model = copy.deepcopy(self.client_model.get_params()) # initialize system metrics self.metrics = Metrics(self.clients, params) def __del__(self): self.client_model.close() def setup_clients(self, dataset, dynamic=0, model=None): '''instantiates clients based on given train and test data directories Return: list of Clients ''' users, groups, train_data, test_data = dataset if len(groups) == 0: groups = [None for _ in users] all_clients = [Client(u, g, train_data[u], test_data[u], dynamic, model) for u, g in zip(users, groups)] return all_clients def train_error(self): num_samples = [] tot_correct = [] losses = [] self.client_model.set_params(self.latest_model) for c in self.clients: ct, cl, ns = c.train_error() tot_correct.append(ct*1.0) losses.append(cl * 1.0) num_samples.append(ns) return np.array(num_samples), np.array(tot_correct), np.array(losses) def test(self): '''tests self.latest_model on given clients ''' num_samples = [] tot_correct = [] losses = [] self.client_model.set_params(self.latest_model) for c in self.clients: ct, cl, ns = c.test() tot_correct.append(ct*1.0) num_samples.append(ns) losses.append(cl * 1.0) return np.array(num_samples), np.array(tot_correct), np.array(losses) def validate(self): '''tests self.latest_model on given clients ''' num_samples = [] tot_correct = [] self.client_model.set_params(self.latest_model) for c in self.clients: ct, ns = c.validate() tot_correct.append(ct*1.0) num_samples.append(ns) return np.array(num_samples), np.array(tot_correct) def test_resulting_model(self): num_samples = [] tot_correct = [] # self.client_model.set_params(self.latest_model) for c in self.clients: ct, ns = c.test() tot_correct.append(ct*1.0) num_samples.append(ns) ids = [c.id for c in self.clients] groups = [c.group for c in self.clients] return ids, groups, num_samples, tot_correct def save(self): pass def select_clients(self, round, corrupt_id, num_clients=20): '''selects num_clients clients weighted by number of samples from possible_clients Args: num_clients: number of clients to select; default 20 note that within function, num_clients is set to min(num_clients, len(possible_clients)) Return: indices: an array of indices self.clients[] ''' num_clients = min(num_clients, len(self.clients)) # number of selected clients per round np.random.seed(round+4) non_corrupt_id = np.setdiff1d(range(len(self.clients)), corrupt_id) corrupt_fraction = len(corrupt_id) / len(self.clients) num_selected_corrupted = int(num_clients * corrupt_fraction) if self.sampling == 0: indices = np.random.choice(range(len(self.clients)), num_clients, replace=False, p=pk) return indices, np.asarray(self.clients)[indices] elif self.sampling == 1: num_samples = [] for client in self.clients: num_samples.append(client.train_samples) total_samples = np.sum(np.asarray(num_samples)) pk = [item * 1.0 / total_samples for item in num_samples] indices1 = np.random.choice(corrupt_id, num_selected_corrupted, replace=False, p=np.asarray(pk)[corrupt_id] / sum(np.asarray(pk)[corrupt_id])) indices2 = np.random.choice(non_corrupt_id, num_clients-num_selected_corrupted, replace=False, p=np.asarray(pk)[non_corrupt_id] / sum(np.asarray(pk)[non_corrupt_id])) indices = np.concatenate((indices1, indices2)) #print(indices1, indices2) return indices,

np.asarray(self.clients)

numpy.asarray

import numpy as np import logging _FFMPEG_INSTALLED = True try: import ffmpeg except Exception: _FFMPEG_INSTALLED = False logger = logging.getLogger(__name__) def video_write(fn, images, framerate=60, vcodec="libx264"): """ Save list of images to a video file. Source: https://github.com/kkroening/ffmpeg-python/issues/246#issuecomment-520200981 Modified so that framerate is given to .input(), as suggested in the thread, to avoid skipping frames. Parameters ---------- fn : string filename images : list or np.array list of images to save to a video. framerate : int """ global _FFMPEG_INSTALLED try: if len(images) == 0: logger.warning("Calling video_write() with empty images.") return if not _FFMPEG_INSTALLED: logger.error( "video_write(): Unable to save video, ffmpeg-python \ package required (https://github.com/kkroening/ffmpeg-python)" ) return if not isinstance(images, np.ndarray): images =

np.asarray(images)

numpy.asarray

# Copyright 2022 <NAME> # # Permission is hereby granted, free of charge, to any person obtaining a copy of this software # and associated documentation files (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all copies or # substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT # LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. import numpy as np class Component: def __init__(self, name, mode_name, current_ma): self.name = name self.mode_name = mode_name self.current_ma = current_ma class Stage: def __init__(self, delta_t_sec, components): self.delta_t_sec = delta_t_sec self.components = components class Thread: def __init__(self, name, stages): self.name = name self.stages = stages self.num_stages = len(stages) self.last_stage_change_t = 0.0 self.next_stage_change_t = self.last_stage_change_t + stages[0].delta_t_sec self.stage_index = 0 class SolarPanel: def __init__(self, rated_power_W, charge_efficiency=0.7, t_offset_sec = 0.0, clouds_tau=3600.0, clouds_cover = 1.0): self.rated_power_W = rated_power_W self.charge_efficiency = charge_efficiency self.t_offset_sec = t_offset_sec self.clouds_tau = clouds_tau self.clouds_cover = clouds_cover self.random_walk_val = 0.0 self.last_time_s = 0.0 self.time = [] self.power_history_W = [] self.random_walk_vals = [] def calculate_power(self, t): dt = t - self.last_time_s if(dt > 0.0): f = np.exp(-dt/self.clouds_tau) self.random_walk_val = f*self.random_walk_val + np.sqrt(1.0-f**2.0) * np.random.randn() power = self.rated_power_W*(1.0/0.65)*(np.sin(((2.0*np.pi)/86400)*(t-self.t_offset_sec)) - 0.35) if power < 0.0: power = 0.0 # Calculate threshold based on normal distribution if

np.abs(self.random_walk_val)

numpy.abs

# coding: utf8 # !/usr/env/python # This file has tests for the old style output writers to ensure backwards # compatibility. All of the existing tests for output writers are kept as is. # There are a few new ones too. import glob import os import numpy as np from terrainbento import Basic, NotCoreNodeBaselevelHandler from terrainbento.utilities import filecmp _TEST_OUTPUT_DIR = os.path.join(os.curdir, "output") _TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), "data") def get_output_filepath(filename): return os.path.join(_TEST_OUTPUT_DIR, filename) def cleanup_files(searchpath): files = glob.glob(searchpath) for f in files: os.remove(f) # Some output writers def output_writer_function_a(model): average_elevation =

np.mean(model.z[model.grid.core_nodes])

numpy.mean

import argparse import glob import os import sys import time from itertools import product, permutations import matplotlib.pyplot as plt import multiprocessing as mp import numpy as np import pandas as pd import seaborn as sns import statsmodels.nonparametric.api as smnp import swifter N_PROC = 1 CHUNK = 25 MIX = False BASE_DIR = '/home/jmcbride/Scales/Compared_data' RAW_DIR = '/home/jmcbride/Scales/Toy_model/Data/Raw/' PRO_DIR = '/home/jmcbride/Scales/Toy_model/Data/Processed/' DIST_DIR = '/home/jmcbride/Scales/Toy_model/Data/None_dist/' REAL_DIR = '/home/jmcbride/Scales/Real_scales' TEMP_MIN = 50. TEMP_MAX = 300. TEMP_LOW_MARGIN = 0.50 TEMP_HI_MARGIN = 1.50 N_TRIALS = 50 ALPHA_W = 0.1 def parse_arguments(): parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument('--partabase', action='store', default='None', type=str) parser.add_argument('-f', action='store', default='None', dest='fName', type=str) parser.add_argument('--sample', action='store_true', default=False, dest='sample',) return parser.parse_args() args = parse_arguments() def get_scale_from_pair_ints(pair_ints): ints = [int(y) for y in pair_ints.split(';')] return ';'.join(['0'] + [str(y) for y in np.cumsum(ints)]) def calculate_most_harmonic_neighbour(int_cents, sim_only=False, CENT_DIFF_MAX=22): best_ratio = [1,1] max_similarity = 0.0 cents = 0.0 for x in np.arange(1,75, dtype=float): cent_diff = 1200.*

np.log10((x+1.)/x)

numpy.log10

import occultquad import numpy as np def s2(r, b): if (b >= 1 + r): return (2 * np.pi / 3) elif (b <= r - 1): return 0 r_ = np.array([r], dtype=float) b_ = np.array([b], dtype=float) flux =

np.empty(1)

numpy.empty

''' Pull out HI properties (and/or others) from a set of point sources. Create a distance map as a function of distance from the nearest source. ''' import astropy.coordinates as coord from astropy.table import Table, Column import astropy.units as u import astropy.constants as const import numpy as np from galaxies import Galaxy import scipy.ndimage as nd from astropy.io import fits from spectral_cube import SpectralCube, Projection from spectral_cube.analysis_utilities import stack_spectra from astropy.utils.console import ProgressBar import matplotlib.pyplot as plt from plotting_styles import default_figure from constants import hi_freq, hi_mass_conversion # from paths import allfigs_path def distance_map_from_catalogue(gal, tab, header, ra_key="RA", dec_key="Dec", diam_key=None): ''' Create a distance map from a set of sky location in a catalogue. ''' if not isinstance(gal, Galaxy): raise TypeError("gal must be a Galaxy instance.") ra = tab[ra_key] dec = tab[dec_key] coords = coord.SkyCoord(ra, dec, frame='icrs', unit=(u.deg, u.deg)) # Assumes that the table column has a unit attached that Table can distinguish if diam_key is not None: # Assume pc. Lost units in the save table?? diams = tab[diam_key].quantity * u.pc radii = gal.radius(header=header) coord_map = gal.skycoord_grid(header=header) object_mask = np.zeros_like(coord_map.ra.value, dtype=int) # Loop through and mask points belonging at a remnant, or the nearest point for i, co in enumerate(coords): mask_index = np.unravel_index(coord_map.separation(co).argmin(), object_mask.shape) if diam_key is not None: # Major axis diameter diam_rad = (diams[i].to(u.pc) / gal.distance).to(u.dimensionless_unscaled).value * u.rad diam_pix = diam_rad.to(u.deg).value / np.abs(header['CDELT2']) # Gather all pixels with a circular region yy, xx = np.mgrid[-(int(diam_pix)//2 + 1):int(diam_pix)//2 + 1, -(int(diam_pix)//2 + 1):int(diam_pix)//2 + 1] # Find all pixels within the diameter valids = np.where(np.sqrt(yy**2 + xx**2) < diam_pix / 2.) y_pts = valids[0] + mask_index[0] x_pts = valids[1] + mask_index[1] mask_index = (y_pts, x_pts) object_mask[mask_index] = i + 1 # print(object_mask[mask_index]) # print(mask_index) # print((object_mask > 0).sum()) dist_transf = nd.distance_transform_edt(~(object_mask > 0)) return object_mask, dist_transf def find_bubble_props(dist_bins, int_profile, lwidth_profile, obj_diam, disk_height=100 * u.pc / np.cos(55.1 * u.deg), mass_conv_factor=None): ''' Dumb estimations of bubble properties based on integrated intensity and line width profiles. ''' # Define the shell radius based on the distance of the peak arg_max = np.argmax(int_profile) # If the centre is the peak, assume it is unresolved if arg_max == 0: shell_rad = obj_diam / 2. else: shell_rad = obj_diam / 2. + dist_bins[arg_max] # Assume a disk scale height and check if the radius of the shell # exceeds it if shell_rad > disk_height: # It has maybe broken out of the disk. Adjust volume as needed # Subtract off caps of the sphere vol = (4 * np.pi / 3.) * shell_rad**3 - \ (2 * np.pi / 3.) * (shell_rad - disk_height)**2 * (2 * shell_rad + disk_height) else: # Likely still contained within the disk vol = (4 * np.pi / 3.) * shell_rad**3 # Awful estimations of the velocity expansion. Assume velocity dispersion # is exactly the same... # Don't know how to do that with any sort of logic applied, so let it be # the dispersion in the peak bin. v_exp = lwidth_profile[arg_max] # Now the integ intensity. If unresolved, we don't have an estimate of the # background. Assume the last distance bin as a background?? Otherwise take # the larger of the innermost and outermost when resolved. peak_int = int_profile[arg_max] if arg_max == 0: bkg_int = int_profile[-1] else: bkg_int = max(int_profile[0], int_profile[-1]) hole_mass = np.pi * shell_rad**2 * bkg_int shell_mass = np.pi * shell_rad**2 * \ (peak_int - bkg_int) if mass_conv_factor is not None: hole_mass *= mass_conv_factor shell_mass *= mass_conv_factor # Estimate an avg volume density within the hole. Don't do this # for unresolved holes if arg_max == 0: energy = np.NaN * u.erg vol_dens = np.NaN * u.cm**-3 else: # Chevalier 74 expansion energy formula vol_dens = ((shell_mass / (1.4 * const.m_p)) / vol).to(u.cm**-3) energy = 5.3e43 * vol_dens.value**1.12 * \ shell_rad.to(u.pc).value**3.12 * v_exp.to(u.km / u.s).value**1.4 * u.erg return shell_rad, vol, v_exp, hole_mass, shell_mass, vol_dens, energy default_figure() # Update this for the server files (geometry should be the same though) gal = Galaxy("M33") gal.distance = 840 * u.kpc hi_cube = SpectralCube.read("/Volumes/Travel_Data/M33_2/HI/M33_14B-088_HI.clean.image.GBT_feathered.pbcov_gt_0.5_masked.fits") peak_vel = Projection.from_hdu(fits.open("/Volumes/Travel_Data/M33_2/HI/M33_14B-088_HI.clean.image.GBT_feathered.pbcov_gt_0.5_masked.peakvels.fits")) mom0 = Projection.from_hdu(fits.open("/Volumes/Travel_Data/M33_2/HI/M33_14B-088_HI.clean.image.GBT_feathered.pbcov_gt_0.5_masked.mom0.fits")) beam = mom0.beam moment0_Kkm_s = beam.jtok(hi_freq).value * mom0.value / 1000. moment0_surfdens = moment0_Kkm_s * hi_mass_conversion * (u.K * u.km / u.s) * np.cos(55.1 * u.deg) lwidth = Projection.from_hdu(fits.open("/Volumes/Travel_Data/M33_2/HI/M33_14B-088_HI.clean.image.GBT_feathered.pbcov_gt_0.5_masked.lwidth.fits")) snr_tab = Table.read("/Volumes/Travel_Data/M33_2/MMT_SNR_catalogue_long18_combined.txt", format='ascii') # Also consider weighting by something like ~1/sqrt(L) to place distances # on a common "scale" index_mask, dist_transf = \ distance_map_from_catalogue(gal, snr_tab, hi_cube.header, diam_key='D') # Get all points within ~100 pc. dist_limit = np.arange(10) * 100 * u.pc stacked_spectra = [] lwidth_bins = [] intint_bins = [] # Pick out individual regions num = index_mask.max() for n in ProgressBar(range(1, num + 1)): reg_mask = index_mask == n dist_transf_reg = nd.distance_transform_edt(~reg_mask) lwidth_reg = [] intint_reg = [] # Calculate avg properties within the region lwidth_reg.append([np.nanmean(lwidth[reg_mask].value), np.nanstd(lwidth[reg_mask].value) / np.sqrt(reg_mask.sum() / 41.)]) intint_reg.append([np.nanmean(moment0_surfdens[reg_mask].value), np.nanstd(moment0_surfdens[reg_mask].value) / np.sqrt(reg_mask.sum() / 41.)]) for i, (low, high) in enumerate(zip(dist_limit[:-1], dist_limit[1:])): # print("On bin {}".format(i + 1)) dist_ang_low = (low / gal.distance.to(u.pc)).value * u.rad dist_pix_low = dist_ang_low.to(u.deg).value / np.abs(hi_cube.header['CDELT2']) dist_ang_high = (high / gal.distance.to(u.pc)).value * u.rad dist_pix_high = dist_ang_high.to(u.deg).value / np.abs(hi_cube.header['CDELT2']) dist_mask = np.logical_and(dist_transf_reg > dist_pix_low, dist_transf_reg <= dist_pix_high) num_beams = dist_mask.sum() / 41. intint_reg.append([np.nanmean(moment0_surfdens[dist_mask].value),

np.nanstd(moment0_surfdens[dist_mask].value)

numpy.nanstd

import numpy as np from numba import * from numba.typed import Dict from numba.types import bool_ import timeit from time import process_time @njit def to_dependency_network(U: np.array, force_select: np.array, extended=False): """Form a dependency network such that the set of nodes S contains all graphlets and graphlets s,k \in U are connected if they share a force selected node v in their intersection. Edge weight $w$ is defined as $$ w_{sk} = |v \cap s \cap k|$$. Parameter `extended` controls for which type of dependency is used. By default the criteria is relaxed such that only graphlets that share force selected node(s) will be considered. Parameters ---------- U : weighted adjacency matrix for the dependency network force_select : set of nodes (as numpy array) that each graphlet configuration is required to contain extended : by default dependency edge is added only if graphlets contain same force selected node(s). Set True for building a network such that it takes into account all overlapping seed nodes Returns ------- U_out : output graphlets as rows of an array with shape (n_out, k_max) where elements are node indices in the adjacency matrix of the input network. Rows are padded from the right with -1 for graphlet sizes < k_max. (Note that n_out <= n) E_const : weighted adjacency matrix of the dependency network where nodes represent graphlets and edges dependencies between graphlets. Numpy array dimensions are (n_out,n_out) fs_map : mapping from force selected nodes to graphlets, numpy array dimensions: (n_out, n_fs) idxs : numpy array of row indeces that can be used for selecting the rows in U_out from U """ n = len(U) n_fs = len(force_select) E_const = np.zeros((n,n)) fs_map = np.zeros((n, n_fs), dtype=bool_) fs_idsn = Dict() fs = set(force_select) for k, v in zip(force_select, np.arange(n_fs)): fs_idsn[k] = v for i, Si in enumerate(U): fs_Si = fs & set(Si) if len(fs_Si) > 0: for u in fs_Si: fs_map[i,fs_idsn[u]] = True for i,Si in enumerate(U): for j,Sj in enumerate(U): if i != j: Si_s = set(Si) Sj_s = set(Sj) l_ij = len(Si_s & Sj_s) if extended else len(Si_s & Sj_s & fs) E_const[j,i] = len(Si_s & Sj_s) if l_ij > 0 else 0 idxs = np.array([(E_const[i,:] > 0).sum() != 0 for i in range(E_const.shape[0])]) E_const = E_const[idxs,:][:,idxs] fs_map = fs_map[idxs,:] return U[idxs,:], E_const, fs_map, idxs @njit def maximal_independent_set(A: np.array) -> np.array: """Sample maximal independent set (numpy implementation) using a greedy strategy. An independent set is a set of nodes such that the subgraph of G induced by these nodes contains no edges. A maximal independent set is an independent set such that it is not possible to add a new node and still get an independent set. Parameters ---------- A : weighted adjacency matrix for the dependency network Returns ------- s : indices of nodes in the found maximal independent set """ n = A.shape[0] D = np.array([False for _ in range(n)]) I = np.array([False for _ in range(n)]) seed_node = np.random.choice(n) neighbors = np.where(A[seed_node,:])[0] D[seed_node] = I[seed_node] = True D[neighbors] = True while np.sum(D) < n: node = np.random.choice(np.where(D != True)[0]) I[node] = True node_neighbors = np.where(A[node,:])[0] D[node_neighbors] = True D[node] = True s = np.where(I)[0] return s def sample_fs_configurations(A: np.array, U, fs, fs_map, target_time=30, adaptive=True, tol=0.99, n_min_target=10000, c_limit: int=10, verbose=False) -> list: """Sample maximal independent sets to determine a configuration of force selected nodes that guarantees near-minimal overlap. Employs adaptive enumeration, where the run length is determined by target_time parameter (and n_min_target sets the absolute minimum requirement). Parameters ---------- A : weighted adjacency matrix of the input network U : input graphlets as rows of an array with shape (n, k_max) where elements are node indices in the adjacency matrix of the input network. Rows are padded from the right with -1 for graphlet sizes < k_max fs : set of nodes that each graphlet configuration is required to contain fs_map : mapping of network nodes to graphlets (see `to_dependency_network` method) target_time : max time threshold for the run (unit in seconds) n_min_target : minimum number of configurations that should be generated Returns ------- ss (list of lists): configurations fs (np.array): updated set of force selected nodes Notes ----- An independent set is a set of nodes such that the subgraph of G induced by these nodes contains no edges. A maximal independent set is an independent set such that it is not possible to add a new node and still get an independent set. """ map_fs = lambda x: np.unique(np.where(fs_map[x,:])[1]) t = timeit.timeit(lambda: maximal_independent_set(A), number=100) n_sample = np.max([np.int32(100 / t), 1000]) rng = np.random.default_rng() u_sel = np.arange(U.shape[0]) n_fsa = len(fs) if verbose: print(':: Sampling configurations of s-nodes for force selection') print(':: --> Targeting {:.0f}s, threshold set @ {:.2f}%' .format(target_time, tol*100)) ls_exs = [] removed = [] enums = set() c = np.zeros(n_fsa, dtype=int) i = n_ss0 = exs0 = i0 = i0a = c_stop = 0 t0 = pt0 = process_time() while pt0 - t0 < target_time or len(enums) < n_min_target: for _ in range(n_sample): s = maximal_independent_set(A) fs_idxs = map_fs(s) c[fs_idxs] += 1 i+=1 l_so = len(set(fs_idxs)) if l_so == len(fs): s_prime = u_sel[s] enums.add(frozenset(s_prime)) else: ls_exs.append(l_so) if verbose: pt0, n_ss0, i0a = __output_stats(fs, enums, i, i0a, c, t0, pt0, n_ss0) # EVALUATE c_stop = c_stop + 1 if len(enums) == n_ss0 else 0 if c_stop == c_limit: if verbose: print(f'Run terminated due to {c_limit} successive empty batches.') break exs_frac = (len(ls_exs)-exs0) / (i - i0) if exs_frac > tol: if not adaptive: raise Exception('Configurations that satisfy the complete set of ' \ 'force selected nodes appear too infrequently. ' \ 'Either remove the problematic nodes or enable '\ 'adaptive running mode by setting `adaptive=True`.') fs, c, fs_map, A, u_sel, removed = __drop(fs, c, fs_map, A, u_sel, removed, verbose) i0 = i exs0 = len(ls_exs) enums = set() c_stop = 0 if verbose: print(':: Discarded {:.2f}% of the configurations'.format(100*exs_frac)) tdelta = process_time() - t0 if verbose: print(':: GENERATION STATS:\n* {} generated\n* {} accepted\n' \ '* {} discarded\n* {:.2f}% (+/-) {:.2f} of the FS nodes included on avg ' \ '\n* elapsed time: {:.2f}s.'.format(i,i-len(ls_exs),len(ls_exs), np.mean(ls_exs) / n_fsa*100, np.std(ls_exs) / n_fsa*100, tdelta)) print(':: Found {} unique configurations, {} ({:.0f}%) force selected nodes were ' \ 'removed: {}'.format(len(enums),len(removed),len(removed)/n_fsa*100,removed)) ss = [[v for v in e] for e in enums] return ss, fs def __drop(fs, c, fs_map, A, u_sel, removed, verbose): """Drop the worst performing node in the current force selected set by occurrence in the generated configurations. """ c_argsort = np.argsort(c) drop = c_argsort[0] keep = c_argsort[1:] if verbose: print(':: Node', fs[drop],'removed.') idxs = np.unique(np.where(fs_map[:, keep])[0]) A = A[idxs,:][:,idxs] fs_map = fs_map[idxs,:][:,keep] c = c[keep] removed.append(fs[drop]) fs = fs[keep] u_sel = u_sel[idxs] return fs, c, fs_map, A, u_sel, removed def __output_stats(fs, enums, i, i0a, c, t0, pt0, n_ss0): """Print iteration statistics in the `sample_fs_configurations` method. """ if i % 10 == 0: pt1 = process_time() if pt1 - pt0 > 10.0: n_ss = len(enums) rate_1_sec = np.round((i-i0a) / (pt1-pt0), 2) rate_2_sec =

np.round((n_ss-n_ss0) / (pt1-pt0), 2)

numpy.round

# this is the python library created for using BigGAN in evolution. import sys from os.path import join sys.path.append("C:/Users/zhanq/OneDrive - Washington University in St. Louis/GitHub/pytorch-pretrained-BigGAN") # sys.path.append("E:\Github_Projects\pytorch-pretrained-BigGAN") from pytorch_pretrained_biggan import (BigGAN, one_hot_from_names, one_hot_from_int, truncated_noise_sample, convert_to_images) import torch import numpy as np import matplotlib.pylab as plt #%% #%% from numpy.linalg import norm def orthonorm(ref, vec2): res = vec2 - vec2 @ ref.T * ref / norm(ref, axis=1)**2 return res / norm(res) * norm(ref) #%% from scipy.stats import truncnorm def convert_to_images_np(obj, scale=1.0): """ Convert an output tensor from BigGAN in a list of images. Params: obj: tensor or numpy array of shape (batch_size, channels, height, width) Output: list of Pillow Images of size (height, width) """ try: from PIL import Image except ImportError: raise ImportError("Please install Pillow to use images: pip install Pillow") if not isinstance(obj, np.ndarray): obj = obj.detach().numpy() obj = obj.transpose((0, 2, 3, 1)) obj = np.clip(((obj + 1) / 2.0) * scale, 0, scale) img = [] for i, out in enumerate(obj): img.append(out) return img def truncated_noise_sample(batch_size=1, dim_z=128, truncation=1., seed=None): """ Create a truncated noise vector. Params: batch_size: batch size. dim_z: dimension of z truncation: truncation value to use seed: seed for the random generator Output: array of shape (batch_size, dim_z) """ state = None if seed is None else np.random.RandomState(seed) values = truncnorm.rvs(-2, 2, size=(batch_size, dim_z), random_state=state).astype(np.float32) return truncation * values #%% # Load pre-trained model tokenizer (vocabulary) model = BigGAN.from_pretrained('biggan-deep-256') model.to('cuda') def BigGAN_render(class_vector, noise_vector, truncation): if class_vector.shape[0] == 1: class_vector = np.tile(class_vector, [noise_vector.shape[0], 1]) if noise_vector.shape[0] == 1: noise_vector = np.tile(noise_vector, [class_vector.shape[0], 1]) class_vector = torch.from_numpy(class_vector.astype(np.float32)).to('cuda') noise_vector = torch.from_numpy(noise_vector.astype(np.float32)).to('cuda') with torch.no_grad(): output = model(noise_vector, class_vector, truncation) imgs = convert_to_images(output.cpu()) return imgs def BigGAN_embed_render(embed_vecs, noise_vecs=None, truncation=0.7, scale=255.0, batch=5): if embed_vecs.shape[1] == 256: input_vecs = torch.from_numpy(embed_vecs) elif embed_vecs.shape[1] == 128: if noise_vecs is None: embed_vecs = torch.from_numpy(embed_vecs) input_vecs = torch.cat((torch.zeros_like(embed_vecs), embed_vecs), dim=1) else: assert noise_vecs.shape[1] == 128 if noise_vecs.shape[0] == embed_vecs[0]: input_vecs = torch.cat((torch.from_numpy(noise_vecs), torch.from_numpy(embed_vecs)), dim=1) else: assert noise_vecs.shape[0] == 1 noise_vecs = np.tile(noise_vecs, [embed_vecs.shape[0], 1]) input_vecs = torch.cat((torch.from_numpy(noise_vecs), torch.from_numpy(embed_vecs)), dim=1) sample_n = input_vecs.shape[0] imgs_all = [] csr = 0 csr_end = 0 while csr_end < sample_n: csr_end = min(csr + batch, sample_n) with torch.no_grad(): output = model.generator(input_vecs[csr:csr_end, :].float().cuda(), truncation).cpu() # imgs = convert_to_images(output.cpu()) # imgs = [np.array(img).astype(np.float64) / 255 * scale for img in imgs] imgs = convert_to_images_np(output, scale) imgs_all.extend(imgs) csr = csr_end return imgs_all if __name__=="__main__": # %% # Prepare a input batch_size = 3 truncation = 0.5 class_vector = one_hot_from_names(['soap bubble', 'coffee', 'mushroom'], batch_size=batch_size) noise_vector = truncated_noise_sample(truncation=truncation, batch_size=batch_size) #noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1) # All in tensors #noise_vector = torch.from_numpy(np.ones([3, 128]).astype(np.float32)) # noise_vector = torch.from_numpy(noise_vector) class_vector = torch.from_numpy(class_vector) # If you have a GPU, put everything on cuda noise_vector = noise_vector.to('cuda') class_vector = class_vector.to('cuda') model.to('cuda') # Generate an image with torch.no_grad(): output = model(noise_vector, class_vector, truncation) imgs = convert_to_images(output.cpu()) #%% 1d interpolation truncation = 0.7 batch_size = 11 class_vector = one_hot_from_names(['mushroom']*batch_size, batch_size=1) noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1) scale_vec = np.arange(-1, 1.1, 0.2) noise_vec_scale = scale_vec.reshape([-1, 1])*noise_vector imgs = BigGAN_render(class_vector, noise_vec_scale, truncation=truncation) #% figh = plt.figure(figsize=[25, 3]) gs = figh.add_gridspec(1, len(imgs)) # 1d interpolation for i, img in enumerate(imgs): plt.subplot(gs[i]) plt.imshow(img) plt.axis('off') plt.title("{0:.2f}".format(scale_vec[i]), fontsize=15,) plt.show() #%% savedir = r"C:\Users\zhanq\OneDrive - Washington University in St. Louis\Generator_Testing\BigGAN256" truncation = 0.7 # batch_size = 11 classname = 'goldfish' class_vector = one_hot_from_names([classname], batch_size=1) #%% 1d interpolation and save truncation = 0.7 noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1) scale_UL = 1; scale_BL = -scale_UL; sample_n = 11 scale_vec = np.linspace(scale_BL, scale_UL, sample_n) # scale_vec = np.linspace(-2.5, -0.9, sample_n) noise_vec_scale = scale_vec.reshape([-1, 1])*noise_vector imgs = BigGAN_render(class_vector, noise_vec_scale, truncation=truncation) figh = plt.figure(figsize=[25, 3]) gs = figh.add_gridspec(1, len(imgs)) # 1d interpolation for i, img in enumerate(imgs): plt.subplot(gs[i]) plt.imshow(img) plt.axis('off') plt.title("{0:.1f}".format(scale_vec[i]), fontsize=15,) plt.savefig(join(savedir, "%s_UL%.1f_BL%.1f_trunc%.1f_%04d.png" % (classname, scale_UL, scale_BL, truncation, np.random.randint(10000)))) plt.show() #%% 2d linear interpolation through center savedir = r"C:\Users\zhanq\OneDrive - Washington University in St. Louis\Generator_Testing\BigGAN256" truncation = 0.7 # batch_size = 11 classname = 'goldfish' class_vector = one_hot_from_names([classname], batch_size=1) truncation = 0.7 noise_vector = truncated_noise_sample(truncation=truncation, batch_size=2) vec1 = noise_vector[0:1, :] vec2 = orthonorm(vec1, noise_vector[1:2, :]) xlim = (-1, 1) ylim = (-1, 1); sample_n = 11 x_scale_vec = np.linspace(*xlim, sample_n) y_scale_vec = np.linspace(*ylim, sample_n) # scale_vec = np.linspace(-2.5, -0.9, sample_n) imgs = [] for ydeg in y_scale_vec: noise_vec_scale = x_scale_vec[:, np.newaxis] * vec1 + ydeg * vec2 img_row = BigGAN_render(class_vector, noise_vec_scale, truncation=truncation) imgs.append(img_row) #% figh = plt.figure(figsize=[25, 25]) gs = figh.add_gridspec(len(y_scale_vec), len(x_scale_vec)) # 2d interpolation for i, img_row in enumerate(imgs): for j, img in enumerate(img_row): plt.subplot(gs[i, j]) plt.imshow(img) plt.axis('off') plt.title("%.1f, %.1f"%(x_scale_vec[i], y_scale_vec[j]), fontsize=15,) plt.tight_layout() plt.savefig(join(savedir, "%s_[%.1f-%.1f]_[%.1f-%.1f]_trunc%.1f_%04d.png" % (classname, *xlim, *ylim, truncation, np.random.randint(10000)))) plt.show() #%% 2d interpolation in sphere savedir = r"C:\Users\zhanq\OneDrive - Washington University in St. Louis\Generator_Testing\BigGAN256" truncation = 0.4 # batch_size = 11 classname = 'goldfish' class_vector = one_hot_from_names([classname], batch_size=1) truncation = 0.4 noise_vector = truncated_noise_sample(truncation=truncation, batch_size=3) vec1 = noise_vector[0:1, :] vec2 = orthonorm(vec1, noise_vector[1:2, :]) vec3 = orthonorm(vec2, noise_vector[2:3, :]) vec3 = orthonorm(vec1, vec3) sample_n = 11 phi_scale_vec =

np.linspace(-90, 90, sample_n)

numpy.linspace

import numpy as np from pkg.dist import dist def weights_c(X, C): w = np.zeros(C.shape[0]) for i in range(len(X)): if X[i] not in C: ind = np.argmin([dist(X[i],c,0) for c in C]) w[ind] +=1 return w def weighted_kmeans(Cen,w,k,random_state=42): rs = np.random.RandomState(random_state) ran = rs.randint(len(Cen)) C1 = Cen[ran,:].reshape(1,Cen.shape[1]) X1 = np.delete(Cen, ran, 0) w=

np.delete(w,ran)

numpy.delete

import copy import glob import os import time import pickle import matchingpennies from collections import deque import gym import gym_compete import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from a2c_ppo_acktr import algo, utils from a2c_ppo_acktr.algo import gail from a2c_ppo_acktr.arguments import get_args from a2c_ppo_acktr.envs import make_vec_envs from a2c_ppo_acktr.model import Policy from a2c_ppo_acktr.storage import RolloutStorage from evaluation import evaluate, evaluate_multi, eval_movie from stable_baselines3.common.running_mean_std import RunningMeanStd class MpPolicy: def __init__(self): self.vals = np.array([1.1,0.1]) self.counts = np.array([1,1]) self.eps = .2 #self.vals = np.zeros(2) + 1e-10 def update(self, obs, action, reward): if isinstance(action, list): for a, r in zip(action, reward): self.vals[action] += reward self.counts[action] += 1 else: self.vals[action] += reward self.counts[action] += 1 @property def probs(self): probs = np.exp(self.vals/self.counts) / np.exp(self.vals/self.counts).sum() return probs def act(self, obs): probs = self.probs actions = [] if isinstance(obs, list): for _ in obs: if np.random.rand() < self.eps: actions.append(np.random.choice([0,1])) else: actions.append(np.random.choice([0,1],p=probs)) else: actions.append(np.random.choice([0,1],p=probs)) return np.array(actions) class Agent: def __init__(self, playerid): self.playerid = playerid self.policies = [] def add_policy(self, policy): # TODO: need to add a wrapper that adds the obs_rms to each actor as we run it... self.policies.append(policy) self.weights = np.arange(1, len(self.policies)+1).astype(float) self.weights[-1] *= 2 self.weights /= self.weights.sum() def sample_policy(self): ind = np.random.choice(range(len(self.policies)), p=self.weights) return self.policies[ind] def init(self, num_envs): self.env_policies = [] for _ in range(num_envs): policy = self.sample_policy() self.env_policies.append(policy) def new_policy(self, ind): policy = self.sample_policy() self.env_policies[ind] = policy def act(self, obs, *args, **kwargs): values = [] actions = [] logprobs = [] rhs = [] with torch.no_grad(): for i, o in enumerate(obs): policy = self.env_policies[i] action = policy.act(o) actions.append(action) actions = np.array(actions) #if len(actions) > 1: # actions = np.concatenate(actions) return actions class EnvWrapper: def __init__(self, env_name, num_envs): self.envs = [] for _ in range(num_envs): self.envs.append(gym.make(env_name)) def step(self, actions): steps = [] for i, a in enumerate(actions): steps.append(self.envs[i].step(a)) obs = [s[0] for s in steps] r = [s[1] for s in steps] dones = [s[2] for s in steps] infos = [s[3] for s in steps] return obs, np.array(r).T, dones, infos def reset(self): obs = [] for env in self.envs: obs.append(env.reset()) return obs def main(): args = get_args() torch.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) if args.cuda and torch.cuda.is_available() and args.cuda_deterministic: torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.set_num_threads(1) device = torch.device("cuda:0" if args.cuda else "cpu") envs = EnvWrapper(args.env_name, args.num_processes) #envs = make_vec_envs(args.env_name, args.seed, args.num_processes, # args.gamma, args.log_dir, device, False, dense=args.dense) envs.reset() agent0 = MpPolicy() agent1 = MpPolicy() player0_policies = [] player1_policies = [] obs_rms_cache = [] episode_rewards0 = deque(maxlen=10) episode_rewards1 = deque(maxlen=10) train_stats = [] eval_p0 = [] eval_p1 = [] cache_freq = args.cache_freq learn_agent0 = agent0 old_agent0 = Agent(0) old_agent0.add_policy(copy.deepcopy(agent0)) old_agent0.init(args.num_processes//2) learn_agent1 = agent1 old_agent1 = Agent(1) old_agent1.add_policy(copy.deepcopy(agent1)) old_agent1.init(args.num_processes//2) p0numep = 0 p1numep = 0 #num_updates = int( # args.num_env_steps) // args.num_steps // args.num_processes num_updates = 50 for j in range(num_updates): eps = -1 p0draws, p1draws = 0, 0 w0, w1 = 0, 0 epoch_p0ep, epoch_p1ep = 0, 0 while eps < args.num_episodes: start = time.time() for step in range(args.num_steps): # Sample actions with torch.no_grad(): action0a = agent0.act([None for _ in range(args.num_processes//2)]) action0b = old_agent0.act([None for _ in range(args.num_processes//2)]) action1a = old_agent1.act([None for _ in range(args.num_processes//2)]) action1b = agent1.act([None for _ in range(args.num_processes//2)]) action0 = np.concatenate((action0a.squeeze(),action0b.squeeze())) action1 = np.concatenate((action1a.squeeze(),action1b.squeeze())) #action0 = torch.tensor(action0)[:,None] #action1 = torch.tensor(action1)[:,None] # Obser reward and next obs obs, reward, done, infos = envs.step(zip(action0, action1)) envs.reset() agent0.update(None, action0, reward[0]) agent1.update(None, action1, reward[1]) for i, info in enumerate(infos[:args.num_processes//2]): d = True if 'episode' in info[0].keys(): old_agent1.new_policy(i) epoch_p0ep += 1 if "winner" in info[0].keys(): d = False w0 += 1 if "winner" in info[1].keys(): d = False eps += 1 episode_rewards0.append(info[0]['episode']['r']) if d: p0draws += 1 p0numep += 1 if p0numep % cache_freq == 0: print(f"ADDING POLICY !! Current length {len(old_agent0.policies)}") old_agent0.add_policy(copy.deepcopy(agent0)) for i, info in enumerate(infos[args.num_processes//2:]): if 'episode' in info[1].keys(): old_agent0.new_policy(i) epoch_p1ep += 1 if "winner" in info[0].keys(): d = False if "winner" in info[1].keys(): d = False w1 += 1 episode_rewards1.append(info[1]['episode']['r']) if d: p1draws += 1 p1numep += 1 if p1numep % cache_freq == 0: old_agent1.add_policy(copy.deepcopy(agent1)) if j % args.log_interval == 0 and len(episode_rewards0) > 1: total_num_steps = args.num_steps * args.num_processes end = time.time() print("=============================================================================================") print(f"Updates {j} player0 rewards {

np.mean(episode_rewards0)

numpy.mean

# module import import gc import os import copy import random import platform import numpy as np import pickle as p import pandas as pd import multiprocessing as mp from numpy.matlib import repmat # scipy module imports from scipy.stats import norm, linregress from scipy.spatial.distance import * from scipy.interpolate import PchipInterpolator as pchip from scipy.interpolate import InterpolatedUnivariateSpline as IUS from scipy.interpolate import interp1d from scipy.optimize import curve_fit from scipy.signal import periodogram, hamming, boxcar, find_peaks # sklearn module imports from sklearn.linear_model import LinearRegression from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA # statsmodels module imports from statsmodels.nonparametric.smoothers_lowess import lowess # pyqt5 module import from PyQt5.QtCore import QThread, pyqtSignal # custom module imports import analysis_guis.common_func as cf import analysis_guis.calc_functions as cfcn import analysis_guis.rotational_analysis as rot from analysis_guis.dialogs.rotation_filter import RotationFilteredData from analysis_guis.cluster_read import ClusterRead from probez.spike_handling import spike_io # other parameters dcopy = copy.deepcopy default_dir_file = os.path.join(os.getcwd(), 'default_dir.p') interp_arr = lambda xi, y: np.vstack([interp1d(np.linspace(0, 1, len(x)), x, kind='nearest')(xi) for x in y]) cell_perm_ind = lambda n_cell_tot, n_cell: np.sort(np.random.permutation(n_cell_tot)[:n_cell]) set_sf_cell_perm = lambda spd_sf, n_pool, n_cell: [x[:, :, cell_perm_ind(n_pool, n_cell)] for x in spd_sf] grp_expt_indices = lambda i_expt0: [np.where(i_expt0 == i)[0] for i in np.unique(i_expt0)] # lambda function declarations lin_func = lambda x, a: a * x ######################################################################################################################## ######################################################################################################################## class WorkerThread(QThread): # creates the signal object work_started = pyqtSignal() work_progress = pyqtSignal(str, float) work_finished = pyqtSignal(object) work_error = pyqtSignal(str, str) work_plot = pyqtSignal(object) def __init__(self, parent=None, main_gui=None): # creates the worker object super(WorkerThread, self).__init__(parent) self.update_pbar = True self.is_running = False self.forced_quit = False self.sub_job = None self.is_ok = True self.data = None # other initialisations self.main_gui = main_gui self.thread_job_primary = None self.thread_job_secondary = None self.thread_job_para = None def set_worker_func_type(self, thread_job_primary, thread_job_secondary=None, thread_job_para=None): ''' :param func_type: :return: ''' # updates the worker primary/secondary job type and parameters self.thread_job_primary = thread_job_primary self.thread_job_secondary = thread_job_secondary self.thread_job_para = thread_job_para def run(self): ''' :return: ''' # initialisations w_prog, w_err = self.work_progress, self.work_error # updates the running/forced quit flagsv self.is_running = True self.forced_quit = False self.is_ok = True # updates the running parameter and enables the progress group parameters self.work_started.emit() # runs the job based on the type thread_data = None if self.thread_job_primary == 'init_data_file': # case is initialising the data file self.init_cluster_data() elif self.thread_job_primary == 'init_pool_object': # case is initialising the pool worker object thread_data = self.init_pool_worker() ################################## #### DATA I/O FUNCTIONS #### ################################## elif self.thread_job_primary == 'load_data_files': # case is loading the data files thread_data = self.load_data_file() elif self.thread_job_primary == 'save_multi_expt_file': # retrieves the parameters data, out_info = self.thread_job_para[0], self.thread_job_para[1] # case is loading the data files thread_data = self.save_multi_expt_file(data, out_info) elif self.thread_job_primary == 'save_multi_comp_file': # retrieves the parameters data, out_info = self.thread_job_para[0], self.thread_job_para[1] # case is loading the data files thread_data = self.save_multi_comp_file(data, out_info) elif self.thread_job_primary == 'run_calc_func': # case is the calculation functions calc_para, plot_para = self.thread_job_para[0], self.thread_job_para[1] data, pool, g_para = self.thread_job_para[2], self.thread_job_para[3], self.thread_job_para[4] ################################################ #### CLUSTER CLASSIFICATION FUNCTIONS #### ################################################ if self.thread_job_secondary == 'Fixed/Free Cluster Matching': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['clust']) # case is determining the cluster matches self.det_cluster_matches(data, calc_para, w_prog) elif self.thread_job_secondary == 'Cluster Cross-Correlogram': # case is the cc-gram type determinations thread_data = self.calc_ccgram_types(calc_para, data.cluster) ###################################### #### AHV ANALYSIS FUNCTIONS #### ###################################### elif ' (Fixed)' in self.thread_job_secondary or \ (self.thread_job_secondary == 'Correlation Significance Overlap'): # ensures the smoothing window is an odd integer (if smoothing) if calc_para['is_smooth']: if calc_para['n_smooth'] % 2 != 1: # if not, then output an error message to screen e_str = 'The median smoothing filter window span must be an odd integer.' w_err.emit(e_str, 'Incorrect Smoothing Window Span') # sets the error flag and exits the function self.is_ok = False self.work_finished.emit(thread_data) return # initialises the rotation filter class object (if not already set) if plot_para['rot_filt'] is None: plot_para['rot_filt'] = cf.init_rotation_filter_data(False) # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['vel', 'vel_sf_fix'], other_para=False) # calculates the shuffled kinematic spiking frequencies cfcn.calc_binned_kinemetic_spike_freq(data, plot_para, dcopy(calc_para), w_prog, roc_calc=False) cfcn.calc_shuffled_kinematic_spike_freq(data, dcopy(calc_para), w_prog) # runs any specific additional function fit_func = ['Correlation Comparison (Fixed)', 'Correlation Fit Parameters (Fixed)', 'Individual Cell Correlation (Fixed)'] if self.thread_job_secondary in fit_func: # case is the correlation fit parameters self.calc_corr_fit_para(data, plot_para, dcopy(calc_para), w_prog) elif (' (Freely Moving)' in self.thread_job_secondary): # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['vel_sf_free'], other_para=False) # updates the bin velocity data.rotation.vel_bin_corr = calc_para['vel_bin'] elif 'Fixed/Free Spiking Correlation' in self.thread_job_secondary: # determines if the freely moving data file has been loaded if not hasattr(data.externd, 'free_data'): # if the data-file has not been loaded then output an error to screen and exit e_str = 'The freely moving spiking frequency/statistics data file must be loaded ' \ 'before being able to run this function.\n\nPlease load this data file and try again.' w_err.emit(e_str, 'Freely Moving Data Missing?') # exits the function with an error flag self.is_ok = False self.work_finished.emit(thread_data) return # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['ff_corr', 'vel'], other_para=False) # calculates the shuffled kinematic spiking frequencies cfcn.calc_binned_kinemetic_spike_freq(data, plot_para, calc_para, w_prog, roc_calc=False, use_raw=True) # calculates the fixed/free correlations (if not already set) if not data.comp.ff_corr.is_set: self.calc_fix_free_correlation(data, calc_para, w_prog) ################################################ #### FREELY MOVING ANALYSIS FUNCTIONS #### ################################################ elif self.thread_job_secondary == 'Freely Moving Cell Fit Residual': # ensures the calculation fields are self.calc_cell_fit_residual(data, calc_para, w_prog) ###################################### #### EYE TRACKING FUNCTIONS #### ###################################### elif self.thread_job_secondary in ['Eye Movement Event Signals']: # check to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['eye_track']) # calculates the eye-tracking metrics (if not calculated) if len(data.externd.eye_track.t_evnt) == 0: self.calc_eye_track_metrics(data, calc_para, w_prog) elif 'Eye Movement Correlation' in self.thread_job_secondary: # check to see if any parameters have been altered/ self.check_altered_para(data, calc_para, plot_para, g_para, ['eye_track']) # calculates the eye-tracking metrics (if not calculated) if len(data.externd.eye_track.t_evnt) == 0: self.calc_eye_track_metrics(data, calc_para, w_prog) # calculates the eye-tracking metrics if len(data.externd.eye_track.t_sp_h) == 0: self.calc_eye_track_corr(data, calc_para, w_prog) ###################################### #### ROC ANALYSIS FUNCTIONS #### ###################################### elif self.thread_job_secondary == 'Direction ROC Curves (Single Cell)': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition']) # case is the shuffled cluster distances if not self.calc_cond_roc_curves(data, pool, calc_para, plot_para, g_para, False, 100.): self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Direction ROC Curves (Whole Experiment)': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition', 'phase']) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(data, pool, calc_para, plot_para, g_para, False, 33.): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(data, calc_para, 66.) self.calc_phase_roc_significance(calc_para, g_para, data, pool, 100.) elif self.thread_job_secondary in ['Direction ROC AUC Histograms', 'Direction ROC Spiking Rate Heatmap']: # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition']) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(data, pool, calc_para, plot_para, g_para, True, 100., True): self.is_ok = False self.work_finished.emit(thread_data) return elif 'Velocity ROC Curves' in self.thread_job_secondary: # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['vel'], other_para=True) # calculates the binned kinematic spike frequencies cfcn.calc_binned_kinemetic_spike_freq(data, plot_para, calc_para, w_prog) self.calc_kinematic_roc_curves(data, pool, calc_para, g_para, 50.) elif self.thread_job_secondary == 'Velocity ROC Significance': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['vel'], other_para=True) # calculates the binned kinematic spike frequencies cfcn.calc_binned_kinemetic_spike_freq(data, plot_para, calc_para, w_prog) # calculates the kinematic roc curves and their significance self.calc_kinematic_roc_curves(data, pool, calc_para, g_para, 0.) self.calc_kinematic_roc_significance(data, calc_para, g_para) elif self.thread_job_secondary == 'Condition ROC Curve Comparison': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['phase']) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(data, pool, calc_para, plot_para, g_para, True, 33.): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(data, calc_para, 66.) self.calc_phase_roc_significance(calc_para, g_para, data, pool, 100.) elif self.thread_job_secondary == 'Direction ROC Significance': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition', 'phase']) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(data, pool, calc_para, plot_para, g_para, True, 33., force_black_calc=True): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(data, calc_para, 66.) self.calc_phase_roc_significance(calc_para, g_para, data, pool, 100.) if cf.det_valid_vis_expt(data, True): if not self.calc_dirsel_group_types(data, pool, calc_para, plot_para, g_para): self.is_ok = False self.work_finished.emit(thread_data) return ############################################### #### COMBINED ANALYSIS LDA FUNCTIONS #### ############################################### elif self.thread_job_secondary == 'Rotation/Visual Stimuli Response Statistics': # calculates the phase roc curve/significance values self.calc_phase_roc_curves(data, calc_para, 50.) # calculates the direction/selection group types if not self.calc_dirsel_group_types(data, pool, calc_para, plot_para, g_para): self.is_ok = False self.work_finished.emit(thread_data) elif self.thread_job_secondary == 'Combined Direction ROC Curves (Whole Experiment)': # checks that the conditions are correct for running the function if not self.check_combined_conditions(calc_para, plot_para): self.is_ok = False self.work_finished.emit(thread_data) return # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition', 'phase', 'visual']) # initisalises the rotational filter (if not initialised already) if plot_para['rot_filt'] is None: plot_para['rot_filt'] = cf.init_rotation_filter_data(False) # adds motordrifting (if the visual expt type) _plot_para, _calc_para = dcopy(plot_para), dcopy(calc_para) if calc_para['vis_expt_type'] == 'MotorDrifting': _plot_para['rot_filt']['t_type'].append('MotorDrifting') # resets the flags to use the full rotation/visual phases _calc_para['use_full_rot'], _calc_para['use_full_vis'] = True, True # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(data, pool, _calc_para, _plot_para, g_para, False, 33.): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(data, _calc_para, 66.) if (calc_para['vis_expt_type'] == 'UniformDrifting') and \ (calc_para['grp_stype'] != 'Wilcoxon Paired Test'): # sets up the visual rotation filter r_filt_v = cf.init_rotation_filter_data(False) r_filt_v['t_type'], r_filt_v['is_ud'], r_filt_v['t_cycle'] = ['UniformDrifting'], [True], ['15'] # retrieves the visual filter object plot_exp_name, plot_all_expt = plot_para['plot_exp_name'], plot_para['plot_all_expt'] r_obj_vis, ind_type = cf.split_unidrift_phases(data, r_filt_v, None, plot_exp_name, plot_all_expt, 'Whole Experiment', 2.) # calculates the full uniform-drifting curves self.calc_ud_roc_curves(data, r_obj_vis, ind_type, 66.) # calculates the direction selection types if not self.calc_dirsel_group_types(data, pool, _calc_para, _plot_para, g_para): self.is_ok = False # calculates the partial roc curves self.calc_partial_roc_curves(data, calc_para, plot_para, 66.) elif self.thread_job_secondary in ['Normalised Kinematic Spiking Frequency']: # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['vel'], other_para=False) # calculates the binned kinematic spike frequencies cfcn.calc_binned_kinemetic_spike_freq(data, plot_para, calc_para, w_prog, roc_calc=False) ###################################################### #### DEPTH-BASED SPIKING ANALYSIS FUNCTIONS #### ###################################################### elif self.thread_job_secondary == 'Depth Spiking Rate Comparison': # make a copy of the plotting/calculation parameters _plot_para, _calc_para, r_data = dcopy(plot_para), dcopy(calc_para), data.depth _plot_para['plot_exp_name'] = None # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition', 'phase', 'visual']) # reduces the data clusters to only include the RSPd/RSPg cells _data = cfcn.get_rsp_reduced_clusters(data) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(_data, pool, _calc_para, _plot_para, g_para, True, 33., r_data=r_data, force_black_calc=True): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(_data, _calc_para, 66., r_data=r_data) ############################################ #### SPIKING FREQUENCY CALCULATION #### ############################################ # initialisations r_filt = _plot_para['rot_filt'] r_data.ch_depth, r_data.ch_region, r_data.ch_layer = \ cfcn.get_channel_depths_tt(_data._cluster, r_filt['t_type']) t_ofs, t_phase = cfcn.get_rot_phase_offsets(calc_para) # rotation filtered object calculation r_obj_rot = RotationFilteredData(_data, r_filt, None, None, True, 'Whole Experiment', False, t_ofs=t_ofs, t_phase=t_phase) # calculates the individual trial/mean spiking rates and sets up the plot/stats arrays sp_f0_rot, sp_f_rot = cf.calc_phase_spike_freq(r_obj_rot) s_plt, _, sf_stats, ind = cf.setup_spike_freq_plot_arrays(r_obj_rot, sp_f0_rot, sp_f_rot, None, 3) r_data.plt, r_data.stats, r_data.ind, r_data.r_filt = s_plt, sf_stats, ind, dcopy(r_filt) elif self.thread_job_secondary == 'Depth Spiking Rate Comparison (Multi-Sensory)': # checks that the conditions are correct for running the function if not self.check_combined_conditions(calc_para, plot_para): self.is_ok = False self.work_finished.emit(thread_data) return else: # otherwise, make a copy of the plotting/calculation parameters _plot_para, _calc_para, r_data = dcopy(plot_para), dcopy(calc_para), data.depth _plot_para['plot_exp_name'], r_filt = None, _plot_para['rot_filt'] t_ofs, t_phase = cfcn.get_rot_phase_offsets(calc_para) # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['condition', 'phase', 'visual']) # adds motordrifting (if it is the visual expt type) if calc_para['vis_expt_type'] == 'MotorDrifting': _plot_para['rot_filt']['t_type'].append('MotorDrifting') # reduces the data clusters to only include the RSPd/RSPg cells _data = cfcn.get_rsp_reduced_clusters(data) # calculates the phase roc-curves for each cell if not self.calc_cond_roc_curves(_data, pool, _calc_para, _plot_para, g_para, False, 33., r_data=r_data): self.is_ok = False self.work_finished.emit(thread_data) return # calculates the phase roc curve/significance values self.calc_phase_roc_curves(_data, _calc_para, 66., r_data=r_data) if (calc_para['vis_expt_type'] == 'UniformDrifting'): # sets up the visual rotation filter r_filt_v = cf.init_rotation_filter_data(False) r_filt_v['t_type'], r_filt_v['is_ud'], r_filt_v['t_cycle'] = ['UniformDrifting'], [True], ['15'] # retrieves the visual filter object r_obj_vis, ind_type = cf.split_unidrift_phases(_data, r_filt_v, None, None, True, 'Whole Experiment', 2., t_phase, t_ofs) # calculates the full uniform-drifting curves self.calc_ud_roc_curves(_data, r_obj_vis, ind_type, 66., r_data=r_data) # calculates the individual trial/mean spiking rates and sets up the plot/stats arrays sp_f0, sp_f = cf.calc_phase_spike_freq(r_obj_vis) s_plt, _, sf_stats, ind = cf.setup_spike_freq_plot_arrays(r_obj_vis, sp_f0, sp_f, ind_type, 2) r_data.plt_vms, r_data.stats_vms, r_data.ind_vms = s_plt, sf_stats, ind, r_filt_v r_data.r_filt_vms = dcopy(r_filt_v) else: # resets the uniform drifting fields r_data.plt_vms, r_data.stats_vms, r_data.ind_vms, r_data.r_filt_vms = None, None, None, None ############################################ #### SPIKING FREQUENCY CALCULATION #### ############################################ # rotation filtered object calculation r_obj_rot = RotationFilteredData(_data, r_filt, None, None, True, 'Whole Experiment', False, t_phase=t_phase, t_ofs=t_ofs) r_data.ch_depth_ms, r_data.ch_region_ms, r_data.ch_layer_ms = \ cfcn.get_channel_depths_tt(_data._cluster, r_filt['t_type']) # calculates the individual trial/mean spiking rates and sets up the plot/stats arrays sp_f0_rot, sp_f_rot = cf.calc_phase_spike_freq(r_obj_rot) s_plt, _, sf_stats, ind = cf.setup_spike_freq_plot_arrays(r_obj_rot, sp_f0_rot, sp_f_rot, None, 3) r_data.plt_rms, r_data.stats_rms, r_data.ind_rms = s_plt, sf_stats, ind r_data.r_filt_rms = dcopy(r_filt) ########################################################## #### ROTATION DISCRIMINATION ANALYSIS FUNCTIONS #### ########################################################## elif self.thread_job_secondary == 'Rotation Direction LDA': # if the solver parameter have not been set, then initalise them d_data = data.discrim.dir # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=d_data) # sets up the lda values r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, d_data, w_prog, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not cfcn.run_rot_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, d_data=d_data, w_prog=w_prog): self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Temporal Duration/Offset LDA': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.temp) # if the temporal data parameters have changed/has not been initialised then calculate the values if data.discrim.temp.lda is None: # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.temp) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.temp, w_prog, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return # if an update in the calculations is required, then run the temporal LDA analysis if status == 2: if not self.run_temporal_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max): # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Individual LDA': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.indiv) self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.dir) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.dir, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not cfcn.run_rot_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, d_data=data.discrim.dir, w_prog=w_prog): self.is_ok = False self.work_finished.emit(thread_data) return # if the individual data parameters have changed/has not been initialised then calculate the values if data.discrim.indiv.lda is None: # runs the individual LDA if not self.run_individual_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max): # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Shuffled LDA': # checks to see if any parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.shuffle) self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.dir) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.dir, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not cfcn.run_rot_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, d_data=data.discrim.dir, w_prog=w_prog): self.is_ok = False self.work_finished.emit(thread_data) return # runs the shuffled LDA if not self.run_shuffled_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max): # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Pooled Neuron LDA': # resets the minimum cell count and checks if the pooled parameters have been altered # calc_para['lda_para']['n_cell_min'] = calc_para['n_cell_min'] self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.part) # if the pooled data parameters have changed/has not been initialised then calculate the values if data.discrim.part.lda is None: # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.dir) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.dir, w_prog, True, w_err=w_err) if not calc_para['pool_expt']: if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return # elif status == 2: # # if an update in the calculations is required, then run the rotation LDA analysis # if not cfcn.run_rot_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, # d_data=data.discrim.dir, w_prog=w_prog): # self.is_ok = False # self.work_finished.emit(thread_data) # return # runs the partial LDA if not self.run_pooled_lda(pool, data, calc_para, r_filt, i_expt, i_cell, n_trial_max): # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Individual Cell Accuracy Filtered LDA': # check to see if the individual LDA calculations have been performed if data.discrim.indiv.lda is None: # if the individual LDA has not been run, then output an error to screen e_str = 'The Individual LDA must be run first before this analysis can be performed' w_err.emit(e_str, 'Missing Individual LDA Data') # sets the ok flag to false and exit the function self.is_ok = False self.work_finished.emit(thread_data) return # _calc_para = dcopy(calc_para) _calc_para['comp_cond'] = dcopy(data.discrim.indiv.ttype) ######################################### #### ROTATION LDA CALCULATIONS #### ######################################### # sets the min/max accuracy values _calc_para['lda_para']['y_acc_min'] = 0 _calc_para['lda_para']['y_acc_max'] = 100 # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, _calc_para, g_para, ['lda'], other_para=data.discrim.dir) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, _calc_para, data.discrim.dir, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not cfcn.run_rot_lda(data, _calc_para, r_filt, i_expt, i_cell, n_trial_max, d_data=data.discrim.dir, w_prog=w_prog, pW=50.): self.is_ok = False self.work_finished.emit(thread_data) return ######################################### #### FILTERED LDA CALCULATIONS #### ######################################### # sets the min/max accuracy values _calc_para['lda_para']['y_acc_min'] = _calc_para['y_acc_min'] _calc_para['lda_para']['y_acc_max'] = _calc_para['y_acc_max'] # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, _calc_para, g_para, ['lda'], other_para=data.discrim.filt) # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, _calc_para, data.discrim.filt, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not cfcn.run_rot_lda(data, _calc_para, r_filt, i_expt, i_cell, n_trial_max, d_data=data.discrim.filt, w_prog=w_prog, pW=50., pW0=50.): self.is_ok = False self.work_finished.emit(thread_data) return else: # otherwise, update the calculation parameters data.discrim.filt.yaccmn = _calc_para['y_acc_min'] data.discrim.filt.yaccmx = _calc_para['y_acc_max'] elif self.thread_job_secondary == 'LDA Group Weightings': # checks to see if the data class as changed parameters d_data, w_prog = data.discrim.wght, self.work_progress self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=d_data) # sets up the lda values r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, d_data, w_prog, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not self.run_wght_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max): self.is_ok = False self.work_finished.emit(thread_data) return ####################################################### #### SPEED DISCRIMINATION ANALYSIS FUNCTIONS #### ####################################################### elif self.thread_job_secondary == 'Speed LDA Accuracy': # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.spdacc) # if the pooled data parameters have changed/has not been initialised then calculate the values if data.discrim.spdc.lda is None: # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.spdacc, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: if not self.run_speed_lda_accuracy(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, w_prog): self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Speed LDA Comparison (Individual Experiments)': # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.spdc) # if the pooled data parameters have changed/has not been initialised then calculate the values if data.discrim.spdc.lda is None: # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.spdc, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: # if an update in the calculations is required, then run the rotation LDA analysis if not self.run_kinematic_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, w_prog): self.is_ok = False self.work_finished.emit(thread_data) return elif self.thread_job_secondary == 'Speed LDA Comparison (Pooled Experiments)': # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.spdcp) # if the pooled data parameters have changed/has not been initialised then calculate the values if data.discrim.spdcp.lda is None: # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.spdcp, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return # elif status == 2:/ # if an update in the calculations is required, then run the rotation LDA analysis if not self.run_pooled_kinematic_lda(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, w_prog): self.is_ok = False self.work_finished.emit(thread_data) return # # calculates the psychometric curves # w_prog.emit('Calculating Pyschometric Curves', 100.) # cfcn.calc_all_psychometric_curves(data.discrim.spdcp, float(calc_para['vel_bin']), calc_para['use_all']) elif self.thread_job_secondary == 'Velocity Direction Discrimination LDA': # checks to see if any base LDA calculation parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['lda'], other_para=data.discrim.spddir) # if the pooled data parameters have changed/has not been initialised then calculate the values if data.discrim.spddir.lda is None: # sets up the important arrays for the LDA r_filt, i_expt, i_cell, n_trial_max, status = cfcn.setup_lda(data, calc_para, data.discrim.spddir, w_prog, True, w_err=w_err) if status == 0: # if there was an error in the calculations, then return an error flag self.is_ok = False self.work_finished.emit(thread_data) return elif status == 2: if not self.run_speed_dir_lda_accuracy(data, calc_para, r_filt, i_expt, i_cell, n_trial_max, w_prog): self.is_ok = False self.work_finished.emit(thread_data) return ####################################### #### MISCELLANEOUS FUNCTIONS #### ####################################### elif self.thread_job_secondary == 'Velocity Multilinear Regression Dataframe Output': # checks to see if any base spiking frequency dataframe parameters have been altered self.check_altered_para(data, calc_para, plot_para, g_para, ['spikedf'], other_para=data.spikedf) # checks to see if the overlap duration is less than the time bin size if calc_para['t_over'] >= calc_para['bin_sz']: # if not, then output an error to screen e_str = 'Bin Overlap Duration must be less than the Time Bin Size.\n' \ 'Reset these parameters before running this function.' w_err.emit(e_str, 'Incorrect Function Parameters') # exits the function with an error flag self.is_ok = False self.work_finished.emit(thread_data) return # only continue if the spiking frequency dataframe has not been set up if not data.spikedf.is_set: self.setup_spiking_freq_dataframe(data, calc_para) elif self.thread_job_secondary == 'Autocorrelogram Theta Index Calculations': # case to see if any parameters have changed self.check_altered_para(data, calc_para, plot_para, g_para, ['theta'], other_para=data.theta_index) # only continue if the theta index dataframe has not been setup if not data.theta_index.is_set: self.calc_auto_ccgram_fft(data, calc_para) ############################### #### OTHER FUNCTIONS #### ############################### elif self.thread_job_secondary == 'Shuffled Cluster Distances': # case is the shuffled cluster distances thread_data = self.calc_shuffled_cluster_dist(calc_para, data.cluster) elif self.thread_job_primary == 'update_plot': pass # emits the finished work signal self.work_finished.emit(thread_data) ############################################ #### THREAD CALCULATION FUNCTIONS #### ############################################ def load_data_file(self): ''' :param exp_file: :return: ''' # retrieves the job parameters load_dlg, loaded_exp, is_multi = self.thread_job_para[0], self.thread_job_para[1], self.thread_job_para[2] if not np.any([not x in loaded_exp for x in load_dlg.exp_name]): # if there are no new experiments to load, then exit the function return None else: n_file = len(load_dlg.exp_files) dpw, p_rlx, data = 1.0 / n_file, 0.05, [] _, f_extn = os.path.splitext(load_dlg.exp_files[0]) # for i_file in range(n_file): if not self.is_running: # if the user cancelled, then exit return None else: # updates the progress bar string p_str, pw0 = 'Loading File {0} of {1}'.format(i_file+1, n_file), i_file / n_file self.work_progress.emit(p_str, 100.0 * pw0) # sets the experiment file and name if load_dlg.exp_name[i_file] not in loaded_exp: # loads the data from the data file with open(load_dlg.exp_files[i_file], 'rb') as fp: data_nw = p.load(fp) # setting of other fields if isinstance(data_nw, dict): data_nw['expFile'] = load_dlg.exp_files[i_file] # re-calculates the signal features (single experiment only) if f_extn == '.cdata': if np.shape(data_nw['sigFeat'])[1] == 5: # memory allocation for the signal features xi = np.array(range(data_nw['nPts'])) sFeat = np.zeros((data_nw['nC'], 2)) for i in range(data_nw['nC']): # creates the piecewise-polynomial of the mean signal pp, t_max = pchip(xi, data_nw['vMu'][:, i]), data_nw['sigFeat'][i, 2] t_min = np.argmin(data_nw['vMu'][int(t_max):, i]) + t_max v_max_2 = data_nw['vMu'][int(t_max), i] / 2.0 v_min = np.min(data_nw['vMu'][int(t_max):, i]) v_half = data_nw['vMu'][int(data_nw['sigFeat'][i, 1]), i] / 2.0 ################################################## #### POST-STIMULI SPIKE HALF-WIDTH TIME #### ################################################## # determines the point/voltage of the pmaximum proceding the minimum bnd_1 = [(data_nw['sigFeat'][i, 0], data_nw['sigFeat'][i, 1])] bnd_2 = [(data_nw['sigFeat'][i, 1], data_nw['sigFeat'][i, 2])] bnd_3 = [(data_nw['sigFeat'][i, 2], t_min)] # determines the location of the half-width points t_hw1_lo = cfcn.opt_time_to_y0((pp, v_half), bnd_1) t_hw1_hi = cfcn.opt_time_to_y0((pp, v_half), bnd_2) t_hw2_lo = cfcn.opt_time_to_y0((pp, v_max_2), bnd_2) t_hw2_hi = cfcn.opt_time_to_y0((pp, v_max_2), bnd_3) t_rlx = cfcn.opt_time_to_y0((pp, v_min + p_rlx * (v_max_2 - v_min)), bnd_3) # determine if it is feasible to find the 2nd peak half-width point if (t_hw2_hi is None) or (t_rlx is None): # if not, then linearly extrapolate past the end point of the signal xi2 = np.array(range(2*xi[-1])) ppL = IUS(xi, data_nw['vMu'][:, i], k=1) # determines the half-width/relaxtion time from the extrapolated signal bnd_4 = [(data_nw['sigFeat'][i, 2], xi2[-1])] t_hw2_hi = cfcn.opt_time_to_y0((ppL, v_max_2), bnd_4) t_rlx = cfcn.opt_time_to_y0((ppL, v_min + p_rlx * (v_max_2 - v_min)), bnd_4) # calculates the new signal features data_nw['sigFeat'][i, 3] = t_hw1_lo data_nw['sigFeat'][i, 4] = t_hw1_hi sFeat[i, 0] = t_hw2_hi - t_hw2_lo sFeat[i, 1] = t_rlx - t_max # concatenates the new signal feature date data_nw['sigFeat'] = np.concatenate((data_nw['sigFeat'], sFeat), axis=1) # sets the cell cluster include indices (if not already set) if 'clInclude' not in data_nw['expInfo']: data_nw['expInfo']['clInclude'] = np.ones(data_nw['nC'], dtype=bool) # appends the new data dictionary to the overall data list data.append(data_nw) # appends the current filename to the data dictionary and returns the object return data def save_multi_expt_file(self, data, out_info): ''' :return: ''' # updates the progressbar self.work_progress.emit('Saving Data To File...', 50.0) # sets the file extension (based on the data type) if hasattr(data.comp, 'data'): f_extn = 'mdata' if len(data.comp.data) == 0 else 'mcomp' else: f_extn = 'mdata' # sets the output file name out_file = os.path.join(out_info['inputDir'], '{0}.{1}'.format(out_info['dataName'], f_extn)) # outputs the data to file with open(out_file, 'wb') as fw: p.dump(data, fw) # updates the progressbar self.work_progress.emit('Data Save Complete!', 100.0) def save_multi_comp_file(self, data, out_info): ''' :return: ''' # updates the progressbar self.work_progress.emit('Saving Data To File...', 50.0) # memory allocation n_file = len(out_info['exptName']) # sets the output file name out_file = os.path.join(out_info['inputDir'], '{0}.mcomp'.format(out_info['dataName'])) # output data file data_out = { 'data': np.empty((n_file, 2), dtype=object), 'c_data': np.empty(n_file, dtype=object), 'ff_corr': data.comp.ff_corr if hasattr(data.comp, 'ff_corr') else None, 'f_data': data.externd.free_data if hasattr(data.externd, 'free_data') else None } for i_file in range(n_file): # retrieves the index of the data field corresponding to the current experiment fix_file = out_info['exptName'][i_file].split('/')[0] i_comp = cf.det_comp_dataset_index(data.comp.data, fix_file) # creates the multi-experiment data file based on the type data_out['c_data'][i_file] = data.comp.data[i_comp] data_out['data'][i_file, 0], data_out['data'][i_file, 1] = \ cf.get_comp_datasets(data, c_data=data_out['c_data'][i_file], is_full=True) # outputs the data to file with open(out_file, 'wb') as fw: p.dump(data_out, fw) # updates the progressbar self.work_progress.emit('Data Save Complete!', 100.0) def init_pool_worker(self): ''' :return: ''' # creates the pool worker object p = mp.Pool(int(np.floor(1.5 * mp.cpu_count()))) # returns the object return p def init_cluster_data(self): ''' :return: ''' def map_cluster_depths(): ''' :param cluster_depth: :return: ''' # retrieves the cluster depths from the spike I/O class object cluster_depth = sp_io.get_cluster_depths(cluster_ids) # sets the mapped cluster depths based on the file type if (exp_info['dmapFile'] is None) or (len(exp_info['dmapFile']) == 0): # no map is given so return the original depth values return cluster_depth, None else: # otherwise, map the cluster depth values from the probe to actual values data = np.array(pd.read_csv(exp_info['dmapFile'])) if np.size(data, axis=1) < 4: # if the mapping file is not correct, then output an error to screen e_str = 'Channel mapping file does not have the correct format.\n\n' \ 'Re-select a valid file before attempting to initialise the combined data files.' self.work_error.emit(e_str, 'Invalid Channel Mapping File') # return none values indicating the error return None, None else: # otherwise, return the mapped channel depths and the other mapping values return np.array([data[data[:, 1] == x, 0][0] for x in cluster_depth]), data[:, :4] # retrieves the job parameters exp_info, out_name, g_para = self.thread_job_para[0], self.thread_job_para[1], self.thread_job_para[2] # sets the global parameters n_hist = int(g_para['n_hist']) n_spike = int(g_para['n_spike']) cluster_ids = None # retrieves the spike I/O data and sets the cluster IDs based on the cluster type sp_io = spike_io.SpikeIo(exp_info['srcDir'], exp_info['traceFile'], int(exp_info['nChan'])) if exp_info['clusterType'] == 'Good': # case is the good clusters if hasattr(sp_io, 'good_cluster_ids'): cluster_ids = sp_io.good_cluster_ids elif exp_info['clusterType'] == 'MUA': # case is the multi-unit clusters if hasattr(sp_io, 'MUA_cluster_ids'): cluster_ids = sp_io.MUA_cluster_ids if cluster_ids is None: e_str = 'Cluster group file is missing? Please re-run with cluster-group file in the source data directory' self.work_error.emit(e_str, 'Cluster Group File Missing!') return # retrieves the clusters spike data and channel depths self.work_progress.emit('Reshaping Cluster Data...', 0.0) clusters = [ClusterRead(sp_io, cid) for cid in cluster_ids] # determines the channel depths mapping depth, channel_map_data = map_cluster_depths() if depth is None: # if the file has an incorrect format, then exit the function return # determines if the mapping values were set correctly if channel_map_data is not None: # if so, then determine the region/recording layers y_coords = channel_map_data[:, 3] depthLo, depthHi = np.array(exp_info['depthLo']).astype(int), np.array(exp_info['depthHi']).astype(int) indD = np.array([next((i for i in range(len(depthHi)) if x <= depthHi[i]), len(depthHi)-1) for x in y_coords]) chRegion = np.array(exp_info['regionName'])[indD][depth.astype(int)] chLayer = np.array(exp_info['recordLayer'])[indD][depth.astype(int)] else: # otherwise, return N/A for the region/recording layers chRegion, chLayer = ['N/A'] * len(clusters), ['N/A'] * len(clusters) depthLo, depthHi = None, None # sets the signal point-wise/ISI bin vectors xi_pts_H = np.linspace(-200, 100, n_hist + 1) xi_isi_H = np.linspace(0, 1000, n_hist + 1) # creates the recording/experimental information sub-dictionaries expInfo = {'name': exp_info['expName'], 'date': exp_info['expDate'], 'cond': exp_info['expCond'], 'type': exp_info['expType'], 'sex': exp_info['expSex'], 'age': exp_info['expAge'], 'probe': exp_info['expProbe'], 'lesion': exp_info['lesionType'], 'channel_map': channel_map_data, 'cluster_type': exp_info['clusterType'], 'other_info': exp_info['otherInfo'], 'record_state': exp_info['recordState'], 'record_coord': exp_info['recordCoord'], 'depth_lo': depthLo, 'depth_hi': depthHi} # memory allocation pW0, pW1, nFeat = 20.0, 60.0, 5 nC, nSample = len(clusters), np.size(sp_io.traces, axis=0) sFreq, vGain = float(exp_info['sFreq']), float(exp_info['vGain']) # sets the data file dictionary object A = { 'vSpike': np.empty(nC, dtype=object), 'tSpike': np.empty(nC, dtype=object), 'vMu': None, 'vSD': None, 'ccGram': None, 'ccGramXi': None, 'sigFeat': np.zeros((nC, nFeat)), 'clustID': cluster_ids, 'expInfo': expInfo, 'chDepth': depth, 'chRegion': chRegion, 'chLayer': chLayer, 'sFreq': sFreq, 'nC': nC, 'nPts': None, 'tExp': nSample / sFreq, 'vGain': vGain, 'isiHist': np.empty(nC, dtype=object), 'isiHistX': xi_isi_H, 'ptsHist': np.empty(nC, dtype=object), 'ptsHistX': xi_pts_H, 'rotInfo': None, } # sets up the rotation analysis data dictionary A['rotInfo'] = rot.load_rot_analysis_data(A, exp_info, sp_io, w_prog=self.work_progress, pW0=pW0) # sets up the sub-job flags self.sub_job = np.zeros(nC, dtype=bool) # retrieves the cluster data for i, c in enumerate(clusters): if not self.is_running: # if the user cancelled, then exit the function return else: # updates the main gui progressnbar pW = pW0 + pW1 * (i + 1) / nC self.work_progress.emit('Processing Cluster {0} of {1}'.format(i + 1, nC), pW) ################################################### #### DATA RETRIEVAL & MEMORY ALLOCATIONS #### ################################################### # retrieves the spike voltage/timing v_spike = c.channel_waveforms t_spike = 1000.0 * sp_io.get_spike_times_in_cluster(cluster_ids[i]) / sFreq # memory allocation (only for the first cluster) if i == 0: A['nPts'] = np.size(v_spike, axis=0) A['vMu'] = np.zeros((A['nPts'], nC), dtype=float) A['vSD'] = np.zeros((A['nPts'], nC), dtype=float) xi = np.array(range(A['nPts'])) ############################################### #### MAIN METRIC CALCULATION/STORAGE #### ############################################### # sets the values into the final array A['vSpike'][i] = v_spike[:, :n_spike] * vGain A['tSpike'][i] = t_spike[:np.size(v_spike, axis=1)] # calculates the mean/standard deviation of the voltage spikes A['vMu'][:, i] = np.mean(v_spike, axis=1) * vGain A['vSD'][:, i] = np.std(v_spike, axis=1) * vGain ###################################### #### HISTOGRAM CALCULATIONS #### ###################################### # calculates the point-wise histograms A['ptsHist'][i] = np.zeros((A['nPts'], n_hist), dtype=int) for iPts in range(A['nPts']): H = np.histogram(v_spike[iPts, :], bins=xi_pts_H) A['ptsHist'][i][iPts, :] = H[0] # calculates the ISI histograms dT = np.diff(A['tSpike'][i]) dT = dT[dT <= xi_isi_H[-1]] H_isi = np.histogram(dT, bins=xi_isi_H, range=(xi_isi_H[0], xi_isi_H[-1])) A['isiHist'][i] = H_isi[0] ########################################### #### SIGNAL FEATURE CALCULATIONS #### ########################################### # creates the piecewise-polynomial of the mean signal pp = pchip(xi, A['vMu'][:, i]) # determines the point/voltage of the pmaximum proceding the minimum i_min = np.argmin(A['vMu'][:, i]) i_max1 = np.argmax(A['vMu'][:i_min, i]) i_max2 = np.argmax(A['vMu'][i_min:, i]) + i_min # determines the location of the half-width points v_half = (min(pp(i_max1), pp(i_max2)) + pp(i_min)) / 2.0 t_lo = cfcn.opt_time_to_y0((pp, v_half), [(i_max1, i_min)]) t_hi = cfcn.opt_time_to_y0((pp, v_half), [(i_min, i_max2)]) # sets the signal features into the final array A['sigFeat'][i, :] = [i_max1, i_min, i_max2, t_lo, t_hi] # memory garbage collection gc.collect() ###################################################### #### CLUSTER CROSS-CORRELOGRAM CALCULATIONS #### ###################################################### # memory allocation win_size = 50 # calculates the cross-correlation between each signal from each cluster for i_row in range(nC): if not self.is_running: # if the user cancelled, then exit the function return else: # updates the main gui progressbar pW = (pW0 + pW1) + (100.0 - (pW0 + pW1)) * (i_row + 1) / (nC + 1) self.work_progress.emit('Calculating CC-Grams...', pW) # calculates the cross-correlograms between each of the other clusters for j_row in range(nC): if (i_row == 0) and (j_row == 0): # case is the first cluster so allocate memory and set the time bin array ccGram, A['ccGramXi'] = cfcn.calc_ccgram(A['tSpike'][i_row], A['tSpike'][j_row], win_size) A['ccGram'] = np.zeros((nC, nC, len(ccGram))) A['ccGram'][i_row, j_row, :] = ccGram else: # otherwise, set the new values directly into the array A['ccGram'][i_row, j_row, :], _ = cfcn.calc_ccgram(A['tSpike'][i_row], A['tSpike'][j_row], win_size) ################################# #### FINAL DATA OUTPUT #### ################################# # dumps the cluster data to file self.work_progress.emit('Outputting Data To File...', 99.0) cf.save_single_file(out_name, A) ########################################## #### CLUSTER MATCHING FUNCTIONS #### ########################################## def det_cluster_matches(self, data, calc_para, w_prog): ''' :param exp_name: :param comp_dlg: :return: ''' # retrieves the comparison dataset i_comp = cf.det_comp_dataset_index(data.comp.data, calc_para['calc_comp']) c_data, data.comp.last_comp = data.comp.data[i_comp], i_comp # if there is no further calculation necessary, then exit the function if c_data.is_set: return # updates the cluster matching parameters c_data.is_set = True c_data.d_max = calc_para['d_max'] c_data.r_max = calc_para['r_max'] c_data.sig_corr_min = calc_para['sig_corr_min'] c_data.isi_corr_min = calc_para['isi_corr_min'] c_data.sig_diff_max = calc_para['sig_diff_max'] c_data.sig_feat_min = calc_para['sig_feat_min'] c_data.w_sig_feat = calc_para['w_sig_feat'] c_data.w_sig_comp = calc_para['w_sig_comp'] c_data.w_isi = calc_para['w_isi'] # retrieves the fixed/free cluster dataframes data_fix, data_free = cf.get_comp_datasets(data, c_data=c_data, is_full=True) def det_overall_cluster_matches(is_feas, D): ''' :param data_fix: :param data_free: :param D: :return: ''' # calculates the pair-wise SS distances between each the fixed/free mean signals iDsort, n_rows = np.argsort(D.T, axis=None), np.size(D, axis=0) # memory allocation isFix = np.zeros(data_fix['nC'], dtype=bool) isFree = np.zeros(data_free['nC'], dtype=bool) i_match = -np.ones(data_fix['nC'], dtype=int) # determines the overall unique for i in range(len(iDsort)): # determines the indices of the next best match iR, iC = cfcn.ind2sub(n_rows, iDsort[i]) if not (isFix[iR] or isFree[iC]) and is_feas[iR, iC]: # if there is not already a match, then update the match arrays i_match[iR] = iC isFix[iR], isFree[iC] = True, True if all(isFix) or all(isFree): # if all matches are found, then exit the loop break # returns the final match array return i_match def det_cluster_matches_old(c_data, is_feas, d_depth): ''' :param data_fix: :param data_free: :return: ''' # parameters z_max = 1.0 # calculates the inter-signal euclidean distances DD = cdist(data_fix['vMu'].T, data_free['vMu'].T) # determines the matches based on the signal euclidean distances c_data.i_match_old = det_overall_cluster_matches(is_feas, DD) # calculates the correlation coefficients between the best matching signals for i in range(data_fix['nC']): # calculation of the z-scores i_match = c_data.i_match_old[i] if i_match >= 0: # z-score calculations dW = data_fix['vMu'][:, i] - data_free['vMu'][:, i_match] c_data.z_score[:, i] = np.divide(dW, data_fix['vSD'][:, i]) # calculates the correlation coefficient CC = np.corrcoef(data_fix['vMu'][:, i], data_free['vMu'][:, i_match]) c_data.sig_corr_old[i] = CC[0, 1] c_data.sig_diff_old[i] = DD[i, i_match] c_data.d_depth_old[i] = d_depth[i, i_match] # sets the acceptance flag. for a cluster to be accepted, the following must be true: # * the maximum absolute z-score must be < z_max # * the correlation coefficient between the fixed/free signals must be > sig_corr_min c_data.is_accept_old[i] = np.max(np.abs(c_data.z_score[:, i])) < z_max and \ c_data.sig_corr_old[i] > c_data.sig_corr_min else: # sets NaN values for all the single value metrics c_data.sig_corr[i] = np.nan c_data.d_depth_old[i] = np.nan # ensures the group is rejected c_data.is_accept_old[i] = False def det_cluster_matches_new(c_data, is_feas, d_depth, r_spike, w_prog): ''' :param data_fix: :param data_free: :return: ''' # parameters pW = 100.0 / 7.0 # memory allocation signal_metrics = np.zeros((data_fix['nC'], data_free['nC'], 4)) isi_metrics = np.zeros((data_fix['nC'], data_free['nC'], 3)) isi_metrics_norm = np.zeros((data_fix['nC'], data_free['nC'], 3)) total_metrics = np.zeros((data_fix['nC'], data_free['nC'], 3)) # initialises the comparison data object w_prog.emit('Calculating Signal DTW Indices', pW) c_data = cfcn.calc_dtw_indices(c_data, data_fix, data_free, is_feas) # calculates the signal feature metrics w_prog.emit('Calculating Signal Feature Metrics', 2.0 * pW) signal_feat = cfcn.calc_signal_feature_diff(data_fix, data_free, is_feas) # calculates the signal direct matching metrics w_prog.emit('Calculating Signal Comparison Metrics', 3.0 * pW) cc_dtw, dd_dtw, dtw_scale = \ cfcn.calc_signal_corr(c_data.i_dtw, data_fix, data_free, is_feas) signal_metrics[:, :, 0] = cc_dtw signal_metrics[:, :, 1] = 1.0 - dd_dtw signal_metrics[:, :, 2] = dtw_scale signal_metrics[:, :, 3] = \ cfcn.calc_signal_hist_metrics(data_fix, data_free, is_feas, cfcn.calc_hist_intersect, max_norm=True) # calculates the ISI histogram metrics w_prog.emit('Calculating ISI Histogram Comparison Metrics', 4.0 * pW) isi_metrics[:, :, 0], isi_metrics_norm[:, :, 0] = \ cfcn.calc_isi_corr(data_fix, data_free, is_feas) isi_metrics[:, :, 1], isi_metrics_norm[:, :, 1] = \ cfcn.calc_isi_hist_metrics(data_fix, data_free, is_feas, cfcn.calc_hist_intersect, max_norm=True) # isi_metrics[:, :, 2], isi_metrics_norm[:, :, 2] = \ # cfcn.calc_isi_hist_metrics(data_fix, data_free, is_feas, cfcn.calc_wasserstein, max_norm=False) # isi_metrics[:, :, 3], isi_metrics_norm[:, :, 3] = \ # cfcn.calc_isi_hist_metrics(data_fix, data_free, is_feas, cfcn.calc_bhattacharyya, max_norm=True) # sets the isi relative spiking rate metrics isi_metrics[:, :, 2] = np.nan for i_row in range(np.size(r_spike, axis=0)): isi_metrics[i_row, is_feas[i_row, :], 2] = r_spike[i_row, is_feas[i_row, :]] isi_metrics_norm[:, :, 2] = cfcn.norm_array_rows(isi_metrics[:, :, 2], max_norm=False) # calculates the array euclidean distances (over all measures/clusters) weight_array = [c_data.w_sig_feat, c_data.w_sig_comp, c_data.w_isi] total_metrics[:, :, 0] = cfcn.calc_array_euclidean(signal_feat) total_metrics[:, :, 1] = cfcn.calc_array_euclidean(signal_metrics) total_metrics[:, :, 2] = cfcn.calc_array_euclidean(isi_metrics_norm) total_metrics_mean = cfcn.calc_weighted_mean(total_metrics, W=weight_array) # determines the unique overall cluster matches w_prog.emit('Determining Overall Cluster Matches', 5.0 * pW) c_data.i_match = det_overall_cluster_matches(is_feas, -total_metrics_mean) # matches which are from different regions are to be removed ii = np.where(c_data.i_match >= 0)[0] same_region = data_fix['chRegion'][ii] == data_free['chRegion'][c_data.i_match[ii]] c_data.i_match[ii[~same_region]] = -1 # calculates the correlation coefficients between the best matching signals w_prog.emit('Setting Final Match Metrics', 6.0 * pW) for i in range(data_fix['nC']): # calculation of the z-scores i_match = c_data.i_match[i] if i_match >= 0: # sets the signal feature metrics c_data.match_intersect[:, i] = cfcn.calc_single_hist_metric(data_fix, data_free, i, i_match, True, cfcn.calc_hist_intersect) c_data.match_wasserstain[:, i] = cfcn.calc_single_hist_metric(data_fix, data_free, i, i_match, True, cfcn.calc_wasserstein) c_data.match_bhattacharyya[:, i] = cfcn.calc_single_hist_metric(data_fix, data_free, i, i_match, True, cfcn.calc_bhattacharyya) # sets the signal difference metrics c_data.d_depth[i] = d_depth[i, i_match] c_data.dtw_scale[i] = dtw_scale[i, i_match] c_data.sig_corr[i] = cc_dtw[i, i_match] c_data.sig_diff[i] = max(0.0, 1 - dd_dtw[i, i_match]) c_data.sig_intersect[i] = signal_metrics[i, i_match, 2] # sets the isi metrics c_data.isi_corr[i] = isi_metrics[i, i_match, 0] c_data.isi_intersect[i] = isi_metrics[i, i_match, 1] # sets the total match metrics c_data.signal_feat[i, :] = signal_feat[i, i_match, :] c_data.total_metrics[i, :] = total_metrics[i, i_match, :] c_data.total_metrics_mean[i] = total_metrics_mean[i, i_match] # sets the acceptance flag. for a cluster to be accepted, the following must be true: # * the ISI correlation coefficient must be > isi_corr_min # * the signal correlation coefficient must be > sig_corr_min # * the inter-signal euclidean distance must be < sig_diff_max # * all signal feature metric similarity scores must be > sig_feat_min c_data.is_accept[i] = (c_data.isi_corr[i] > c_data.isi_corr_min) and \ (c_data.sig_corr[i] > c_data.sig_corr_min) and \ (c_data.sig_diff[i] > (1 - c_data.sig_diff_max)) and \ (np.all(c_data.signal_feat[i, :] > c_data.sig_feat_min)) else: # sets NaN values for all the single value metrics c_data.d_depth[i] = np.nan c_data.dtw_scale[i] = np.nan c_data.sig_corr[i] = np.nan c_data.sig_diff[i] = np.nan c_data.sig_intersect[i] = np.nan c_data.isi_corr[i] = np.nan c_data.isi_intersect[i] = np.nan c_data.signal_feat[i, :] = np.nan c_data.total_metrics[i, :] = np.nan c_data.total_metrics_mean[i] = np.nan # ensures the group is rejected c_data.is_accept[i] = False # determines the number of spikes n_spike_fix = [len(x) / data_fix['tExp'] for x in data_fix['tSpike']] n_spike_free = [len(x) / data_free['tExp'] for x in data_free['tSpike']] # calculates the relative spiking rates (note - ratios are coverted so that they are all > 1) r_spike = np.divide(repmat(n_spike_fix, data_free['nC'], 1).T, repmat(n_spike_free, data_fix['nC'], 1)) r_spike[r_spike < 1] = 1 / r_spike[r_spike < 1] # calculates the pair-wise distances between the fixed/free probe depths d_depth = np.abs(np.subtract(repmat(data_fix['chDepth'], data_free['nC'], 1).T, repmat(data_free['chDepth'], data_fix['nC'], 1))) # determines the feasible fixed/free cluster groupings such that: # 1) the channel depth has to be <= d_max # 2) the relative spiking rates between clusters is <= r_max is_feas = np.logical_and(r_spike <= c_data.r_max, d_depth <= c_data.d_max) # determines the cluster matches from the old/new methods det_cluster_matches_old(c_data, is_feas, d_depth) det_cluster_matches_new(c_data, is_feas, d_depth, r_spike, w_prog) def calc_ccgram_types(self, calc_para, data): ''' :return: ''' # determines the indices of the experiment to be analysed if calc_para['calc_all_expt']: # case is all experiments are to be analysed i_expt = list(range(len(data))) else: # case is a single experiment is being analysed i_expt = [cf.get_expt_index(calc_para['calc_exp_name'], data)] # memory allocation d_copy = copy.deepcopy A, B, C = np.empty(len(i_expt), dtype=object), [[] for _ in range(5)], [[] for _ in range(4)] c_type, t_dur, t_event, ci_lo, ci_hi, ccG_T = d_copy(A), d_copy(A), d_copy(A), d_copy(A), d_copy(A), d_copy(A) # for i_ex in i_expt: # sets the experiment ID info based on the number of experiments being analysed if len(i_expt) == 1: # only one experiment is being analysed expt_id = None else: # multiple experiments are being analysed expt_id = [(i_ex+1), len(i_expt)] # retrieves the cluster information t_dur[i_ex], t_event[i_ex] = d_copy(C), d_copy(C) c_type[i_ex], ci_lo[i_ex], ci_hi[i_ex], ccG_T[i_ex] = d_copy(B), d_copy(B), d_copy(B), d_copy(B) ccG, ccG_xi, t_spike = data[i_ex]['ccGram'], data[i_ex]['ccGramXi'], data[i_ex]['tSpike'] c_id = data[i_ex]['clustID'] # runs the cc-gram type calculation function c_type0, t_dur[i_ex], t_event[i_ex], ci_hi0, ci_lo0, ccG_T0 = cfcn.calc_ccgram_types( ccG, ccG_xi, t_spike, calc_para=calc_para, expt_id=expt_id, w_prog=self.work_progress, c_id=c_id) # sets the final values into their respective groupings for i in range(5): # sets the final type values and lower/upper bound confidence interval signals if len(c_type0[i]): # c_type[i_ex][i] =

