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

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import trimesh import numpy as np import sys from matplotlib.cm import get_cmap as colormap import os from glob import glob import cv2 import shutil import json import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt sys.path.append('../') from evaluate import Renderer from vPlaneRecover.transforms import NYU40_COLORMAP from vPlaneRecover.datasets.scannet import load_scannet_label_mapping, load_scannet_nyu40_mapping from fit_plane.util import get_nyu_id2labl NYU_ID2LAEBL = get_nyu_id2labl() CLASS_W_LONGSTRIKE = set(['table', 'desk', 'chair', 'window','bookshelf','shelves']) NONE_PLANE_CLASS = set(['window', 'lamp', 'pillow', 'bag','curtain', 'shower curtain', 'toilet', 'bag', 'mirror', 'person', 'clothes', 'sink']) INVALID_ID = 16777216 // 100 - 1 # (255,255,255) refer to fit_plane code get_gtPlane_segmt.py PLANE_AREA_THRES = 0.1 PLANE_EDGE_RATIO_THRES = 0.1 PLANE_AREA_RATIO_THRES = 0.5 PLANE_SEC_EDGE_THRES = 0.2 N_DEPTH_THRES = 0.8 DEPTH_ABSDIFF_THRES = 0.1 DEPTH_RELDIFF_THRES = 0.05 N_VERT_THRES = 120 # same as MergedPlaneAreaThreshold = 120 in get_gtPlane_segmt.py DEPTH_SHIFT = 1000 width, height = 640, 480 zoom_x, zoom_y = width / 1296., height/ 968. def get_planeColor_map( n_plane): # get color _cmap = np.array(NYU40_COLORMAP[1:]) if n_plane - 40 > 0: cmap = (colormap('jet')(np.linspace(0, 1, n_plane - 40))[:, :3] * 255).astype(np.uint8) cmap = cmap[np.random.permutation(n_plane - 40), :] plane_color = np.concatenate([_cmap, cmap], axis=0) else: plane_color = _cmap return plane_color def color2plane(mesh): # convert color w.r.t. plane id vert_colors = mesh.visual.vertex_colors.view(np.ndarray) chan_0 = vert_colors[:, 2] chan_1 = vert_colors[:, 1] chan_2 = vert_colors[:, 0] plane_id = (chan_2 * 256 ** 2 + chan_1 * 256 + chan_0) // 100 - 1 # there is no (0,0,0) color in fitting mesh # mesh.vertex_attributes['plane_id'] = plane_id return plane_id def filter_plane_area_only(mesh): unique_id = np.unique(mesh.vertex_attributes['plane_ins']) face_color = mesh.visual.face_colors # filter them with face area again, we cannot compute bbox, because # hull precision error: Initial simplex is flat, because every piece is flat for i, cur_id in enumerate(unique_id): if cur_id == INVALID_ID: continue # use plane id select vertex vert_mask = mesh.vertex_attributes['plane_ins'] == cur_id trg_color = mesh.visual.vertex_colors[vert_mask][0] face_mask = face_color == trg_color sub_mesh = mesh.submesh(np.nonzero(face_mask.all(axis=1)))[0] surface_area = sub_mesh.area_faces.sum() if surface_area <= PLANE_AREA_THRES: mesh.visual.vertex_colors[vert_mask] = np.array([255, 255, 255, 255]).astype(np.uint8) return mesh def filter_plane_instance(mesh, semseg, plane_param, n_plane): unique_id = np.unique( mesh.vertex_attributes['plane_ins']) face_color = mesh.visual.face_colors verts = mesh.vertices add_addtion = 0 for cur_id in unique_id: if cur_id == INVALID_ID: continue # use plane id select vertex vert_mask = mesh.vertex_attributes['plane_ins'] == cur_id param = plane_param[cur_id:cur_id+1] # debug # mesh.visual.vertex_colors[vert_mask] = np.array([255, 0,0,255]) # load semseg id sem_id = np.unique(semseg[vert_mask]) assert len(sem_id>1) #use face color get submesh # ensure the plane vert >= 120 as state in fitting if vert_mask.sum() < N_VERT_THRES or NYU_ID2LAEBL[sem_id[0]] in NONE_PLANE_CLASS: mesh.visual.vertex_colors[vert_mask] = np.array([255, 255, 255, 255]).astype(np.uint8) continue trg_color = mesh.visual.vertex_colors[vert_mask][0] face_mask = face_color == trg_color sub_mesh = mesh.submesh(np.nonzero(face_mask.all(axis=1)))[0] # NOTE, some verts may do not have a face or have a face with different color with the verts color # we ask all verts become white first, and give the largest one meets the requirement their original color mesh.visual.vertex_colors[vert_mask] = np.array([255, 255, 255, 255]).astype(np.uint8) # if sem_id == 5: # mesh.visual.vertex_colors[vert_mask] = np.array([255, 0, 0, 255]).astype(np.uint8) # mesh.show() split_mesh = sub_mesh.split(only_watertight=False) # we cannot sort the mesh w.r.t. bbox first, because to compute bbox, there must be at least 4 verts, # but some split piece may not have verts_masks, surfaces, edge_ratios, sec_edges, area_ratios = [], [], [], [], [] for i, m in enumerate(split_mesh): # https://stackoverflow.com/questions/16210738/implementation-of-numpy-in1d-for-2d-arrays vmask = np.in1d(verts.view(dtype='f8,f8,f8').reshape(verts.shape[0]), m.vertices.view(dtype='f8,f8, f8').reshape(m.vertices.shape[0])) if vmask.sum() < N_VERT_THRES: continue # for each spart bbox = m.bounding_box_oriented # which is oriented bounding box, align with object instead of axis (so it is more tight) edges = bbox.primitive.extents max_edge, sec_edge = edges[edges.argsort()[-2:][::-1]] edge_ratio = sec_edge/max_edge # to remove single long thin surface bbox_area = max_edge * sec_edge surface_area = m.area_faces.sum() area_ratio = surface_area / bbox_area # to remove L, M, T shape surface # if surface_area > max_surface : verts_masks.append(vmask) surfaces.append(surface_area) edge_ratios.append(edge_ratio) sec_edges.append(sec_edge) area_ratios.append(area_ratio) n_assgined = -1 for mask, surface, edge_ratio, area_ratio, sec_edge in \ zip(verts_masks, surfaces, edge_ratios, area_ratios, sec_edges): if surface >= PLANE_AREA_THRES and mask.sum() > N_VERT_THRES \ and sec_edge>=PLANE_SEC_EDGE_THRES and edge_ratio >= PLANE_EDGE_RATIO_THRES: if NYU_ID2LAEBL[sem_id[0]] in CLASS_W_LONGSTRIKE: if area_ratio >= PLANE_AREA_RATIO_THRES: n_assgined += 1 else: n_assgined += 1 # mesh.visual.vertex_colors[mask] = trg_color #np.array([255, 0, 255, 255]).astype(np.uint8) # uncomment it if wish to split a large one into pieces to ensure connection # as one plane is splited into 2+, add new param to the result # if n_assgined > 0: # add_addtion += 1 # more_color = (n_plane + add_addtion) * 100 # new_color = np.array([more_color / (256 * 256), more_color / 256 % 256, more_color % 256, 255]) # plane_param = np.concatenate([plane_param, param]) # mesh.visual.vertex_colors[mask] = new_color # all part meet the standard will be preserved as the original one if n_assgined >= 0: mesh.visual.vertex_colors[mask] = trg_color # if n_assgined == -1 and sem_id == 5: # mesh.visual.vertex_colors[vert_mask] = np.array([255, 255, 255, 255]).astype(np.uint8) # print(cur_id, sem_id, ":", surface, PLANE_AREA_THRES, '/', edge_ratio, PLANE_EDGE_RATIO_THRES, '/', # area_ratio, PLANE_AREA_RATIO_THRES, '/', sec_edge, PLANE_SEC_EDGE_THRES) # # if sem_id == 5: # mesh.show() return mesh, plane_param def load_ply(src, data_pth, scene_name, label2id, scanNet2nyu): src_mesh_coarse = trimesh.load(src, process=False) # ============================== # load instance label agregated idx # offer the instance id, and all the over_segmt_pieceID belong to this instance # ============================== # each vertex in gt mesh indexs a seg group segIndices = \ json.load(open('{}/{}/{}_vh_clean_2.0.010000.segs.json'.format(data_pth, scene_name, scene_name), 'r'))[ 'segIndices'] # maps seg groups to instances segGroups = json.load(open('{}/{}/{}.aggregation.json'.format(data_pth, scene_name, scene_name), 'r'))['segGroups'] mapping = {ind: group['label'] for group in segGroups for ind in group['segments']} # get per vertex instance ids (0 is unknown, [1,...] are objects) n = len(segIndices) label_verts = np.zeros(n, dtype=np.long) for i in range(n): if segIndices[i] in mapping: label_verts[i] = scanNet2nyu[label2id[mapping[segIndices[i]]]] assert len(src_mesh_coarse.vertices) == n return src_mesh_coarse, label_verts def main(): train_pth = '/data/ScanNet/ScanNet_raw_data/scannet/scannetv2_train.txt' data_pth = '/data/ScanNet/ScanNet_raw_data/scannet/scans/' frm_err_pth = '/data/ScanNet/ScanNet_raw_data/video_plane_fitting/N_depth_thres=0.8/frm_err_pth/' if not os.path.isdir(frm_err_pth): os.makedirs((frm_err_pth)) train_scenes = [] with open(train_pth) as vf: for line in vf: tmp = line.strip() # if tmp[-2:] == '00': train_scenes.append(tmp) train_scenes.sort() # train_scenes = train_scenes[:3] # sum err, num_frame total_absDiff = np.zeros([len(train_scenes), 2]) total_absRel = np.zeros([len(train_scenes),2]) all_absDiff, all_absRel = [], [] for k, src in enumerate(train_scenes): print('process:', src) # read orignal face for depth render, plane fitting mesh have lots of holes between planes # Our plane fitting algorithm remove the face if the 3 verts have different plane ins id src_orginal_mesh = trimesh.load('{}/{}/{}_vh_clean_2.ply'.format(data_pth, src, src), process=False) # ========== render the depth and compare with gt depth ============ # if inconsistent #frame > N, remove the scene, because either the plane fitting or the gt pose is inaccurate # =================================================================== # load the gt_depth gt_depth_pth = '{}/{}/depth/.png'.format(data_pth, src) depth_list = sorted(glob(gt_depth_pth), key= lambda x: int(os.path.basename(x)[:-4])) # load pose path gt_pose_pth = '{}/{}/pose/'.format(data_pth, src) # load intrinsic with open(os.path.join(data_pth, src, '%s.txt' % src)) as info_f: info = [line.rstrip().split(' = ') for line in info_f] info = {key: value for key, value in info} intrinsics = np.array([ [float(info['fx_color'])zoom_x, 0., float(info['mx_color'])zoom_x], [0., float(info['fy_color'])zoom_y, float(info['my_color'])*zoom_y], [0., 0., 1.]]) # init render renderer = Renderer() mesh_opengl = renderer.mesh_opengl(src_orginal_mesh) # we use the mesh_viz as it has same face as original scannet mesh # compare gt depth with render depth # as we will use the rendered_depth to generate tsdf_mesh, the mesh will be prune naturally during that process sum_absDiff, sum_absRel, n_frm = 0, 0, 0 scene_stat = {} save_pth = frm_err_pth + '/' + src + '.npz' if os.path.isfile(save_pth): data =	np.load(save_pth)	numpy.load
import numpy as np from agents.common import * def generate_move_minimax( board: np.ndarray, player: BoardPiece, saved_state: Optional[SavedState] ) -> Tuple[PlayerAction, Optional[SavedState]]: """ generate move function employing minimax algorithm with/without alpha-beta pruning Parameters ----------- board: np.ndarray board reflecting current game state player: BoardPiece player for which the minimax algorithm is supposed to calculate an action Return ----------- action action for player, generated by minimax """ depth = 5 """change comment to use/not use alpha-beta pruning""" #action, _ = minimax(board, depth, player, True) action, _ = minimax_alpha_beta_pruning(board, depth, player, True, -np.inf, +np.inf) return action, saved_state def minimax_alpha_beta_pruning( board: np.ndarray, depth, player: BoardPiece, maximizing: bool, alpha, beta ): """ Minimax algorithm with alpha-beta pruning Parameters ----------- board: np.ndarray board reflecting current game state depth: int depth explored by the minimax algorithm player: BoardPiece player for which the minimax algorithm is supposed to calculate an action maximizing : Bool if True, result is maximized for the given player alpha alpha-value for pruning, if MAXIMIZING = True, should be initialized to -np.inf beta beta-value for pruning, if MAXIMIZING = True, should be initialized to +np.inf Return ----------- Tuple [action (best column), heuristic_value] """ opponent = PLAYER2 if player == PLAYER1 else PLAYER1 """Edge Case: (terminal node) AI or Player is winning with the next piece or the board is full (draw)""" terminal_state = check_end_state(board, player) if terminal_state == GameState.IS_WIN and maximizing: # Maximizing Player is Winning # print("winning") return -1, np.inf elif terminal_state == GameState.IS_WIN and maximizing == False: # Maximizing Player looses because Minimizing player wins # print("loosing") return -1, -np.inf elif terminal_state == GameState.IS_DRAW: # Game over because draw return -1, None if depth == 0: # case where depth = 0 value_for_depth0 = evaluate_board(board, player) return -1, value_for_depth0 valid_columns = get_valid_columns(board) best_column = np.random.choice(valid_columns, 1) """ recursive minimizing/maximizing case""" if maximizing: # maximizing for player value = -np.inf for column in valid_columns: c_board = apply_player_action(board, column, player, True) ### player _, new_score = minimax_alpha_beta_pruning(c_board, depth - 1, opponent, False, alpha, beta) ### maximizingTrue is False for next iter, because will be oppnent if new_score > value: value = new_score best_column = column alpha = max(alpha, new_score) if alpha >= beta: break ### beta cut-off return best_column, value else: # minimizing for opponent value = np.inf for column in valid_columns: c_board = apply_player_action(board, column, opponent, True) ### opponent is minimizing player _, new_score = minimax_alpha_beta_pruning(c_board, depth - 1, player, True, alpha, beta) ### maximizingTrue is True for next iter, because will be player if new_score < value: value = new_score best_column = column beta = min(beta, new_score) if alpha >= beta: break ### alpha cut off return best_column, value def minimax( board: np.ndarray, depth: int, player: BoardPiece, MAXIMIZING: bool ): """ Minimax algorithm Parameters ----------- board: np.ndarray board reflecting current game state depth: int depth explored by the minimax algorithm player: BoardPiece player for which the minimax algorithm is supposed to calculate an action MAXIMIZING : Bool if True, result is maximized for the given player Return ----------- Tuple [action (best column), heuristic_value] """ opponent = PLAYER2 if player == PLAYER1 else PLAYER1 """Edge Case: (terminal node) AI or Player is winning with the next piece or the board is full (draw)""" terminal_state = check_end_state(board, player) if terminal_state == GameState.IS_WIN and MAXIMIZING == True: # Maximizing Player is Winning # print("winning") return -1, np.inf elif terminal_state == GameState.IS_WIN and MAXIMIZING == False: # Maximizing Player looses because Minimizing player wins # print("loosing") return -1, -np.inf elif terminal_state == GameState.IS_DRAW: # Game over because draw return -1, None if depth == 0: # case where depth = 0 value_for_depth0 = evaluate_board(board, player) return -1, value_for_depth0 valid_columns = get_valid_columns(board) best_column = np.random.choice(valid_columns, 1) """ recursive minimizing/maximizing case""" if MAXIMIZING: # maximizing for player # print("maximizing works") value = -np.inf for column in valid_columns: c_board = apply_player_action(board, column, player, True) ### player _, new_score = minimax(c_board, depth - 1, opponent, False) ### maximizingTrue is False for next iter, because will be oppnent if new_score > value: value = new_score best_column = column # print("after column", column, "best column is", best_column) return best_column, value else: # minimizing for opponent # print("minimizing works") value = np.inf for column in valid_columns: c_board = apply_player_action(board, column, opponent, True) ### opponent is minimizing player _, new_score = minimax(c_board, depth - 1, player, True) ### maximizingTrue is True for next iter, because will be player if new_score < value: value = new_score best_column = column # print("after column", column, "best column is", best_column) return best_column, value def get_valid_columns(board: np.ndarray) -> np.ndarray: """ Function returning list of all columns with possible valid moves for current board Parameters ----------- board: np.ndarray board reflecting current game state Return ----------- valid_columns_array: array containing indices of all valid columns """ valid_columns_array = [] # initialize empty list for m, col in enumerate(board.T): for n, row in enumerate(col): if board.T[m, n] == 0: valid_columns_array.append(m) break return valid_columns_array def evaluate_window(window: np.ndarray, player: BoardPiece) -> int: """ evaluate a window (fraction of the board) passed into the function by func: evaluate_board in terms of player pieces, opponent pieces and empty slots Parameters ----------- window: array of shape (4,) player: BoardPiece Player for whom the window (i.e. the board) is evaluated Return ----------- window_score: int score for the respective window """ window_score = 0 opponent = PLAYER2 if player == PLAYER1 else PLAYER1 """Raise score if Player has 1 or more pieces in a window""" if sum(window == player) == 3 and sum(window == NO_PLAYER) == 1: window_score += 70 if sum(window == player) == 2 and sum(window == NO_PLAYER) == 2: window_score += 5 if sum(window == player) == 1 and sum(window == NO_PLAYER) == 3: window_score += 2 """penalty if opponent hast 1 or more pieces in a window with empty space next to it""" if sum(window == opponent) == 3 and sum(window == NO_PLAYER) == 1: window_score -= 70 if sum(window == opponent) == 2 and sum(window == NO_PLAYER) == 2: window_score -= 5 if sum(window == opponent) == 1 and sum(window == NO_PLAYER) == 3: window_score -= 2 return window_score def evaluate_board( board: np.ndarray, player: BoardPiece ) -> int: """ return a score reflecting the state of the board for the given player Parameters ----------- board: np.ndarray board reflecting current game state player: BoardPiece Player for whom the the board) is evaluated Return ----------- board_score: int score for the respective window """ WINDOW_LENGTH = 4 # Window length to be evaluated (=4 for connect 4) slide = WINDOW_LENGTH - 1 # reducing parameter based on window length when iterating over the board board_score = 0 # initialize board score to 0 n_rows, m_columns = board.shape # initialize board shape """ evaluate horizontally """ for r, row in enumerate(board): for c in range(m_columns - slide): window = row[c:c + WINDOW_LENGTH] board_score += evaluate_window(window, player) """ evaluate horizontally """ tboard =	np.transpose(board)	numpy.transpose
import numpy as np import matplotlib.pyplot as plt def line_intersect(p,q): P = np.matrix([[p[0][0],-1],[q[0][0],-1]]) c = -	np.matrix([[p[1][0]],[q[1][0]]])	numpy.matrix
import argparse import numpy as np from os import path import struct from internal import db_handling def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--sift_feature_dir', required=True) parser.add_argument('--query_txt_file', required=True) parser.add_argument('--database_file', required=True) args = parser.parse_args() return args def main(): args = parse_args() db = db_handling.COLMAPDatabase.connect(args.database_file) db.create_tables() with open(args.query_txt_file) as f: for line in f: name, _, h, w, fx, fy, cx, cy = line.split(' ') params = np.array([float(fx), float(fy), float(cx), float(cy)]) camera_id = db.add_camera(1, int(h), int(w), params) image_id = db.add_image(path.join('images', name), camera_id) featurefile = path.join(args.sift_feature_dir, path.splitext(name)[0] + '.sift') with open(featurefile, 'rb') as f: data = f.read() header = struct.unpack_from('iiiii', data, 0) _, _, num_points, num_entries, desc_size = header assert num_entries == 5 and desc_size == 128 offset = 20 keypoints = np.zeros((num_points, 2)) for i in range(num_points): point = struct.unpack_from('fffff', data, offset) offset += 20 keypoints[i, :] = np.array((point[1], point[0])) descriptors = np.zeros((num_points, desc_size)) for i in range(num_points): descriptor = struct.unpack_from('128B', data, offset) offset += desc_size descriptors[i, :] =	np.asarray(descriptor)	numpy.asarray
import numpy as np import pandas as pd import os import sys import inspect import unittest from joblib import load currentdir = os.path.dirname( os.path.abspath( inspect.getfile( inspect.currentframe()))) parentdir = os.path.dirname(currentdir) sys.path.insert(0, parentdir) from src.lr.text_processing.util import pre_process_nli_df from src.lr.text_processing.util import get_corpus from src.lr.text_processing.transformations.wordnet import get_noun_syn_dict from src.lr.text_processing.transformations.wordnet import p_h_transformation_syn_dict from src.lr.text_processing.transformations.wordnet import parallelize from src.lr.training.util import filter_df_by_label # data_path = parentdir + "/src/data/toy/train.csv" # assert os.path.exists(data_path) class SynTrans(unittest.TestCase): @classmethod def setUp(cls): folder = "toy" cls.n_cores = 2 train_path = parentdir + "/src/data/{}/train.csv".format(folder) dev_path = parentdir + "/src/data/{}/dev.csv".format(folder) veto_path = parentdir + "/src/data/{}/syn_veto.csv".format(folder) cls.syn_path = parentdir + "/src/data/{}/syn_noun.csv".format(folder) cls.train_path_mod = parentdir + "/src/data/{}/train_p_h_syn_noun.csv".format(folder) cls.dev_path_mod = parentdir + "/src/data/{}/dev_p_h_syn_noun.csv".format(folder) train = pd.read_csv(train_path) dev = pd.read_csv(dev_path) train = filter_df_by_label(train.dropna()).reset_index(drop=True) dev = filter_df_by_label(dev.dropna()).reset_index(drop=True) pre_process_nli_df(train) pre_process_nli_df(dev) cls.train = train cls.dev = dev cls.veto = pd.read_csv(veto_path).veto.values @classmethod def tearDown(cls): for path in [cls.train_path_mod, cls.dev_path_mod, cls.syn_path]: if os.path.exists(path): os.remove(path) def test_syn_transformation(self): # get syn dict syn_dict = get_noun_syn_dict(df=self.train, n_cores=self.n_cores, veto=self.veto) # removing possible verbs syn_dict = {k: syn_dict[k] for k in syn_dict if k[-3:] != "ing"} # saving to a dataframe key = sorted(syn_dict.keys()) value = [syn_dict[k] for k in key] syn_df = pd.DataFrame({"key": key, "value": value}) train_t = p_h_transformation_syn_dict(df=self.train, syn_dict=syn_dict) dev_t = p_h_transformation_syn_dict(df=self.dev, syn_dict=syn_dict) self.assertTrue(not np.any(self.train.premise == train_t.premise)) self.assertTrue(not np.any(self.train.hypothesis == train_t.hypothesis)) self.assertTrue(np.all(self.train.label == train_t.label)) self.assertTrue(not np.any(self.dev.premise == dev_t.premise)) self.assertTrue(not	np.any(self.dev.hypothesis == dev_t.hypothesis)	numpy.any
#!/usr/bin/env python3 """ Created on Wed Feb 12 10:44:59 2020 @author: <NAME> Skeleton modified from https://www.tensorflow.org/tutorials/customization/custom_training https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough Training of an RBM parametrization of the unitary matrix that diagonalises the extended HAMILTONIAN of harmonically driven quantum systems ==================== IMPORTANT NOTE ======================== ============================================================ """ ############### IMPORT WHAT WE NEED FROM THE OS/PYTHON DISTRIBUTION ##################### from __future__ import absolute_import, division, print_function, unicode_literals try: # %tensorflow_version only exists in Colab. get_ipython().run_line_magic('tensorflow_version', '2.x') except Exception: pass import tensorflow as tf import numpy as np import math as m #import matplotlib as mpl import matplotlib.pyplot as plt ############### IMPORT WHAT WE NEED FROM THIS PROJECT ##################### import model as Model import matrixmanipulation as mp import NN_model as NN_Model import Training as training ############### FULL RUN ################################ # 1. CALCULATE THE EXACT FLOQUET STATES # 2. CALCULATE THE RBM REPRESENTATION H = Model.Hamiltonian() N = 32 CentralFloquetE = np.zeros([N,2],dtype=np.float64) CentralFloquetEVec = np.zeros([N,H.dim,2],dtype=np.complex128) CentralFloquetE_RBM = np.zeros([N,2],dtype=np.float64) CentralFloquetEVec_RBM = np.zeros([N,H.dim,2],dtype=np.complex128) loss_list_RWA = tf.Variable([],dtype=tf.float64) N_training = 4 N_tr_ph = 2 N_tr_rho = 2 file = open('RBM_TrainingFloquet.dat','wb') optimizer = tf.keras.optimizers.Adam(learning_rate=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False,name='Adam') #optimizer = tf.keras.optimizers.SGD(learning_rate=0.1) #%% file_psi = open('absPsi_comparison_Floquet_3D.dat','w') for i in [4,16,31]:#[16,18]:#:,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]:#range(N): delta = 1.7 Omega = 10.0i/N phase = np.arctan(1.0)/4.1341341 print(i,delta,Omega,phase) N_training = 256 H = Model.Hamiltonian(delta,Omega,phase) ###### FITTING A WAVEFUNCTION: ###### loss_Floquet,loss_Floquet_phase : fits the Floquet wavefunction ###### loss_RWA,loss_RWA_phase : fits the RWA wavefunction trained_parameters,loss_history = training.RMB_training_Psi(N_training,H, Model.loss_Floquet, Model.loss_Floquet_Phase) #trained_parameters,loss_history = training.RMB_training_Psi(N_training,H, # Model.loss_RWA, # Model.loss_RWA_Phase) model = Model.RBM_Model(delta,Omega,phase,trained_parameters) UF = Model.Unitary_Matrix(model) U_ = tf.transpose(tf.math.conj(UF))@model.H_TLS@UF if (UF.shape[1] > model.S): index_ = int(model.S((UF.shape[1]/model.S -1))/2) else: index_ = 0 CentralFloquetEVec_RBM[i,:,0] = UF[:,index_ ].numpy() CentralFloquetEVec_RBM[i,:,1] = UF[:,index_+1].numpy() CentralFloquetE_RBM[i,0] = tf.math.real(U_[index_ , index_ ]).numpy() CentralFloquetE_RBM[i,1] = tf.math.real(U_[index_ + 1 , index_ + 1]).numpy() absPsi_comparison = np.zeros([UF.shape[0],11],dtype=np.double) absPsi_comparison[:,0] = np.linspace(0,UF.shape[0]-1,UF.shape[0],dtype=np.int) absPsi_comparison[:,1] = np.mod(np.linspace(0,UF.shape[0]-1,UF.shape[0],dtype=np.int),2) - 0.5 absPsi_comparison[:,2] = absPsi_comparison[:,0]//2 - 16 absPsi_comparison[:,3:5] = Model.Rect2Pol(UF[:,model.N_Floquet_UFmodel.S:model.N_Floquet_UFmodel.S+1]) absPsi_comparison[:,5:7] = Model.Rect2Pol(UF[:,model.N_Floquet_UFmodel.S+1:model.N_Floquet_UFmodel.S+2]) aux = np.zeros([66,1],dtype=np.complex) aux[:,0] = model.U_Floquet[:,0] absPsi_comparison[:,7:9] = Model.Rect2Pol(aux) aux[:,0] = model.U_Floquet[:,1] absPsi_comparison[:,9:11] = Model.Rect2Pol(aux) np.savetxt(file_psi,absPsi_comparison) file_psi.write("\n\n") np.savetxt(file_psi,loss_history) file_psi.write("\n\n") plt.plot(loss_history) plt.show() plt.plot(np.abs(UF[:,model.N_Floquet_UFmodel.S:model.N_Floquet_UFmodel.S+2])) plt.show() plt.plot(np.abs(model.U_Floquet[:,:])) plt.show() plt.imshow(np.abs(U_.numpy())) plt.show() file_psi.close() #%% for i in [16]:#:,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]:#range(N): delta = 1.7 Omega = 10.0i/N phase = np.arctan(1.0)/4.1341341 N_training= 12000 trained_parameters,loss_history = training.RBM_training_FloquetStates(N_training,H) model = Model.RBM_Model(delta,Omega,phase,trained_parameters) UF = Model.Unitary_Matrix(model) U_ = tf.transpose(tf.math.conj(UF))@model.H_TLS@UF if (UF.shape[1] > model.S): index_ = int(model.S((UF.shape[1]/model.S -1))/2) else: index_ = 0 CentralFloquetEVec_RBM[i,:,0] = UF[:,index_ ].numpy() CentralFloquetEVec_RBM[i,:,1] = UF[:,index_+1].numpy() CentralFloquetE_RBM[i,0] = tf.math.real(U_[index_ , index_ ]).numpy() CentralFloquetE_RBM[i,1] = tf.math.real(U_[index_ + 1 , index_ + 1]).numpy() plt.plot(np.abs(UF[:,model.N_Floquet_UFmodel.S:model.N_Floquet_UFmodel.S+2])) plt.show() plt.plot(np.abs(model.U_Floquet[:,:])) plt.show() plt.imshow(np.abs(U_.numpy())) plt.show() #%% ########## TRAINING AGAINST THE RWA ########################### ########## TO OBTAIN AN INITIAL SET RBM PARAMETERS ################ ########## THIS IS EQUIVALENT TO RECONSTRUCT A SET OF WAVE FUNCTIONS ###### for i in [14,16,18]:#:,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]:#range(N): delta = 1.7 Omega = 10.0i/N phase = np.arctan(1.0)/4.1341341 print(i,delta,Omega,phase) N_training = 4 H = Model.Hamiltonian(delta,Omega,phase) model = Model.RBM_Model(delta,Omega,phase) CentralFloquetEVec[i,:,0] = model.U_Floquet[:,0].numpy() CentralFloquetEVec[i,:,1] = model.U_Floquet[:,1].numpy() CentralFloquetE[i,0] = tf.math.real(model.E_Floquet[0]).numpy() CentralFloquetE[i,1] = tf.math.real(model.E_Floquet[1]).numpy() optimizer = tf.keras.optimizers.Adam(learning_rate=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False,name='Adam') for j in range(N_training):#[0,1,2,3,5]: # Fit the norm for i_ in range(N_tr_rho): #loss_value, grads = Model.grad_fun(model,Model.loss_Psi_rho) loss_value, grads = Model.grad_fun(model,Model.loss_RWA) #loss_value, grads = Model.grad_fun(model,Model.loss_Floquet) loss_list_RWA = tf.concat([loss_list_RWA,[loss_value.numpy()]],axis=0) #optimizer.apply_gradients(zip(grads, model.trainable_variables)) optimizer.apply_gradients(zip(grads[0:3], model.trainable_variables[0:3])) # Fit the phase for i_ in range(N_tr_ph): #loss_value, grads = Model.grad_Phase(model,Model.loss_Psi_Phase) loss_value, grads = Model.grad_Phase(model,Model.loss_RWA_Phase) #loss_value, grads = Model.grad_Phase(model,Model.loss_Floquet_Phase) loss_list_RWA = tf.concat([loss_list_RWA,[loss_value.numpy()]],axis=0) optimizer.apply_gradients(zip(grads, model.trainable_variables[3:6])) UF = Model.Unitary_Matrix(model) #print("Hamiltonian in the dressed basis (RBM parametrisation): ") U_ = tf.transpose(tf.math.conj(UF))@model.H_TLS@UF #print(U_) if (UF.shape[1] > model.S): index_ = int(model.S((UF.shape[1]/model.S -1))/2) else: index_ = 0 CentralFloquetEVec_RBM[i,:,0] = UF[:,index_ ].numpy() CentralFloquetEVec_RBM[i,:,1] = UF[:,index_+1].numpy() CentralFloquetE_RBM[i,0] = tf.math.real(U_[index_ , index_ ]).numpy() CentralFloquetE_RBM[i,1] = tf.math.real(U_[index_ + 1 , index_ + 1]).numpy() #################################################################### #################################################################### #################################################################### #################################################################### #################################################################### ####### FINDING THE FLOQUET OPERATOR TRAINING A RBM ############## #################################################################### optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False,name='Adam') #optimizer = tf.keras.optimizers.SGD(learning_rate=0.00001) loss_list = tf.Variable([],dtype=tf.float64) CentralFloquetE_RBM2 = np.zeros([N,2], dtype = np.float64) CentralFloquetEVec_RBM2 =	np.zeros([N,H.dim,2],dtype = np.complex128)	numpy.zeros
import numpy as np import scipy.special as ss import scipy.signal as ss2 import scipy from numpy import abs, sin, cos, real, exp, pi, sqrt def psi_s(z, x, beta): """ 2D longitudinal potential Eq. (23) from Ref[1] with no constant factor (ebeta2/2/rho2). Ref[1]: <NAME> and <NAME>, PRAB 23, 014402 (2020). Note that 'x' here corresponds to 'chi = x / rho' in the paper. """ #try: out = (cos(2 alpha(z, x, beta)) - 1 / (1+x)) / ( kappa(z, x, beta) - beta * (1+x) * sin(2alpha(z, x, beta))) #except ZeroDivisionError: # out = 0 # print(f"Oops! ZeroDivisionError at (z,x)= ({z:5.2f},{x:5.2f}). Returning 0.") return np.nan_to_num(out) def psi_x_where_x_equals_zero(z, dx, beta): """ Evaluate psi_x close to x = 0 This is a rough approximation of the singularity across x = 0 """ return (psi_x(z, -dx/2, beta) + psi_x(z, dx/2, beta))/2 @np.vectorize def ss_ellipf(phi, m): y = ss.ellipkinc(phi, m) # y = np.float(y) return y @np.vectorize def ss_ellipe(phi, m): y = ss.ellipeinc(phi, m) # y = np.float(y) return y def psi_x(z, x, beta): """ Eq.(24) from Ref[1] with argument zeta=0 and no constant factor ebeta2/2/rho2. Note that 'x' here corresponds to 'chi = x/rho', and 'z' here corresponds to 'xi = z/2/rho' in the paper. """ # z = np.float(z) # x = np.float(x) kap = kappa(z, x, beta) alp = alpha(z, x, beta) arg2 = -4 * (1+x) / x*2 try: T1 = (1/abs(x)/(1 + x) ((2 + 2x + x2) ss.ellipkinc(alp, arg2)- x*2 ss.ellipeinc(alp, arg2))) D = kap2 - beta2 * (1 + x)*2	sin(2*alp)	numpy.sin
import numpy as np import os defargs = { 'VERBOSE':'TRUE', 'LOGFILE':'snap.logfile', 'TILECOSTTHRESH':500, 'MINREGIONSIZE':100, 'TILEEDGEWEIGHT':2.5, 'SCNDRYARCFLOWMAX':8} def Snaphu(C,*kwargs): #get the file names outfile = '' while outfile == '' or os.path.isfile(outfile): num = np.random.randint(1000000) outfile = '{:0d}.out'.format(num) infile = '{:0d}.in'.format(num) cfgfile = '{:0d}.cfg'.format(num) #save the config file f = open(cfgfile,'w') f.write('INFILE {:s}\n'.format(infile)) f.write('LINELENGTH {:d}\n'.format(C.shape[1])) f.write('OUTFILE {:s}\n'.format(outfile)) keys = list(defargs.keys()) for k in keys: v = kwargs.get(k,defargs[k]) if isinstance(v,str): o = k + ' {:s}\n'.format(v) elif isinstance(v,(np.int,np.int32,np.int64)): o = k + ' {:d}\n'.format(v) elif isinstance(v,(np.float,np.float32,np.float64)): o = k + ' {:f}\n'.format(v) f.write(o) f.close() #save the input data f = open(infile,'wb') C.flatten().astype('complex64').tofile(f) f.close() #run snaphu os.system('snaphu -f {:s}'.format(cfgfile)) #remove input file os.system('rm -v {:s}'.format(infile)) #read the output file f = open(outfile,'rb') c = np.fromfile(f,dtype='float32',count=C.size2).reshape((C.shape[0]2,C.shape[1])) f.close() #create output arrays of amplitude and phase ia = np.arange(C.shape[0])2 ip =	np.arange(C.shape[0])	numpy.arange