np.vstack(c_type0[i])

numpy.vstack

import numpy as np from numba import vectorize # Size 为列表,为神经网络结构，比如[3，5，5，4，2]，3是输入层神经元个数，中间为隐藏层每层神经元个数，2为输出层个数 class nn_Creat(): def __init__(self,Size,active_fun='sigmoid',learning_rate=1.5,batch_normalization=1,objective_fun='MSE', output_function='sigmoid',optimization_method='normal',weight_decay=0): self.Size=Size # 初始化网络参数，并进行打印 print('the structure of the NN is \n', self.Size) self.active_fun=active_fun print('active function is %s '% active_fun) self.learning_rate=learning_rate print('learning_rate is %s '% learning_rate) self.batch_normalization=batch_normalization print('batch_normalization is %d '% batch_normalization) self.objective_fun=objective_fun print('objective_function is %s '% objective_fun) self.optimization_method=optimization_method print('optimization_method is %s '% optimization_method) self.weight_decay = weight_decay print('weight_decay is %f '% weight_decay) # 初始化网络权值和梯度 self.vecNum=0 self.depth=len(Size) self.W=[] self.b=[] self.W_grad=[] self.b_grad=[] self.cost=[] if self.batch_normalization: # 是否运用批量归一化，如果用，则引入期望E和方差S，以及缩放因子Gamma、Beta self.E = [] self.S = [] self.Gamma = [] self.Beta = [] if objective_fun=='Cross Entropy': # 目标函数是否为交叉墒函数 self.output_function='softmax' else: self.output_function='sigmoid' print('output_function is %s \n'% self.output_function) print('Start training NN \n') for item in range(self.depth-1): width=self.Size[item] height=self.Size[item+1] q=2*np.random.rand(height,width)/np.sqrt(width)-1/np.sqrt(width) #初始化权系数W self.W.append(q) if self.active_fun=='relu': # 判断激活函数是否为relu函数，以决定b的初始化形式 self.b.append(np.random.rand(height,1)+0.01) else: self.b.append(2*np.random.rand(height,1)/np.sqrt(width)-1/np.sqrt(width)) if self.optimization_method=='Momentum': #优化方向是否使用矩形式，即为之前梯度的叠加 if item!=0: self.vW.append(np.zeros([height,width])) self.vb.append(np.zeros([height, 1])) else: self.vW=[] self.vb=[] self.vW.append(np.zeros([height, width])) self.vb.append(np.zeros([height, 1])) if self.optimization_method=='AdaGrad'or optimization_method=='RMSProp' or optimization_method=='Adam': #优化方法是否使用上述方法 if item!=0: self.rW.append(np.zeros([height,width])) self.rb.append(np.zeros([height, 1])) else: self.rW=[] self.rb=[] self.rW.append(np.zeros([height, width])) self.rb.append(np.zeros([height, 1])) if self.optimization_method == 'Adam': #优化方法是否为Adam方法 if item!=0: self.sW.append(np.zeros([height, width])) self.sb.append(np.zeros([height, 1])) else: self.sW = [] self.sb = [] self.sW.append(np.zeros([height, width])) self.sb.append(np.zeros([height, 1])) if self.batch_normalization: #是否对每层进行归一化 self.Gamma.append(

np.array([1])

numpy.array

# ________ # / # \ / # \ / # \/ import random import textwrap import emd_mean import AdvEMDpy import emd_basis import emd_utils import numpy as np import pandas as pd import cvxpy as cvx import seaborn as sns import matplotlib.pyplot as plt from scipy.integrate import odeint from scipy.ndimage import gaussian_filter from emd_utils import time_extension, Utility from scipy.interpolate import CubicSpline from emd_hilbert import Hilbert, hilbert_spectrum from emd_preprocess import Preprocess from emd_mean import Fluctuation from AdvEMDpy import EMD # alternate packages from PyEMD import EMD as pyemd0215 import emd as emd040 sns.set(style='darkgrid') pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001) pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time) pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series) # plot 0 - addition fig = plt.figure(figsize=(9, 4)) ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title('First Iteration of Sifting Algorithm') plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1) plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()], pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()], c='r', label=r'$M(t_i)$', zorder=2) plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4) plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()], pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()], c='c', label=r'$m(t_j)$', zorder=3) plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5) plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5) plt.yticks(ticks=[-2, -1, 0, 1, 2]) plt.xticks(ticks=[0, np.pi, 2 * np.pi], labels=[r'0', r'$\pi$', r'$2\pi$']) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/pseudo_algorithm.png') plt.show() knots = np.arange(12) time = np.linspace(0, 11, 1101) basis = emd_basis.Basis(time=time, time_series=time) b_spline_basis = basis.cubic_b_spline(knots) chsi_basis = basis.chsi_basis(knots) # plot 1 plt.title('Non-Natural Cubic B-Spline Bases at Boundary') plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $') plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $') plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $') plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $') plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $') plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $']) plt.xlim(4.4, 6.6) plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') plt.legend(loc='upper left') plt.savefig('jss_figures/boundary_bases.png') plt.show() # plot 1a - addition knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001) knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time) knots_uniform = np.linspace(0, 2 * np.pi, 51) emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series) imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0] fig, axs = plt.subplots(3, 1) fig.subplots_adjust(hspace=0.6) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Time Series and Uniform Knots') axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100) axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].set_title('IMF 1 and Uniform Knots') axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[2].set_title('IMF 2 and Uniform Knots') axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100) axs[2].set_yticks(ticks=[-2, 0, 2]) axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[0].legend(loc='lower left') axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') for i in range(3): for j in range(1, len(knots_uniform)): axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey') plt.savefig('jss_figures/knot_uniform.png') plt.show() # plot 1b - addition knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001) knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time) emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series) imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric', optimise_knots=1, verbose=False) fig, axs = plt.subplots(3, 1) fig.subplots_adjust(hspace=0.6) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Time Series and Statically Optimised Knots') axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100) axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].set_title('IMF 1 and Statically Optimised Knots') axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[2].set_title('IMF 2 and Statically Optimised Knots') axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100) axs[2].set_yticks(ticks=[-2, 0, 2]) axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[0].legend(loc='lower left') axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') for i in range(3): for j in range(1, len(knots)): axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey') plt.savefig('jss_figures/knot_1.png') plt.show() # plot 1c - addition knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001) knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time) emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series) imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric', optimise_knots=2, verbose=False) fig, axs = plt.subplots(3, 1) fig.subplots_adjust(hspace=0.6) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Time Series and Dynamically Optimised Knots') axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100) axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].set_title('IMF 1 and Dynamically Knots') axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[2].set_title('IMF 2 and Dynamically Knots') axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100) axs[2].set_yticks(ticks=[-2, 0, 2]) axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[0].legend(loc='lower left') axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots') for i in range(3): for j in range(1, len(knots[i])): axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey') plt.savefig('jss_figures/knot_2.png') plt.show() # plot 1d - addition window = 81 fig, axs = plt.subplots(2, 1) fig.subplots_adjust(hspace=0.4) figure_size = plt.gcf().get_size_inches() factor = 0.8 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Preprocess Filtering Demonstration') axs[1].set_title('Zoomed Region') preprocess_time = pseudo_alg_time.copy() np.random.seed(1) random.seed(1) preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time)) for i in random.sample(range(1000), 500): preprocess_time_series[i] += np.random.normal(0, 1) preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series) axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12)) axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13)) axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12)) axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize interpolation filter', 14)) axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey', label=textwrap.fill('Quantile window', 12)) axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey') axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black', label=textwrap.fill('Zoomed region', 10)) axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black') axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12)) axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13)) axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12)) axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize interpolation filter', 14)) axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey', label=textwrap.fill('Quantile window', 12)) axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey') axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi) axs[1].set_ylim(-3, 3) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[np.pi]) axs[1].set_xticklabels(labels=[r'$\pi$']) box_0 = axs[0].get_position() axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15)) box_1 = axs[1].get_position() axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height]) plt.savefig('jss_figures/preprocess_filter.png') plt.show() # plot 1e - addition fig, axs = plt.subplots(2, 1) fig.subplots_adjust(hspace=0.4) figure_size = plt.gcf().get_size_inches() factor = 0.8 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.gcf().subplots_adjust(bottom=0.10) axs[0].set_title('Preprocess Smoothing Demonstration') axs[1].set_title('Zoomed Region') axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[0].plot(preprocess_time, preprocess.hp()[1], label=textwrap.fill('Hodrick-Prescott smoothing', 12)) axs[0].plot(preprocess_time, preprocess.hw(order=51)[1], label=textwrap.fill('Henderson-Whittaker smoothing', 13)) downsampled_and_decimated = preprocess.downsample() axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1], label=textwrap.fill('Downsampled & decimated', 11)) downsampled = preprocess.downsample(decimate=False) axs[0].plot(downsampled[0], downsampled[1], label=textwrap.fill('Downsampled', 13)) axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black', label=textwrap.fill('Zoomed region', 10)) axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black') axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black') axs[0].set_yticks(ticks=[-2, 0, 2]) axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi]) axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$']) axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)') axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12)) axs[1].plot(preprocess_time, preprocess.hp()[1], label=textwrap.fill('Hodrick-Prescott smoothing', 12)) axs[1].plot(preprocess_time, preprocess.hw(order=51)[1], label=textwrap.fill('Henderson-Whittaker smoothing', 13)) axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1], label=textwrap.fill('Downsampled & decimated', 13)) axs[1].plot(downsampled[0], downsampled[1], label=textwrap.fill('Downsampled', 13)) axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi) axs[1].set_ylim(-3, 3) axs[1].set_yticks(ticks=[-2, 0, 2]) axs[1].set_xticks(ticks=[np.pi]) axs[1].set_xticklabels(labels=[r'$\pi$']) box_0 = axs[0].get_position() axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height]) axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15)) box_1 = axs[1].get_position() axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height]) plt.savefig('jss_figures/preprocess_smooth.png') plt.show() # plot 2 fig, axs = plt.subplots(1, 2, sharey=True) axs[0].set_title('Cubic B-Spline Bases') axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1') axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2') axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3') axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4') axs[0].legend(loc='upper left') axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-') axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-') axs[0].set_xticks([5, 6]) axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $']) axs[0].set_xlim(4.5, 6.5) axs[1].set_title('Cubic Hermite Spline Bases') axs[1].plot(time, chsi_basis[10, :].T, '--') axs[1].plot(time, chsi_basis[11, :].T, '--') axs[1].plot(time, chsi_basis[12, :].T, '--') axs[1].plot(time, chsi_basis[13, :].T, '--') axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-') axs[1].set_xticks([5, 6]) axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $']) axs[1].set_xlim(4.5, 6.5) plt.savefig('jss_figures/comparing_bases.png') plt.show() # plot 3 a = 0.25 width = 0.2 time = np.linspace(0, (5 - a) * np.pi, 1001) time_series = np.cos(time) + np.cos(5 * time) utils = emd_utils.Utility(time=time, time_series=time_series) max_bool = utils.max_bool_func_1st_order_fd() maxima_x = time[max_bool] maxima_y = time_series[max_bool] min_bool = utils.min_bool_func_1st_order_fd() minima_x = time[min_bool] minima_y = time_series[min_bool] max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101) max_dash = maxima_y[-1] * np.ones_like(max_dash_time) min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101) min_dash = minima_y[-1] * np.ones_like(min_dash_time) dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101) dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101) max_discard = maxima_y[-1] max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1] max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101) max_discard_dash = max_discard * np.ones_like(max_discard_dash_time) dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101) dash_2 = np.linspace(minima_y[-1], max_discard, 101) end_point_time = time[-1] end_point = time_series[-1] time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101) time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi, (5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi, (5 - a) * np.pi, 101))) time_series_anti_reflect = time_series_reflect[0] - time_series_reflect utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect) anti_max_bool = utils.max_bool_func_1st_order_fd() anti_max_point_time = time_reflect[anti_max_bool] anti_max_point = time_series_anti_reflect[anti_max_bool] utils = emd_utils.Utility(time=time, time_series=time_series_reflect) no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()] no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()] point_1 = 5.4 length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101) length_distance_time = point_1 * np.pi * np.ones_like(length_distance) length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101) length_top = maxima_y[-1] * np.ones_like(length_time) length_bottom = minima_y[-1] * np.ones_like(length_time) point_2 = 5.2 length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101) length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2) length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101) length_top_2 = time_series[-1] * np.ones_like(length_time_2) length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2) symmetry_axis_1_time = minima_x[-1] * np.ones(101) symmetry_axis_2_time = time[-1] * np.ones(101) symmetry_axis = np.linspace(-2, 2, 101) end_time = np.linspace(time[-1] - width, time[-1] + width, 101) end_signal = time_series[-1] * np.ones_like(end_time) anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101) anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time) ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.plot(time, time_series, LineWidth=2, label='Signal') plt.title('Symmetry Edge Effects Example') plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10)) plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2, label=textwrap.fill('Anti-symmetric signal', 10)) plt.plot(max_dash_time, max_dash, 'k-') plt.plot(min_dash_time, min_dash, 'k-') plt.plot(dash_1_time, dash_1, 'k--') plt.plot(dash_2_time, dash_2, 'k--') plt.plot(length_distance_time, length_distance, 'k--') plt.plot(length_distance_time_2, length_distance_2, 'k--') plt.plot(length_time, length_top, 'k-') plt.plot(length_time, length_bottom, 'k-') plt.plot(length_time_2, length_top_2, 'k-') plt.plot(length_time_2, length_bottom_2, 'k-') plt.plot(end_time, end_signal, 'k-') plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1) plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1) plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1) plt.text(5.1 * np.pi, -0.7, r'$\beta$L') plt.text(5.34 * np.pi, -0.05, 'L') plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima') plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima') plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10)) plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10)) plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10)) plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10)) plt.xlim(3.9 * np.pi, 5.5 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/edge_effects_symmetry_anti.png') plt.show() # plot 4 a = 0.21 width = 0.2 time = np.linspace(0, (5 - a) * np.pi, 1001) time_series = np.cos(time) + np.cos(5 * time) utils = emd_utils.Utility(time=time, time_series=time_series) max_bool = utils.max_bool_func_1st_order_fd() maxima_x = time[max_bool] maxima_y = time_series[max_bool] min_bool = utils.min_bool_func_1st_order_fd() minima_x = time[min_bool] minima_y = time_series[min_bool] max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101) max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101) max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1) max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1) min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101) min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101) min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1) min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1) dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101) dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101) dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101) dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101) s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1]) slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2]) slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1 max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1) max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101) dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101) dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101) s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1]) slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2]) slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2 min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1) min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101) dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time) dash_4 = np.linspace(slope_based_maximum, slope_based_minimum) maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101) maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash) maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash) maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash) maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101) maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time) minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101) minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash) minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash) minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash) minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101) minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time) # slightly edit signal to make difference between slope-based method and improved slope-based method more clear time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \ time_series[time == minima_x[-1]] improved_slope_based_maximum_time = time[-1] improved_slope_based_maximum = time_series[-1] improved_slope_based_minimum_time = slope_based_minimum_time improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time - improved_slope_based_maximum_time) min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101) min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4) dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101) dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101) ax = plt.subplot(111) figure_size = plt.gcf().get_size_inches() factor = 0.9 plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1])) plt.gcf().subplots_adjust(bottom=0.10) plt.plot(time, time_series, LineWidth=2, label='Signal') plt.title('Slope-Based Edge Effects Example') plt.plot(max_dash_time_1, max_dash_1, 'k-') plt.plot(max_dash_time_2, max_dash_2, 'k-') plt.plot(max_dash_time_3, max_dash_3, 'k-') plt.plot(min_dash_time_1, min_dash_1, 'k-') plt.plot(min_dash_time_2, min_dash_2, 'k-') plt.plot(min_dash_time_3, min_dash_3, 'k-') plt.plot(min_dash_time_4, min_dash_4, 'k-') plt.plot(maxima_dash_time_1, maxima_dash, 'k-') plt.plot(maxima_dash_time_2, maxima_dash, 'k-') plt.plot(maxima_dash_time_3, maxima_dash, 'k-') plt.plot(minima_dash_time_1, minima_dash, 'k-') plt.plot(minima_dash_time_2, minima_dash, 'k-') plt.plot(minima_dash_time_3, minima_dash, 'k-') plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$') plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$') plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$') plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$') plt.text(4.30 * np.pi, 0.35, r'$s_1$') plt.text(4.43 * np.pi, -0.20, r'$s_2$') plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$') plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]), -0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$') plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]), 1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$') plt.plot(minima_line_dash_time, minima_line_dash, 'k--') plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--') plt.plot(dash_1_time, dash_1, 'k--') plt.plot(dash_2_time, dash_2, 'k--') plt.plot(dash_3_time, dash_3, 'k--') plt.plot(dash_4_time, dash_4, 'k--') plt.plot(dash_final_time, dash_final, 'k--') plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima') plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima') plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4, label=textwrap.fill('Slope-based maximum', 11)) plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4, label=textwrap.fill('Slope-based minimum', 11)) plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4, label=textwrap.fill('Improved slope-based maximum', 11)) plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4, label=textwrap.fill('Improved slope-based minimum', 11)) plt.xlim(3.9 * np.pi, 5.5 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/edge_effects_slope_based.png') plt.show() # plot 5 a = 0.25 width = 0.2 time = np.linspace(0, (5 - a) * np.pi, 1001) time_series = np.cos(time) + np.cos(5 * time) utils = emd_utils.Utility(time=time, time_series=time_series) max_bool = utils.max_bool_func_1st_order_fd() maxima_x = time[max_bool] maxima_y = time_series[max_bool] min_bool = utils.min_bool_func_1st_order_fd() minima_x = time[min_bool] minima_y = time_series[min_bool] A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2 A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2 P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2]) P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1]) Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1] Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1] Coughlin_time = Huang_time Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0])) Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2]) Average_max = (maxima_y[-2] + maxima_y[-1]) / 2 Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2]) Average_min = (minima_y[-2] + minima_y[-1]) / 2 utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave) Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd() Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd() utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave) Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd() Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd() Huang_max_time = Huang_time[Huang_max_bool] Huang_max = Huang_wave[Huang_max_bool] Huang_min_time = Huang_time[Huang_min_bool] Huang_min = Huang_wave[Huang_min_bool] Coughlin_max_time = Coughlin_time[Coughlin_max_bool] Coughlin_max = Coughlin_wave[Coughlin_max_bool] Coughlin_min_time = Coughlin_time[Coughlin_min_bool] Coughlin_min = Coughlin_wave[Coughlin_min_bool] max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101) max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101) max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time) min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101) min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101) min_2_x = minima_y[-2] * np.ones_like(min_2_x_time) dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101) dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x) max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101) max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101) max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y) min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101) min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101) min_2_y_time = minima_x[-2] * np.ones_like(min_2_y) dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101) dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time) max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101) max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101) max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time) min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101) min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101) min_1_x = minima_y[-1] * np.ones_like(min_1_x_time) dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101) dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x) max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101) max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101) max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y) min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101) min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101) min_1_y_time = minima_x[-1] * np.ones_like(min_1_y) dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101) dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time) ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title('Characteristic Wave Effects Example') plt.plot(time, time_series, LineWidth=2, label='Signal') plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10)) plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10)) plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4, label=textwrap.fill('Coughlin maximum', 14)) plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4, label=textwrap.fill('Coughlin minimum', 14)) plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4, label=textwrap.fill('Average maximum', 14)) plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4, label=textwrap.fill('Average minimum', 14)) plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima') plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima') plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14)) plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14)) plt.plot(max_2_x_time, max_2_x, 'k-') plt.plot(max_2_x_time_side, max_2_x, 'k-') plt.plot(min_2_x_time, min_2_x, 'k-') plt.plot(min_2_x_time_side, min_2_x, 'k-') plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--') plt.text(5.16 * np.pi, 0.85, r'$2a_2$') plt.plot(max_2_y_time, max_2_y, 'k-') plt.plot(max_2_y_time, max_2_y_side, 'k-') plt.plot(min_2_y_time, min_2_y, 'k-') plt.plot(min_2_y_time, min_2_y_side, 'k-') plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--') plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$') plt.plot(max_1_x_time, max_1_x, 'k-') plt.plot(max_1_x_time_side, max_1_x, 'k-') plt.plot(min_1_x_time, min_1_x, 'k-') plt.plot(min_1_x_time_side, min_1_x, 'k-') plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--') plt.text(5.42 * np.pi, -0.1, r'$2a_1$') plt.plot(max_1_y_time, max_1_y, 'k-') plt.plot(max_1_y_time, max_1_y_side, 'k-') plt.plot(min_1_y_time, min_1_y, 'k-') plt.plot(min_1_y_time, min_1_y_side, 'k-') plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--') plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$') plt.xlim(3.9 * np.pi, 5.6 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/edge_effects_characteristic_wave.png') plt.show() # plot 6 t = np.linspace(5, 95, 100) signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200) util_nn = emd_utils.Utility(time=t, time_series=signal_orig) maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()] minima = signal_orig[util_nn.min_bool_func_1st_order_fd()] cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima) cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima) time = np.linspace(0, 5 * np.pi, 1001) lsq_signal = np.cos(time) + np.cos(5 * time) knots = np.linspace(0, 5 * np.pi, 101) time_extended = time_extension(time) time_series_extended = np.zeros_like(time_extended) / 0 time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal neural_network_m = 200 neural_network_k = 100 # forward -> P = np.zeros((int(neural_network_k + 1), neural_network_m)) for col in range(neural_network_m): P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))] P[-1, col] = 1 # for additive constant t = lsq_signal[-neural_network_m:] # test - top seed_weights = np.ones(neural_network_k) / neural_network_k weights = 0 * seed_weights.copy() train_input = P[:-1, :] lr = 0.01 for iterations in range(1000): output = np.matmul(weights, train_input) error = (t - output) gradients = error * (- train_input) # guess average gradients average_gradients = np.mean(gradients, axis=1) # steepest descent max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients))) adjustment = - lr * average_gradients # adjustment = - lr * max_gradient_vector weights += adjustment # test - bottom weights_right = np.hstack((weights, 0)) max_count_right = 0 min_count_right = 0 i_right = 0 while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1): time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \ sum(weights_right * np.hstack((time_series_extended[ int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right): int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1))) i_right += 1 if i_right > 1: emd_utils_max = \ emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)], time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)]) if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0: max_count_right += 1 emd_utils_min = \ emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)], time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1): int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)]) if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0: min_count_right += 1 # backward <- P = np.zeros((int(neural_network_k + 1), neural_network_m)) for col in range(neural_network_m): P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)] P[-1, col] = 1 # for additive constant t = lsq_signal[:neural_network_m] vx = cvx.Variable(int(neural_network_k + 1)) objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary prob = cvx.Problem(objective) result = prob.solve(verbose=True, solver=cvx.ECOS) weights_left = np.array(vx.value) max_count_left = 0 min_count_left = 0 i_left = 0 while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1): time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \ 2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left): int(len(lsq_signal) - 1 - i_left + neural_network_k)], 1))) + 1 i_left += 1 if i_left > 1: emd_utils_max = \ emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))], time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))]) if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0: max_count_left += 1 emd_utils_min = \ emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))], time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))]) if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0: min_count_left += 1 lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal) utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended) maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()] maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()] maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1] maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1] minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()] minima_time = time[lsq_utils.min_bool_func_1st_order_fd()] minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:] minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:] ax = plt.subplot(111) plt.gcf().subplots_adjust(bottom=0.10) plt.title('Single Neuron Neural Network Example') plt.plot(time, lsq_signal, zorder=2, label='Signal') plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12)) plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima') plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima') plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3, label=textwrap.fill('Extrapolated maxima', 12)) plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4, label=textwrap.fill('Extrapolated minima', 12)) plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k', label=textwrap.fill('Neural network inputs', 13)) plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100), -2.75 * np.ones(100), c='k') plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100), 2.75 * np.ones(100), c='k') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2), ((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2), ((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k') plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k') plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed', label=textwrap.fill('Neural network targets', 13)) plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100), -2.75 * np.ones(100), c='gray') plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100), 2.75 * np.ones(100), c='gray') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2), ((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray') plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2), ((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray') plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed') plt.xlim(3.4 * np.pi, 5.6 * np.pi) plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$')) plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2')) box_0 = ax.get_position() ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height]) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) plt.savefig('jss_figures/neural_network.png') plt.show() # plot 6a np.random.seed(0) time = np.linspace(0, 5 * np.pi, 1001) knots_51 = np.linspace(0, 5 * np.pi, 51) time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time) noise = np.random.normal(0, 1, len(time_series)) time_series += noise advemdpy = EMD(time=time, time_series=time_series) imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3, edge_effect='symmetric_anchor', verbose=False)[:3] knots_31 = np.linspace(0, 5 * np.pi, 31) imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2, edge_effect='symmetric_anchor', verbose=False)[:3] knots_11 = np.linspace(0, 5 * np.pi, 11) imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1, edge_effect='symmetric_anchor', verbose=False)[:3] fig, axs = plt.subplots(3, 1) plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40)) plt.subplots_adjust(hspace=0.1) axs[0].plot(time, time_series, label='Time series') axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21)) print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}') for knot in knots_51: axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1) axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots') axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi]) axs[0].set_xticklabels(['', '', '', '', '', '']) axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--') axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--') axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--') axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region') box_0 = axs[0].get_position() axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height]) axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8) axs[1].plot(time, time_series, label='Time series') axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19)) axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19)) print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}') for knot in knots_31: axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1) axs[1].plot(knot * np.ones(101),

np.linspace(-5, 5, 101)

numpy.linspace

import sys # We need sys so that we can pass argv to QApplication from pathlib import Path from dataclasses import dataclass import numpy as np from PyQt5 import QtWidgets, QtCore, QtGui, uic import pyqtgraph as pg import easyqc.qt COLOR_PLOTS = (pg.mkColor((31, 119, 180)),) class EasyQC(QtWidgets.QMainWindow): """ This is the view in the MVC approach """ layers = None # used for additional scatter layers @staticmethod def _instances(): app = QtWidgets.QApplication.instance() return [w for w in app.topLevelWidgets() if isinstance(w, EasyQC)] @staticmethod def _get_or_create(title=None): eqc = next(filter(lambda e: e.isVisible() and e.windowTitle() == title, EasyQC._instances()), None) if eqc is None: eqc = EasyQC() eqc.setWindowTitle(title) return eqc def __init__(self, *args, **kwargs): super(EasyQC, self).__init__(*args, **kwargs) # wave by <NAME> from the Noun Project self.layers = {} self.ctrl = Controller(self) uic.loadUi(Path(__file__).parent.joinpath('easyqc.ui'), self) self.setWindowIcon(QtGui.QIcon(str(Path(__file__).parent.joinpath('easyqc.svg')))) background_color = self.palette().color(self.backgroundRole()) # init the seismic density display self.plotItem_seismic.setAspectLocked(False) self.imageItem_seismic = pg.ImageItem() self.plotItem_seismic.setBackground(background_color) self.plotItem_seismic.addItem(self.imageItem_seismic) self.viewBox_seismic = self.plotItem_seismic.getPlotItem().getViewBox() self._init_cmenu() # init the header display and link X and Y axis with density display self.plotDataItem_header_h = pg.PlotDataItem() self.plotItem_header_h.addItem(self.plotDataItem_header_h) self.plotItem_seismic.setXLink(self.plotItem_header_h) self.plotDataItem_header_v = pg.PlotDataItem() self.plotItem_header_h.setBackground(background_color) self.plotItem_header_v.addItem(self.plotDataItem_header_v) self.plotItem_header_v.setBackground(background_color) self.plotItem_seismic.setYLink(self.plotItem_header_v) # set the ticks so that they don't auto scale and ruin the axes link ax = self.plotItem_seismic.getAxis('left') ax.setStyle(tickTextWidth=60, autoReduceTextSpace=False, autoExpandTextSpace=False) ax = self.plotItem_header_h.getAxis('left') ax.setStyle(tickTextWidth=60, autoReduceTextSpace=False, autoExpandTextSpace=False) ax = self.plotItem_header_v.getAxis('left') ax.setStyle(showValues=False) # prepare placeholders for hover windows self.hoverPlotWidgets = {'Trace': None, 'Spectrum': None, 'Spectrogram': None} # connect signals and slots s = self.viewBox_seismic.scene() # vb.scene().sigMouseMoved.connect(self.mouseMoveEvent) self.proxy = pg.SignalProxy(s.sigMouseMoved, rateLimit=60, slot=self.mouseMoveEvent) s.sigMouseClicked.connect(self.mouseClick) self.lineEdit_gain.returnPressed.connect(self.editGain) self.lineEdit_sort.returnPressed.connect(self.editSort) self.comboBox_header.activated[str].connect(self.ctrl.set_header) self.viewBox_seismic.sigRangeChanged.connect(self.on_sigRangeChanged) self.horizontalScrollBar.sliderMoved.connect(self.on_horizontalSliderChange) self.verticalScrollBar.sliderMoved.connect(self.on_verticalSliderChange) def _init_cmenu(self): """ Setup context menus - on instantiation only """ self.viewBox_seismic.scene().contextMenu = None # this gets rid of the export context menu self.plotItem_seismic.plotItem.ctrlMenu = None # this gets rid of the plot context menu for act in self.viewBox_seismic.menu.actions(): if act.text() == 'View All': continue self.viewBox_seismic.menu.removeAction(act) # and add ours self.viewBox_seismic.menu.addSeparator() act = QtWidgets.QAction("View Trace", self.viewBox_seismic.menu) act.triggered.connect(self.cmenu_ViewTrace) self.viewBox_seismic.menu.addAction(act) act = QtWidgets.QAction("View Spectrum", self.viewBox_seismic.menu) act.triggered.connect(self.cmenu_ViewSpectrum) self.viewBox_seismic.menu.addAction(act) act = QtWidgets.QAction("View Spectrogram", self.viewBox_seismic.menu) act.triggered.connect(self.cmenu_ViewSpectrogram) self.viewBox_seismic.menu.addAction(act) """ View Methods """ def closeEvent(self, event): self.destroy() def keyPressEvent(self, e): """ page-up / ctrl + a : gain up page-down / ctrl + z : gain down ctrl + p : propagate display to current windows up/down/left/right arrows: pan using keys :param e: """ k, m = (e.key(), e.modifiers()) # page up / ctrl + a if k == QtCore.Qt.Key_PageUp or ( m == QtCore.Qt.ControlModifier and k == QtCore.Qt.Key_A): self.ctrl.set_gain(self.ctrl.gain - 3) # page down / ctrl + z elif k == QtCore.Qt.Key_PageDown or ( m == QtCore.Qt.ControlModifier and k == QtCore.Qt.Key_Z): self.ctrl.set_gain(self.ctrl.gain + 3) # control + P: propagate elif m == QtCore.Qt.ControlModifier and k == QtCore.Qt.Key_P: self.ctrl.propagate() # arrows keys move seismic elif k in (QtCore.Qt.Key_Up, QtCore.Qt.Key_Left, QtCore.Qt.Key_Right, QtCore.Qt.Key_Down): self.translate_seismic(k, m == QtCore.Qt.ControlModifier) # ctrl + s: screenshot to clipboard elif m == QtCore.Qt.ControlModifier and k == QtCore.Qt.Key_S: qtapp = QtWidgets.QApplication.instance() qtapp.clipboard().setPixmap(self.grab()) def editGain(self): self.ctrl.set_gain() def editSort(self): keys = self.lineEdit_sort.text().split(' ') self.ctrl.sort(keys) def mouseClick(self, event): if not event.double(): return qxy = self.imageItem_seismic.mapFromScene(event.scenePos()) tr, s = (qxy.x(), qxy.y()) print(tr, s) def mouseMoveEvent(self, scenepos): if isinstance(scenepos, tuple): scenepos = scenepos[0] else: return qpoint = self.imageItem_seismic.mapFromScene(scenepos) c, t, a, h = self.ctrl.cursor2timetraceamp(qpoint) self.label_x.setText(f"{c:.0f}") self.label_t.setText(f"{t:.4f}") self.label_amp.setText(f"{a:2.2E}") htxt = h if isinstance(h, str) else f"{h:.4f}" self.label_h.setText(htxt) for key in self.hoverPlotWidgets: if self.hoverPlotWidgets[key] is not None and self.hoverPlotWidgets[key].isVisible(): self.ctrl.update_hover(qpoint, key) def translate_seismic(self, k, cm): """ Resizes vertical or horizontal on a KeyPress :param k: translate by 1./7 :param cm (bool): if the control modifier has been pressed, translate by 1./2 :return: """ r = self.viewBox_seismic.viewRect() xlim, ylim = self.ctrl.limits() FAC = 1 / 2 if cm else 1 / 7 dy = FAC * r.height() dx = FAC * r.width() if k == QtCore.Qt.Key_Down: yr = np.array([r.y(), r.y() + r.height()]) + dy yr += np.min([0, ylim[1] - yr[1]]) self.viewBox_seismic.setYRange(yr[0], yr[1], padding=0) elif k == QtCore.Qt.Key_Left: xr = np.array([r.x(), r.x() + r.width()]) - dx xr += np.max([0, xlim[0] - xr[0]]) self.viewBox_seismic.setXRange(xr[0], xr[1], padding=0) elif k == QtCore.Qt.Key_Right: xr = np.array([r.x(), r.x() + r.width()]) + dx xr += np.min([0, xlim[1] - xr[1]]) self.viewBox_seismic.setXRange(xr[0], xr[1], padding=0) elif k == QtCore.Qt.Key_Up: yr = np.array([r.y(), r.y() + r.height()]) - dy yr += np.max([0, ylim[0] - yr[0]]) self.viewBox_seismic.setYRange(yr[0], yr[1], padding=0) def on_sigRangeChanged(self, r): def set_scroll(sb, r, b): # sb: scroll bar object, r: current range, b: axes limits (bounds) # cf. https://doc.qt.io/qt-5/qscrollbar.html range = (r[1] - r[0]) doclength = (b[1] - b[0]) maximum = int((doclength - range) / doclength * 65536) sb.setMaximum(maximum) sb.setPageStep(65536 - maximum) sb.setValue(int((r[0] - b[0]) / doclength * 65536)) xr, yr = self.viewBox_seismic.viewRange() xl, yl = self.ctrl.limits() set_scroll(self.horizontalScrollBar, xr, xl) set_scroll(self.verticalScrollBar, yr, yl) def on_horizontalSliderChange(self, r): b = self.ctrl.limits()[0] r = self.viewBox_seismic.viewRange()[0] x = float(self.horizontalScrollBar.value()) / 65536 * (b[1] - b[0]) + b[0] self.viewBox_seismic.setXRange(x, x + r[1] - r[0], padding=0) def on_verticalSliderChange(self, r): b = self.ctrl.limits()[1] r = self.viewBox_seismic.viewRange()[1] y = float(self.verticalScrollBar.value()) / 65536 * (b[1] - b[0]) + b[0] self.viewBox_seismic.setYRange(y, y + r[1] - r[0], padding=0) def _cmenu_hover(self, key, image=False): """Creates the plot widget for a given key: could be 'Trace', 'Spectrum', or 'Spectrogram'""" if self.hoverPlotWidgets[key] is None: from easyqc.pgtools import ImShowItem if image: self.hoverPlotWidgets[key] = ImShowItem().plotwidget else: self.hoverPlotWidgets[key] = pg.plot([0], [0], pen=pg.mkPen(color=COLOR_PLOTS[0])) self.hoverPlotWidgets[key].setBackground(pg.mkColor('#ffffff')) self.hoverPlotWidgets[key].setVisible(True) def cmenu_ViewTrace(self): self._cmenu_hover('Trace') def cmenu_ViewSpectrum(self): self._cmenu_hover('Spectrum') def cmenu_ViewSpectrogram(self): self._cmenu_hover('Spectrogram', image=True) class Controller: def __init__(self, view): self.view = view self.model = Model(None, None) self.order = None self.transform = None # affine transform image indices 2 data domain self.trace_indices = None self.hkey = None def remove_all_layers(self): layers_dict = self.view.layers.copy() for label in layers_dict: self.remove_layer_from_label(label) def remove_layer_from_label(self, label): current_layer = self.view.layers.get(label) if current_layer is not None: current_layer['layer'].clear() self.view.plotItem_seismic.removeItem(current_layer['layer']) self.view.layers.pop(label) def add_scatter(self, x, y, rgb=None, label='default'): """ Adds a scatter layer to the display (removing any previous one if any) """ rgb = rgb or (0, 255, 0) self.remove_layer_from_label(label) new_scatter = pg.ScatterPlotItem() self.view.layers[label] = {'layer': new_scatter, 'type': 'scatter'} self.view.plotItem_seismic.addItem(new_scatter) new_scatter.setData(x=x, y=y, brush=pg.mkBrush(rgb), name=label) def cursor2timetraceamp(self, qpoint): """Used for the mouse hover function over seismic display, returns trace, time, amplitude,and header""" ixy = self.cursor2ind(qpoint) a = self.model.data[ixy[0], ixy[1]] xy_ = np.matmul(self.transform, np.array([ixy[0], ixy[1], 1])) t = xy_[self.model.taxis] c = xy_[self.model.caxis] h = self.model.header[self.hkey][ixy[self.model.caxis]] return c, t, a, h def cursor2ind(self, qpoint): """ image coordinates over the seismic display""" ix = np.max((0, np.min((int(np.floor(qpoint.x())), self.model.nx - 1)))) iy = np.max((0, np.min((int(np.round(qpoint.y())), self.model.ny - 1)))) return ix, iy def limits(self): # returns the xlims and ylims of the data in the data space (time, trace) ixlim = [0, self.model.nx] iylim = [0, self.model.ny] x, y, _ = np.matmul(self.transform, np.c_[ixlim, iylim, [1, 1]].T) return x, y def propagate(self): """ set all the eqc instances at the same position/gain scales for flip comparisons """ eqcs = self.view._instances() for eqc in eqcs: if eqc is self.view: continue else: eqc.setGeometry(self.view.geometry()) eqc.ctrl.set_gain(self.gain) eqc.plotItem_seismic.setXLink(self.view.plotItem_seismic) eqc.plotItem_seismic.setYLink(self.view.plotItem_seismic) # also propagate sorting eqc.lineEdit_sort.setText(self.view.lineEdit_sort.text()) eqc.ctrl.sort(eqc.lineEdit_sort.text()) def redraw(self): """ redraw seismic and headers with order and selection""" # np.take could look neater but it's actually much slower than straight indexing if self.model.taxis == 1: self.view.imageItem_seismic.setImage(self.model.data[self.trace_indices, :]) elif self.model.taxis == 0: self.view.imageItem_seismic.setImage(self.model.data[:, self.trace_indices]) self.set_header() self.set_gain() def set_gain(self, gain=None): if gain is None: gain = self.gain levels = 10 ** (gain / 20) * 4 * np.array([-1, 1]) self.view.imageItem_seismic.setLevels(levels) self.view.lineEdit_gain.setText(f"{gain:.1f}") @property def gain(self): return float(self.view.lineEdit_gain.text()) or self.model.auto_gain() def set_header(self): key = self.view.comboBox_header.currentText() if key not in self.model.header.keys(): return self.hkey = key traces = np.arange(self.trace_indices.size) values = self.model.header[self.hkey][self.trace_indices] # skip the plotting part for non-numeric arrays if not np.issubdtype(values.dtype, np.number): return if self.model.taxis == 1: self.view.plotDataItem_header_h.setData(x=traces, y=values) elif self.model.taxis == 0: self.view.plotDataItem_header_v.setData(y=traces, x=values) def sort(self, keys): if not(set(keys).issubset(set(self.model.header.keys()))): print("Wrong input") return elif len(keys) == 0: return self.trace_indices = np.lexsort([self.model.header[k] for k in keys]) self.redraw() def update_data(self, data, h=None, si=.002, gain=None, x0=0, t0=0, taxis=1): """ data is a 2d array [ntr, nsamples] if 3d the first dimensions are merged in ntr and the last is nsamples update_data(self, data=None, h=0.002, gain=None) """ # reshape a 3d+ array in 2d to plot as an image self.remove_all_layers() # if the data has the same shape as the current model data, keep axis all the same update_axis = self.model.data is None or self.model.data.shape != data.shape if data.ndim >= 3: data = np.reshape(data, (-1, data.shape[-1])) self.model.set_data(data, si=si, header=h, x0=x0, t0=t0, taxis=taxis) self.trace_indices = np.arange(self.model.ntr) # this will contain selection and sort clim = [x0 - .5, x0 + self.model.ntr - .5] tlim = [t0, t0 + self.model.ns * self.model.si] if taxis == 0: # time is the 0 dimension and the horizontal axis xlim, ylim = (tlim, clim) transform = [si, 0., 0., 0., 1, 0., t0 - si / 2, x0 - .5, 1.] self.view.imageItem_seismic.setImage(data[:, self.trace_indices]) elif taxis == 1: # time is the 1 dimension and vertical axis xlim, ylim = (clim, tlim) transform = [1., 0., 0., 0., si, 0., x0 - .5, t0 - si / 2, 1.] self.view.imageItem_seismic.setImage(data[self.trace_indices, :]) self.view.plotItem_seismic.invertY() else: ValueError('taxis must be 0 (horizontal axis) or 1 (vertical axis)') self.transform = np.array(transform).reshape((3, 3)).T self.view.imageItem_seismic.setTransform(QtGui.QTransform(*transform)) self.view.plotItem_header_h.setLimits(xMin=xlim[0], xMax=xlim[1]) self.view.plotItem_header_v.setLimits(yMin=ylim[0], yMax=ylim[1]) self.view.plotItem_seismic.setLimits(xMin=xlim[0], xMax=xlim[1], yMin=ylim[0], yMax=ylim[1]) # reset the view if update_axis: xlim, ylim = self.limits() self.view.viewBox_seismic.setXRange(*xlim, padding=0) self.view.viewBox_seismic.setYRange(*ylim, padding=0) # set the header combo box keys if isinstance(self.model.header, dict): self.view.comboBox_header.clear() for hname in self.model.header.keys(): self.view.comboBox_header.addItem(hname) self.set_gain(gain=gain) self.set_header() def update_hover(self, qpoint, key): c, _, _, _ = self.cursor2timetraceamp(qpoint) if key == 'Trace': plotitem = self.view.hoverPlotWidgets[key].getPlotItem() plotitem.items[0].setData(self.model.tscale, self.model.get_trace(c)) plotitem.setXRange(*self.trange) elif key == 'Spectrum': plotitem = self.view.hoverPlotWidgets[key].getPlotItem() plotitem.items[0].setData(*self.model.get_trace_spectrum(c, trange=self.trange)) elif key == 'Spectrogram': imageshowitem = self.view.hoverPlotWidgets[key].imageshowitem fscale, tscale, tf = self.model.get_trace_spectrogram(c, trange=self.trange) imageshowitem.set_image(tf, tscale, fscale) @property def trange(self): """ returns the current time range of the view :return: 2 floats list """ return self.view.viewBox_seismic.viewRange()[self.model.taxis] @property def crange(self): """ returns the current channel range of the view :return: 2 floats list """ return self.view.viewBox_seismic.viewRange()[self.model.caxis] @dataclass class Model: """Class for keeping track of the visualized data""" data: np.array header: np.array si: float = 1. def auto_gain(self) -> float: rmsnan = np.nansum(self.data ** 2, axis=self.taxis) / np.sum( ~np.isnan(self.data), axis=self.taxis) return 20 * np.log10(np.median(np.sqrt(rmsnan))) def get_trace_spectrogram(self, c, trange=None): from scipy.signal import spectrogram tr = self.get_trace(c, trange=trange) fscale, tscale, tf = spectrogram(tr, fs=1 / self.si, nperseg=50, nfft=512, window='cosine', noverlap=48) tscale += trange[0] tf = 20 * np.log10(tf + np.finfo(float).eps) return fscale, tscale, tf def get_trace_spectrum(self, c, trange=None): tr = self.get_trace(c, trange=trange) psd = 20 * np.log10(np.abs(np.fft.rfft(tr)) -

np.finfo(float)