import numpy as np import pandas as pd import joblib import tensorflow as tf import sys import functools import os import tensorflow.keras.backend as K from matplotlib import pyplot as plt # from IPython.display import clear_output from scipy.stats import gaussian_kde, binned_statistic as binstat from tensorflow.keras.preprocessing.sequence import pad_sequences from sklearn.model_selection import ShuffleSplit, GroupShuffleSplit from sklearn.preprocessing import MinMaxScaler, StandardScaler from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error, median_absolute_error from tensorflow.keras.losses import Loss from scipy.spatial.distance import jensenshannon as js class HuberLoss(Loss): """ Custom TensorFlow Loss subclass implementing the Huber loss. """ def __init__(self, threshold: float = 1): """ :param threshold: float The Huber threshold between L1 and L2 losses. """ super().__init__() self.threshold = threshold def call(self, y_true, y_pred): error = y_true - y_pred is_small_error = tf.abs(error) <= self.threshold small_error_loss = tf.square(error) / 2 big_error_loss = self.threshold * (tf.abs(error) - (0.5 * self.threshold)) return tf.where(is_small_error, small_error_loss, big_error_loss) def root_mean_squared_error(y, y_pred, sample_weight=None): """ Compute the root mean squared error metric. """ value = mean_squared_error(y, y_pred, sample_weight=sample_weight) return np.sqrt(value) def process_input_parameters(pars, min_folds_cv=5): """ Check the consistency of the input parameters and make modifications if necessary. :param pars: argparse.Namespace An argparse namespace object containing the input parameters. :param min_folds_cv: int The minimum number of folds required for K-fold cross-validation. :return: pars, argparse.Namespace The processed version of the input namespace object. """ if len(pars.lcdir) > 1: assert len(pars.wavebands) == len(pars.lcdir), "The number of items in lcdir must either be 1 or match " \ "the number of items in wavebands." assert len(pars.wavebands) == len(pars.lcfile_suffices), \ "The number of items in wavebands and lcfile_suffices must match." if not os.path.isdir(os.path.join(pars.rootdir, pars.outdir)): os.mkdir(os.path.join(pars.rootdir, pars.outdir)) pars.hparam_grid = np.array(pars.hpars) # Check if only the CPU is to be used: if pars.cpu: os.environ["CUDA_VISIBLE_DEVICES"] = "" # Join the list elements of pars.subset into a long string: if pars.subset: pars.subset = ' '.join(pars.subset) # Check the number of meta input features: if pars.meta_input is None: pars.n_meta = 0 else: pars.n_meta = len(pars.meta_input) if pars.nn_type == 'cnn': pars.n_channels = len(pars.wavebands) else: pars.n_channels = 2 * len(pars.wavebands) if pars.weighing_by_density: print("Density weighing is ON with cutoff {}".format(pars.weighing_by_density)) else: print("Density weighing is OFF.") print("Number of input channels: {}".format(pars.n_channels)) print("Number of meta features: {}".format(pars.n_meta)) if pars.train: pars.predict = False # We want to train a regression model. if pars.pick_fold is not None: for ii in pars.pick_fold: print(type(ii)) assert isinstance(ii, int) and 0 < ii <= pars.k_fold, \ "pick_fold must be > 0 AND <= k_fold integer" assert pars.k_fold >= min_folds_cv, \ "pick_fold requires k_fold >= {}".format(min_folds_cv) pars.refit = False if not pars.cross_validate: assert len(pars.hparam_grid) == 1, "Cannot do grid-search of hyper-parameters if cross_validate is False." pars.refit = True if pars.explicit_test_frac: assert pars.refit or pars.ensemble, \ "For the evaluation of the model on the test set, 'refit' or 'ensemble' must be set." if pars.optimize_lr: pars.n_epochs = 100 pars.decay = 0.0 pars.save_model = False pars.cross_validate = False pars.refit = True return pars def read_dataset(filename: str, columns: list = None, subset_expr: str = None, input_feature_names: list = None, trim_quantiles: list = None, qlo: float = 0.25, qhi: float = 0.75, plothist: bool = False, histfig: str = "hist.png", dropna_cols: list = None, comment: str = '#', dtype=None): """ Loads, trims, and exports dataset to numpy arrays. :param filename: str The name of the input file. :param columns: list of strings Passed to the usecols parameter of pandas.read_csv() :param subset_expr: str Expression for subsetting the input data, passed as the first parameter of pandas.DataFrame.query() :param input_feature_names: list of strings An optional subset of the usecols parameter, including the names of the columns to be returned as features. If None, all columns in usecols will be returned. :param trim_quantiles: list An optional subset of the usecols parameter, including the names of the columns to be threshold-rejected beyond the quantiles specified by qlo and qhi. If None, no quantile-trimming will be performed. :param qlo: float Lower quantile for threshold rejection. :param qhi: float Upper quantile for threshold rejection. :param plothist: bool If True, the histograms of the columns in usecols will be plotted before and, if performed, after quantile trimming. :param histfig: str The name of the output histogram figure file if plothist is True. :param dropna_cols: :param comment: :param dtype: :return: """ with open(filename) as f: header = f.readline() cols = header.strip('#').split() df = pd.read_csv(filename, names=cols, header=None, sep="\s+", usecols=columns, comment=comment, dtype=dtype) if dropna_cols is not None: df.dropna(inplace=True, subset=dropna_cols) ndata = len(df) print(df.head()) print("----------\n{} lines read from {}\n".format(ndata, filename)) df_orig = df # Apply threshold rejections: if subset_expr is not None: df = df.query(subset_expr) ndata = len(df) print("{} lines after threshold rejections\n".format(ndata)) # plot histogram for each column in original dataset if plothist: fig, ax = plt.subplots(figsize=(20, 10)) fig.clf() _ = pd.DataFrame.hist(df, bins=int(np.ceil(np.cbrt(ndata) * 2)), figsize=(20, 10), grid=False, color='red', ax=ax) plt.savefig(histfig) # omit data beyond specific quantiles [qlo, qhi] if trim_quantiles is not None: dfq = df[trim_quantiles] quantiles = pd.DataFrame.quantile(dfq, q=[qlo, qhi], axis=0, numeric_only=True, interpolation='linear') print("Values at [{},{}] quantiles to be applied for data trimming:".format(qlo, qhi)) print(quantiles.sum) mask = (dfq > dfq.quantile(qlo)) & (dfq < dfq.quantile(qhi)) # print(mask) mask = mask.all(axis=1) # print(mask.shape) df = pd.DataFrame.dropna(df[mask]) ndata = len(df) print("\n{} lines remained after quantile rejection.\n".format(ndata)) # plot histogram for each column in trimmed dataset if plothist: fig, ax = plt.subplots(figsize=(20, 10)) _ = pd.DataFrame.hist(df, bins=int(np.ceil(np.cbrt(ndata) * 2)), figsize=(20, 10), grid=False, color='green', ax=ax) fig.savefig("hist_trim.png", format="png") if input_feature_names is not None: return df.loc[:, input_feature_names], df_orig else: return df, df_orig def read_time_series_for_rnn(name_list, source_dir, nts, input_wavebands, ts_file_suffix, rootdir="", periods=None, max_phase=1.0, phase_shift=None, nbins=None): print("Reading time series...", file=sys.stderr) n_data = len(name_list) scaler = StandardScaler(copy=True, with_mean=True, with_std=False) X_list = list() times_dict = dict() mags_dict = dict() phases_dict = dict() if nbins is not None: print("Light curves will be binned to max. {0} points in [0, {1:.1f}].".format(nbins, max_phase)) for iband, waveband in enumerate(input_wavebands): X = np.zeros((n_data, nts, 2)) # Input shape required by an RNN: (batch_size, time_steps, features) phases = list() times = list() mags = list() if len(source_dir) > 1: directory = source_dir[iband] else: directory = source_dir[0] for ii, name in enumerate(name_list): print('Reading data for {}\r'.format(name), end="", file=sys.stderr) pp, mm = np.genfromtxt(os.path.join(rootdir, directory, name + ts_file_suffix[iband]), unpack=True, comments='#') phasemask = (pp < max_phase) pp = pp[phasemask] mm = mm[phasemask] if phase_shift is not None: pp = get_phases(1.0, pp, shift=phase_shift, all_positive=True) inds = np.argsort(pp) pp = pp[inds] mm = mm[inds] if nbins is not None: pp, mm = binlc(pp, mm, nbins=nbins, max_y=max_phase) if periods is not None: tt = pp * periods[ii] else: tt = pp # here we only subtract the mean: mm = scaler.fit_transform(mm.reshape(-1, 1)).flatten() times.append(tt) mags.append(mm) phases.append(pp) times_padded = pad_sequences(times, maxlen=nts, dtype='float64', padding='post', truncating='post', value=-1) mags_padded = pad_sequences(mags, maxlen=nts, dtype='float64', padding='post', truncating='post', value=-1) X[:, :, 0] = times_padded X[:, :, 1] = mags_padded X_list.append(X) times_dict[waveband] = times mags_dict[waveband] = mags phases_dict[waveband] = phases # Create final data matrix for the time series: X = np.concatenate(X_list, axis=2) print("") return X, times_dict, mags_dict, phases_dict def read_time_series_for_cnn(name_list, source_dir, nts, input_wavebands, ts_file_suffix, nuse=1, rootdir="", n_aug=None): nmags = int(nts / nuse) n_data = len(name_list) if n_aug is not None: assert isinstance(n_aug, int) and n_aug > 0, \ "n_aug must be a positive integer" dict_x_ts = dict() for waveband in input_wavebands: dict_x_ts[waveband] = np.zeros((n_data, nmags)) if n_aug is not None: dict_x_ts[waveband] = np.zeros((n_data * n_aug, nmags)) groups = np.zeros((n_data * n_aug)) dict_x_ts_scaled = dict() print("Reading time series...", file=sys.stderr) for ii, name in enumerate(name_list): print('Reading data for {}\r'.format(name), end="", file=sys.stderr) for iband, waveband in enumerate(input_wavebands): if len(source_dir) > 1: directory = source_dir[iband] else: directory = source_dir[0] if n_aug is None: phases, timeseries = np.genfromtxt(os.path.join(directory, name + ts_file_suffix[iband]), unpack=True, comments='#') phases = phases[0:nts] timeseries = timeseries[0:nts] dict_x_ts[waveband][ii][:] = timeseries[nuse - 1::nuse] groups = None else: tsinput = np.genfromtxt(os.path.join(directory, name + ts_file_suffix[iband]), unpack=False, comments='#') # check if there are n_aug+1 columns in the data matrix assert tsinput.shape[1] == n_aug + 1, \ "data matrix in " + os.path.join(directory, name + ts_file_suffix[iband]) + " has wrong shape" phases = tsinput[0:nts, 0] for jj in range(n_aug): timeseries = tsinput[0:nts, jj + 1] dict_x_ts[waveband][jj + ii * n_aug][:] = timeseries[nuse - 1::nuse] groups[jj + ii * n_aug] = ii phases = phases[nuse - 1::nuse] # Scale the time series to the [0,1] range scaler = MinMaxScaler(copy=True, feature_range=(0, 1)) ts_list = list() for ii, waveband in enumerate(input_wavebands): scaler.fit(dict_x_ts[waveband].T) dict_x_ts_scaled[waveband] = (scaler.transform(dict_x_ts[waveband].T)).T ts_list.append(np.expand_dims(dict_x_ts_scaled[waveband], axis=2)) # Create final data matrix for the time series: X = np.concatenate(ts_list, axis=2) print("") return X, dict_x_ts, dict_x_ts_scaled, phases, groups def cross_validate(model, folds: list, x_list: list or tuple, y, model_kwargs: dict = {}, compile_kwargs: dict = {}, initial_weights: list = None, sample_weight_fit=None, sample_weight_eval=None, ids=None, indices_to_scale: list or tuple = None, scaler=None, n_epochs: int = 1, batch_size: int = None, shuffle=True, verbose: int = 0, callbacks: list = [], metrics: list or tuple = None, log_training=True, log_prefix='', pick_fold: list or tuple = None, save_data=True, rootdir='.', filename_train='train.dat', filename_val='val.dat', strategy=None, n_devices=1, validation_freq=1, seed=1): # Initialize variables: histories = list() model_weights = list() scalers_folds = list() Y_train_collected = np.array([]) Y_val_collected = np.array([]) Y_train_pred_collected = np.array([]) Y_val_pred_collected = np.array([]) fitting_weights_train_collected = np.array([]) fitting_weights_val_collected = np.array([]) eval_weights_train_collected = np.array([]) eval_weights_val_collected = np.array([]) ids_train_collected = np.array([]) ids_val_collected = np.array([]) numcv_t = np.array([]) numcv_v = np.array([]) # callbacks.append(PrintLearningRate()) if ids is None: # create IDs by simply numbering the data ids = np.linspace(1, y.shape[0], y.shape[0]).astype(int) first_fold = True for i_cv, (train_index, val_index) in enumerate(folds): # if pick_fold is not None and pick_fold != i_cv + 1: if pick_fold is not None and i_cv + 1 not in pick_fold: continue # Create and compile the model: tf.keras.backend.clear_session() tf.random.set_seed(seed) if strategy is not None: # Apply distributed strategy on model if multiple devices are present: with strategy.scope(): model_ = model(model_kwargs) else: model_ = model(model_kwargs) if first_fold: first_fold = False model_.summary() if initial_weights is None: initial_weights = model_.get_weights() else: # Initialize model weights: model_.set_weights(initial_weights) if strategy is not None: with strategy.scope(): model_.compile(compile_kwargs) else: model_.compile(compile_kwargs) print("fold " + str(i_cv + 1) + "/" + str(len(folds))) print("n_train = {} ; n_val = {}".format(train_index.shape[0], val_index.shape[0])) if log_training: callbacks_fold = callbacks + [tf.keras.callbacks.CSVLogger( os.path.join(rootdir, log_prefix + f"_fold{i_cv + 1}.log"))] else: callbacks_fold = callbacks # -------------------------------------------------- # Split the arrays to training and validations sets: x_train_list = list() x_val_list = list() scalers = list() for i, x in enumerate(x_list): x_t, x_v = x[train_index], x[val_index] if indices_to_scale is not None and i in indices_to_scale: scaler.fit(x_t) x_t = scaler.transform(x_t) x_v = scaler.transform(x_v) scalers.append(scaler.copy()) x_train_list.append(x_t) x_val_list.append(x_v) y_train, y_val = y[train_index], y[val_index] if sample_weight_fit is not None: fitting_weights_train, fitting_weights_val = sample_weight_fit[train_index], sample_weight_fit[val_index] else: fitting_weights_train, fitting_weights_val = None, None if sample_weight_eval is not None: eval_weights_train, eval_weights_val = sample_weight_eval[train_index], sample_weight_eval[val_index] else: eval_weights_train, eval_weights_val = None, None ids_t, ids_v = ids[train_index], ids[val_index] # -------------------------------------------------- # Fit and evaluate the model for this fold: history = model_.fit(x=x_train_list, y=y_train, sample_weight=fitting_weights_train, epochs=n_epochs, initial_epoch=0, batch_size=batch_size, shuffle=shuffle, validation_data=(x_val_list, y_val, fitting_weights_val), verbose=verbose, callbacks=callbacks_fold, validation_freq=validation_freq) Y_train_pred = (model_.predict(x_train_list)).flatten() Y_val_pred = (model_.predict(x_val_list)).flatten() histories.append(history) model_weights.append(model_.get_weights()) scalers_folds.append(scalers.copy()) # -------------------------------------------------- # Append the values of this fold to those from the previous fold(s). Y_train_collected = np.hstack((Y_train_collected, y_train)) Y_val_collected = np.hstack((Y_val_collected, y_val)) Y_train_pred_collected = np.hstack((Y_train_pred_collected, Y_train_pred)) Y_val_pred_collected = np.hstack((Y_val_pred_collected, Y_val_pred)) if sample_weight_fit is not None: fitting_weights_train_collected = np.hstack((fitting_weights_train_collected, fitting_weights_train)) fitting_weights_val_collected =	np.hstack((fitting_weights_val_collected, fitting_weights_val))	numpy.hstack