numpy.finfo

import numpy as np import os import re import requests import sys import time from netCDF4 import Dataset import pandas as pd from bs4 import BeautifulSoup from tqdm import tqdm # setup constants used to access the data from the different M2M interfaces BASE_URL = 'https://ooinet.oceanobservatories.org/api/m2m/' # base M2M URL SENSOR_URL = '12576/sensor/inv/' # Sensor Information # setup access credentials AUTH = ['OOIAPI-853A3LA6QI3L62', '<KEY>'] def M2M_Call(uframe_dataset_name, start_date, end_date): options = '?beginDT=' + start_date + '&endDT=' + end_date + '&format=application/netcdf' r = requests.get(BASE_URL + SENSOR_URL + uframe_dataset_name + options, auth=(AUTH[0], AUTH[1])) if r.status_code == requests.codes.ok: data = r.json() else: return None # wait until the request is completed print('Waiting for OOINet to process and prepare data request, this may take up to 20 minutes') url = [url for url in data['allURLs'] if re.match(r'.*async_results.*', url)][0] check_complete = url + '/status.txt' with tqdm(total=400, desc='Waiting') as bar: for i in range(400): r = requests.get(check_complete) bar.update(1) if r.status_code == requests.codes.ok: bar.n = 400 bar.last_print_n = 400 bar.refresh() print('\nrequest completed in %f minutes.' % elapsed) break else: time.sleep(3) elapsed = (i * 3) / 60 return data def M2M_Files(data, tag=''): """ Use a regex tag combined with the results of the M2M data request to collect the data from the THREDDS catalog. Collected data is gathered into an xarray dataset for further processing. :param data: JSON object returned from M2M data request with details on where the data is to be found for download :param tag: regex tag to use in discriminating the data files, so we only collect the correct ones :return: the collected data as an xarray dataset """ # Create a list of the files from the request above using a simple regex as a tag to discriminate the files url = [url for url in data['allURLs'] if re.match(r'.*thredds.*', url)][0] files = list_files(url, tag) return files def list_files(url, tag=''): """ Function to create a list of the NetCDF data files in the THREDDS catalog created by a request to the M2M system. :param url: URL to user's THREDDS catalog specific to a data request :param tag: regex pattern used to distinguish files of interest :return: list of files in the catalog with the URL path set relative to the catalog """ page = requests.get(url).text soup = BeautifulSoup(page, 'html.parser') pattern = re.compile(tag) return [node.get('href') for node in soup.find_all('a', text=pattern)] def M2M_Data(nclist,variables): thredds = 'https://opendap.oceanobservatories.org/thredds/dodsC/ooi/' #nclist is going to contain more than one url eventually for jj in range(len(nclist)): url=nclist[jj] url=url[25:] dap_url = thredds + url + '#fillmismatch' openFile = Dataset(dap_url,'r') for ii in range(len(variables)): dum = openFile.variables[variables[ii].name] variables[ii].data = np.append(variables[ii].data, dum[:].data) tmp = variables[0].data/60/60/24 time_converted = pd.to_datetime(tmp, unit='D', origin=pd.Timestamp('1900-01-01')) return variables, time_converted class var(object): def __init__(self): """A Class that generically holds data with a variable name and the units as attributes""" self.name = '' self.data = np.array([]) self.units = '' def __repr__(self): return_str = "name: " + self.name + '\n' return_str += "units: " + self.units + '\n' return_str += "data: size: " + str(self.data.shape) return return_str class structtype(object): def __init__(self): """ A class that imitates a Matlab structure type """ self._data = [] def __getitem__(self, index): """implement index behavior in the struct""" if index == len(self._data): self._data.append(var()) return self._data[index] def __len__(self): return len(self._data) def M2M_URLs(platform_name,node,instrument_class,method): var_list = structtype() #MOPAK if platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #METBK elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/telemetered/metbk_a_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' #FLORT elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/06-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/06-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/02-FLORTD000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/04-FLORTK000/telemetered/flort_sample' var_list[0].name = 'time' var_list[1].name = 'seawater_scattering_coefficient' var_list[2].name = 'fluorometric_chlorophyll_a' var_list[3].name = 'fluorometric_cdom' var_list[4].name = 'total_volume_scattering_coefficient' var_list[5].name = 'optical_backscatter' var_list[6].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm-1' var_list[2].units = 'ug/L' var_list[3].units = 'ppb' var_list[4].units = 'm-1 sr-1' var_list[5].units = 'm-1' var_list[6].units = 'dbar' #FDCHP elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'FDCHP' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/08-FDCHPA000/telemetered/fdchp_a_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #DOSTA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/04-DOSTAD000/telemetered/dosta_abcdjm_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'estimated_oxygen_concentration' var_list[3].name = 'optode_temperature' var_list[4].name = 'dosta_abcdjm_cspp_tc_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' var_list[3].units = 'degC' var_list[4].units = 'umol/L' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/03-DOSTAD000/telemetered/dosta_abcdjm_ctdbp_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'dissolved_oxygen' var_list[2].name = 'dosta_ln_optode_oxygen' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'dofst_k_oxygen_l2' var_list[2].name = 'dofst_k_oxygen' var_list[3].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/kg' var_list[2].units = 'Hz' var_list[3].units = 'dbar' #ADCP elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/01-ADCPTA000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/01-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/01-ADCPTA000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/01-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/04-ADCPTM000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/04-ADCPTM000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/04-ADCPTC000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ADCP' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/04-ADCPSJ000/telemetered/adcp_velocity_earth' var_list[0].name = 'time' var_list[1].name = 'bin_depths' var_list[2].name = 'heading' var_list[3].name = 'pitch' var_list[4].name = 'roll' var_list[5].name = 'eastward_seawater_velocity' var_list[6].name = 'northward_seawater_velocity' var_list[7].name = 'upward_seawater_velocity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'meters' var_list[2].units = 'deci-degrees' var_list[3].units = 'deci-degrees' var_list[4].units = 'deci-degrees' var_list[5].units = 'm/s' var_list[6].units = 'm/s' var_list[7].units = 'm/s' #ZPLSC elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/07-ZPLSCC000/telemetered/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'ZPLSC' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/MFD37/07-ZPLSCC000/recovered_host/zplsc_c_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #WAVSS elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'WAVSS_Stats' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/05-WAVSSA000/telemetered/wavss_a_dcl_statistics' var_list[0].name = 'time' var_list[1].name = 'number_zero_crossings' var_list[2].name = 'average_wave_height' var_list[3].name = 'mean_spectral_period' var_list[4].name = 'max_wave_height' var_list[5].name = 'significant_wave_height' var_list[6].name = 'significant_period' var_list[7].name = 'wave_height_10' var_list[8].name = 'wave_period_10' var_list[9].name = 'mean_wave_period' var_list[10].name = 'peak_wave_period' var_list[11].name = 'wave_period_tp5' var_list[12].name = 'wave_height_hmo' var_list[13].name = 'mean_direction' var_list[14].name = 'mean_spread' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'counts' var_list[2].units = 'm' var_list[3].units = 'sec' var_list[4].units = 'm' var_list[5].units = 'm' var_list[6].units = 'sec' var_list[7].units = 'm' var_list[8].units = 'sec' var_list[9].units = 'sec' var_list[10].units = 'sec' var_list[11].units = 'sec' var_list[12].units = 'm' var_list[13].units = 'degrees' var_list[14].units = 'degrees' #VELPT elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD11/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'VELPT' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/04-VELPTA000/telemetered/velpt_ab_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'eastward_velocity' var_list[2].name = 'northward_velocity' var_list[3].name = 'upward_velocity' var_list[4].name = 'heading_decidegree' var_list[5].name = 'roll_decidegree' var_list[6].name = 'pitch_decidegree' var_list[7].name = 'temperature_centidegree' var_list[8].name = 'pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'deci-degrees' var_list[5].units = 'deci-degrees' var_list[6].units = 'deci-degrees' var_list[7].units = '0.01degC' var_list[8].units = '0.001dbar' #PCO2W elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PCO2W' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/05-PCO2WB000/telemetered/pco2w_abc_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'pco2w_thermistor_temperature' var_list[2].name = 'pco2_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'uatm' #PHSEN elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PHSEN' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/06-PHSEND000/telemetered/phsen_abcdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'phsen_thermistor_temperature' var_list[2].name = 'phsen_abcdef_ph_seawater' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' #SPKIR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'SPKIR' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/08-SPKIRB000/telemetered/spkir_abj_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'spkir_abj_cspp_downwelling_vector' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uW cm-2 nm-1' #PRESF elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/02-PRESFA000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/02-PRESFA000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/02-PRESFB000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'PRESF' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/02-PRESFC000/telemetered/presf_abc_dcl_tide_measurement' var_list[0].name = 'time' var_list[1].name = 'abs_seafloor_pressure' var_list[2].name = 'seawater_temperature' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'dbar' var_list[2].units = 'degC' #CTDBP elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/SBD17/06-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/SBD17/06-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/03-CTDBPC000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/03-CTDBPE000/telemetered/ctdbp_cdef_dcl_instrument' var_list[0].name = 'time' var_list[1].name = 'temp' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'pressure' var_list[5].name = 'conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' #VEL3D elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD35/01-VEL3DD000/telemetered/vel3d_cd_dcl_velocity_data' var_list[0].name = 'time' var_list[1].name = 'vel3d_c_eastward_turbulent_velocity' var_list[2].name = 'vel3d_c_northward_turbulent_velocity' var_list[3].name = 'vel3d_c_upward_turbulent_velocity' var_list[4].name = 'seawater_pressure_mbar' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = '0.001dbar' #VEL3DK elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument' var_list[0].name = 'time' var_list[1].name = 'vel3d_k_eastward_velocity' var_list[2].name = 'vel3d_k_northward_velocity' var_list[3].name = 'vel3d_k_upward_velocity' var_list[4].name = 'vel3d_k_heading' var_list[5].name = 'vel3d_k_pitch' var_list[6].name = 'vel3d_k_roll' var_list[7].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'm/s' var_list[2].units = 'm/s' var_list[3].units = 'm/s' var_list[4].units = 'ddegrees' var_list[5].units = 'ddegrees' var_list[6].units = 'ddegrees' var_list[7].units = 'dbar' elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument' var_list[0].name = 'time' var_list[1].name = 'ctdpf_ckl_seawater_temperature' var_list[2].name = 'practical_salinity' var_list[3].name = 'density' var_list[4].name = 'ctdpf_ckl_seawater_pressure' var_list[5].name = 'ctdpf_ckl_seawater_conductivity' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'unitless' var_list[3].units = 'kg/m3' var_list[4].units = 'dbar' var_list[5].units = 'S/m' #PCO2A elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'PCO2A' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/SBD12/04-PCO2AA000/telemetered/pco2a_a_dcl_instrument_water' var_list[0].name = 'time' var_list[1].name = 'partial_pressure_co2_ssw' var_list[2].name = 'partial_pressure_co2_atm' var_list[3].name = 'pco2_co2flux' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'uatm' var_list[2].units = 'uatm' var_list[3].units = 'mol m-2 s-1' #PARAD elif platform_name == 'CE09OSPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered': uframe_dataset_name = 'CE09OSPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument' var_list[0].name = 'time' var_list[1].name = 'parad_k_par' var_list[2].name = 'int_ctd_pressure' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol photons m-2 s-1' var_list[2].units = 'dbar' #OPTAA elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID27/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE01ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/MFD37/01-OPTAAD000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'MFN' and instrument_class == 'OPTAA' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/MFD37/01-OPTAAC000/telemetered/optaa_dj_dcl_instrument' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #NUTNR elif platform_name == 'CE01ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE01ISSM/RID16/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE02SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE02SHSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE04OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE04OSSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE06ISSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE06ISSM/RID16/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE07SHSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE07SHSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' elif platform_name == 'CE09OSSM' and node == 'NSIF' and instrument_class == 'NUTNR' and method == 'Telemetered': uframe_dataset_name = 'CE09OSSM/RID26/07-NUTNRB000/telemetered/suna_dcl_recovered' var_list[0].name = 'time' var_list[1].name = 'nitrate_concentration' var_list[2].name = 'salinity_corrected_nitrate' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'umol/L' var_list[2].units = 'umol/L' ## #MOPAK elif platform_name == 'CE01ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE01ISSM/SBD17/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE06ISSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE06ISSM/SBD17/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' elif platform_name == 'CE09OSPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered' var_list[0].name = 'time' var_list[0].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' #METBK elif platform_name == 'CE02SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE02SHSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE04OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE04OSSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE07SHSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE07SHSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data = np.array([]) var_list[8].data = np.array([]) var_list[9].data = np.array([]) var_list[10].data = np.array([]) var_list[11].data = np.array([]) var_list[12].data = np.array([]) var_list[13].data = np.array([]) var_list[14].data = np.array([]) var_list[15].data = np.array([]) var_list[16].data = np.array([]) var_list[17].data = np.array([]) var_list[18].data = np.array([]) var_list[0].units = 'seconds since 1900-01-01' var_list[1].units = 'degC' var_list[2].units = 'S/m' var_list[3].units = 'unitless' var_list[4].units = 'm/s' var_list[5].units = 'm/s' var_list[6].units = 'mbar' var_list[7].units = 'degC' var_list[8].units = '#' var_list[9].units = 'W/m' var_list[10].units = 'W/m' var_list[11].units = 'mm' var_list[12].units = 'W/m' var_list[13].units = 'W/m' var_list[14].units = 'W/m' var_list[15].units = 'W/m' var_list[16].units = 'm/s' var_list[17].units = 'm/s' var_list[18].units = 'g/kg' elif platform_name == 'CE09OSSM' and node == 'BUOY' and instrument_class == 'METBK1' and method == 'RecoveredHost': uframe_dataset_name = 'CE09OSSM/SBD11/06-METBKA000/recovered_host/metbk_a_dcl_instrument_recovered' var_list[0].name = 'time' var_list[1].name = 'sea_surface_temperature' var_list[2].name = 'sea_surface_conductivity' var_list[3].name = 'met_salsurf' var_list[4].name = 'met_windavg_mag_corr_east' var_list[5].name = 'met_windavg_mag_corr_north' var_list[6].name = 'barometric_pressure' var_list[7].name = 'air_temperature' var_list[8].name = 'relative_humidity' var_list[9].name = 'longwave_irradiance' var_list[10].name = 'shortwave_irradiance' var_list[11].name = 'precipitation' var_list[12].name = 'met_heatflx_minute' var_list[13].name = 'met_latnflx_minute' var_list[14].name = 'met_netlirr_minute' var_list[15].name = 'met_sensflx_minute' var_list[16].name = 'eastward_velocity' var_list[17].name = 'northward_velocity' var_list[18].name = 'met_spechum' var_list[0].data = np.array([]) var_list[1].data = np.array([]) var_list[2].data = np.array([]) var_list[3].data = np.array([]) var_list[4].data = np.array([]) var_list[5].data = np.array([]) var_list[6].data = np.array([]) var_list[7].data =

np.array([])

numpy.array

import numpy as np from scipy.optimize import minimize import math """Diagonalize the Jaynes-Cummings ladder of energies""" def get_order(omega_r, qubit_energy_list): """Get the order of eigenenergies that diagonalizer produces. Use the bare energy of the system as reference. Args: omega_r (float): Resonator frequency qubit_energy_list (List[float]): Qubit energies Returns: (List[int]): Indices giving the proper order of the elements in qubit_energy_list. """ tmax = len(qubit_energy_list) # Maximum number of transmon levels order = np.zeros(tmax) # Bare energies of the system diag_bare = np.array([-i*omega_r + qubit_energy_list[i] for i in range(tmax)]) # Eigenenergies in the order produced by diagonalizer eigensolver_order = np.linalg.eigvalsh(np.diag(diag_bare)) # Find where the diagonalizer puts the energies for i in range(tmax): index, =

np.where(eigensolver_order==diag_bare[i])

numpy.where

import os import numpy as np import matplotlib.pyplot as plt import cv2 import torch import torch.nn as nn import torchvision.models as models from model import Unet from utils.dataloader import read_data_path, MaskDataset from torch.utils.data import DataLoader from utils.config import Config from utils.loss import dice_score # Hyperparameter config = Config() TRAIN_TEST_SPLIT = config.TRAIN_TEST_SPLIT BATCH_SIZE_VALIDATION = config.BATCH_SIZE_VALIDATION BATCH_SIZE_TESTING = config.BATCH_SIZE_TESTING PRED_SAVE_DIR = config.PRED_SAVE_DIR os.makedirs(PRED_SAVE_DIR, exist_ok=True) INFERENCE_WEIGHT = config.INFERENCE_WEIGHT # Use torch cuda device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Import Resnet-50 as base network, modify first layer model_ft = models.resnet50(pretrained=True) model_ft.conv1 = nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(2, 2), padding=(3, 3), bias=False) # Add Residual layer in unet model = Unet(model_ft) model.to(device) if INFERENCE_WEIGHT: model.load_state_dict(torch.load(INFERENCE_WEIGHT)) # Read data path, make in dataloader """ read_data_path input: (float), the split of train and test return: (list, list, list), train & valid & test file path list list -> (img_path, mask_path) """ training_list, validation_list, testing_list = read_data_path(TRAIN_TEST_SPLIT) val_dataset = MaskDataset(validation_list) val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE_VALIDATION, shuffle=False, drop_last=True) # Confusion matrix, postive = abnormal; negative = normal TP = 0; FP = 0 FN = 0; TN = 0 """ TP => mask: abnormal, pred: abnormal FP => mask: normal, pred: abnormal FN => mask: abnormal, pred: normal TN => mask: normal, pred: normal """ dice_score_list = [] number = 0 with torch.no_grad(): for imgs, masks in val_loader: imgs_gpu = imgs.to(device) outputs = model(imgs_gpu) outputs = torch.round(outputs) * 255 masks = masks.to(device) # Dice score list dice_scores = dice_score(outputs, masks) dice_score_list.extend([dice_scores.item()]) for index in range(BATCH_SIZE_VALIDATION): img_origin = np.reshape(imgs_gpu[index].cpu().numpy(), (256, 256)) pred_img = np.reshape(outputs[index].cpu().numpy(), (256, 256)) mask_img = np.reshape(masks[index].cpu().numpy()*255, (256, 256)) # Confusion Matrix if np.sum(mask_img)!=0 and np.sum(pred_img)!=0: TP += 1 if np.sum(mask_img)==0 and np.sum(pred_img)!=0: FP += 1 if np.sum(mask_img)!=0 and np.sum(pred_img)==0: FN += 1 if np.sum(mask_img)==0 and np.sum(pred_img)==0: TN += 1 number += 1 print(number) if

np.all(mask_img==0)

numpy.all

#!/usr/bin/env python # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # <NAME> # California Institute of Technology # (C) 2006-2010 All Rights Reserved # # {LicenseText} # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # standalone = True import unittestX as unittest import journal debug = journal.debug( "Broadened_E_Q_Kernel_TestCase" ) warning = journal.warning( "Broadened_E_Q_Kernel_TestCase" ) import mcni from mccomposite import mccompositebp from mccomponents import mccomponentsbp class TestCase(unittest.TestCase): def test(self): E_Q = "Q*Q/3." S_Q = "1" sigma_Q = "Q/2." Qmin = 0; Qmax = 10 absorption_coefficient = scattering_coefficient = 1. kernel = mccomponentsbp.create_Broadened_E_Q_Kernel( E_Q, S_Q, sigma_Q, Qmin, Qmax, absorption_coefficient, scattering_coefficient, ) ei = 500 # meV from mcni.utils import conversion vil = conversion.e2v(ei) vi = (0,0,vil) import numpy.linalg as nl import numpy as np for i in range(10): event = mcni.neutron( r = (0,0,0), v = vi, prob = 1, time = 0 ) kernel.scatter( event ); vf = np.array(event.state.velocity) diffv = vi - vf Q = conversion.v2k(

nl.norm(diffv)

numpy.linalg.norm

import pytest, numbers, warnings import numpy as np from numpy.testing import assert_array_equal, assert_allclose, assert_equal from scipy.sparse import rand as sprand from scipy import optimize from pyuoi import UoI_L1Logistic from pyuoi.linear_model.logistic import (fit_intercept_fixed_coef, MaskedCoefLogisticRegression, LogisticInterceptFitterNoFeatures, _logistic_regression_path, _multinomial_loss_grad, _logistic_loss_and_grad) from sklearn.metrics import accuracy_score from sklearn.preprocessing import LabelEncoder, OneHotEncoder from sklearn.utils import (compute_class_weight, check_consistent_length, check_array) from sklearn.exceptions import ConvergenceWarning from pyuoi.datasets import make_classification from pyuoi.lbfgs import fmin_lbfgs, AllZeroLBFGSError def _logistic_regression_path_old(X, y, Cs=48, fit_intercept=True, max_iter=100, tol=1e-4, verbose=0, coef=None, class_weight=None, penalty='l2', multi_class='auto', check_input=True, sample_weight=None, l1_ratio=None, coef_mask=None): """Compute a Logistic Regression model for a list of regularization parameters. This is the original function used to check the new indexing-based version rather than the masking version implemented here. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Input data. y : array-like, shape (n_samples,) or (n_samples, n_targets) Input data, target values. Cs : int | array-like, shape (n_cs,) List of values for the regularization parameter or integer specifying the number of regularization parameters that should be used. In this case, the parameters will be chosen in a logarithmic scale between 1e-4 and 1e4. fit_intercept : bool Whether to fit an intercept for the model. In this case the shape of the returned array is (n_cs, n_features + 1). max_iter : int Maximum number of iterations for the solver. tol : float Stopping criterion. For the newton-cg and lbfgs solvers, the iteration will stop when ``max{|g_i | i = 1, ..., n} <= tol`` where ``g_i`` is the i-th component of the gradient. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. coef : array-like, shape (n_features,), default None Initialization value for coefficients of logistic regression. Useless for liblinear solver. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. multi_class : str, {'multinomial', 'auto'}, default: 'auto' For 'multinomial' the loss minimised is the multinomial loss fit across the entire probability distribution, *even when the data is binary*. 'auto' selects binary if the data is binary and otherwise selects 'multinomial'. check_input : bool, default True If False, the input arrays X and y will not be checked. sample_weight : array-like, shape(n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. coef_mask : array-like, shape (n_features), (n_classes, n_features) optional Masking array for coef. Returns ------- coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1) List of coefficients for the Logistic Regression model. If fit_intercept is set to True then the second dimension will be n_features + 1, where the last item represents the intercept. For ``multiclass='multinomial'``, the shape is (n_classes, n_cs, n_features) or (n_classes, n_cs, n_features + 1). Cs : ndarray Grid of Cs used for cross-validation. n_iter : array, shape (n_cs,) Actual number of iteration for each Cs. """ if isinstance(Cs, numbers.Integral): Cs = np.logspace(-4, 4, Cs) # Preprocessing. if check_input: X = check_array(X, accept_sparse='csr', dtype=np.float64, accept_large_sparse=True) y = check_array(y, ensure_2d=False, dtype=None) check_consistent_length(X, y) _, n_features = X.shape classes = np.unique(y) if multi_class == 'auto': if len(classes) > 2: multi_class = 'multinomial' else: multi_class = 'ovr' # If sample weights exist, convert them to array (support for lists) # and check length # Otherwise set them to 1 for all examples if sample_weight is not None: sample_weight = np.array(sample_weight, dtype=X.dtype, order='C') check_consistent_length(y, sample_weight) else: sample_weight = np.ones(X.shape[0], dtype=X.dtype) # If class_weights is a dict (provided by the user), the weights # are assigned to the original labels. If it is "balanced", then # the class_weights are assigned after masking the labels with a OvR. le = LabelEncoder() if isinstance(class_weight, dict) or multi_class == 'multinomial': class_weight_ = compute_class_weight(class_weight, classes=classes, y=y) sample_weight *= class_weight_[le.fit_transform(y)] # For doing a ovr, we need to mask the labels first. for the # multinomial case this is not necessary. if multi_class == 'ovr': coef_size = n_features w0 = np.zeros(n_features + int(fit_intercept), dtype=X.dtype) mask_classes = np.array([-1, 1]) mask = (y == 1) y_bin = np.ones(y.shape, dtype=X.dtype) y_bin[~mask] = -1. # for compute_class_weight if class_weight == "balanced": class_weight_ = compute_class_weight(class_weight, classes=mask_classes, y=y_bin) sample_weight *= class_weight_[le.fit_transform(y_bin)] else: coef_size = classes.size * n_features lbin = OneHotEncoder(categories=[range(classes.size)], sparse=False) Y_multi = lbin.fit_transform(y[:, np.newaxis]) if Y_multi.shape[1] == 1: Y_multi = np.hstack([1 - Y_multi, Y_multi]) w0 = np.zeros((classes.size, n_features + int(fit_intercept)), dtype=X.dtype) w0[:, -1] = LogisticInterceptFitterNoFeatures(y, classes.size).intercept_ if coef is not None: # it must work both giving the bias term and not if multi_class == 'ovr': if coef.size not in (n_features, w0.size): raise ValueError( 'Initialization coef is of shape %d, expected shape ' '%d or %d' % (coef.size, n_features, w0.size)) w0[:coef.size] = coef else: w0[:, :coef.shape[1]] = coef # Mask initial array if coef_mask is not None: if multi_class == 'ovr': w0[:n_features] *= coef_mask else: w0[:, :n_features] *= coef_mask if multi_class == 'multinomial': # fmin_l_bfgs_b and newton-cg accepts only ravelled parameters. target = Y_multi if penalty == 'l2': w0 = w0.ravel() def func(x, *args): return _multinomial_loss_grad(x, *args)[0:2] else: w0 = w0.T.ravel().copy() def inner_func(x, *args): return _multinomial_loss_grad(x, *args)[0:2] def func(x, g, *args): x = x.reshape(-1, classes.size).T.ravel() loss, grad = inner_func(x, *args) grad = grad.reshape(classes.size, -1).T.ravel() g[:] = grad return loss else: target = y_bin if penalty == 'l2': func = _logistic_loss_and_grad else: def func(x, g, *args): loss, grad = _logistic_loss_and_grad(x, *args) g[:] = grad return loss coefs = list() n_iter = np.zeros(len(Cs), dtype=np.int32) for i, C in enumerate(Cs): iprint = [-1, 50, 1, 100, 101][ np.searchsorted(np.array([0, 1, 2, 3]), verbose)] if penalty == 'l2': w0, loss, info = optimize.fmin_l_bfgs_b( func, w0, fprime=None, args=(X, target, 1. / C, coef_mask, sample_weight), iprint=iprint, pgtol=tol, maxiter=max_iter) else: zeros_seen = [0] def zero_coef(x, *args): if multi_class == 'multinomial': x = x.reshape(-1, classes.size)[:-1] else: x = x[:-1] now_zeros = np.array_equiv(x, 0.) if now_zeros: zeros_seen[0] += 1 else: zeros_seen[0] = 0 if zeros_seen[0] > 1: return -2048 try: w0 = fmin_lbfgs(func, w0, orthantwise_c=1. / C, args=(X, target, 0., coef_mask, sample_weight), max_iterations=max_iter, epsilon=tol, orthantwise_end=coef_size, progress=zero_coef) except AllZeroLBFGSError: w0 *= 0. info = None if info is not None and info["warnflag"] == 1: warnings.warn("lbfgs failed to converge. Increase the number " "of iterations.", ConvergenceWarning) # In scipy <= 1.0.0, nit may exceed maxiter. # See https://github.com/scipy/scipy/issues/7854. if info is None: n_iter_i = -1 else: n_iter_i = min(info['nit'], max_iter) if multi_class == 'multinomial': n_classes = max(2, classes.size) if penalty == 'l2': multi_w0 = np.reshape(w0, (n_classes, -1)) else: multi_w0 = np.reshape(w0, (-1, n_classes)).T if coef_mask is not None: multi_w0[:, :n_features] *= coef_mask coefs.append(multi_w0.copy()) else: if coef_mask is not None: w0[:n_features] *= coef_mask coefs.append(w0.copy()) n_iter[i] = n_iter_i return np.array(coefs), np.array(Cs), n_iter def test_fit_intercept_fixed_coef(): """Test that the intercept in fit correctly for fixed coefficients.""" X = np.zeros((6, 5)) coef = np.ones((1, 5)) y = np.ones(6, dtype=int) y[:3] = 0 b = fit_intercept_fixed_coef(X, coef, y, 2) assert_allclose(b, 0.) X = np.zeros((7, 5)) y = np.ones(7, dtype=int) y[:3] = 0 b = fit_intercept_fixed_coef(X, coef, y, 3) assert_allclose(b.argmax(), 1) assert_allclose(b.argmin(), 2) def test_fit_intercept_no_features(): """Test that the intercept in fit correctly for fixed coefficients.""" X = np.zeros((5, 1)) y = np.ones(6, dtype=int) y[:3] = 0 LR = LogisticInterceptFitterNoFeatures(y, 1) b = LR.intercept_ assert_allclose(b, 0.) y = np.ones(7, dtype=int) y[:3] = 0 LR = LogisticInterceptFitterNoFeatures(y, 1) yhat = LR.predict(X) assert_allclose(yhat, 1) py = LR.predict_proba(X) assert np.all(py > .5) y = np.ones(7, dtype=int) y[:3] = 0 LR = LogisticInterceptFitterNoFeatures(y, 3) yhat = LR.predict(X) assert_allclose(yhat, 1) py = LR.predict_proba(X) assert_allclose(py.argmax(axis=1), 1) assert_allclose(py.argmin(axis=1), 2) def test_l1logistic_intercept(): """Test that binary L1 Logistic fits an intercept when run.""" for fi in [True, False]: X, y, w, b = make_classification(n_samples=100, random_state=11, n_features=4, w_scale=4., include_intercept=fi) l1log = UoI_L1Logistic(fit_intercept=fi, n_boots_sel=3, n_boots_est=3).fit(X, y) if not fi: assert_array_equal(l1log.intercept_, 0.) else: l1log.intercept_ def test_l1logistic_binary(): """Test that binary L1 Logistic runs in the UoI framework.""" n_inf = 10 X, y, w, b = make_classification(n_samples=200, random_state=6, n_informative=n_inf, n_features=20, w_scale=4., include_intercept=True) l1log = UoI_L1Logistic(random_state=10).fit(X, y) l1log = UoI_L1Logistic(random_state=10, fit_intercept=False).fit(X, y) l1log.predict_proba(X) l1log.predict_log_proba(X) y_hat = l1log.predict(X) assert_equal(accuracy_score(y, y_hat), l1log.score(X, y)) assert (np.sign(abs(w)) == np.sign(abs(l1log.coef_))).mean() >= .8 def test_l1logistic_binary_multinomial(): """Test that binary L1 Logistic runs in the UoI framework using multi_class='multinomial'.""" n_inf = 10 X, y, w, b = make_classification(n_samples=200, random_state=6, n_informative=n_inf, n_features=20, w_scale=4., include_intercept=True) UoI_L1Logistic(random_state=10, multi_class='multinomial').fit(X, y) UoI_L1Logistic(random_state=10, fit_intercept=False, multi_class='multinomial').fit(X, y) def test_l1logistic_no_ovr(): """Test that binary L1 Logistic model raises an error for multiclass='ovr'.""" with pytest.raises(ValueError): UoI_L1Logistic(multi_class='ovr') def test_l1logistic_multiclass(): """Test that multiclass L1 Logistic runs in the UoI framework when all classes share a support.""" n_features = 20 n_inf = 10 X, y, w, b = make_classification(n_samples=200, random_state=10, n_classes=5, n_informative=n_inf, n_features=n_features, shared_support=True, w_scale=4.) l1log = UoI_L1Logistic().fit(X, y) l1log.predict_proba(X) l1log.predict_log_proba(X) y_hat = l1log.predict(X) assert_equal(accuracy_score(y, y_hat), l1log.score(X, y)) assert (np.sign(abs(w)) == np.sign(abs(l1log.coef_))).mean() >= .8 def test_l1logistic_multiclass_not_shared(): """Test that multiclass L1 Logistic runs in the UoI framework when all classes share a support.""" n_features = 20 n_inf = 10 X, y, w, b = make_classification(n_samples=400, random_state=10, n_classes=5, n_informative=n_inf, n_features=n_features, shared_support=False, w_scale=4.) l1log = UoI_L1Logistic(shared_support=False).fit(X, y) l1log.predict_log_proba(X) y_hat = l1log.predict(X) assert_equal(accuracy_score(y, y_hat), l1log.score(X, y)) assert (np.sign(abs(w)) == np.sign(abs(l1log.coef_))).mean() >= .7 def test_masked_logistic(): """Test the masked logistic regression class.""" n_features = 20 n_inf = 10 for shared_support in [True, False]: for n_classes in [2, 3]: for intercept in [True, False]: X, y, w, b = make_classification(n_samples=200, random_state=10, n_classes=n_classes, n_informative=n_inf, n_features=n_features, shared_support=shared_support, include_intercept=intercept, w_scale=4.) mask = np.squeeze(np.logical_not(np.equal(w, 0))) for penalty in ['l1', 'l2']: lr = MaskedCoefLogisticRegression(penalty=penalty, C=10., warm_start=True, fit_intercept=intercept) lr.fit(X, y, coef_mask=mask) coef_idxs = np.flatnonzero(np.equal(lr.coef_, 0.)) coef_idxs = set(coef_idxs.tolist()) mask_idxs = np.flatnonzero(np.equal(mask, 0)) mask_idxs = set(mask_idxs.tolist()) assert mask_idxs.issubset(coef_idxs) lr.fit(X, y, coef_mask=mask) def test_masked_logistic_standardize(): """Test the masked logistic regression class with `standardize=True`.""" n_features = 20 n_inf = 10 for shared_support in [True, False]: for n_classes in [2, 3]: for intercept in [True, False]: X, y, w, b = make_classification(n_samples=200, random_state=10, n_classes=n_classes, n_informative=n_inf, n_features=n_features, shared_support=shared_support, include_intercept=intercept, w_scale=4.) mask = np.squeeze(np.logical_not(np.equal(w, 0))) for penalty in ['l1', 'l2']: lr = MaskedCoefLogisticRegression(penalty=penalty, C=10., warm_start=True, fit_intercept=intercept, standardize=True) lr.fit(X, y, coef_mask=mask) coef_idxs = np.flatnonzero(np.equal(lr.coef_, 0.)) coef_idxs = set(coef_idxs.tolist()) mask_idxs = np.flatnonzero(np.equal(mask, 0)) mask_idxs = set(mask_idxs.tolist()) assert mask_idxs.issubset(coef_idxs) lr.fit(X, y, coef_mask=mask) @pytest.mark.parametrize("n_classes,penalty,fit_intercept", [(3, "l2", True), (3, "l2", False), (3, "l1", True), (3, "l1", False), (2, "l2", True), (2, "l2", False), (2, "l1", True), (2, "l1", False)]) def test_masking_with_indexing(n_classes, penalty, fit_intercept): """Check that indexing the masks gives the same results as masking with logistic regression. """ X, y, w, intercept = make_classification(n_samples=1000, n_classes=n_classes, n_features=20, n_informative=10, random_state=0) mask = w != 0. if n_classes == 2: mask = mask.ravel() coefs, _, _ = _logistic_regression_path(X, y, [10.], coef_mask=mask, penalty=penalty, fit_intercept=fit_intercept) coefs_old, _, _ = _logistic_regression_path_old(X, y, [10.], coef_mask=mask, penalty=penalty, fit_intercept=fit_intercept) assert_allclose(coefs, coefs_old) coefs, _, _ = _logistic_regression_path(X, y, [10.], penalty=penalty, fit_intercept=fit_intercept) coefs_old, _, _ = _logistic_regression_path_old(X, y, [10.], penalty=penalty, fit_intercept=fit_intercept) assert_allclose(coefs, coefs_old) @pytest.mark.parametrize("n_classes,penalty,fit_intercept", [(3, "l2", True), (3, "l2", False), (3, "l1", True), (3, "l1", False), (2, "l2", True), (2, "l2", False), (2, "l1", True), (2, "l1", False)]) def test_all_masked_with_indexing(n_classes, penalty, fit_intercept): """Check masking all of the coef either works with intercept or raises an error. """ X, y, w, intercept = make_classification(n_samples=1000, n_classes=n_classes, n_features=20, n_informative=10, random_state=0) mask = np.zeros_like(w) if n_classes == 2: mask = mask.ravel() coefs, _, _ = _logistic_regression_path(X, y, [10.], coef_mask=mask, fit_intercept=fit_intercept) if fit_intercept: if n_classes == 2: assert_equal(coefs[0][:-1], 0.) else: assert_equal(coefs[0][:, :-1], 0.) else: assert_equal(coefs[0], 0.) def test_estimation_score_usage(): """Test the ability to change the estimation score in UoI L1Logistic""" methods = ('acc', 'log', 'BIC', 'AIC', 'AICc') X, y, w, b = make_classification(n_samples=200, random_state=6, n_informative=5, n_features=10) scores = [] for method in methods: l1log = UoI_L1Logistic(random_state=12, estimation_score=method, tol=1e-2, n_boots_sel=24, n_boots_est=24) assert_equal(l1log.estimation_score, method) l1log.fit(X, y) scores.append(l1log.scores_) scores = np.stack(scores) assert_equal(len(np.unique(scores, axis=0)), len(methods)) def test_set_random_state(): """Tests whether random states are handled correctly.""" X, y, w, b = make_classification(n_samples=100, random_state=60, n_informative=4, n_features=5, w_scale=4.) # same state l1log_0 = UoI_L1Logistic(random_state=13) l1log_1 = UoI_L1Logistic(random_state=13) l1log_0.fit(X, y) l1log_1.fit(X, y) assert_array_equal(l1log_0.coef_, l1log_1.coef_) # different state l1log_1 = UoI_L1Logistic(random_state=14) l1log_1.fit(X, y) assert not np.array_equal(l1log_0.coef_, l1log_1.coef_) # different state, not set l1log_0 = UoI_L1Logistic() l1log_1 = UoI_L1Logistic() l1log_0.fit(X, y) l1log_1.fit(X, y) assert not np.array_equal(l1log_0.coef_, l1log_1.coef_) def test_normalization_by_samples(): """Test that coef_ does not depend directly on the number of samples.""" n_features = 20 for n_classes in [2, 3]: X, y, w, b = make_classification(n_samples=200, random_state=10, n_classes=n_classes, n_informative=n_features, n_features=n_features, w_scale=4.) for penalty in ['l1', 'l2']: lr1 = MaskedCoefLogisticRegression(penalty=penalty, C=1e2) lr1.fit(X, y) lr3 = MaskedCoefLogisticRegression(penalty=penalty, C=1e2) lr3.fit(np.tile(X, (3, 1)), np.tile(y, 3)) assert_allclose(lr1.coef_, lr3.coef_) def test_l1logistic_binary_strings(): """Test that binary L1 Logistic runs in the UoI framework.""" n_inf = 10 X, y, w, b = make_classification(n_samples=200, random_state=6, n_informative=n_inf, n_features=20, w_scale=4., include_intercept=True) classes = ['a', 'b'] lb = LabelEncoder() lb.fit(classes) y = lb.inverse_transform(y) l1log = UoI_L1Logistic(random_state=10).fit(X, y) y_hat = l1log.predict(X) assert set(classes) >= set(y_hat) def test_l1logistic_multiclass_strings(): """Test that multiclass L1 Logistic runs in the UoI framework when all classes share a support.""" n_features = 20 n_inf = 10 X, y, w, b = make_classification(n_samples=200, random_state=10, n_classes=5, n_informative=n_inf, n_features=n_features, shared_support=True, w_scale=4.) classes = ['a', 'b', 'c', 'd', 'e'] lb = LabelEncoder() lb.fit(classes) y = lb.inverse_transform(y) l1log = UoI_L1Logistic(random_state=10).fit(X, y) y_hat = l1log.predict(X) assert set(classes) >= set(y_hat) def test_l1logistic_sparse_input(): """Test that multiclass L1 Logistic works when using sparse matrix inputs""" rs = np.random.RandomState(17) X = sprand(100, 100, random_state=rs) classes = ['abc', 'de', 'fgh'] y = np.array(classes)[rs.randint(3, size=100)] kwargs = dict( fit_intercept=False, random_state=rs, n_boots_sel=4, n_boots_est=4, n_C=7, ) l1log = UoI_L1Logistic(**kwargs).fit(X, y) y_hat = l1log.predict(X) assert set(classes) >= set(y_hat) def test_l1logistic_sparse_input_no_center(): """Test that multiclass L1 Logistic raises an error when asked to center sparse data. """ rs = np.random.RandomState(17) X = sprand(10, 10, random_state=rs) classes = ['abc', 'de', 'fgh'] y = np.array(classes)[rs.randint(3, size=10)] with pytest.raises(ValueError): UoI_L1Logistic(fit_intercept=True).fit(X, y) def test_l1logistic_bad_est_score(): """Test that multiclass L1 Logistic raises an error when given a bad estimation_score value. """ X =

np.random.randn(20, 5)

numpy.random.randn

# 'source /home/voanna/TimePrediction/src/bash/gpu_caffe_env_variables ') from __future__ import print_function import os import time_to_label import glob import math import numpy as np import scipy.io import json import argparse import HONHelpers as hon import random parser = argparse.ArgumentParser() parser.add_argument("webcam", help="either the name of the webcam you want to use from {} or 'all'".format(hon.webcams), type=str) parser.add_argument("GPU_ID", help="gpu core to run the caffe training on", type=int) args = parser.parse_args() np.random.seed(6) random.seed(6) if args.webcam == 'all': webcams = hon.webcams else: assert args.webcam in hon.webcams webcams = [args.webcam] CAFFE_PATH = hon.gpu_caffe_root CAFFE_MODEL = hon.VGG16_caffemodel_path DATA_ROOT = hon.hon_data_root EXPERIMENT_ROOT = os.path.join(os.path.dirname(os.path.realpath(__file__)), args.webcam) training_frac = 0.8 MIN_TEMPERATURE = 5 with open(os.path.join(EXPERIMENT_ROOT, 'train.txt'), 'w') as ftrain, \ open(os.path.join(EXPERIMENT_ROOT, 'val.txt'), 'w') as fval: for webcam in webcams: matfile = os.path.join(DATA_ROOT, webcam, 'train_data_aligned.mat') labels = scipy.io.loadmat(matfile) labels = labels['y'] labels = labels[~

np.isnan(labels)

numpy.isnan

import struct import numpy as np import random def norm_sgy(data): maxval=max(max(data)) minval=min(min(data)) return [[(float(i)-minval)/float(maxval-minval) for i in j] for j in data] def norm_sgy1(data): #maxval=max(max(data)) #minval=min(min(data)) # return [[(float(i)-min(j))/float(max(j)-min(j)) for i in j] for j in data] return [[float(i)/float(max(np.abs(j))) for i in j] for j in data] def norm_sgy2(data): maxval=np.max(np.abs(data),axis=1) index=[i for i in range(len(data))] return [data[j]/maxval[j] for j in index] def norm_tgy(data): # maxval=np.max(np.abs(data),axis=1) maxval=np.max(1000) index=[i for i in range(len(data))] return [data[j]/maxval for j in index] def norm_ydata(data): maxval=np.max(np.abs(data),axis=0) minval=np.min(np.abs(data),axis=0) # maxval=np.max(data) # minval=np.min(data) # return [(j-minval[np.where(j)])/(maxval[np.where(j)]-minval[np.where(j)]) for j in data], maxval, minval return [j/maxval for j in data], maxval, minval # return [[(float(i)-float(minval))/float(maxval-minval) for i in j] for j in data] def read_sgy(sgynam): # print "sgynam: "+sgynam try: binsgy = open(sgynam,'rb') except IOError: return 0,0,[] fhead=binsgy.read(3600); # print fhead[3213:3215] nr=struct.unpack(">H",fhead[3212:3214]) print (nr) nsmp=struct.unpack(">H",fhead[3220:3222]) print(nsmp) data = [] for ir in range(0,nr[0]): trchead=binsgy.read(240) trcdata=binsgy.read(nsmp[0]*4) data1 = [] for i in range(0,nsmp[0]): # print(trcdata[i*4:i*4+4]) data1=data1+list(struct.unpack(">f",trcdata[i*4:i*4+4])) data.append(data1) print("read 1sgy end") binsgy.close() return nr,nsmp,data; def read_egy(egynam): try: binegy = open(egynam,'rb') except IOError: return 0,[] data=[] fbinegy=binegy.read() ndata=len(fbinegy)//4 for i in range(ndata): data1=[] data1=struct.unpack("f",fbinegy[i*4:i*4+4]) data.append(data1) print("read 1egy end") binegy.close() return ndata, data def read_tgy(tgynam): try: binegy = open(tgynam,'rb') except IOError: return 0,[] data=[] fbinegy=binegy.read() ndata=len(fbinegy)//4 for i in range(ndata): data1=[] data1=struct.unpack("f",fbinegy[i*4:i*4+4]) data.append(data1) print("read 1tgy end") binegy.close() return data def load_data(sgynam='sgy',sgyf1=0,sgyt1=300,sgyf2=0,sgyt2=300,shuffle='true'): data= [] ydata=[] for i in range(sgyf1,sgyt1): print(sgynam+"/event/event%04d.sgy" %(i)) nr,nsmp,data1 = read_sgy(sgynam+"/event/event%04d.sgy" %(i)); if nr != 0: data1=norm_sgy1(data1) data.append(data1) ydata.append(1); else: print('1 event sgy not found') for i in range(sgyf2,sgyt2): nr,nsmp,data1 = read_sgy(sgynam+"/noise/noise%04d.sgy" %(i)); if nr != 0: data1=norm_sgy1(data1) data.append(data1) ydata.append(0); else: print('1 noise sgy not found') index=[i for i in range(len(ydata))] random.seed(7) if shuffle == 'true': random.shuffle(index) data = [data[i] for i in index] ydata = [ydata[i] for i in index] data=np.array(data) ydata=np.array(ydata) return data.shape[0],(data.shape[1]*data.shape[2]),data,ydata def load_sgylist(sgylist,floc,shuffle='false'): data= [] ydata=[] lines=open(sgylist,'r').readlines() lines2=open(floc,'r').readlines() for i in range(0,len(lines)): egynam=lines[i][:lines[i].find(' ')] print(egynam) ndata, data1 = read_egy(egynam) tgynam=lines2[i][:lines2[i].find(' ')] print(tgynam) data2 = read_tgy(tgynam); if ndata != 0: data2=norm_tgy(data2) data.append(data1) ydata.append(data2) else: print('1 event tgy not found') index=[i for i in range(len(ydata))] random.seed(7) if shuffle == 'true': random.shuffle(index) data = [data[i] for i in index] ydata = [ydata[i] for i in index] data=np.array(data) ydata=

np.array(ydata)

numpy.array

"""Collection of functions to run forwards and backwards algorithms on haploid genotype data, where the data is structured as samples x variants.""" import numba as nb import numpy as np @nb.jit def forwards_ls_hap(n, m, H, s, e, r, norm=True): """Matrix based haploid LS forward algorithm using numpy vectorisation.""" # Initialise F = np.zeros((n, m)) c =

np.ones(m)

numpy.ones

import unittest from ancb import NumpyCircularBuffer from ancb import ( # type: ignore star_can_broadcast, can_broadcast ) from numpy import array_equal, allclose, shares_memory from numpy import array, zeros, arange, ndarray, ones, empty from numpy.random import rand, randint from numpy import fill_diagonal, roll from itertools import zip_longest from operator import ( matmul, add, sub, mul, truediv, mod, floordiv, pow, rshift, lshift, and_, or_, xor, neg, pos, abs, inv, invert, iadd, iand, ifloordiv, ilshift, imod, imul, ior, ipow, irshift, isub, itruediv, ixor ) class TestBroadcastability(unittest.TestCase): def test_broadcastablity(self): x = zeros((1, 2, 3, 4, 5)) y = zeros((1, 1, 1, 4, 5)) z = zeros((1, 1, 1, 3, 5)) w = zeros(1) self.assertTrue(can_broadcast(x.shape, y.shape)) self.assertFalse(can_broadcast(x.shape, z.shape)) self.assertFalse(can_broadcast(y.shape, z.shape)) self.assertTrue(can_broadcast(x.shape, x.shape)) self.assertTrue(can_broadcast(y.shape, y.shape)) self.assertTrue(can_broadcast(z.shape, z.shape)) self.assertTrue(can_broadcast(w.shape, w.shape)) self.assertTrue(can_broadcast(x.shape, w.shape)) self.assertTrue(can_broadcast(y.shape, w.shape)) self.assertTrue(can_broadcast(z.shape, w.shape)) def test_star_broadcastablity(self): x = zeros((1, 2, 3, 4, 5)) y = zeros((1, 1, 1, 4, 5)) z = zeros((1, 1, 1, 3, 5)) w = zeros(1) starexpr = zip_longest(x.shape, y.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(x.shape, z.shape, fillvalue=1) self.assertFalse(star_can_broadcast(starexpr)) starexpr = zip_longest(y.shape, z.shape, fillvalue=1) self.assertFalse(star_can_broadcast(starexpr)) starexpr = zip_longest(x.shape, x.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(y.shape, y.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(z.shape, z.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(w.shape, w.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(x.shape, w.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(y.shape, w.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(y.shape, w.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) starexpr = zip_longest(z.shape, w.shape, fillvalue=1) self.assertTrue(star_can_broadcast(starexpr)) class OperatorTestFactory(type): def __new__(cls, name, bases, dct): obj = super().__new__(cls, name, bases, dct) bin_operators = [ matmul, add, sub, mul, truediv, mod, floordiv, pow ] un_operators = [neg, pos, abs, invert, inv] bitbin_operators = [rshift, lshift, and_, or_, xor] i_operators = [ iadd, ifloordiv, imul, ipow, isub, itruediv ] bit_ioperators = [ ilshift, irshift, ior, iand, ixor, imod ] def unop_testcase(op): def f(self): data = zeros(3, dtype=int) test = -arange(3, dtype=int) buffer = NumpyCircularBuffer(data) buffer.append(0) buffer.append(-1) buffer.append(-2) res = op(buffer) self.assertIsInstance(res, ndarray) self.assertTrue(array_equal(res, op(test))) # unfrag buffer.append(-3) test -= 1 res = op(buffer) self.assertIsInstance(res, ndarray) self.assertTrue(array_equal(res, op(test))) # frag return f def bitbinop_testcase(op): def f(self): data = zeros(3, dtype=int) test = arange(1, 4, dtype=int) x = randint(3) buffer = NumpyCircularBuffer(data) buffer.append(1) buffer.append(2) buffer.append(3) res1 = op(buffer, x) res2 = op(x, buffer) self.assertIsInstance(res1, ndarray) self.assertIsInstance(res2, ndarray) self.assertTrue(array_equal(res1, op(test, x))) self.assertTrue(array_equal(res2, op(x, test))) buffer.append(4) test += 1 res1 = op(buffer, x) res2 = op(x, buffer) self.assertIsInstance(res1, ndarray) self.assertIsInstance(res2, ndarray) self.assertTrue(array_equal(res1, op(test, x))) self.assertTrue(array_equal(res2, op(x, test))) return f def binop_testcase(op): def f(self): data = zeros(3, dtype=float) test = arange(1, 4, dtype=float) x = rand(3) buffer = NumpyCircularBuffer(data) buffer.append(1) buffer.append(2) buffer.append(3) res1 = op(buffer, x) self.assertIsInstance(res1, ndarray) self.assertTrue(allclose(res1, op(test, x))) res2 = op(x, buffer) self.assertIsInstance(res2, ndarray) self.assertTrue(allclose(res2, op(x, test))) buffer.append(4) test += 1 res1 = op(buffer, x) self.assertIsInstance(res1, ndarray) self.assertTrue(allclose(res1, op(test, x))) res2 = op(x, buffer) self.assertIsInstance(res2, ndarray) self.assertTrue(allclose(res2, op(x, test))) return f def iop_testcase(op): def f(self): data = zeros(3, dtype=float) data2 = zeros(3, dtype=float) test1 = arange(1, 4, dtype=float) test2 = arange(2, 5, dtype=float) x = rand(3) buffer1 = NumpyCircularBuffer(data) buffer2 = NumpyCircularBuffer(data2) buffer1.append(1) buffer1.append(2) buffer1.append(3) buffer2.append(1) buffer2.append(2) buffer2.append(3) op(buffer1, x) op(test1, x) self.assertIsInstance(buffer1, NumpyCircularBuffer) self.assertTrue(array_equal(buffer1 + 0, test1)) buffer2.append(4) op(buffer2, x) op(test2, x) self.assertIsInstance(buffer2, NumpyCircularBuffer) self.assertTrue(array_equal(buffer2 + 0, test2)) return f def bitiop_testcase(op): def f(self): data = zeros(3, dtype=int) data2 = zeros(3, dtype=int) test1 = arange(1, 4, dtype=int) test2 = arange(2, 5, dtype=int) x = randint(low=1, high=100, size=3) buffer1 = NumpyCircularBuffer(data) buffer2 = NumpyCircularBuffer(data2) buffer1.append(1) buffer1.append(2) buffer1.append(3) buffer2.append(1) buffer2.append(2) buffer2.append(3) op(buffer1, x) op(test1, x) self.assertIsInstance(buffer1, NumpyCircularBuffer) self.assertTrue(allclose(buffer1 + 0, test1)) buffer2.append(4) op(buffer2, x) op(test2, x) self.assertIsInstance(buffer2, NumpyCircularBuffer) self.assertTrue(allclose(buffer2 + 0, test2)) return f for op in bin_operators: setattr(obj, 'test_{}'.format(op.__name__), binop_testcase(op)) for op in bitbin_operators: setattr(obj, 'test_{}'.format(op.__name__), bitbinop_testcase(op)) for op in un_operators: setattr(obj, 'test_{}'.format(op.__name__), unop_testcase(op)) for op in i_operators: setattr(obj, 'test_{}'.format(op.__name__), iop_testcase(op)) for op in bit_ioperators: setattr(obj, 'test_{}'.format(op.__name__), bitiop_testcase(op)) return(obj) class TestNumpyCircularBuffer( unittest.TestCase, metaclass=OperatorTestFactory ): """ NumpyCircularBuffer tests """ def test_init(self): data = zeros(3) buffer = NumpyCircularBuffer(data) self.assertTrue(array_equal(data, buffer)) def test_fragmentation(self): data = zeros(3) buffer = NumpyCircularBuffer(data) self.assertFalse(buffer.fragmented) buffer.append(0) self.assertFalse(buffer.fragmented) buffer.append(1) self.assertFalse(buffer.fragmented) buffer.append(2) self.assertFalse(buffer.fragmented) buffer.append(3) self.assertTrue(buffer.fragmented) buffer.append(4) self.assertTrue(buffer.fragmented) buffer.append(5) self.assertFalse(buffer.fragmented) buffer.pop() self.assertFalse(buffer.fragmented) buffer.pop() self.assertFalse(buffer.fragmented) buffer.pop() self.assertFalse(buffer.fragmented) def test_matmul_1d1d(self): """Tests buffer @ X where buffer.ndim == 1 and X.ndim == 1""" data = zeros(3) C = rand(3) buffer = NumpyCircularBuffer(data) buffer.append(0) self.assertTrue(allclose(buffer @ C[:1], arange(1) @ C[:1])) buffer.append(1) self.assertTrue(allclose(buffer @ C[:2], arange(2) @ C[:2])) buffer.append(2) self.assertTrue(allclose(buffer @ C, arange(3) @ C)) buffer.append(3) self.assertTrue(allclose(buffer @ C, (arange(1, 4)) @ C)) buffer.append(4) self.assertTrue(allclose(buffer @ C, (arange(2, 5)) @ C)) buffer.append(5) self.assertTrue(allclose(buffer @ C, (arange(3, 6)) @ C)) buffer.append(6) self.assertTrue(allclose(buffer @ C, (arange(4, 7)) @ C)) buffer.pop() self.assertTrue(allclose(buffer @ C[1:], (arange(5, 7)) @ C[1:])) buffer.pop() self.assertTrue(allclose(buffer @ C[2:], (arange(6, 7)) @ C[2:])) def test_matmul_1d2d(self): """Tests buffer @ X where buffer.ndim == 1 and X.ndim == 2""" data = zeros(3) A = zeros((3, 3)) B = rand(9).reshape(3, 3) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) buffer.append(0) buffer.append(1) buffer.append(2) res_a = buffer @ A res_b = buffer @ B self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, arange(3) @ A)) self.assertTrue(allclose(res_b, arange(3) @ B)) buffer.append(3) res_a = buffer @ A res_b = buffer @ B self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(allclose(res_a, arange(1, 4) @ A)) self.assertTrue(allclose(res_b, arange(1, 4) @ B)) def test_matmul_2d2d(self): """Tests buffer @ X where buffer.ndim == 2""" data = zeros((3, 3)) A = zeros(9).reshape(3, 3) B = rand(9).reshape(3, 3) fill_diagonal(A, arange(1, 4)) buffer = NumpyCircularBuffer(data) buffer.append(arange(3)) buffer.append(arange(3, 6)) buffer.append(arange(6, 9)) test = arange(9).reshape(3, 3) self.assertTrue(array_equal(buffer, test)) res_a = buffer @ A res_b = buffer @ B self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) buffer.append(arange(9, 12)) test += 3 res_a = buffer @ A res_b = buffer @ B self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) def test_matmul_ndnd(self): """Tests buffer @ X where X.ndim > 2 and buffer.ndim > 2""" data = zeros((3, 3, 3)) A = zeros((3, 3, 3)) B = rand(27).reshape(3, 3, 3) C = rand(12).reshape(3, 4) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) filler = arange(9).reshape(3, 3) buffer.append(filler) buffer.append(filler + 9) buffer.append(filler + 18) test = arange(27).reshape(3, 3, 3) res_a = buffer @ A res_b = buffer @ B self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) buffer.append(filler + 27) test += 9 res_a = buffer @ A res_b = buffer @ B res_c = buffer @ C self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) self.assertTrue(allclose(res_c, test @ C)) def test_rmatmul_1d1d(self): """Tests X @ buffer where X.ndim == 1 and buffer.ndim == 1""" data = zeros(3) C = rand(3) buffer = NumpyCircularBuffer(data) buffer.append(0) res_c = C[:1] @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C[:1] @ arange(1))) buffer.append(1) res_c = C[:2] @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C[:2] @ arange(2))) buffer.append(2) res_c = C @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C @ arange(3))) buffer.append(3) res_c = C @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C @ arange(1, 4))) buffer.append(4) res_c = C @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C @ arange(2, 5))) buffer.append(5) res_c = C @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C @ arange(3, 6))) buffer.append(6) res_c = C @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C @ arange(4, 7))) buffer.pop() res_c = C[1:] @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C[1:] @ arange(5, 7))) buffer.pop() res_c = C[2:] @ buffer self.assertIsInstance(res_c, ndarray) self.assertTrue(allclose(res_c, C[2:] @ arange(6, 7))) def test_rmatmul_nd1d(self): """Tests X @ buffer where X.ndim == 1 and buffer.ndim > 1""" data = zeros(3) A = zeros(9).reshape(3, 3) B = arange(9).reshape(3, 3) C = arange(3) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) buffer.append(0) buffer.append(1) buffer.append(2) res_a = A @ buffer self.assertIsInstance(res_a, ndarray) self.assertTrue(array_equal(A @ buffer, A @ array([0, 1, 2]))) buffer.append(3) res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ array([1, 2, 3]))) self.assertTrue(allclose(res_b, B @ array([1, 2, 3]))) self.assertTrue(allclose(res_c, C @ array([1, 2, 3]))) buffer.append(4) res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ arange(2, 5))) self.assertTrue(allclose(res_b, B @ arange(2, 5))) self.assertTrue(allclose(res_c, C @ arange(2, 5))) buffer.append(5) res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ arange(3, 6))) self.assertTrue(allclose(res_b, B @ arange(3, 6))) self.assertTrue(allclose(res_c, C @ arange(3, 6))) def test_rmatmul_1dnd(self): """Tests X @ buffer where X.ndim == 1 and buffer.ndim > 1""" data1 = zeros((3, 3)) data2 = zeros((3, 3, 3)) A = rand(3) test1 = arange(9).reshape(3, 3) test2 = arange(27).reshape(3, 3, 3) buffer1 = NumpyCircularBuffer(data1) buffer2 = NumpyCircularBuffer(data2) buffer1.append(arange(3)) buffer1.append(arange(3, 6)) buffer1.append(arange(6, 9)) buffer2.append(arange(9).reshape(3, 3)) buffer2.append(arange(9, 18).reshape(3, 3)) buffer2.append(arange(18, 27).reshape(3, 3)) res_buf1 = A @ buffer1 res_buf2 = A @ buffer2 self.assertIsInstance(res_buf1, ndarray) self.assertIsInstance(res_buf2, ndarray) self.assertTrue(allclose(res_buf1, A @ test1)) self.assertTrue(allclose(res_buf2, A @ test2)) buffer1.append(arange(9, 12)) buffer2.append(arange(27, 36).reshape(3, 3)) test1 += 3 test2 += 9 res_buf1 = A @ buffer1 res_buf2 = A @ buffer2 self.assertIsInstance(res_buf1, ndarray) self.assertIsInstance(res_buf2, ndarray) self.assertTrue(allclose(res_buf1, A @ test1)) self.assertTrue(allclose(res_buf2, A @ test2)) buffer1.append(arange(12, 15)) buffer2.append(arange(36, 45).reshape(3, 3)) test1 += 3 test2 += 9 res_buf1 = A @ buffer1 res_buf2 = A @ buffer2 self.assertIsInstance(res_buf1, ndarray) self.assertIsInstance(res_buf2, ndarray) self.assertTrue(allclose(res_buf1, A @ test1)) self.assertTrue(allclose(res_buf2, A @ test2)) buffer1.append(arange(15, 18)) buffer2.append(arange(45, 54).reshape(3, 3)) test1 += 3 test2 += 9 res_buf1 = A @ buffer1 res_buf2 = A @ buffer2 self.assertIsInstance(res_buf1, ndarray) self.assertIsInstance(res_buf2, ndarray) self.assertTrue(allclose(res_buf1, A @ test1)) self.assertTrue(allclose(res_buf2, A @ test2)) def test_rmatmul_2d2d(self): data = zeros((3, 3)) A = zeros(9).reshape(3, 3) B = rand(9).reshape(3, 3) C = rand(12).reshape(4, 3) fill_diagonal(A, arange(1, 4)) buffer = NumpyCircularBuffer(data) buffer.append(arange(3)) buffer.append(arange(3, 6)) buffer.append(arange(6, 9)) test = arange(9).reshape(3, 3) self.assertTrue(array_equal(buffer, test)) res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ test)) self.assertTrue(allclose(res_b, B @ test)) self.assertTrue(allclose(res_c, C @ test)) buffer.append([9, 10, 11]) test += 3 res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ test)) self.assertTrue(allclose(res_b, B @ test)) self.assertTrue(allclose(res_c, C @ test)) def test_rmatmul_ndnd(self): data = zeros((3, 3, 3)) A = zeros(27).reshape(3, 3, 3) B = arange(27).reshape(3, 3, 3) C = arange(3*8*3).reshape(3, 8, 3) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) filler = arange(9).reshape(3, 3) buffer.append(filler) buffer.append(filler + 9) buffer.append(filler + 18) test = arange(27).reshape(3, 3, 3) res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ test)) self.assertTrue(allclose(res_b, B @ test)) self.assertTrue(allclose(res_c, C @ test)) buffer.append(filler + 27) test += 9 res_a = A @ buffer res_b = B @ buffer res_c = C @ buffer self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertIsInstance(res_c, ndarray) self.assertTrue(array_equal(res_a, A @ test)) self.assertTrue(allclose(res_b, B @ test)) self.assertTrue(allclose(res_c, C @ test)) def test_matmul2_1d1d(self): """Tests buffer @ X where buffer.ndim == 1 and X.ndim == 1""" data = zeros(3) C = rand(3) buffer = NumpyCircularBuffer(data) buffer.append(0) self.assertTrue(allclose( buffer.matmul(C[:1], empty(1)), arange(1) @ C[:1] ) ) buffer.append(1) self.assertTrue(allclose( buffer.matmul(C[:2], empty(2)), arange(2) @ C[:2] ) ) buffer.append(2) self.assertTrue(allclose( buffer.matmul(C, empty(3)), arange(3) @ C ) ) buffer.append(3) self.assertTrue(allclose( buffer.matmul(C, empty(3)), arange(1, 4) @ C ) ) buffer.append(4) self.assertTrue(allclose( buffer.matmul(C, empty(3)), arange(2, 5) @ C ) ) buffer.append(5) self.assertTrue(allclose( buffer.matmul(C, empty(3)), arange(3, 6) @ C ) ) buffer.append(6) self.assertTrue(allclose( buffer.matmul(C, empty(3)), arange(4, 7) @ C ) ) buffer.pop() self.assertTrue(allclose( buffer.matmul(C[1:], empty(2)), arange(5, 7) @ C[1:] ) ) buffer.pop() self.assertTrue(allclose( buffer.matmul(C[2:], empty(1)), arange(6, 7) @ C[2:] ) ) def test_matmul2_1d2d(self): """Tests buffer @ X where buffer.ndim == 1 and X.ndim == 2""" data = zeros(3) A = zeros((3, 3)) B = rand(9).reshape(3, 3) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) buffer.append(0) buffer.append(1) buffer.append(2) res_a = buffer.matmul(A, empty(3)) res_b = buffer.matmul(B, empty(3)) self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, arange(3) @ A)) self.assertTrue(allclose(res_b, arange(3) @ B)) buffer.append(3) res_a = buffer.matmul(A, empty(3)) res_b = buffer.matmul(B, empty(3)) self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(allclose(res_a, arange(1, 4) @ A)) self.assertTrue(allclose(res_b, arange(1, 4) @ B)) def test_matmul2_2d2d(self): """Tests buffer @ X where buffer.ndim == 2""" data = zeros((3, 3)) A = zeros(9).reshape(3, 3) B = rand(9).reshape(3, 3) fill_diagonal(A, arange(1, 4)) buffer = NumpyCircularBuffer(data) buffer.append(arange(3)) buffer.append(arange(3, 6)) buffer.append(arange(6, 9)) test = arange(9).reshape(3, 3) self.assertTrue(array_equal(buffer, test)) res_a = buffer.matmul(A, empty((3, 3))) res_b = buffer.matmul(B, empty((3, 3))) self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) buffer.append(arange(9, 12)) test += 3 res_a = buffer.matmul(A, empty((3, 3))) res_b = buffer.matmul(B, empty((3, 3))) self.assertIsInstance(res_a, ndarray) self.assertIsInstance(res_b, ndarray) self.assertTrue(array_equal(res_a, test @ A)) self.assertTrue(allclose(res_b, test @ B)) def test_matmul2_ndnd(self): """Tests buffer @ X where X.ndim > 2 and buffer.ndim > 2""" data = zeros((3, 3, 3)) A = zeros((3, 3, 3)) B = rand(27).reshape(3, 3, 3) C = rand(12).reshape(3, 4) fill_diagonal(A, [1, 2, 3]) buffer = NumpyCircularBuffer(data) filler =

arange(9)

numpy.arange

# Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections from functools import partial import itertools import operator import unittest from unittest import SkipTest from absl.testing import absltest from absl.testing import parameterized import numpy as np import jax import jax.numpy as jnp from jax import core from jax._src import dtypes from jax import lax from jax._src import test_util as jtu from jax import tree_util from jax._src import lax_reference from jax.test_util import check_grads import jax.util from jax._src.util import prod from jax._src.lax.lax import _device_put_raw from jax.config import config config.parse_flags_with_absl() ### lax tests # For standard unops and binops, we can generate a large number of tests on # arguments of appropriate shapes and dtypes using the following table. float_dtypes = jtu.dtypes.all_floating complex_elem_dtypes = jtu.dtypes.floating complex_dtypes = jtu.dtypes.complex inexact_dtypes = jtu.dtypes.all_inexact int_dtypes = jtu.dtypes.all_integer uint_dtypes = jtu.dtypes.all_unsigned bool_dtypes = jtu.dtypes.boolean default_dtypes = float_dtypes + int_dtypes all_dtypes = float_dtypes + complex_dtypes + int_dtypes + uint_dtypes + bool_dtypes python_scalar_types = [bool, int, float, complex] compatible_shapes = [[(3,)], [(3, 4), (3, 1), (1, 4)], [(2, 3, 4), (2, 1, 4)]] # We check cases where the preferred type is at least as wide as the input # type and where both are either both floating-point or both integral, # which are the only supported configurations. preferred_type_combinations = [ (np.float16, np.float16), (np.float16, np.float32), (np.float16, np.float64), (dtypes.bfloat16, dtypes.bfloat16), (dtypes.bfloat16, np.float32), (dtypes.bfloat16, np.float64), (np.float32, np.float32), (np.float32, np.float64), (np.float64, np.float64), (np.int8, np.int8), (np.int8, np.int16), (np.int8, np.int32), (np.int8, np.int64), (np.int16, np.int16), (np.int16, np.int32), (np.int16, np.int64), (np.int32, np.int32), (np.int32, np.int64), (np.int64, np.int64), (np.complex64, np.complex64), (np.complex64, np.complex128), (np.complex128, np.complex128)] OpRecord = collections.namedtuple( "OpRecord", ["op", "nargs", "dtypes", "rng_factory", "tol"]) def op_record(op, nargs, dtypes, rng_factory, tol=None): return OpRecord(op, nargs, dtypes, rng_factory, tol) LAX_OPS = [ op_record("neg", 1, default_dtypes + complex_dtypes, jtu.rand_small), op_record("sign", 1, default_dtypes + uint_dtypes, jtu.rand_small), op_record("floor", 1, float_dtypes, jtu.rand_small), op_record("ceil", 1, float_dtypes, jtu.rand_small), op_record("round", 1, float_dtypes, jtu.rand_default), op_record("nextafter", 2, [f for f in float_dtypes if f != dtypes.bfloat16], jtu.rand_default, tol=0), op_record("is_finite", 1, float_dtypes, jtu.rand_small), op_record("exp", 1, float_dtypes + complex_dtypes, jtu.rand_small), # TODO(b/142975473): on CPU, expm1 for float64 is only accurate to ~float32 # precision. op_record("expm1", 1, float_dtypes + complex_dtypes, jtu.rand_small, {np.float64: 1e-8}), op_record("log", 1, float_dtypes + complex_dtypes, jtu.rand_positive), op_record("log1p", 1, float_dtypes + complex_dtypes, jtu.rand_positive), # TODO(b/142975473): on CPU, tanh for complex128 is only accurate to # ~float32 precision. # TODO(b/143135720): on GPU, tanh has only ~float32 precision. op_record("tanh", 1, float_dtypes + complex_dtypes, jtu.rand_small, {np.float64: 1e-9, np.complex128: 1e-7}), op_record("sin", 1, float_dtypes + complex_dtypes, jtu.rand_default), op_record("cos", 1, float_dtypes + complex_dtypes, jtu.rand_default), op_record("atan2", 2, float_dtypes, jtu.rand_default), op_record("sqrt", 1, float_dtypes, jtu.rand_positive), op_record("sqrt", 1, complex_dtypes, jtu.rand_default), op_record("rsqrt", 1, float_dtypes, jtu.rand_positive), op_record("rsqrt", 1, complex_dtypes, jtu.rand_default), op_record("cbrt", 1, float_dtypes, jtu.rand_default), op_record("square", 1, float_dtypes + complex_dtypes, jtu.rand_default), op_record("reciprocal", 1, float_dtypes + complex_dtypes, jtu.rand_positive), op_record("tan", 1, float_dtypes + complex_dtypes, jtu.rand_default, {np.float32: 3e-5}), op_record("asin", 1, float_dtypes + complex_dtypes, jtu.rand_small), op_record("acos", 1, float_dtypes + complex_dtypes, jtu.rand_small), op_record("atan", 1, float_dtypes + complex_dtypes, jtu.rand_small), op_record("asinh", 1, float_dtypes + complex_dtypes, jtu.rand_default, tol={np.complex64: 1E-4, np.complex128: 1E-5}), op_record("acosh", 1, float_dtypes + complex_dtypes, jtu.rand_positive), # TODO(b/155331781): atanh has only ~float precision op_record("atanh", 1, float_dtypes + complex_dtypes, jtu.rand_small, {np.float64: 1e-9}), op_record("sinh", 1, float_dtypes + complex_dtypes, jtu.rand_default), op_record("cosh", 1, float_dtypes + complex_dtypes, jtu.rand_default), op_record("lgamma", 1, float_dtypes, jtu.rand_positive, {np.float32: 1e-3 if jtu.device_under_test() == "tpu" else 1e-5, np.float64: 1e-14}), op_record("digamma", 1, float_dtypes, jtu.rand_positive, {np.float64: 1e-14}), op_record("betainc", 3, float_dtypes, jtu.rand_positive, {np.float64: 1e-14}), op_record("igamma", 2, [f for f in float_dtypes if f not in [dtypes.bfloat16, np.float16]], jtu.rand_positive, {np.float64: 1e-14}), op_record("igammac", 2, [f for f in float_dtypes if f not in [dtypes.bfloat16, np.float16]], jtu.rand_positive, {np.float64: 1e-14}), op_record("erf", 1, float_dtypes, jtu.rand_small), op_record("erfc", 1, float_dtypes, jtu.rand_small), # TODO(b/142976030): the approximation of erfinf used by XLA is only # accurate to float32 precision. op_record("erf_inv", 1, float_dtypes, jtu.rand_small, {np.float64: 1e-9}), op_record("bessel_i0e", 1, float_dtypes, jtu.rand_default), op_record("bessel_i1e", 1, float_dtypes, jtu.rand_default), op_record("real", 1, complex_dtypes, jtu.rand_default), op_record("imag", 1, complex_dtypes, jtu.rand_default), op_record("complex", 2, complex_elem_dtypes, jtu.rand_default), op_record("conj", 1, complex_elem_dtypes + complex_dtypes, jtu.rand_default), op_record("abs", 1, default_dtypes + complex_dtypes, jtu.rand_default), op_record("pow", 2, float_dtypes + complex_dtypes, jtu.rand_positive), op_record("bitwise_and", 2, bool_dtypes, jtu.rand_small), op_record("bitwise_not", 1, bool_dtypes, jtu.rand_small), op_record("bitwise_or", 2, bool_dtypes, jtu.rand_small), op_record("bitwise_xor", 2, bool_dtypes, jtu.rand_small), op_record("population_count", 1, int_dtypes + uint_dtypes, jtu.rand_int), op_record("clz", 1, int_dtypes + uint_dtypes, jtu.rand_int), op_record("add", 2, default_dtypes + complex_dtypes, jtu.rand_small), op_record("sub", 2, default_dtypes + complex_dtypes, jtu.rand_small), op_record("mul", 2, default_dtypes + complex_dtypes, jtu.rand_small), op_record("div", 2, default_dtypes + complex_dtypes, jtu.rand_nonzero), op_record("rem", 2, default_dtypes, jtu.rand_nonzero), op_record("max", 2, all_dtypes, jtu.rand_small), op_record("min", 2, all_dtypes, jtu.rand_small), op_record("eq", 2, all_dtypes, jtu.rand_some_equal), op_record("ne", 2, all_dtypes, jtu.rand_small), op_record("ge", 2, default_dtypes, jtu.rand_small), op_record("gt", 2, default_dtypes, jtu.rand_small), op_record("le", 2, default_dtypes, jtu.rand_small), op_record("lt", 2, default_dtypes, jtu.rand_small), ] class LaxTest(jtu.JaxTestCase): """Numerical tests for LAX operations.""" @parameterized.named_parameters(itertools.chain.from_iterable( jtu.cases_from_list( {"testcase_name": jtu.format_test_name_suffix( rec.op, shapes, itertools.repeat(dtype)), "op_name": rec.op, "rng_factory": rec.rng_factory, "shapes": shapes, "dtype": dtype} for shape_group in compatible_shapes for shapes in itertools.combinations_with_replacement(shape_group, rec.nargs) for dtype in rec.dtypes) for rec in LAX_OPS)) def testOp(self, op_name, rng_factory, shapes, dtype): rng = rng_factory(self.rng()) args_maker = lambda: [rng(shape, dtype) for shape in shapes] op = getattr(lax, op_name) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(itertools.chain.from_iterable( jtu.cases_from_list( {"testcase_name": jtu.format_test_name_suffix( rec.op, shapes, itertools.repeat(dtype)), "op_name": rec.op, "rng_factory": rec.rng_factory, "shapes": shapes, "dtype": dtype, "tol": rec.tol} for shape_group in compatible_shapes for shapes in itertools.combinations_with_replacement(shape_group, rec.nargs) for dtype in rec.dtypes) for rec in LAX_OPS)) def testOpAgainstNumpy(self, op_name, rng_factory, shapes, dtype, tol): if (not config.x64_enabled and op_name == "nextafter" and dtype == np.float64): raise SkipTest("64-bit mode disabled") rng = rng_factory(self.rng()) args_maker = lambda: [rng(shape, dtype) for shape in shapes] op = getattr(lax, op_name) numpy_op = getattr(lax_reference, op_name) self._CheckAgainstNumpy(numpy_op, op, args_maker, tol=tol) # TODO test shift_left, shift_right_arithmetic, shift_right_logical @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_from_dtype={}_to_dtype={}_weak_type={}".format( from_dtype, to_dtype, weak_type), "from_dtype": from_dtype, "to_dtype": to_dtype, "weak_type": weak_type} for from_dtype, to_dtype in itertools.product( [None, np.float32, np.int32, "float32", "int32"], repeat=2) for weak_type in [True, False])) def testConvertElementType(self, from_dtype, to_dtype, weak_type): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng((2, 3), from_dtype)] op = lambda x: lax._convert_element_type(x, to_dtype, weak_type) self._CompileAndCheck(op, args_maker) x = rng((1,), from_dtype) out = op(x) self.assertEqual(out.dtype, dtypes.canonicalize_dtype(to_dtype or x.dtype)) self.assertEqual(out.aval.weak_type, weak_type) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_from_dtype={}_to_dtype={}" .format(from_dtype, to_dtype), "from_dtype": from_dtype, "to_dtype": to_dtype} for from_dtype, to_dtype in itertools.product( [np.float32, np.int32, "float32", "int32"], repeat=2))) def testConvertElementTypeAgainstNumpy(self, from_dtype, to_dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng((2, 3), from_dtype)] op = lambda x: lax.convert_element_type(x, to_dtype) numpy_op = lambda x: lax_reference.convert_element_type(x, to_dtype) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_from_dtype={}_to_dtype={}" .format(from_dtype, to_dtype), "from_dtype": from_dtype, "to_dtype": to_dtype} for from_dtype, to_dtype in itertools.product( [np.float32, np.int32, "float32", "int32"], repeat=2))) def testBitcastConvertType(self, from_dtype, to_dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng((2, 3), from_dtype)] op = lambda x: lax.bitcast_convert_type(x, to_dtype) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_from_dtype={}_to_dtype={}" .format(from_dtype, to_dtype), "from_dtype": from_dtype, "to_dtype": to_dtype} for from_dtype, to_dtype in itertools.product( [np.float32, np.int32, "float32", "int32"], repeat=2))) def testBitcastConvertTypeAgainstNumpy(self, from_dtype, to_dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng((2, 3), from_dtype)] op = lambda x: lax.bitcast_convert_type(x, to_dtype) numpy_op = lambda x: lax_reference.bitcast_convert_type(x, to_dtype) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_from_dtype={}_to_dtype={}_weak_type={}" .format(from_dtype, to_dtype, weak_type), "from_dtype": from_dtype, "to_dtype": to_dtype, "weak_type": weak_type} for from_dtype, to_dtype in itertools.product( [np.float32, np.int32, "float32", "int32"], repeat=2) for weak_type in [True, False])) def testBitcastConvertWeakType(self, from_dtype, to_dtype, weak_type): rng = jtu.rand_default(self.rng()) x_in = lax._convert_element_type(rng((2, 3), from_dtype), weak_type=weak_type) op = lambda x: lax.bitcast_convert_type(x, to_dtype) self.assertEqual(dtypes.is_weakly_typed(x_in), weak_type) x_out = op(x_in) self.assertEqual(dtypes.is_weakly_typed(x_out), False) x_out_jit = jax.jit(op)(x_in) self.assertEqual(dtypes.is_weakly_typed(x_out_jit), False) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_min_shape={}_operand_shape={}_max_shape={}".format( jtu.format_shape_dtype_string(min_shape, dtype), jtu.format_shape_dtype_string(operand_shape, dtype), jtu.format_shape_dtype_string(max_shape, dtype)), "min_shape": min_shape, "operand_shape": operand_shape, "max_shape": max_shape, "dtype": dtype} for min_shape, operand_shape, max_shape in [ [(), (2, 3), ()], [(2, 3), (2, 3), ()], [(), (2, 3), (2, 3)], [(2, 3), (2, 3), (2, 3)], ] for dtype in default_dtypes)) def testClamp(self, min_shape, operand_shape, max_shape, dtype): rng = jtu.rand_default(self.rng()) shapes = [min_shape, operand_shape, max_shape] args_maker = lambda: [rng(shape, dtype) for shape in shapes] self._CompileAndCheck(lax.clamp, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_min_shape={}_operand_shape={}_max_shape={}".format( jtu.format_shape_dtype_string(min_shape, dtype), jtu.format_shape_dtype_string(operand_shape, dtype), jtu.format_shape_dtype_string(max_shape, dtype)), "min_shape": min_shape, "operand_shape": operand_shape, "max_shape": max_shape, "dtype": dtype} for min_shape, operand_shape, max_shape in [ [(), (2, 3), ()], [(2, 3), (2, 3), ()], [(), (2, 3), (2, 3)], [(2, 3), (2, 3), (2, 3)], ] for dtype in default_dtypes)) def testClampAgainstNumpy(self, min_shape, operand_shape, max_shape, dtype): rng = jtu.rand_default(self.rng()) shapes = [min_shape, operand_shape, max_shape] args_maker = lambda: [rng(shape, dtype) for shape in shapes] self._CheckAgainstNumpy(lax_reference.clamp, lax.clamp, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_dim={}_baseshape=[{}]_dtype={}_narrs={}".format( dim, ",".join(str(d) for d in base_shape), np.dtype(dtype).name, num_arrs), "dim": dim, "base_shape": base_shape, "dtype": dtype, "num_arrs": num_arrs} for num_arrs in [3] for dtype in default_dtypes for base_shape in [(4,), (3, 4), (2, 3, 4)] for dim in range(len(base_shape)))) def testConcatenate(self, dim, base_shape, dtype, num_arrs): rng = jtu.rand_default(self.rng()) shapes = [base_shape[:dim] + (size,) + base_shape[dim+1:] for size, _ in zip(itertools.cycle([3, 1, 4]), range(num_arrs))] args_maker = lambda: [rng(shape, dtype) for shape in shapes] op = lambda *args: lax.concatenate(args, dim) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_dim={}_baseshape=[{}]_dtype={}_narrs={}".format( dim, ",".join(str(d) for d in base_shape), np.dtype(dtype).name, num_arrs), "dim": dim, "base_shape": base_shape, "dtype": dtype, "num_arrs": num_arrs} for num_arrs in [3] for dtype in default_dtypes for base_shape in [(4,), (3, 4), (2, 3, 4)] for dim in range(len(base_shape)))) def testConcatenateAgainstNumpy(self, dim, base_shape, dtype, num_arrs): rng = jtu.rand_default(self.rng()) shapes = [base_shape[:dim] + (size,) + base_shape[dim+1:] for size, _ in zip(itertools.cycle([3, 1, 4]), range(num_arrs))] args_maker = lambda: [rng(shape, dtype) for shape in shapes] op = lambda *args: lax.concatenate(args, dim) numpy_op = lambda *args: lax_reference.concatenate(args, dim) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding} for lhs_shape, rhs_shape in [ ((b, i, 9, 10), (j, i, 4, 5)) for b, i, j in itertools.product([2, 3], repeat=3)] for dtype in float_dtypes for strides in [(1, 1), (1, 2), (2, 1)] for padding in ["VALID", "SAME"])) def testConv(self, lhs_shape, rhs_shape, dtype, strides, padding): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv(lhs, rhs, strides, padding) self._CompileAndCheck(fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_preferred_element_type={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), preferred_element_type.__name__), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "preferred_element_type": preferred_element_type} for lhs_shape, rhs_shape in [ ((b, i, 9, 10), (j, i, 4, 5)) for b, i, j in itertools.product([2, 3], repeat=3)] for dtype, preferred_element_type in preferred_type_combinations)) def testConvPreferredElement(self, lhs_shape, rhs_shape, dtype, preferred_element_type): if (not config.x64_enabled and (dtype == np.float64 or preferred_element_type == np.float64 or dtype == np.int64 or preferred_element_type == np.int64 or dtype == np.complex128 or preferred_element_type == np.complex128)): raise SkipTest("64-bit mode disabled") if jtu.device_under_test() == "gpu" and np.issubdtype(dtype, np.integer): # TODO(b/183565702): Support integer convolutions on CPU/GPU. raise SkipTest("Integer convolution not yet supported on GPU") if (jtu.device_under_test() == "tpu" and (dtype == np.complex128 or preferred_element_type == np.complex128)): raise SkipTest("np.complex128 is not yet supported on TPU") # x64 implementation is only accurate to ~float32 precision for this case. if dtype == np.complex64 and preferred_element_type == np.complex128: tol = 1e-5 else: tol = {np.float64: 1e-14} rng = jtu.rand_default(self.rng()) x = rng(lhs_shape, dtype) y = rng(rhs_shape, dtype) # We first compute the conv when both inputs are a lower-precision type and # preferred_element_type is a higher-precision type. We then compute results # where the inputs are first upcast to the higher-precision type and no # `preferred_element_type` is given. We expect the result to be extremely # similar given the semantics of `preferred_element_type`. result_with_preferred_type = lax.conv( x, y, (1, 1), "VALID", preferred_element_type=preferred_element_type) result_with_upcast_inputs = lax.conv( x.astype(preferred_element_type), y.astype(preferred_element_type), (1, 1), "VALID") self.assertArraysAllClose( result_with_preferred_type, result_with_upcast_inputs, rtol=tol, atol=tol) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding} for lhs_shape, rhs_shape in [ ((b, i, 9, 10), (j, i, 4, 5)) for b, i, j in itertools.product([2, 3], repeat=3)] for dtype in float_dtypes for strides in [(1, 1), (1, 2), (2, 1)] for padding in ["VALID", "SAME"])) def testConvAgainstNumpy(self, lhs_shape, rhs_shape, dtype, strides, padding): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] op = lambda lhs, rhs: lax.conv(lhs, rhs, strides, padding) numpy_op = lambda lhs, rhs: lax_reference.conv(lhs, rhs, strides, padding) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}" "_lhs_dilation={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, lhs_dilation, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "lhs_dilation": lhs_dilation, "rhs_dilation": rhs_dilation} for lhs_shape, rhs_shape in [ ((b, i, 9, 10), (j, i, 4, 5)) for b, i, j in itertools.product([1, 2, 3], repeat=3)] for dtype in float_dtypes for strides in [(1, 1), (1, 2), (2, 1)] for padding in [((0, 0), (0, 0)), ((1, 2), (2, 0))] for lhs_dilation, rhs_dilation in itertools.product( [(1, 1), (1, 2), (2, 2)], repeat=2))) def testConvWithGeneralPadding(self, lhs_shape, rhs_shape, dtype, strides, padding, lhs_dilation, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv_with_general_padding( lhs, rhs, strides, padding, lhs_dilation, rhs_dilation) self._CompileAndCheck(fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}" "_lhs_dilation={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, lhs_dilation, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "lhs_dilation": lhs_dilation, "rhs_dilation": rhs_dilation} for lhs_shape, rhs_shape in [ ((b, i, 9, 10), (j, i, 4, 5)) for b, i, j in itertools.product([1, 2, 3], repeat=3)] for dtype in [np.float32] for strides in [(1, 1), (1, 2), (2, 1)] for padding in [((0, 0), (0, 0)), ((1, 2), (2, 0))] for lhs_dilation, rhs_dilation in itertools.product( [(1, 1), (1, 2), (2, 2)], repeat=2))) def testConvWithGeneralPaddingAgainstNumpy( self, lhs_shape, rhs_shape, dtype, strides, padding, lhs_dilation, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv_with_general_padding( lhs, rhs, strides, padding, lhs_dilation, rhs_dilation, precision=lax.Precision.HIGHEST) def numpy_fun(lhs, rhs): return lax_reference.conv_with_general_padding( lhs, rhs, strides, padding, lhs_dilation, rhs_dilation) self._CheckAgainstNumpy(numpy_fun, fun, args_maker) @parameterized.named_parameters(jtu.named_cases_from_sampler(lambda s: ({ "testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}" "_lhs_dilation={}_rhs_dilation={}" "_dims={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, lhs_dilation, rhs_dilation, ",".join(dim_nums)), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "lhs_dilation": lhs_dilation, "rhs_dilation": rhs_dilation, "dimension_numbers": dim_nums, "feature_group_count": feature_group_count, "batch_group_count": batch_group_count, "perms": perms } for batch_group_count, feature_group_count in s([(1, 1), (2, 1), (1, 2)]) for lhs_shape, rhs_shape in s([ ((b * batch_group_count, i * feature_group_count, 9, w), (j * feature_group_count * batch_group_count, i, 4, 5)) for w in [0, 10] for b, i, j in itertools.product([2, 3], repeat=3)]) for dtype in s(all_dtypes) for strides in s([(1, 1), (2, 1)]) for padding in s([((1, 2), (2, 0)), ((10, 8), (7, 13))]) for lhs_dilation, rhs_dilation in s(itertools.product( [(1, 1), (1, 2), (1, 4)], repeat=2)) for dim_nums, perms in s([ (("NCHW", "OIHW", "NCHW"), ([0, 1, 2, 3], [0, 1, 2, 3])), (("NHWC", "HWIO", "NHWC"), ([0, 2, 3, 1], [2, 3, 1, 0])), (("NCHW", "HWIO", "NHWC"), ([0, 1, 2, 3], [2, 3, 1, 0])), ])))) def testConvGeneralDilated(self, lhs_shape, rhs_shape, dtype, strides, padding, lhs_dilation, rhs_dilation, feature_group_count, batch_group_count, dimension_numbers, perms): if np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.bool_): # TODO(b/183565702): Support integer convolutions on CPU/GPU. if jtu.device_under_test() == "gpu": raise SkipTest("Integer convolution not yet supported on GPU") rng = jtu.rand_small(self.rng()) lhs_perm, rhs_perm = perms # permute to compatible shapes def args_maker(): return [lax.transpose(rng(lhs_shape, dtype), lhs_perm), lax.transpose(rng(rhs_shape, dtype), rhs_perm)] def fun(lhs, rhs): return lax.conv_general_dilated( lhs, rhs, strides, padding, lhs_dilation, rhs_dilation, dimension_numbers, feature_group_count=feature_group_count, batch_group_count=batch_group_count) self._CompileAndCheck(fun, args_maker) def testConvGeneralDilatedPatchesOverlapping1D(self): lhs = np.array([[1]], np.float32).reshape((1, 1)) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=(), window_strides=(), padding='SAME' ) self.assertAllClose(lhs, patches) dn = ('NHC', 'OIH', 'NHC') lhs = np.array([1, 2, 3, 4, 5], np.float32).reshape((1, -1, 1)) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=(2,), window_strides=(2,), padding='VALID', dimension_numbers=dn ) self.assertAllClose( np.array([[1, 2], [3, 4]], np.float32).reshape((1, 2, 2)), patches) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=(3,), window_strides=(1,), padding='SAME', dimension_numbers=dn ) self.assertAllClose( np.array([[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 0]], np.float32).reshape((1, 5, 3)), patches) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=(3,), window_strides=(1,), padding='SAME', rhs_dilation=(2,), dimension_numbers=dn ) self.assertAllClose( np.array([[0, 1, 3], [0, 2, 4], [1, 3, 5], [2, 4, 0], [3, 5, 0]], np.float32).reshape((1, 5, 3)), patches) def testConvGeneralDilatedPatchesOverlapping2D(self): lhs = np.array([[1, 2, 3], [4, 5, 6]], np.float32).reshape((1, 2, 3, 1)) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=(2, 2), window_strides=(1, 1), padding='SAME', dimension_numbers=('NHWC', 'OIHW', 'NHWC') ) self.assertAllClose(np.array([[1, 2, 4, 5], [2, 3, 5, 6], [3, 0, 6, 0], [4, 5, 0, 0], [5, 6, 0, 0], [6, 0, 0, 0]], np.float32).reshape((1, 2, 3, 4)), patches) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_filter_shape={}_strides={}_padding={}" "_dims={}_precision={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(filter_shape, dtype), strides, padding, "None" if dim_nums is None else ",".join(dim_nums), precision ), "lhs_shape": lhs_shape, "filter_shape": filter_shape, "dtype": dtype, "strides": strides, "padding": padding, "dimension_numbers": dim_nums, "precision": precision } for dtype in all_dtypes for lhs_shape, filter_shape, strides, padding, dim_nums in [ ((2, 5), (), (), [], ("NC", "OI", "CN")), ((2, 3, 4), (2,), (2,), [(0, 2)], ("CNH", "OHI", "HNC")), ((3, 1, 4, 5), (1, 3), (1, 3), [(3, 1), (2, 2)], ("NCHW", "OIHW", "NCHW")), ((3, 2, 5, 6), (4, 3), (4, 3), [(5, 2), (2, 4)], None), ((1, 2, 3, 4), (1, 1), (1, 1), [(0, 0), (0, 0)], ("NCWH", "OHWI", "CNHW")), ((1, 2, 3, 4), (3, 2), (1, 1), [(0, 0), (0, 0)], ("CWHN", "HOWI", "NCHW")), ((2, 3, 4, 5, 6), (2, 1, 3), (2, 1, 3), [(1, 2), (5, 3), (3, 5)], ("NHWDC", "HDIWO", "DCWNH")) ] for precision in [None, lax.Precision.DEFAULT, lax.Precision.HIGH, lax.Precision.HIGHEST] )) def testConvGeneralDilatedPatchesNonOverlapping(self, lhs_shape, filter_shape, dtype, strides, padding, dimension_numbers, precision): if np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.bool_): # TODO(b/183565702): Support integer convolutions on CPU/GPU. if jtu.device_under_test() == "gpu": raise SkipTest("Integer convolution not yet supported on GPU") rng = jtu.rand_small(self.rng()) lhs = rng(lhs_shape, dtype) if dimension_numbers is None: lhs_spec, rhs_spec, out_spec = "NCHW", "OIHW", "NCHW" else: lhs_spec, rhs_spec, out_spec = dimension_numbers filter_spec = ''.join(c for c in rhs_spec if c not in ('I', 'O')) patches_spec = out_spec.replace('C', 'C' + filter_spec.lower()) full_padding = [] for c in lhs_spec: if c in ('N', 'C'): full_padding += [(0, 0)] else: full_padding += [padding[filter_spec.index(c)]] lhs_padded = np.pad(lhs, full_padding, 'constant') out = lax.transpose(lhs_padded, [lhs_spec.index(c) for c in out_spec]) patches = lax.conv_general_dilated_patches( lhs=lhs, filter_shape=filter_shape, window_strides=strides, padding=padding, dimension_numbers=dimension_numbers, precision=precision ) source = [] # Test that output spatial shape is factored into `#patches x patch_size`. for c in out_spec: out_c = out.shape[out_spec.index(c)] patch_c = patches.shape[out_spec.index(c)] if c == 'N': self.assertEqual(out_c, patch_c) elif c == 'C': self.assertEqual(out_c * np.prod(filter_shape), patch_c) else: self.assertEqual(out_c, patch_c * filter_shape[filter_spec.index(c)]) source += [patches_spec.index(c), patches_spec.index(c.lower())] # Test that stacking patches together gives the source image, padded. c = out_spec.index('C') patches = patches.reshape(patches.shape[:c] + (lhs_shape[lhs_spec.index('C')],) + filter_shape + patches.shape[c + 1:] ) patches = np.moveaxis(patches, source, range(len(source))) for i in range(len(filter_shape)): patches = patches.reshape(patches.shape[:i] + (-1,) + patches.shape[2 + i:]) patches = np.moveaxis( patches, range(len(filter_shape)), [out_spec.index(c) for c in out_spec if c not in ('N', 'C')]) self.assertAllClose(out, patches) # TODO(mattjj): test conv_general_dilated against numpy def testConv0DIsDot(self): rng = jtu.rand_default(self.rng()) def args_maker(): return [rng((10, 5), np.float32), rng((5, 7), np.float32)] jnp_fun = partial(lax.conv_general_dilated, window_strides=(), padding='VALID', dimension_numbers=('NC', 'IO', 'NC')) self._CompileAndCheck(jnp_fun, args_maker) self._CheckAgainstNumpy(np.dot, jnp_fun, args_maker, tol=.1) def testGradConv0D(self): # Reproduces a failure in neural_tangents not caught in our presubmit tests # See cl/367416742. lhs = np.ones((2, 5), dtype=np.float32) rhs = np.ones((5, 10), dtype=np.float32) def f_jax(lhs, rhs): return lax.conv_general_dilated( lhs, rhs, window_strides=(), padding=(), lhs_dilation=(), rhs_dilation=(), dimension_numbers=lax.ConvDimensionNumbers((0, 1), (1, 0), (0, 1)), batch_group_count=1, feature_group_count=1, precision=None, preferred_element_type=None) res, pullback = jax.vjp(f_jax, lhs, rhs) grad = pullback(np.ones_like(res)) self.assertAllClose((lhs * 10., rhs * 2.), grad) @staticmethod def _conv_transpose_via_grad(data, kernel, strides, padding, rhs_dilation=None, dimension_numbers=None): """Helper method: calculates conv transpose via grad for testing.""" assert len(data.shape) == len(kernel.shape) nspatial = len(data.shape) - 2 one = (1,) * nspatial rhs_dilation = rhs_dilation or one dn = lax.conv_dimension_numbers(data.shape, kernel.shape, dimension_numbers) in_shape = np.take(data.shape, dn.lhs_spec) in_sdims = in_shape[2:] k_shape = np.take(kernel.shape, dn.rhs_spec) k_sdims = k_shape[2:] e_k_sdims = [(k-1) * r + 1 for k, r in zip(k_sdims, rhs_dilation)] if padding == 'VALID': o_sdims = [in_sdims[i]*strides[i] + max(e_k_sdims[i]-strides[i],0) for i in range(nspatial)] elif padding == 'SAME': o_sdims = [in_sdims[i]*strides[i] for i in range(nspatial)] o_shape = [in_shape[0], k_shape[1]] + o_sdims out_spec_inv = [x[0] for x in sorted(enumerate(dn.out_spec), key=lambda x: x[1])] o_layout = np.take(np.array(o_shape), out_spec_inv) placeholder = np.ones(o_layout, data.dtype) conv = lambda x: lax.conv_general_dilated(x, kernel, strides, padding, one, rhs_dilation, dn) _, g = jax.vjp(conv, placeholder) return g(data)[0] @staticmethod def _transpose_conv_kernel(data, kernel, dimension_numbers): dn = lax.conv_dimension_numbers(data.shape, kernel.shape, dimension_numbers) spatial_axes = np.array(dn.rhs_spec)[2:] for axis in spatial_axes: kernel = np.flip(kernel, axis) kernel = np.swapaxes(kernel, dn.rhs_spec[0], dn.rhs_spec[1]) return kernel @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "rhs_dilation": rhs_dilation, "dspec": dspec} for lhs_shape, rhs_shape in [ ((b, 9, 10, i), (k, k, j, i)) # NB: i,j flipped in RHS for transpose for b, i, j, k in itertools.product([2,3],[2,3],[2,3],[3,4,5])] for dtype in float_dtypes for strides in [(1, 1), (1, 2), (2, 1), (2, 2), (3, 3)] for padding in ["VALID", "SAME"] for dspec in [('NHWC', 'HWIO', 'NHWC'),] for rhs_dilation in [None, (2, 2)])) @jtu.skip_on_flag("jax_skip_slow_tests", True) def testConvTranspose2DT(self, lhs_shape, rhs_shape, dtype, strides, padding, dspec, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] # NB: this test calculates conv_transpose performing identically to the # lhs-grad of conv. def fun(lhs, rhs): return lax.conv_transpose(lhs, rhs, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec, transpose_kernel=True) def fun_via_grad(lhs, rhs): return self._conv_transpose_via_grad(lhs, rhs, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec) # NB: below just checks for agreement, we're not calling numpy. self._CheckAgainstNumpy(fun_via_grad, fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "rhs_dilation": rhs_dilation, "dspec": dspec} for lhs_shape, rhs_shape in [ ((b, 9, 10, i), (k, k, i, j)) for b, i, j, k in itertools.product([2,3],[2,3],[2,3],[3,4,5])] for dtype in float_dtypes for strides in [(1, 1), (1, 2), (2, 1), (2, 2), (3, 3)] for padding in ["VALID", "SAME"] for dspec in [('NHWC', 'HWIO', 'NHWC'),] for rhs_dilation in [None, (2, 2)])) @jtu.skip_on_flag("jax_skip_slow_tests", True) def testConvTranspose2D(self, lhs_shape, rhs_shape, dtype, strides, padding, dspec, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv_transpose(lhs, rhs, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec, transpose_kernel=False) def fun_via_grad(lhs, rhs): rhs_t = self._transpose_conv_kernel(lhs, rhs, dimension_numbers=dspec) return self._conv_transpose_via_grad(lhs, rhs_t, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec) # NB: below just checks for agreement, we're not calling numpy. self._CheckAgainstNumpy(fun_via_grad, fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "rhs_dilation": rhs_dilation, "dspec": dspec} for lhs_shape, rhs_shape in [ ((b, 10, i), (k, i, j)) for b, i, j, k in itertools.product([2,3],[2,3],[2,3],[3,4,5])] for dtype in float_dtypes for strides in [(1,), (2,), (3,)] for padding in ["VALID", "SAME"] for dspec in [('NHC', 'HIO', 'NHC'),] for rhs_dilation in [None, (2,)])) def testConvTranspose1D(self, lhs_shape, rhs_shape, dtype, strides, padding, dspec, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv_transpose(lhs, rhs, strides, padding, dimension_numbers=dspec, rhs_dilation=rhs_dilation, transpose_kernel=False) def fun_via_grad(lhs, rhs): rhs_t = self._transpose_conv_kernel(lhs, rhs, dimension_numbers=dspec) return self._conv_transpose_via_grad(lhs, rhs_t, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec) # NB: below just checks for agreement, we're not calling numpy. self._CheckAgainstNumpy(fun_via_grad, fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_strides={}_padding={}_rhs_dilation={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), strides, padding, rhs_dilation), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "strides": strides, "padding": padding, "rhs_dilation": rhs_dilation, "dspec": dspec} for lhs_shape, rhs_shape in [ ((b, i), (i, j)) for b, i, j in itertools.product([2,3],[2,3],[2,3])] for dtype in float_dtypes for strides in [()] for padding in ["VALID", "SAME"] for dspec in [('NC', 'IO', 'NC'),] for rhs_dilation in [None, ()])) def testConvTranspose0D(self, lhs_shape, rhs_shape, dtype, strides, padding, dspec, rhs_dilation): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.conv_transpose(lhs, rhs, strides, padding, dimension_numbers=dspec, rhs_dilation=rhs_dilation, transpose_kernel=False) def fun_via_grad(lhs, rhs): rhs_t = self._transpose_conv_kernel(lhs, rhs, dimension_numbers=dspec) return self._conv_transpose_via_grad(lhs, rhs_t, strides, padding, rhs_dilation=rhs_dilation, dimension_numbers=dspec) # NB: below just checks for agreement, we're not calling numpy. self._CheckAgainstNumpy(fun_via_grad, fun, args_maker) def testConvTransposePaddingList(self): # Regression test for https://github.com/google/jax/discussions/8695 a = jnp.ones((28,28)) b = jnp.ones((3,3)) c = lax.conv_general_dilated(a[None, None], b[None, None], (1,1), [(0,0),(0,0)], (1,1)) self.assertArraysEqual(c, 9 * jnp.ones((1, 1, 26, 26))) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_precision={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), precision), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "precision": precision} for lhs_shape in [(3,), (4, 3)] for rhs_shape in [(3,), (3, 6)] for dtype in all_dtypes for precision in [None, lax.Precision.DEFAULT, lax.Precision.HIGH, lax.Precision.HIGHEST, (lax.Precision.DEFAULT, lax.Precision.HIGHEST)])) def testDot(self, lhs_shape, rhs_shape, dtype, precision): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] self._CompileAndCheck(partial(lax.dot, precision=precision), args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_preferred_element_type={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), jtu.format_shape_dtype_string((), preferred_element_type) ), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "preferred_element_type": preferred_element_type } for lhs_shape in [(3,), (4, 3)] for rhs_shape in [(3,), (3, 6)] for dtype, preferred_element_type in preferred_type_combinations)) def testDotPreferredElement(self, lhs_shape, rhs_shape, dtype, preferred_element_type): if (not config.x64_enabled and (dtype == np.float64 or preferred_element_type == np.float64 or dtype == np.int64 or preferred_element_type == np.int64)): raise SkipTest("64-bit mode disabled") if (jtu.device_under_test() == "tpu" and (dtype == np.complex128 or preferred_element_type == np.complex128)): raise SkipTest("np.complex128 is not yet supported on TPU") if jtu.device_under_test() == "gpu": # TODO(b/189287598) raise SkipTest("dot_general with preferred_element_type returns NaN non-deterministically on GPU") rng = jtu.rand_default(self.rng()) x = rng(lhs_shape, dtype) y = rng(rhs_shape, dtype) # We first compute the dot when both inputs are a lower-precision type and # preferred_element_type is a higher-precision type. We then compute results # where the inputs are first upcast to the higher-precision type and no # `preferred_element_type` is given. We expect the result to be extremely # similar given the semantics of `preferred_element_type`. result_with_preferred_type = lax.dot(x, y, preferred_element_type=preferred_element_type) result_with_upcast_inputs = lax.dot( x.astype(preferred_element_type), y.astype(preferred_element_type)) self.assertArraysAllClose(result_with_preferred_type, result_with_upcast_inputs) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}".format( jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype)), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype} for lhs_shape in [(3,), (4, 3)] for rhs_shape in [(3,), (3, 6)] for dtype in all_dtypes)) def testDotAgainstNumpy(self, lhs_shape, rhs_shape, dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] tol = { np.float16: 1e-2, np.float64: max(jtu.default_tolerance()[np.dtype(np.float64)], 1e-14), np.complex128: max(jtu.default_tolerance()[np.dtype(np.complex128)], 1e-14) } lax_op = partial(lax.dot, precision=lax.Precision.HIGHEST) self._CheckAgainstNumpy(lax_reference.dot, lax_op, args_maker, tol=tol) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_lhs_contracting={}_rhs_contracting={}" .format(jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), lhs_contracting, rhs_contracting), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "lhs_contracting": lhs_contracting, "rhs_contracting": rhs_contracting} for lhs_shape, rhs_shape, lhs_contracting, rhs_contracting in [ [(5,), (5,), [0], [0]], [(5, 7), (5,), [0], [0]], [(7, 5), (5,), [1], [0]], [(3, 5), (2, 5), [1], [1]], [(5, 3), (5, 2), [0], [0]], [(5, 3, 2), (5, 2, 4), [0], [0]], [(5, 3, 2), (5, 2, 4), [0,2], [0,1]], [(5, 3, 2), (3, 5, 2, 4), [0,2], [1,2]], [(1, 2, 2, 3), (1, 2, 3, 1), [1], [1]], [(3, 2), (2, 4), [1], [0]], ] for dtype in all_dtypes)) def testDotGeneralContractOnly(self, lhs_shape, rhs_shape, dtype, lhs_contracting, rhs_contracting): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] dimension_numbers = ((lhs_contracting, rhs_contracting), ([], [])) def fun(lhs, rhs): return lax.dot_general(lhs, rhs, dimension_numbers) self._CompileAndCheck(fun, args_maker, check_dtypes=False) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_dimension_numbers={}" .format(jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), dimension_numbers), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "dimension_numbers": dimension_numbers} for lhs_shape, rhs_shape, dimension_numbers in [ ((3, 3, 2), (3, 2, 4), (([2], [1]), ([0], [0]))), ((3, 3, 2), (2, 3, 4), (([2], [0]), ([0], [1]))), ((3, 4, 2, 4), (3, 4, 3, 2), (([2], [3]), ([0, 1], [0, 1]))), ] for dtype in all_dtypes)) def testDotGeneralContractAndBatch(self, lhs_shape, rhs_shape, dtype, dimension_numbers): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] def fun(lhs, rhs): return lax.dot_general(lhs, rhs, dimension_numbers) self._CompileAndCheck(fun, args_maker, check_dtypes=False) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_lhs_shape={}_rhs_shape={}_dimension_numbers={}" .format(jtu.format_shape_dtype_string(lhs_shape, dtype), jtu.format_shape_dtype_string(rhs_shape, dtype), dimension_numbers), "lhs_shape": lhs_shape, "rhs_shape": rhs_shape, "dtype": dtype, "dimension_numbers": dimension_numbers} for lhs_shape, rhs_shape, dimension_numbers in [ ((3, 3, 2), (3, 2, 4), (([2], [1]), ([0], [0]))), ((3, 3, 2), (2, 3, 4), (([2], [0]), ([0], [1]))), ((3, 4, 2, 4), (3, 4, 3, 2), (([2], [3]), ([0, 1], [0, 1]))), ] for dtype in all_dtypes)) def testDotGeneralAgainstNumpy(self, lhs_shape, rhs_shape, dtype, dimension_numbers): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)] op = lambda x, y: lax.dot_general(x, y, dimension_numbers) numpy_op = lambda x, y: lax_reference.dot_general(x, y, dimension_numbers) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_dtype={}_broadcast_sizes={}".format( shape, np.dtype(dtype).name, broadcast_sizes), "shape": shape, "dtype": dtype, "broadcast_sizes": broadcast_sizes} for shape in [(), (2, 3)] for dtype in default_dtypes for broadcast_sizes in [(), (2,), (1, 2)])) def testBroadcast(self, shape, dtype, broadcast_sizes): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.broadcast(x, broadcast_sizes) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_broadcast_sizes={}".format( jtu.format_shape_dtype_string(shape, dtype), broadcast_sizes), "shape": shape, "dtype": dtype, "broadcast_sizes": broadcast_sizes} for shape in [(), (2, 3)] for dtype in default_dtypes for broadcast_sizes in [(), (2,), (1, 2)])) def testBroadcastAgainstNumpy(self, shape, dtype, broadcast_sizes): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.broadcast(x, broadcast_sizes) numpy_op = lambda x: lax_reference.broadcast(x, broadcast_sizes) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_outshape={}_bcdims={}".format( jtu.format_shape_dtype_string(inshape, dtype), outshape, broadcast_dimensions), "inshape": inshape, "dtype": dtype, "outshape": outshape, "dimensions": broadcast_dimensions} for inshape, outshape, broadcast_dimensions in [ ([2], [2, 2], [0]), ([2], [2, 2], [1]), ([2], [2, 3], [0]), ([], [2, 3], []), ([1], [2, 3], [1]), ] for dtype in default_dtypes)) def testBroadcastInDim(self, inshape, dtype, outshape, dimensions): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(inshape, dtype)] op = lambda x: lax.broadcast_in_dim(x, outshape, dimensions) self._CompileAndCheck(op, args_maker) def testBroadcastInDimOperandShapeTranspose(self): # Regression test for https://github.com/google/jax/issues/5276 def f(x): return lax.broadcast_in_dim(x, (2, 3, 4), broadcast_dimensions=(0, 1, 2)).sum() def g(x): return lax.broadcast_in_dim(x.reshape((3,)), (2, 3, 4), broadcast_dimensions=(1,)).sum() x = np.ones((1, 3, 1)) self.assertArraysEqual(jax.grad(f)(x), jax.grad(g)(x)) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_outshape={}_bcdims={}".format( jtu.format_shape_dtype_string(inshape, np.float32), outshape, broadcast_dimensions), "inshape": inshape, "outshape": outshape, "broadcast_dimensions": broadcast_dimensions, "err_msg": err_msg} for inshape, outshape, broadcast_dimensions, err_msg in [ ([2], [2, 2], [0, 1], ('broadcast_dimensions must have length equal to ' 'operand ndim')), ([2, 2], [2], [0, 1], ('target broadcast shape must have equal or higher rank ' 'to the operand shape')), ([2], [2, 3], [2], ('broadcast_in_dim broadcast_dimensions must be a subset of output ' 'dimensions')), ([2], [3], [0], ('operand dimension sizes must either be 1, or be ' 'equal to their corresponding dimensions in the target broadcast shape')), ([2, 2], [2, 2], [1, 0], ('broadcast_dimensions must be strictly increasing')), ])) def testBroadcastInDimShapeCheck(self, inshape, outshape, broadcast_dimensions, err_msg): rng = jtu.rand_default(self.rng()) x = rng(inshape, np.float32) with self.assertRaisesRegex(TypeError, err_msg): lax.broadcast_in_dim(x, shape=outshape, broadcast_dimensions=broadcast_dimensions) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_outshape={}_bcdims={}".format( jtu.format_shape_dtype_string(inshape, dtype), outshape, broadcast_dimensions), "inshape": inshape, "dtype": dtype, "outshape": outshape, "dimensions": broadcast_dimensions} for inshape, outshape, broadcast_dimensions in [ ([2], [2, 2], [0]), ([2], [2, 2], [1]), ([2], [2, 3], [0]), ([], [2, 3], []), ([1], [2, 3], [1]), ] for dtype in default_dtypes)) def testBroadcastInDimAgainstNumpy(self, inshape, dtype, outshape, dimensions): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(inshape, dtype)] op = lambda x: lax.broadcast_in_dim(x, outshape, dimensions) numpy_op = lambda x: lax_reference.broadcast_in_dim(x, outshape, dimensions) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_dimensions={}".format( jtu.format_shape_dtype_string(inshape, np.float32), dimensions), "inshape": inshape, "dimensions": dimensions, "error_type": error_type, "err_msg": err_msg} for inshape, dimensions, error_type, err_msg in [ ((1, 2, 3), (0, 0), ValueError, 'dimensions are not unique'), ((1, 2, 3), (3,), ValueError, 'axis 3 is out of bounds'), ((1, 2, 3), (-4,), ValueError, 'axis -4 is out of bounds'), ((1, 2, 3), (1,), ValueError, 'cannot select an axis to squeeze out'), ((1, 2, 3), (None,), TypeError, 'cannot be interpreted as an integer'), ])) def testSqueezeShapeCheck(self, inshape, dimensions, error_type, err_msg): rng = jtu.rand_default(self.rng()) x = rng(inshape, np.float32) with self.assertRaisesRegex(error_type, err_msg): lax.squeeze(x, dimensions=dimensions) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_dimensions={}".format( jtu.format_shape_dtype_string(arg_shape, np.float32), dimensions), "arg_shape": arg_shape, "dimensions": dimensions} for arg_shape, dimensions in [ [(1,), (0,)], [(1,), (-1,)], [(2, 1, 4), (1,)], [(2, 1, 3, 1), (1,)], [(2, 1, 3, 1), (1, 3)], [(2, 1, 3, 1), (3,)], ])) def testSqueeze(self, arg_shape, dimensions): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(arg_shape, np.float32)] op = lambda x: lax.squeeze(x, dimensions) numpy_op = lambda x: lax_reference.squeeze(x, dimensions) self._CompileAndCheck(op, args_maker) self._CheckAgainstNumpy(numpy_op, op, args_maker) check_grads(op, args_maker(), 2, ["fwd", "rev"], eps=1.) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_outshape={}".format( jtu.format_shape_dtype_string(arg_shape, dtype), jtu.format_shape_dtype_string(out_shape, dtype)), "arg_shape": arg_shape, "out_shape": out_shape, "dtype": dtype} for dtype in default_dtypes for arg_shape, out_shape in [ [(3, 4), (12,)], [(2, 1, 4), (8,)], [(2, 2, 4), (2, 8)] ])) def testReshape(self, arg_shape, out_shape, dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(arg_shape, dtype)] op = lambda x: lax.reshape(x, out_shape) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_outshape={}".format( jtu.format_shape_dtype_string(arg_shape, dtype), jtu.format_shape_dtype_string(out_shape, dtype)), "arg_shape": arg_shape, "out_shape": out_shape, "dtype": dtype} for dtype in default_dtypes for arg_shape, out_shape in [ [(3, 4), (12,)], [(2, 1, 4), (8,)], [(2, 2, 4), (2, 8)] ])) def testReshapeAgainstNumpy(self, arg_shape, out_shape, dtype): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(arg_shape, dtype)] op = lambda x: lax.reshape(x, out_shape) numpy_op = lambda x: lax_reference.reshape(x, out_shape) self._CheckAgainstNumpy(numpy_op, op, args_maker) def testRoundRoundingMethods(self): x = np.array([-2.5, -1.5, -0.5, 0.5, 1.5, 2.5], dtype=np.float32) self.assertAllClose(lax.round(x, lax.RoundingMethod.AWAY_FROM_ZERO), np.array([-3, -2, -1, 1, 2, 3], dtype=np.float32)) self.assertAllClose(lax.round(x, lax.RoundingMethod.TO_NEAREST_EVEN), np.array([-2, -2, 0, 0, 2, 2], dtype=np.float32)) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_pads={}" .format(jtu.format_shape_dtype_string(shape, dtype), pads), "shape": shape, "dtype": dtype, "pads": pads} for dtype in default_dtypes for shape, pads in [ ((0, 2), [(1, 2, 1), (0, 1, 0)]), ((2, 3), [(1, 2, 1), (0, 1, 0)]), ((2,), [(1, 2, 0)]), ((1, 2), [(1, 2, 0), (3, 4, 0)]), ((1, 2), [(0, 0, 0), (0, 0, 0)]), ((2,), [(1, 2, 3),]), ((3, 2), [(1, 2, 1), (3, 4, 2)]), ((2,), [(-1, 2, 0),]), ((4, 2), [(-1, -2, 0), (1, 2, 0)]), ((4, 2), [(-1, 2, 0), (1, 2, 2)]), ((5,), [(-1, -2, 2),]), ((4, 2), [(-1, -2, 1), (1, 2, 2)]) ])) def testPad(self, shape, dtype, pads): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(shape, dtype)] fun = lambda operand: lax.pad(operand, np.array(0, dtype), pads) self._CompileAndCheck(fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_inshape={}_pads={}" .format(jtu.format_shape_dtype_string(shape, dtype), pads), "shape": shape, "dtype": dtype, "pads": pads} for shape in [(2, 3)] for dtype in default_dtypes for pads in [ [(0, 0, 0), (0, 0, 0)], # no padding [(1, 1, 0), (2, 2, 0)], # only positive edge padding [(1, 2, 1), (0, 1, 0)], # edge padding and interior padding [(0, 0, 0), (-1, -1, 0)], # negative padding [(0, 0, 0), (-2, -2, 4)], # add big dilation then remove from edges [(0, 0, 0), (-2, -3, 1)], # remove everything in one dimension ])) def testPadAgainstNumpy(self, shape, dtype, pads): rng = jtu.rand_small(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.pad(x, np.array(0, dtype), pads) numpy_op = lambda x: lax_reference.pad(x, np.array(0, dtype), pads) self._CheckAgainstNumpy(numpy_op, op, args_maker) def testPadErrors(self): with self.assertRaisesRegex(ValueError, "padding_config"): lax.pad(np.zeros(2), 0., [(0, 1, 0), (0, 1, 0)]) with self.assertRaisesRegex(ValueError, "interior padding in padding_config must be nonnegative"): lax.pad(np.zeros(2), 0., [(0, 1, -1)]) with self.assertRaisesRegex(ValueError, "Dimension size after padding is not at least 0"): lax.pad(np.zeros(2), 0., [(-3, 0, 0)]) with self.assertRaisesRegex(ValueError, "Dimension size after padding is not at least 0"): lax.pad(np.zeros(2), 0., [(-4, 0, 1)]) def testReverse(self): rev = jax.jit(lambda operand: lax.rev(operand, dimensions)) dimensions = [] self.assertAllClose(np.array([0, 1, 2, 3]), rev(np.array([0, 1, 2, 3])), check_dtypes=False) dimensions = [0] self.assertAllClose(np.array([3, 2, 1]), rev(np.array([1, 2, 3])), check_dtypes=False) dimensions = [0, 1] self.assertAllClose(np.array([[6, 5, 4], [3, 2, 1]]), rev(np.array([[1, 2, 3], [4, 5, 6]])), check_dtypes=False) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_predshape={}_argshapes={}".format( jtu.format_shape_dtype_string(pred_shape, np.bool_), jtu.format_shape_dtype_string(arg_shape, arg_dtype)), "pred_shape": pred_shape, "arg_shape": arg_shape, "arg_dtype": arg_dtype} for arg_shape in [(), (3,), (2, 3)] for pred_shape in ([(), arg_shape] if arg_shape else [()]) for arg_dtype in default_dtypes)) def testSelect(self, pred_shape, arg_shape, arg_dtype): rng = jtu.rand_default(self.rng()) def args_maker(): return [rng(pred_shape, np.bool_), rng(arg_shape, arg_dtype), rng(arg_shape, arg_dtype)] return self._CompileAndCheck(lax.select, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_predshape={}_argshapes={}".format( jtu.format_shape_dtype_string(pred_shape, np.bool_), jtu.format_shape_dtype_string(arg_shape, arg_dtype)), "pred_shape": pred_shape, "arg_shape": arg_shape, "arg_dtype": arg_dtype} for arg_shape in [(), (3,), (2, 3)] for pred_shape in ([(), arg_shape] if arg_shape else [()]) for arg_dtype in default_dtypes)) def testSelectAgainstNumpy(self, pred_shape, arg_shape, arg_dtype): rng = jtu.rand_default(self.rng()) def args_maker(): return [rng(pred_shape, np.bool_), rng(arg_shape, arg_dtype), rng(arg_shape, arg_dtype)] return self._CheckAgainstNumpy(lax_reference.select, lax.select, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_limit_indices={}_strides={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, limit_indices, strides), "shape": shape, "dtype": dtype, "starts": indices, "limits": limit_indices, "strides": strides} for shape, indices, limit_indices, strides in [ [(3,), (1,), (2,), None], [(7,), (4,), (7,), None], [(5,), (1,), (5,), (2,)], [(8,), (1,), (6,), (2,)], [(5, 3), (1, 1), (3, 2), None], [(5, 3), (1, 1), (3, 1), None], [(7, 5, 3), (4, 0, 1), (7, 1, 3), None], [(5, 3), (1, 1), (2, 1), (1, 1)], [(5, 3), (1, 1), (5, 3), (2, 1)], ] for dtype in default_dtypes)) def testSlice(self, shape, dtype, starts, limits, strides): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.slice(x, starts, limits, strides) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_limit_indices={}_strides={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, limit_indices, strides), "shape": shape, "dtype": dtype, "starts": indices, "limits": limit_indices, "strides": strides} for shape, indices, limit_indices, strides in [ [(3,), (1,), (2,), None], [(7,), (4,), (7,), None], [(5,), (1,), (5,), (2,)], [(8,), (1,), (6,), (2,)], [(5, 3), (1, 1), (3, 2), None], [(5, 3), (1, 1), (3, 1), None], [(7, 5, 3), (4, 0, 1), (7, 1, 3), None], [(5, 3), (1, 1), (2, 1), (1, 1)], [(5, 3), (1, 1), (5, 3), (2, 1)], ] for dtype in default_dtypes)) def testSliceAgainstNumpy(self, shape, dtype, starts, limits, strides): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.slice(x, starts, limits, strides) numpy_op = lambda x: lax_reference.slice(x, starts, limits, strides) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_size_indices={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, size_indices), "shape": shape, "dtype": dtype, "indices": indices, "size_indices": size_indices} for shape, indices, size_indices in [ [(3,), np.array((1,)), (1,)], [(5, 3), (1, 1), (3, 1)], [(5, 3), np.array((1, 1)), (3, 1)], [(7, 5, 3), np.array((4, 1, 0)), (2, 0, 1)], ] for dtype in default_dtypes)) def testDynamicSlice(self, shape, dtype, indices, size_indices): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype), np.array(indices)] op = lambda x, starts: lax.dynamic_slice(x, starts, size_indices) self._CompileAndCheck(op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_size_indices={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, size_indices), "shape": shape, "dtype": dtype, "indices": indices, "size_indices": size_indices} for shape, indices, size_indices in [ [(3,), (1,), (1,)], [(5, 3), (1, 1), (3, 1)], [(7, 5, 3), (4, 1, 0), (2, 0, 1)], ] for dtype in default_dtypes)) def testDynamicSliceAgainstNumpy(self, shape, dtype, indices, size_indices): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype), np.array(indices)] op = lambda x, s: lax.dynamic_slice(x, s, size_indices) numpy_op = lambda x, s: lax_reference.dynamic_slice(x, s, size_indices) self._CheckAgainstNumpy(numpy_op, op, args_maker) def testDynamicSliceInDim(self): # Regression test for mixed type problem in dynamic_slice_in_dim. rng = jtu.rand_default(self.rng()) x = rng((6, 7), np.int32) np.testing.assert_equal(lax.dynamic_slice_in_dim(x, 2, 3), x[2:5]) def testDynamicSliceArraySliceSizes(self): rng = jtu.rand_default(self.rng()) x = rng((6, 7), np.int32) np.testing.assert_equal(lax.dynamic_slice(x, [2, 3], jnp.array([2, 2])), x[2:4, 3:5]) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_update_shape={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, update_shape), "shape": shape, "dtype": dtype, "indices": indices, "update_shape": update_shape} for shape, indices, update_shape in [ [(3,), (1,), (1,)], [(5, 3), (1, 1), (3, 1)], [(7, 5, 3), (4, 1, 0), (2, 0, 1)], ] for dtype in default_dtypes)) def testDynamicUpdateSlice(self, shape, dtype, indices, update_shape): rng = jtu.rand_default(self.rng()) def args_maker(): return [rng(shape, dtype), rng(update_shape, dtype), np.array(indices)] self._CompileAndCheck(lax.dynamic_update_slice, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_indices={}_update_shape={}".format( jtu.format_shape_dtype_string(shape, dtype), indices, update_shape), "shape": shape, "dtype": dtype, "indices": indices, "update_shape": update_shape} for shape, indices, update_shape in [ [(3,), (1,), (1,)], [(5, 3), (1, 1), (3, 1)], [(7, 5, 3), (4, 1, 0), (2, 0, 1)], ] for dtype in default_dtypes)) def testDynamicUpdateSliceAgainstNumpy(self, shape, dtype, indices, update_shape): rng = jtu.rand_default(self.rng()) def args_maker(): return [rng(shape, dtype), rng(update_shape, dtype), np.array(indices)] self._CheckAgainstNumpy(lax_reference.dynamic_update_slice, lax.dynamic_update_slice, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_perm={}".format( jtu.format_shape_dtype_string(shape, dtype), perm), "shape": shape, "dtype": dtype, "perm": perm} for shape, perm in [ [(3, 4), (1, 0)], [(3, 4), (0, 1)], [(3, 4, 5), (2, 1, 0)], [(3, 4, 5), (1, 0, 2)], ] for dtype in default_dtypes)) def testTranspose(self, shape, dtype, perm): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.transpose(x, perm) self._CompileAndCheck(op, args_maker) def testTransposeWithArrayPermutation(self): x = lax.transpose(np.ones((2, 3)), jnp.array([1, 0])) self.assertEqual((3, 2), x.shape) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_shape={}_perm={}".format( jtu.format_shape_dtype_string(shape, dtype), perm), "shape": shape, "dtype": dtype, "perm": perm} for shape, perm in [ [(3, 4), (1, 0)], [(3, 4), (0, 1)], [(3, 4, 5), (2, 1, 0)], [(3, 4, 5), (1, 0, 2)], ] for dtype in default_dtypes)) def testTransposeAgainstNumpy(self, shape, dtype, perm): rng = jtu.rand_default(self.rng()) args_maker = lambda: [rng(shape, dtype)] op = lambda x: lax.transpose(x, perm) numpy_op = lambda x: lax_reference.transpose(x, perm) self._CheckAgainstNumpy(numpy_op, op, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_op={}_inshape={}_reducedims={}_initval={}" .format(op.__name__, jtu.format_shape_dtype_string(shape, dtype), dims, init_val), "op": op, "init_val": init_val, "shape": shape, "dtype": dtype, "dims": dims} for init_val, op, types in [ (0, lax.add, default_dtypes), (1, lax.mul, default_dtypes), (0, lax.max, all_dtypes), # non-monoidal (-np.inf, lax.max, float_dtypes), (dtypes.iinfo(np.int32).min, lax.max, [np.int32]), (dtypes.iinfo(np.int64).min, lax.max, [np.int64]), (np.inf, lax.min, float_dtypes), (dtypes.iinfo(np.int32).max, lax.min, [np.int32]), (dtypes.iinfo(np.int64).max, lax.min, [np.int64]), (dtypes.iinfo(np.uint32).max, lax.min, [np.uint32]), (dtypes.iinfo(np.uint64).max, lax.min, [np.uint64]), ] for dtype in types for shape, dims in [ [(3, 4, 5), (0,)], [(3, 4, 5), (1, 2)], [(3, 4, 5), (0, 2)], [(3, 4, 5), (0, 1, 2)] ])) def testReduce(self, op, init_val, shape, dtype, dims): rng_factory = (jtu.rand_default if dtypes.issubdtype(dtype, np.integer) else jtu.rand_small) rng = rng_factory(self.rng()) init_val = np.asarray(init_val, dtype=dtype) fun = lambda operand, init_val: lax.reduce(operand, init_val, op, dims) args_maker = lambda: [rng(shape, dtype), init_val] self._CompileAndCheck(fun, args_maker) # we separately test the version that uses a concrete init_val because it # can hit different code paths fun = lambda operand: lax.reduce(operand, init_val, op, dims) args_maker = lambda: [rng(shape, dtype)] self._CompileAndCheck(fun, args_maker) @parameterized.named_parameters(jtu.cases_from_list( {"testcase_name": "_op={}.{}_arr_weak_type={}_init_weak_type={}" .format(op_namespace.__name__, op, arr_weak_type, init_weak_type), "op": op, "op_namespace": op_namespace, "arr_weak_type": arr_weak_type, "init_weak_type": init_weak_type} for op in ["add", "mul"] for op_namespace in [lax, operator] for arr_weak_type in [True, False] for init_weak_type in [True, False])) def testReduceWeakType(self, op_namespace, op, arr_weak_type, init_weak_type): op = getattr(op_namespace, op) arr = lax._convert_element_type(

np.arange(10)

numpy.arange

import controlSBML.constants as cn import controlSBML as ctl from controlSBML.option_management.option_manager import OptionManager from controlSBML.option_management.options import Options from docstring_expander.expander import Expander import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import urllib.request REPO_URL = "https://github.com/ModelEngineering/controlSBML/raw/main" MODULE_URLPAT = REPO_URL + "/controlSBML/%s.py" # Pattern for module URL MODEL_URLPAT = REPO_URL + "/models/%s" MODEL_823_FILE = "biomodels_823.ant" LOCAL_FILE = "local.txt" def calculateMatrixDistance(mat1, mat2): """ Calculates the distance between two matrices with the same shape. Parameters ---------- mat1 - np.array mat2 - np.array Returns ------- float """ if

np.shape(mat1)

numpy.shape

"""Write a python module (e.g. function) that returns movies randomly""" import numpy as np from sklearn.decomposition import NMF import pandas as pd import random import pickle MOVIES = [x[:-7] for x in (pd.read_csv('movies.csv'))['title'].values.tolist()] #MOVIES = ['Shawshank Redemption', # 'Wizard of Oz', # 'Pulp Fiction', # 'Kill Bill', # 'Rings, The Lord of (2002)', # 'RegEx: The Movie', # 'FuzzyWuzzy Bubbly Buddy', # 'Docker: Unleashed', # 'Docker: FML', # 'Flask Fun', # 'Django Girls Unchained'] # Add userinput as parameter to this FUNCTION and give the funcion a user user_input # transform user input so that it is a list of 3 with length equal to number of columns # replace the columns that represent the user id with the values the user has given # give that resulting list to line 40 (P = model.transform(user_input2)) # nR is going to be the new recommendation list # find top values in nR (np.argsort or np.argmax) # find the index (or movieId) of those columns # find the name for those movieIds (iloc) or pandas.columns def random_recommend(user_input_movies, user_input_ratings): ratings = pd.read_csv('ratings.csv') movies = pd.read_csv('movies.csv') moviesIdlist=movies['movieId'].tolist() MOVIES = [x[:-7] for x in (pd.read_csv('movies.csv'))['title'].values.tolist()] MOVIEID = [x for x in (pd.read_csv('movies.csv'))['movieId'].values.tolist()] movies['title'] = MOVIES ratings2=ratings.set_index(['userId']) ratings3 = ratings.drop(['timestamp'], axis=1) newmoviesratings = pd.merge(ratings3, movies, on='movieId') newmoviesratings2=newmoviesratings[['userId','title','rating']] # newf = newmoviesratings2.set_index('userId') newf = ratings3.pivot(index='userId', columns='movieId') actualmovieIdslist=ratings3['movieId'].tolist() newmoviesIDlist = [x for x in moviesIdlist if x in actualmovieIdslist] newf2=newf.fillna(3.000000) R=newf2.values model = pickle.load(open('finalized_model.sav', 'rb')) Q = model.components_ P = model.transform(R) nR =

np.dot(P, Q)

numpy.dot

# -*- coding: utf-8 -*- """ Created on 2020/8/12 @project: SPAIC @filename: Monitor @author: <NAME> @contact: <EMAIL> @description: 定义神经集群放电以及神经元状态量、连接状态量的仿真记录模块 """ from ..Network.Assembly import BaseModule, Assembly from ..Network.Connection import Connection from ..Backend.Backend import Backend import numpy as np import torch class Monitor(BaseModule): def __init__(self, target, var_name, index='full', dt=None, get_grad=False, nbatch=True): super().__init__() if isinstance(target, Assembly): self.target = target self.target_type = 'Assembly' elif isinstance(target, Connection): self.target = target self.target_type = 'Connection' elif target == None: self.target = None self.target_type = None else: raise ValueError("The target does not belong to types that can be watched (Assembly, Connection).") self.var_name = '{'+var_name+'}' self.index = index self.var_container = None self.get_grad = get_grad self.nbatch = nbatch self._nbatch_records = [] # all time window's record self._nbatch_times = [] self._records = [] # single time window's record self._times = [] self.dt = dt self.is_recording = True self.new_record = True def check_var_name(self, var_name): ''' Check if variable is in the traget model, and add the target id label to the variable name. Parameters ---------- var_name : original variable name Returns : modified variable name ------- ''' tar_var_name = None if var_name[1:-1] in self.backend._variables.keys(): tar_var_name = var_name[1:-1] else: for tar_name in self.target.get_var_names(): # 没有中间变量 if var_name in tar_name: tar_var_name = tar_name break if tar_var_name is not None: return tar_var_name else: raise ValueError(" Variable %s is not in the target model"%var_name) def get_str(self, level): pass def monitor_on(self): self.is_recording = True def monitor_off(self): self.is_recording = False def clear(self): NotImplementedError() def build(self, backend: Backend): NotImplementedError() def init_record(self): NotImplementedError() def update_step(self): NotImplementedError() def push_data(self, data, time): "push data to monitor by backend" self._records.append(data) self._times.append(time) class SpikeMonitor(Monitor): def __init__(self, target, var_name='O', index='full', dt=None, get_grad=False, nbatch=False): super().__init__(target=target, var_name=var_name, index=index, dt=dt, get_grad=get_grad, nbatch=nbatch) self._transform_len = 0 self._nbatch_index = [] # all time window's record self._nbatch_times = [] self._spk_index = [] self._spk_times = [] self._records = [] # single time window's record self._times = [] def build(self, backend: Backend): self.backend = backend self.backend._monitors.append(self) self.var_name = self.check_var_name(self.var_name) self.shape = self.backend._variables[self.var_name].shape if self.dt is None: self.dt = self.backend.dt def clear(self): self._transform_len = -1 self._nbatch_index = [] # all time window's record self._nbatch_times = [] self._spk_index = [] self._spk_times = [] self._records = [] # single time window's record self._times = [] def init_record(self): self.new_record = True if len(self._spk_index) > 0: if self.nbatch is True: if isinstance(self._spk_index[0], torch.Tensor): self._nbatch_index.append(torch.stack(self._spk_index[1:], dim=-1).cpu().detach().numpy()) else: self._nbatch_index.append(np.stack(self._spk_index[1:], axis=-1)) self._nbatch_times.append(self._times[1:]) elif self.nbatch > 0: if isinstance(self._spk_index[0], torch.Tensor): self._nbatch_index.append(torch.stack(self._spk_index[1:], dim=-1).cpu().detach().numpy()) else: self._nbatch_index.append(np.stack(self._spk_index[1:], axis=-1)) self._nbatch_times.append(self._times[1:]) if len(self._nbatch_times) > self.nbatch: self._nbatch_index = self._nbatch_index[-self.nbatch:] self._nbatch_times = self._nbatch_times[-self.nbatch:] self._records = [] # single time window's record self._times = [] self._transform_len = -1 def push_spike_train(self, spk_times, spk_index, batch_index=0): if len(self._spk_index) < batch_index+1: add_num = batch_index + 1 - len(self._spk_index) for _ in range(add_num): self._spk_index.append([]) self._spk_times.append([]) if isinstance(spk_times, list) or isinstance(spk_times, tuple): self._spk_times[batch_index].extend(spk_times) self._spk_index[batch_index].extend(spk_index) else: self._spk_times[batch_index].append(spk_times) self._spk_index[batch_index].append(spk_index) #to override the _spike_transform function when getting spk_times and spk_index self._transform_len = 1 def update_step(self, variables): ''' Recoding the variable values of the current step. Returns ------- ''' if self.is_recording is False: return if int(10000 * self.backend.time / self.dt) % 10000 == 0: record_value = variables[self.var_name] if self.get_grad: variables[self.var_name].retain_grad() if self.index == 'full': self._records.append(record_value) self._times.append(self.backend.time) else: if len(self.index) == record_value.ndim: self._records.append(record_value[self.index]) self._times.append(self.backend.time) else: assert len(self.index) == record_value.ndim -1 if self.backend.backend_name == 'pytorch': record_value = torch.movedim(record_value, 0, -1) indexed_value = record_value[tuple(self.index)] indexed_value = torch.movedim(indexed_value, -1, 0) else: record_value = np.array(record_value) record_value = np.moveaxis(record_value, 0, -1) indexed_value = record_value[tuple(self.index)] indexed_value = np.moveaxis(indexed_value, -1, 0) self._records.append(indexed_value) self._times.append(self.backend.time) def _spike_transform(self): batch_size = self.backend.get_batch_size() if len(self._records) > self._transform_len: self._transform_len = len(self._records) self._spk_index = [] self._spk_times = [] if isinstance(self._records[0], torch.Tensor): step = len(self._records) rec_spikes = torch.stack(self._records, dim=-1).cpu().detach() if '{[2]' in self.var_name: for ii in range(batch_size): rec_spikes_i = rec_spikes[ii,0,...].bool().reshape(-1) rec_spikes_t = rec_spikes[ii,1,...].reshape(-1) num = int(rec_spikes_i.size(0)/step) time_seq = torch.tensor(self._times).unsqueeze(dim=0).expand(num, -1).reshape(-1) indx_seq = torch.arange(0, num).unsqueeze(dim=1).expand(-1, step).reshape(-1) time_seq = (torch.masked_select(time_seq - rec_spikes_t, rec_spikes_i) ).numpy() indx_seq = torch.masked_select(indx_seq, rec_spikes_i).numpy() self._spk_index.append(indx_seq) self._spk_times.append(time_seq) else: for ii in range(batch_size): rec_spikes_i = rec_spikes[ii,...].bool().reshape(-1) num = int(rec_spikes_i.size(0)/step) time_seq = torch.tensor(self._times).unsqueeze(dim=0).expand(num, -1).reshape(-1) indx_seq = torch.arange(0, num).unsqueeze(dim=1).expand(-1, step).reshape(-1) time_seq = torch.masked_select(time_seq, rec_spikes_i).numpy() indx_seq = torch.masked_select(indx_seq, rec_spikes_i).numpy() self._spk_index.append(indx_seq) self._spk_times.append(time_seq) @property def spk_times(self): self._spike_transform() return self._spk_times @property def spk_index(self): self._spike_transform() return self._spk_index @property def spk_grad(self): pass return None @property def time_spk_rate(self): if isinstance(self._records[0], torch.Tensor): if '{[2]' in self.var_name: spike = torch.stack(self._records, dim=-1).cpu().detach()[:,0,...] else: spike = torch.stack(self._records, dim=-1).cpu().detach() return torch.mean(spike, dim=0).numpy() else: if '{[2]' in self.var_name: spike = np.stack(self._records, axis=-1)[:,0,...] else: spike = np.stack(self._records, axis=-1) return np.mean(spike, axis=0).numpy() @property def time(self): return np.stack(self._times, axis=-1) class StateMonitor(Monitor): def __init__(self, target, var_name, index='full', dt=None, get_grad=False, nbatch=False): # TODO: 初始化有点繁琐，需要知道record的变量，考虑采用更直接的监控函数 super().__init__(target=target, var_name=var_name, index=index, dt=dt, get_grad=get_grad, nbatch=nbatch) self._nbatch_records = [] # all time window's record self._nbatch_times = [] self._records = [] # single time window's record self._times = [] def build(self, backend: Backend): self.backend = backend self.backend._monitors.append(self) self.var_name = self.check_var_name(self.var_name) if self.index != 'full': self.index = tuple(self.index) if self.dt is None: self.dt = self.backend.dt def clear(self): self._nbatch_records = [] # all time window's record self._nbatch_times = [] self._records = [] # single time window's record self._times = [] def init_record(self): ''' Inite record of new trial Returns: ''' self.new_record = True if len(self._records) > 0: if self.nbatch is True: if isinstance(self._records[0], torch.Tensor): self._nbatch_records.append(torch.stack(self._records, dim=-1).cpu().detach().numpy()) else: self._nbatch_records.append(np.stack(self._records, axis=-1)) self._nbatch_times.append(self._times) elif self.nbatch > 0: if isinstance(self._records[0], torch.Tensor): self._nbatch_records.append(torch.stack(self._records, dim=-1).cpu().detach().numpy()) else: self._nbatch_records.append(