# pylint: disable=E1101 import torch import torch.nn as nn from torch.utils.data import DataLoader, TensorDataset from sklearn.metrics import average_precision_score, confusion_matrix import numpy as np from physionet import PhysioNet, get_data_min_max, variable_time_collate_fn2 from sklearn import model_selection from sklearn import metrics from sklearn.metrics import precision_score, recall_score, f1_score from person_activity import PersonActivity def one_hot(y_): # Function to encode output labels from number indexes # e.g.: [[5], [0], [3]] --> [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]] y_ = y_.reshape(len(y_)) y_ = [int(x) for x in y_] n_values = np.max(y_) + 1 return np.eye(n_values)[np.array(y_, dtype=np.int32)] def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) def log_normal_pdf(x, mean, logvar, mask): const = torch.from_numpy(np.array([2. * np.pi])).float().to(x.device) const = torch.log(const) return -.5 * (const + logvar + (x - mean) ** 2. / torch.exp(logvar)) * mask def normal_kl(mu1, lv1, mu2, lv2): v1 = torch.exp(lv1) v2 = torch.exp(lv2) lstd1 = lv1 / 2. lstd2 = lv2 / 2. kl = lstd2 - lstd1 + ((v1 + (mu1 - mu2) ** 2.) / (2. * v2)) - .5 return kl def mean_squared_error(orig, pred, mask): error = (orig - pred) ** 2 error = error * mask return error.sum() / mask.sum() def normalize_masked_data(data, mask, att_min, att_max): # we don't want to divide by zero att_max[att_max == 0.] = 1. if (att_max != 0.).all(): data_norm = (data - att_min) / att_max else: raise Exception("Zero!") if torch.isnan(data_norm).any(): raise Exception("nans!") # set masked out elements back to zero data_norm[mask == 0] = 0 return data_norm, att_min, att_max def evaluate(dim, rec, dec, test_loader, args, num_sample=10, device="cuda"): mse, test_n = 0.0, 0.0 with torch.no_grad(): for test_batch in test_loader: test_batch = test_batch.to(device) observed_data, observed_mask, observed_tp = ( test_batch[:, :, :dim], test_batch[:, :, dim: 2 * dim], test_batch[:, :, -1], ) if args.sample_tp and args.sample_tp < 1: subsampled_data, subsampled_tp, subsampled_mask = subsample_timepoints( observed_data.clone(), observed_tp.clone(), observed_mask.clone(), args.sample_tp) else: subsampled_data, subsampled_tp, subsampled_mask = \ observed_data, observed_tp, observed_mask out = rec(torch.cat((subsampled_data, subsampled_mask), 2), subsampled_tp) qz0_mean, qz0_logvar = ( out[:, :, : args.latent_dim], out[:, :, args.latent_dim:], ) epsilon = torch.randn( num_sample, qz0_mean.shape[0], qz0_mean.shape[1], qz0_mean.shape[2] ).to(device) z0 = epsilon * torch.exp(0.5 * qz0_logvar) + qz0_mean z0 = z0.view(-1, qz0_mean.shape[1], qz0_mean.shape[2]) batch, seqlen = observed_tp.size() time_steps = ( observed_tp[None, :, :].repeat(num_sample, 1, 1).view(-1, seqlen) ) pred_x = dec(z0, time_steps) pred_x = pred_x.view(num_sample, -1, pred_x.shape[1], pred_x.shape[2]) pred_x = pred_x.mean(0) mse += mean_squared_error(observed_data, pred_x, observed_mask) * batch test_n += batch return mse / test_n def compute_losses(dim, dec_train_batch, qz0_mean, qz0_logvar, pred_x, args, device): observed_data, observed_mask \ = dec_train_batch[:, :, :dim], dec_train_batch[:, :, dim:2dim] noise_std = args.std noise_std_ = torch.zeros(pred_x.size()).to(device) + noise_std noise_logvar = 2. torch.log(noise_std_).to(device) logpx = log_normal_pdf(observed_data, pred_x, noise_logvar, observed_mask).sum(-1).sum(-1) pz0_mean = pz0_logvar = torch.zeros(qz0_mean.size()).to(device) analytic_kl = normal_kl(qz0_mean, qz0_logvar, pz0_mean, pz0_logvar).sum(-1).sum(-1) if args.norm: logpx /= observed_mask.sum(-1).sum(-1) analytic_kl /= observed_mask.sum(-1).sum(-1) return logpx, analytic_kl def evaluate_classifier(model, test_loader, dec=None, args=None, classifier=None, dim=41, device='cuda', reconst=False, num_sample=1, dataset='P12'): pred = [] true = [] test_loss = 0 for test_batch, label in test_loader: test_batch, label = test_batch.to(device), label.to(device) batch_len = test_batch.shape[0] observed_data, observed_mask, observed_tp \ = test_batch[:, :, :dim], test_batch[:, :, dim:2dim], test_batch[:, :, -1] with torch.no_grad(): out = model( torch.cat((observed_data, observed_mask), 2), observed_tp) if reconst: qz0_mean, qz0_logvar = out[:, :, :args.latent_dim], out[:, :, args.latent_dim:] epsilon = torch.randn( num_sample, qz0_mean.shape[0], qz0_mean.shape[1], qz0_mean.shape[2]).to(device) z0 = epsilon torch.exp(.5 * qz0_logvar) + qz0_mean z0 = z0.view(-1, qz0_mean.shape[1], qz0_mean.shape[2]) if args.classify_pertp: pred_x = dec(z0, observed_tp[None, :, :].repeat( num_sample, 1, 1).view(-1, observed_tp.shape[1])) out = classifier(pred_x) else: out = classifier(z0) if args.classify_pertp: N = label.size(-1) out = out.view(-1, N) label = label.view(-1, N) _, label = label.max(-1) test_loss += nn.CrossEntropyLoss()(out, label.long()).item() * batch_len * 50. else: label = label.unsqueeze(0).repeat_interleave( num_sample, 0).view(-1) test_loss += nn.CrossEntropyLoss()(out, label).item() * batch_len * num_sample pred.append(out.cpu().numpy()) true.append(label.cpu().numpy()) pred = np.concatenate(pred, 0) true = np.concatenate(true, 0) acc = np.mean(pred.argmax(1) == true) if dataset == 'P12' or dataset == 'P19' or dataset == 'eICU': auc = metrics.roc_auc_score(true, pred[:, 1]) if not args.classify_pertp else 0. aupr = average_precision_score(true, pred[:, 1]) if not args.classify_pertp else 0. return test_loss / pred.shape[0], acc, auc, aupr, None, None, None elif dataset == 'PAM': auc = metrics.roc_auc_score(one_hot(true), pred) if not args.classify_pertp else 0. aupr = average_precision_score(one_hot(true), pred) if not args.classify_pertp else 0. precision = precision_score(true, pred.argmax(1), average='macro', ) if not args.classify_pertp else 0. recall = recall_score(true, pred.argmax(1), average='macro', ) if not args.classify_pertp else 0. F1 = 2 * (precision * recall) / (precision + recall) if not args.classify_pertp else 0. return test_loss/pred.shape[0], acc, auc, aupr, precision, recall, F1 def random_sample(idx_0, idx_1, batch_size): """ Returns a balanced sample by randomly sampling without replacement. :param idx_0: indices of negative samples :param idx_1: indices of positive samples :param batch_size: batch size :return: indices of balanced batch of negative and positive samples """ idx0_batch = np.random.choice(idx_0, size=int(batch_size / 2), replace=False) idx1_batch = np.random.choice(idx_1, size=int(batch_size / 2), replace=False) idx = np.concatenate([idx0_batch, idx1_batch], axis=0) return idx def preprocess_P19(PT_dict, arr_outcomes, labels_ts): total = [] for i, patient in enumerate(PT_dict): length = patient['length'] record_id = patient['id'] tt = torch.squeeze(torch.tensor(patient['time'][:length]), 1) vals = torch.tensor(patient['arr'][:length, :], dtype=torch.float32) m = np.zeros(shape=patient['arr'][:length, :].shape) m[patient['arr'][:length, :].nonzero()] = 1 mask = torch.tensor(m, dtype=torch.float32) outcome = torch.tensor(arr_outcomes[i][0], dtype=torch.float32) total.append((record_id, tt, vals, mask, outcome)) ''' # calculate and save P19 statistics - age, gender, density scores (can be used for all algorithms) idx_under_65 = [] idx_over_65 = [] idx_male = [] idx_female = [] for i in range(len(PT_dict)): if total[i][0] == PT_dict[i]['id']: age, gender, _, _, _, _ = PT_dict[i]['extended_static'] if age > 0: if age < 65: idx_under_65.append(i) else: idx_over_65.append(i) if gender == 0: idx_female.append(i) if gender == 1: idx_male.append(i) np.save('P19_idx_under_65.npy', np.array(idx_under_65), allow_pickle=True) np.save('P19_idx_over_65.npy', np.array(idx_over_65), allow_pickle=True) np.save('P19_idx_male.npy', np.array(idx_male), allow_pickle=True) np.save('P19_idx_female.npy', np.array(idx_female), allow_pickle=True) # save density scores X_features = np.array([d['arr'] for d in PT_dict]) counts = np.count_nonzero(X_features, axis=(0, 1)) ascending_indices = np.argsort(counts) density_scores = counts / (X_features.shape[0] * 60) res = [[ind, density_scores[ind], labels_ts[:-1][ind]] for ind in ascending_indices] np.save('P19_density_scores.npy', res, allow_pickle=True) ''' return total def preprocess_eICU(PT_dict, arr_outcomes, labels_ts): total = [] for i, patient in enumerate(PT_dict): record_id = str(i) tt = torch.squeeze(torch.tensor(patient['time']), 1) vals = torch.tensor(patient['arr'], dtype=torch.float32) m = np.zeros(shape=patient['arr'].shape) m[patient['arr'].nonzero()] = 1 mask = torch.tensor(m, dtype=torch.float32) outcome = torch.tensor(arr_outcomes[i], dtype=torch.float32) total.append((record_id, tt, vals, mask, outcome)) ''' # calculate and save P19 statistics - gender, density scores (can be used for all algorithms) idx_male = [] idx_female = [] for i in range(len(PT_dict)): if total[i][0] == str(i): vec = PT_dict[i]['extended_static'] if vec[-3] > 0: idx_female.append(i) if vec[-4] > 0: idx_male.append(i) print('\nOnly 1.329/36.443 samples have gender data available.\n') np.save('eICU_idx_male.npy', np.array(idx_male), allow_pickle=True) np.save('eICU_idx_female.npy', np.array(idx_female), allow_pickle=True) # save density scores X_features = np.array([d['arr'] for d in PT_dict]) counts = np.count_nonzero(X_features, axis=(0, 1)) ascending_indices = np.argsort(counts) density_scores = counts / (X_features.shape[0] * 300) res = [[ind, density_scores[ind], labels_ts[ind]] for ind in ascending_indices] np.save('eICU_density_scores.npy', res, allow_pickle=True) ''' return total def preprocess_PAM(PT_dict, arr_outcomes): length = 600 total = [] for i, patient in enumerate(PT_dict): record_id = str(i) tt = torch.tensor(list(range(length))) vals = torch.tensor(patient, dtype=torch.float32) m = np.zeros(shape=patient.shape) m[patient.nonzero()] = 1 mask = torch.tensor(m, dtype=torch.float32) outcome = torch.tensor(arr_outcomes[i][0], dtype=torch.float32) total.append((record_id, tt, vals, mask, outcome)) return total def random_sample_8(ytrain, B, replace=False): """ Returns a balanced sample of tensors by randomly sampling without replacement. """ idx0_batch = np.random.choice(np.where(ytrain == 0)[0], size=int(B / 8), replace=replace) idx1_batch = np.random.choice(np.where(ytrain == 1)[0], size=int(B / 8), replace=replace) idx2_batch = np.random.choice(np.where(ytrain == 2)[0], size=int(B / 8), replace=replace) idx3_batch = np.random.choice(np.where(ytrain == 3)[0], size=int(B / 8), replace=replace) idx4_batch = np.random.choice(np.where(ytrain == 4)[0], size=int(B / 8), replace=replace) idx5_batch = np.random.choice(np.where(ytrain == 5)[0], size=int(B / 8), replace=replace) idx6_batch = np.random.choice(np.where(ytrain == 6)[0], size=int(B / 8), replace=replace) idx7_batch = np.random.choice(np.where(ytrain == 7)[0], size=int(B / 8), replace=replace) idx = np.concatenate([idx0_batch, idx1_batch, idx2_batch, idx3_batch, idx4_batch, idx5_batch, idx6_batch, idx7_batch], axis=0) return idx def get_data(args, dataset, device, q, upsampling_batch, split_type, feature_removal_level, missing_ratio, flag=1, reverse=False, predictive_label='mortality'): if dataset == 'P12': train_dataset_obj_1 = PhysioNet('data/physionet', train=True, quantization=q, download=True, n_samples=12000, device=device, set_letter='a') train_dataset_obj_2 = PhysioNet('data/physionet', train=True, quantization=q, download=True, n_samples=12000, device=device, set_letter='b') train_dataset_obj_3 = PhysioNet('data/physionet', train=True, quantization=q, download=True, n_samples=12000, device=device, set_letter='c') dataset_1 = train_dataset_obj_1[:len(train_dataset_obj_1)] dataset_2 = train_dataset_obj_2[:len(train_dataset_obj_2)] dataset_3 = train_dataset_obj_3[:len(train_dataset_obj_3)] total_dataset = dataset_1 + dataset_2 + dataset_3 if predictive_label == 'LoS': los_outcomes = np.load('../saved/LoS_y1_out.npy', allow_pickle=True) for i, tpl in enumerate(total_dataset): a, b, c, d, _ = tpl los_label = los_outcomes[i][0] los_label = torch.tensor(los_label, dtype=torch.float32) total_dataset[i] = (a, b, c, d, los_label) ''' # calculate and save statistics idx_under_65 = [] idx_over_65 = [] idx_male = [] idx_female = [] P_list = np.load('P_list.npy', allow_pickle=True) for i in range(len(P_list)): if total_dataset[i][0] == P_list[i]['id']: age, gender, _, _, _ = P_list[i]['static'] if age > 0: if age < 65: idx_under_65.append(i) else: idx_over_65.append(i) if gender == 0: idx_female.append(i) if gender == 1: idx_male.append(i) np.save('mtand_idx_under_65.npy', np.array(idx_under_65), allow_pickle=True) np.save('mtand_idx_over_65.npy', np.array(idx_over_65), allow_pickle=True) np.save('mtand_idx_male.npy', np.array(idx_male), allow_pickle=True) np.save('mtand_idx_female.npy', np.array(idx_female), allow_pickle=True) ''' elif dataset == 'P19': PT_dict = np.load('../../../P19data/processed_data/PT_dict_list_6.npy', allow_pickle=True) labels_ts = np.load('../../../P19data/processed_data/labels_ts.npy', allow_pickle=True) labels_demogr = np.load('../../../P19data/processed_data/labels_demogr.npy', allow_pickle=True) arr_outcomes = np.load('../../../P19data/processed_data/arr_outcomes_6.npy', allow_pickle=True) total_dataset = preprocess_P19(PT_dict, arr_outcomes, labels_ts) elif dataset == 'eICU': PT_dict = np.load('../../../eICUdata/processed_data/PTdict_list.npy', allow_pickle=True) labels_ts = np.load('../../../eICUdata/processed_data/eICU_ts_vars.npy', allow_pickle=True) labels_demogr = np.load('../../../eICUdata/processed_data/eICU_static_vars.npy', allow_pickle=True) arr_outcomes = np.load('../../../eICUdata/processed_data/arr_outcomes.npy', allow_pickle=True) total_dataset = preprocess_eICU(PT_dict, arr_outcomes, labels_ts) elif dataset == 'PAM': PT_dict = np.load('../../../PAMdata/processed_data/PTdict_list.npy', allow_pickle=True) arr_outcomes = np.load('../../../PAMdata/processed_data/arr_outcomes.npy', allow_pickle=True) total_dataset = preprocess_PAM(PT_dict, arr_outcomes) print('len(total_dataset):', len(total_dataset)) if split_type == 'random': # Shuffle and split train_data, test_data = model_selection.train_test_split(total_dataset, train_size=0.9, # 80% train, 10% validation, 10% test shuffle=True) elif split_type == 'age' or split_type == 'gender': if dataset == 'P12': prefix = 'mtand' elif dataset == 'P19': prefix = 'P19' elif dataset == 'eICU': # possible only with split_type == 'gender' prefix = 'eICU' if split_type == 'age': if dataset == 'eICU': print('\nCombination of eICU dataset and age split is not possible.