np.stack(self._records, axis=-1)

numpy.stack

import numpy as np from scipy.optimize import curve_fit from scipy.optimize import fsolve, brentq from scipy.interpolate import interp1d import scipy.integrate import sys import os import writeproperties.velociraptor_python_tools as vpt from scipy.spatial import cKDTree import h5py import re from constants import * from snapshot import * import copy import itertools def getHaloCoord(catalog, halo, z=0): #Mpc/h coords = np.zeros(3) coords[0] = (catalog['Xcmbp'][halo]+catalog['Xc'][halo])*h*(1+z) coords[1] = (catalog['Ycmbp'][halo]+catalog['Yc'][halo])*h*(1+z) coords[2] = (catalog['Zcmbp'][halo]+catalog['Zc'][halo])*h*(1+z) return coords def getHaloRadius(catalog, halo, z=0): #Mpc/h return catalog['R_200crit'][halo]*h*(1+z) def getHaloCoordCOM(catalog, halo, z=0): #Mpc/h coords = np.zeros(3) coords[0] = catalog['Xc'][halo]*h*(1+z) coords[1] = catalog['Yc'][halo]*h*(1+z) coords[2] = catalog['Zc'][halo]*h*(1+z) return coords def readHaloFile(halofile): atime,tree,numhalos,halodata,cosmodata,unitdata = vpt.ReadUnifiedTreeandHaloCatalog(halofile, desiredfields=[], icombinedfile=1,iverbose=0) return atime,tree,numhalos,halodata,cosmodata,unitdata def findSurroundingHaloProperties(hp, halolist, d_snap, boxsize=32.): coords = hp['Coord'] halotree = cKDTree(coords, boxsize=boxsize) for k in halolist: if hp['R200'][k] == -1: continue halostring = hp['HaloIndex'][k] length_of_neighbours = len(np.array(halotree.query_ball_point([hp['Coord'][k]], r=hp['R200'][k]*5)[0])) distance, indices = halotree.query([hp['Coord'][k]], k=length_of_neighbours) indices = np.array(indices[0])[1:] distance = np.array(distance[0])[1:] hp['Neighbours'][halostring] = hp['HaloIndex'][indices] hp['Neighbour_distance'][halostring] = distance hp['Neighbour_Velrad'][halostring] = np.zeros(len(distance)) j=0 for i in indices: partindices = hp['Partindices'][hp['HaloIndex'][i]] hp['Neighbour_Velrad'][halostring][j] = np.sum(d_snap['File'].get_radialvelocity(hp['Coord'][k], indices=partindices))/len(partindices) j+=1 def fixSatelliteProblems(hp, Hydro=False, TEMPORALHALOIDVAL=1000000000000): halotree = cKDTree(hp['Coord'], boxsize=32) toolarge = np.where(hp['R200'] > hp['R200'][0]*1.2)[0] #print(i, toolarge) if len(toolarge) != 0: for tl in toolarge: hp['M200'][tl] = -1 hp['R200'][tl] = -1 if Hydro: hp['DMFraction'][tl] = -1 hp['hostHaloIndex'][hp['HaloIndex'][tl]==hp['hostHaloIndex']] = -2 for halo in range(len(hp['M200'])): if hp['M200'][halo] == -1: continue buren = np.array(halotree.query_ball_point(hp['Coord'][halo], r = 2*hp['R200'][halo])) if len(buren) <= 1: continue buren = buren[hp['R200'][buren] != -1] i_largest = np.argmax(hp['n_part'][buren]) index_largest = buren[i_largest] buren = np.delete(buren,i_largest) coords = hp['Coord'][buren] - hp['Coord'][index_largest] coords = np.where(np.abs(coords) > 0.5*32, coords - coords/np.abs(coords)*32, coords) rad = np.sqrt(np.sum(coords*coords, axis=1)) burentemp = np.where(hp['R200'][buren]-rad+hp['R200'][index_largest] > 0)[0] if len(burentemp) == 0: continue buren = buren[burentemp] hp['hostHaloIndex'][buren] = index_largest hp['M200'][buren] = -1 hp['R200'][buren] = -1 if Hydro: hp['DMFraction'][buren] = -1 def findSubHaloFraction(hp, catalog): if len(hp['hostHaloIndex']) < 10: hp['Msub'] = np.zeros(len(hp['M200'])) return 0 i_hostH = np.where(hp['hostHaloIndex'] > -1)[0] hp['Msub'] = np.zeros(len(hp['M200'])) for i in i_hostH: isattemp = np.where(hp['HaloID'][i] == catalog['ID'])[0] hp['Msub'][hp['hostHaloIndex'][i]] += catalog['Mass_FOF'][isattemp] def buildHaloDictionary(Hydro=False, multiple=False, mainBranch=False): haloproperties = {} if Hydro: haloarray = (['HaloIndex', 'HaloID', 'Coord', 'R200', 'M200', 'redshift', 'snapshot', 'lambda', 'lambdaDM', 'lambdaH', 'DensityDM', 'DensityH', 'Npart', 'DMpartIDs', 'Partindices', 'HpartIDs', 'NpartDM_profile', 'Npart_profile', 'DMFraction', 'DMFraction_profile', 'MassH_profile', 'VelradDM', 'Velrad', 'VelradH', 'Vel', 'Temperature', 'Mass_profile', 'MassDM_profile', 'COM_offset', 'AngularMomentumDM', 'AngularMomentumH', 'AngularMomentum', 'Radius', 'MaxRadIndex', 'hostHaloIndex', 'n_part', 'Msub', 'CrossTime', 'Virial_ratio']) else: haloarray = (['HaloIndex', 'HaloID', 'Coord', 'R200', 'M200', 'redshift', 'snapshot', 'DMpartIDs', 'lambda', 'Density', 'Npart', 'AngularMomentum', 'Npart_profile', 'Radius', 'Velrad', 'Vel', 'MassDM_profile', 'Partindices', 'n_part', 'MaxRadIndex', 'Virial_ratio', 'COM_offset', 'Msub', 'CrossTime', 'hostHaloIndex']) if mainBranch: haloarray.append('Head') haloarray.append('Tail') haloarray.append('TreeBool') for key in haloarray: if multiple and (key == 'DMpartIDs' or key == 'HpartIDs' or key=='Partindices' or key=='Neighbours' or key=='Neighbour_distance' or key=='Neighbour_Velrad'): haloproperties[key] = {} else: haloproperties[key] = np.zeros(0) return haloproperties def findHaloPropertiesInSnap(catalog, snappath, snapshot, particledata=[], Hydro=False, Nhalo=100, startHalo=0, softeningLength=0.002, r200fac=1., mass=False, partlim=200, savePartData=False): print("Computing properties for %i haloes in snapshot %i" %(Nhalo, snapshot)) haloproperties = buildHaloDictionary(Hydro = Hydro, multiple=True) if len(catalog['Mass_tot']) == 0: return haloproperties sortorder = np.argsort(catalog['Mass_tot'][:]) d_snap = {} d_snap['snapshot'] = snapshot limiet = 0 if Hydro: d_snap['File'] = Snapshot(snappath, snapshot, useIDs=False, partType=7, softeningLength=softeningLength) d_snap['File'].makeCoordTree() else: d_snap['File'] = Snapshot(snappath, snapshot, useIDs=False, partType=1, softeningLength=softeningLength) d_snap['File'].makeCoordTree() for key in catalog.keys(): catalog[key][:] = catalog[key][sortorder] catalog[key][:] = catalog[key][::-1] if len(particledata) > 0: mass=True for key in particledata.keys(): particledata[key][:] = np.array(particledata[key])[sortorder] particledata[key][:] = particledata[key][::-1] for halo in range(startHalo, startHalo+Nhalo): masshier = False if mass: masshier=catalog['Mass_200crit'][halo]*h if masshier <= 0.000001: startHalo += 1 limiet += 1 continue if halo > len(catalog['Xc'])-1 or limiet > 500: print("Halo limit reached: nhalo = %i, hlim = %i" %(halo, limiet)) print("Coordinates: ", coords) break halopropertiestemp = {} coords = getHaloCoord(catalog, halo, z=d_snap['File'].redshift) if coords[0] < 0 or coords[1] < 0 or coords[2] < 0 or coords[0]%32 < 0.5 or coords[1]%32 < 0.5 or coords[2]%32 < 0.5: startHalo += 1 continue coords = coords%32 radhier = getHaloRadius(catalog, halo, z=d_snap['File'].redshift) if ((coords[0] < 2.*radhier) or (coords[1] < 2.*radhier) or (coords[2] < 2.*radhier) or (np.abs(coords[0]-32) < 2.*radhier) or (np.abs(coords[1]-32) < 2.*radhier) or (np.abs(coords[2]-32) < 2.*radhier)): startHalo += 1 continue if (catalog['hostHaloID'][halo] != -1) or (catalog['npart'][halo] < 20) or (catalog['Mass_200crit'][halo]*h == 0):# or ((catalog['Mass_200crit'][halo]/4.e3)**(1./3) < catalog['R_200crit'][halo]): startHalo += 1 continue halopropertiestemp = findHaloProperties(halo, catalog, d_snap, particledata=particledata, Hydro=Hydro, r200fac=r200fac, mass=masshier, partlim=partlim) if len(halopropertiestemp) == 0: startHalo += 1 limiet += 1 continue if halopropertiestemp['Npart'] < partlim: startHalo += 1 limiet += 1 continue limiet = 0 for key in haloproperties.keys(): if key == 'Neighbours' or key == 'Neighbour_distance' or key == 'Neighbour_Velrad': iets = 1 elif key == 'DMpartIDs' or key == 'HpartIDs' or key=='Partindices': if key=='Partindices': haloproperties[key][halopropertiestemp['HaloIndex']] = halopropertiestemp[key][:] elif savePartData: haloproperties[key][halopropertiestemp['HaloIndex']] = halopropertiestemp[key][:] elif halo == startHalo: haloproperties[key] = [halopropertiestemp[key]] else: haloproperties[key] = np.concatenate((haloproperties[key], [halopropertiestemp[key]])) #if startHalo + Nhalo >= len(catalog['npart']) and len(haloproperties['Npart']) > partlim: print("Compute temperatures outside haloes...") everythingOutside(haloproperties, d_snap) print("finding surrounding haloes...") if len(haloproperties['M200']) > 100: findSurroundingHaloProperties(haloproperties, np.arange(0, 100, 1).astype(int), d_snap) return haloproperties def findHaloPropertiesInSnap_fromUnifiedTreeCatalog(catalog, snappath, snapshot, Hydro=False, Nhalo=100, startHalo=0, softeningLength=0.002, Radius=1., partlim=200, savePartData=False, sortorder=[], boxsize=32, TEMPORALHALOIDVAL=1000000000000): print("Computing properties for %i haloes in snapshot %i" %(Nhalo, snapshot)) haloproperties = buildHaloDictionary(Hydro = Hydro, multiple=True, mainBranch=True) if len(catalog['Mass_tot']) == 0: return haloproperties if len(sortorder)==0: sortorder = np.argsort(catalog['Mass_tot'][:])[::-1] sortorderinvert = np.argsort(sortorder) else: sortorderinvert = np.argsort(sortorder) d_snap = {} d_snap['snapshot'] = snapshot limiet = 0 if Hydro: d_snap['File'] = Snapshot(snappath, snapshot, useIDs=False, partType=7, softeningLength=softeningLength) d_snap['File'].makeCoordTree() else: d_snap['File'] = Snapshot(snappath, snapshot, useIDs=False, partType=1, softeningLength=softeningLength) d_snap['File'].makeCoordTree() for key in catalog.keys(): catalog[key][:] = catalog[key][sortorder] #catalog[key][:] = catalog[key][::-1] #haloproperties['TreeBool'] = np.ones(len(tails), dtype=int) for halo in range(startHalo, startHalo+Nhalo): #start_time = time.time() #print(halo) #print(catalog['npart'][halo]) if halo%1000==0: print('Computing properties for halo %i-%i' %(halo, halo+1000)) if halo > len(catalog['Xc'])-1: print("Halo limit reached: nhalo = %i, hlim = %i" %(halo, limiet)) print("Coordinates: ", coords) break if limiet > 500: #Only computing sats if catalog['hostHaloID'][halo] == -1: # haloproperties['TreeBool'][halo] = 0 continue halopropertiestemp = {} coords = getHaloCoord(catalog, halo, z=d_snap['File'].redshift) coords = coords%32 radhier = getHaloRadius(catalog, halo, z=d_snap['File'].redshift) satellite = False if (catalog['npart'][halo] < 20) or (catalog['Mass_200crit'][halo]*h == 0): startHalo += 1 # haloproperties['TreeBool'][halo] = 0 continue if (catalog['hostHaloID'][halo] != -1) and len(haloproperties['HaloID'])>1: haloindextemp = np.where((haloproperties['HaloID']%TEMPORALHALOIDVAL)==catalog['hostHaloID'][halo]%TEMPORALHALOIDVAL)[0] if len(haloindextemp) == 0: hostHaloIDtemp = -1 if catalog['npart'][halo] < 50: hostHaloIDtemp = -2 satellite = True else: afstandtemp = (haloproperties['Coord'][haloindextemp[0]]-coords) afstandtemp = np.where(np.abs(afstandtemp)>0.5*boxsize, np.abs(afstandtemp) - boxsize, afstandtemp) afstandtemp = (np.sum(afstandtemp*afstandtemp))**0.5 if afstandtemp < haloproperties['R200'][haloindextemp[0]]: # and catalog['npart'][halo] > 50: #print(afstandtemp, haloproperties['R200'][haloindextemp[0]], haloproperties['Coord'][haloindextemp[0]], coords) hostHaloIDtemp = haloindextemp[0] satellite = True else: #print(afstandtemp, haloproperties['R200'][haloindextemp[0]], haloproperties['Coord'][haloindextemp[0]], coords) hostHaloIDtemp = -1 else: hostHaloIDtemp = -1 #All happens here halopropertiestemp = findHaloPropertiesFixedRadius(d_snap, halo, coords, np.logspace(-3, 0, 60)*Radius, Hydro=Hydro, rad=radhier, mass=False, satellite=satellite, mainBranch=True) #print("--- %s seconds ---" % (time.time() - start_time), 'halopropertiestemp computed') if len(halopropertiestemp) == 0: startHalo += 1 limiet += 1 # haloproperties['TreeBool'][halo] = 0 continue if satellite == False and halopropertiestemp['Npart'] < partlim: startHalo += 1 limiet += 1 # haloproperties['TreeBool'][halo] = 0 continue limiet = 0 if satellite: halopropertiestemp['Npart'] = catalog['npart'][halo] #start_time = time.time() halopropertiestemp['n_part'] = catalog['npart'][halo] halopropertiestemp['HaloID'] = catalog['ID'][halo] halopropertiestemp['hostHaloIndex'] = hostHaloIDtemp if not satellite: afstandtemp = coords - getHaloCoordCOM(catalog, halo, z=d_snap['File'].redshift) rhier = np.where(np.abs(afstandtemp)>0.5*boxsize, np.abs(afstandtemp) - boxsize, afstandtemp) halopropertiestemp['COM_offset'] = np.sqrt(np.sum(rhier**2))/halopropertiestemp['R200'] halopropertiestemp['CrossTime'] = (2.*halopropertiestemp['R200']*Mpc_to_km / np.sqrt(G_Mpc_km2_Msi_si2*halopropertiestemp['M200']*1e10/halopropertiestemp['R200']))*s_to_yr/1.e6 else: halopropertiestemp['COM_offset'] = -1 halopropertiestemp['CrossTime'] = -1 for key in haloproperties.keys(): if key == 'TreeBool' or key == 'Tail' or key == 'Head' or key == 'Radius': continue elif key == 'Neighbours' or key == 'Neighbour_distance' or key == 'Neighbour_Velrad': continue elif key == 'DMpartIDs' or key == 'HpartIDs' or key=='Partindices': if key=='Partindices': haloproperties[key][halopropertiestemp['HaloIndex']] = halopropertiestemp[key][:] elif savePartData: haloproperties[key][halopropertiestemp['HaloIndex']] = halopropertiestemp[key][:] elif halo == startHalo: haloproperties[key] = [halopropertiestemp[key]] else: #print(key) haloproperties[key] = np.concatenate((haloproperties[key], [halopropertiestemp[key]])) #print("--- %s seconds ---" % (time.time() - start_time), 'haloproperties updated') # print("Correcting hostHaloIndices...") # for i in range(len(haloproperties['HaloID'])): # if (haloproperties['hostHaloIndex'][i] != -1): # haloindextemp = np.where((haloproperties['HaloID']%TEMPORALHALOIDVAL)==haloproperties['hostHaloIndex'][i]%TEMPORALHALOIDVAL)[0] # if len(haloindextemp) == 0: # haloproperties['hostHaloIndex'][i] = -2 # else: # haloproperties['hostHaloIndex'][i] = haloindextemp[0] # else: # haloproperties['hostHaloIndex'][i] = -1 # haloproperties['Tail'] = tails # haloproperties['Head'] = heads haloproperties['Radius'] =

np.logspace(-3, 0, 60)

numpy.logspace

#!/usr/bin/env python ''' Filename: test_flags.py Description: unit tests to test flag_timepoints.py ''' __author__ = '<NAME>' __email__ = '<EMAIL>' __status__ = 'prototype' # standard imports import os import sys import unittest import numpy as np HERE = os.path.dirname(os.path.realpath(__file__)) sys.path.append(os.path.abspath(HERE +'/../../importing')) from flags_and_breaks import * class TestFlagTimepoints(unittest.TestCase): def test_consolidate_all_true_flags(self): all_flags = {'f1': [True, True, True], 'f2': [True, True, True],} flags = consolidate_flags(all_flags) soln = [True, True, True] self.assertEqual(flags, soln) def test_consolidate_all_False_flags(self): all_flags = {'f1': [False, False], 'f2': [False, False],} flags = consolidate_flags(all_flags) soln = [False, False] self.assertEqual(flags, soln) def test_consolidate_mixed_flags(self): all_flags = {'f1': [False, True, True, True], 'f2': [False, True, True, True], 'f2': [True, True, False, True]} flags = consolidate_flags(all_flags) soln = [False, True, False, True] self.assertEqual(flags, soln) def test_flag_no_outliers(self): fail_num = 0 for i in range(10): normal_dataset = np.random.normal(loc=5.0, scale=1.0, size=1000) flags = flag_outliers(normal_dataset) soln = [True for i in range(1000)] if flags != soln: fail_num += 1 self.assertTrue(fail_num <= 2) def test_flag_lage_and_small_outliers(self): N_normal, N_out1, N_out2 = 200, 50, 20 normal_dataset = np.random.normal(loc=10.0, scale=1.0, size=N_normal) outliers1 = np.random.normal(loc=20.0, scale=1.0, size=N_out1) outliers2 = np.random.normal(loc=1.0, scale=0.5, size=N_out2) test_set = list(normal_dataset) + list(outliers1) + list(outliers2) flags = flag_outliers(test_set) soln = ([True] * N_normal) + ([False] * (N_out1 + N_out2)) self.assertEqual(flags, soln) def test_flag_just_small_outliers(self): N_normal, N_out1, N_out2 = 200, 10, 30 normal_dataset = np.random.normal(loc=10.0, scale=1.0, size=N_normal) outliers1 = np.random.normal(loc=20.0, scale=1.0, size=N_out1) outliers2 = np.random.normal(loc=1.0, scale=0.5, size=N_out2) test_set = list(normal_dataset) + list(outliers1) + list(outliers2) flags = flag_outliers(test_set, options='short') soln = ([True] * (N_normal+N_out1)) + ([False] * N_out2) self.assertEqual(flags, soln) def test_flag_just_large_outliers(self): N_normal, N_out1, N_out2 = 200, 10, 20 normal_dataset = np.random.normal(loc=10.0, scale=1.0, size=N_normal) outliers1 = np.random.normal(loc=20.0, scale=1.0, size=N_out1) outliers2 = np.random.normal(loc=1.0, scale=0.5, size=N_out2) test_set = list(normal_dataset) + list(outliers1) + list(outliers2) flags = flag_outliers(test_set, options='long') soln = [True] * N_normal + [False] * N_out1 + [True] * N_out2 self.assertEqual(flags, soln) def test_flag_outliers_with_nulls(self): N_normal = 1000 normal_dataset =

np.random.normal(loc=10.0, scale=1.0, size=N_normal)

numpy.random.normal

# Copyright 2019 <NAME> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 import numpy as np from scipy.optimize import curve_fit from auspex.log import logger from copy import copy import matplotlib.pyplot as plt from .fits import AuspexFit, Auspex2DFit from .signal_analysis import KT_estimation class RabiAmpFit(AuspexFit): """A fit to a Rabi amplitude curve, assuming a cosine model. """ xlabel = "Amplitude" ylabel = r"<$\sigma_z$>" title = "Rabi Amp Fit" @staticmethod def _model(x, *p): return p[0] - p[1]*np.cos(2*np.pi*p[2]*(x - p[3])) def _initial_guess(self): #seed Rabi frequency from largest FFT component N = len(self.ypts) yfft = np.fft.fft(self.ypts) f_max_ind = np.argmax(np.abs(yfft[1:N//2])) f_0 = 0.5 * max([1, f_max_ind]) / self.xpts[-1] amp_0 = 0.5*(self.ypts.max() - self.ypts.min()) offset_0 = np.mean(self.ypts) phase_0 = 0 if self.ypts[N//2 - 1] > offset_0: amp_0 = -amp_0 return [offset_0, amp_0, f_0, phase_0] def _fit_dict(self, p): return {"y0": p[0], "Api": p[1], "f": p[2], "phi": p[3]} def __str__(self): return "y0 - Api*cos(2*pi*f*(t - phi))" @property def pi_amp(self): """Returns the pi-pulse amplitude of the fit. """ return 0.5/self.fit_params["f"] def annotation(self): return r"$A_\pi$ = {0:.2e} {1} {2:.2e}".format(self.pi_amp, chr(177), self.fit_errors["Api"]) class RabiWidthFit(AuspexFit): """Fit to a single-frequency decaying cosine for fitting Rabi-vs-time experiments """ xlabel = "Delay" ylabel = r"<$\sigma_z$>" title = "Rabi Width Fit" @staticmethod def _model(x, *p): return p[0] + p[1]*np.exp(-x/p[2])*np.cos(2*np.pi*p[3]*(x - p[4])) def _initial_guess(self): frabi, Tcs, amps = KT_estimation(self.ypts-np.mean(self.ypts), self.xpts, 1) offset = np.average(self.xpts) amp = np.max(self.ypts) trabi = self.xpts[np.size(self.ypts) // 3]# assume Trabi is 1/3 of the scan phase = 90.0 return [offset, amp, trabi, frabi[0], phase] def _fit_dict(self, p): return {"y0": p[0], "A": p[1], 'T': p[2], "f": p[3], "phi": p[4]} def __str__(self): return "y0 + A*exp(-x/T)*cos(2*pi*f*(t - phi))" @property def t_rabi(self): return self.fit_params["T"] def annotation(self): return r"$T_\pi$ = {0:.2e} {1} {2:.2e}".format(self.fit_params["T"], chr(177), self.fit_errors["T"]) class T1Fit(AuspexFit): """Fit to a decaying exponential for T1 measurement experiments. """ xlabel = "Delay" ylabel = r"<$\sigma_z$>" title = r"$T_1$ Fit" @staticmethod def _model(x, *p): return p[0]*np.exp(-x/p[1]) + p[2] def _initial_guess(self): ## Initial guess using method of linear regression via integral equations ## https://www.scribd.com/doc/14674814/Regressions-et-equations-integrales N = len(self.xpts) S = np.zeros(N) for j in range(2, N): S[j] = S[j-1] + 0.5*((self.ypts[j] + self.ypts[j-1]) * (self.xpts[j] - self.xpts[j-1])) xs = self.xpts - self.xpts[0] ys = self.ypts - self.ypts[0] M = np.array([[np.sum(xs**2), np.sum(xs * S)], [np.sum(xs * S), np.sum(S**2)]]) B1 = (np.linalg.inv(M) @ np.array([np.sum(ys * xs), np.sum(ys * S)]).T)[1] theta = np.exp(B1 * self.xpts) M2 = np.array([[N, np.sum(theta)], [np.sum(theta), np.sum(theta**2)]]) A = np.linalg.inv(M2) @ np.array([np.sum(self.ypts), np.sum(self.ypts * theta)]).T return [A[1], -1.0/B1, A[0]] def _fit_dict(self, p): return {"A": p[0], "T1": p[1], "A0": p[2]} def __str__(self): return "A0 + A*exp(-t/T1)" @property def T1(self): """Return the measured T1 (i.e. decay constant of exponential). """ return self.fit_params["T1"] def make_plots(self): """Create plot on both linear and semilog scale """ logger.info("Semilog plot of |1> state probability requires calibrated data.") plt.figure(figsize=(2*6.4, 4.8)) plt.subplot(121) plt.plot(self.xpts, self.ypts, ".", markersize=15, label="Data") plt.plot(self.xpts, self.model(self.xpts), "-", linewidth=3, label="Fit") plt.xlabel(self.xlabel, fontsize=14) plt.ylabel(self.ylabel, fontsize=14) plt.annotate(self.annotation(), xy=(0.4, 0.10), xycoords='axes fraction', size=12) plt.subplot(122) plt.semilogy(self.xpts, -1/2*(self.ypts - self.fit_params["A0"]), ".", markersize=15, label="Data") plt.semilogy(self.xpts, -1/2*(self.model(self.xpts) - self.fit_params["A0"]), "-", linewidth=3, label="Fit") plt.xlabel(self.xlabel, fontsize=14) plt.ylabel('|1> probability', fontsize=14) plt.suptitle(self.title, fontsize=14) def annotation(self): return r"$T_1$ = {0:.2e} {1} {2:.2e}".format(self.fit_params["T1"], chr(177), self.fit_errors["T1"]) class RamseyFit(AuspexFit): """Fit to a Ramsey experiment using either a one or two frequency decaying sine model. """ xlabel = "Delay" ylabel = r"<$\sigma_z$>" title = "Ramsey Fit" def __init__(self, xpts, ypts, two_freqs=True, AIC=True, make_plots=False, force=False, ax=None): """One or two frequency Ramsey experiment fit. If a two-frequency fit is selected by the user or by comparing AIC scores, fit parameters are returned as tuples instead of single numbers. Args: xpts (numpy.array): Time data points. ypts (numpy.array): Qubit measurements. two_freqs (Bool): If true, attempt a two-frequency fit of the data. AIC (Bool): Decide between one and two frequency fits using the Akaike information criterion. make_plots (Bool): Display a plot of data and fit result. ax (Axes, optional): Axes on which to draw plot. If None, new figure is created force (Bool): Force the selection of a two-frequency fit regardless of AIC score. """ self.AIC = AIC self.dict_option = two_freqs self.two_freqs = two_freqs self.force = force self.plots = make_plots self.ax = ax assert len(xpts) == len(ypts), "Length of X and Y points must match!" self.xpts = xpts self.ypts = ypts self._do_fit() def _initial_guess_1f(self): freqs, Tcs, amps = KT_estimation(self.ypts-np.mean(self.ypts), self.xpts, 1) return [freqs[0], abs(amps[0]), Tcs[0], np.angle(amps[0]), np.mean(self.ypts)] def _initial_guess_2f(self): freqs, Tcs, amps = KT_estimation(self.ypts-np.mean(self.ypts), self.xpts, 2) return [*freqs, *abs(amps), *Tcs, *np.angle(amps), np.mean(self.ypts)] @staticmethod def _ramsey_1f(x, f, A, tau, phi, y0): return A*np.exp(-x/tau)*np.cos(2*np.pi*f*x + phi) + y0 @staticmethod def _model_2f(x, *p): return (RamseyFit._ramsey_1f(x, p[0], p[2], p[4], p[6], p[8]) + RamseyFit._ramsey_1f(x, p[1], p[3], p[5], p[7], p[8])) @staticmethod def _model_1f(x, *p): return RamseyFit._ramsey_1f(x, p[0], p[1], p[2], p[3], p[4]) def _aicc(self, e, k, n): return 2*k+e+(2*k*(k+1))/(n-k-1) def _do_fit(self): if self.two_freqs: self.dict_option = True self._initial_guess = self._initial_guess_2f self._model = self._model_2f try: super()._do_fit() two_freq_chi2 = self.sq_error except: self.two_freqs = False logger.info("Two-frequency fit failed. Trying single-frequency fit.") if self.two_freqs and self.AIC: #Compare the one and two frequency fits self.dict_option = False self._initial_guess = self._initial_guess_1f self._model = self._model_1f super()._do_fit() one_freq_chi2 = self.sq_error aic = self._aicc(two_freq_chi2, 9, len(self.xpts)) - self._aicc(one_freq_chi2, 5, len(self.xpts)) if aic > 0 and not self.force: self.two_freqs = False rl = 100*np.exp(-aic/2) logger.info(f"Selecting one-frequency fit with relative likelihood = {rl:.2f}%") if rl>33: logger.info("Relative likelihood of 2nd frequency high, take more averages or set force = True.") else: self.dict_option = True self._initial_guess = self._initial_guess_2f self._model = self._model_2f super()._do_fit() if not self.two_freqs: self.dict_option = False self._initial_guess = self._initial_guess_1f self._model = self._model_1f super()._do_fit() if self.plots: self.make_plots() def annotation(self): if self.two_freqs: return r"$T_2$ = {0:.2e} {1} {2:.2e} "'\n'"$T_2$ = {3:.2e} {4} {5:.2e}".format(self.fit_params["tau1"], chr(177), self.fit_errors["tau1"], self.fit_params["tau2"], chr(177), self.fit_errors["tau2"]) else: return r"$T_2$ = {0:.2e} {1} {2:.2e}".format(self.fit_params["tau"], chr(177), self.fit_errors["tau"]) @property def T2(self): if self.two_freqs: return self.fit_params["tau1"], self.fit_params["tau2"] else: return self.fit_params["tau"] @property def ramsey_freq(self): if self.two_freqs: return self.fit_params["f1"], self.fit_params["f2"] else: return self.fit_params["f"] def _fit_dict(self, p): if self.dict_option: return {"f1": p[0], "A1": p[2], "tau1": p[4], "phi1": p[6], "f2": p[1], "A2": p[3], "tau2": p[5], "phi2": p[7], "y0": p[8]} else: return {"f": p[0], "A": p[1], "tau": p[2], "phi": p[3], "y0": p[4]} class SingleQubitRBFit(AuspexFit): """Fit to an RB decay curve using the model A*(r^n) + B """ ylabel = r"<$\sigma_z$>" title = "Single Qubit RB Fit" def __init__(self, lengths, data, make_plots=False, log_scale_x=True, smart_guess=True, bounded_fit=True, ax=None): self.lengths = sorted(list(set(lengths))) repeats = len(data) // len(self.lengths) xpts = np.array(self.lengths) ypts = np.mean(np.reshape(data,(len(self.lengths),repeats)),1) self.data = data self.data_points = np.reshape(data,(len(self.lengths),repeats)) self.errors = np.std(self.data_points, 1) self.log_scale_x = log_scale_x self.ax = ax self.smart_guess = smart_guess if log_scale_x: self.xlabel = r"$log_2$ Clifford Number" else: self.xlabel = "Clifford Number" if bounded_fit: self.bounds = ((0, -np.inf, 0), (1, np.inf, 1)) super().__init__(xpts, ypts, make_plots=make_plots, ax=ax) @staticmethod def _model(x, *p): return p[0] * (1-p[1])**x + p[2] def _initial_guess(self): if self.smart_guess: ## Initial guess using method of linear regression via integral equations ## https://www.scribd.com/doc/14674814/Regressions-et-equations-integrales N = len(self.xpts) S = np.zeros(N) for j in range(2, N): S[j] = S[j-1] + 0.5*((self.ypts[j] + self.ypts[j-1]) * (self.xpts[j] - self.xpts[j-1])) xs = self.xpts - self.xpts[0] ys = self.ypts - self.ypts[0] M = np.array([[np.sum(xs**2), np.sum(xs * S)], [

np.sum(xs * S)

numpy.sum

from typing import List import numpy as np from scipy.interpolate import interp1d import matplotlib.pyplot as plt import pandas as pd from modules.CD_parameters import * from modules.utilities import * # check if the output directory exists check_dir("".join((project_dir, '/figures/foo.eps'))) def flatten_list(list_of_lists: List) -> np.ndarray: return np.array([item for sub_list in list_of_lists for item in sub_list]) start_line_number = 93 # The first line end_line_number = 93 # The last line following_the_spectra_catlogue = True # Read spectrumIDs or read SampleIDs first? rows = np.array(range(start_line_number, end_line_number + 1)) - 2 # Read the files if following_the_spectra_catlogue: Sample_catalogue = pd.read_excel("".join((path_relab, 'Sample_Catalogue.xlsx')), index_col=None, na_values=['NA'], usecols="A, C, Ag", engine='openpyxl').to_numpy() Spectra_catalogue = pd.read_excel("".join((path_relab, 'Spectra_Catalogue.xlsx')), index_col=None, na_values=['NA'], usecols="A, B, F:H", engine='openpyxl').to_numpy()[rows] SpectrumIDs = Spectra_catalogue[:, 0] SampleIDs = Spectra_catalogue[:, 1] # Range for interpolation Start = np.array(Spectra_catalogue[:, 2]) Stop = np.array(Spectra_catalogue[:, 3]) Step = np.array(Spectra_catalogue[:, 4]) Weathering = flatten_list([Sample_catalogue[np.where(Sample_catalogue[:, 0] == SampleID)[0], 2] for SampleID in SampleIDs]) else: Sample_catalogue = pd.read_excel("".join((path_relab, 'Sample_Catalogue.xlsx')), index_col=None, na_values=['NA'], usecols="A, C, Ag", engine='openpyxl').to_numpy()[rows] Spectra_catalogue = pd.read_excel("".join((path_relab, 'Spectra_Catalogue.xlsx')), index_col=None, na_values=['NA'], usecols="A, B, F:H", engine='openpyxl').to_numpy() SampleIDs = np.array(Sample_catalogue[:, 0]) # Find Spectrum ID SpectrumIDs = flatten_list([Spectra_catalogue[np.where(Spectra_catalogue[:, 1] == SampleID)[0], 0] for SampleID in SampleIDs]) # Range for interpolation Start = flatten_list([Spectra_catalogue[np.where(Spectra_catalogue[:, 1] == SampleID)[0], 2] for SampleID in SampleIDs]) Stop = flatten_list([Spectra_catalogue[np.where(Spectra_catalogue[:, 1] == SampleID)[0], 3] for SampleID in SampleIDs]) Step = flatten_list([Spectra_catalogue[np.where(Spectra_catalogue[:, 1] == SampleID)[0], 4] for SampleID in SampleIDs]) # Find samples for the spectra (this is necessary because a spectrum can have same SampleID) SampleIDs = flatten_list([Spectra_catalogue[np.where(Spectra_catalogue[:, 0] == SpectrumID)[0], 1] for SpectrumID in SpectrumIDs]) Weathering = flatten_list([Sample_catalogue[np.where(Sample_catalogue[:, 0] == SampleID)[0], 2] for SampleID in SampleIDs]) # Take only these spectra mask = np.array(np.where((Start <= lambda_min) & (Stop >= lambda_max) & (Step <= resolution_max))).ravel() SpectrumIDs = SpectrumIDs[mask] SampleIDs = SampleIDs[mask] X = np.arange(lambda_min, lambda_max + resolution_final / 2, resolution_final) # Find corresponding PIs PIs = flatten_list([Sample_catalogue[np.where(Sample_catalogue[:, 0] == SampleID)[0], 1] for SampleID in SampleIDs]) """ # Sorting idx = np.argsort(SampleIDs) SampleIDs = SampleIDs[idx] SpectrumIDs = SpectrumIDs[idx] PIs = PIs[idx] """ if denoise: width = 9 cent = np.int(

np.round(width / 2)

numpy.round

import argparse import numpy as np import torch import plyfile import skimage.measure from tqdm import tqdm import yaml import os.path as osp import skimage import time def convert_sigma_samples_to_ply( input_3d_sigma_array: np.ndarray, voxel_grid_origin, volume_size, ply_filename_out, level=5.0, offset=None, scale=None,): """ Convert density samples to .ply :param input_3d_sdf_array: a float array of shape (n,n,n) :voxel_grid_origin: a list of three floats: the bottom, left, down origin of the voxel grid :volume_size: a list of three floats :ply_filename_out: string, path of the filename to save to This function adapted from: https://github.com/RobotLocomotion/spartan """ start_time = time.time() verts, faces, normals, values = skimage.measure.marching_cubes( input_3d_sigma_array, level=level, spacing=volume_size ) # transform from voxel coordinates to camera coordinates # note x and y are flipped in the output of marching_cubes mesh_points =

np.zeros_like(verts)

numpy.zeros_like

import os import pybullet as p import numpy as np import time from itertools import product from .utils import unit_pose, safe_zip, multiply, Pose, AABB, create_box, set_pose, get_all_links, LockRenderer, \ get_aabb, pairwise_link_collision, remove_body, draw_aabb, get_box_geometry, create_shape, create_body, STATIC_MASS, \ unit_quat, unit_point, CLIENT, create_shape_array, set_color, get_point, clip, load_model, TEMP_DIR, NULL_ID, elapsed_time MAX_TEXTURE_WIDTH = 418 # max square dimension MAX_PIXEL_VALUE = 255 MAX_LINKS = 125 # Max links seems to be 126 class VoxelGrid(object): # https://github.mit.edu/caelan/ROS/blob/master/sparse_voxel_grid.py # https://github.mit.edu/caelan/ROS/blob/master/base_navigation.py # https://github.mit.edu/caelan/ROS/blob/master/utils.py # https://github.mit.edu/caelan/ROS/blob/master/voxel_detection.py # TODO: can always display the grid in RVIZ after filtering # TODO: compute the maximum sized cuboid (rectangle) in a grid (matrix) def __init__(self, resolutions, color=(1, 0, 0, 0.5)): #def __init__(self, sizes, centers, pose=unit_pose()): #assert len(sizes) == len(centers) self.resolutions = resolutions self.occupied = set() self.world_from_grid = unit_pose() # TODO: support for real self.color = color #self.bodies = None # TODO: store voxels more intelligently spatially def __len__(self): return len(self.occupied) def voxel_from_point(self, point): return tuple(np.floor(np.divide(point, self.resolutions)).astype(int)) def voxels_from_aabb(self, aabb): lower_voxel, upper_voxel = map(self.voxel_from_point, aabb) return map(tuple, product(*[range(l, u + 1) for l, u in safe_zip(lower_voxel, upper_voxel)])) def lower_from_voxel(self, voxel): return np.multiply(voxel, self.resolutions) def center_from_voxel(self, voxel): return self.lower_from_voxel(voxel) + self.resolutions/2. def upper_from_voxel(self, voxel): return self.lower_from_voxel(voxel) + self.resolutions def pose_from_voxel(self, voxel): return multiply(self.world_from_grid, Pose(self.center_from_voxel(voxel))) def aabb_from_voxel(self, voxel): return AABB(self.lower_from_voxel(voxel), self.upper_from_voxel(voxel)) def is_occupied(self, voxel): return voxel in self.occupied def set_occupied(self, voxel): if self.is_occupied(voxel): return False self.occupied.add(voxel) return True def set_free(self, voxel): if not self.is_occupied(voxel): return False self.occupied.remove(voxel) return True def get_neighbors(self, index): for i in range(len(index)): direction = np.zeros(len(index), dtype=int) for n in (-1, +1): direction[i] = n yield tuple(np.array(index) + direction) def get_clusters(self, voxels=None): if voxels is None: voxels = self.occupied clusters = [] assigned = set() def dfs(current): if (current in assigned) or (not self.is_occupied(current)): return [] cluster = [current] assigned.add(current) for neighbor in self.get_neighbors(current): cluster.extend(dfs(neighbor)) return cluster for voxel in voxels: cluster = dfs(voxel) if cluster: clusters.append(cluster) return clusters # TODO: implicitly check collisions def create_box(self): color = (0, 0, 0, 0) #color = None box = create_box(*self.resolutions, color=color) #set_color(box, color=color) set_pose(box, self.world_from_grid) return box def get_affected(self, bodies, occupied): assert self.world_from_grid == unit_pose() check_voxels = {} for body in bodies: for link in get_all_links(body): aabb = get_aabb(body, link) # TODO: pad using threshold for voxel in self.voxels_from_aabb(aabb): if self.is_occupied(voxel) == occupied: check_voxels.setdefault(voxel, []).append((body, link)) return check_voxels def check_collision(self, box, voxel, pairs, threshold=0.): box_pairs = [(box, link) for link in get_all_links(box)] set_pose(box, self.pose_from_voxel(voxel)) return any(pairwise_link_collision(body1, link1, body2, link2, max_distance=threshold) for (body1, link1), (body2, link2) in product(pairs, box_pairs)) def add_point(self, point): self.set_occupied(self.voxel_from_point(point)) def add_aabb(self, aabb): for voxel in self.voxels_from_aabb(aabb): self.set_occupied(voxel) def add_bodies(self, bodies, threshold=0.): # Otherwise, need to transform bodies check_voxels = self.get_affected(bodies, occupied=False) box = self.create_box() for voxel, pairs in check_voxels.items(): # pairs typically only has one element if self.check_collision(box, voxel, pairs, threshold=threshold): self.set_occupied(voxel) remove_body(box) def remove_bodies(self, bodies, threshold=1e-2): # TODO: could also just iterate over the voxels directly check_voxels = self.get_affected(bodies, occupied=True) box = self.create_box() for voxel, pairs in check_voxels.items(): if self.check_collision(box, voxel, pairs, threshold=threshold): self.set_free(voxel) remove_body(box) def draw_voxel_bodies(self): # TODO: transform into the world frame with LockRenderer(): handles = [] for voxel in sorted(self.occupied): handles.extend(draw_aabb(self.aabb_from_voxel(voxel), color=self.color[:3])) return handles def create_voxel_bodies1(self): start_time = time.time() geometry = get_box_geometry(*self.resolutions) collision_id, visual_id = create_shape(geometry, color=self.color) bodies = [] for voxel in sorted(self.occupied): body = create_body(collision_id, visual_id) #scale = self.resolutions[0] #body = load_model('models/voxel.urdf', fixed_base=True, scale=scale) set_pose(body, self.pose_from_voxel(voxel)) bodies.append(body) # 0.0462474774444 / voxel print(elapsed_time(start_time)) return bodies def create_voxel_bodies2(self): geometry = get_box_geometry(*self.resolutions) collision_id, visual_id = create_shape(geometry, color=self.color) ordered_voxels = sorted(self.occupied) bodies = [] for start in range(0, len(ordered_voxels), MAX_LINKS): voxels = ordered_voxels[start:start + MAX_LINKS] body = p.createMultiBody(#baseMass=STATIC_MASS, #baseCollisionShapeIndex=-1, #baseVisualShapeIndex=-1, #basePosition=unit_point(), #baseOrientation=unit_quat(), #baseInertialFramePosition=unit_point(), #baseInertialFrameOrientation=unit_quat(), linkMasses=len(voxels)*[STATIC_MASS], linkCollisionShapeIndices=len(voxels)*[collision_id], linkVisualShapeIndices=len(voxels)*[visual_id], linkPositions=list(map(self.center_from_voxel, voxels)), linkOrientations=len(voxels)*[unit_quat()], linkInertialFramePositions=len(voxels)*[unit_point()], linkInertialFrameOrientations=len(voxels)*[unit_quat()], linkParentIndices=len(voxels)*[0], linkJointTypes=len(voxels)*[p.JOINT_FIXED], linkJointAxis=len(voxels)*[unit_point()], physicsClientId=CLIENT) set_pose(body, self.world_from_grid) bodies.append(body) # 0.0163199263677 / voxel return bodies def create_voxel_bodies3(self): ordered_voxels = sorted(self.occupied) geoms = [get_box_geometry(*self.resolutions) for _ in ordered_voxels] poses = list(map(self.pose_from_voxel, ordered_voxels)) #colors = [list(self.color) for _ in self.voxels] # TODO: colors don't work colors = None collision_id, visual_id = create_shape_array(geoms, poses, colors) body = create_body(collision_id, visual_id) # Max seems to be 16 #dump_body(body) set_color(body, self.color) return [body] def create_voxel_bodies(self): with LockRenderer(): return self.create_voxel_bodies1() #return self.create_voxel_bodies2() #return self.create_voxel_bodies3() def project2d(self): # TODO: combine adjacent voxels into larger lines # TODO: greedy algorithm that combines lines/boxes tallest_voxel = {} for i, j, k in self.occupied: tallest_voxel[i, j] = max(k, tallest_voxel.get((i, j), k)) return {(i, j, k) for (i, j), k in tallest_voxel.items()} def create_height_map(self, plane, plane_size, width=MAX_TEXTURE_WIDTH, height=MAX_TEXTURE_WIDTH): min_z, max_z = 0., 2. plane_extent = plane_size*np.array([1, 1, 0]) plane_lower = get_point(plane) - plane_extent/2. #plane_aabb = (plane_lower, plane_lower + plane_extent) #plane_aabb = get_aabb(plane) # TODO: bounding box is effectively empty #plane_lower, plane_upper = plane_aabb #plane_extent = (plane_upper - plane_lower) image_size = np.array([width, height]) # TODO: fix width/height order pixel_from_point = lambda point: np.floor( image_size * (point - plane_lower)[:2] / plane_extent[:2]).astype(int) # TODO: last row/col doesn't seem to be filled height_map = np.zeros(image_size) for voxel in self.project2d(): voxel_aabb = self.aabb_from_voxel(voxel) #if not aabb_contains_aabb(aabb2d_from_aabb(voxel_aabb), aabb2d_from_aabb(plane_aabb)): # continue (x1, y1), (x2, y2) = map(pixel_from_point, voxel_aabb) if (x1 < 0) or (width <= x2) or (y1 < 0) or (height <= y2): continue scaled_z = (clip(voxel_aabb[1][2], min_z, max_z) - min_z) / max_z for c in range(x1, x2+1): for y in range(y1, y2+1): r = height - y - 1 # TODO: can also just set in bulk if using height_map height_map[r, c] = max(height_map[r, c], scaled_z) return height_map def create_textured_square(size, color=None, width=MAX_TEXTURE_WIDTH, height=MAX_TEXTURE_WIDTH): body = load_model('models/square.urdf', scale=size) if color is not None: set_color(body, color) path = os.path.join(TEMP_DIR, 'texture.png') image = MAX_PIXEL_VALUE*np.ones((width, height, 3), dtype=np.uint8) import scipy.misc scipy.misc.imsave(path, image) texture = p.loadTexture(path) p.changeVisualShape(body, NULL_ID, textureUniqueId=texture, physicsClientId=CLIENT) return body, texture def set_texture(texture, image): # Alias/WaveFront Material (.mtl) File Format # https://people.cs.clemson.edu/~dhouse/courses/405/docs/brief-mtl-file-format.html #print(get_visual_data(body)) width, height, channels = image.shape pixels = image.flatten().tolist() assert len(pixels) <= 524288 # b3Printf: uploadBulletFileToSharedMemory 747003 exceeds max size 524288 p.changeTexture(texture, pixels, width, height, physicsClientId=CLIENT) # TODO: it's important that width and height are the same as the original def rgb_interpolate(grey_image, min_color, max_color): width, height = grey_image.shape channels = 3 rgb_image =

np.zeros((width, height, channels), dtype=np.uint8)

numpy.zeros

# -*- coding: utf-8 -*- from __future__ import print_function from __future__ import absolute_import import os import sys import tempfile import warnings import numpy from numpy import testing as npt import tables from tables import Atom, ClosedNodeError, NoSuchNodeError from tables.utils import byteorders from tables.tests import common from tables.tests.common import allequal from tables.tests.common import unittest, test_filename from tables.tests.common import PyTablesTestCase as TestCase from six.moves import range #warnings.resetwarnings() class BasicTestCase(TestCase): """Basic test for all the supported typecodes present in numpy. All of them are included on pytables. """ endiancheck = False def write_read(self, testarray): a = testarray if common.verbose: print('\n', '-=' * 30) print("Running test for array with type '%s'" % a.dtype.type, end=' ') print("for class check:", self.title) # Create an instance of HDF5 file filename = tempfile.mktemp(".h5") try: with tables.open_file(filename, mode="w") as fileh: root = fileh.root # Create the array under root and name 'somearray' if self.endiancheck and a.dtype.kind != "S": b = a.byteswap() b.dtype = a.dtype.newbyteorder() a = b fileh.create_array(root, 'somearray', a, "Some array") # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: root = fileh.root # Read the saved array b = root.somearray.read() # Compare them. They should be equal. if common.verbose and not allequal(a, b): print("Write and read arrays differ!") # print("Array written:", a) print("Array written shape:", a.shape) print("Array written itemsize:", a.itemsize) print("Array written type:", a.dtype.type) # print("Array read:", b) print("Array read shape:", b.shape) print("Array read itemsize:", b.itemsize) print("Array read type:", b.dtype.type) if a.dtype.kind != "S": print("Array written byteorder:", a.dtype.byteorder) print("Array read byteorder:", b.dtype.byteorder) # Check strictly the array equality self.assertEqual(a.shape, b.shape) self.assertEqual(a.shape, root.somearray.shape) if a.dtype.kind == "S": self.assertEqual(root.somearray.atom.type, "string") else: self.assertEqual(a.dtype.type, b.dtype.type) self.assertEqual(a.dtype.type, root.somearray.atom.dtype.type) abo = byteorders[a.dtype.byteorder] bbo = byteorders[b.dtype.byteorder] if abo != "irrelevant": self.assertEqual(abo, root.somearray.byteorder) self.assertEqual(bbo, sys.byteorder) if self.endiancheck: self.assertNotEqual(bbo, abo) obj = root.somearray self.assertEqual(obj.flavor, 'numpy') self.assertEqual(obj.shape, a.shape) self.assertEqual(obj.ndim, a.ndim) self.assertEqual(obj.chunkshape, None) if a.shape: nrows = a.shape[0] else: # scalar nrows = 1 self.assertEqual(obj.nrows, nrows) self.assertTrue(allequal(a, b)) finally: # Then, delete the file os.remove(filename) def write_read_out_arg(self, testarray): a = testarray if common.verbose: print('\n', '-=' * 30) print("Running test for array with type '%s'" % a.dtype.type, end=' ') print("for class check:", self.title) # Create an instance of HDF5 file filename = tempfile.mktemp(".h5") try: with tables.open_file(filename, mode="w") as fileh: root = fileh.root # Create the array under root and name 'somearray' if self.endiancheck and a.dtype.kind != "S": b = a.byteswap() b.dtype = a.dtype.newbyteorder() a = b fileh.create_array(root, 'somearray', a, "Some array") # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: root = fileh.root # Read the saved array b = numpy.empty_like(a, dtype=a.dtype) root.somearray.read(out=b) # Check strictly the array equality self.assertEqual(a.shape, b.shape) self.assertEqual(a.shape, root.somearray.shape) if a.dtype.kind == "S": self.assertEqual(root.somearray.atom.type, "string") else: self.assertEqual(a.dtype.type, b.dtype.type) self.assertEqual(a.dtype.type, root.somearray.atom.dtype.type) abo = byteorders[a.dtype.byteorder] bbo = byteorders[b.dtype.byteorder] if abo != "irrelevant": self.assertEqual(abo, root.somearray.byteorder) self.assertEqual(abo, bbo) if self.endiancheck: self.assertNotEqual(bbo, sys.byteorder) self.assertTrue(allequal(a, b)) finally: # Then, delete the file os.remove(filename) def write_read_atom_shape_args(self, testarray): a = testarray atom = Atom.from_dtype(a.dtype) shape = a.shape byteorder = None if common.verbose: print('\n', '-=' * 30) print("Running test for array with type '%s'" % a.dtype.type, end=' ') print("for class check:", self.title) # Create an instance of HDF5 file filename = tempfile.mktemp(".h5") try: with tables.open_file(filename, mode="w") as fileh: root = fileh.root # Create the array under root and name 'somearray' if self.endiancheck and a.dtype.kind != "S": b = a.byteswap() b.dtype = a.dtype.newbyteorder() if b.dtype.byteorder in ('>', '<'): byteorder = byteorders[b.dtype.byteorder] a = b ptarr = fileh.create_array(root, 'somearray', atom=atom, shape=shape, title="Some array", # specify the byteorder explicitly # since there is no way to deduce # it in this case byteorder=byteorder) self.assertEqual(shape, ptarr.shape) self.assertEqual(atom, ptarr.atom) ptarr[...] = a # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: root = fileh.root # Read the saved array b = root.somearray.read() # Compare them. They should be equal. if common.verbose and not allequal(a, b): print("Write and read arrays differ!") # print("Array written:", a) print("Array written shape:", a.shape) print("Array written itemsize:", a.itemsize) print("Array written type:", a.dtype.type) # print("Array read:", b) print("Array read shape:", b.shape) print("Array read itemsize:", b.itemsize) print("Array read type:", b.dtype.type) if a.dtype.kind != "S": print("Array written byteorder:", a.dtype.byteorder) print("Array read byteorder:", b.dtype.byteorder) # Check strictly the array equality self.assertEqual(a.shape, b.shape) self.assertEqual(a.shape, root.somearray.shape) if a.dtype.kind == "S": self.assertEqual(root.somearray.atom.type, "string") else: self.assertEqual(a.dtype.type, b.dtype.type) self.assertEqual(a.dtype.type, root.somearray.atom.dtype.type) abo = byteorders[a.dtype.byteorder] bbo = byteorders[b.dtype.byteorder] if abo != "irrelevant": self.assertEqual(abo, root.somearray.byteorder) self.assertEqual(bbo, sys.byteorder) if self.endiancheck: self.assertNotEqual(bbo, abo) obj = root.somearray self.assertEqual(obj.flavor, 'numpy') self.assertEqual(obj.shape, a.shape) self.assertEqual(obj.ndim, a.ndim) self.assertEqual(obj.chunkshape, None) if a.shape: nrows = a.shape[0] else: # scalar nrows = 1 self.assertEqual(obj.nrows, nrows) self.assertTrue(allequal(a, b)) finally: # Then, delete the file os.remove(filename) def setup00_char(self): """Data integrity during recovery (character objects)""" if not isinstance(self.tupleChar, numpy.ndarray): a = numpy.array(self.tupleChar, dtype="S") else: a = self.tupleChar return a def test00_char(self): a = self.setup00_char() self.write_read(a) def test00_char_out_arg(self): a = self.setup00_char() self.write_read_out_arg(a) def test00_char_atom_shape_args(self): a = self.setup00_char() self.write_read_atom_shape_args(a) def test00b_char(self): """Data integrity during recovery (string objects)""" a = self.tupleChar filename = tempfile.mktemp(".h5") try: # Create an instance of HDF5 file with tables.open_file(filename, mode="w") as fileh: fileh.create_array(fileh.root, 'somearray', a, "Some array") # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: # Read the saved array b = fileh.root.somearray.read() if isinstance(a, bytes): self.assertEqual(type(b), bytes) self.assertEqual(a, b) else: # If a is not a python string, then it should be a list # or ndarray self.assertTrue(type(b) in [list, numpy.ndarray]) finally: # Then, delete the file os.remove(filename) def test00b_char_out_arg(self): """Data integrity during recovery (string objects)""" a = self.tupleChar filename = tempfile.mktemp(".h5") try: # Create an instance of HDF5 file with tables.open_file(filename, mode="w") as fileh: fileh.create_array(fileh.root, 'somearray', a, "Some array") # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: # Read the saved array b = numpy.empty_like(a) if fileh.root.somearray.flavor != 'numpy': self.assertRaises(TypeError, lambda: fileh.root.somearray.read(out=b)) else: fileh.root.somearray.read(out=b) self.assertTrue(type(b), numpy.ndarray) finally: # Then, delete the file os.remove(filename) def test00b_char_atom_shape_args(self): """Data integrity during recovery (string objects)""" a = self.tupleChar filename = tempfile.mktemp(".h5") try: # Create an instance of HDF5 file with tables.open_file(filename, mode="w") as fileh: nparr = numpy.asarray(a) atom = Atom.from_dtype(nparr.dtype) shape = nparr.shape if nparr.dtype.byteorder in ('>', '<'): byteorder = byteorders[nparr.dtype.byteorder] else: byteorder = None ptarr = fileh.create_array(fileh.root, 'somearray', atom=atom, shape=shape, byteorder=byteorder, title="Some array") self.assertEqual(shape, ptarr.shape) self.assertEqual(atom, ptarr.atom) ptarr[...] = a # Re-open the file in read-only mode with tables.open_file(filename, mode="r") as fileh: # Read the saved array b = numpy.empty_like(a) if fileh.root.somearray.flavor != 'numpy': self.assertRaises(TypeError, lambda: fileh.root.somearray.read(out=b)) else: fileh.root.somearray.read(out=b) self.assertTrue(type(b), numpy.ndarray) finally: # Then, delete the file os.remove(filename) def setup01_char_nc(self): """Data integrity during recovery (non-contiguous character objects)""" if not isinstance(self.tupleChar, numpy.ndarray): a = numpy.array(self.tupleChar, dtype="S") else: a = self.tupleChar if a.ndim == 0: b = a.copy() else: b = a[::2] # Ensure that this numpy string is non-contiguous if len(b) > 1: self.assertEqual(b.flags.contiguous, False) return b def test01_char_nc(self): b = self.setup01_char_nc() self.write_read(b) def test01_char_nc_out_arg(self): b = self.setup01_char_nc() self.write_read_out_arg(b) def test01_char_nc_atom_shape_args(self): b = self.setup01_char_nc() self.write_read_atom_shape_args(b) def test02_types(self): """Data integrity during recovery (numerical types)""" typecodes = ['int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64', 'float32', 'float64', 'complex64', 'complex128'] for name in ('float16', 'float96', 'float128', 'complex192', 'complex256'): atomname = name.capitalize() + 'Atom' if hasattr(tables, atomname): typecodes.append(name) for typecode in typecodes: a = numpy.array(self.tupleInt, typecode) self.write_read(a) b = numpy.array(self.tupleInt, typecode) self.write_read_out_arg(b) c = numpy.array(self.tupleInt, typecode) self.write_read_atom_shape_args(c) def test03_types_nc(self): """Data integrity during recovery (non-contiguous numerical types)""" typecodes = ['int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64', 'float32', 'float64', 'complex64', 'complex128', ] for name in ('float16', 'float96', 'float128', 'complex192', 'complex256'): atomname = name.capitalize() + 'Atom' if hasattr(tables, atomname): typecodes.append(name) for typecode in typecodes: a = numpy.array(self.tupleInt, typecode) if a.ndim == 0: b1 = a.copy() b2 = a.copy() b3 = a.copy() else: b1 = a[::2] b2 = a[::2] b3 = a[::2] # Ensure that this array is non-contiguous if len(b1) > 1: self.assertEqual(b1.flags.contiguous, False) if len(b2) > 1: self.assertEqual(b2.flags.contiguous, False) if len(b3) > 1: self.assertEqual(b3.flags.contiguous, False) self.write_read(b1) self.write_read_out_arg(b2) self.write_read_atom_shape_args(b3) class Basic0DOneTestCase(BasicTestCase): # Scalar case title = "Rank-0 case 1" tupleInt = 3 tupleChar = b"3" endiancheck = True class Basic0DTwoTestCase(BasicTestCase): # Scalar case title = "Rank-0 case 2" tupleInt = 33 tupleChar = b"33" endiancheck = True class Basic1DZeroTestCase(BasicTestCase): # This test case is not supported by PyTables (HDF5 limitations) # 1D case title = "Rank-1 case 0" tupleInt = () tupleChar = () endiancheck = False class Basic1DOneTestCase(BasicTestCase): # 1D case title = "Rank-1 case 1" tupleInt = (3,) tupleChar = (b"a",) endiancheck = True class Basic1DTwoTestCase(BasicTestCase): # 1D case title = "Rank-1 case 2" tupleInt = (3, 4) tupleChar = (b"aaa",) endiancheck = True class Basic1DThreeTestCase(BasicTestCase): # 1D case title = "Rank-1 case 3" tupleInt = (3, 4, 5) tupleChar = (b"aaa", b"bbb",) endiancheck = True class Basic2DOneTestCase(BasicTestCase): # 2D case title = "Rank-2 case 1" tupleInt = numpy.array(numpy.arange((4)**2)) tupleInt.shape = (4,)*2 tupleChar = numpy.array(["abc"]*3**2, dtype="S3") tupleChar.shape = (3,)*2 endiancheck = True class Basic2DTwoTestCase(BasicTestCase): # 2D case, with a multidimensional dtype title = "Rank-2 case 2" tupleInt = numpy.array(numpy.arange((4)), dtype=(numpy.int_, (4,))) tupleChar = numpy.array(["abc"]*3, dtype=("S3", (3,))) endiancheck = True class Basic10DTestCase(BasicTestCase): # 10D case title = "Rank-10 test" tupleInt = numpy.array(numpy.arange((2)**10)) tupleInt.shape = (2,)*10 tupleChar = numpy.array( ["abc"]*2**10, dtype="S3") tupleChar.shape = (2,)*10 endiancheck = True class Basic32DTestCase(BasicTestCase): # 32D case (maximum) title = "Rank-32 test" tupleInt = numpy.array((32,)) tupleInt.shape = (1,)*32 tupleChar = numpy.array(["121"], dtype="S3") tupleChar.shape = (1,)*32 class ReadOutArgumentTests(common.TempFileMixin, TestCase): def setUp(self): super(ReadOutArgumentTests, self).setUp() self.size = 1000 def create_array(self): array = numpy.arange(self.size, dtype='f8') disk_array = self.h5file.create_array('/', 'array', array) return array, disk_array def test_read_entire_array(self): array, disk_array = self.create_array() out_buffer = numpy.empty((self.size, ), 'f8') disk_array.read(out=out_buffer) numpy.testing.assert_equal(out_buffer, array) def test_read_contiguous_slice1(self): array, disk_array = self.create_array() out_buffer = numpy.arange(self.size, dtype='f8') out_buffer = numpy.random.permutation(out_buffer) out_buffer_orig = out_buffer.copy() start = self.size // 2 disk_array.read(start=start, stop=self.size, out=out_buffer[start:]) numpy.testing.assert_equal(out_buffer[start:], array[start:]) numpy.testing.assert_equal(out_buffer[:start], out_buffer_orig[:start]) def test_read_contiguous_slice2(self): array, disk_array = self.create_array() out_buffer = numpy.arange(self.size, dtype='f8') out_buffer = numpy.random.permutation(out_buffer) out_buffer_orig = out_buffer.copy() start = self.size // 4 stop = self.size - start disk_array.read(start=start, stop=stop, out=out_buffer[start:stop]) numpy.testing.assert_equal(out_buffer[start:stop], array[start:stop]) numpy.testing.assert_equal(out_buffer[:start], out_buffer_orig[:start]) numpy.testing.assert_equal(out_buffer[stop:], out_buffer_orig[stop:]) def test_read_non_contiguous_slice_contiguous_buffer(self): array, disk_array = self.create_array() out_buffer = numpy.empty((self.size // 2, ), dtype='f8') disk_array.read(start=0, stop=self.size, step=2, out=out_buffer) numpy.testing.assert_equal(out_buffer, array[0:self.size:2]) def test_read_non_contiguous_buffer(self): array, disk_array = self.create_array() out_buffer = numpy.empty((self.size, ), 'f8') out_buffer_slice = out_buffer[0:self.size:2] # once Python 2.6 support is dropped, this could change # to assertRaisesRegexp to check exception type and message at once self.assertRaises(ValueError, disk_array.read, 0, self.size, 2, out_buffer_slice) try: disk_array.read(0, self.size, 2, out_buffer_slice) except ValueError as exc: self.assertEqual('output array not C contiguous', str(exc)) def test_buffer_too_small(self): array, disk_array = self.create_array() out_buffer = numpy.empty((self.size // 2, ), 'f8') self.assertRaises(ValueError, disk_array.read, 0, self.size, 1, out_buffer) try: disk_array.read(0, self.size, 1, out_buffer) except ValueError as exc: self.assertTrue('output array size invalid, got' in str(exc)) def test_buffer_too_large(self): array, disk_array = self.create_array() out_buffer = numpy.empty((self.size + 1, ), 'f8') self.assertRaises(ValueError, disk_array.read, 0, self.size, 1, out_buffer) try: disk_array.read(0, self.size, 1, out_buffer) except ValueError as exc: self.assertTrue('output array size invalid, got' in str(exc)) class SizeOnDiskInMemoryPropertyTestCase(common.TempFileMixin, TestCase): def setUp(self): super(SizeOnDiskInMemoryPropertyTestCase, self).setUp() self.array_size = (10, 10) self.array = self.h5file.create_array( '/', 'somearray', numpy.zeros(self.array_size, 'i4')) def test_all_zeros(self): self.assertEqual(self.array.size_on_disk, 10 * 10 * 4) self.assertEqual(self.array.size_in_memory, 10 * 10 * 4) class UnalignedAndComplexTestCase(common.TempFileMixin, TestCase): """Basic test for all the supported typecodes present in numpy. Most of them are included on PyTables. """ def setUp(self): super(UnalignedAndComplexTestCase, self).setUp() self.root = self.h5file.root def write_read(self, testArray): if common.verbose: print('\n', '-=' * 30) print("\nRunning test for array with type '%s'" % testArray.dtype.type) # Create the array under root and name 'somearray' a = testArray if self.endiancheck: byteorder = {"little": "big", "big": "little"}[sys.byteorder] else: byteorder = sys.byteorder self.h5file.create_array(self.root, 'somearray', a, "Some array", byteorder=byteorder) if self.reopen: self._reopen() self.root = self.h5file.root # Read the saved array b = self.root.somearray.read() # Get an array to be compared in the correct byteorder c = a.newbyteorder(byteorder) # Compare them. They should be equal. if not allequal(c, b) and common.verbose: print("Write and read arrays differ!") print("Array written:", a) print("Array written shape:", a.shape) print("Array written itemsize:", a.itemsize) print("Array written type:", a.dtype.type) print("Array read:", b) print("Array read shape:", b.shape) print("Array read itemsize:", b.itemsize) print("Array read type:", b.dtype.type) # Check strictly the array equality self.assertEqual(a.shape, b.shape) self.assertEqual(a.shape, self.root.somearray.shape) if a.dtype.byteorder != "|": self.assertEqual(a.dtype, b.dtype) self.assertEqual(a.dtype, self.root.somearray.atom.dtype) self.assertEqual(byteorders[b.dtype.byteorder], sys.byteorder) self.assertEqual(self.root.somearray.byteorder, byteorder) self.assertTrue(allequal(c, b)) def test01_signedShort_unaligned(self): """Checking an unaligned signed short integer array""" r = numpy.rec.array(b'a'*200, formats='i1,f4,i2', shape=10) a = r["f2"] # Ensure that this array is non-aligned self.assertEqual(a.flags.aligned, False) self.assertEqual(a.dtype.type, numpy.int16) self.write_read(a) def test02_float_unaligned(self): """Checking an unaligned single precision array""" r = numpy.rec.array(b'a'*200, formats='i1,f4,i2', shape=10) a = r["f1"] # Ensure that this array is non-aligned self.assertEqual(a.flags.aligned, 0) self.assertEqual(a.dtype.type, numpy.float32) self.write_read(a) def test03_byte_offset(self): """Checking an offsetted byte array""" r = numpy.arange(100, dtype=numpy.int8) r.shape = (10, 10) a = r[2] self.write_read(a) def test04_short_offset(self): """Checking an offsetted unsigned short int precision array""" r = numpy.arange(100, dtype=numpy.uint32) r.shape = (10, 10) a = r[2] self.write_read(a) def test05_int_offset(self): """Checking an offsetted integer array""" r = numpy.arange(100, dtype=numpy.int32) r.shape = (10, 10) a = r[2] self.write_read(a) def test06_longlongint_offset(self): """Checking an offsetted long long integer array""" r = numpy.arange(100, dtype=numpy.int64) r.shape = (10, 10) a = r[2] self.write_read(a) def test07_float_offset(self): """Checking an offsetted single precision array""" r = numpy.arange(100, dtype=numpy.float32) r.shape = (10, 10) a = r[2] self.write_read(a) def test08_double_offset(self): """Checking an offsetted double precision array""" r = numpy.arange(100, dtype=numpy.float64) r.shape = (10, 10) a = r[2] self.write_read(a) def test09_float_offset_unaligned(self): """Checking an unaligned and offsetted single precision array""" r = numpy.rec.array(b'a'*200, formats='i1,3f4,i2', shape=10) a = r["f1"][3] # Ensure that this array is non-aligned self.assertEqual(a.flags.aligned, False) self.assertEqual(a.dtype.type, numpy.float32) self.write_read(a) def test10_double_offset_unaligned(self): """Checking an unaligned and offsetted double precision array""" r = numpy.rec.array(b'a'*400, formats='i1,3f8,i2', shape=10) a = r["f1"][3] # Ensure that this array is non-aligned self.assertEqual(a.flags.aligned, False) self.assertEqual(a.dtype.type, numpy.float64) self.write_read(a) def test11_int_byteorder(self): """Checking setting data with different byteorder in a range (integer)""" # Save an array with the reversed byteorder on it a = numpy.arange(25, dtype=numpy.int32).reshape(5, 5) a = a.byteswap() a = a.newbyteorder() array = self.h5file.create_array( self.h5file.root, 'array', a, "byteorder (int)") # Read a subarray (got an array with the machine byteorder) b = array[2:4, 3:5] b = b.byteswap() b = b.newbyteorder() # Set this subarray back to the array array[2:4, 3:5] = b b = b.byteswap() b = b.newbyteorder() # Set this subarray back to the array array[2:4, 3:5] = b # Check that the array is back in the correct byteorder c = array[...] if common.verbose: print("byteorder of array on disk-->", array.byteorder) print("byteorder of subarray-->", b.dtype.byteorder) print("subarray-->", b) print("retrieved array-->", c) self.assertTrue(allequal(a, c)) def test12_float_byteorder(self): """Checking setting data with different byteorder in a range (float)""" # Save an array with the reversed byteorder on it a = numpy.arange(25, dtype=numpy.float64).reshape(5, 5) a = a.byteswap() a = a.newbyteorder() array = self.h5file.create_array( self.h5file.root, 'array', a, "byteorder (float)") # Read a subarray (got an array with the machine byteorder) b = array[2:4, 3:5] b = b.byteswap() b = b.newbyteorder() # Set this subarray back to the array array[2:4, 3:5] = b b = b.byteswap() b = b.newbyteorder() # Set this subarray back to the array array[2:4, 3:5] = b # Check that the array is back in the correct byteorder c = array[...] if common.verbose: print("byteorder of array on disk-->", array.byteorder) print("byteorder of subarray-->", b.dtype.byteorder) print("subarray-->", b) print("retrieved array-->", c) self.assertTrue(allequal(a, c)) class ComplexNotReopenNotEndianTestCase(UnalignedAndComplexTestCase): endiancheck = False reopen = False class ComplexReopenNotEndianTestCase(UnalignedAndComplexTestCase): endiancheck = False reopen = True class ComplexNotReopenEndianTestCase(UnalignedAndComplexTestCase): endiancheck = True reopen = False class ComplexReopenEndianTestCase(UnalignedAndComplexTestCase): endiancheck = True reopen = True class GroupsArrayTestCase(common.TempFileMixin, TestCase): """This test class checks combinations of arrays with groups.""" def test00_iterativeGroups(self): """Checking combinations of arrays with groups.""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test00_iterativeGroups..." % self.__class__.__name__) # Get the root group group = self.h5file.root # Set the type codes to test # The typecodes below does expose an ambiguity that is reported in: # http://projects.scipy.org/scipy/numpy/ticket/283 and # http://projects.scipy.org/scipy/numpy/ticket/290 typecodes = ['b', 'B', 'h', 'H', 'i', 'I', 'l', 'L', 'q', 'f', 'd', 'F', 'D'] if hasattr(tables, 'Float16Atom'): typecodes.append('e') if hasattr(tables, 'Float96Atom') or hasattr(tables, 'Float128Atom'): typecodes.append('g') if (hasattr(tables, 'Complex192Atom') or hasattr(tables, 'Complex256Atom')): typecodes.append('G') for i, typecode in enumerate(typecodes): a = numpy.ones((3,), typecode) dsetname = 'array_' + typecode if common.verbose: print("Creating dataset:", group._g_join(dsetname)) self.h5file.create_array(group, dsetname, a, "Large array") group = self.h5file.create_group(group, 'group' + str(i)) # Reopen the file self._reopen() # Get the root group group = self.h5file.root # Get the metadata on the previosly saved arrays for i in range(len(typecodes)): # Create an array for later comparison a = numpy.ones((3,), typecodes[i]) # Get the dset object hanging from group dset = getattr(group, 'array_' + typecodes[i]) # Get the actual array b = dset.read() if common.verbose: print("Info from dataset:", dset._v_pathname) print(" shape ==>", dset.shape, end=' ') print(" type ==> %s" % dset.atom.dtype) print("Array b read from file. Shape: ==>", b.shape, end=' ') print(". Type ==> %s" % b.dtype) self.assertEqual(a.shape, b.shape) self.assertEqual(a.dtype, b.dtype) self.assertTrue(allequal(a, b)) # Iterate over the next group group = getattr(group, 'group' + str(i)) def test01_largeRankArrays(self): """Checking creation of large rank arrays (0 < rank <= 32) It also uses arrays ranks which ranges until maxrank. """ # maximum level of recursivity (deepest group level) achieved: # maxrank = 32 (for a effective maximum rank of 32) # This limit is due to HDF5 library limitations. minrank = 1 maxrank = 32 if common.verbose: print('\n', '-=' * 30) print("Running %s.test01_largeRankArrays..." % self.__class__.__name__) print("Maximum rank for tested arrays:", maxrank) group = self.h5file.root if common.verbose: print("Rank array writing progress: ", end=' ') for rank in range(minrank, maxrank + 1): # Create an array of integers, with incrementally bigger ranges a = numpy.ones((1,) * rank, numpy.int32) if common.verbose: print("%3d," % (rank), end=' ') self.h5file.create_array(group, "array", a, "Rank: %s" % rank) group = self.h5file.create_group(group, 'group' + str(rank)) # Reopen the file self._reopen() group = self.h5file.root if common.verbose: print() print("Rank array reading progress: ") # Get the metadata on the previosly saved arrays for rank in range(minrank, maxrank + 1): # Create an array for later comparison a = numpy.ones((1,) * rank, numpy.int32) # Get the actual array b = group.array.read() if common.verbose: print("%3d," % (rank), end=' ') if common.verbose and not allequal(a, b): print("Info from dataset:", group.array._v_pathname) print(" Shape: ==>", group.array.shape, end=' ') print(" typecode ==> %c" % group.array.typecode) print("Array b read from file. Shape: ==>", b.shape, end=' ') print(". Type ==> %c" % b.dtype) self.assertEqual(a.shape, b.shape) self.assertEqual(a.dtype, b.dtype) self.assertTrue(allequal(a, b)) # print(self.h5file) # Iterate over the next group group = self.h5file.get_node(group, 'group' + str(rank)) if common.verbose: print() # This flush the stdout buffer class CopyTestCase(common.TempFileMixin, TestCase): def test01_copy(self): """Checking Array.copy() method.""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test01_copy..." % self.__class__.__name__) # Create an Array arr = numpy.array([[456, 2], [3, 457]], dtype='int16') array1 = self.h5file.create_array( self.h5file.root, 'array1', arr, "title array1") # Copy to another Array array2 = array1.copy('/', 'array2') if self.close: if common.verbose: print("(closing file version)") self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.array2 if common.verbose: print("array1-->", array1.read()) print("array2-->", array2.read()) # print("dirs-->", dir(array1), dir(array2)) print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Check that all the elements are equal self.assertTrue(allequal(array1.read(), array2.read())) # Assert other properties in array self.assertEqual(array1.nrows, array2.nrows) self.assertEqual(array1.flavor, array2.flavor) self.assertEqual(array1.atom.dtype, array2.atom.dtype) self.assertEqual(array1.title, array2.title) def test02_copy(self): """Checking Array.copy() method (where specified)""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test02_copy..." % self.__class__.__name__) # Create an Array arr = numpy.array([[456, 2], [3, 457]], dtype='int16') array1 = self.h5file.create_array( self.h5file.root, 'array1', arr, "title array1") # Copy to another Array group1 = self.h5file.create_group("/", "group1") array2 = array1.copy(group1, 'array2') if self.close: if common.verbose: print("(closing file version)") self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.group1.array2 if common.verbose: print("array1-->", array1.read()) print("array2-->", array2.read()) # print("dirs-->", dir(array1), dir(array2)) print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Check that all the elements are equal self.assertTrue(allequal(array1.read(), array2.read())) # Assert other properties in array self.assertEqual(array1.nrows, array2.nrows) self.assertEqual(array1.flavor, array2.flavor) self.assertEqual(array1.atom.dtype, array2.atom.dtype) self.assertEqual(array1.title, array2.title) def test03_copy(self): """Checking Array.copy() method (checking title copying)""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test04_copy..." % self.__class__.__name__) # Create an Array arr = numpy.array([[456, 2], [3, 457]], dtype='int16') array1 = self.h5file.create_array( self.h5file.root, 'array1', arr, "title array1") # Append some user attrs array1.attrs.attr1 = "attr1" array1.attrs.attr2 = 2 # Copy it to another Array array2 = array1.copy('/', 'array2', title="title array2") if self.close: if common.verbose: print("(closing file version)") self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.array2 # Assert user attributes if common.verbose: print("title of destination array-->", array2.title) self.assertEqual(array2.title, "title array2") def test04_copy(self): """Checking Array.copy() method (user attributes copied)""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test05_copy..." % self.__class__.__name__) # Create an Array arr = numpy.array([[456, 2], [3, 457]], dtype='int16') array1 = self.h5file.create_array( self.h5file.root, 'array1', arr, "title array1") # Append some user attrs array1.attrs.attr1 = "attr1" array1.attrs.attr2 = 2 # Copy it to another Array array2 = array1.copy('/', 'array2', copyuserattrs=1) if self.close: if common.verbose: print("(closing file version)") self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.array2 if common.verbose: print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Assert user attributes self.assertEqual(array2.attrs.attr1, "attr1") self.assertEqual(array2.attrs.attr2, 2) def test04b_copy(self): """Checking Array.copy() method (user attributes not copied)""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test05b_copy..." % self.__class__.__name__) # Create an Array arr = numpy.array([[456, 2], [3, 457]], dtype='int16') array1 = self.h5file.create_array( self.h5file.root, 'array1', arr, "title array1") # Append some user attrs array1.attrs.attr1 = "attr1" array1.attrs.attr2 = 2 # Copy it to another Array array2 = array1.copy('/', 'array2', copyuserattrs=0) if self.close: if common.verbose: print("(closing file version)") self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.array2 if common.verbose: print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Assert user attributes self.assertEqual(hasattr(array2.attrs, "attr1"), 0) self.assertEqual(hasattr(array2.attrs, "attr2"), 0) class CloseCopyTestCase(CopyTestCase): close = 1 class OpenCopyTestCase(CopyTestCase): close = 0 class CopyIndexTestCase(common.TempFileMixin, TestCase): def test01_index(self): """Checking Array.copy() method with indexes.""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test01_index..." % self.__class__.__name__) # Create a numpy r = numpy.arange(200, dtype='int32') r.shape = (100, 2) # Save it in a array: array1 = self.h5file.create_array( self.h5file.root, 'array1', r, "title array1") # Copy to another array array2 = array1.copy("/", 'array2', start=self.start, stop=self.stop, step=self.step) if common.verbose: print("array1-->", array1.read()) print("array2-->", array2.read()) print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Check that all the elements are equal r2 = r[self.start:self.stop:self.step] self.assertTrue(allequal(r2, array2.read())) # Assert the number of rows in array if common.verbose: print("nrows in array2-->", array2.nrows) print("and it should be-->", r2.shape[0]) self.assertEqual(r2.shape[0], array2.nrows) def test02_indexclosef(self): """Checking Array.copy() method with indexes (close file version)""" if common.verbose: print('\n', '-=' * 30) print("Running %s.test02_indexclosef..." % self.__class__.__name__) # Create a numpy r = numpy.arange(200, dtype='int32') r.shape = (100, 2) # Save it in a array: array1 = self.h5file.create_array( self.h5file.root, 'array1', r, "title array1") # Copy to another array array2 = array1.copy("/", 'array2', start=self.start, stop=self.stop, step=self.step) # Close and reopen the file self._reopen() array1 = self.h5file.root.array1 array2 = self.h5file.root.array2 if common.verbose: print("array1-->", array1.read()) print("array2-->", array2.read()) print("attrs array1-->", repr(array1.attrs)) print("attrs array2-->", repr(array2.attrs)) # Check that all the elements are equal r2 = r[self.start:self.stop:self.step] self.assertTrue(allequal(r2, array2.read())) # Assert the number of rows in array if common.verbose: print("nrows in array2-->", array2.nrows) print("and it should be-->", r2.shape[0]) self.assertEqual(r2.shape[0], array2.nrows) class CopyIndex1TestCase(CopyIndexTestCase): start = 0 stop = 7 step = 1 class CopyIndex2TestCase(CopyIndexTestCase): start = 0 stop = -1 step = 1 class CopyIndex3TestCase(CopyIndexTestCase): start = 1 stop = 7 step = 1 class CopyIndex4TestCase(CopyIndexTestCase): start = 0 stop = 6 step = 1 class CopyIndex5TestCase(CopyIndexTestCase): start = 3 stop = 7 step = 1 class CopyIndex6TestCase(CopyIndexTestCase): start = 3 stop = 6 step = 2 class CopyIndex7TestCase(CopyIndexTestCase): start = 0 stop = 7 step = 10 class CopyIndex8TestCase(CopyIndexTestCase): start = 6 stop = -1 # Negative values means starting from the end step = 1 class CopyIndex9TestCase(CopyIndexTestCase): start = 3 stop = 4 step = 1 class CopyIndex10TestCase(CopyIndexTestCase): start = 3 stop = 4 step = 2 class CopyIndex11TestCase(CopyIndexTestCase): start = -3 stop = -1 step = 2 class CopyIndex12TestCase(CopyIndexTestCase): start = -1 # Should point to the last element stop = None # None should mean the last element (including it) step = 1 class GetItemTestCase(common.TempFileMixin, TestCase): def test00_single(self): """Single element access (character types)""" # Create the array under root and name 'somearray' a = self.charList arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original first element:", a[0], type(a[0])) print("Read first element:", arr[0], type(arr[0])) self.assertTrue(allequal(a[0], arr[0])) self.assertEqual(type(a[0]), type(arr[0])) def test01_single(self): """Single element access (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalList arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original first element:", a[0], type(a[0])) print("Read first element:", arr[0], type(arr[0])) self.assertEqual(a[0], arr[0]) self.assertEqual(type(a[0]), type(arr[0])) def test02_range(self): """Range element access (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original elements:", a[1:4]) print("Read elements:", arr[1:4]) self.assertTrue(allequal(a[1:4], arr[1:4])) def test03_range(self): """Range element access (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original elements:", a[1:4]) print("Read elements:", arr[1:4]) self.assertTrue(allequal(a[1:4], arr[1:4])) def test04_range(self): """Range element access, strided (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original elements:", a[1:4:2]) print("Read elements:", arr[1:4:2]) self.assertTrue(allequal(a[1:4:2], arr[1:4:2])) def test05_range(self): """Range element access, strided (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original elements:", a[1:4:2]) print("Read elements:", arr[1:4:2]) self.assertTrue(allequal(a[1:4:2], arr[1:4:2])) def test06_negativeIndex(self): """Negative Index element access (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original last element:", a[-1]) print("Read last element:", arr[-1]) self.assertTrue(allequal(a[-1], arr[-1])) def test07_negativeIndex(self): """Negative Index element access (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original before last element:", a[-2]) print("Read before last element:", arr[-2]) if isinstance(a[-2], numpy.ndarray): self.assertTrue(allequal(a[-2], arr[-2])) else: self.assertEqual(a[-2], arr[-2]) def test08_negativeRange(self): """Negative range element access (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original last elements:", a[-4:-1]) print("Read last elements:", arr[-4:-1]) self.assertTrue(allequal(a[-4:-1], arr[-4:-1])) def test09_negativeRange(self): """Negative range element access (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen() arr = self.h5file.root.somearray # Get and compare an element if common.verbose: print("Original last elements:", a[-4:-1]) print("Read last elements:", arr[-4:-1]) self.assertTrue(allequal(a[-4:-1], arr[-4:-1])) class GI1NATestCase(GetItemTestCase, TestCase): title = "Rank-1 case 1" numericalList = numpy.array([3]) numericalListME = numpy.array([3, 2, 1, 0, 4, 5, 6]) charList = numpy.array(["3"], 'S') charListME = numpy.array( ["321", "221", "121", "021", "421", "521", "621"], 'S') class GI1NAOpenTestCase(GI1NATestCase): close = 0 class GI1NACloseTestCase(GI1NATestCase): close = 1 class GI2NATestCase(GetItemTestCase): # A more complex example title = "Rank-1,2 case 2" numericalList = numpy.array([3, 4]) numericalListME = numpy.array([[3, 2, 1, 0, 4, 5, 6], [2, 1, 0, 4, 5, 6, 7], [4, 3, 2, 1, 0, 4, 5], [3, 2, 1, 0, 4, 5, 6], [3, 2, 1, 0, 4, 5, 6]]) charList = numpy.array(["a", "b"], 'S') charListME = numpy.array( [["321", "221", "121", "021", "421", "521", "621"], ["21", "21", "11", "02", "42", "21", "61"], ["31", "21", "12", "21", "41", "51", "621"], ["321", "221", "121", "021", "421", "521", "621"], ["3241", "2321", "13216", "0621", "4421", "5421", "a621"], ["a321", "s221", "d121", "g021", "b421", "5vvv21", "6zxzxs21"]], 'S') class GI2NAOpenTestCase(GI2NATestCase): close = 0 class GI2NACloseTestCase(GI2NATestCase): close = 1 class SetItemTestCase(common.TempFileMixin, TestCase): def test00_single(self): """Single element update (character types)""" # Create the array under root and name 'somearray' a = self.charList arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify a single element of a and arr: a[0] = b"b" arr[0] = b"b" # Get and compare an element if common.verbose: print("Original first element:", a[0]) print("Read first element:", arr[0]) self.assertTrue(allequal(a[0], arr[0])) def test01_single(self): """Single element update (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalList arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: a[0] = 333 arr[0] = 333 # Get and compare an element if common.verbose: print("Original first element:", a[0]) print("Read first element:", arr[0]) self.assertEqual(a[0], arr[0]) def test02_range(self): """Range element update (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: a[1:3] = b"xXx" arr[1:3] = b"xXx" # Get and compare an element if common.verbose: print("Original elements:", a[1:4]) print("Read elements:", arr[1:4]) self.assertTrue(allequal(a[1:4], arr[1:4])) def test03_range(self): """Range element update (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = slice(1, 3, None) rng = numpy.arange(a[s].size)*2 + 3 rng.shape = a[s].shape a[s] = rng arr[s] = rng # Get and compare an element if common.verbose: print("Original elements:", a[1:4]) print("Read elements:", arr[1:4]) self.assertTrue(allequal(a[1:4], arr[1:4])) def test04_range(self): """Range element update, strided (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = slice(1, 4, 2) a[s] = b"xXx" arr[s] = b"xXx" # Get and compare an element if common.verbose: print("Original elements:", a[1:4:2]) print("Read elements:", arr[1:4:2]) self.assertTrue(allequal(a[1:4:2], arr[1:4:2])) def test05_range(self): """Range element update, strided (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = slice(1, 4, 2) rng = numpy.arange(a[s].size)*2 + 3 rng.shape = a[s].shape a[s] = rng arr[s] = rng # Get and compare an element if common.verbose: print("Original elements:", a[1:4:2]) print("Read elements:", arr[1:4:2]) self.assertTrue(allequal(a[1:4:2], arr[1:4:2])) def test06_negativeIndex(self): """Negative Index element update (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = -1 a[s] = b"xXx" arr[s] = b"xXx" # Get and compare an element if common.verbose: print("Original last element:", a[-1]) print("Read last element:", arr[-1]) self.assertTrue(allequal(a[-1], arr[-1])) def test07_negativeIndex(self): """Negative Index element update (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = -2 a[s] = a[s]*2 + 3 arr[s] = arr[s]*2 + 3 # Get and compare an element if common.verbose: print("Original before last element:", a[-2]) print("Read before last element:", arr[-2]) if isinstance(a[-2], numpy.ndarray): self.assertTrue(allequal(a[-2], arr[-2])) else: self.assertEqual(a[-2], arr[-2]) def test08_negativeRange(self): """Negative range element update (character types)""" # Create the array under root and name 'somearray' a = self.charListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = slice(-4, -1, None) a[s] = b"xXx" arr[s] = b"xXx" # Get and compare an element if common.verbose: print("Original last elements:", a[-4:-1]) print("Read last elements:", arr[-4:-1]) self.assertTrue(allequal(a[-4:-1], arr[-4:-1])) def test09_negativeRange(self): """Negative range element update (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of a and arr: s = slice(-3, -1, None) rng = numpy.arange(a[s].size)*2 + 3 rng.shape = a[s].shape a[s] = rng arr[s] = rng # Get and compare an element if common.verbose: print("Original last elements:", a[-4:-1]) print("Read last elements:", arr[-4:-1]) self.assertTrue(allequal(a[-4:-1], arr[-4:-1])) def test10_outOfRange(self): """Out of range update (numerical types)""" # Create the array under root and name 'somearray' a = self.numericalListME arr = self.h5file.create_array( self.h5file.root, 'somearray', a, "Some array") if self.close: self._reopen('a') arr = self.h5file.root.somearray # Modify elements of arr that are out of range: s = slice(1, a.shape[0]+1, None) s2 = slice(1, 1000, None) rng = numpy.arange(a[s].size)*2 + 3 rng.shape = a[s].shape a[s] = rng rng2 = numpy.arange(a[s2].size)*2 + 3 rng2.shape = a[s2].shape arr[s2] = rng2 # Get and compare an element if common.verbose: print("Original last elements:", a[-4:-1]) print("Read last elements:", arr[-4:-1]) self.assertTrue(allequal(a[-4:-1], arr[-4:-1])) class SI1NATestCase(SetItemTestCase, TestCase): title = "Rank-1 case 1" numericalList = numpy.array([3]) numericalListME = numpy.array([3, 2, 1, 0, 4, 5, 6]) charList = numpy.array(["3"], 'S') charListME = numpy.array( ["321", "221", "121", "021", "421", "521", "621"], 'S') class SI1NAOpenTestCase(SI1NATestCase): close = 0 class SI1NACloseTestCase(SI1NATestCase): close = 1 class SI2NATestCase(SetItemTestCase): # A more complex example title = "Rank-1,2 case 2" numericalList =

numpy.array([3, 4])

numpy.array

# coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. r"""Script for running experiments. Example to run locally: python experiments.py --output_dir=may19_3d --hidden_state_dim=3 \ --min_seq_len=100 --max_seq_len=2000 --num_sampled_seq_len=20 \ --num_systems=100 --num_repeat=100 \ --cluster_center_dist_lower_bound=0.1 --hide_inputs=true The outputs will show up in output_dir may19_3d. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import logging import os # pylint: disable=g-bad-import-order from absl import app from absl import flags import matplotlib matplotlib.use('Agg') from matplotlib import pylab # pylint: disable=g-import-not-at-top import numpy as np import pandas as pd import seaborn as sns import six import sklearn import tqdm import arma import clustering import lds sns.set(style='whitegrid') FLAGS = flags.FLAGS # Flags for IO and plotting. flags.DEFINE_string('output_dir', None, 'Output filepath.') flags.DEFINE_boolean( 'load_results', False, 'Whether to skip experiments ' 'and only plot existing results from output_dir.') flags.DEFINE_boolean( 'plot_clusters', False, 'Whether to visualize each ' 'experiment run and plot clustering results.') # Flags for generating simulated clusters of LDSs. flags.DEFINE_boolean('generate_diagonalizable_only', False, 'Whether to only ' 'generate diagonalizable LDSs.') flags.DEFINE_integer('num_clusters', 2, 'Number of clusters in experiments.') flags.DEFINE_integer('num_systems', 100, 'Number of dynamical systems to cluster.') flags.DEFINE_integer('hidden_state_dim', 2, 'Hidden state dim in experiments.') flags.DEFINE_integer('input_dim', 1, 'Input dim in experiments.') flags.DEFINE_boolean( 'hide_inputs', True, 'Whether the inputs are observable ' 'to the clustering algorithm.') flags.DEFINE_spaceseplist( 'cluster_center_eigvalues', None, 'Optional List of lists of eigenvalues ' 'for each cluster. The outer list is space separated, and the inner list ' 'is comma separated. E.g. `0.9,0.1 0.5,0.1`. When null, generate random ' 'clusters centers by drawing eigenvalues uniformly from [-1, 1].') flags.DEFINE_float( 'cluster_center_dist_lower_bound', 0.2, 'Desired distance lower bound ' 'between cluster centers. Only applicable when cluster_center_eigvalues ' 'is None. Generate cluster centers until distance >= this val.') flags.DEFINE_float('cluster_radius', 0.05, 'Radius of each dynamical system cluster.') flags.DEFINE_integer('random_seed', 0, 'Random seed.') flags.DEFINE_integer('num_repeat', 1, 'Number of repeated runs for each fixed seq len.') # Flags for output sequences from LDSs. flags.DEFINE_integer('min_seq_len', 10, 'Min seq len in experiments.') flags.DEFINE_integer('max_seq_len', 1000, 'Max seq len in experiments.') flags.DEFINE_integer( 'num_sampled_seq_len', 10, 'Number of sampled seq len ' 'values in between min and max seq len.') flags.DEFINE_float('input_mean', 0.0, 'Input mean.') flags.DEFINE_float('input_stddev', 1.0, 'Input stddev.') flags.DEFINE_float('output_noise_stddev', 0.01, 'Output noise stddev.') flags.DEFINE_float('init_state_mean', 0.0, 'Init state mean.') flags.DEFINE_float('init_state_stddev', 0.0, 'Init state stddev.') # Flags for hparams in clustering algorithms. flags.DEFINE_integer('guessed_hidden_dim', 0, 'Assumed hidden dim. If 0, use true hidden dim.') flags.DEFINE_integer( 'guessed_num_clusters', 0, 'Desired number of clusters. If 0, find best number ' 'adaptively from maximizing kmeans objective score.') flags.DEFINE_integer( 'LDS_GIBBS_num_update_samples', 100, 'Number of update ' 'samples used for fitting LDS in pylds package.') flags.DEFINE_integer('spectral_filtering_num_filters', 25, 'Number of filters ' 'used in spectral filtering method.') flags.DEFINE_float('spectral_filtering_learning_rate', 0.0001, 'Learning rate ' 'in spectral filtering method.') # Flags for whether to include certain baselines. flags.DEFINE_boolean( 'include_LDS_MLE', False, 'Whether to include MLE ' 'estimation for LDS in the experiments. Could be slow.') flags.DEFINE_boolean( 'include_tslearn', True, 'Whether to include time series ' 'clustering methods from the tslearn package in the ' 'experiments.') flags.DEFINE_boolean( 'include_tslearn_slow', False, 'Whether to include time ' 'series clustering methods from the tslearn package ' 'that are slow: DTW and GAK.') flags.DEFINE_boolean('include_LDS_GIBBS', True, 'Whether to include the ' 'Gibbs sampling method for LDS.') flags.DEFINE_boolean('include_ARMA_MLE', False, 'Whether to include the ' 'MLE method for ARMA.') def create_model_fns(hdim): """Util function to create model fns to fit model params to sequences. Args: hdim: Guessed hidden dimension for model fitting. Returns: A dictionary mapping method names to model_fns. Each model_fn takes output seq and input seq, and returns fitted model params. """ model_fns = collections.OrderedDict() # Using raw outputs. # model_fns['raw_output'] = lambda o, i: o # pylint: disable=g-long-lambda # Pure AR. model_fns['AR'] = lambda o, i: arma.fit_ar(o, i, hdim) # Iterated regression without regularization and with regularization. model_fns['ARMA_OLS'] = lambda o, i: arma.fit_arma_iter(o, i, hdim) model_fns['ARMA_RLS'] = lambda o, i: arma.fit_arma_iter( o, i, hdim, l2_reg=0.01) # Fit AR model and cluster based on AR param roots. # model_fns['AR_roots'] = lambda o, i: arma.get_eig_from_arparams( # arma.fit_ar(o, i, hdim)) # Fit ARMA model and cluster based on AR param roots. # model_fns['ARMA_OLS_roots'] = lambda o, i: arma.get_eig_from_arparams( # arma.fit_arma_iter(o, i, hdim)) # model_fns['ARMA_RLS_roots_0.01'] = lambda o, i: arma.get_eig_from_arparams( # arma.fit_arma_iter(o, i, hdim, l2_reg=0.01)) if FLAGS.include_LDS_GIBBS: model_fns['LDS_GIBBS'] = lambda o, i: lds.fit_lds_gibbs( o, i, hdim, num_update_samples=FLAGS.LDS_GIBBS_num_update_samples) if FLAGS.include_ARMA_MLE: model_fns['ARMA_MLE'] = lambda o, i: arma.fit_arma_mle(o, i, hdim) if FLAGS.include_LDS_MLE: model_fns['LDS_MLE'] = lambda o, i: lds.fit_lds_mle(o, i, hdim) return model_fns def _compose_model_fn(model_fn): if FLAGS.hide_inputs: return lambda seq: model_fn(seq.outputs, None) return lambda seq: model_fn(seq.outputs, seq.inputs) def _create_pca_model_fn(pca_model): return lambda o, _: pca_model.transform(o.flatten()).flatten() # pylint: disable=g-doc-args def get_results(cluster_center_eigvalues, cluster_center_dist_lower_bound, hidden_state_dim, input_dim, guessed_hidden_dim, num_clusters, guessed_num_clusters, min_seq_len, max_seq_len, num_sampled_seq_len, num_repeat, num_systems, cluster_radius, input_mean, input_stddev, output_noise_stddev, init_state_mean=0.0, init_state_stddev=0.0, generate_diagonalizable_only=False, random_seed=0, results_path=None): """Get results for varying sequence lengths. Args: cluster_center_eigvalues: List of lists of eigenvalues for each cluster. E.g. [[0.9,0.1], [0.5,0.1], [0.2,0.2], or None. If None, eigenvalues will be generated from uniform(-1,1) with respect to cluster_center_dist_lower_bound. cluster_center_dist_lower_bound: Desired distance lower bound between clusters. When generating cluster centers, try repeatedly until distance is greater than cluster_center_dist_lower_bound. hidden_state_dim: True hidden state dim. input_dim: The input dim. guessed_hidden_dim: Assumed hidden dim. If 0, use true hidden dim. num_clusters: True number of clusters. guessed_num_clusters: Desired number of clusters. If 0, use true number. min_seq_len: Min seq len in experiments. max_seq_len: Max seq len in experiments. num_sampled_seq_len: Number of sampled seq len values in between min and max seq len. num_repeat: Number of repeated experiments for each seq_len. num_systems: Number of dynamical system in each clustering experiments. cluster_radius: Expected distance of generated systems from cluster centers. input_mean: Scalar or 1D array of length hidden state dim. input_stddev: Scalar of 1D array of length hidden state dim. output_noise_stddev: Scalar. init_state_mean: Scalar or 1D array of length hidden state dim. init_state_stddev: Scalar of 1D array of length hidden state dim. random_seed: Random seed, integer. Returns: A pandas DataFrame with columns `method`, `seq_len`, `t_secs`, `failed_ratio`, and columns for clustering metrics such as `adj_mutual_info` and `v_measure`. The same method and seq_len will appear in num_repeat many rows. """ if cluster_center_eigvalues is not None: if len(cluster_center_eigvalues) <= 1: raise ValueError('Need at least two cluster centers.') cluster_center_eigvalues = np.array(cluster_center_eigvalues) if cluster_center_eigvalues.shape != (num_clusters, hidden_state_dim): raise ValueError( 'Cluter center eig has shape %s, expected (%d, %d).' % (str(cluster_center_eigvalues.shape), num_clusters, hidden_state_dim))

np.random.seed(random_seed)

numpy.random.seed

import numpy as np from pyfmmlib2d import FMM from pyfmmlib2d.periodized.real_laplace import periodized_laplace_fmm from pyfmmlib2d.utilities.random import float_random, complex_random import matplotlib as mpl import matplotlib.pyplot as plt from pyfmmlib2d import RFMM import time plt.ion() ################################################################################ # Periodic Laplace FMM # tested against FFT based grid solve; compared outside of support of forces # the grid has been spaced so a grid node comes within ~10^{-12} of a check pt # should work in all corners and sides n_grid = 150 # get uniform grid on [0, 2π] grid_v, grid_h = np.linspace(0, 2*np.pi, n_grid, endpoint=False, retstep=True) grid_xv = grid_v + 3.381898123243e-02 grid_yv = grid_v + 1e-12 grid_x, grid_y = np.meshgrid(grid_xv, grid_yv, indexing='ij') grid = np.row_stack([grid_x.ravel(), grid_y.ravel()]) k = np.fft.fftfreq(n_grid, grid_h/(2*np.pi)) kx, ky = np.meshgrid(k, k, indexing='ij') k2 = kx*kx + ky*ky k2[0,0] = np.Inf ilap = -1.0/k2 # put a test pulse at location in center k = 30 for center_x in (0.0, np.pi, 2*np.pi): for center_y in (0.0, np.pi, 2*np.pi): print('\nCenter x: {:0.2f}'.format(center_x)) print( 'Center y: {:0.2f}'.format(center_y)) def get_shift(xs, ys): lax = center_x+0.2 + xs lay = center_y+0.2 + ys lbx = center_x-0.2 + xs lby = center_y-0.2 + ys d2a = (grid_x-lax)**2 + (grid_y-lay)**2 d2b = (grid_x-lbx)**2 + (grid_y-lby)**2 fa = np.exp(-k*d2a) fb = -np.exp(-k*d2b) return fa + fb f = np.zeros_like(grid_x) shift_vec = [-2*np.pi, 0.0, 2*np.pi] for xs in shift_vec: for ys in shift_vec: f += get_shift(xs, ys) # solve Poisson problem on grid ua = np.fft.ifft2(ilap*np.fft.fft2(f)).real # get charge qw = grid_h**2 charge = f.ravel() * qw / (2*np.pi) # evaluate FMM st1 = time.time() pfmm = periodized_laplace_fmm(p=16, N=4) st2 = time.time() out = pfmm(grid, charge=charge, compute_source_potential=True) periodized_time_with_setup = time.time() - st1 periodized_time_without_setup = time.time() - st2 ue = out['self']['u'].reshape([n_grid, n_grid]) # compare to raw FMM speed st = time.time() _ = RFMM(grid, grid, charge=charge, compute_source_potential=True) raw_time = time.time() - st # now ignore everything well outside of the support of f r = 2 bad = np.zeros(grid_x.shape, dtype=bool) for xs in shift_vec: for ys in shift_vec: d2 = (grid_x-center_x-xs)**2 + (grid_y-center_y-ys)**2 bad = np.logical_or(bad, d2<r) good = ~bad # get error with adjusting mean ue -= np.mean(ue) error = np.abs(ue-ua)/np.abs(ua[good]).max() me =

np.ma.array(error, mask=bad)

numpy.ma.array

import numpy as np import scipy.optimize as optimization import matplotlib.pyplot as plt try: from submm_python_routines.KIDs import calibrate except: from KIDs import calibrate from numba import jit # to get working on python 2 I had to downgrade llvmlite pip install llvmlite==0.31.0 # module for fitting resonances curves for kinetic inductance detectors. # written by <NAME> 12/21/16 # for example see test_fit.py in this directory # To Do # I think the error analysis on the fit_nonlinear_iq_with_err probably needs some work # add in step by step fitting i.e. first amplitude normalizaiton, then cabel delay, then i0,q0 subtraction, then phase rotation, then the rest of the fit. # need to have fit option that just specifies tau becuase that never really changes for your cryostat #Change log #JDW 2017-08-17 added in a keyword/function to allow for gain varation "amp_var" to be taken out before fitting #JDW 2017-08-30 added in fitting for magnitude fitting of resonators i.e. not in iq space #JDW 2018-03-05 added more clever function for guessing x0 for fits #JDW 2018-08-23 added more clever guessing for resonators with large phi into guess seperate functions J=np.exp(2j*np.pi/3) Jc=1/J @jit(nopython=True) def cardan(a,b,c,d): ''' analytical root finding fast: using numba looks like x10 speed up returns only the largest real root ''' u=np.empty(2,np.complex128) z0=b/3/a a2,b2 = a*a,b*b p=-b2/3/a2 +c/a q=(b/27*(2*b2/a2-9*c/a)+d)/a D=-4*p*p*p-27*q*q r=np.sqrt(-D/27+0j) u=((-q-r)/2)**(1/3.)#0.33333333333333333333333 v=((-q+r)/2)**(1/3.)#0.33333333333333333333333 w=u*v w0=np.abs(w+p/3) w1=np.abs(w*J+p/3) w2=np.abs(w*Jc+p/3) if w0<w1: if w2<w0 : v*=Jc elif w2<w1 : v*=Jc else: v*=J roots = np.asarray((u+v-z0, u*J+v*Jc-z0,u*Jc+v*J-z0)) #print(roots) where_real = np.where(np.abs(np.imag(roots)) < 1e-15) #if len(where_real)>1: print(len(where_real)) #print(D) if D>0: return np.max(np.real(roots)) # three real roots else: return np.real(roots[np.argsort(np.abs(np.imag(roots)))][0]) #one real root get the value that has smallest imaginary component #return np.max(np.real(roots[where_real])) #return np.asarray((u+v-z0, u*J+v*Jc-z0,u*Jc+v*J-z0)) # function to descript the magnitude S21 of a non linear resonator @jit(nopython=True) def nonlinear_mag(x,fr,Qr,amp,phi,a,b0,b1,flin): ''' # x is the frequeciesn your iq sweep covers # fr is the center frequency of the resonator # Qr is the quality factor of the resonator # amp is Qr/Qc # phi is a rotation paramter for an impedance mismatch between the resonaotor and the readout system # a is the non-linearity paramter bifurcation occurs at a = 0.77 # b0 DC level of s21 away from resonator # b1 Frequency dependant gain varation # flin is probably the frequency of the resonator when a = 0 # # This is based of fitting code from MUSIC # The idea is we are producing a model that is described by the equation below # the frist two terms in the large parentasis and all other terms are farmilar to me # but I am not sure where the last term comes from though it does seem to be important for fitting # # / (j phi) (j phi) \ 2 #|S21|^2 = (b0+b1 x_lin)* |1 -amp*e^ +amp*(e^ -1) |^ # | ------------ ---- | # \ (1+ 2jy) 2 / # # where the nonlineaity of y is described by the following eqution taken from Response of superconducting microresonators # with nonlinear kinetic inductance # yg = y+ a/(1+y^2) where yg = Qr*xg and xg = (f-fr)/fr # ''' xlin = (x - flin)/flin xg = (x-fr)/fr yg = Qr*xg y = np.zeros(x.shape[0]) #find the roots of the y equation above for i in range(0,x.shape[0]): # 4y^3+ -4yg*y^2+ y -(yg+a) #roots = np.roots((4.0,-4.0*yg[i],1.0,-(yg[i]+a))) #roots = cardan(4.0,-4.0*yg[i],1.0,-(yg[i]+a)) #print(roots) #roots = np.roots((16.,-16.*yg[i],8.,-8.*yg[i]+4*a*yg[i]/Qr-4*a,1.,-yg[i]+a*yg[i]/Qr-a+a**2/Qr)) #more accurate version that doesn't seem to change the fit at al # only care about real roots #where_real = np.where(np.imag(roots) == 0) #where_real = np.where(np.abs(np.imag(roots)) < 1e-10) #analytic version has some floating point error accumulation y[i] = cardan(4.0,-4.0*yg[i],1.0,-(yg[i]+a))#np.max(np.real(roots[where_real])) z = (b0 +b1*xlin)*np.abs(1.0 - amp*np.exp(1.0j*phi)/ (1.0 +2.0*1.0j*y) + amp/2.*(np.exp(1.0j*phi) -1.0))**2 return z @jit(nopython=True) def linear_mag(x,fr,Qr,amp,phi,b0): ''' # simplier version for quicker fitting when applicable # x is the frequeciesn your iq sweep covers # fr is the center frequency of the resonator # Qr is the quality factor of the resonator # amp is Qr/Qc # phi is a rotation paramter for an impedance mismatch between the resonaotor and the readout system # b0 DC level of s21 away from resonator # # This is based of fitting code from MUSIC # The idea is we are producing a model that is described by the equation below # the frist two terms in the large parentasis and all other terms are farmilar to me # but I am not sure where the last term comes from though it does seem to be important for fitting # # / (j phi) (j phi) \ 2 #|S21|^2 = (b0)* |1 -amp*e^ +amp*(e^ -1) |^ # | ------------ ---- | # \ (1+ 2jxg) 2 / # # no y just xg # with no nonlinear kinetic inductance ''' if not np.isscalar(fr): #vectorize x = np.reshape(x,(x.shape[0],1,1,1,1,1)) xg = (x-fr)/fr z = (b0)*np.abs(1.0 - amp*np.exp(1.0j*phi)/ (1.0 +2.0*1.0j*xg*Qr) + amp/2.*(np.exp(1.0j*phi) -1.0))**2 return z # function to describe the i q loop of a nonlinear resonator @jit(nopython=True) def nonlinear_iq(x,fr,Qr,amp,phi,a,i0,q0,tau,f0): ''' # x is the frequeciesn your iq sweep covers # fr is the center frequency of the resonator # Qr is the quality factor of the resonator # amp is Qr/Qc # phi is a rotation paramter for an impedance mismatch between the resonaotor and the readou system # a is the non-linearity paramter bifurcation occurs at a = 0.77 # i0 # q0 these are constants that describes an overall phase rotation of the iq loop + a DC gain offset # tau cabel delay # f0 is all the center frequency, not sure why we include this as a secondary paramter should be the same as fr # # This is based of fitting code from MUSIC # # The idea is we are producing a model that is described by the equation below # the frist two terms in the large parentasis and all other terms are farmilar to me # but I am not sure where the last term comes from though it does seem to be important for fitting # # (-j 2 pi deltaf tau) / (j phi) (j phi) \ # (i0+j*q0)*e^ *|1 -amp*e^ +amp*(e^ -1) | # | ------------ ---- | # \ (1+ 2jy) 2 / # # where the nonlineaity of y is described by the following eqution taken from Response of superconducting microresonators # with nonlinear kinetic inductance # yg = y+ a/(1+y^2) where yg = Qr*xg and xg = (f-fr)/fr # ''' deltaf = (x - f0) xg = (x-fr)/fr yg = Qr*xg y = np.zeros(x.shape[0]) #find the roots of the y equation above for i in range(0,x.shape[0]): # 4y^3+ -4yg*y^2+ y -(yg+a) #roots = np.roots((4.0,-4.0*yg[i],1.0,-(yg[i]+a))) #roots = np.roots((16.,-16.*yg[i],8.,-8.*yg[i]+4*a*yg[i]/Qr-4*a,1.,-yg[i]+a*yg[i]/Qr-a+a**2/Qr)) #more accurate version that doesn't seem to change the fit at al # only care about real roots #where_real = np.where(np.imag(roots) == 0) #y[i] = np.max(np.real(roots[where_real])) y[i] = cardan(4.0,-4.0*yg[i],1.0,-(yg[i]+a)) z = (i0 +1.j*q0)* np.exp(-1.0j* 2* np.pi *deltaf*tau) * (1.0 - amp*np.exp(1.0j*phi)/ (1.0 +2.0*1.0j*y) + amp/2.*(np.exp(1.0j*phi) -1.0)) return z def nonlinear_iq_for_fitter(x,fr,Qr,amp,phi,a,i0,q0,tau,f0,**keywords): ''' when using a fitter that can't handel complex number one needs to return both the real and imaginary components seperatly ''' if ('tau' in keywords): use_given_tau = True tau = keywords['tau'] print("hello") else: use_given_tau = False deltaf = (x - f0) xg = (x-fr)/fr yg = Qr*xg y = np.zeros(x.shape[0]) for i in range(0,x.shape[0]): #roots = np.roots((4.0,-4.0*yg[i],1.0,-(yg[i]+a))) #where_real = np.where(np.imag(roots) == 0) #y[i] = np.max(np.real(roots[where_real])) y[i] = cardan(4.0,-4.0*yg[i],1.0,-(yg[i]+a)) z = (i0 +1.j*q0)* np.exp(-1.0j* 2* np.pi *deltaf*tau) * (1.0 - amp*np.exp(1.0j*phi)/ (1.0 +2.0*1.0j*y) + amp/2.*(np.exp(1.0j*phi) -1.0)) real_z = np.real(z) imag_z = np.imag(z) return np.hstack((real_z,imag_z)) def brute_force_linear_mag_fit(x,z,ranges,n_grid_points,error = None, plot = False,**keywords): ''' x frequencies Hz z complex or abs of s21 ranges is the ranges for each parameter i.e. np.asarray(([f_low,Qr_low,amp_low,phi_low,b0_low],[f_high,Qr_high,amp_high,phi_high,b0_high])) n_grid_points how finely to sample each parameter space. this can be very slow for n>10 an increase by a factor of 2 will take 2**5 times longer to marginalize over you must minimize over the unwanted axies of sum_dev i.e for fr np.min(np.min(np.min(np.min(fit['sum_dev'],axis = 4),axis = 3),axis = 2),axis = 1) ''' if error is None: error = np.ones(len(x)) fs = np.linspace(ranges[0][0],ranges[1][0],n_grid_points) Qrs = np.linspace(ranges[0][1],ranges[1][1],n_grid_points) amps = np.linspace(ranges[0][2],ranges[1][2],n_grid_points) phis = np.linspace(ranges[0][3],ranges[1][3],n_grid_points) b0s = np.linspace(ranges[0][4],ranges[1][4],n_grid_points) evaluated_ranges = np.vstack((fs,Qrs,amps,phis,b0s)) a,b,c,d,e = np.meshgrid(fs,Qrs,amps,phis,b0s,indexing = "ij") #always index ij evaluated = linear_mag(x,a,b,c,d,e) data_values = np.reshape(np.abs(z)**2,(abs(z).shape[0],1,1,1,1,1)) error = np.reshape(error,(abs(z).shape[0],1,1,1,1,1)) sum_dev = np.sum(((np.sqrt(evaluated)-np.sqrt(data_values))**2/error**2),axis = 0) # comparing in magnitude space rather than magnitude squared min_index = np.where(sum_dev == np.min(sum_dev)) index1 = min_index[0][0] index2 = min_index[1][0] index3 = min_index[2][0] index4 = min_index[3][0] index5 = min_index[4][0] fit_values = np.asarray((fs[index1],Qrs[index2],amps[index3],phis[index4],b0s[index5])) fit_values_names = ('f0','Qr','amp','phi','b0') fit_result = linear_mag(x,fs[index1],Qrs[index2],amps[index3],phis[index4],b0s[index5]) marginalized_1d = np.zeros((5,n_grid_points)) marginalized_1d[0,:] = np.min(np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 2),axis = 1) marginalized_1d[1,:] = np.min(np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 2),axis = 0) marginalized_1d[2,:] = np.min(np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 1),axis = 0) marginalized_1d[3,:] = np.min(np.min(np.min(np.min(sum_dev,axis = 4),axis = 2),axis = 1),axis = 0) marginalized_1d[4,:] = np.min(np.min(np.min(np.min(sum_dev,axis = 3),axis = 2),axis = 1),axis = 0) marginalized_2d = np.zeros((5,5,n_grid_points,n_grid_points)) #0 _ #1 x _ #2 x x _ #3 x x x _ #4 x x x x _ # 0 1 2 3 4 marginalized_2d[0,1,:] = marginalized_2d[1,0,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 2) marginalized_2d[2,0,:] = marginalized_2d[0,2,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 1) marginalized_2d[2,1,:] = marginalized_2d[1,2,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 3),axis = 0) marginalized_2d[3,0,:] = marginalized_2d[0,3,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 2),axis = 1) marginalized_2d[3,1,:] = marginalized_2d[1,3,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 2),axis = 0) marginalized_2d[3,2,:] = marginalized_2d[2,3,:] = np.min(np.min(np.min(sum_dev,axis = 4),axis = 1),axis = 0) marginalized_2d[4,0,:] = marginalized_2d[0,4,:] = np.min(np.min(np.min(sum_dev,axis = 3),axis = 2),axis = 1) marginalized_2d[4,1,:] = marginalized_2d[1,4,:] = np.min(np.min(np.min(sum_dev,axis = 3),axis = 2),axis = 0) marginalized_2d[4,2,:] = marginalized_2d[2,4,:] = np.min(np.min(np.min(sum_dev,axis = 3),axis = 1),axis = 0) marginalized_2d[4,3,:] = marginalized_2d[3,4,:] = np.min(np.min(np.min(sum_dev,axis = 2),axis = 1),axis = 0) if plot: levels = [2.3,4.61] #delta chi squared two parameters 68 90 % confidence fig_fit = plt.figure(-1) axs = fig_fit.subplots(5, 5) for i in range(0,5): # y starting from top for j in range(0,5): #x starting from left if i > j: #plt.subplot(5,5,i+1+5*j) #axs[i, j].set_aspect('equal', 'box') extent = [evaluated_ranges[j,0],evaluated_ranges[j,n_grid_points-1],evaluated_ranges[i,0],evaluated_ranges[i,n_grid_points-1]] axs[i,j].imshow(marginalized_2d[i,j,:]-np.min(sum_dev),extent =extent,origin = 'lower', cmap = 'jet') axs[i,j].contour(evaluated_ranges[j],evaluated_ranges[i],marginalized_2d[i,j,:]-np.min(sum_dev),levels = levels,colors = 'white') axs[i,j].set_ylim(evaluated_ranges[i,0],evaluated_ranges[i,n_grid_points-1]) axs[i,j].set_xlim(evaluated_ranges[j,0],evaluated_ranges[j,n_grid_points-1]) axs[i,j].set_aspect((evaluated_ranges[j,0]-evaluated_ranges[j,n_grid_points-1])/(evaluated_ranges[i,0]-evaluated_ranges[i,n_grid_points-1])) if j == 0: axs[i, j].set_ylabel(fit_values_names[i]) if i == 4: axs[i, j].set_xlabel("\n"+fit_values_names[j]) if i<4: axs[i,j].get_xaxis().set_ticks([]) if j>0: axs[i,j].get_yaxis().set_ticks([]) elif i < j: fig_fit.delaxes(axs[i,j]) for i in range(0,5): #axes.subplot(5,5,i+1+5*i) axs[i,i].plot(evaluated_ranges[i,:],marginalized_1d[i,:]-np.min(sum_dev)) axs[i,i].plot(evaluated_ranges[i,:],np.ones(len(evaluated_ranges[i,:]))*1.,color = 'k') axs[i,i].plot(evaluated_ranges[i,:],np.ones(len(evaluated_ranges[i,:]))*2.7,color = 'k') axs[i,i].yaxis.set_label_position("right") axs[i,i].yaxis.tick_right() axs[i,i].xaxis.set_label_position("top") axs[i,i].xaxis.tick_top() axs[i,i].set_xlabel(fit_values_names[i]) #axs[0,0].set_ylabel(fit_values_names[0]) #axs[4,4].set_xlabel(fit_values_names[4]) axs[4,4].xaxis.set_label_position("bottom") axs[4,4].xaxis.tick_bottom() #make a dictionary to return fit_dict = {'fit_values': fit_values,'fit_values_names':fit_values_names, 'sum_dev': sum_dev, 'fit_result': fit_result,'marginalized_2d':marginalized_2d,'marginalized_1d':marginalized_1d,'evaluated_ranges':evaluated_ranges}#, 'x0':x0, 'z':z} return fit_dict # function for fitting an iq sweep with the above equation def fit_nonlinear_iq(x,z,**keywords): ''' # keywards are # bounds ---- which is a 2d tuple of low the high values to bound the problem by # x0 --- intial guess for the fit this can be very important becuase because least square space over all the parameter is comple # amp_norm --- do a normalization for variable amplitude. usefull when tranfer function of the cryostat is not flat # tau forces tau to specific value # tau_guess fixes the guess for tau without have to specifiy all of x0 ''' if ('tau' in keywords): use_given_tau = True tau = keywords['tau'] else: use_given_tau = False if ('bounds' in keywords): bounds = keywords['bounds'] else: #define default bounds print("default bounds used") bounds = ([np.min(x),50,.01,-np.pi,0,-np.inf,-np.inf,0,np.min(x)],[np.max(x),200000,1,np.pi,5,np.inf,np.inf,1*10**-6,np.max(x)]) if ('x0' in keywords): x0 = keywords['x0'] else: #define default intial guess print("default initial guess used") #fr_guess = x[np.argmin(np.abs(z))] #x0 = [fr_guess,10000.,0.5,0,0,np.mean(np.real(z)),np.mean(np.imag(z)),3*10**-7,fr_guess] x0 = guess_x0_iq_nonlinear(x,z,verbose = True) print(x0) if ('fr_guess' in keywords): x0[0] = keywords['fr_guess'] if ('tau_guess' in keywords): x0[7] = keywords['tau_guess'] #Amplitude normalization? do_amp_norm = 0 if ('amp_norm' in keywords): amp_norm = keywords['amp_norm'] if amp_norm == True: do_amp_norm = 1 elif amp_norm == False: do_amp_norm = 0 else: print("please specify amp_norm as True or False") if do_amp_norm == 1: z = amplitude_normalization(x,z) z_stacked = np.hstack((np.real(z),np.imag(z))) if use_given_tau == True: del bounds[0][7] del bounds[1][7] del x0[7] fit = optimization.curve_fit(lambda x_lamb,a,b,c,d,e,f,g,h: nonlinear_iq_for_fitter(x_lamb,a,b,c,d,e,f,g,tau,h), x, z_stacked,x0,bounds = bounds) fit_result = nonlinear_iq(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],tau,fit[0][7]) x0_result = nonlinear_iq(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],tau,x0[7]) else: fit = optimization.curve_fit(nonlinear_iq_for_fitter, x, z_stacked,x0,bounds = bounds) fit_result = nonlinear_iq(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7],fit[0][8]) x0_result = nonlinear_iq(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7],x0[8]) #make a dictionary to return fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z} return fit_dict def fit_nonlinear_iq_sep(fine_x,fine_z,gain_x,gain_z,**keywords): ''' # same as above funciton but takes fine and gain scans seperatly # keywards are # bounds ---- which is a 2d tuple of low the high values to bound the problem by # x0 --- intial guess for the fit this can be very important becuase because least square space over all the parameter is comple # amp_norm --- do a normalization for variable amplitude. usefull when tranfer function of the cryostat is not flat ''' if ('bounds' in keywords): bounds = keywords['bounds'] else: #define default bounds print("default bounds used") bounds = ([np.min(fine_x),500.,.01,-np.pi,0,-np.inf,-np.inf,1*10**-9,np.min(fine_x)],[np.max(fine_x),1000000,1,np.pi,5,np.inf,np.inf,1*10**-6,np.max(fine_x)]) if ('x0' in keywords): x0 = keywords['x0'] else: #define default intial guess print("default initial guess used") #fr_guess = x[np.argmin(np.abs(z))] #x0 = [fr_guess,10000.,0.5,0,0,np.mean(np.real(z)),np.mean(np.imag(z)),3*10**-7,fr_guess] x0 = guess_x0_iq_nonlinear_sep(fine_x,fine_z,gain_x,gain_z) #print(x0) #Amplitude normalization? do_amp_norm = 0 if ('amp_norm' in keywords): amp_norm = keywords['amp_norm'] if amp_norm == True: do_amp_norm = 1 elif amp_norm == False: do_amp_norm = 0 else: print("please specify amp_norm as True or False") if (('fine_z_err' in keywords) & ('gain_z_err' in keywords)): use_err = True fine_z_err = keywords['fine_z_err'] gain_z_err = keywords['gain_z_err'] else: use_err = False x = np.hstack((fine_x,gain_x)) z = np.hstack((fine_z,gain_z)) if use_err: z_err = np.hstack((fine_z_err,gain_z_err)) if do_amp_norm == 1: z = amplitude_normalization(x,z) z_stacked = np.hstack((np.real(z),np.imag(z))) if use_err: z_err_stacked = np.hstack((np.real(z_err),np.imag(z_err))) fit = optimization.curve_fit(nonlinear_iq_for_fitter, x, z_stacked,x0,sigma = z_err_stacked,bounds = bounds) else: fit = optimization.curve_fit(nonlinear_iq_for_fitter, x, z_stacked,x0,bounds = bounds) fit_result = nonlinear_iq(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7],fit[0][8]) x0_result = nonlinear_iq(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7],x0[8]) if use_err: #only do it for fine data #red_chi_sqr = np.sum(z_stacked-np.hstack((np.real(fit_result),np.imag(fit_result))))**2/z_err_stacked**2)/(len(z_stacked)-8.) #only do it for fine data red_chi_sqr = np.sum((np.hstack((np.real(fine_z),np.imag(fine_z)))-np.hstack((np.real(fit_result[0:len(fine_z)]),np.imag(fit_result[0:len(fine_z)]))))**2/np.hstack((np.real(fine_z_err),np.imag(fine_z_err)))**2)/(len(fine_z)*2.-8.) #make a dictionary to return if use_err: fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z,'fit_freqs':x,'red_chi_sqr':red_chi_sqr} else: fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z,'fit_freqs':x} return fit_dict # same function but double fits so that it can get error and a proper covariance matrix out def fit_nonlinear_iq_with_err(x,z,**keywords): ''' # keywards are # bounds ---- which is a 2d tuple of low the high values to bound the problem by # x0 --- intial guess for the fit this can be very important becuase because least square space over all the parameter is comple # amp_norm --- do a normalization for variable amplitude. usefull when tranfer function of the cryostat is not flat ''' if ('bounds' in keywords): bounds = keywords['bounds'] else: #define default bounds print("default bounds used") bounds = ([np.min(x),2000,.01,-np.pi,0,-5,-5,1*10**-9,np.min(x)],[np.max(x),200000,1,np.pi,5,5,5,1*10**-6,np.max(x)]) if ('x0' in keywords): x0 = keywords['x0'] else: #define default intial guess print("default initial guess used") fr_guess = x[np.argmin(np.abs(z))] x0 = guess_x0_iq_nonlinear(x,z) #Amplitude normalization? do_amp_norm = 0 if ('amp_norm' in keywords): amp_norm = keywords['amp_norm'] if amp_norm == True: do_amp_norm = 1 elif amp_norm == False: do_amp_norm = 0 else: print("please specify amp_norm as True or False") if do_amp_norm == 1: z = amplitude_normalization(x,z) z_stacked = np.hstack((np.real(z),np.imag(z))) fit = optimization.curve_fit(nonlinear_iq_for_fitter, x, z_stacked,x0,bounds = bounds) fit_result = nonlinear_iq(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7],fit[0][8]) fit_result_stacked = nonlinear_iq_for_fitter(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7],fit[0][8]) x0_result = nonlinear_iq(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7],x0[8]) # get error var = np.sum((z_stacked-fit_result_stacked)**2)/(z_stacked.shape[0] - 1) err = np.ones(z_stacked.shape[0])*np.sqrt(var) # refit fit = optimization.curve_fit(nonlinear_iq_for_fitter, x, z_stacked,x0,err,bounds = bounds) fit_result = nonlinear_iq(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7],fit[0][8]) x0_result = nonlinear_iq(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7],x0[8]) #make a dictionary to return fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z} return fit_dict # function for fitting an iq sweep with the above equation def fit_nonlinear_mag(x,z,**keywords): ''' # keywards are # bounds ---- which is a 2d tuple of low the high values to bound the problem by # x0 --- intial guess for the fit this can be very important becuase because least square space over all the parameter is comple # amp_norm --- do a normalization for variable amplitude. usefull when tranfer function of the cryostat is not flat ''' if ('bounds' in keywords): bounds = keywords['bounds'] else: #define default bounds print("default bounds used") bounds = ([np.min(x),100,.01,-np.pi,0,-np.inf,-np.inf,np.min(x)],[np.max(x),200000,1,np.pi,5,np.inf,np.inf,np.max(x)]) if ('x0' in keywords): x0 = keywords['x0'] else: #define default intial guess print("default initial guess used") fr_guess = x[np.argmin(np.abs(z))] #x0 = [fr_guess,10000.,0.5,0,0,np.abs(z[0])**2,np.abs(z[0])**2,fr_guess] x0 = guess_x0_mag_nonlinear(x,z,verbose = True) fit = optimization.curve_fit(nonlinear_mag, x, np.abs(z)**2 ,x0,bounds = bounds) fit_result = nonlinear_mag(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7]) x0_result = nonlinear_mag(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7]) #make a dictionary to return fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z} return fit_dict def fit_nonlinear_mag_sep(fine_x,fine_z,gain_x,gain_z,**keywords): ''' # same as above but fine and gain scans are provided seperatly # keywards are # bounds ---- which is a 2d tuple of low the high values to bound the problem by # x0 --- intial guess for the fit this can be very important becuase because least square space over all the parameter is comple # amp_norm --- do a normalization for variable amplitude. usefull when tranfer function of the cryostat is not flat ''' if ('bounds' in keywords): bounds = keywords['bounds'] else: #define default bounds print("default bounds used") bounds = ([np.min(fine_x),100,.01,-np.pi,0,-np.inf,-np.inf,np.min(fine_x)],[np.max(fine_x),1000000,100,np.pi,5,np.inf,np.inf,np.max(fine_x)]) if ('x0' in keywords): x0 = keywords['x0'] else: #define default intial guess print("default initial guess used") x0 = guess_x0_mag_nonlinear_sep(fine_x,fine_z,gain_x,gain_z) if (('fine_z_err' in keywords) & ('gain_z_err' in keywords)): use_err = True fine_z_err = keywords['fine_z_err'] gain_z_err = keywords['gain_z_err'] else: use_err = False #stack the scans for curvefit x = np.hstack((fine_x,gain_x)) z = np.hstack((fine_z,gain_z)) if use_err: z_err = np.hstack((fine_z_err,gain_z_err)) z_err = np.sqrt(4*np.real(z_err)**2*np.real(z)**2+4*np.imag(z_err)**2*np.imag(z)**2) #propogation of errors left out cross term fit = optimization.curve_fit(nonlinear_mag, x, np.abs(z)**2 ,x0,sigma = z_err,bounds = bounds) else: fit = optimization.curve_fit(nonlinear_mag, x, np.abs(z)**2 ,x0,bounds = bounds) fit_result = nonlinear_mag(x,fit[0][0],fit[0][1],fit[0][2],fit[0][3],fit[0][4],fit[0][5],fit[0][6],fit[0][7]) x0_result = nonlinear_mag(x,x0[0],x0[1],x0[2],x0[3],x0[4],x0[5],x0[6],x0[7]) #compute reduced chi squared print(len(z)) if use_err: #red_chi_sqr = np.sum((np.abs(z)**2-fit_result)**2/z_err**2)/(len(z)-7.) # only use fine scan for reduced chi squared. red_chi_sqr = np.sum((np.abs(fine_z)**2-fit_result[0:len(fine_z)])**2/z_err[0:len(fine_z)]**2)/(len(fine_z)-7.) #make a dictionary to return if use_err: fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z,'fit_freqs':x,'red_chi_sqr':red_chi_sqr} else: fit_dict = {'fit': fit, 'fit_result': fit_result, 'x0_result': x0_result, 'x0':x0, 'z':z,'fit_freqs':x} return fit_dict def amplitude_normalization(x,z): ''' # normalize the amplitude varation requires a gain scan #flag frequencies to use in amplitude normaliztion ''' index_use = np.where(np.abs(x-np.median(x))>100000) #100kHz away from resonator poly = np.polyfit(x[index_use],np.abs(z[index_use]),2) poly_func = np.poly1d(poly) normalized_data = z/poly_func(x)*np.median(np.abs(z[index_use])) return normalized_data def amplitude_normalization_sep(gain_x,gain_z,fine_x,fine_z,stream_x,stream_z): ''' # normalize the amplitude varation requires a gain scan # uses gain scan to normalize does not use fine scan #flag frequencies to use in amplitude normaliztion ''' index_use = np.where(np.abs(gain_x-np.median(gain_x))>100000) #100kHz away from resonator poly = np.polyfit(gain_x[index_use],np.abs(gain_z[index_use]),2) poly_func = np.poly1d(poly) poly_data = poly_func(gain_x) normalized_gain = gain_z/poly_data*np.median(np.abs(gain_z[index_use])) normalized_fine = fine_z/poly_func(fine_x)*np.median(np.abs(gain_z[index_use])) normalized_stream = stream_z/poly_func(stream_x)*np.median(np.abs(gain_z[index_use])) amp_norm_dict = {'normalized_gain':normalized_gain, 'normalized_fine':normalized_fine, 'normalized_stream':normalized_stream, 'poly_data':poly_data} return amp_norm_dict def guess_x0_iq_nonlinear(x,z,verbose = False): ''' # this is lest robust than guess_x0_iq_nonlinear_sep # below. it is recommended to use that instead #make sure data is sorted from low to high frequency ''' sort_index = np.argsort(x) x = x[sort_index] z = z[sort_index] #extract just fine data df = np.abs(x-np.roll(x,1)) fine_df = np.min(df[np.where(df != 0)]) fine_z_index = np.where(df<fine_df*1.1) fine_z = z[fine_z_index] fine_x = x[fine_z_index] #extract the gain scan gain_z_index = np.where(df>fine_df*1.1) gain_z = z[gain_z_index] gain_x = x[gain_z_index] gain_phase = np.arctan2(np.real(gain_z),np.imag(gain_z)) #guess f0 fr_guess_index = np.argmin(np.abs(z)) #fr_guess = x[fr_guess_index] fr_guess_index_fine = np.argmin(np.abs(fine_z)) # below breaks if there is not a right and left side in the fine scan if fr_guess_index_fine == 0: fr_guess_index_fine = len(fine_x)//2 elif fr_guess_index_fine == (len(fine_x)-1): fr_guess_index_fine = len(fine_x)//2 fr_guess = fine_x[fr_guess_index_fine] #guess Q mag_max = np.max(np.abs(fine_z)**2) mag_min = np.min(np.abs(fine_z)**2) mag_3dB = (mag_max+mag_min)/2. half_distance = np.abs(fine_z)**2-mag_3dB right = half_distance[fr_guess_index_fine:-1] left = half_distance[0:fr_guess_index_fine] right_index = np.argmin(np.abs(right))+fr_guess_index_fine left_index = np.argmin(np.abs(left)) Q_guess_Hz = fine_x[right_index]-fine_x[left_index] Q_guess = fr_guess/Q_guess_Hz #guess amp d = np.max(20*np.log10(np.abs(z)))-np.min(20*np.log10(np.abs(z))) amp_guess = 0.0037848547850284574+0.11096782437821565*d-0.0055208783469291173*d**2+0.00013900471000261687*d**3+-1.3994861426891861e-06*d**4#polynomial fit to amp verus depth #guess impedance rotation phi phi_guess = 0 #guess non-linearity parameter #might be able to guess this by ratioing the distance between min and max distance between iq points in fine sweep a_guess = 0 #i0 and iq guess if np.max(np.abs(fine_z))==np.max(np.abs(z)): #if the resonator has an impedance mismatch rotation that makes the fine greater that the cabel delay i0_guess = np.real(fine_z[np.argmax(np.abs(fine_z))]) q0_guess = np.imag(fine_z[np.argmax(np.abs(fine_z))]) else: i0_guess = (np.real(fine_z[0])+np.real(fine_z[-1]))/2. q0_guess = (np.imag(fine_z[0])+np.imag(fine_z[-1]))/2. #cabel delay guess tau #y = mx +b #m = (y2 - y1)/(x2-x1) #b = y-mx if len(gain_z)>1: #is there a gain scan? m = (gain_phase - np.roll(gain_phase,1))/(gain_x-np.roll(gain_x,1)) b = gain_phase -m*gain_x m_best = np.median(m[~np.isnan(m)]) tau_guess = m_best/(2*np.pi) else: tau_guess = 3*10**-9 if verbose == True: print("fr guess = %.2f MHz" %(fr_guess/10**6)) print("Q guess = %.2f kHz, %.1f" % ((Q_guess_Hz/10**3),Q_guess)) print("amp guess = %.2f" %amp_guess) print("i0 guess = %.2f" %i0_guess) print("q0 guess = %.2f" %q0_guess) print("tau guess = %.2f x 10^-7" %(tau_guess/10**-7)) x0 = [fr_guess,Q_guess,amp_guess,phi_guess,a_guess,i0_guess,q0_guess,tau_guess,fr_guess] return x0 def guess_x0_mag_nonlinear(x,z,verbose = False): ''' # this is lest robust than guess_x0_mag_nonlinear_sep #below it is recommended to use that instead #make sure data is sorted from low to high frequency ''' sort_index = np.argsort(x) x = x[sort_index] z = z[sort_index] #extract just fine data #this will probably break if there is no fine scan df = np.abs(x-np.roll(x,1)) fine_df = np.min(df[np.where(df != 0)]) fine_z_index = np.where(df<fine_df*1.1) fine_z = z[fine_z_index] fine_x = x[fine_z_index] #extract the gain scan gain_z_index = np.where(df>fine_df*1.1) gain_z = z[gain_z_index] gain_x = x[gain_z_index] gain_phase = np.arctan2(np.real(gain_z),np.imag(gain_z)) #guess f0 fr_guess_index = np.argmin(np.abs(z)) #fr_guess = x[fr_guess_index] fr_guess_index_fine = np.argmin(np.abs(fine_z)) if fr_guess_index_fine == 0: fr_guess_index_fine = len(fine_x)//2 elif fr_guess_index_fine == (len(fine_x)-1): fr_guess_index_fine = len(fine_x)//2 fr_guess = fine_x[fr_guess_index_fine] #guess Q mag_max = np.max(np.abs(fine_z)**2) mag_min = np.min(np.abs(fine_z)**2) mag_3dB = (mag_max+mag_min)/2. half_distance = np.abs(fine_z)**2-mag_3dB right = half_distance[fr_guess_index_fine:-1] left = half_distance[0:fr_guess_index_fine] right_index = np.argmin(np.abs(right))+fr_guess_index_fine left_index = np.argmin(np.abs(left)) Q_guess_Hz = fine_x[right_index]-fine_x[left_index] Q_guess = fr_guess/Q_guess_Hz #guess amp d = np.max(20*np.log10(np.abs(z)))-np.min(20*np.log10(np.abs(z))) amp_guess = 0.0037848547850284574+0.11096782437821565*d-0.0055208783469291173*d**2+0.00013900471000261687*d**3+-1.3994861426891861e-06*d**4#polynomial fit to amp verus depth #guess impedance rotation phi phi_guess = 0 #guess non-linearity parameter #might be able to guess this by ratioing the distance between min and max distance between iq points in fine sweep a_guess = 0 #b0 and b1 guess if len(gain_z)>1: xlin = (gain_x - fr_guess)/fr_guess b1_guess = (np.abs(gain_z)[-1]**2-np.abs(gain_z)[0]**2)/(xlin[-1]-xlin[0]) else: xlin = (fine_x - fr_guess)/fr_guess b1_guess = (np.abs(fine_z)[-1]**2-np.abs(fine_z)[0]**2)/(xlin[-1]-xlin[0]) b0_guess = np.median(np.abs(gain_z)**2) if verbose == True: print("fr guess = %.2f MHz" %(fr_guess/10**6)) print("Q guess = %.2f kHz, %.1f" % ((Q_guess_Hz/10**3),Q_guess)) print("amp guess = %.2f" %amp_guess) print("phi guess = %.2f" %phi_guess) print("b0 guess = %.2f" %b0_guess) print("b1 guess = %.2f" %b1_guess) x0 = [fr_guess,Q_guess,amp_guess,phi_guess,a_guess,b0_guess,b1_guess,fr_guess] return x0 def guess_x0_iq_nonlinear_sep(fine_x,fine_z,gain_x,gain_z,verbose = False): ''' # this is the same as guess_x0_iq_nonlinear except that it takes # takes the fine scan and the gain scan as seperate variables # this runs into less issues when trying to sort out what part of # data is fine and what part is gain for the guessing #make sure data is sorted from low to high frequency ''' #gain phase gain_phase = np.arctan2(np.real(gain_z),np.imag(gain_z)) #guess f0 fr_guess_index = np.argmin(np.abs(fine_z)) # below breaks if there is not a right and left side in the fine scan if fr_guess_index == 0: fr_guess_index = len(fine_x)//2 elif fr_guess_index == (len(fine_x)-1): fr_guess_index = len(fine_x)//2 fr_guess = fine_x[fr_guess_index] #guess Q mag_max = np.max(np.abs(fine_z)**2) mag_min = np.min(np.abs(fine_z)**2) mag_3dB = (mag_max+mag_min)/2. half_distance = np.abs(fine_z)**2-mag_3dB right = half_distance[fr_guess_index:-1] left = half_distance[0:fr_guess_index] right_index = np.argmin(np.abs(right))+fr_guess_index left_index = np.argmin(np.abs(left)) Q_guess_Hz = fine_x[right_index]-fine_x[left_index] Q_guess = fr_guess/Q_guess_Hz #guess amp d = np.max(20*np.log10(np.abs(gain_z)))-np.min(20*np.log10(np.abs(fine_z))) amp_guess = 0.0037848547850284574+0.11096782437821565*d-0.0055208783469291173*d**2+0.00013900471000261687*d**3+-1.3994861426891861e-06*d**4#polynomial fit to amp verus depth #guess impedance rotation phi #phi_guess = 0 #guess impedance rotation phi #fit a circle to the iq loop xc, yc, R, residu = calibrate.leastsq_circle(np.real(fine_z),np.imag(fine_z)) #compute angle between (off_res,off_res),(0,0) and (off_ress,off_res),(xc,yc) of the the fitted circle off_res_i,off_res_q = (np.real(fine_z[0])+np.real(fine_z[-1]))/2.,(np.imag(fine_z[0])+np.imag(fine_z[-1]))/2. x1, y1, = -off_res_i,-off_res_q x2, y2 = xc-off_res_i,yc-off_res_q dot = x1*x2 + y1*y2 # dot product det = x1*y2 - y1*x2 # determinant angle = np.arctan2(det, dot) phi_guess = angle # if phi is large better re guess f0 # f0 should be the farthers from the off res point if (np.abs(phi_guess)>0.3): dist1 = np.sqrt((np.real(fine_z[0])-np.real(fine_z))**2+(np.imag(fine_z[0])-np.imag(fine_z))**2) dist2 = np.sqrt((np.real(fine_z[-1])-np.real(fine_z))**2+(np.imag(fine_z[-1])-np.imag(fine_z))**2) fr_guess_index = np.argmax((dist1+dist2)) fr_guess = fine_x[fr_guess_index] #also fix the Q gues fine_z_derot = (fine_z-(off_res_i+1.j*off_res_q))*np.exp(1j*(-phi_guess))+(off_res_i+1.j*off_res_q) #fr_guess_index = np.argmin(np.abs(fine_z_derot)) #fr_guess = fine_x[fr_guess_index] mag_max = np.max(np.abs(fine_z_derot)**2) mag_min = np.min(np.abs(fine_z_derot)**2) mag_3dB = (mag_max+mag_min)/2. half_distance = np.abs(fine_z_derot)**2-mag_3dB right = half_distance[np.argmin(np.abs(fine_z_derot)):-1] left = half_distance[0:np.argmin(np.abs(fine_z_derot))] right_index = np.argmin(np.abs(right))+np.argmin(np.abs(fine_z_derot)) left_index = np.argmin(np.abs(left)) Q_guess_Hz = fine_x[right_index]-fine_x[left_index] Q_guess = fr_guess/Q_guess_Hz #also fix amp guess d = np.max(20*np.log10(np.abs(gain_z)))-np.min(20*np.log10(np.abs(fine_z_derot))) amp_guess = 0.0037848547850284574+0.11096782437821565*d-0.0055208783469291173*d**2+0.00013900471000261687*d**3+-1.3994861426891861e-06*d**4 #guess non-linearity parameter #might be able to guess this by ratioing the distance between min and max distance between iq points in fine sweep a_guess = 0 #i0 and iq guess if np.max(np.abs(fine_z))>np.max(np.abs(gain_z)): #if the resonator has an impedance mismatch rotation that makes the fine greater that the cabel delay i0_guess = np.real(fine_z[np.argmax(np.abs(fine_z))]) q0_guess = np.imag(fine_z[np.argmax(np.abs(fine_z))]) else: i0_guess = (np.real(fine_z[0])+np.real(fine_z[-1]))/2. q0_guess = (np.imag(fine_z[0])+np.imag(fine_z[-1]))/2. #cabel delay guess tau #y = mx +b #m = (y2 - y1)/(x2-x1) #b = y-mx m = (gain_phase - np.roll(gain_phase,1))/(gain_x-np.roll(gain_x,1)) b = gain_phase -m*gain_x m_best = np.median(m[~np.isnan(m)]) tau_guess = m_best/(2*np.pi) if verbose == True: print("fr guess = %.3f MHz" %(fr_guess/10**6)) print("Q guess = %.2f kHz, %.1f" % ((Q_guess_Hz/10**3),Q_guess)) print("amp guess = %.2f" %amp_guess) print("phi guess = %.2f" %phi_guess) print("i0 guess = %.2f" %i0_guess) print("q0 guess = %.2f" %q0_guess) print("tau guess = %.2f x 10^-7" %(tau_guess/10**-7)) x0 = [fr_guess,Q_guess,amp_guess,phi_guess,a_guess,i0_guess,q0_guess,tau_guess,fr_guess] return x0 def guess_x0_mag_nonlinear_sep(fine_x,fine_z,gain_x,gain_z,verbose = False): ''' # this is the same as guess_x0_mag_nonlinear except that it takes # takes the fine scan and the gain scan as seperate variables # this runs into less issues when trying to sort out what part of # data is fine and what part is gain for the guessing #make sure data is sorted from low to high frequency ''' #phase of gain gain_phase = np.arctan2(np.real(gain_z),np.imag(gain_z)) #guess f0 fr_guess_index = np.argmin(np.abs(fine_z)) #protect against guessing the first or last data points if fr_guess_index == 0: fr_guess_index = len(fine_x)//2 elif fr_guess_index == (len(fine_x)-1): fr_guess_index = len(fine_x)//2 fr_guess = fine_x[fr_guess_index] #guess Q mag_max = np.max(np.abs(fine_z)**2) mag_min = np.min(np.abs(fine_z)**2) mag_3dB = (mag_max+mag_min)/2. half_distance = np.abs(fine_z)**2-mag_3dB right = half_distance[fr_guess_index:-1] left = half_distance[0:fr_guess_index] right_index = np.argmin(np.abs(right))+fr_guess_index left_index = np.argmin(np.abs(left)) Q_guess_Hz = fine_x[right_index]-fine_x[left_index] Q_guess = fr_guess/Q_guess_Hz #guess amp d = np.max(20*np.log10(np.abs(gain_z)))-np.min(20*np.log10(np.abs(fine_z))) amp_guess = 0.0037848547850284574+0.11096782437821565*d-0.0055208783469291173*d**2+0.00013900471000261687*d**3+-1.3994861426891861e-06*d**4 #polynomial fit to amp verus depth calculated emperically #guess impedance rotation phi #fit a circle to the iq loop xc, yc, R, residu = calibrate.leastsq_circle(np.real(fine_z),np.imag(fine_z)) #compute angle between (off_res,off_res),(0,0) and (off_ress,off_res),(xc,yc) of the the fitted circle off_res_i,off_res_q = (np.real(fine_z[0])+np.real(fine_z[-1]))/2.,(np.imag(fine_z[0])+np.imag(fine_z[-1]))/2. x1, y1, = -off_res_i,-off_res_q x2, y2 = xc-off_res_i,yc-off_res_q dot = x1*x2 + y1*y2 # dot product det = x1*y2 - y1*x2 # determinant angle =