\n') if reverse == False: idx_train = np.load('%s_idx_under_65.npy' % prefix, allow_pickle=True) idx_vt = np.load('%s_idx_over_65.npy' % prefix, allow_pickle=True) else: idx_train = np.load('%s_idx_over_65.npy' % prefix, allow_pickle=True) idx_vt = np.load('%s_idx_under_65.npy' % prefix, allow_pickle=True) elif split_type == 'gender': if reverse == False: idx_train = np.load('%s_idx_male.npy' % prefix, allow_pickle=True) idx_vt = np.load('%s_idx_female.npy' % prefix, allow_pickle=True) else: idx_train = np.load('%s_idx_female.npy' % prefix, allow_pickle=True) idx_vt = np.load('%s_idx_male.npy' % prefix, allow_pickle=True) np.random.shuffle(idx_train) np.random.shuffle(idx_vt) train_data = [total_dataset[i] for i in idx_train] test_data = [total_dataset[i] for i in idx_vt] record_id, tt, vals, mask, labels = train_data[0] input_dim = vals.size(-1) data_min, data_max = get_data_min_max(total_dataset, device) batch_size = 128 if flag: if args.classif: if split_type == 'random': train_data, val_data = model_selection.train_test_split(train_data, train_size=0.8889, shuffle=True) # 80% train, 10% validation, 10% test elif split_type == 'age' or split_type == 'gender': val_data, test_data = model_selection.train_test_split(test_data, train_size=0.5, shuffle=False) if dataset == 'P12': num_all_features = 36 elif dataset == 'P19': num_all_features = 34 elif dataset == 'eICU': num_all_features = 14 elif dataset == 'PAM': num_all_features = 17 num_missing_features = round(missing_ratio * num_all_features) if feature_removal_level == 'sample': for i, tpl in enumerate(val_data): idx = np.random.choice(num_all_features, num_missing_features, replace=False) _, _, values, _, _ = tpl tpl = list(tpl) values[:, idx] = torch.zeros(values.shape[0], num_missing_features) tpl[2] = values val_data[i] = tuple(tpl) for i, tpl in enumerate(test_data): idx = np.random.choice(num_all_features, num_missing_features, replace=False) _, _, values, _, _ = tpl tpl = list(tpl) values[:, idx] = torch.zeros(values.shape[0], num_missing_features) tpl[2] = values test_data[i] = tuple(tpl) elif feature_removal_level == 'set': if dataset == 'P12': dict_params = train_dataset_obj_1.params_dict density_scores_names = np.load('../saved/IG_density_scores_P12.npy', allow_pickle=True)[:, 1] idx = [dict_params[name] for name in density_scores_names[:num_missing_features]] elif dataset == 'P19': labels_ts = np.load('../../../P19data/processed_data/labels_ts.npy', allow_pickle=True) dict_params = {label: i for i, label in enumerate(labels_ts[:-1])} density_scores_names = np.load('../saved/IG_density_scores_P19.npy', allow_pickle=True)[:, 1] idx = [dict_params[name] for name in density_scores_names[:num_missing_features]] elif dataset == 'eICU': labels_ts = np.load('../../../eICUdata/processed_data/eICU_ts_vars.npy', allow_pickle=True) dict_params = {label: i for i, label in enumerate(labels_ts)} density_scores_names = np.load('../saved/IG_density_scores_eICU.npy', allow_pickle=True)[:, 1] idx = [dict_params[name] for name in density_scores_names[:num_missing_features]] elif dataset == 'PAM': density_scores_indices = np.load('../saved/IG_density_scores_PAM.npy', allow_pickle=True)[:, 0] idx = list(map(int, density_scores_indices[:num_missing_features])) for i, tpl in enumerate(val_data): _, _, values, _, _ = tpl tpl = list(tpl) values[:, idx] = torch.zeros(values.shape[0], num_missing_features) tpl[2] = values val_data[i] = tuple(tpl) for i, tpl in enumerate(test_data): _, _, values, _, _ = tpl tpl = list(tpl) values[:, idx] = torch.zeros(values.shape[0], num_missing_features) tpl[2] = values test_data[i] = tuple(tpl) if upsampling_batch: train_data_upsamled = [] true_labels = np.array(list(map(lambda x: float(x[7]), np.array(train_data)[:, 4]))) if dataset == 'P12' or dataset == 'P19' or dataset == 'eICU': # 2 classes idx_0 = np.where(true_labels == 0)[0] idx_1 = np.where(true_labels == 1)[0] for _ in range(len(true_labels) // batch_size): indices = random_sample(idx_0, idx_1, batch_size) for i in indices: train_data_upsamled.append(train_data[i]) elif dataset == 'PAM': # 8 classes for b in range(len(true_labels) // batch_size): indices = random_sample_8(true_labels, batch_size) for i in indices: train_data_upsamled.append(train_data[i]) train_data = train_data_upsamled test_data_combined = variable_time_collate_fn(test_data, device, classify=args.classif, data_min=data_min, data_max=data_max) train_data_combined = variable_time_collate_fn(train_data, device, classify=args.classif, data_min=data_min, data_max=data_max) val_data_combined = variable_time_collate_fn( val_data, device, classify=args.classif, data_min=data_min, data_max=data_max) print(train_data_combined[1].sum( ), val_data_combined[1].sum(), test_data_combined[1].sum()) print(train_data_combined[0].size(), train_data_combined[1].size(), val_data_combined[0].size(), val_data_combined[1].size(), test_data_combined[0].size(), test_data_combined[1].size()) train_data_combined = TensorDataset( train_data_combined[0], train_data_combined[1].long().squeeze()) val_data_combined = TensorDataset( val_data_combined[0], val_data_combined[1].long().squeeze()) test_data_combined = TensorDataset( test_data_combined[0], test_data_combined[1].long().squeeze()) else: train_data_combined = variable_time_collate_fn( train_data, device, classify=args.classif, data_min=data_min, data_max=data_max) train_dataloader = DataLoader( train_data_combined, batch_size=batch_size, shuffle=False) test_dataloader = DataLoader( test_data_combined, batch_size=batch_size, shuffle=False) else: train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=False, collate_fn=lambda batch: variable_time_collate_fn2(batch, args, device, data_type="train", data_min=data_min, data_max=data_max)) test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, collate_fn=lambda batch: variable_time_collate_fn2(batch, args, device, data_type="test", data_min=data_min, data_max=data_max)) data_objects = {"dataset_obj": {}, "train_dataloader": train_dataloader, "test_dataloader": test_dataloader, "input_dim": input_dim, "n_train_batches": len(train_dataloader), "n_test_batches": len(test_dataloader), "attr": {}, # optional "classif_per_tp": False, # optional "n_labels": 1} # optional if args.classif: val_dataloader = DataLoader( val_data_combined, batch_size=batch_size, shuffle=False) data_objects["val_dataloader"] = val_dataloader return data_objects def variable_time_collate_fn(batch, device=torch.device("cpu"), classify=False, activity=False, data_min=None, data_max=None): """ Expects a batch of time series data in the form of (record_id, tt, vals, mask, labels) where - record_id is a patient id - tt is a 1-dimensional tensor containing T time values of observations. - vals is a (T, D) tensor containing observed values for D variables. - mask is a (T, D) tensor containing 1 where values were observed and 0 otherwise. - labels is a list of labels for the current patient, if labels are available. Otherwise None. Returns: combined_tt: The union of all time observations. combined_vals: (M, T, D) tensor containing the observed values. combined_mask: (M, T, D) tensor containing 1 where values were observed and 0 otherwise. """ D = batch[0][2].shape[1] # number of labels N = batch[0][-1].shape[1] if activity else 1 len_tt = [ex[1].size(0) for ex in batch] maxlen = np.max(len_tt) enc_combined_tt = torch.zeros([len(batch), maxlen]).to(device) enc_combined_vals = torch.zeros([len(batch), maxlen, D]).to(device) enc_combined_mask = torch.zeros([len(batch), maxlen, D]).to(device) if classify: if activity: combined_labels = torch.zeros([len(batch), maxlen, N]).to(device) else: combined_labels = torch.zeros([len(batch), N]).to(device) for b, (record_id, tt, vals, mask, labels) in enumerate(batch): currlen = tt.size(0) enc_combined_tt[b, :currlen] = tt.to(device) enc_combined_vals[b, :currlen] = vals.to(device) enc_combined_mask[b, :currlen] = mask.to(device) if classify: if activity: combined_labels[b, :currlen] = labels.to(device) else: if labels is not None: combined_labels[b] = labels.to(device) if not activity: enc_combined_vals, _, _ = normalize_masked_data(enc_combined_vals, enc_combined_mask, att_min=data_min, att_max=data_max) if torch.max(enc_combined_tt) != 0.: enc_combined_tt = enc_combined_tt / torch.max(enc_combined_tt) combined_data = torch.cat( (enc_combined_vals, enc_combined_mask, enc_combined_tt.unsqueeze(-1)), 2) if classify: return combined_data, combined_labels else: return combined_data def get_activity_data(args, device): n_samples = min(10000, args.n) dataset_obj = PersonActivity('data/PersonActivity', download=True, n_samples=n_samples, device=device) print(dataset_obj) train_data, test_data = model_selection.train_test_split(dataset_obj, train_size=0.8, random_state=42, shuffle=True) record_id, tt, vals, mask, labels = train_data[0] input_dim = vals.size(-1) batch_size = min(min(len(dataset_obj), args.batch_size), args.n) test_data_combined = variable_time_collate_fn(test_data, device, classify=args.classif, activity=True) train_data, val_data = model_selection.train_test_split(train_data, train_size=0.8, random_state=11, shuffle=True) train_data_combined = variable_time_collate_fn( train_data, device, classify=args.classif, activity=True) val_data_combined = variable_time_collate_fn( val_data, device, classify=args.classif, activity=True) print(train_data_combined[1].sum( ), val_data_combined[1].sum(), test_data_combined[1].sum()) print(train_data_combined[0].size(), train_data_combined[1].size(), val_data_combined[0].size(), val_data_combined[1].size(), test_data_combined[0].size(), test_data_combined[1].size()) train_data_combined = TensorDataset( train_data_combined[0], train_data_combined[1].long()) val_data_combined = TensorDataset( val_data_combined[0], val_data_combined[1].long()) test_data_combined = TensorDataset( test_data_combined[0], test_data_combined[1].long()) train_dataloader = DataLoader( train_data_combined, batch_size=batch_size, shuffle=False) test_dataloader = DataLoader( test_data_combined, batch_size=batch_size, shuffle=False) val_dataloader = DataLoader( val_data_combined, batch_size=batch_size, shuffle=False) data_objects = {"train_dataloader": train_dataloader, "test_dataloader": test_dataloader, "val_dataloader": val_dataloader, "input_dim": input_dim, "n_train_batches": len(train_dataloader), "n_test_batches": len(test_dataloader), "classif_per_tp": False, # optional "n_labels": 1} # optional return data_objects def irregularly_sampled_data_gen(n=10, length=20, seed=0): np.random.seed(seed) obs_values, ground_truth, obs_times = [], [], [] for i in range(n): t1 = np.sort(np.random.uniform(low=0.0, high=1.0, size=length)) t2 = np.sort(np.random.uniform(low=0.0, high=1.0, size=length)) t3 = np.sort(np.random.uniform(low=0.0, high=1.0, size=length)) a = 10 * np.random.randn() b = 10 * np.random.rand() f1 = .8 * np.sin(20(t1+a) + np.sin(20(t1+a))) + \ 0.01 * np.random.randn() f2 = -.5 * np.sin(20(t2+a + 20) + np.sin(20(t2+a + 20)) ) + 0.01 * np.random.randn() f3 = np.sin(12(t3+b)) + 0.01 np.random.randn() obs_times.append(np.stack((t1, t2, t3), axis=0)) obs_values.append(np.stack((f1, f2, f3), axis=0)) t = np.linspace(0, 1, 100) fg1 = .8 * np.sin(20(t+a) + np.sin(20(t+a))) fg2 = -.5 * np.sin(20(t+a + 20) + np.sin(20(t+a + 20))) fg3 = np.sin(12(t+b)) ground_truth.append(np.stack((fg1, fg2, fg3), axis=0)) return obs_values, ground_truth, obs_times def sine_wave_data_gen(args, seed=0): np.random.seed(seed) obs_values, ground_truth, obs_times = [], [], [] for _ in range(args.n): t = np.sort(np.random.choice(np.linspace( 0, 1., 101), size=args.length, replace=True)) b = 10 np.random.rand() f = np.sin(12(t+b)) + 0.1 np.random.randn() obs_times.append(t) obs_values.append(f) tc = np.linspace(0, 1, 100) fg = np.sin(12(tc + b)) ground_truth.append(fg) obs_values = np.array(obs_values) obs_times = np.array(obs_times) ground_truth = np.array(ground_truth) print(obs_values.shape, obs_times.shape, ground_truth.shape) mask = np.ones_like(obs_values) combined_data = np.concatenate((np.expand_dims(obs_values, axis=2), np.expand_dims( mask, axis=2), np.expand_dims(obs_times, axis=2)), axis=2) print(combined_data.shape) print(combined_data[0]) train_data, test_data = model_selection.train_test_split(combined_data, train_size=0.8, random_state=42, shuffle=True) print(train_data.shape, test_data.shape) train_dataloader = DataLoader(torch.from_numpy( train_data).float(), batch_size=args.batch_size, shuffle=False) test_dataloader = DataLoader(torch.from_numpy( test_data).float(), batch_size=args.batch_size, shuffle=False) data_objects = {"dataset_obj": combined_data, "train_dataloader": train_dataloader, "test_dataloader": test_dataloader, "input_dim": 1, "ground_truth": np.array(ground_truth)} return data_objects def kernel_smoother_data_gen(args, alpha=100., seed=0, ref_points=10): np.random.seed(seed) obs_values, ground_truth, obs_times = [], [], [] for _ in range(args.n): key_values = np.random.randn(ref_points) key_points = np.linspace(0, 1, ref_points) query_points = np.sort(np.random.choice( np.linspace(0, 1., 101), size=args.length, replace=True)) weights = np.exp(-alpha(np.expand_dims(query_points, 1) -	np.expand_dims(key_points, 0)	numpy.expand_dims
from pixell import enmap, curvedsky, fft as enfft, sharp, wcsutils from enlib import array_ops, bench from soapack import interfaces as sints from optweight import alm_c_utils import numpy as np from scipy.interpolate import interp1d, RectBivariateSpline from scipy import ndimage import numba import healpy as hp from astropy.io import fits import yaml import pkgutil from concurrent import futures import multiprocessing import os import hashlib # Utility functions to support tiling classes and functions. Just keeping code organized so I don't get whelmed. # copied from soapack.interfaces def config_from_yaml_file(filename): """Returns a dictionary from a yaml file given by absolute filename. """ with open(filename) as f: config = yaml.safe_load(f) return config def config_from_yaml_resource(resource): """Returns a dictionary from a yaml file given by the resource name (relative to tacos package). """ f = pkgutil.get_data('mnms', resource).decode() config = yaml.safe_load(f) return config def get_default_data_model(): """Returns a soapack.interfaces.DataModel instance depending on the name of the data model specified in the users's soapack config 'mnms' block, under key 'default_data_model'. Returns ------- soapack.interfaces.DataModel instance An object used for loading raw data from disk. """ config = sints.dconfig['mnms'] dm_name = config['default_data_model'] # capitalize all chars except v3 dm_name = ''.join( [dm_name[i].upper() if dm_name[i] != 'v' else 'v' for i in range(len(dm_name))] ) return getattr(sints, dm_name)() def get_default_mask_version(): """Returns the mask version (string) depending on what is specified in the user's soapack config. If the 'mnms' block has a key 'default_mask_version', return the value of that key. If not, return the 'default_mask_version' from the user's default data model block. Returns ------- str A default mask version string to help find an analysis mask. With no arguments, a NoiseModel constructor will use this mask version to seek the analysis mask in directory mask_path/mask_version. """ config = sints.dconfig['mnms'] try: mask_version = config['default_mask_version'] except KeyError: dm_name = config['default_data_model'].lower() config = sints.dconfig[dm_name] mask_version = config['default_mask_version'] return mask_version def get_nsplits_by_qid(qid, data_model): """Get the number of splits in the raw data corresponding to this array 'qid'""" return int(data_model.adf[data_model.adf['#qid']==qid]['nsplits']) def slice_geometry_by_pixbox(ishape, iwcs, pixbox): pb = np.asarray(pixbox) return enmap.slice_geometry( ishape[-2:], iwcs, (slice(pb[:, -2]), slice(pb[:, -1])), nowrap=True ) def slice_geometry_by_geometry(ishape, iwcs, oshape, owcs): pb = enmap.pixbox_of(iwcs, oshape, owcs) return enmap.slice_geometry( ishape[-2:], iwcs, (slice(pb[:, -2]), slice(pb[:, -1])), nowrap=True ) # users must be careful with indices shape. it gets promoted to 2D array by # prepending a dimension if necessary. afterward, indices[i] (ie, indexing along # axis=0 of 2D indices array) corresponds to axis[i] def get_take_indexing_obj(arr, indices, axis=None): if axis is not None: shape = arr.shape slicelist = [slice(dim) for dim in shape] axis = np.atleast_1d(axis) indices = atleast_nd(indices, 2) for i in range(len(axis)): slicelist[axis[i]] = tuple(indices[i]) return tuple(slicelist) else: return np.array(indices) # this is like enmap.partial_flatten except you give the axes you want to flatten def flatten_axis(imap, axis=None, pos=0): if axis is None: return imap.reshape(-1) else: imap = np.moveaxis(imap, axis, range(len(axis))) # put the axes to be flattened in front imap = imap.reshape((-1,) + imap.shape[len(axis):]) # flatten the front dimensions imap = np.moveaxis(imap, 0, pos) # put front dimension in pos return imap # this is like enmap.partial_expand except you give the axes you want to restore # shape is the shape of the restored axes or the full original array with restored axes def unflatten_axis(imap, shape, axis=None, pos=0): if axis is None: return imap else: imap = np.moveaxis(imap, pos, 0) # put pos in front dimension shape = np.atleast_1d(shape) axis = np.atleast_1d(axis) if len(shape) == len(axis): shape = tuple(shape) elif len(shape) == len(axis) + len(imap.shape[1:]): shape = tuple(shape[axis]) else: raise ValueError('Shape arg must either be the same length as axis or the restored array shape') imap = imap.reshape(shape + imap.shape[1:]) # expand the front dimension into restored shape imap = np.moveaxis(imap, range(len(axis)), axis) # put the front restored axes into correct positions return imap def atleast_nd(arr, n, axis=None): arr = np.asanyarray(arr) if (axis is None) or (arr.ndim >= n): oaxis=tuple(range(n - arr.ndim)) # prepend the dims or do nothing in n < arr.ndim else: axis = np.atleast_1d(axis) assert (n - arr.ndim) >= len(axis), 'More axes than dimensions to add' oaxis = tuple(range(n - arr.ndim - len(axis))) + tuple(axis) # prepend the extra dims return np.expand_dims(arr, oaxis) def triu_indices(N): """Gives the upper triangular indices of an NxN matrix in diagonal-major order (compatible with healpy spectra ordering with new=True) Parameters ---------- N : int Side-length of matrix Returns ------- tuple of ndarray A row array and column array of ints, such that if arr is NxN, then arr[triu_indices(N)] gives the 1D elements of the upper triangle of arr in diagonal-major order. """ num_idxs = N(N+1)//2 rows = [] cols = [] rowoffset = 0 coloffset = 0 for i in range(num_idxs): rownum = i - rowoffset colnum = rownum + coloffset rows.append(rownum) cols.append(colnum) if colnum >= N-1: # N-1 is last column rowoffset = i+1 # restart rows next iteration coloffset += 1 # cols start one higher return np.array(rows), np.array(cols) def triu_indices_1d(N): idxs = np.arange(N2).reshape(N, N) return idxs[triu_indices(N)] def is_triangular(i): return np.roots((1, 1, -2i))[1].is_integer() def triangular(N): return N(N+1)//2 def triangular_idx(i): assert is_triangular(i), 'Arg must be a triangular number' return np.roots((1, 1, -2i)).astype(int)[1] def triu_pos(i, N): return np.stack(triu_indices(N))[:, i] def triu_to_symm(arr, copy=False, axis1=0, axis2=1): assert arr.shape[axis1] == arr.shape[axis2], 'Axes of matrix must at axis1, axis2 dimension must have same size' if copy: arr = arr.copy() ncomp = arr.shape[axis1] for i in range(ncomp): for j in range(i+1, ncomp): # i+1 to only do upper triangle, not diagonal getslice = get_take_indexing_obj(arr, [[i], [j]], axis=(axis1, axis2)) setslice = get_take_indexing_obj(arr, [[j], [i]], axis=(axis1, axis2)) arr[setslice] = arr[getslice] return arr # we can collapse a symmetric 2D matrix whose first axis is passed as the arg # into a 1D list of the upper triangular components. # this will clobber if axis1 or axis2 is in map axes, see __array_wrap__ def to_flat_triu(arr, axis1=0, axis2=None, flat_triu_axis=None): # force axes to be positive axis1 = axis1%arr.ndim if axis2 is None: axis2 = axis1+1 axis2 = axis2%arr.ndim assert axis1 < axis2 assert arr.shape[axis1] == arr.shape[axis2], 'Axes of matrix at axis1, axis2 dimension must have same size' ncomp = arr.shape[axis1] # put flattened dims at flat_triu_axis position if flat_triu_axis is None: flat_triu_axis = axis1 arr = flatten_axis(arr, axis=(axis1, axis2), pos=flat_triu_axis) # get triangular indices for axis to be expanded slice_obj = get_take_indexing_obj(arr, triu_indices_1d(ncomp), axis=flat_triu_axis) return arr[slice_obj] # this clobbers ndarray subclasses because of call to append! # axis2 is position in expanded array, while flat_triu_axis is position in passed array def from_flat_triu(arr, axis1=0, axis2=None, flat_triu_axis=None): if flat_triu_axis is None: flat_triu_axis = axis1 # force axes to be positive axis1 = axis1%arr.ndim if axis2 is None: axis2 = axis1+1 axis2 = axis2%(arr.ndim+1) assert axis1 < axis2 assert is_triangular(arr.shape[flat_triu_axis]), 'flat_triu_axis length must be a triangular number' # place in new array with ncomp2 instead of n_triu at flat_triu_axis ncomp = triangular_idx(arr.shape[flat_triu_axis]) ishape = list(arr.shape) ishape[flat_triu_axis] = ncomp2 - triangular(ncomp) # this will be concatenated to arr arr = np.append(arr, np.zeros(ishape), axis=flat_triu_axis).astype(arr.dtype) # get triangular indices for axis to be expanded getslice = get_take_indexing_obj(arr, np.arange(triangular(ncomp)), axis=flat_triu_axis) setslice = get_take_indexing_obj(arr, triu_indices_1d(ncomp), axis=flat_triu_axis) arr[setslice] = arr[getslice] # reshape into correct shape arr = unflatten_axis(arr, (ncomp, ncomp), axis=(axis1, axis2), pos=flat_triu_axis) return triu_to_symm(arr, axis1=axis1, axis2=axis2) # get a logical mask that can be used to index an array, eg to build a mask # out of conditions. # if not keep_prepend_dims, perform op over all dims up to map dims; else # perform over specified axes def get_logical_mask(cond, op=np.logical_or, keep_prepend_dims=False, axis=0): if not keep_prepend_dims: axis = tuple(range(cond.ndim - 2)) return op.reduce(cond, axis=axis) def get_coadd_map(imap, ivar): """Return sum(imap[i]ivar[i]) / sum(ivar[i]), where each sum is over splits. Parameters ---------- imap : (..., nsplit, 3, ny, nx) ndmap Data maps for N splits. ivar : (..., nsplit, 1, ny, nx) ndmap Inverse variance maps for N splits. Returns ------- coadd : (..., 1, 3, ny, nx) ndmap Inverse variance weighted coadd over splits, with singleton dimension in split axis. """ if hasattr(imap, 'wcs'): is_enmap = True wcs = imap.wcs else: is_enmap = False imap = atleast_nd(imap, 4) # make 4d by prepending ivar = atleast_nd(ivar, 4) # due to floating point precision, the coadd is not exactly the same # as a split where that split is the only non-zero ivar in that pixel num = np.sum(imap ivar, axis=-4, keepdims=True) den = np.sum(ivar, axis=-4, keepdims=True) mask = den != 0 coadd = np.zeros_like(num) np.divide(num, den, where=mask, out=coadd) # find pixels where exactly one split has a nonzero ivar single_nonzero_ivar_mask = np.sum(ivar!=0, axis=-4, keepdims=True) == 1 # set the coadd in those pixels to be equal to the imap value of that split (ie, avoid floating # point errors in naive coadd calculation) single_nonzero_fill = np.sum(imap * (ivar!=0), axis=-4, where=single_nonzero_ivar_mask, keepdims=True) coadd = np.where(single_nonzero_ivar_mask, single_nonzero_fill, coadd) if is_enmap: coadd = enmap.ndmap(coadd, wcs) return coadd def get_ivar_eff(ivar, use_inf=False): """ Return ivar_eff = 1 / (1 / ivar - 1 / sum_ivar), where sum_ivar is the sum over splits. Parameters ---------- ivar : (..., nsplit, 1, ny, nx) ndmap Inverse variance maps for N splits. use_inf : bool, optional If set, use np.inf for values that approach infinity, instead of large numerical values. Returns ------- ivar_eff : (..., nsplit, 1, ny, nx) enmap Ivar_eff for each split. """ # Make 4d by prepending splits along -4 axis. ivar = atleast_nd(ivar, 4) # We want to calculate 1 / (1/ivar - 1/sum(ivar). It easier to do # ivar * sum(ivar) / (sum(ivar) - ivar) to avoid (some) divisions by zero. sum_ivar = np.sum(ivar, axis=-4, keepdims=True) num = sum_ivar * ivar # Numerator. den = sum_ivar - ivar # Denominator. # In pixels were ivar == sum_ivar we get inf. mask = den != 0 out = np.divide(num, den, where=mask, out=num) if use_inf: out[~mask] = np.inf else: # Fill with largest value allowed by dtype to mimic np.nan_to_num. out[~mask] = np.finfo(out.dtype).max return out def get_corr_fact(ivar): ''' Get correction factor sqrt(ivar_eff / ivar) that converts a draw from split difference d_i to a draw from split noise n_i. Parameters ---------- ivar : (..., nsplit, 1, ny, nx) enmap Inverse variance maps for N splits. Returns ------- corr_fact : (..., nsplit, 1, ny, nx) enmap Correction factor for each split. ''' corr_fact = get_ivar_eff(ivar, use_inf=True) corr_fact[~np.isfinite(corr_fact)] = 0 np.divide(corr_fact, ivar, out=corr_fact, where=ivar!=0) corr_fact[ivar==0] = 0 corr_fact *= 0.5 return corr_fact def get_noise_map(imap, ivar): return imap - get_coadd_map(imap, ivar) def get_whitened_noise_map(imap, ivar): return get_noise_map(imap, ivar)np.sqrt(get_ivar_eff(ivar)) def rolling_average(x, N): cumsum = np.cumsum(np.insert(x, 0, 0)) return (cumsum[N:] - cumsum[:-N]) / float(N) def linear_crossfade(cNy,cNx,npix_y,npix_x=None, dtype=np.float32): if npix_x is None: npix_x = npix_y fys =	np.ones(cNy, dtype=dtype)	numpy.ones
import numpy as np a = np.array([1, 3, 6, 9, -3, -6]) b = np.array([1, 2, 3, 40, -9, 5]) # 1. print(np.add(a, b)) # 2. print(np.subtract(a, b)) # 3. print(np.multiply(a, b)) # 4. print(np.divide(a, b)) # 5. print(np.mod(a, b)) # 6. print(np.remainder(a, b)) # 7. print(np.divmod(a, b)) # 8. print(np.absolute(a)) print(np.absolute(b)) # 9. a =	np.array([1, 3, 6, 9, 3, 6])	numpy.array
import numpy as np import pytest from snc.environments.job_generators.discrete_review_job_generator \ import DeterministicDiscreteReviewJobGenerator as drjg from snc.environments.job_generators.discrete_review_job_generator \ import PoissonDiscreteReviewJobGenerator as prjg from snc.environments.controlled_random_walk import ControlledRandomWalk import snc.utils.snc_tools as snc import snc.environments.state_initialiser as stinit import snc.environments.examples as examples import \ snc.environments.examples_distribution_with_rebalancing as examples_distribution_with_rebalancing from snc.environments.job_generators.scaled_bernoulli_services_poisson_arrivals_generator import \ ScaledBernoulliServicesPoissonArrivalsGenerator from snc.environments.state_initialiser import DeterministicCRWStateInitialiser def test_is_binary(): c = np.ones((1, 1)) assert (snc.is_binary(c)) c = np.zeros((1, 1)) assert (snc.is_binary(c)) c = np.ones((5, 4)) assert (snc.is_binary(c)) c = np.zeros((5, 4)) assert (snc.is_binary(c)) c = np.random.randint(0, 1, (3, 6)) assert (snc.is_binary(c)) c = [] assert (not snc.is_binary(c)) c = np.random.random_sample((3, 5)) c[0] = 1 assert (not snc.is_binary(c)) def test_index_phys_resources_with_negative_values(): index_phys_resources = (-1, 0) # Other needed parameters cost_per_buffer = np.zeros((2, 1)) demand_rate = np.zeros((2, 1)) initial_state = np.zeros((2, 1)) capacity = np.zeros((2, 1)) constituency_mat = np.eye(2) buffer_processing_mat = np.eye(2) job_generator = drjg(demand_rate, buffer_processing_mat, sim_time_interval=1) s0 = stinit.DeterministicCRWStateInitialiser(initial_state) with pytest.raises(AssertionError): ControlledRandomWalk(cost_per_buffer, capacity, constituency_mat, job_generator, s0, index_phys_resources=index_phys_resources) def test_index_phys_resources_with_index_higher_than_num_resources(): index_phys_resources = (0, 2) # Other needed parameters cost_per_buffer = np.zeros((2, 1)) demand_rate = np.zeros((2, 1)) initial_state = np.zeros((2, 1)) capacity = np.zeros((2, 1)) constituency_mat = np.eye(2) buffer_processing_mat = np.eye(2) job_generator = drjg(demand_rate, buffer_processing_mat, sim_time_interval=1) s0 = stinit.DeterministicCRWStateInitialiser(initial_state) with pytest.raises(AssertionError): ControlledRandomWalk(cost_per_buffer, capacity, constituency_mat, job_generator, s0, index_phys_resources=index_phys_resources) def test_index_phys_resources_with_repeated_indexes(): index_phys_resources = (0, 0) # Other needed parameters cost_per_buffer = np.zeros((2, 1)) demand_rate = np.zeros((2, 1)) initial_state = np.zeros((2, 1)) capacity = np.zeros((2, 1)) constituency_mat = np.eye(2) buffer_processing_mat = np.eye(2) job_generator = drjg(demand_rate, buffer_processing_mat, sim_time_interval=1) s0 = stinit.DeterministicCRWStateInitialiser(initial_state) with pytest.raises(AssertionError): ControlledRandomWalk(cost_per_buffer, capacity, constituency_mat, job_generator, s0, index_phys_resources=index_phys_resources) def test_valid_index_phys_resources_1(): index_phys_resources = (0,) # Other needed parameters cost_per_buffer = np.zeros((2, 1)) demand_rate = np.zeros((2, 1)) initial_state = np.zeros((2, 1)) capacity = np.zeros((2, 1)) constituency_mat = np.eye(2) buffer_processing_mat = np.eye(2) job_generator = drjg(demand_rate, buffer_processing_mat, sim_time_interval=1) s0 = stinit.DeterministicCRWStateInitialiser(initial_state) env = ControlledRandomWalk(cost_per_buffer, capacity, constituency_mat, job_generator, s0, index_phys_resources=index_phys_resources) assert env.index_phys_resources == index_phys_resources def test_valid_index_phys_resources_1_2(): index_phys_resources = (0, 1) # Other needed parameters cost_per_buffer = np.zeros((2, 1)) demand_rate = np.zeros((2, 1)) initial_state = np.zeros((2, 1)) capacity = np.zeros((2, 1)) constituency_mat = np.eye(2) buffer_processing_mat = np.eye(2) job_generator = drjg(demand_rate, buffer_processing_mat, sim_time_interval=1) s0 = stinit.DeterministicCRWStateInitialiser(initial_state) env = ControlledRandomWalk(cost_per_buffer, capacity, constituency_mat, job_generator, s0, index_phys_resources=index_phys_resources) assert env.index_phys_resources == index_phys_resources def test_state_initialiser_uniform(): num_buffers = 5 capacity = 10 s0 = stinit.UniformRandomCRWStateInitialiser(num_buffers, capacity) init_state = np.zeros((num_buffers, 1)) num_samples = 100000 for i in range(num_samples): init_state += s0.get_initial_state() init_state /= num_samples np.all(np.isclose(init_state, ((capacity - 1) / 2) * np.ones((num_buffers, 1)))) def test_state_initialiser_deterministic(): initial_state = np.array([2, 3, 4, 5])[:, None] s0 = stinit.DeterministicCRWStateInitialiser(initial_state) assert np.all(s0.get_initial_state() == initial_state) def test_unfeasible_action(): """Check that the step method doesn't allow actions that violate the one action per resource constraint: C u <= 1.""" np.random.seed(42) env = examples.double_reentrant_line_only_shared_resources_model(alpha=0) env.reset_with_random_state(42) action =	np.array([[1], [0], [0], [1]])	numpy.array
import cv2 import matplotlib.pyplot as plt import numpy as np kernel =	np.ones((5, 5), np.uint8)	numpy.ones
import numpy as np from typing import * from numpy.typing import ArrayLike from scipy.spatial import Delaunay from tallem.utility import ask_package_install, package_exists def flywing(): ''' Fly wings example (Klingenberg, 2015 \| https://en.wikipedia.org/wiki/Procrustes_analysis) ''' arr1 = np.array([[588.0, 443.0], [178.0, 443.0], [56.0, 436.0], [50.0, 376.0], [129.0, 360.0], [15.0, 342.0], [92.0, 293.0], [79.0, 269.0], [276.0, 295.0], [281.0, 331.0], [785.0, 260.0], [754.0, 174.0], [405.0, 233.0], [386.0, 167.0], [466.0, 59.0]]) arr2 = np.array([[477.0, 557.0], [130.129, 374.307], [52.0, 334.0], [67.662, 306.953], [111.916, 323.0], [55.119, 275.854], [107.935, 277.723], [101.899, 259.73], [175.0, 329.0], [171.0, 345.0], [589.0, 527.0], [591.0, 468.0], [299.0, 363.0], [306.0, 317.0], [406.0, 288.0]]) return([arr1, arr2]) def gaussian_blob(n_pixels: int, r: float): ''' Generates a closure which, given a 2D location mu=(x,y), generates a white blob with [normalized] radius 0 < r <= 1 in a (n_pixels x n_pixels) image. If mu is in [0,1] x [0,1], the center of the white blob should be visible If mu has as both of its coordinates outside of [0,1]x[0,1], the blob may be partially visible If mu has both of its coordinates outside of [-r, 1+r]x[-r, 1+r], then image should be essentially black The returned closure completely autograd's numpy wrapper to do the image generation. Thus, the resulting function can be differentiated (w.r.t mu) using the reverse-mode differentiation process that autograd provides. This function also returns the global normalizing constant needed normalize the pixel intensities in [0,1], for plotting or other purposes. Return: (blob, c) where - blob := differentiable closure which, given a vector (x,y), generates the blob image a flat vector. - c := maximum value of the intensity of any given pixel for any choice of mu. ''' import autograd.numpy as auto_np sd = r/3.090232 sigma = sd*2 sigma_inv = 1.0/sigma denom = np.sqrt(((2auto_np.pi)*2) (sigma*2)) def blob(mu): # mu can be anywhere; center of image is [0.5, 0.5] loc = auto_np.linspace(0, 1, n_pixels, False) + 1/(2n_pixels) x,y = auto_np.meshgrid(loc, loc) grid = auto_np.exp(-0.5(sigma_inv ((x-mu[0])2 + (y-mu[1])2)))/denom return(auto_np.ravel(grid).flatten()) return(blob, auto_np.exp(0)/denom) def plot_image(P, figsize=(8,8), max_val = "default"): if max_val == "default": max_val = np.max(P) import matplotlib.pyplot as plt fig = plt.figure(figsize=figsize) plt.imshow(P, cmap='gray', vmin=0, vmax=max_val) fig.gca().axes.get_xaxis().set_visible(False) fig.gca().axes.get_yaxis().set_visible(False) def plot_images(P, shape, max_val = "default", figsize=(8,8), layout = None): ''' P := numpy array where each row is a grayscale image shape := the shape to reshape each row of P prior to plotting ''' import matplotlib.pyplot as plt if max_val == "default": max_val = np.max(P) if P.ndim == 1: fig = plt.figure(figsize=figsize) plt.imshow(P.reshape(shape), cmap='gray', vmin=0, vmax=max_val) fig.gca().axes.get_xaxis().set_visible(False) fig.gca().axes.get_yaxis().set_visible(False) return(fig, ax) else: assert layout is not None, "missing layout" fig, axs = plt.subplots(layout, figsize=figsize) axs = axs.flatten() for i, (img, ax) in enumerate(zip(P, axs)): #fig.add_subplot(layout[0], layout[1], i+1) plt.axis("off") ax.imshow(P[i,:].reshape(shape), cmap='gray', vmin=0, vmax=max_val, aspect='auto') ax.axes.xaxis.set_visible(False) ax.axes.yaxis.set_visible(False) plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.1, hspace=0.1) return(fig, axs) def scatter2D(P, layout = None, figsize=(8,8), kwargs): import matplotlib.pyplot as plt if isinstance(P, np.ndarray): if "fig" in kwargs.keys() and "ax" in kwargs.keys(): fig, ax = kwargs["fig"], kwargs["ax"] kwargs.pop('fig', None) kwargs.pop('ax', None) else: fig = plt.figure(figsize=figsize) ax = fig.add_subplot() ax.scatter(P.T, *kwargs) return(fig, ax) elif isinstance(P, Iterable): assert layout is not None, "missing layout" assert len(P) == np.prod(layout) if "fig" in kwargs.keys() and "ax" in kwargs.keys(): fig, ax = kwargs["fig"], kwargs["ax"] kwargs.pop('fig', None) else: fig = plt.figure(figsize=figsize) for i, p in enumerate(P): ax = fig.add_subplot(layout[0], layout[1], i+1) ax.scatter(p.T, kwargs) return(fig, ax) def scatter3D(P, angles = None, layout = None, figsize=(8,8), kwargs): import matplotlib.pyplot as plt if isinstance(P, np.ndarray): import numbers if angles is not None: if isinstance(angles, numbers.Integral): angles = np.linspace(0, 360, angles, endpoint=False) assert len(angles) == np.prod(layout) if "fig" in kwargs.keys() and "ax" in kwargs.keys(): fig, ax = kwargs["fig"], kwargs["ax"] kwargs.pop('fig', None) kwargs.pop('ax', None) else: fig, ax = plt.subplots(layout, figsize=figsize) for i, theta in enumerate(angles): ax = fig.add_subplot(layout[0], layout[1], i+1, projection='3d') ax.scatter3D(P.T, *kwargs) ax.view_init(30, theta) else: if "fig" in kwargs.keys() and "ax" in kwargs.keys(): fig, ax = kwargs["fig"], kwargs["ax"] kwargs.pop('fig', None) kwargs.pop('ax', None) else: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(projection='3d') ax.scatter3D(P.T, *kwargs) elif isinstance(P, Iterable): import numbers assert layout is not None, "missing layout" if angles is None: angles = np.repeat(60, len(P)) elif isinstance(angles, numbers.Integral): angles = np.linspace(0, 2np.pi, len(P), endpoint=False) assert len(angles) == np.prod(layout) if "fig" in kwargs.keys() and "ax" in kwargs.keys(): fig, ax = kwargs["fig"], kwargs["ax"] kwargs.pop('fig', None) kwargs.pop('ax', None) else: fig, ax = plt.subplots(layout, figsize=figsize) for i, p in enumerate(P): ax = fig.add_subplot(layout[0], layout[1], i+1, projection='3d') ax.scatter3D(p.T, *kwargs) ax.view_init(30, angles[i]) plt.setp(plt.gcf().get_axes(), xticks=[], yticks=[]); return(fig, ax) def rotating_disk(n_pixels: int, r: float, sigma: float = 1.0): from scipy.ndimage import gaussian_filter import numpy as np I = np.zeros(shape=(n_pixels, n_pixels)) p = np.linspace(0, 1, n_pixels, False) + 1/(2n_pixels) # center locations of pixels, in normalized space z = np.array([r, 0.0]).reshape((2,1)) d = np.array([0.5, 0.5]).reshape((2,1)) x,y = np.meshgrid(p, p) def disk_image(theta: float): R = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) c = (R @ z) + d # center of disk in [0,1]^2 D = np.flipud(np.sqrt((x - c[0])2 + (y - c[1])2)) D[D <= r] = -1.0 D[D > r] = 0.0 D[D == -1.0] = 1.0 return(np.ravel(gaussian_filter(D, sigma=1.0)).flatten()) return(disk_image, 1.0) # D = np.zeros(np.prod(x.shape)) # for i, (xi,yi) in enumerate(zip(x.flatten(),y.flatten())): # p = np.array([xi,yi]) # D[i] = np.dot(p-b, u)# np.linalg.norm(z-v) def white_bars(n_pixels: int, r: float, sigma: float = 1.0): ''' Returns a parameterization that yields a white vertical bar at various orientations in an image. Fixed parameters: n_pixels := number of pixels to make square image r := constant between [0,1] indicating how wide to make the bar sigma := kernel parameter for gaussian blur Returns: bar := closure w/ parameters y_offset in [0, 1] and theta in [0, pi] c := normalizing constant for plotting ''' from scipy.ndimage import gaussian_filter import numpy as np w = r p = np.linspace(0, 1, n_pixels, False) + 1/(2n_pixels) # center locations of pixels, in normalized space x,y = np.meshgrid(p,p) c = np.array([0.5, 0.5]) # center of image def bar(theta: float, d: float): assert np.all(np.bitwise_and(d >= -1.0, d <= 1.0)), "d must be in the range [-1, 1]" assert np.all(np.bitwise_and(theta >= 0.0, theta <= np.pi)), "theta must be in the range [0, pi]" u = np.array([ 1.0, np.tan(theta) ]) u = u / np.linalg.norm(u) c = np.array([0.5, 0.5]) # center of image d = d (np.sqrt(2) / 2) # scale where to place center of bar if theta > np.pi/2: d = -d b = c + du # center of bar D = np.zeros(np.prod(x.shape)) for i, (xi,yi) in enumerate(zip(x.flatten(),y.flatten())): p = np.array([xi,yi]) D[i] = np.dot(p-b, u)# np.linalg.norm(z-v) I = abs(D.reshape((n_pixels, n_pixels))).T I[I > w] = 1 I = 1.0 - I return(gaussian_filter(I, sigma=sigma)) c = np.max(bar(0.0, 0.0)) return(bar, c) # u = np.array([ 1.0, np.tan(theta) ]) # u = u / np.linalg.norm(u) # d = np.array([-di if ti <= np.pi/2 else di for di,ti in zip(d, theta)])(np.sqrt(2) / 2) # U = np.c_[np.repeat(1.0, len(theta)), theta] # U = U / np.linalg.norm(U, axis = 1, keepdims = True) # B = c + d.reshape((len(d), 1)) * U # center of bars # D = [abs((x - b[0])u[0] + (y - b[1])u[1]).T for (u, b) in zip(U, B)] # # b = c + du # center of bar # # D = (x - b[0])u[0] + (y - b[1])u[1] # # I = abs(D.reshape((n_pixels, n_pixels))).T # images = np.zeros((B.shape[0], n_pixels2)) # for i, img in enumerate(D): # img[img > w] = 1 # img = 1.0 - img # images[i,:] = np.ravel(gaussian_filter(img, sigma=sigma).flatten()) # return(images) # from scipy.ndimage import gaussian_filter # import numpy as np # w = rnp.sqrt(2) # p = np.linspace(0, 1, n_pixels, False) + 1/(2n_pixels) # center locations of pixels, in normalized space # x,y = np.meshgrid(p,p) # def bar(y_offset: float, theta: float): # assert y_offset >= 0.0 and y_offset <= 1.0 # assert theta >= 0.0 and theta <= np.pi # # z = np.array([0.5, y_offset]) # intercept # # dist_to_line = np.cos(theta)(z[1] - y) - np.sin(theta)(z[0]-x) # # dist_to_line = ((y - y_offset)/np.tan(theta))np.sin(theta) # # Z = np.array([np.array([xi,yi]) for xi,yi in zip(x.flatten(),y.flatten())]) # # fig,ax = scatter2D(Z, c="blue") # # fig,ax = scatter2D(np.array(P), c="red", fig=fig, ax=ax) # # fig,ax = scatter2D(np.array([0.5, 0.5]), c="green", fig=fig, ax=ax) # # fig,ax = scatter2D(np.c_[x.flatten(), x.flatten()m + b], c="purple", fig=fig, ax=ax) # m, b = np.tan(theta), y_offset # #pt = np.array([1.0, m + b]) # z1 = np.array([0.50, b]) # z2 = np.array([1.0, 1.0m + b]) # pt = z2 - z1 # d = [] # P = [] # for xi,yi in zip(x.flatten(),y.flatten()): # u = pt / np.linalg.norm(pt) # v = np.array([xi,yi]) # z = unp.dot(v-np.array([0.5, b]), u)+np.array([0.5, b]) # d.append(np.linalg.norm(z-v)) # P.append(z) # dist_to_line = np.array(d) # # fig, ax = scatter2D(np.array(P)) # I = abs(dist_to_line.reshape((n_pixels, n_pixels))) # I = np.flipud(I) # make origin lower-left, not top-left # # I = (np.sqrt(2)/2)(I/np.max(I)) # I[I > w] = np.sqrt(2) # I = np.sqrt(2) - I ## invert intensity # # I[I < (np.sqrt(2) - w)] = 0.0 # # B = I.copy() # # I[I <= w] = -1.0 # # I[I > w] = 0.0 # # I[I == -1.0] = np.max(B[B <= w]) - B[B <= w] # 1.0 # return(gaussian_filter(I, sigma=sigma)) # c = np.max(bar(0.0, 0.0)) # return(bar, c) # def _gaussian_pixel(d, n_pixels): # from scipy.stats import norm # sigma = d/3.0 # Sigma = auto_np.diag([sigma, sigma]) # sigma_inv = auto_np.linalg.inv(Sigma)[0,0] # denom = np.sqrt(((2np.pi)2) auto_np.linalg.det(Sigma)) # normal_constant = norm.pdf(0, loc=0, scale=sigma) # def blob(mu): # generates blob at location mu # # mu = mu.reshape((2, 1)) # # np.exp(-0.5 * ((x - mu).T @ SigmaI @ (x - mu))).flatten() # #x, y = auto_np.meshgrid(auto_np.arange(n_pixels), auto_np.arange(n_pixels)) # loc = auto_np.linspace(0, 1, n_pixels, False) + (1/(2n_pixels)) # x,y = auto_np.meshgrid(loc, loc) # grid = auto_np.exp(-0.5(sigma_inv * ((x-mu[0])2 + (y-mu[1])2)))/denom # #grid = auto_np.exp(-0.5((x - mu[0])2 + (y - mu[1])2))/denom # #return(auto_np.ravel(grid).flatten()) # return(grid/normal_constant) # return(blob) # plot_image(gaussian_pixel2(1/32, 11)([-0.5, 0.5])) def white_dot(n_pixels: int, r: float, n: Optional[int], method: Optional[str] = "grid", mu: Optional[ArrayLike] = None): ''' Generates a grayscale image data set where white blobs are placed on a (n_pixels x n_pixels) grid using a multivariate normal density whose standard deviation sigma (in both directions) is sigma=d/3. If 'n' is specified, then 'n' samples are generated from a larger space s([-d, 1+d]^2) where s() denotes the scaling of the interval [-d,1+d] by 'n_pixels'. Parameters: n_pixels := number of pixels wide/tall to make the resulting images r := relative radius of dot (in (0, 1]) n := (optional) number of samples desired method := (optional) how to generate samples in the parameter space. Can be either "grid" or "random". mu := (optional) locations of dot centers to generate the dots at Returns: samples := generated image samples params := (x,y,i) parameters associated with each sample, f := closure for generating more samples. See gaussian_blob() for more details. c := normalizing constant. See gaussian_blob() for more details. ''' assert r > 0 and r <= 1.0, "r must be in the range 0 < r <= 1.0" assert isinstance(n, int) or isinstance(n, tuple), "n must be integer of tuple of integers" ask_package_install("autograd") import numpy as np import autograd.numpy as auto_np ## First generate the closure to make the images blob, c = gaussian_blob(n_pixels, r) if not(mu is None): samples = np.vstack([blob(auto_np.array([x,y])) for x,y in mu]) params = mu elif method == "random": ## Generate uniformly random locations (in domain) assert n is not None, "'n' must be supplied if 'mu' is not." n1, n2 = (n, n) if isinstance(n, int) else (n[0], n[1]) samples, params = [], [] X, Y = np.random.uniform(size=n1,low=-r,high=1+r), np.random.uniform(size=n1,low=-r,high=1+r) for x,y in zip(X, Y): samples.append(blob(auto_np.array([x,y]))) params.append([x, y, 1.0]) NP = blob(auto_np.array([0.5, 0.5])) for t in np.random.uniform(size=n2, low=0.0, high=1.0): samples.append(tNP) params.append([0.5, 0.5, 1-t]) ## Vertically stack samples, params = np.vstack(samples), np.vstack(params) elif method == "grid": assert n is not None, "'n' must be supplied if 'mu' is not." if isinstance(n, int): n1, n2 = (n, n) else: n1, n2 = (n[0], n[1]) ng = int(np.floor(np.sqrt(n1))) samples, params = [], [] for x in np.linspace(0.0-r,1.0+r,ng): for y in np.linspace(0.0-r,1.0+r,ng): samples.append(blob(auto_np.array([x, y]))) params.append([x, y, 1.0]) ## Generate the pole NP = blob(auto_np.array([0.5, 0.5])) for t in np.linspace(0, 1, n2): samples.append(tNP) params.append([0.5, 0.5, 1-t]) ## Vertically stack samples, params = np.vstack(samples), np.vstack(params) ## Return the data return(samples, params, blob, c) def mobius_band(n_polar=66, n_wide=9, scale_band=0.25): ''' Generates stratified samples on a Mobius band embedded in R^3 To get uniform samples, N = (n_polarn_wide) uniformly spaced coordinates are generated initially from the intrinsic space of M. These points are converted to their extrinsic (3D) coordinates and are then triangulated using a Delaunay triangulation. Finally, using the Delaunay triangles as stratum, a stratified sampling scheme is employed by sampling randomly from each triangle using its barycentric coordinates. This stratification ensures the samples are both sufficiently random but sufficiently "uniformly spaced" around the band. Returns: - M := (n x 3) matrix of embedding coordinates - B := (n x 2) matrix of intrinsic coordinates In the intrinsic coordinates, B[:,0] is the width parameter and B[:,1] is the angular coordinate ''' ## Generate random (deterministic) polar coordinates around Mobius Band np.random.seed(0) s = np.linspace(-scale_band, scale_band, 2n_wide) # width/radius t = np.linspace(0, 2np.pi, n_polar) # circular coordinate s, t = np.meshgrid(s, t) ## Triangulate to allow stratification M = np.c_[np.ravel(s), np.ravel(t)] V = M[Delaunay(M).simplices] ## Sample within each strata via random barycentric coordinates normalize = lambda x: x / np.sum(x) Y = np.array([np.sum(v normalize(np.random.uniform(size=(3,1))), axis = 0) for v in V]) ## Convert to 3d S, T = Y[:,0], Y[:,1] phi = 0.5 * T r = 1 + S * np.cos(phi) MB = np.c_[r * np.cos(T), r * np.sin(T), S * np.sin(phi)] ## Return both 3D embedding + original parameters return(MB, Y) def embed(a: ArrayLike, D: int, method="givens"): ''' Embeds a point cloud into D dimensions using random orthogonal rotations ''' def givens(i,j,theta,n=2): G = np.eye(n) G[i,i] = np.cos(theta) G[j,j] = np.cos(theta) G[i,j] = -np.sin(theta) G[j,i] =	np.sin(theta)	numpy.sin
#!/usr/bin/env python # -- coding: utf-8 -- # # @Author: <NAME> (<EMAIL>) # @Filename: tiledb.py # @License: BSD 3-clause (http://www.opensource.org/licenses/BSD-3-Clause) import itertools import astropy import cycler import matplotlib.pyplot as plt import numpy from matplotlib import animation import lvmsurveysim.target from lvmsurveysim.schedule.tiledb import TileDB from lvmsurveysim.schedule.scheduler import Scheduler from lvmsurveysim.schedule.plan import ObservingPlan from lvmsurveysim import IFU, config from lvmsurveysim.schedule.plan import ObservingPlan from lvmsurveysim.utils.plot import __MOLLWEIDE_ORIGIN__, get_axes, transform_patch_mollweide, convert_to_mollweide numpy.seterr(invalid='raise') __all__ = ['Simulator'] class Simulator(object): """Simulates an observing schedule for a list of targets (tile database) following and observing plan. Parameters ---------- tiledb : ~lvmsurveysim.schedule.tiledb.TileDB The `~lvmsurveysim.schedule.tiledb.TileDB` instance with the table of tiles to schedule. observing_plan : l`.ObservingPlan` or None The `.ObservingPlan` to use (one for each observatory). If `None`, it will be created from the ``observing_plan`` section in the configuration file. Contains dates and sun/moon data for the duration of the survey as well as Observatory data. ifu : ~lvmsurveysim.ifu.IFU The `~lvmsurveysim.ifu.IFU` to use. Defaults to the one from the configuration file. Used only for plotting the survey footprint. Attributes ---------- tiledb : ~lvmsurveysim.schedule.tiledb.TileDB Instance of the tile database to observe. schedule : ~astropy.table.Table An astropy table with the results of the scheduling. Includes information about the JD of each observation, the target observed, the index of the pointing in the target tiling, coordinates, etc. """ def __init__(self, tiledb, observing_plan=None, ifu=None): assert isinstance(tiledb, lvmsurveysim.schedule.tiledb.TileDB), \ 'tiledb must be a lvmsurveysim.schedule.tiledb.TileDB instances.' # get rid of the special tiles, we do not need them for the simulator tdb = tiledb.tile_table tiledb.tile_table = tdb[numpy.where(tdb['TileID'] >= tiledb.tileid_start)[0]] if observing_plan is None: observing_plan = self._create_observing_plan() assert isinstance(observing_plan, ObservingPlan), 'observing_plan is not an instance of ObservingPlan.' self.zenith_avoidance = config['scheduler']['zenith_avoidance'] self.time_step = config['scheduler']['timestep'] self.observing_plan = observing_plan self.tiledb = tiledb self.targets = tiledb.targets self.ifu = ifu or IFU.from_config() self.schedule = None def __repr__(self): return (f'<Simulator (observing_plan={self.observing_plan.observatory.name}, ' f'tiles={len(self.tiledb.tile_table)})>') def save(self, path, overwrite=False): """ Saves the results of the scheduling simulation to a FITS file. """ assert isinstance(self.schedule, astropy.table.Table), \ 'cannot save empty schedule. Execute Scheduler.run() first.' targfile = str(self.targets.filename) if