np.arctan2(det, dot)

numpy.arctan2

import sys, traceback import numpy as np import torch from dynamical_system.modulation import Modulator from imitation_learning.model import BinClassifier from rapidly_exploring_random_tree.rrt import RRT class Controller(): ''' This is an abstract super-class for all controllers. At the very least, get_trajectory() and log_prior() need to be implemented. ''' def __init__(self): pass def get_trajectory(self, env, kernel): ''' given an environment (i.e. a specific task instantiation), roll out the controller with randomness specified in the kernel and return the trajectory of shape T x 2. ''' raise NotImplementedError def log_prior(self, kernel): ''' return the prior probability of the controller kernel, for stochastic controllers. return 0 for deterministic controllers. ''' raise NotImplementedError class DSController(Controller): def __init__(self): super().__init__() self.modulator = Modulator() self.warned = False def get_trajectory(self, env, kernel=None): assert env.oob_termination is False, 'early termination needs to be disabled' assert env.time_limit >= 500, 'time limit should be greater than or equal to 500' if env.enable_lidar is True and not self.warned: print('WARNING: Not turning off lidar on env could be significantly slower') self.warned = True try: self.modulator.set_arena(env.arena) epsilon = sys.float_info.epsilon done = False s = env.s()[:2] traj = [s] while not done: d = self.modulator.modulate(s) d = d / max([0.3, d[0] + epsilon, d[1] + epsilon]) s, _, done, _ = env.step(d) s = s[:2] traj.append(s) traj = np.array(traj) return traj except KeyboardInterrupt: raise except: traceback.print_exc() return None def log_prior(self, kernel): return 0 class ILController(Controller): def __init__(self, model_fn='imitation_learning/data_and_model/best.pt', device=None): super().__init__() if device is None: device = ['cpu', 'cuda'][torch.cuda.is_available()] model_data = torch.load(model_fn) self.MEAN = model_data['MEAN'] self.STD = model_data['STD'] self.N_BINS = model_data['N_BINS'] self.policy = BinClassifier(18, self.N_BINS) model_data = torch.load(model_fn, map_location='cpu') self.policy.load_state_dict(model_data['model']) self.BIN_RES = 2 * np.pi / self.N_BINS self.BIN_LOW = - np.pi self.device = device self.policy.to(self.device) def get_trajectory(self, env, kernel=None): try: traj = [] with torch.no_grad(): done = False s = env.s() traj.append(s[:2]) while not done: s_scaled = torch.tensor((s - self.MEAN) / self.STD).float().to(self.device) bin_idx = self.policy(s_scaled).cpu().numpy().argmax() angle = bin_idx * self.BIN_RES - self.BIN_LOW dx =

np.cos(angle)

numpy.cos

import numpy as np import netCDF4 import torch from torch.utils.data import Dataset class iciData(Dataset): """ Pytorch dataset for the ICI training data. """ def __init__(self, path, inChannels, target, T_rec = None, batch_size = None): """ Create instance of the dataset from a given file path. Args: path: Path to the NetCDF4 containing the data. batch_size: If positive, data is provided in batches of this size """ super().__init__() self.batch_size = batch_size self.file = netCDF4.Dataset(path, mode = "r") TB = self.file.variables["TB"][:] channels = self.file.variables["channels"][:] self.surface = self.file.variables["cases"][:] self.channels = inChannels idx = [] for i in range(len(inChannels)): idx.append(np.argwhere(channels == inChannels[i])[0][0]) self.index = idx self.itarget = np.argwhere(np.array(channels) == target)[0] C = [] for i in range(len(inChannels)): ic = self.index[i] C.append(TB[1, :, ic]) self.x = np.float32(np.stack(C, axis = 1)) #store mean and std to normalise data x_noise = self.add_noise(self.x, self.index) self.std = np.std(x_noise, axis = 0) self.mean = np.mean(x_noise, axis = 0) self.y = np.float32(TB[0, :, self.itarget[0]]) self.y_noise = self.add_noise(self.y, self.itarget) self.x = self.x.data self.y = self.y.data self.y_noise = self.y_noise.data def __len__(self): """ The number of entries in the training data. This is part of the pytorch interface for datasets. Return: int: The number of samples in the data set """ if self.batch_size is None: return self.x.shape[0] else: return int(np.ceil(self.x.shape[0] / self.batch_size)) def __getitem__(self, i): """ Return element from the dataset. This is part of the pytorch interface for datasets. Args: i: The index of the sample to return """ if (i == 0): indices = np.random.permutation(self.x.shape[0]) self.x = self.x[indices, :] self.y = self.y[indices] if self.batch_size is None: return (torch.tensor(self.x[[i], :]), torch.tensor(self.y[[i]])) else: i_start = self.batch_size * i i_end = self.batch_size * (i + 1) x = self.x[i_start : i_end, :].copy() x_noise = np.float32(self.add_noise(x, self.index)) x_norm = np.float32(self.normalise(x_noise)) return (torch.tensor(x_norm), torch.tensor(self.y[i_start : i_end])) def add_noise(self, x, index): """ Gaussian noise is added to every measurement before used for training again. Args: the input TB in one batch of size (batch_size x number of channels) Returns: input TB with noise """ nedt = np.array([0.8, 0.8, 0.8, #183Ghz 0.7, 0.7, #243Ghz 1.2, 1.3, 1.5, #325Ghz 1.4, 1.6, 2.0, #448Ghz 1.6, 1.6]) #664Ghz nedt_subset = nedt[index] size_TB = int(x.size/len(nedt_subset)) x_noise = x.copy() if len(index) > 1: for ic in range(len(self.index)): noise = np.random.normal(0, nedt_subset[ic], size_TB) x_noise[:, ic] += noise else: noise =

np.random.normal(0, nedt_subset, size_TB)

numpy.random.normal

""" @file setup.py """ import numpy as np import copy # DOT_assignment from DOT_assignment import assignments from DOT_assignment import controls from DOT_assignment import dynamics from DOT_assignment import engine from DOT_assignment import linear_models_2D from DOT_assignment import linear_models_3D from DOT_assignment import run from DOT_assignment import distributions def setup_simulation(sim_profile): """ Returns dictionary of controls, dynamics, decision-making policy, and initial state parameters Input: Standard python dict containing descriptors outlining simulation requirements Output: Standard python dict containing controls, dynamics, assignment, etc. data structures """ x0 = None stationary_states = None agent_model = sim_profile["agent_model"] agent_control_policy = sim_profile["agent_control_policy"] agent_formation = sim_profile["agent_formation"] target_formation = sim_profile["target_formation"] assignment_policy = sim_profile["assignment_policy"] assignment_epoch = sim_profile["assignment_epoch"] nagents = sim_profile["nagents"] ntargets = sim_profile["ntargets"] collisions = sim_profile["collisions"] collision_tol = sim_profile["collision_tol"] dim = sim_profile["dim"] dt = sim_profile["dt"] maxtime = sim_profile["maxtime"] initial_conditions = sim_profile['initial_conditions'] if initial_conditions == None: initial_formation_params = { 'nagents': nagents, 'agent_model': agent_model, 'agent_swarm_formation': agent_formation, 'ntargets': ntargets, 'target_swarm_formation': target_formation } initial_conditions = generate_initial_conditions(dim, initial_formation_params) x0 = ic[0] targets = ic[1] else: x0 = initial_conditions[0] targets = initial_conditions[1] sim = {} parameters = ['agent_model', 'dx', 'du', 'A', 'B', 'agent_dyn', 'agent_pol', 'asst_pol', 'x0'] sim.fromkeys(parameters) ##### Dynamic Model ##### if dim == 2: if agent_model == "Double_Integrator": A, B, C, D, dx, du, statespace = linear_models_2D.double_integrator_2D() ### runner sim_runner = run.run_identical_doubleint_2D if agent_model == "Linearized_Quadcopter": A, B, C, D, dx, du, statespace = linear_models_2D.quadcopter_2D() ### runner sim_runner = run.run_identical_linearized_quadcopter_2D if dim == 3: if agent_model == "Double_Integrator": A, B, C, D, dx, du, statespace = linear_models_3D.double_integrator_3D() ### runner sim_runner = run.run_identical_doubleint_3D if agent_model == "Linearized_Quadcopter": A, B, C, D, dx, du, statespace = linear_models_3D.quadcopter_3D() ### runner sim_runner = run.run_identical_linearized_quadcopter_3D Q = np.eye(dx) R = np.eye(du) # TODO - remove # DEBUG control terms Q2 = None Q3 = None ###################### if dim == 2: if agent_model == 'Double_Integrator': Q2 = copy.deepcopy(Q) Q2[2,2] = 0.0 Q2[3,3] = 0.0 Q3 = copy.deepcopy(Q) Q3[0, 0] = 100 Q3[1, 1] = 100 Q3[2,2] = 0.0 Q3[3,3] = 0.0 if agent_model == 'Linearized_Quadcopter': Q3 = copy.deepcopy(Q) Q3[0, 0] = 100 Q3[1, 1] = 100 Q3[2,2] = 100 Q3[3,3] = 100 Q3[4,4] = 0.0 Q3[5,5] = 0.0 Q3[6, 6] = 0.0 Q3[7, 7] = 0.0 if dim == 3: if agent_model == 'Double_Integrator': Q2 = copy.deepcopy(Q) Q2[3,3] = 0.0 Q2[4,4] = 0.0 Q2[5,5] = 0.0 Q3 = copy.deepcopy(Q) Q3[0, 0] = 1000 Q3[1, 1] = 1000 Q3[2, 2] = 1000 Q3[3,3] = 0.0 Q3[4,4] = 0.0 Q3[5,5] = 0.0 if agent_model == 'Linearized_Quadcopter': Q3 = copy.deepcopy(Q) Q3[0, 0] = 1000 Q3[1, 1] = 1000 Q3[2, 2] = 1000 Q3[3,3] = 1000 Q3[4,4] = 1000 Q3[5,5] = 1000 Q3[6,6] = 0.0 Q3[7,7] = 0.0 Q3[8,8] = 0.0 Q3[9, 9] = 0.0 Q3[10, 10] = 0.0 Q3[11, 11] = 0.0 ###################### ### Agent control law if agent_control_policy == "LQR": poltrack = [controls.LinearFeedbackConstTracker(A, B, Q, R, t) for t in targets] ### Agent Dynamics ltidyn = dynamics.LTIDyn(A, B) ### Assignment Policy if assignment_policy == 'AssignmentCustom': apol = assignments.AssignmentCustom(nagents, ntargets) if assignment_policy == 'AssignmentEMD': apol = assignments.AssignmentEMD(nagents, ntargets) ### CONSTRUCT SIMULATION DICTIONARY sim['agent_control_policy'] = agent_control_policy sim['agent_model'] = agent_model sim['agent_formation'] = agent_formation sim['target_formation'] = target_formation sim['collisions'] = collisions sim['collision_tol'] = collision_tol sim['dt'] = dt sim['maxtime'] = maxtime sim['dx'] = dx sim['du'] = du sim['statespace'] = statespace sim['x0'] = x0 sim['agent_dyn'] = ltidyn sim['agent_pol'] = poltrack sim['asst_pol'] = apol sim['asst_epoch'] = assignment_epoch sim['nagents'] = nagents sim['ntargets'] = ntargets sim['runner'] = sim_runner return sim def generate_distribution(dim, space, num_particles, distribution): """ Returns discrete distribution of states (ie. X,Y,Z positions) Input: - dim: dimension - space: range of values that distribution can take - num_particles: number of particles within the distribution - distribution: name of distribution Output: - states: vector consisting of n-dimensional states corresponding to a desired distribution """ if distribution == 'uniform_distribution': states = np.random.uniform(-space, space, (num_particles,dim)) elif distribution == 'circle': radius = space states = [distributions.circle(dim, radius, num_particles, t) for t in range(num_particles)] # circle elif distribution == 'fibonacci_sphere': radius = space states = [distributions.fibonacci_sphere(radius, num_particles, t) for t in range(num_particles)] # sphere return states # TODO breakdown into more functions def generate_initial_conditions(dim, initial_formation_params): """ Returns initial states for agents, targets, and target terminal locations """ x0 = None cities = None nagents = initial_formation_params['nagents'] agent_model = initial_formation_params['agent_model'] agent_swarm_formation = initial_formation_params['agent_swarm_formation'] ntargets = initial_formation_params['ntargets'] target_swarm_formation = initial_formation_params['target_swarm_formation'] r = 100 # agent position distribution (ie. x, y, z state components) x0p = generate_distribution(dim, r, nagents, agent_swarm_formation) # target position distribution x02p = generate_distribution(dim, r, ntargets, target_swarm_formation) # TODO Place these into separate function # populate the rest of the agent/target state components given the dynamics models if dim == 2: ###### DOUBLE_INTEGRATOR ###### if agent_model == "Double_Integrator": A, B, C, D, dx, du, statespace = linear_models_2D.double_integrator_2D() ### Initial conditions # populate agent state x0 = np.zeros((nagents, dx)) # NOTE user-defined how the intial state is constructed vel_range = 500 for ii, tt in enumerate(x0): x0[ii] = np.array([x0p[ii][0], x0p[ii][1], np.random.uniform(-vel_range, vel_range, 1)[0], np.random.uniform(-vel_range, vel_range, 1)[0]]) x0 = x0.flatten() # populate target state rot_x02p = np.random.uniform(-2*np.pi, 2*np.pi, (ntargets,dim)) # position spread vel_range = 50 rot_vel_range = 25 x02 = np.zeros((ntargets, dx)) for ii, tt in enumerate(x02): x02[ii] = np.array([ x02p[ii][0], x02p[ii][1], 0, 0]) targets = x02.flatten() x0 = np.hstack((x0, targets)) ###### LINEARIZED_QUADCOPTER ###### if agent_model == "Linearized_Quadcopter": A, B, C, D, dx, du, statespace = linear_models_2D.quadcopter_2D() # Agents # populate agent state rot_x0p = np.random.uniform(-2*np.pi, 2*np.pi, (nagents,dim)) # position spread vel_range = 500 rot_vel_range = 25 x0 =

np.zeros((nagents, dx))

numpy.zeros

""" 1次元離散フーリエ変換 """ import numpy as np from scipy.fftpack import fft, ifft import matplotlib.pyplot as plt # 時系列のサンプルデータの作成 N = 120 # データ数 T=10 # サンプリング幅 del_t= T/N # サンプリング間隔 del_w=2*np.pi/T # 離散フーリエ変換の振動数の間隔 # # 離散点　生成 xs = np.arange(0,T-del_t,del_t) w=np.arange(2*np.pi/T, 2*np.pi*N/T, del_w) # f1,f2=3,4 f=

np.sin(2*np.pi*f1*xs)

numpy.sin

# coding=utf-8 # Copyright 2021 The TensorFlow Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Common corruptions to images. Define 15+4 common image corruptions: Gaussian noise, shot noise, impulse_noise, defocus blur, frosted glass blur, zoom blur, fog, brightness, contrast, elastic, pixelate, jpeg compression, frost, snow, and motion blur. 4 extra corruptions: gaussian blur, saturate, spatter, and speckle noise. """ import io import subprocess import tempfile import numpy as np import tensorflow as tf import tensorflow_datasets.public_api as tfds # To be populated by download_manager FROST_FILENAMES = [] def _imagemagick_bin(): return 'imagemagick' # pylint: disable=unreachable # /////////////// Corruption Helpers /////////////// def around_and_astype(x): """Round a numpy array, and convert to uint8. Args: x: numpy array. Returns: numpy array with dtype uint8. """ return np.around(x).astype(np.uint8) def disk(radius, alias_blur=0.1, dtype=np.float32): """Generating a Gaussian blurring kernel with disk shape. Generating a Gaussian blurring kernel with disk shape using cv2 API. Args: radius: integer, radius of blurring kernel. alias_blur: float, standard deviation of Gaussian blurring. dtype: data type of kernel Returns: cv2 object of the Gaussian blurring kernel. """ if radius <= 8: length = np.arange(-8, 8 + 1) ksize = (3, 3) else: length = np.arange(-radius, radius + 1) ksize = (5, 5) x_axis, y_axis = np.meshgrid(length, length) aliased_disk = np.array((x_axis**2 + y_axis**2) <= radius**2, dtype=dtype) aliased_disk /= np.sum(aliased_disk) # supersample disk to antialias return tfds.core.lazy_imports.cv2.GaussianBlur( aliased_disk, ksize=ksize, sigmaX=alias_blur) def clipped_zoom(img, zoom_factor): """Zoom image with clipping. Zoom the central part of the image and clip extra pixels. Args: img: numpy array, uncorrupted image. zoom_factor: numpy array, a sequence of float numbers for zoom factor. Returns: numpy array, zoomed image after clipping. """ h = img.shape[0] ch = int(np.ceil(h / float(zoom_factor))) top_h = (h - ch) // 2 w = img.shape[1] cw = int(np.ceil(w / float(zoom_factor))) top_w = (w - cw) // 2 img = tfds.core.lazy_imports.scipy.ndimage.zoom( img[top_h:top_h + ch, top_w:top_w + cw], (zoom_factor, zoom_factor, 1), order=1) # trim off any extra pixels trim_top_h = (img.shape[0] - h) // 2 trim_top_w = (img.shape[1] - w) // 2 return img[trim_top_h:trim_top_h + h, trim_top_w:trim_top_w + w] def plasma_fractal(mapsize=512, wibbledecay=3): """Generate a heightmap using diamond-square algorithm. Modification of the algorithm in https://github.com/FLHerne/mapgen/blob/master/diamondsquare.py Args: mapsize: side length of the heightmap, must be a power of two. wibbledecay: integer, decay factor. Returns: numpy 2d array, side length 'mapsize', of floats in [0,255]. """ if mapsize & (mapsize - 1) != 0: raise ValueError('mapsize must be a power of two.') maparray = np.empty((mapsize, mapsize), dtype=np.float_) maparray[0, 0] = 0 stepsize = mapsize wibble = 100 def wibbledmean(array): return array / 4 + wibble * np.random.uniform(-wibble, wibble, array.shape) def fillsquares(): """For each square, calculate middle value as mean of points + wibble.""" cornerref = maparray[0:mapsize:stepsize, 0:mapsize:stepsize] squareaccum = cornerref + np.roll(cornerref, shift=-1, axis=0) squareaccum += np.roll(squareaccum, shift=-1, axis=1) maparray[stepsize // 2:mapsize:stepsize, stepsize // 2:mapsize:stepsize] = wibbledmean(squareaccum) def filldiamonds(): """For each diamond, calculate middle value as meanof points + wibble.""" mapsize = maparray.shape[0] drgrid = maparray[stepsize // 2:mapsize:stepsize, stepsize // 2:mapsize:stepsize] ulgrid = maparray[0:mapsize:stepsize, 0:mapsize:stepsize] ldrsum = drgrid + np.roll(drgrid, 1, axis=0) lulsum = ulgrid + np.roll(ulgrid, -1, axis=1) ltsum = ldrsum + lulsum maparray[0:mapsize:stepsize, stepsize // 2:mapsize:stepsize] = wibbledmean(ltsum) tdrsum = drgrid + np.roll(drgrid, 1, axis=1) tulsum = ulgrid + np.roll(ulgrid, -1, axis=0) ttsum = tdrsum + tulsum maparray[stepsize // 2:mapsize:stepsize, 0:mapsize:stepsize] = wibbledmean(ttsum) while stepsize >= 2: fillsquares() filldiamonds() stepsize //= 2 wibble /= wibbledecay maparray -= maparray.min() return maparray / maparray.max() # /////////////// End Corruption Helpers /////////////// # /////////////// Corruptions /////////////// def gaussian_noise(x, severity=1): """Gaussian noise corruption to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Added Gaussian noise. """ c = [.08, .12, 0.18, 0.26, 0.38][severity - 1] x = np.array(x) / 255. x_clip = np.clip(x + np.random.normal(size=x.shape, scale=c), 0, 1) * 255 return around_and_astype(x_clip) def shot_noise(x, severity=1): """Shot noise corruption to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Added shot noise. """ c = [60, 25, 12, 5, 3][severity - 1] x = np.array(x) / 255. x_clip = np.clip(np.random.poisson(x * c) / float(c), 0, 1) * 255 return around_and_astype(x_clip) def impulse_noise(x, severity=1): """Impulse noise corruption to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Added impulse noise. """ c = [.03, .06, .09, 0.17, 0.27][severity - 1] x = tfds.core.lazy_imports.skimage.util.random_noise( np.array(x) / 255., mode='s&p', amount=c) x_clip = np.clip(x, 0, 1) * 255 return around_and_astype(x_clip) def defocus_blur(x, severity=1): """Defocus blurring to images. Apply defocus blurring to images using Gaussian kernel. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied defocus blur. """ c = [(3, 0.1), (4, 0.5), (6, 0.5), (8, 0.5), (10, 0.5)][severity - 1] x = np.array(x) / 255. kernel = disk(radius=c[0], alias_blur=c[1]) channels = [] for d in range(3): channels.append(tfds.core.lazy_imports.cv2.filter2D(x[:, :, d], -1, kernel)) channels = np.array(channels).transpose((1, 2, 0)) # 3x224x224 -> 224x224x3 x_clip = np.clip(channels, 0, 1) * 255 return around_and_astype(x_clip) def glass_blur(x, severity=1): """Frosted glass blurring to images. Apply frosted glass blurring to images by shuffling pixels locally. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied frosted glass blur. """ # sigma, max_delta, iterations c = [(0.7, 1, 2), (0.9, 2, 1), (1, 2, 3), (1.1, 3, 2), (1.5, 4, 2)][severity - 1] x = np.uint8( tfds.core.lazy_imports.skimage.filters.gaussian( np.array(x) / 255., sigma=c[0], multichannel=True) * 255) # locally shuffle pixels for _ in range(c[2]): for h in range(x.shape[0] - c[1], c[1], -1): for w in range(x.shape[1] - c[1], c[1], -1): dx, dy = np.random.randint(-c[1], c[1], size=(2,)) h_prime, w_prime = h + dy, w + dx # swap x[h, w], x[h_prime, w_prime] = x[h_prime, w_prime], x[h, w] x_clip = np.clip( tfds.core.lazy_imports.skimage.filters.gaussian( x / 255., sigma=c[0], multichannel=True), 0, 1) x_clip *= 255 return around_and_astype(x_clip) def zoom_blur(x, severity=1): """Zoom blurring to images. Applying zoom blurring to images by zooming the central part of the images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied zoom blur. """ c = [ np.arange(1, 1.11, 0.01), np.arange(1, 1.16, 0.01), np.arange(1, 1.21, 0.02), np.arange(1, 1.26, 0.02), np.arange(1, 1.31, 0.03) ][severity - 1] x = (np.array(x) / 255.).astype(np.float32) out = np.zeros_like(x) for zoom_factor in c: out += clipped_zoom(x, zoom_factor) x = (x + out) / (len(c) + 1) x_clip = np.clip(x, 0, 1) * 255 return around_and_astype(x_clip) def fog(x, severity=1): """Fog corruption to images. Adding fog to images. Fog is generated by diamond-square algorithm. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Added fog. """ c = [(1.5, 2), (2., 2), (2.5, 1.7), (2.5, 1.5), (3., 1.4)][severity - 1] x = np.array(x) / 255. max_val = x.max() mapsize = 512 shape = x.shape max_length = max(shape[0], shape[1]) if max_length > mapsize: mapsize = 2**int(np.ceil(np.log2(float(max_length)))) tmp = plasma_fractal(mapsize=mapsize, wibbledecay=c[1]) tmp = tmp[:x.shape[0], :x.shape[1]] tmp = tmp[..., np.newaxis] x += c[0] * tmp x_clip = np.clip(x * max_val / (max_val + c[0]), 0, 1) * 255 return around_and_astype(x_clip) def brightness(x, severity=1): """Change brightness of images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Changed brightness. """ c = [.1, .2, .3, .4, .5][severity - 1] x = np.array(x) / 255. x = tfds.core.lazy_imports.skimage.color.rgb2hsv(x) x[:, :, 2] = np.clip(x[:, :, 2] + c, 0, 1) x = tfds.core.lazy_imports.skimage.color.hsv2rgb(x) x_clip = np.clip(x, 0, 1) * 255 return around_and_astype(x_clip) def contrast(x, severity=1): """Change contrast of images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Changed contrast. """ c = [0.4, .3, .2, .1, .05][severity - 1] x = np.array(x) / 255. means = np.mean(x, axis=(0, 1), keepdims=True) x_clip = np.clip((x - means) * c + means, 0, 1) * 255 return around_and_astype(x_clip) def elastic_transform(x, severity=1): """Conduct elastic transform to images. Elastic transform is performed on small patches of the images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied elastic transform. """ c = [(244 * 2, 244 * 0.7, 244 * 0.1), (244 * 2, 244 * 0.08, 244 * 0.2), (244 * 0.05, 244 * 0.01, 244 * 0.02), (244 * 0.07, 244 * 0.01, 244 * 0.02), (244 * 0.12, 244 * 0.01, 244 * 0.02)][severity - 1] image = np.array(x, dtype=np.float32) / 255. shape = image.shape shape_size = shape[:2] # random affine center_square = np.float32(shape_size) // 2 square_size = min(shape_size) // 3 pts1 = np.float32([ center_square + square_size, [center_square[0] + square_size, center_square[1] - square_size], center_square - square_size ]) pts2 = pts1 + np.random.uniform( -c[2], c[2], size=pts1.shape).astype(np.float32) affine_trans = tfds.core.lazy_imports.cv2.getAffineTransform(pts1, pts2) image = tfds.core.lazy_imports.cv2.warpAffine( image, affine_trans, shape_size[::-1], borderMode=tfds.core.lazy_imports.cv2.BORDER_REFLECT_101) dx = (tfds.core.lazy_imports.skimage.filters.gaussian( np.random.uniform(-1, 1, size=shape[:2]), c[1], mode='reflect', truncate=3) * c[0]).astype(np.float32) dy = (tfds.core.lazy_imports.skimage.filters.gaussian( np.random.uniform(-1, 1, size=shape[:2]), c[1], mode='reflect', truncate=3) * c[0]).astype(np.float32) dx, dy = dx[..., np.newaxis], dy[..., np.newaxis] x, y, z = np.meshgrid( np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2])) indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1)) x_clip = np.clip( tfds.core.lazy_imports.scipy.ndimage.interpolation.map_coordinates( image, indices, order=1, mode='reflect').reshape(shape), 0, 1) * 255 return around_and_astype(x_clip) def pixelate(x, severity=1): """Pixelate images. Conduct pixelating corruptions to images by first shrinking the images and then resizing to original size. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied pixelating corruption. """ c = [0.6, 0.5, 0.4, 0.3, 0.25][severity - 1] shape = x.shape x = tfds.core.lazy_imports.PIL_Image.fromarray(x.astype(np.uint8)) x = x.resize((int(shape[1] * c), int(shape[0] * c))) x = x.resize((shape[1], shape[0])) return np.asarray(x) def jpeg_compression(x, severity=1): """Conduct jpeg compression to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied jpeg compression. """ c = [25, 18, 15, 10, 7][severity - 1] x = tfds.core.lazy_imports.PIL_Image.fromarray(x.astype(np.uint8)) output = io.BytesIO() x.save(output, 'JPEG', quality=c) output.seek(0) x = tfds.core.lazy_imports.PIL_Image.open(output) return np.asarray(x) def frost(x, severity=1): """Apply frost to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied frost. """ c = [(1, 0.4), (0.8, 0.6), (0.7, 0.7), (0.65, 0.7), (0.6, 0.75)][severity - 1] filename = FROST_FILENAMES[np.random.randint(5)] with tempfile.NamedTemporaryFile() as im_frost: tf.io.gfile.copy(filename, im_frost.name, overwrite=True) frost_img = tfds.core.lazy_imports.cv2.imread(im_frost.name) # randomly crop and convert to rgb x_start, y_start = np.random.randint( 0, frost_img.shape[0] - 224), np.random.randint(0, frost_img.shape[1] - 224) frost_img = frost_img[x_start:x_start + 224, y_start:y_start + 224][..., [2, 1, 0]] x = np.clip(c[0] * np.array(x) + c[1] * frost_img, 0, 255) return around_and_astype(x) def snow(x, severity=1): """Apply snow to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied snow. """ cv2 = tfds.core.lazy_imports.cv2 PIL_Image = tfds.core.lazy_imports.PIL_Image # pylint: disable=invalid-name c = [(0.1, 0.3, 3, 0.5, 10, 4, 0.8), (0.2, 0.3, 2, 0.5, 12, 4, 0.7), (0.55, 0.3, 4, 0.9, 12, 8, 0.7), (0.55, 0.3, 4.5, 0.85, 12, 8, 0.65), (0.55, 0.3, 2.5, 0.85, 12, 12, 0.55)][severity - 1] x = np.array(x, dtype=np.float32) / 255. snow_layer = np.random.normal( size=x.shape[:2], loc=c[0], scale=c[1]) # [:2] for monochrome snow_layer = clipped_zoom(snow_layer[..., np.newaxis], c[2]) snow_layer[snow_layer < c[3]] = 0 snow_layer = PIL_Image.fromarray( (np.clip(snow_layer.squeeze(), 0, 1) * 255).astype(np.uint8), mode='L') with tempfile.NamedTemporaryFile() as im_input: with tempfile.NamedTemporaryFile() as im_output: snow_layer.save(im_input.name, format='PNG') convert_bin = _imagemagick_bin() radius = c[4] sigma = c[5] angle = np.random.uniform(-135, -45) subprocess.check_output([ convert_bin, '-motion-blur', '{}x{}+{}'.format(radius, sigma, angle), im_input.name, im_output.name ]) with open(im_output.name, 'rb') as f: output = f.read() snow_layer = cv2.imdecode( np.frombuffer(output, np.uint8), cv2.IMREAD_UNCHANGED) / 255. snow_layer = snow_layer[..., np.newaxis] x = c[6] * x + (1 - c[6]) * np.maximum( x, cv2.cvtColor(x, cv2.COLOR_RGB2GRAY).reshape(224, 224, 1) * 1.5 + 0.5) x = np.clip(x + snow_layer + np.rot90(snow_layer, k=2), 0, 1) * 255 return around_and_astype(x) def motion_blur(x, severity=1): """Apply motion blur to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied motion blur. """ c = [(10, 3), (15, 5), (15, 8), (15, 12), (20, 15)][severity - 1] x = tfds.core.lazy_imports.PIL_Image.fromarray(x.astype(np.uint8)) with tempfile.NamedTemporaryFile() as im_input: with tempfile.NamedTemporaryFile() as im_output: x.save(im_input.name, format='PNG') convert_bin = _imagemagick_bin() radius = c[0] sigma = c[1] angle = np.random.uniform(-45, -45) subprocess.check_output([ convert_bin, '-motion-blur', '{}x{}+{}'.format(radius, sigma, angle), im_input.name, im_output.name ]) with open(im_output.name, 'rb') as f: output = f.read() x = tfds.core.lazy_imports.cv2.imdecode( np.frombuffer(output, np.uint8), tfds.core.lazy_imports.cv2.IMREAD_UNCHANGED) if x.shape != (224, 224): x = np.clip(x[..., [2, 1, 0]], 0, 255) # BGR to RGB else: # greyscale to RGB x = np.clip(np.array([x, x, x]).transpose((1, 2, 0)), 0, 255) return around_and_astype(x) # /////////////// Extra Corruptions /////////////// def gaussian_blur(x, severity=1): """Apply gaussian blur to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied gaussian blur. """ c = [1, 2, 3, 4, 6][severity - 1] x = tfds.core.lazy_imports.skimage.filters.gaussian( np.array(x) / 255., sigma=c, multichannel=True) x = np.clip(x, 0, 1) * 255 return around_and_astype(x) def saturate(x, severity=1): """Increase saturation of images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied saturation. """ c = [(0.3, 0), (0.1, 0), (2, 0), (5, 0.1), (20, 0.2)][severity - 1] x = np.array(x) / 255. x = tfds.core.lazy_imports.skimage.color.rgb2hsv(x) x[:, :, 1] = np.clip(x[:, :, 1] * c[0] + c[1], 0, 1) x = tfds.core.lazy_imports.skimage.color.hsv2rgb(x) x = np.clip(x, 0, 1) * 255 return around_and_astype(x) def spatter(x, severity=1): """Apply spatter to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Applied spatter. """ cv2 = tfds.core.lazy_imports.cv2 skimage = tfds.core.lazy_imports.skimage c = [(0.65, 0.3, 4, 0.69, 0.6, 0), (0.65, 0.3, 3, 0.68, 0.6, 0), (0.65, 0.3, 2, 0.68, 0.5, 0), (0.65, 0.3, 1, 0.65, 1.5, 1), (0.67, 0.4, 1, 0.65, 1.5, 1)][severity - 1] x = np.array(x, dtype=np.float32) / 255. liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1]) liquid_layer = skimage.filters.gaussian(liquid_layer, sigma=c[2]) liquid_layer[liquid_layer < c[3]] = 0 if c[5] == 0: liquid_layer = (liquid_layer * 255).astype(np.uint8) dist = 255 - cv2.Canny(liquid_layer, 50, 150) dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5) _, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC) dist = cv2.blur(dist, (3, 3)).astype(np.uint8) dist = cv2.equalizeHist(dist) # ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32) # ker -= np.mean(ker) ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]]) dist = cv2.filter2D(dist, cv2.CVX_8U, ker) dist = cv2.blur(dist, (3, 3)).astype(np.float32) m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA) m /= np.max(m, axis=(0, 1)) m *= c[4] # water is pale turqouise color = np.concatenate( (175 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1])), axis=2) color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA) x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA) x = cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255 else: m =

np.where(liquid_layer > c[3], 1, 0)

numpy.where

from keras.models import Sequential from keras.layers import Conv2D, Conv2DTranspose, Input, BatchNormalization, PReLU from keras.callbacks import ModelCheckpoint, Callback, TensorBoard from keras.optimizers import SGD, Adam import numpy as np import math import os import random from os import listdir, makedirs from os.path import isfile, join, exists from PIL import Image import os.path, sys sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir)) from s3sync import S3SyncCallback def model(scale = 2): d = 56 s = 12 m = 4 c = 3 SRCNN = Sequential() SRCNN.add(Conv2D(nb_filter=d, nb_row=5, nb_col=5, init='glorot_uniform', border_mode='same', bias=True, input_shape=(100, 100, 3))) SRCNN.add(PReLU(shared_axes=[1, 2])) SRCNN.add(Conv2D(nb_filter=s, nb_row=1, nb_col=1, init='glorot_uniform', border_mode='same', bias=True)) SRCNN.add(PReLU(shared_axes=[1, 2])) for i in range(m): SRCNN.add(Conv2D(nb_filter=s, nb_row=3, nb_col=3, init='glorot_uniform', border_mode='same', bias=True)) SRCNN.add(PReLU(shared_axes=[1, 2])) SRCNN.add(Conv2D(nb_filter=d, nb_row=1, nb_col=1, init='glorot_uniform', border_mode='same', bias=True)) SRCNN.add(PReLU(shared_axes=[1, 2])) SRCNN.add(Conv2DTranspose(filters=3, kernel_size=(9,9), strides=(scale, scale), init='glorot_uniform', border_mode='same', bias=True)) adam = Adam(lr=0.0003) SRCNN.compile(optimizer=adam, loss='mean_squared_error', metrics=['mean_squared_error']) return SRCNN class MyDataGenerator(object): def flow_from_directory(self, input_dir, label_dir, batch_size=32): images = [] labels = [] while True: files = listdir(input_dir) random.shuffle(files) for f in files: images.append(self.load_image(input_dir, f)) labels.append(self.load_image(label_dir, f)) if len(images) == batch_size: x_inputs =

np.asarray(images)

numpy.asarray

"""Testing for K-means""" import re import sys import numpy as np from scipy import sparse as sp from threadpoolctl import threadpool_limits import pytest from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils.fixes import _astype_copy_false from sklearn.base import clone from sklearn.exceptions import ConvergenceWarning from sklearn.utils.extmath import row_norms from sklearn.metrics import pairwise_distances_argmin from sklearn.metrics.cluster import v_measure_score from sklearn.cluster import KMeans, k_means from sklearn.cluster import MiniBatchKMeans from sklearn.cluster._kmeans import _labels_inertia from sklearn.cluster._kmeans import _mini_batch_step from sklearn.cluster._k_means_fast import _relocate_empty_clusters_dense from sklearn.cluster._k_means_fast import _relocate_empty_clusters_sparse from sklearn.cluster._k_means_fast import _euclidean_dense_dense_wrapper from sklearn.cluster._k_means_fast import _euclidean_sparse_dense_wrapper from sklearn.cluster._k_means_fast import _inertia_dense from sklearn.cluster._k_means_fast import _inertia_sparse from sklearn.datasets import make_blobs from io import StringIO from sklearn.metrics.cluster import homogeneity_score # non centered, sparse centers to check the centers = np.array([ [0.0, 5.0, 0.0, 0.0, 0.0], [1.0, 1.0, 4.0, 0.0, 0.0], [1.0, 0.0, 0.0, 5.0, 1.0], ]) n_samples = 100 n_clusters, n_features = centers.shape X, true_labels = make_blobs(n_samples=n_samples, centers=centers, cluster_std=1., random_state=42) X_csr = sp.csr_matrix(X) @pytest.mark.parametrize("array_constr", [np.array, sp.csr_matrix], ids=["dense", "sparse"]) @pytest.mark.parametrize("algo", ["full", "elkan"]) @pytest.mark.parametrize("dtype", [np.float32, np.float64]) def test_kmeans_results(array_constr, algo, dtype): # Checks that KMeans works as intended on toy dataset by comparing with # expected results computed by hand. X = array_constr([[0, 0], [0.5, 0], [0.5, 1], [1, 1]], dtype=dtype) sample_weight = [3, 1, 1, 3] init_centers = np.array([[0, 0], [1, 1]], dtype=dtype) expected_labels = [0, 0, 1, 1] expected_inertia = 0.375 expected_centers = np.array([[0.125, 0], [0.875, 1]], dtype=dtype) expected_n_iter = 2 kmeans = KMeans(n_clusters=2, n_init=1, init=init_centers, algorithm=algo) kmeans.fit(X, sample_weight=sample_weight) assert_array_equal(kmeans.labels_, expected_labels) assert_allclose(kmeans.inertia_, expected_inertia) assert_allclose(kmeans.cluster_centers_, expected_centers) assert kmeans.n_iter_ == expected_n_iter @pytest.mark.parametrize("array_constr", [np.array, sp.csr_matrix], ids=['dense', 'sparse']) @pytest.mark.parametrize("algo", ['full', 'elkan']) def test_relocated_clusters(array_constr, algo): # check that empty clusters are relocated as expected X = array_constr([[0, 0], [0.5, 0], [0.5, 1], [1, 1]]) # second center too far from others points will be empty at first iter init_centers = np.array([[0.5, 0.5], [3, 3]]) expected_labels = [0, 0, 1, 1] expected_inertia = 0.25 expected_centers = [[0.25, 0], [0.75, 1]] expected_n_iter = 3 kmeans = KMeans(n_clusters=2, n_init=1, init=init_centers, algorithm=algo) kmeans.fit(X) assert_array_equal(kmeans.labels_, expected_labels) assert_almost_equal(kmeans.inertia_, expected_inertia) assert_array_almost_equal(kmeans.cluster_centers_, expected_centers) assert kmeans.n_iter_ == expected_n_iter @pytest.mark.parametrize("representation", ["dense", "sparse"]) def test_relocate_empty_clusters(representation): # test for the _relocate_empty_clusters_(dense/sparse) helpers # Synthetic dataset with 3 obvious clusters of different sizes X = np.array( [-10., -9.5, -9, -8.5, -8, -1, 1, 9, 9.5, 10]).reshape(-1, 1) if representation == "sparse": X = sp.csr_matrix(X) sample_weight = np.full(shape=10, fill_value=1.) # centers all initialized to the first point of X centers_old = np.array([-10., -10, -10]).reshape(-1, 1) # With this initialization, all points will be assigned to the first center # At this point a center in centers_new is the weighted sum of the points # it contains if it's not empty, otherwise it is the same as before. centers_new = np.array([-16.5, -10, -10]).reshape(-1, 1) weight_in_clusters = np.array([10., 0, 0]) labels = np.zeros(10, dtype=np.int32) if representation == "dense": _relocate_empty_clusters_dense(X, sample_weight, centers_old, centers_new, weight_in_clusters, labels) else: _relocate_empty_clusters_sparse(X.data, X.indices, X.indptr, sample_weight, centers_old, centers_new, weight_in_clusters, labels) # The relocation scheme will take the 2 points farthest from the center and # assign them to the 2 empty clusters, i.e. points at 10 and at 9.9. The # first center will be updated to contain the other 8 points. assert_array_equal(weight_in_clusters, [8, 1, 1]) assert_allclose(centers_new, [[-36], [10], [9.5]]) @pytest.mark.parametrize("distribution", ["normal", "blobs"]) @pytest.mark.parametrize("array_constr", [np.array, sp.csr_matrix], ids=["dense", "sparse"]) @pytest.mark.parametrize("tol", [1e-2, 1e-8, 1e-100, 0]) def test_kmeans_elkan_results(distribution, array_constr, tol): # Check that results are identical between lloyd and elkan algorithms rnd = np.random.RandomState(0) if distribution == 'normal': X = rnd.normal(size=(5000, 10)) else: X, _ = make_blobs(random_state=rnd) km_full = KMeans(algorithm='full', n_clusters=5, random_state=0, n_init=1, tol=tol) km_elkan = KMeans(algorithm='elkan', n_clusters=5, random_state=0, n_init=1, tol=tol) km_full.fit(X) km_elkan.fit(X) assert_allclose(km_elkan.cluster_centers_, km_full.cluster_centers_) assert_array_equal(km_elkan.labels_, km_full.labels_) assert km_elkan.n_iter_ == km_full.n_iter_ assert km_elkan.inertia_ == pytest.approx(km_full.inertia_, rel=1e-6) @pytest.mark.parametrize('algorithm', ['full', 'elkan']) def test_kmeans_convergence(algorithm): # Check that KMeans stops when convergence is reached when tol=0. (#16075) rnd = np.random.RandomState(0) X = rnd.normal(size=(5000, 10)) max_iter = 300 km = KMeans(algorithm=algorithm, n_clusters=5, random_state=0, n_init=1, tol=0, max_iter=max_iter).fit(X) assert km.n_iter_ < max_iter @pytest.mark.parametrize('distribution', ['normal', 'blobs']) def test_elkan_results_sparse(distribution): # check that results are identical between lloyd and elkan algorithms # with sparse input rnd = np.random.RandomState(0) if distribution == 'normal': X = sp.random(100, 100, density=0.1, format='csr', random_state=rnd) X.data = rnd.randn(len(X.data)) else: X, _ = make_blobs(n_samples=100, n_features=100, random_state=rnd) X = sp.csr_matrix(X) km_full = KMeans(algorithm='full', n_clusters=5, random_state=0, n_init=1) km_elkan = KMeans(algorithm='elkan', n_clusters=5, random_state=0, n_init=1) km_full.fit(X) km_elkan.fit(X) assert_allclose(km_elkan.cluster_centers_, km_full.cluster_centers_) assert_allclose(km_elkan.labels_, km_full.labels_) def test_labels_assignment_and_inertia(): # pure numpy implementation as easily auditable reference gold # implementation rng = np.random.RandomState(42) noisy_centers = centers + rng.normal(size=centers.shape) labels_gold = np.full(n_samples, -1, dtype=np.int) mindist = np.empty(n_samples) mindist.fill(np.infty) for center_id in range(n_clusters): dist = np.sum((X - noisy_centers[center_id]) ** 2, axis=1) labels_gold[dist < mindist] = center_id mindist = np.minimum(dist, mindist) inertia_gold = mindist.sum() assert (mindist >= 0.0).all() assert (labels_gold != -1).all() sample_weight = None # perform label assignment using the dense array input x_squared_norms = (X ** 2).sum(axis=1) labels_array, inertia_array = _labels_inertia( X, sample_weight, x_squared_norms, noisy_centers) assert_array_almost_equal(inertia_array, inertia_gold) assert_array_equal(labels_array, labels_gold) # perform label assignment using the sparse CSR input x_squared_norms_from_csr = row_norms(X_csr, squared=True) labels_csr, inertia_csr = _labels_inertia( X_csr, sample_weight, x_squared_norms_from_csr, noisy_centers) assert_array_almost_equal(inertia_csr, inertia_gold) assert_array_equal(labels_csr, labels_gold) def test_minibatch_update_consistency(): # Check that dense and sparse minibatch update give the same results rng = np.random.RandomState(42) old_centers = centers + rng.normal(size=centers.shape) new_centers = old_centers.copy() new_centers_csr = old_centers.copy() weight_sums = np.zeros(new_centers.shape[0], dtype=np.double) weight_sums_csr = np.zeros(new_centers.shape[0], dtype=np.double) x_squared_norms = (X ** 2).sum(axis=1) x_squared_norms_csr = row_norms(X_csr, squared=True) buffer = np.zeros(centers.shape[1], dtype=np.double) buffer_csr = np.zeros(centers.shape[1], dtype=np.double) # extract a small minibatch X_mb = X[:10] X_mb_csr = X_csr[:10] x_mb_squared_norms = x_squared_norms[:10] x_mb_squared_norms_csr = x_squared_norms_csr[:10] sample_weight_mb = np.ones(X_mb.shape[0], dtype=np.double) # step 1: compute the dense minibatch update old_inertia, incremental_diff = _mini_batch_step( X_mb, sample_weight_mb, x_mb_squared_norms, new_centers, weight_sums, buffer, 1, None, random_reassign=False) assert old_inertia > 0.0 # compute the new inertia on the same batch to check that it decreased labels, new_inertia = _labels_inertia( X_mb, sample_weight_mb, x_mb_squared_norms, new_centers) assert new_inertia > 0.0 assert new_inertia < old_inertia # check that the incremental difference computation is matching the # final observed value effective_diff = np.sum((new_centers - old_centers) ** 2) assert_almost_equal(incremental_diff, effective_diff) # step 2: compute the sparse minibatch update old_inertia_csr, incremental_diff_csr = _mini_batch_step( X_mb_csr, sample_weight_mb, x_mb_squared_norms_csr, new_centers_csr, weight_sums_csr, buffer_csr, 1, None, random_reassign=False) assert old_inertia_csr > 0.0 # compute the new inertia on the same batch to check that it decreased labels_csr, new_inertia_csr = _labels_inertia( X_mb_csr, sample_weight_mb, x_mb_squared_norms_csr, new_centers_csr) assert new_inertia_csr > 0.0 assert new_inertia_csr < old_inertia_csr # check that the incremental difference computation is matching the # final observed value effective_diff = np.sum((new_centers_csr - old_centers) ** 2) assert_almost_equal(incremental_diff_csr, effective_diff) # step 3: check that sparse and dense updates lead to the same results assert_array_equal(labels, labels_csr) assert_array_almost_equal(new_centers, new_centers_csr) assert_almost_equal(incremental_diff, incremental_diff_csr) assert_almost_equal(old_inertia, old_inertia_csr) assert_almost_equal(new_inertia, new_inertia_csr) def _check_fitted_model(km): # check that the number of clusters centers and distinct labels match # the expectation centers = km.cluster_centers_ assert centers.shape == (n_clusters, n_features) labels = km.labels_ assert np.unique(labels).shape[0] == n_clusters # check that the labels assignment are perfect (up to a permutation) assert v_measure_score(true_labels, labels) == 1.0 assert km.inertia_ > 0.0 # check error on dataset being too small assert_raise_message(ValueError, "n_samples=1 should be >= n_clusters=%d" % km.n_clusters, km.fit, [[0., 1.]]) def test_k_means_new_centers(): # Explore the part of the code where a new center is reassigned X = np.array([[0, 0, 1, 1], [0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0]]) labels = [0, 1, 2, 1, 1, 2] bad_centers = np.array([[+0, 1, 0, 0], [.2, 0, .2, .2], [+0, 0, 0, 0]]) km = KMeans(n_clusters=3, init=bad_centers, n_init=1, max_iter=10, random_state=1) for this_X in (X, sp.coo_matrix(X)): km.fit(this_X) this_labels = km.labels_ # Reorder the labels so that the first instance is in cluster 0, # the second in cluster 1, ... this_labels = np.unique(this_labels, return_index=True)[1][this_labels] np.testing.assert_array_equal(this_labels, labels) @pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse']) @pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()]) def test_k_means_init(data, init): km = KMeans(init=init, n_clusters=n_clusters, random_state=42, n_init=1) km.fit(data) _check_fitted_model(km) @pytest.mark.parametrize("init", ["random", "k-means++", centers, lambda X, k, random_state: centers], ids=["random", "k-means++", "ndarray", "callable"]) def test_minibatch_kmeans_partial_fit_init(init): # Check MiniBatchKMeans init with partial_fit km = MiniBatchKMeans(init=init, n_clusters=n_clusters, random_state=0) for i in range(100): # "random" init requires many batches to recover the true labels. km.partial_fit(X) _check_fitted_model(km) def test_k_means_fortran_aligned_data(): # Check the KMeans will work well, even if X is a fortran-aligned data. X = np.asfortranarray([[0, 0], [0, 1], [0, 1]]) centers = np.array([[0, 0], [0, 1]]) labels = np.array([0, 1, 1]) km = KMeans(n_init=1, init=centers, random_state=42, n_clusters=2) km.fit(X) assert_array_almost_equal(km.cluster_centers_, centers) assert_array_equal(km.labels_, labels) @pytest.mark.parametrize('algo', ['full', 'elkan']) @pytest.mark.parametrize('dtype', [np.float32, np.float64]) @pytest.mark.parametrize('constructor', [np.asarray, sp.csr_matrix]) @pytest.mark.parametrize('seed, max_iter, tol', [ (0, 2, 1e-7), # strict non-convergence (1, 2, 1e-1), # loose non-convergence (3, 300, 1e-7), # strict convergence (4, 300, 1e-1), # loose convergence ]) def test_k_means_fit_predict(algo, dtype, constructor, seed, max_iter, tol): # check that fit.predict gives same result as fit_predict # There's a very small chance of failure with elkan on unstructured dataset # because predict method uses fast euclidean distances computation which # may cause small numerical instabilities. # NB: This test is largely redundant with respect to test_predict and # test_predict_equal_labels. This test has the added effect of # testing idempotence of the fittng procesdure which appears to # be where it fails on some MacOS setups. if sys.platform == "darwin": pytest.xfail( "Known failures on MacOS, See " "https://github.com/scikit-learn/scikit-learn/issues/12644") rng = np.random.RandomState(seed) X = make_blobs(n_samples=1000, n_features=10, centers=10, random_state=rng)[0].astype(dtype, copy=False) X = constructor(X) kmeans = KMeans(algorithm=algo, n_clusters=10, random_state=seed, tol=tol, max_iter=max_iter) labels_1 = kmeans.fit(X).predict(X) labels_2 = kmeans.fit_predict(X) # Due to randomness in the order in which chunks of data are processed when # using more than one thread, the absolute values of the labels can be # different between the 2 strategies but they should correspond to the same # clustering. assert v_measure_score(labels_1, labels_2) == 1 def test_minibatch_kmeans_verbose(): # Check verbose mode of MiniBatchKMeans for better coverage. km = MiniBatchKMeans(n_clusters=n_clusters, random_state=42, verbose=1) old_stdout = sys.stdout sys.stdout = StringIO() try: km.fit(X) finally: sys.stdout = old_stdout @pytest.mark.parametrize("algorithm", ["full", "elkan"]) @pytest.mark.parametrize("tol", [1e-2, 0]) def test_kmeans_verbose(algorithm, tol, capsys): # Check verbose mode of KMeans for better coverage. X = np.random.RandomState(0).normal(size=(5000, 10)) KMeans(algorithm=algorithm, n_clusters=n_clusters, random_state=42, init="random", n_init=1, tol=tol, verbose=1).fit(X) captured = capsys.readouterr() assert re.search(r"Initialization complete", captured.out) assert re.search(r"Iteration [0-9]+, inertia", captured.out) if tol == 0: assert re.search(r"strict convergence", captured.out) else: assert re.search(r"center shift .* within tolerance", captured.out) def test_minibatch_kmeans_warning_init_size(): # Check that a warning is raised when init_size is smaller than n_clusters with pytest.warns(RuntimeWarning, match=r"init_size.* should be larger than n_clusters"): MiniBatchKMeans(init_size=10, n_clusters=20).fit(X) def test_minibatch_k_means_init_multiple_runs_with_explicit_centers(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=10) assert_warns(RuntimeWarning, mb_k_means.fit, X) @pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse']) @pytest.mark.parametrize('init', ["random", 'k-means++', centers.copy()]) def test_minibatch_k_means_init(data, init): mb_k_means = MiniBatchKMeans(init=init, n_clusters=n_clusters, random_state=42, n_init=10) mb_k_means.fit(data) _check_fitted_model(mb_k_means) def test_minibatch_sensible_reassign_fit(): # check if identical initial clusters are reassigned # also a regression test for when there are more desired reassignments than # samples. zeroed_X, true_labels = make_blobs(n_samples=100, centers=5, cluster_std=1., random_state=42) zeroed_X[::2, :] = 0 mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=10, random_state=42, init="random") mb_k_means.fit(zeroed_X) # there should not be too many exact zero cluster centers assert mb_k_means.cluster_centers_.any(axis=1).sum() > 10 # do the same with batch-size > X.shape[0] (regression test) mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=201, random_state=42, init="random") mb_k_means.fit(zeroed_X) # there should not be too many exact zero cluster centers assert mb_k_means.cluster_centers_.any(axis=1).sum() > 10 def test_minibatch_sensible_reassign_partial_fit(): zeroed_X, true_labels = make_blobs(n_samples=n_samples, centers=5, cluster_std=1., random_state=42) zeroed_X[::2, :] = 0 mb_k_means = MiniBatchKMeans(n_clusters=20, random_state=42, init="random") for i in range(100): mb_k_means.partial_fit(zeroed_X) # there should not be too many exact zero cluster centers assert mb_k_means.cluster_centers_.any(axis=1).sum() > 10 def test_minibatch_reassign(): # Give a perfect initialization, but a large reassignment_ratio, # as a result all the centers should be reassigned and the model # should no longer be good sample_weight = np.ones(X.shape[0], dtype=X.dtype) for this_X in (X, X_csr): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, random_state=42) mb_k_means.fit(this_X) score_before = mb_k_means.score(this_X) try: old_stdout = sys.stdout sys.stdout = StringIO() # Turn on verbosity to smoke test the display code _mini_batch_step(this_X, sample_weight, (X ** 2).sum(axis=1), mb_k_means.cluster_centers_, mb_k_means.counts_, np.zeros(X.shape[1], np.double), False, distances=np.zeros(X.shape[0]), random_reassign=True, random_state=42, reassignment_ratio=1, verbose=True) finally: sys.stdout = old_stdout assert score_before > mb_k_means.score(this_X) # Give a perfect initialization, with a small reassignment_ratio, # no center should be reassigned for this_X in (X, X_csr): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, init=centers.copy(), random_state=42, n_init=1) mb_k_means.fit(this_X) clusters_before = mb_k_means.cluster_centers_ # Turn on verbosity to smoke test the display code _mini_batch_step(this_X, sample_weight, (X ** 2).sum(axis=1), mb_k_means.cluster_centers_, mb_k_means.counts_, np.zeros(X.shape[1], np.double), False, distances=np.zeros(X.shape[0]), random_reassign=True, random_state=42, reassignment_ratio=1e-15) assert_array_almost_equal(clusters_before, mb_k_means.cluster_centers_) def test_minibatch_with_many_reassignments(): # Test for the case that the number of clusters to reassign is bigger # than the batch_size n_samples = 550 rnd = np.random.RandomState(42) X = rnd.uniform(size=(n_samples, 10)) # Check that the fit works if n_clusters is bigger than the batch_size. # Run the test with 550 clusters and 550 samples, because it turned out # that this values ensure that the number of clusters to reassign # is always bigger than the batch_size n_clusters = 550 MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, init_size=n_samples, random_state=42).fit(X) def test_sparse_mb_k_means_callable_init(): def test_init(X, k, random_state): return centers mb_k_means = MiniBatchKMeans(n_clusters=3, init=test_init, random_state=42).fit(X_csr) _check_fitted_model(mb_k_means) def test_mini_batch_k_means_random_init_partial_fit(): km = MiniBatchKMeans(n_clusters=n_clusters, init="random", random_state=42) # use the partial_fit API for online learning for X_minibatch in np.array_split(X, 10): km.partial_fit(X_minibatch) # compute the labeling on the complete dataset labels = km.predict(X) assert v_measure_score(true_labels, labels) == 1.0 def test_minibatch_kmeans_default_init_size(): # Check the internal _init_size attribute of MiniBatchKMeans # default init size should be 3 * batch_size km = MiniBatchKMeans(n_clusters=10, batch_size=5, n_init=1).fit(X) assert km._init_size == 15 # if 3 * batch size < n_clusters, it should then be 3 * n_clusters km = MiniBatchKMeans(n_clusters=10, batch_size=1, n_init=1).fit(X) assert km._init_size == 30 # it should not be larger than n_samples km = MiniBatchKMeans(n_clusters=10, batch_size=5, n_init=1, init_size=n_samples + 1).fit(X) assert km._init_size == n_samples def test_minibatch_tol(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=10, random_state=42, tol=.01).fit(X) _check_fitted_model(mb_k_means) def test_minibatch_set_init_size(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, init_size=666, random_state=42, n_init=1).fit(X) assert mb_k_means.init_size == 666 assert mb_k_means.init_size_ == n_samples _check_fitted_model(mb_k_means) def test_k_means_copyx(): # Check if copy_x=False returns nearly equal X after de-centering. my_X = X.copy() km = KMeans(copy_x=False, n_clusters=n_clusters, random_state=42) km.fit(my_X) _check_fitted_model(km) # check if my_X is centered assert_array_almost_equal(my_X, X) def test_k_means_non_collapsed(): # Check k_means with a bad initialization does not yield a singleton # Starting with bad centers that are quickly ignored should not # result in a repositioning of the centers to the center of mass that # would lead to collapsed centers which in turns make the clustering # dependent of the numerical unstabilities. my_X = np.array([[1.1, 1.1], [0.9, 1.1], [1.1, 0.9], [0.9, 1.1]]) array_init = np.array([[1.0, 1.0], [5.0, 5.0], [-5.0, -5.0]]) km = KMeans(init=array_init, n_clusters=3, random_state=42, n_init=1) km.fit(my_X) # centers must not been collapsed assert len(np.unique(km.labels_)) == 3 centers = km.cluster_centers_ assert np.linalg.norm(centers[0] - centers[1]) >= 0.1 assert np.linalg.norm(centers[0] - centers[2]) >= 0.1 assert np.linalg.norm(centers[1] - centers[2]) >= 0.1 @pytest.mark.parametrize('algo', ['full', 'elkan']) def test_score(algo): # Check that fitting k-means with multiple inits gives better score km1 = KMeans(n_clusters=n_clusters, max_iter=1, random_state=42, n_init=1, algorithm=algo) s1 = km1.fit(X).score(X) km2 = KMeans(n_clusters=n_clusters, max_iter=10, random_state=42, n_init=1, algorithm=algo) s2 = km2.fit(X).score(X) assert s2 > s1 @pytest.mark.parametrize('Estimator', [KMeans, MiniBatchKMeans]) @pytest.mark.parametrize('data', [X, X_csr], ids=['dense', 'sparse']) @pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()]) def test_predict(Estimator, data, init): k_means = Estimator(n_clusters=n_clusters, init=init, n_init=10, random_state=0).fit(data) # sanity check: re-predict labeling for training set samples assert_array_equal(k_means.predict(data), k_means.labels_) # sanity check: predict centroid labels pred = k_means.predict(k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # re-predict labels for training set using fit_predict pred = k_means.fit_predict(data) assert_array_equal(pred, k_means.labels_) @pytest.mark.parametrize('init', ['random', 'k-means++', centers.copy()]) def test_predict_minibatch_dense_sparse(init): # check that models trained on sparse input also works for dense input at # predict time mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init=init, n_init=10, random_state=0).fit(X_csr) assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_int_input(): X_list = [[0, 0], [10, 10], [12, 9], [-1, 1], [2, 0], [8, 10]] for dtype in [np.int32, np.int64]: X_int = np.array(X_list, dtype=dtype) X_int_csr = sp.csr_matrix(X_int) init_int = X_int[:2] fitted_models = [ KMeans(n_clusters=2).fit(X_int), KMeans(n_clusters=2, init=init_int, n_init=1).fit(X_int), # mini batch kmeans is very unstable on such a small dataset hence # we use many inits MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_int), MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit( X_int_csr), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int, n_init=1).fit(X_int), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int, n_init=1).fit(X_int_csr), ] for km in fitted_models: assert km.cluster_centers_.dtype == np.float64 expected_labels = [0, 1, 1, 0, 0, 1] scores = np.array([v_measure_score(expected_labels, km.labels_) for km in fitted_models]) assert_array_almost_equal(scores, np.ones(scores.shape[0])) def test_transform(): km = KMeans(n_clusters=n_clusters) km.fit(X) X_new = km.transform(km.cluster_centers_) for c in range(n_clusters): assert X_new[c, c] == 0 for c2 in range(n_clusters): if c != c2: assert X_new[c, c2] > 0 def test_fit_transform(): X1 = KMeans(n_clusters=3, random_state=51).fit(X).transform(X) X2 = KMeans(n_clusters=3, random_state=51).fit_transform(X) assert_array_almost_equal(X1, X2) @pytest.mark.parametrize('algo', ['full', 'elkan']) def test_predict_equal_labels(algo): km = KMeans(random_state=13, n_init=1, max_iter=1, algorithm=algo) km.fit(X) assert_array_equal(km.predict(X), km.labels_) def test_full_vs_elkan(): km1 = KMeans(algorithm='full', random_state=13).fit(X) km2 = KMeans(algorithm='elkan', random_state=13).fit(X) assert homogeneity_score( km1.predict(X), km2.predict(X) ) == pytest.approx(1.0) def test_n_init(): # Check that increasing the number of init increases the quality n_runs = 5 n_init_range = [1, 5, 10] inertia = np.zeros((len(n_init_range), n_runs)) for i, n_init in enumerate(n_init_range): for j in range(n_runs): km = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, random_state=j).fit(X) inertia[i, j] = km.inertia_ inertia = inertia.mean(axis=1) failure_msg = ("Inertia %r should be decreasing" " when n_init is increasing.") % list(inertia) for i in range(len(n_init_range) - 1): assert inertia[i] >= inertia[i + 1], failure_msg def test_k_means_function(): # test calling the k_means function directly # catch output old_stdout = sys.stdout sys.stdout = StringIO() try: cluster_centers, labels, inertia = k_means(X, n_clusters=n_clusters, sample_weight=None, verbose=True) finally: sys.stdout = old_stdout centers = cluster_centers assert centers.shape == (n_clusters, n_features) labels = labels assert np.unique(labels).shape[0] == n_clusters # check that the labels assignment are perfect (up to a permutation) assert v_measure_score(true_labels, labels) == 1.0 assert inertia > 0.0 # check warning when centers are passed assert_warns(RuntimeWarning, k_means, X, n_clusters=n_clusters, sample_weight=None, init=centers) def test_x_squared_norms_init_centroids(): # Test that x_squared_norms can be None in _init_centroids from sklearn.cluster._kmeans import _init_centroids X_norms = np.sum(X**2, axis=1) precompute = _init_centroids( X, 3, "k-means++", random_state=0, x_squared_norms=X_norms) assert_array_almost_equal( precompute, _init_centroids(X, 3, "k-means++", random_state=0)) @pytest.mark.parametrize("data", [X, X_csr], ids=["dense", "sparse"]) @pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans]) def test_float_precision(Estimator, data): # Check that the results are the same for single and double precision. km = Estimator(n_init=1, random_state=0) inertia = {} Xt = {} centers = {} labels = {} for dtype in [np.float64, np.float32]: X = data.astype(dtype, **_astype_copy_false(data)) km.fit(X) inertia[dtype] = km.inertia_ Xt[dtype] = km.transform(X) centers[dtype] = km.cluster_centers_ labels[dtype] = km.labels_ # dtype of cluster centers has to be the dtype of the input data assert km.cluster_centers_.dtype == dtype # same with partial_fit if Estimator is MiniBatchKMeans: km.partial_fit(X[0:3]) assert km.cluster_centers_.dtype == dtype # compare arrays with low precision since the difference between 32 and # 64 bit comes from an accumulation of rounding errors. assert_allclose(inertia[np.float32], inertia[np.float64], rtol=1e-5) assert_allclose(Xt[np.float32], Xt[np.float64], rtol=1e-5) assert_allclose(centers[np.float32], centers[np.float64], rtol=1e-5) assert_array_equal(labels[np.float32], labels[np.float64]) def test_k_means_init_centers(): # This test is used to check KMeans won't mutate the user provided input # array silently even if input data and init centers have the same type X_small = np.array([[1.1, 1.1], [-7.5, -7.5], [-1.1, -1.1], [7.5, 7.5]]) init_centers = np.array([[0.0, 0.0], [5.0, 5.0], [-5.0, -5.0]]) for dtype in [np.int32, np.int64, np.float32, np.float64]: X_test = dtype(X_small) init_centers_test = dtype(init_centers) assert_array_equal(init_centers, init_centers_test) km = KMeans(init=init_centers_test, n_clusters=3, n_init=1) km.fit(X_test) assert np.may_share_memory(km.cluster_centers_, init_centers) is False @pytest.mark.parametrize("data", [X, X_csr], ids=["dense", "sparse"]) def test_k_means_init_fitted_centers(data): # Get a local optimum centers = KMeans(n_clusters=3).fit(X).cluster_centers_ # Fit starting from a local optimum shouldn't change the solution new_centers = KMeans(n_clusters=3, init=centers, n_init=1).fit(X).cluster_centers_ assert_array_almost_equal(centers, new_centers) def test_less_centers_than_unique_points(): X = np.asarray([[0, 0], [0, 1], [1, 0], [1, 0]]) # last point is duplicated km = KMeans(n_clusters=4).fit(X) # only three distinct points, so only three clusters # can have points assigned to them assert set(km.labels_) == set(range(3)) # k_means should warn that fewer labels than cluster # centers have been used msg = ("Number of distinct clusters (3) found smaller than " "n_clusters (4). Possibly due to duplicate points in X.") assert_warns_message(ConvergenceWarning, msg, k_means, X, sample_weight=None, n_clusters=4) def _sort_centers(centers): return

np.sort(centers, axis=0)

numpy.sort

# -*- coding: utf-8 -*- """ Created on Thu Jun 11 10:17:34 2020 compare the features calculated by kubios and calculated by our own algorithm @author: skjerns """ import os import matplotlib.pyplot as plt import numpy as np from tqdm import tqdm import config as cfg from sleep import SleepSet import matplotlib.pyplot as plt import scipy import pandas as pd from scipy.ndimage import median_filter, convolve from scipy.ndimage.filters import gaussian_filter1d os.makedirs(os.path.join(cfg.documents, 'reports', 'feat_comparison'), exist_ok=True) ss = SleepSet(cfg.folder_unisens) p = ss[16] p.reset() matfile = dict(p.feats.get_data()) kubios = matfile['TimeVar'] def best_corr(kubios, feat1): """ we correlate all features of kubios with this feature this is a somewhat sound way to check whether our feature has the best correlation with what is actually calculated """ df = pd.DataFrame(columns=['Name', 'corr', 'data']) for feat2_name, feat2 in kubios.items(): if abs(len(feat2)-len(feat1))>10: continue if np.isnan(feat2).all(): continue min_len = min(len(feat1), len(feat2)) mean = np.nan_to_num(np.nanmean(feat2)) feat2 =

np.nan_to_num(feat2[:min_len], nan=mean)

numpy.nan_to_num

import numpy as np import pytest from continuum.scenarios import Rotations from tests.test_classorder import InMemoryDatasetTest from continuum.datasets import MNIST, CIFAR100 @pytest.fixture def numpy_data(): nb_classes = 6 nb_data = 100 x_train = [] y_train = [] for i in range(nb_classes): x_train.append(np.ones((nb_data, 4, 4, 3), dtype=np.uint8) * i) y_train.append(

np.ones(nb_data)

numpy.ones

""" Potential field transformations, like upward continuation and derivatives. .. note:: Most, if not all, functions here required gridded data. **Transformations** * :func:`~fatiando.gravmag.transform.upcontinue`: Upward continuation of gridded potential field data on a level surface. * :func:`~fatiando.gravmag.transform.reduce_to_pole`: Reduce the total field magnetic anomaly to the pole. * :func:`~fatiando.gravmag.transform.tga`: Calculate the amplitude of the total gradient (also called the analytic signal) * :func:`~fatiando.gravmag.transform.tilt`: Calculates the tilt angle * :func:`~fatiando.gravmag.transform.power_density_spectra`: Calculates the Power Density Spectra of a gridded potential field data. * :func:`~fatiando.gravmag.transform.radial_average`: Calculates the the radial average of a Power Density Spectra using concentring rings. **Derivatives** * :func:`~fatiando.gravmag.transform.derivx`: Calculate the n-th order derivative of a potential field in the x-direction (North-South) * :func:`~fatiando.gravmag.transform.derivy`: Calculate the n-th order derivative of a potential field in the y-direction (East-West) * :func:`~fatiando.gravmag.transform.derivz`: Calculate the n-th order derivative of a potential field in the z-direction ---- """ from __future__ import division, absolute_import import warnings import numpy from .. import utils def reduce_to_pole(x, y, data, shape, inc, dec, sinc, sdec): r""" Reduce total field magnetic anomaly data to the pole. The reduction to the pole if a phase transformation that can be applied to total field magnetic anomaly data. It "simulates" how the data would be if **both** the Geomagnetic field and the magnetization of the source were vertical (:math:`90^\circ` inclination) (Blakely, 1996). This functions performs the reduction in the frequency domain (using the FFT). The transform filter is (in the frequency domain): .. math:: RTP(k_x, k_y) = \frac{|k|}{ a_1 k_x^2 + a_2 k_y^2 + a_3 k_x k_y + i|k|(b_1 k_x + b_2 k_y)} in which :math:`k_x` and :math:`k_y` are the wave-numbers in the x and y directions and .. math:: |k| = \sqrt{k_x^2 + k_y^2} \\ a_1 = m_z f_z - m_x f_x \\ a_2 = m_z f_z - m_y f_y \\ a_3 = -m_y f_x - m_x f_y \\ b_1 = m_x f_z + m_z f_x \\ b_2 = m_y f_z + m_z f_y :math:`\mathbf{m} = (m_x, m_y, m_z)` is the unit-vector of the total magnetization of the source and :math:`\mathbf{f} = (f_x, f_y, f_z)` is the unit-vector of the Geomagnetic field. .. note:: Requires gridded data. .. warning:: The magnetization direction of the anomaly source is crucial to the reduction-to-the-pole. **Wrong values of *sinc* and *sdec* will lead to a wrong reduction.** Parameters: * x, y : 1d-arrays The x, y, z coordinates of each data point. * data : 1d-array The total field anomaly data at each point. * shape : tuple = (nx, ny) The shape of the data grid * inc, dec : floats The inclination and declination of the inducing Geomagnetic field * sinc, sdec : floats The inclination and declination of the total magnetization of the anomaly source. The total magnetization is the vector sum of the induced and remanent magnetization. If there is only induced magnetization, use the *inc* and *dec* of the Geomagnetic field. Returns: * rtp : 1d-array The data reduced to the pole. References: Blakely, <NAME>. (1996), Potential Theory in Gravity and Magnetic Applications, Cambridge University Press. """ fx, fy, fz = utils.ang2vec(1, inc, dec) if sinc is None or sdec is None: mx, my, mz = fx, fy, fz else: mx, my, mz = utils.ang2vec(1, sinc, sdec) kx, ky = [k for k in _fftfreqs(x, y, shape, shape)] kz_sqr = kx**2 + ky**2 a1 = mz*fz - mx*fx a2 = mz*fz - my*fy a3 = -my*fx - mx*fy b1 = mx*fz + mz*fx b2 = my*fz + mz*fy # The division gives a RuntimeWarning because of the zero frequency term. # This suppresses the warning. with numpy.errstate(divide='ignore', invalid='ignore'): rtp = (kz_sqr)/(a1*kx**2 + a2*ky**2 + a3*kx*ky + 1j*numpy.sqrt(kz_sqr)*(b1*kx + b2*ky)) rtp[0, 0] = 0 ft_pole = rtp*numpy.fft.fft2(numpy.reshape(data, shape)) return numpy.real(numpy.fft.ifft2(ft_pole)).ravel() def upcontinue(x, y, data, shape, height): r""" Upward continuation of potential field data. Calculates the continuation through the Fast Fourier Transform in the wavenumber domain (Blakely, 1996): .. math:: F\{h_{up}\} = F\{h\} e^{-\Delta z |k|} and then transformed back to the space domain. :math:`h_{up}` is the upward continue data, :math:`\Delta z` is the height increase, :math:`F` denotes the Fourier Transform, and :math:`|k|` is the wavenumber modulus. .. note:: Requires gridded data. .. note:: x, y, z and height should be in meters. .. note:: It is not possible to get the FFT of a masked grid. The default :func:`fatiando.gridder.interp` call using minimum curvature will not be suitable. Use ``extrapolate=True`` or ``algorithm='nearest'`` to get an unmasked grid. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * height : float The height increase (delta z) in meters. Returns: * cont : array The upward continued data References: <NAME>. (1996), Potential Theory in Gravity and Magnetic Applications, Cambridge University Press. """ assert x.shape == y.shape, \ "x and y arrays must have same shape" if height <= 0: warnings.warn("Using 'height' <= 0 means downward continuation, " + "which is known to be unstable.") nx, ny = shape # Pad the array with the edge values to avoid instability padded, padx, pady = _pad_data(data, shape) kx, ky = _fftfreqs(x, y, shape, padded.shape) kz = numpy.sqrt(kx**2 + ky**2) upcont_ft = numpy.fft.fft2(padded)*numpy.exp(-height*kz) cont = numpy.real(numpy.fft.ifft2(upcont_ft)) # Remove padding cont = cont[padx: padx + nx, pady: pady + ny].ravel() return cont def _upcontinue_space(x, y, data, shape, height): """ Upward continuation using the space-domain formula. DEPRECATED. Use the better implementation using FFT. Kept here for historical reasons. """ nx, ny = shape dx = (x.max() - x.min())/(nx - 1) dy = (y.max() - y.min())/(ny - 1) area = dx*dy deltaz_sqr = (height)**2 cont = numpy.zeros_like(data) for i, j, g in zip(x, y, data): cont += g*area*((x - i)**2 + (y - j)**2 + deltaz_sqr)**(-1.5) cont *= abs(height)/(2*numpy.pi) return cont def tga(x, y, data, shape, method='fd'): r""" Calculate the total gradient amplitude (TGA). This the same as the `3D analytic signal` of Roest et al. (1992), but we prefer the newer, more descriptive nomenclature suggested by Reid (2012). The TGA is defined as the amplitude of the gradient vector of a potential field :math:`T` (e.g. the magnetic total field anomaly): .. math:: TGA = \sqrt{ \left(\frac{\partial T}{\partial x}\right)^2 + \left(\frac{\partial T}{\partial y}\right)^2 + \left(\frac{\partial T}{\partial z}\right)^2 } .. note:: Requires gridded data. .. warning:: If the data is not in SI units, the derivatives will be in strange units and so will the total gradient amplitude! I strongly recommend converting the data to SI **before** calculating the TGA is you need the gradient in Eotvos (use one of the unit conversion functions of :mod:`fatiando.utils`). Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * method : string The method used to calculate the horizontal derivatives. Options are: ``'fd'`` for finite-difference (more stable) or ``'fft'`` for the Fast Fourier Transform. The z derivative is always calculated by FFT. Returns: * tga : 1D-array The amplitude of the total gradient References: <NAME>. (2012), Forgotten truths, myths and sacred cows of Potential Fields Geophysics - II, in SEG Technical Program Expanded Abstracts 2012, pp. 1-3, Society of Exploration Geophysicists. <NAME>., <NAME>, and <NAME> (1992), Magnetic interpretation using the 3-D analytic signal, GEOPHYSICS, 57(1), 116-125, doi:10.1190/1.1443174. """ dx = derivx(x, y, data, shape, method=method) dy = derivy(x, y, data, shape, method=method) dz = derivz(x, y, data, shape) res = numpy.sqrt(dx ** 2 + dy ** 2 + dz ** 2) return res def tilt(x, y, data, shape, xderiv=None, yderiv=None, zderiv=None): r""" Calculates the potential field tilt, as defined by Miller and Singh (1994) .. math:: tilt(f) = tan^{-1}\left( \frac{ \frac{\partial T}{\partial z}}{ \sqrt{\frac{\partial T}{\partial x}^2 + \frac{\partial T}{\partial y}^2}} \right) When used on magnetic total field anomaly data, works best if the data is reduced to the pole. It's useful to plot the zero contour line of the tilt to represent possible outlines of the source bodies. Use matplotlib's ``pyplot.contour`` or ``pyplot.tricontour`` for this. .. note:: Requires gridded data if ``xderiv``, ``yderiv`` and ``zderiv`` are not given. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid. Ignored if *xderiv*, *yderiv* and *zderiv* are given. * xderiv : 1D-array or None Optional. Values of the derivative in the x direction. If ``None``, will calculated using the default options of :func:`~fatiando.gravmag.transform.derivx` * yderiv : 1D-array or None Optional. Values of the derivative in the y direction. If ``None``, will calculated using the default options of :func:`~fatiando.gravmag.transform.derivy` * zderiv : 1D-array or None Optional. Values of the derivative in the z direction. If ``None``, will calculated using the default options of :func:`~fatiando.gravmag.transform.derivz` Returns: * tilt : 1D-array The tilt angle of the total field in radians. References: Miller, <NAME>, and <NAME>. 1994. "Potential Field Tilt --- a New Concept for Location of Potential Field Sources." Journal of Applied Geophysics 32 (2--3): 213-17. doi:10.1016/0926-9851(94)90022-1. """ if xderiv is None: xderiv = derivx(x, y, data, shape) if yderiv is None: yderiv = derivy(x, y, data, shape) if zderiv is None: zderiv = derivz(x, y, data, shape) horiz_deriv = numpy.sqrt(xderiv**2 + yderiv**2) tilt = numpy.arctan2(zderiv, horiz_deriv) return tilt def derivx(x, y, data, shape, order=1, method='fd'): """ Calculate the derivative of a potential field in the x direction. .. note:: Requires gridded data. .. warning:: If the data is not in SI units, the derivative will be in strange units! I strongly recommend converting the data to SI **before** calculating the derivative (use one of the unit conversion functions of :mod:`fatiando.utils`). This way the derivative will be in SI units and can be easily converted to what unit you want. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * order : int The order of the derivative * method : string The method used to calculate the derivatives. Options are: ``'fd'`` for central finite-differences (more stable) or ``'fft'`` for the Fast Fourier Transform. Returns: * deriv : 1D-array The derivative """ nx, ny = shape assert method in ['fft', 'fd'], \ 'Invalid method "{}".'.format(method) if method == 'fft': # Pad the array with the edge values to avoid instability padded, padx, pady = _pad_data(data, shape) kx, _ = _fftfreqs(x, y, shape, padded.shape) deriv_ft = numpy.fft.fft2(padded)*(kx*1j)**order deriv_pad = numpy.real(numpy.fft.ifft2(deriv_ft)) # Remove padding from derivative deriv = deriv_pad[padx: padx + nx, pady: pady + ny] elif method == 'fd': datamat = data.reshape(shape) dx = (x.max() - x.min())/(nx - 1) deriv = numpy.empty_like(datamat) deriv[1:-1, :] = (datamat[2:, :] - datamat[:-2, :])/(2*dx) deriv[0, :] = deriv[1, :] deriv[-1, :] = deriv[-2, :] if order > 1: deriv = derivx(x, y, deriv, shape, order=order - 1, method='fd') return deriv.ravel() def derivy(x, y, data, shape, order=1, method='fd'): """ Calculate the derivative of a potential field in the y direction. .. note:: Requires gridded data. .. warning:: If the data is not in SI units, the derivative will be in strange units! I strongly recommend converting the data to SI **before** calculating the derivative (use one of the unit conversion functions of :mod:`fatiando.utils`). This way the derivative will be in SI units and can be easily converted to what unit you want. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * order : int The order of the derivative * method : string The method used to calculate the derivatives. Options are: ``'fd'`` for central finite-differences (more stable) or ``'fft'`` for the Fast Fourier Transform. Returns: * deriv : 1D-array The derivative """ nx, ny = shape assert method in ['fft', 'fd'], \ 'Invalid method "{}".'.format(method) if method == 'fft': # Pad the array with the edge values to avoid instability padded, padx, pady = _pad_data(data, shape) _, ky = _fftfreqs(x, y, shape, padded.shape) deriv_ft = numpy.fft.fft2(padded)*(ky*1j)**order deriv_pad = numpy.real(numpy.fft.ifft2(deriv_ft)) # Remove padding from derivative deriv = deriv_pad[padx: padx + nx, pady: pady + ny] elif method == 'fd': datamat = data.reshape(shape) dy = (y.max() - y.min())/(ny - 1) deriv = numpy.empty_like(datamat) deriv[:, 1:-1] = (datamat[:, 2:] - datamat[:, :-2])/(2*dy) deriv[:, 0] = deriv[:, 1] deriv[:, -1] = deriv[:, -2] if order > 1: deriv = derivy(x, y, deriv, shape, order=order - 1, method='fd') return deriv.ravel() def derivz(x, y, data, shape, order=1, method='fft'): """ Calculate the derivative of a potential field in the z direction. .. note:: Requires gridded data. .. warning:: If the data is not in SI units, the derivative will be in strange units! I strongly recommend converting the data to SI **before** calculating the derivative (use one of the unit conversion functions of :mod:`fatiando.utils`). This way the derivative will be in SI units and can be easily converted to what unit you want. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * order : int The order of the derivative * method : string The method used to calculate the derivatives. Options are: ``'fft'`` for the Fast Fourier Transform. Returns: * deriv : 1D-array The derivative """ assert method == 'fft', \ "Invalid method '{}'".format(method) nx, ny = shape # Pad the array with the edge values to avoid instability padded, padx, pady = _pad_data(data, shape) kx, ky = _fftfreqs(x, y, shape, padded.shape) deriv_ft = numpy.fft.fft2(padded)*numpy.sqrt(kx**2 + ky**2)**order deriv = numpy.real(numpy.fft.ifft2(deriv_ft)) # Remove padding from derivative return deriv[padx: padx + nx, pady: pady + ny].ravel() def power_density_spectra(x, y, data, shape): r""" Calculates the Power Density Spectra of a 2D gridded potential field through the FFT: .. math:: \Phi_{\Delta T}(k_x, k_y) = | F\left{\Delta T \right}(k_x, k_y) |^2 .. note:: Requires gridded data. .. note:: x, y, z and height should be in meters. Parameters: * x, y : 1D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid Returns: * kx, ky : 2D-arrays The wavenumbers of each Power Density Spectra point * pds : 2D-array The Power Density Spectra of the data """ kx, ky = _fftfreqs(x, y, shape, shape) pds = abs(numpy.fft.fft2(numpy.reshape(data, shape)))**2 return kx, ky, pds def radial_average_spectrum(kx, ky, pds, max_radius=None, ring_width=None): r""" Calculates the average of the Power Density Spectra points that falls inside concentric rings built around the origin of the wavenumber coordinate system with constant width. The width of the rings and the inner radius of the biggest ring can be changed by setting the optional parameters ring_width and max_radius, respectively. .. note:: To calculate the radially averaged power density spectra use the outputs of the function power_density_spectra as input of this one. Parameters: * kx, ky : 2D-arrays The x and y coordinates of the grid points * data : 1D-array The potential field at the grid points * shape : tuple = (nx, ny) The shape of the grid * max_radius : float (optional) Inner radius of the biggest ring. By default it's set as the minimum of kx.max() and ky.max(). Making it smaller leaves points outside of the averaging, and making it bigger includes points nearer to the boundaries. * ring_width : float (optional) Width of the rings. By default it's set as the largest value of :math:`\Delta k_x` and :math:`\Delta k_y`, being them the equidistances of the kx and ky arrays. Making it bigger gives more populated averages, and making it smaller lowers the ammount of points per ring (use it carefully). Returns: * k_radial : 1D-array Wavenumbers of each Radially Averaged Power Spectrum point. Also, the inner radius of the rings. * pds_radial : 1D array Radially Averaged Power Spectrum """ nx, ny = pds.shape if max_radius is None: max_radius = min(kx.max(), ky.max()) if ring_width is None: ring_width = max(kx[1, 0], ky[0, 1]) k = numpy.sqrt(kx**2 + ky**2) pds_radial = [] k_radial = [] radius_i = -1 while True: radius_i += 1 if radius_i*ring_width > max_radius: break else: if radius_i == 0: inside = k <= 0.5*ring_width else: inside = numpy.logical_and(k > (radius_i - 0.5)*ring_width, k <= (radius_i + 0.5)*ring_width) pds_radial.append(pds[inside].mean()) k_radial.append(radius_i*ring_width) return numpy.array(k_radial), numpy.array(pds_radial) def _pad_data(data, shape): n = _nextpow2(numpy.max(shape)) nx, ny = shape padx = (n - nx)//2 pady = (n - ny)//2 padded = numpy.pad(data.reshape(shape), ((padx, padx), (pady, pady)), mode='edge') return padded, padx, pady def _nextpow2(i): buf = numpy.ceil(numpy.log(i)/numpy.log(2)) return int(2**buf) def _fftfreqs(x, y, shape, padshape): """ Get two 2D-arrays with the wave numbers in the x and y directions. """ nx, ny = shape dx = (x.max() - x.min())/(nx - 1) fx = 2*numpy.pi*numpy.fft.fftfreq(padshape[0], dx) dy = (y.max() - y.min())/(ny - 1) fy = 2*numpy.pi*numpy.fft.fftfreq(padshape[1], dy) return

numpy.meshgrid(fy, fx)

numpy.meshgrid

""" April 2018 Simulates the trajectory implementing a CZ gate. June 2018 Included noise in the simulation. July 2018 Added distortions to simulation. September 2018 Added flux noise as a quasi-static component with Gaussian distribution """ import time import numpy as np import qutip as qtp from pycqed.measurement import detector_functions as det from scipy.interpolate import interp1d from pycqed.measurement.waveform_control_CC import waveforms_flux as wfl import scipy import matplotlib.pyplot as plt import logging #np.set_printoptions(threshold=np.inf) # operators b = qtp.tensor(qtp.destroy(3), qtp.qeye(3)) # LSB is static qubit a = qtp.tensor(qtp.qeye(3), qtp.destroy(3)) n_q0 = a.dag() * a n_q1 = b.dag() * b H_coupling = (a.dag() + a) * (b + b.dag()) H_c = n_q0 scalefactor=1 # scalefactor not used anymore # Hamiltonian def coupled_transmons_hamiltonian(w_q0, w_q1, alpha_q0, alpha_q1, J, w_bus): """ Hamiltonian of two coupled anharmonic transmons. Because the intention is to tune one qubit into resonance with the other, the number of levels is limited. q1 -> static qubit, 3-levels q0 -> fluxing qubit, 3-levels intended avoided crossing: 11 <-> 02 (q1 is the first (left) qubit and q0 the second (right) one) N.B. the frequency of q0 is expected to be larger than that of q1 w_q0 > w_q1 and the anharmonicities alpha negative """ raise NotImplementedError("Old way of handling the hamiltonian H_0. Use calc_hamiltonian") eps=0 delta_q1=w_q1-w_bus delta_q0_interactionpoint=(w_q1-alpha_q0)-w_bus delta_q0=(w_q0+eps)-w_bus J_new = J / ((delta_q1+delta_q0_interactionpoint)/(delta_q1*delta_q0_interactionpoint)) * (delta_q1+delta_q0)/(delta_q1*delta_q0) H_0 = w_q0 * n_q0 + w_q1 * n_q1 + \ 1/2*alpha_q0*(a.dag()*a.dag()*a*a) + 1/2*alpha_q1*(b.dag()*b.dag()*b*b) +\ J_new * (a.dag() + a) * (b + b.dag()) return H_0 def hamiltonian_timedependent(H_0,eps,w_bus): raise NotImplementedError("Old way of handling the hamiltonian time-dependent. Use calc_hamiltonian") w_q0=np.real(H_0[1,1]) w_q1=np.real(H_0[3,3]) alpha_q0=np.real(H_0[2,2])-2*w_q0 J=np.real(H_0[1,3]) delta_q1=w_q1-w_bus delta_q0_sweetspot=(w_q0)-w_bus delta_q0=(w_q0+eps)-w_bus J_new = J / ((delta_q1+delta_q0_sweetspot)/(delta_q1*delta_q0_sweetspot)) * (delta_q1+delta_q0)/(delta_q1*delta_q0) return H_0+eps*H_c+(J_new-J)*H_coupling # target in the case with no noise # note that the Hilbert space is H_q1 /otimes H_q0 # so the ordering of basis states below is 00,01,02,10,11,12,20,21,22 U_target = qtp.Qobj([[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, -1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]], type='oper', dims=[[3, 3], [3, 3]]) #U_target._type = 'oper' U_target_diffdims = qtp.Qobj([[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, -1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]], type='oper', dims=[[9], [9]]) # otherwise average_gate_fidelity doesn't work # if there is noise the target is the corresponding superoperator U_super_target = qtp.to_super(U_target) ''' remember that qutip uses the Liouville (matrix) representation for superoperators, with column stacking. This means that rho_{xy,x'y'}=rho[3*x+y,3*x'+y'] rho_{xy,x'y'}=operator_to_vector(rho)[3*x+y+27*x'+9*y'] where xy is the row and x'y' is the column ''' def plot(x_plot_vec,y_plot_vec,title='No title',xlabel='No xlabel',ylabel='No ylabel',legend_labels=list(),yscale='linear'): # tool for plotting # x_plot_vec and y_plot_vec should be passed as either lists or np.array if isinstance(y_plot_vec,list): y_length=len(y_plot_vec) else: y_length=np.size(y_plot_vec) if legend_labels==[]: legend_labels=np.arange(y_length) for i in range(y_length): if isinstance(y_plot_vec[i],list): y_plot_vec[i]=np.array(y_plot_vec[i]) if isinstance(legend_labels[i],int): legend_labels[i]=str(legend_labels[i]) if len(x_plot_vec)==1: if isinstance(x_plot_vec[0],list): x_plot_vec[0]=np.array(x_plot_vec[0]) plt.plot(x_plot_vec[0], y_plot_vec[i], label=legend_labels[i]) else: if isinstance(x_plot_vec[i],list): x_plot_vec[i]=np.array(x_plot_vec[i]) plt.plot(x_plot_vec[i], y_plot_vec[i], label=legend_labels[i]) plt.legend() plt.title(title) plt.xlabel(xlabel) plt.ylabel(ylabel) plt.yscale(yscale) plt.show() def jump_operators(T1_q0,T1_q1): c_ops=[] if T1_q0 != 0: c_ops.append(np.sqrt(1/T1_q0)*a) if T1_q1 != 0: c_ops.append(np.sqrt(1/T1_q1)*b) return c_ops def c_ops_amplitudedependent(T1_q0,T1_q1,Tphi01_q0_vec,Tphi01_q1): # case where the pure decoherence for qubit q0 is time dependent, or better pulse-amplitude dependent c_ops=[] if T1_q0 != 0: c_ops.append(np.sqrt(1/T1_q0)*a) if T1_q1 != 0: c_ops.append(np.sqrt(1/T1_q1)*b) if Tphi01_q1 != 0: # we automatically put also the decoherence for 12 and 02 sigmaZinqutrit = qtp.Qobj([[1,0,0], [0,-1,0], [0,0,0]]) collapse=qtp.tensor(sigmaZinqutrit,qtp.qeye(3)) c_ops.append(collapse*np.sqrt(1/(2*Tphi01_q1))) Tphi12_q1=Tphi01_q1 sigmaZinqutrit = qtp.Qobj([[0,0,0], [0,1,0], [0,0,-1]]) collapse=qtp.tensor(sigmaZinqutrit,qtp.qeye(3)) c_ops.append(collapse*np.sqrt(1/(2*Tphi12_q1))) Tphi02_q1=Tphi01_q1/2 sigmaZinqutrit = qtp.Qobj([[1,0,0], [0,0,0], [0,0,-1]]) collapse=qtp.tensor(sigmaZinqutrit,qtp.qeye(3)) c_ops.append(collapse*np.sqrt(1/(2*Tphi02_q1))) if Tphi01_q0_vec != []: # we automatically put also the decoherence for 12 and 02 sigmaZinqutrit = qtp.Qobj([[1,0,0], [0,-1,0], [0,0,0]]) collapse=qtp.tensor(qtp.qeye(3),sigmaZinqutrit) c_ops.append([collapse,np.sqrt(1/(2*Tphi01_q0_vec))]) Tphi12_q0_vec=Tphi01_q0_vec sigmaZinqutrit = qtp.Qobj([[0,0,0], [0,1,0], [0,0,-1]]) collapse=qtp.tensor(qtp.qeye(3),sigmaZinqutrit) c_ops.append([collapse,np.sqrt(1/(2*Tphi12_q0_vec))]) Tphi02_q0_vec=Tphi01_q0_vec/2 sigmaZinqutrit = qtp.Qobj([[1,0,0], [0,0,0], [0,0,-1]]) collapse=qtp.tensor(qtp.qeye(3),sigmaZinqutrit) c_ops.append([collapse,np.sqrt(1/(2*Tphi02_q0_vec))]) return c_ops def rotating_frame_transformation_propagator(U, t: float, w_q0: float=0, w_q1: float =0): """ Transforms the frame of the unitary according to U' = U_{RF}*U NOTE: remember that this is how the time evolution operator changes from one picture to another with U_{RF} = e^{-i w_q0 a^dag a t } otimes e^{-i w_q1 b^dag b t } (method for the case where we are simply rotating away the two qubit frequencies) Args: U (QObj): Unitary to be transformed t (float): time at which to transform w_q0 (float): freq of frame for q0 w_q1 (float): freq of frame for q1 """ logging.warning('Recommended to use rotating_frame_transformation_new passing the hamiltonian as an argument.') U_RF = (1j*w_q0*n_q0*t).expm() * (1j*w_q1*n_q1*t).expm() if U.type=='super': U_RF=qtp.to_super(U_RF) U_prime = U_RF * U """ U_RF only on one side because that's the operator that satisfies the Schroedinger equation in the interaction picture. """ return U_prime def rotating_frame_transformation_propagator_new(U, t: float, H): """ Transforms the frame of the unitary according to U' = U_{RF}*U NOTE: remember that this is how the time evolution operator changes from one picture to another Args: U (QObj): Unitary to be transformed t (float): time at which to transform H (QObj): hamiltonian to be rotated away """ U_RF = (1j*H*t).expm() if U.type=='super': U_RF=qtp.to_super(U_RF) U_prime = U_RF * U """ U_RF only on one side because that's the operator that satisfies the Schroedinger equation in the interaction picture. """ return U_prime def rotating_frame_transformation_operators(operator, t: float, H): """ Transforms the frame of an operator (hamiltonian, or jump operator) according to O' = U_{RF}*O*U_{RF}^dag Args: operator (QObj): operator to be transformed t (float): time at which to transform H (QObj): hamiltonian to be rotated away """ U_RF = (1j*H*t).expm() return U_RF * H * U_RF.dag() def correct_reference(U,w_q1,w_q0,t): # w_qi should be a frequency (not including the 2*pi factor). Moreover they and t should be in the same scale. # this functions should be used just to make sanity checks. phase_to_correct_q1 = w_q1*(2*np.pi)*t phase_to_correct_q0 = w_q0*(2*np.pi)*t Ucorrection = qtp.Qobj([[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, np.exp(1j*phase_to_correct_q0), 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, np.exp(1j*phase_to_correct_q1), 0, 0, 0, 0, 0], [0, 0, 0, 0, np.exp(1j*(phase_to_correct_q0+phase_to_correct_q1)), 0, 0, 0, 0], [0, 0, 0, 0, 0, np.exp(1j*phase_to_correct_q1), 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, np.exp(1j*phase_to_correct_q0), 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]], type='oper', dims=[[3, 3], [3, 3]]) if U.type=='oper': return Ucorrection*U elif U.type=='super': return qtp.to_super(Ucorrection)*U def phases_from_superoperator(U): """ Returns the phases from the unitary or superoperator U """ if U.type=='oper': phi_00 = np.rad2deg(np.angle(U[0, 0])) # expected to equal 0 because of our # choice for the energy, not because of rotating frame. But not guaranteed including the coupling phi_01 = np.rad2deg(np.angle(U[1, 1])) phi_10 = np.rad2deg(np.angle(U[3, 3])) phi_11 = np.rad2deg(np.angle(U[4, 4])) phi_02 = np.rad2deg(np.angle(U[2, 2])) # used only for avgatefid_superoperator_phasecorrected phi_20 = np.rad2deg(np.angle(U[6, 6])) # used only for avgatefid_superoperator_phasecorrected elif U.type=='super': phi_00 = 0 # we set it to 0 arbitrarily but it is indeed not knowable phi_01 = np.rad2deg(np.angle(U[1, 1])) # actually phi_01-phi_00 etc phi_10 = np.rad2deg(np.angle(U[3, 3])) phi_11 = np.rad2deg(np.angle(U[4, 4])) phi_02 = np.rad2deg(np.angle(U[2, 2])) phi_20 = np.rad2deg(np.angle(U[6, 6])) phi_cond = (phi_11 - phi_01 - phi_10 + phi_00) % 360 # still the right formula independently from phi_00 return phi_00, phi_01, phi_10, phi_11, phi_02, phi_20, phi_cond def pro_avfid_superoperator_compsubspace(U,L1): """ Average process (gate) fidelity in the qubit computational subspace for two qutrits. Leakage has to be taken into account, see Woods & Gambetta. The function assumes that the computational subspace (:= the 4 energy levels chosen as the two qubits) is given by the standard basis |0> /otimes |0>, |0> /otimes |1>, |1> /otimes |0>, |1> /otimes |1>. If this is not the case, one need to change the basis to that one, before calling this function. """ if U.type=='oper': inner = U.dag()*U_target part_idx = [0, 1, 3, 4] # only computational subspace ptrace = 0 for i in part_idx: ptrace += inner[i, i] dim = 4 # 2 qubits comp subspace return np.real(((np.abs(ptrace))**2+dim*(1-L1))/(dim*(dim+1))) elif U.type=='super': kraus_form = qtp.to_kraus(U) dim=4 # 2 qubits in the computational subspace part_idx = [0, 1, 3, 4] # only computational subspace psum=0 for A_k in kraus_form: ptrace = 0 inner = U_target_diffdims.dag()*A_k # otherwise dimension mismatch for i in part_idx: ptrace += inner[i, i] psum += (np.abs(ptrace))**2 return np.real((dim*(1-L1) + psum) / (dim*(dim + 1))) def pro_avfid_superoperator_compsubspace_phasecorrected(U,L1,phases): """ Average process (gate) fidelity in the qubit computational subspace for two qutrits Leakage has to be taken into account, see Woods & Gambetta The phase is corrected with Z rotations considering both transmons as qubits. The correction is done perfectly. The function assumes that the computational subspace (:= the 4 energy levels chosen as the two qubits) is given by the standard basis |0> /otimes |0>, |0> /otimes |1>, |1> /otimes |0>, |1> /otimes |1>. If this is not the case, one need to change the basis to that one, before calling this function. """ Ucorrection = qtp.Qobj([[np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0, 0], [0, np.exp(-1j*np.deg2rad(phases[1])), 0, 0, 0, 0, 0, 0, 0], [0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0], [0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0, 0, 0], [0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[3]-phases[-1])), 0, 0, 0, 0], [0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[2])), 0, 0, 0], [0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0], [0, 0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[1])), 0], [0, 0, 0, 0, 0, 0, 0, 0, np.exp(-1j*np.deg2rad(phases[0]))]], type='oper', dims=[[3, 3], [3, 3]]) if U.type=='oper': U=Ucorrection*U inner = U.dag()*U_target part_idx = [0, 1, 3, 4] # only computational subspace ptrace = 0 for i in part_idx: ptrace += inner[i, i] dim = 4 # 2 qubits comp subspace return np.real(((np.abs(ptrace))**2+dim*(1-L1))/(dim*(dim+1))) elif U.type=='super': U=qtp.to_super(Ucorrection)*U kraus_form = qtp.to_kraus(U) dim=4 # 2 qubits in the computational subspace part_idx = [0, 1, 3, 4] # only computational subspace psum=0 for A_k in kraus_form: ptrace = 0 inner = U_target_diffdims.dag()*A_k # otherwise dimension mismatch for i in part_idx: ptrace += inner[i, i] psum += (np.abs(ptrace))**2 return np.real((dim*(1-L1) + psum) / (dim*(dim + 1))) def pro_avfid_superoperator_compsubspace_phasecorrected_onlystaticqubit(U,L1,phases): """ Average process (gate) fidelity in the qubit computational subspace for two qutrits Leakage has to be taken into account, see Woods & Gambetta The phase is corrected with Z rotations considering both transmons as qubits. The correction is done perfectly. The function assumes that the computational subspace (:= the 4 energy levels chosen as the two qubits) is given by the standard basis |0> /otimes |0>, |0> /otimes |1>, |1> /otimes |0>, |1> /otimes |1>. If this is not the case, one need to change the basis to that one, before calling this function. """ Ucorrection = qtp.Qobj([[np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0, 0], [0, np.exp(-1j*np.deg2rad(phases[0])), 0, 0, 0, 0, 0, 0, 0], [0, 0, np.exp(-1j*

np.deg2rad(phases[0])

numpy.deg2rad

""" isicarchive.jitfunc This module provides JIT (numba) helper functions and doesn't have to be imported from outside the main package functionality (isicapi). Functions --------- conv_kernel Generate convolution smoothing kernel image_mix Mix two images (RGB and/or gray scale, alpha parameter supported) image_resample_u1 Cheap (!) image resampling for uint8 images image_resample_f4 Cheap (!) image resampling for float32 images superpixel_contour Extract superpixel contour superpixel_decode Converts an RGB superpixel image to a 2D superpixel index array superpixel_map Decodes a superpixel (index) array into a 2D mapping array superpixel_outline_dir Extract SVG path directions from binary mask of outline superpixel_path Extract superpixel path svg_coord_list Generate SVG-path-suitable list of directions from coordinates list svg_path_from_list Generate SVG-path-suitable list of directions from v/h list """ __version__ = '0.4.8' from typing import Optional, Tuple import numba from numba import jit, prange import numpy # convolution (smoothing) kernel @jit('f4[:](f4)', nopython=True) def conv_kernel(fwhm:numpy.float32 = 2.0) -> numpy.ndarray: """ Generate convolution smoothing kernel Parameters ---------- fwhm : numpy scalar float32 Gaussian kernel size in FWHM (full-width at half-maximum) Returns ------- kernel : ndarray Gaussian smoothing kernel (numpy.float32) """ if fwhm <= 0.29: return numpy.asarray([0,1,0]).astype(numpy.float32) fwhm = fwhm / numpy.sqrt(8.0 * numpy.log(2.0)) if fwhm < 2.0: md = numpy.trunc(0.5 + 6.0 * fwhm) else: md = numpy.trunc(0.5 + 6.0 * numpy.log2(fwhm) * fwhm) k = numpy.exp(-((numpy.arange(-md,md+1.0,1.0) ** 2) / (2.0 * fwhm * fwhm))) k = k[k >= 0.00000001] return (k / numpy.sum(k)).astype(numpy.float32) # image convolution (cheap!) @jit('f4[:,:](f4[:,:],f4[:])', nopython=True) def image_conv_float( data:numpy.ndarray, kernel:numpy.ndarray, ) -> numpy.ndarray: """ Two-dimensional image convolution with kernel vector (staggered) Parameters ---------- data : ndarray Image data (must be 2D numpy.float32!) kernel : ndarray Kernel vector (must be numpy.float32!) Returns ------- conv_data : ndarray Convolved data array """ if (kernel.size) == 1: kernel = conv_kernel(kernel[0]) if (kernel.size % 2) != 1: raise ValueError('Parameter kernel must have odd length of elements.') s = numpy.sum(kernel) if s <= 0.0: raise ValueError('Parameter kernel must have a positive sum.') if s < 0.999999 or s > 1.000001: kernel = kernel / s ds0 = data.shape[0] ds1 = data.shape[1] kh = kernel.size // 2 temp = numpy.zeros(data.size, dtype=numpy.float32).reshape(data.shape) tempv = numpy.zeros(ds0, dtype=numpy.float32) for c in prange(ds0): #pylint: disable=not-an-iterable col = temp[c,:] colv = 0.0 for k in range(kernel.size): dc = c + k - kh if dc < 0 or dc >= ds0: continue colv += kernel[k] col += kernel[k] * data[dc,:] temp[c,:] = col tempv[c] = colv temp = numpy.true_divide(temp, tempv.reshape((ds0,1,))) out = numpy.zeros(data.size, dtype=numpy.float32).reshape(data.shape) tempv = numpy.zeros(ds1, dtype=numpy.float32) for c in prange(ds1): #pylint: disable=not-an-iterable col = out[:,c] colv = 0.0 for k in range(kernel.size): dc = c + k - kh if dc < 0 or dc >= ds1: continue colv += kernel[k] col += kernel[k] * temp[:,dc] out[:,c] = col tempv[c] = colv return numpy.true_divide(out, tempv.reshape((1,ds1,))) # image mixing @jit('u1[:,:](u1[:,:],u1[:,:],optional(f4[:]))', nopython=True) def image_mix( i1:numpy.ndarray, i2:numpy.ndarray, a2:numpy.ndarray = None, ) -> numpy.ndarray: """ Mix two images with optional alpha channel Parameters ---------- i1, i2 : ndarray Image vectors array (second dimension is RGB color!) a2 : ndarray Optional alpha (opacity) array for second image Returns ------- mixed : ndarray Mixed image vectors array """ ishape = i1.shape i2shape = i2.shape oi = numpy.zeros(i1.size, dtype=numpy.uint8).reshape(ishape) num_pix = ishape[0] if i2shape[0] != num_pix: raise ValueError('Images mismatch in number of pixels') if (not a2 is None) and (a2.size != num_pix): raise ValueError('Alpha mismatch in number of pixels') if ishape[1] == 1: if i2shape[1] == 1: if a2 is None: for p in prange(num_pix): #pylint: disable=not-an-iterable oi[p,0] = max(i1[p,0], i2[p,0]) else: o = numpy.float32(1.0) for p in prange(num_pix): #pylint: disable=not-an-iterable a = a2[p] ia = o - a oi[p,0] = round( ia * numpy.float32(i1[p,0]) + a * numpy.float32(i2[p,0])) elif i2shape[1] != 3: raise ValueError('i2 not a valid image array') else: th = numpy.float32(1.0) / numpy.float32(3) if a2 is None: for p in prange(num_pix): #pylint: disable=not-an-iterable i2m = round(th * ( numpy.float32(i2[p,0]) + numpy.float32(i2[p,1]) + numpy.float32(i2[p,2]))) oi[p,0] = max(i1[p,0], i2m) else: o = numpy.float32(1.0) for p in prange(num_pix): #pylint: disable=not-an-iterable a = a2[p] ia = o - a i2m = th * ( numpy.float32(i2[p,0]) + numpy.float32(i2[p,1]) + numpy.float32(i2[p,2])) oi[p,0] = round(ia * numpy.float32(i1[p,0]) + a * i2m) elif ishape[1] != 3: raise ValueError('i1 not a valid image array') else: if i2shape[1] == 1: if a2 is None: for p in prange(num_pix): #pylint: disable=not-an-iterable oi[p,0] = max(i1[p,0], i2[p,0]) oi[p,1] = max(i1[p,1], i2[p,0]) oi[p,2] = max(i1[p,2], i2[p,0]) else: o = numpy.float32(1.0) for p in prange(num_pix): #pylint: disable=not-an-iterable a = a2[p] ia = o - a i2ap = a * numpy.float32(i2[p,0]) oi[p,0] = round(ia * numpy.float32(i1[p,0]) + i2ap) oi[p,1] = round(ia * numpy.float32(i1[p,1]) + i2ap) oi[p,2] = round(ia * numpy.float32(i1[p,2]) + i2ap) elif i2shape[1] != 3: raise ValueError('i2 not a valid image array') else: if a2 is None: for p in prange(num_pix): #pylint: disable=not-an-iterable oi[p,0] = max(i1[p,0], i2[p,0]) oi[p,1] = max(i1[p,1], i2[p,1]) oi[p,2] = max(i1[p,2], i2[p,2]) else: o = numpy.float32(1.0) for p in prange(num_pix): #pylint: disable=not-an-iterable a = a2[p] ia = o - a oi[p,0] = round( ia * numpy.float32(i1[p,0]) + a * numpy.float32(i2[p,0])) oi[p,1] = round( ia * numpy.float32(i1[p,1]) + a * numpy.float32(i2[p,1])) oi[p,2] = round( ia * numpy.float32(i1[p,2]) + a * numpy.float32(i2[p,2])) return oi # image resampling (cheap!) @jit('u1[:,:,:](u1[:,:,:],i4,i4)', nopython=True) def image_resample_u1(image:numpy.ndarray, d0:numpy.int, d1:numpy.int) -> numpy.ndarray: """ Cheap (!) image resampling for uint8 images Parameters ---------- image : ndarray Image array d0, d1 : int Target image size in first and second dimension Returns ------- res : ndarray Resampled image array """ im_shape = image.shape f0 = numpy.float(im_shape[0]) / numpy.float(d0) f1 = numpy.float(im_shape[1]) / numpy.float(d1) temp = numpy.zeros(im_shape[0] * d1 * im_shape[2], dtype=numpy.uint8).reshape( (numpy.int64(im_shape[0]),numpy.int64(d1),numpy.int64(im_shape[2]),)) for c in prange(d1): #pylint: disable=not-an-iterable ffrom = f1 * numpy.float(c) + 0.5 fto = ffrom + f1 - 1.0 ifrom = numpy.int64(

numpy.trunc(ffrom)

numpy.trunc

import utm as UTM import unittest import numpy as np class UTMTestCase(unittest.TestCase): def assert_utm_equal(self, a, b): self.assertTrue(np.allclose(a[0], b[0])) self.assertTrue(np.allclose(a[1], b[1])) self.assertEqual(a[2], b[2]) self.assertEqual(a[3].upper(), b[3].upper()) def assert_latlon_equal(self, a, b): self.assertTrue(np.allclose(a[0], b[0], rtol=1e-4, atol=1e-4)) self.assertTrue(np.allclose(a[1], b[1], rtol=1e-4, atol=1e-4)) class KnownValues(UTMTestCase): known_values = [ # Aachen, Germany ( (50.77535, 6.08389), (294409, 5628898, 32, 'U'), {'northern': True}, ), # New York, USA ( (40.71435, -74.00597), (583960, 4507523, 18, 'T'), {'northern': True}, ), # Wellington, New Zealand ( (-41.28646, 174.77624), (313784, 5427057, 60, 'G'), {'northern': False}, ), # Capetown, South Africa ( (-33.92487, 18.42406), (261878, 6243186, 34, 'H'), {'northern': False}, ), # Mendoza, Argentina ( (-32.89018, -68.84405), (514586, 6360877, 19, 'h'), {'northern': False}, ), # Fairbanks, Alaska, USA ( (64.83778, -147.71639), (466013, 7190568, 6, 'W'), {'northern': True}, ), # <NAME>, Scotland, UK ( (56.79680, -5.00601), (377486, 6296562, 30, 'V'), {'northern': True}, ), # Latitude 84 ( (84, -5.00601), (476594, 9328501, 30, 'X'), {'northern': True}, ), ] def test_from_latlon(self): lats = np.array([0.0, 3.0, 6.0]) lons = np.array([0.0, 1.0, 3.4]) result = UTM.from_latlon(lats, lons) self.assert_utm_equal((np.array([166021.44317933032, 277707.83075574087, 544268.12794623]), np.array([0.0, 331796.29167519242, 663220.7198366751]), 31, 'N'), result) for latlon, utm, _ in self.known_values: result = UTM.from_latlon(*[np.array([x]) for x in latlon]) self.assert_utm_equal(utm, result) def test_to_latlon(self): result = UTM.to_latlon(np.array([166021.44317933032, 277707.83075574087, 544268.12794623]), np.array([0.0, 331796.29167519242, 663220.7198366751]), 31, 'N') self.assert_latlon_equal((np.array([0.0, 3.0, 6.0]), np.array([0.0, 1.0, 3.4])), result) for latlon, utm, utm_kw in self.known_values: utm = [np.array([x]) for x in utm[:2]] + list(utm[2:]) result = UTM.to_latlon(*utm) self.assert_latlon_equal(latlon, result) class BadInput(UTMTestCase): def test_from_latlon_range_checks(self): '''from_latlon should fail with out-of-bounds input''' self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(-100), np.array(0)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(-80.1), np.array(0)) for i in range(-8000, 8400): UTM.from_latlon(np.array(i / 100.0), np.array(0)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(84.1), np.array(0)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(100), np.array(0)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(0), np.array(-300)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(0), np.array(-180.1)) for i in range(-18000, 18000): UTM.from_latlon(np.array(0), np.array(i / 100.0)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(0), np.array(180.1)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(0), np.array(300)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(-100), np.array(-300)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(100), np.array(-300)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(-100), np.array(300)) self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(100), np.array(300)) # test forcing zone ranges # NYC should be zone 18T self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(40.71435), np.array(-74.00597), 70, 'T') self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, np.array(40.71435), np.array(-74.00597), 18, 'A') def test_to_latlon_range_checks(self): '''to_latlon should fail with out-of-bounds input''' # test easting range self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(0), np.array(5000000), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(99999), np.array(5000000), 32, 'U') for i in range(100000, 999999, 1000): UTM.to_latlon(np.array(i), np.array(5000000), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(1000000), np.array(5000000), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(100000000000), np.array(5000000), 32, 'U') # test northing range self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(-100000), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(-1), 32, 'U') for i in range(10, 10000000, 1000): UTM.to_latlon(np.array(500000), np.array(i), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(10000001), 32, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(50000000), 32, 'U') # test zone numbers self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 0, 'U') for i in range(1, 60): UTM.to_latlon(np.array(500000), np.array(5000000), i, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 61, 'U') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 1000, 'U') # test zone letters self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'A') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'B') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'I') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'O') for i in range(ord('C'), ord('X')): i = chr(i) if i != 'I' and i != 'O': UTM.to_latlon(np.array(500000), np.array(5000000), 32, i) self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'Y') self.assertRaises( UTM.OutOfRangeError, UTM.to_latlon, np.array(500000), np.array(5000000), 32, 'Z') class Zone32V(unittest.TestCase): def assert_zone_equal(self, result, expected_number, expected_letter): self.assertEqual(result[2], expected_number) self.assertEqual(result[3].upper(), expected_letter.upper()) def test_inside(self): self.assert_zone_equal(UTM.from_latlon( np.array(56), np.array(3)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(56), np.array(6)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(56), np.array(9)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(56), np.array(11.999999)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(60), np.array(3)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(60), np.array(6)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(60), np.array(9)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(60), np.array(11.999999)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(63.999999), np.array(3)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(63.999999), np.array(6)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(63.999999), np.array(9)), 32, 'V') self.assert_zone_equal(UTM.from_latlon( np.array(63.999999),

np.array(11.999999)

numpy.array

import numpy as np import mc.util import matplotlib.pyplot as plt import matplotlib as mpl from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm class Likelihood: def __init__(self, true_tx_power=0.8, true_min_power=1e-5, true_path_loss_exp=3, n=10) -> None: self.true_tx_power = true_tx_power self.true_min_power = true_min_power self.true_path_loss_exp = true_path_loss_exp self.tx_power_array = np.linspace(true_tx_power/2., true_tx_power*2., num=n) self.min_power_array = np.linspace(true_min_power / 2., true_min_power * 2., num=n) self.path_loss_exp_array = np.linspace(true_path_loss_exp / 2., true_path_loss_exp * 2., num=n) self.i_max = None self.i_closest = None self.likelihood = None # non-blocking plots plt.ion() self.tx_power_grid_1st, self.min_power_grid_2nd = np.meshgrid(self.tx_power_array, self.min_power_array) _, self.path_loss_exp_grid_2nd = np.meshgrid(self.tx_power_array, self.path_loss_exp_array) self.min_power_grid_1st, _ = np.meshgrid(self.min_power_array, self.path_loss_exp_array) self.plots_z_value = 10000 # to be run from, at least, mc.pmc.PopulationMonteCarlo.step def explore(self, pf, observations): log_tx_power_array, log_min_power_array, log_path_loss_exp_array =\ np.log(self.tx_power_array), np.log(self.min_power_array), np.log(self.path_loss_exp_array) self.likelihood = np.empty((len(self.tx_power_array), len(self.min_power_array), len(self.path_loss_exp_array))) for i_log_tx_power, log_tx_power in enumerate(log_tx_power_array): print('log_tx_power = {}'.format(log_tx_power)) for i_log_min_power, log_min_power in enumerate(log_min_power_array): for i_log_path_loss_exp, log_path_loss_exp in enumerate(log_path_loss_exp_array): self.likelihood[i_log_tx_power, i_log_min_power, i_log_path_loss_exp] =\ mc.util.loglikelihood(pf, observations, log_tx_power, log_min_power, log_path_loss_exp) print(self.likelihood) # this is a tuple self.i_max = np.unravel_index(self.likelihood.argmax(), self.likelihood.shape) print('maximum likelihood = {} at transmiter power = {}, min power = {}, path loss exponent = {} ({})'.format( self.likelihood[self.i_max], self.tx_power_array[self.i_max[0]], self.min_power_array[self.i_max[1]], self.path_loss_exp_array[self.i_max[2]], self.i_max)) likelihood_at_true = mc.util.loglikelihood( pf, observations, np.log(self.true_tx_power), np.log(self.true_min_power), np.log(self.true_path_loss_exp)) print('likelihood at true parameters: {}'.format(likelihood_at_true)) self.i_closest = ( np.abs(self.tx_power_array - self.true_tx_power).argmin(), np.abs(self.min_power_array - self.true_min_power).argmin(), np.abs(self.path_loss_exp_array - self.true_path_loss_exp).argmin()) print('closest point to ground truth is transmiter power = {}, min power = {}, path loss exponent = {}, likelihood {} ({})'.format( self.tx_power_array[self.i_closest[0]], self.min_power_array[self.i_closest[1]], self.path_loss_exp_array[self.i_closest[2]], self.likelihood[self.i_closest], self.i_closest)) def plot3d(self, x, y, Z): fig = plt.figure() ax = fig.gca(projection='3d') X, Y =

np.meshgrid(x, y)

numpy.meshgrid

# -*- coding: utf-8 -*- import sys import os import toml import librosa import librosa.display import matplotlib.pyplot as plt import soundfile as sf import numpy as np from tqdm import tqdm from joblib import Parallel, delayed import paddle from paddle.io import DataLoader from paddle.signal import stft, istft from visualdl import LogWriter sys.path.append("./") from FullBandNet.model import FullBandNet from dataset.dataset import DNS_Dataset from audio.feature import is_clipped from audio.mask import decompress_cIRM from audio.metrics import STOI, WB_PESQ from audio.utils import prepare_empty_path plt.switch_backend("agg") class Inferencer: def __init__(self, model, test_iter, config): # get checkpoints path self.checkpoints_path = os.path.join(os.path.dirname(__file__), "checkpoints") # get output path self.output_path = os.path.join(os.path.dirname(__file__), "enhanced") # get logs path self.logs_path = os.path.join(os.path.dirname(__file__), "logs", "inference") prepare_empty_path([self.output_path, self.logs_path]) # set iter self.test_iter = test_iter # get model self.model = model self.load_checkpoint() # get dataset args self.sr = config["dataset"]["sr"] self.n_fft = config["dataset"]["n_fft"] self.win_len = config["dataset"]["win_len"] self.hop_len = config["dataset"]["hop_len"] self.window = paddle.to_tensor(np.hanning(self.win_len), dtype=paddle.float32) # get inference args self.audio_visual_samples = config["inference"]["audio_visual_samples"] # config logs self.writer = LogWriter(logdir=self.logs_path, max_queue=5, flush_secs=60) self.writer_text_enh_clipped_step = 1 self.writer.add_text( tag="config", text_string=f"<pre \n{toml.dumps(config)} \n</pre>", step=1, ) def load_checkpoint(self): best_model_path = os.path.join(self.checkpoints_path, "best_model.tar") assert os.path.exists(best_model_path) checkpoint = paddle.load(best_model_path) self.epoch = checkpoint["epoch"] self.model.set_state_dict(checkpoint["model"]) print(f"Loading model checkpoint (epoch == {self.epoch})...") def check_clipped(self, enh, enh_file): if is_clipped(enh): self.writer.add_text( tag="enh_clipped", text_string=enh_file, step=self.writer_text_enh_clipped_step, ) self.writer_text_enh_clipped_step += 1 def audio_visualization(self, noisy, clean, enh, name): self.writer.add_audio("audio/noisy", noisy, 1, sample_rate=self.sr) self.writer.add_audio("audio/clean", clean, 1, sample_rate=self.sr) self.writer.add_audio("audio/enh", enh, 1, sample_rate=self.sr) # Visualize the spectrogram of noisy speech, clean speech, and enhanced speech noisy_mag, _ = librosa.magphase(librosa.stft(noisy, n_fft=320, hop_length=160, win_length=320)) clean_mag, _ = librosa.magphase(librosa.stft(clean, n_fft=320, hop_length=160, win_length=320)) enh_mag, _ = librosa.magphase(librosa.stft(enh, n_fft=320, hop_length=160, win_length=320)) fig, axes = plt.subplots(3, 1, figsize=(6, 6)) for k, mag in enumerate([noisy_mag, clean_mag, enh_mag]): axes[k].set_title( f"mean: {np.mean(mag):.3f}, " f"std: {np.std(mag):.3f}, " f"max: {np.max(mag):.3f}, " f"min: {np.min(mag):.3f}" ) librosa.display.specshow(librosa.amplitude_to_db(mag), cmap="magma", y_axis="linear", ax=axes[k], sr=16000) plt.tight_layout() self.writer.add_figure(f"spec/{name}", fig, 1) def metrics_visualization(self, noisy_list, clean_list, enh_list, n_jobs=8): noisy_stoi_score = Parallel(n_jobs=n_jobs)( delayed(STOI)(noisy, clean) for noisy, clean in tqdm(zip(noisy_list, clean_list)) ) enh_stoi_score = Parallel(n_jobs=n_jobs)( delayed(STOI)(noisy, clean) for noisy, clean in tqdm(zip(enh_list, clean_list)) ) noisy_stoi_score_mean = np.mean(noisy_stoi_score) enh_stoi_score_mean = np.mean(enh_stoi_score) self.writer.add_scalar("STOI/test/noisy", noisy_stoi_score_mean, 1) self.writer.add_scalar("STOI/test/enh", enh_stoi_score_mean, 1) noisy_wb_pesq_score = Parallel(n_jobs=n_jobs)( delayed(WB_PESQ)(noisy, clean) for noisy, clean in tqdm(zip(noisy_list, clean_list)) ) enh_wb_pesq_score = Parallel(n_jobs=n_jobs)( delayed(WB_PESQ)(noisy, clean) for noisy, clean in tqdm(zip(enh_list, clean_list)) ) noisy_wb_pesq_score_mean =

np.mean(noisy_wb_pesq_score)

numpy.mean

import numpy as np import os import mxnet as mx import gluonnlp as nlp def prepare_dataset(filename, allow_pickle=False): return nlp.data.NumpyDataset(filename[0], allow_pickle=allow_pickle) def prepare_bucket_sampler(dataset, batch_size, shuffle=False, num_buckets=1): lengths = dataset.transform(lambda x: len(x)) sampler = nlp.data.FixedBucketSampler(lengths, batch_size=batch_size, num_buckets=num_buckets, ratio=0, shuffle=shuffle) return sampler def test_dataset_loader(): num_files = 5 for i in range(num_files): np.save(os.path.join('tests', 'data', 'part_{}.npy'.format(i)),

np.random.uniform(size=(100, 20))

numpy.random.uniform