self.targets.filename != None else 'NA' self.schedule.meta['targfile'] = targfile self.schedule.write(path+'.fits', format='fits', overwrite=overwrite) @classmethod def load(cls, path, tiledb=None, observing_plan=None): """Creates a new instance from a schedule file. Parameters ---------- path : str or ~pathlib.Path The path to the schedule file and the basename, no extension. The routine expects to find path.fits and path.npy tiledb : ~lvmsurveysim.schedule.tiledb.TileDB or path-like Instance of the tile database to observe. observing_plan : `.ObservingPlan` or None The `.ObservingPlan` to use (one for each observatory). """ schedule = astropy.table.Table.read(path+'.fits') if not isinstance(tiledb, lvmsurveysim.schedule.tiledb.TileDB): assert tiledb != None and tiledb != 'NA', \ 'invalid or unavailable tiledb file path.' tiledb = TileDB.load(tiledb) observing_plan = observing_plan or [] sim = cls(tiledb, observing_plan=observing_plan) sim.schedule = schedule return sim def run(self, progress_bar=True): """Schedules the pointings for the whole survey defined in the observing plan. Parameters ---------- progress_bar : bool If `True`, shows a progress bar. """ # Make self.schedule a list so that we can add rows. Later we'll make # this an Astropy Table. self.schedule = [] plan = self.observing_plan # Instance of the Scheduler scheduler = Scheduler(plan) # observed exposure time for each pointing observed = numpy.zeros(len(self.tiledb.tile_table), dtype=numpy.float) # range of dates for the survey min_date = numpy.min(plan['JD']) max_date = numpy.max(plan['JD']) dates = range(min_date, max_date + 1) if progress_bar: generator = astropy.utils.console.ProgressBar(dates) else: generator = dates for jd in generator: if progress_bar is False: print(f'scheduling JD={jd}.') # Skips JDs not found in the plan or those that don't have good weather. if jd not in plan['JD'] or plan[plan['JD'] == jd]['is_clear'][0] == 0: continue observed += self.schedule_one_night(jd, scheduler, observed) # Convert schedule to Astropy Table. self.schedule = astropy.table.Table( rows=self.schedule, names=['JD', 'observatory', 'target', 'group', 'tileid', 'index', 'ra', 'dec', 'pa', 'airmass', 'lunation', 'shadow_height', "moon_dist", 'lst', 'exptime', 'totaltime'], dtype=[float, 'S10', 'S20', 'S20', int, int, float, float, float, float, float, float, float, float, float, float]) def schedule_one_night(self, jd, scheduler, observed): """Schedules a single night at a single observatory. This method is not intended to be called directly. Instead, use `.run`. Parameters ---------- jd : int The Julian Date to schedule. Must be included in ``plan``. scheduler : .Scheduler The Scheduler instance that will determine the observing sequence. observed : ~numpy.array An array of the length of the tiledb that records the observing time accumulated on each tile thus far Returns ------- exposure_times : `~numpy.ndarray` Array with the exposure times in seconds added to each tile during this night. """ # initialize the scheduler for the night scheduler.prepare_for_night(jd, self.observing_plan, self.tiledb) # shortcut tdb = self.tiledb.tile_table # begin at twilight current_jd = scheduler.evening_twi # While the current time is before morning twilight ... while current_jd < scheduler.morning_twi: # obtain the next tile to observe observed_idx, current_lst, hz, alt, lunation = scheduler.get_optimal_tile(current_jd, observed) if observed_idx == -1: # nothing available self._record_observation(current_jd, self.observing_plan.observatory, lst=current_lst, exptime=self.time_step, totaltime=self.time_step) current_jd += (self.time_step) / 86400.0 continue # observe it, give it one quantum of exposure exptime = tdb['VisitExptime'].data[observed_idx] observed[observed_idx] += exptime # collect observation data to put in table tileid_observed = tdb['TileID'].data[observed_idx] target_index = tdb['TargetIndex'].data[observed_idx] target_name = self.targets[target_index].name groups = self.targets[target_index].groups target_group = groups[0] if groups else 'None' target_overhead = self.targets[target_index].overhead # Get the index of the first value in index_to_target that matches # the index of the target. target_index_first = numpy.nonzero(tdb['TargetIndex'].data == target_index)[0][0] # Get the index of the pointing within its target. pointing_index = observed_idx - target_index_first # Record angular distance to moon dist_to_moon = scheduler.moon_to_pointings[observed_idx] # Update the table with the schedule. airmass = 1.0 / numpy.cos(numpy.radians(90.0 - alt)) self._record_observation(current_jd, self.observing_plan.observatory, target_name=target_name, target_group=target_group, tileid = tileid_observed, pointing_index=pointing_index, ra=tdb['RA'].data[observed_idx], dec=tdb['DEC'].data[observed_idx], pa=tdb['PA'].data[observed_idx], airmass=airmass, lunation=lunation, shadow_height= hz, #hz[valid_priority_idx[obs_tile_idx]], dist_to_moon=dist_to_moon, lst=current_lst, exptime=exptime, totaltime=exptime * target_overhead) current_jd += exptime * target_overhead / 86400.0 return observed def animate_survey(self, filename='lvm_survey.mp4', step=100, observatory=None, projection='mollweide'): """Create an animation of the survey progress and save as an mp4 file. Parameters ---------- filename : str Name of the mp4 file, defaults to ``'lvm_survey.mp4'``. step : int Number of observations per frame of movie. observatory : str Either ``'LCO'`` or ``'APO'`` or `None` (plots both). projection : str Which projection of the sphere to use. Defaults to Mollweide. """ data = self.schedule[self.schedule['target'] != '-'] if observatory: data = data[data['observatory'] == observatory] ll = int(len(data) / step) x,y = convert_to_mollweide(data['ra'], data['dec']) tt = [target.name for target in self.targets] g = numpy.array([tt.index(i) for i in data['target']], dtype=float) t = data['JD'] fig, ax = get_axes(projection=projection) # scat = ax.scatter(x[:1], y[:1], c=g[:1], s=1, edgecolor=None, edgecolors=None) scat = ax.scatter(x, y, c=g % 19, s=0.05, edgecolor=None, edgecolors=None, cmap='tab20') # fig.show() # return def animate(ii): if ii % 10 == 0: print('%.1f %% done\r' % (ii / ll * 100)) scat.set_offsets(numpy.stack((x[:ii * step], y[:ii * step]), axis=0).T) scat.set_array(g[:ii * step]) ax.set_title(str(t[ii])) return scat, anim = animation.FuncAnimation(fig, animate, frames=range(1, ll), interval=1, blit=True, repeat=False) anim.save(filename, fps=24, extra_args=['-vcodec', 'libx264']) def plot(self, observatory=None, projection='mollweide', tname=None, fast=False, annotate=False, edge=False): """Plots the observed pointings. Parameters ---------- observatory : str Plot only the points for that observatory. Otherwise, plots all the pointings. projection : str The projection to use, either ``'mollweide'`` or ``'rectangular'``. tname : str Select only a particular target name to plot fast : bool Plot IFU sized and shaped patches if `False`. This is the default. Allows accurate zooming and viewing. If `True`, plot scatter-plot dots instead of IFUs, for speed sacrificing accuracy. This is MUCH faster. annotate : bool Write the targets' names next to the target coordinates. Implies ``fast=True``. edge : bool Draw tile edges and make tiles partly transparent to better judge overlap. Makes zoomed-out view look odd, so use default False. Returns ------- figure : `matplotlib.figure.Figure` The figure with the plot. """ if annotate is True: fast = True color_cycler = cycler.cycler(bgcolor=['b', 'r', 'g', 'y', 'm', 'c', 'k']) fig, ax = get_axes(projection=projection) if tname != None: data = self.schedule[self.schedule['target'] == tname] else: data = self.schedule[self.schedule['target'] != '-'] if observatory: data = data[data['observatory'] == observatory] if fast is True: if projection == 'mollweide': x,y = convert_to_mollweide(data['ra'], data['dec']) else: x,y = data['ra'], data['dec'] tt = [target.name for target in self.targets] g = numpy.array([tt.index(i) for i in data['target']], dtype=float) ax.scatter(x, y, c=g % 19, s=0.05, edgecolor=None, edgecolors=None, cmap='tab20') if annotate is True: _, text_indices = numpy.unique(g, return_index=True) for i in range(len(tt)): plt.text(x[text_indices[i]], y[text_indices[i]], tt[i], fontsize=9) else: for ii, sty in zip(range(len(self.targets)), itertools.cycle(color_cycler)): target = self.targets[ii] name = target.name target_data = data[data['target'] == name] if edge: patches = [self.ifu.get_patch(scale=target.telescope.plate_scale, centre=[p['ra'], p['dec']], pa=p['pa'], edgecolor='k', linewidth=1, alpha=0.5, facecolor=sty['bgcolor'])[0] for p in target_data] else: patches = [self.ifu.get_patch(scale=target.telescope.plate_scale, centre=[p['ra'], p['dec']], pa=p['pa'], edgecolor='None', linewidth=0.0, facecolor=sty['bgcolor'])[0] for p in target_data] if projection == 'mollweide': patches = [transform_patch_mollweide(patch) for patch in patches] for patch in patches: ax.add_patch(patch) if observatory != None: ax.set_title(f'Observatory: {observatory}') return fig def _create_observing_plan(self): """Returns an `.ObservingPlan` from the configuration file.""" observatory = config['observing_plan'] obs_data = config['observing_plan'][observatory] start_date = obs_data['start_date'] end_date = obs_data['end_date'] return ObservingPlan(start_date, end_date, observatory=observatory) def _record_observation(self, jd, observatory, target_name='-', target_group='-', tileid=-1, pointing_index=-1, ra=-999., dec=-999., pa=-999., airmass=-999., lunation=-999., shadow_height=-999., dist_to_moon=-999., lst=-999., exptime=0., totaltime=0.): """Adds a row to the schedule.""" self.schedule.append((jd, observatory, target_name, target_group, tileid, pointing_index, ra, dec, pa, airmass, lunation, shadow_height, dist_to_moon, lst, exptime, totaltime)) def get_target_time(self, tname, group=False, observatory=None, lunation=None, return_lst=False): """Returns the JDs or LSTs for a target at an observatory. Parameters ---------- tname : str The name of the target or group. Use ``'-'`` for unused time. group : bool If not true, ``tname`` will be the name of a group not a single target. observatory : str The observatory to filter for. lunation : list Restrict the data to a range in lunation. Defaults to returning all lunations. Set to ``[lmin, lmax]`` to return values of ``lmin < lunation <= lmax``. return_lst : bool If `True`, returns an array with the LSTs of all the unobserved times. Returns ------- table : `~numpy.ndarray` An array containing the times the target is observed at an observatory, as JDs. If ``return_lst=True`` returns an array of the corresponding LSTs. """ column = 'group' if group is True else 'target' t = self.schedule[self.schedule[column] == tname] if observatory: t = t[t['observatory'] == observatory] if lunation != None: t = t[(t['lunation'] > lunation[0]) * (t['lunation'] <= lunation[1])] if return_lst: return t['lst'].data else: return t['JD'].data def print_statistics(self, out_file=None, out_format="ascii", overwrite_out=True, observatory=None, targets=None, return_table=False): """Prints a summary of observations at a given observatory. Parameters ---------- observatory : str The observatory to filter for. targets : `~lvmsurveysim.target.TargetList` The targets to summarize. If `None`, use ``self.targets``. return_table : bool If `True`, return a `~astropy.table.Table` with the results. out_file : str Outfile to write statistics. out_format : str Outfile format consistent with astropy.table dumps """ if targets is None: targets = self.targets names = [t.name for t in targets] time_on_target = {} # time spent exposing target exptime_on_target = {} # total time (exp + overhead) on target tile_area = {} # area of a single tile target_ntiles = {} # number of tiles in a target tiling target_ntiles_observed = {} # number of observed tiles target_nvisits = {} # number of visits for each tile surveytime = 0.0 # total time of survey names.append('-') # deals with unused time for tname, i in zip(names, range(len(names))): if (tname != '-'): target = self.targets[i] tile_area[tname] = target.get_pixarea(ifu=self.ifu) target_ntiles[tname] = len(numpy.where(self.tiledb.tile_table['TargetIndex'] == i)[0]) target_nvisits[tname] = float(target.n_exposures / target.min_exposures) else: tile_area[tname] = -999 target_ntiles[tname] = -999 target_nvisits[tname] = 1 tdata = self.schedule[self.schedule['target'] == tname] if observatory: tdata = tdata[tdata['observatory'] == observatory] exptime_on_target[tname] = numpy.sum(tdata['exptime'].data) target_ntiles_observed[tname] = len(tdata) / target_nvisits[tname] target_total_time = numpy.sum(tdata['totaltime'].data) time_on_target[tname] = target_total_time surveytime += target_total_time # targets that completely overlap with others have no tiles for t in self.targets: if target_ntiles[t.name] == 0: print(t.name + ' has no tiles') target_ntiles[t.name] = 1 rows = [ (t if t != '-' else 'unused', numpy.float(target_ntiles[t]), numpy.around(target_ntiles_observed[t], decimals=2), numpy.around(time_on_target[t] / 3600.0, decimals=2), numpy.around(exptime_on_target[t] / 3600.0, decimals=2), numpy.around(time_on_target[t] / surveytime, decimals=2), numpy.around(target_ntiles_observed[t] * tile_area[t], decimals=2) if t != '-' else -999, numpy.around(float(target_ntiles_observed[t]) / float(target_ntiles[t]), decimals=2) if t != '-' else -999) for t in names] stats = astropy.table.Table(rows=rows, names=['Target', 'tiles', 'tiles_obs', 'tottime/h', 'exptime/h', 'timefrac', 'area', 'areafrac'], dtype=('S8', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4')) print('%s :' % (observatory if observatory != None else 'APO+LCO')) stats.pprint(max_lines=-1, max_width=-1) if out_file != None: stats.write(out_file, format=out_format, overwrite=overwrite_out) if return_table: return stats def plot_survey(self, observatory=None, bin_size=30., targets=None, groups=None, use_groups=False, use_primary_group=True, show_ungrouped=True, cumulative=False, lst=False, lunation=None, show_unused=True, skip_fast=False, show_mpld3=False): """Plot the hours spent on target. Parameters ---------- observatory : str The observatory to plot. If `None`, all observatories. bin_size : int The number of days in each bin of the plot. targets : list A list with the names of the targets to plot. If empty, plots all targets. groups : list A list with the names of the groups to plot. If empty, plots all groups. use_groups : bool If set, the targets are grouped together using the ``Target.groups`` list. use_primary_group : bool If `True`, a target will only be added to its primary group (the first one in the group list). Only used when ``use_groups=True``. show_ungrouped : bool If `True`, targets that don't belong to any group are plotted individually. Only used when ``use_groups=True``. cumulative : bool or str If `True`, plots the cumulative sum of hours spent on each target. If ``'group'``, it plots the cumulative on-target hours normalised by the total hours needed to observe the target group. If ``'survey'``, plots the cumulative hours normalised by the total survey hours. When ``cumulative`` is not `False`, ``bin_size`` is set to 1. lst : bool Whether to bin the used time by LST instead of JD. show_unused : bool Display the unused time. lunation : list Range of lunations to include in statistics. Defaults to all lunations. Set to ``[lmin, lmax]`` to return values of ``lmin < lunation <= lmax``. Can be used to restrict lst usage plots to only bright, grey, or dark time. skip_fast : bool If set, do not plot targets that complete in the first 20% of the survey. Return ------ fig : `~matplotlib.figure.Figure` The Matplotlib figure of the plot. """ assert self.schedule != None, 'you still have not run a simulation.' if not targets: targets = [target.name for target in self.targets] ncols = 2 if len(targets) > 15 else 1 if lst: bin_size = 1. if bin_size == 30. else bin_size assert cumulative is False, 'cumulative cannot be used with lst=True.' if cumulative != False: bin_size = 1 fig, ax = plt.subplots(figsize=(12, 8)) # Leaves a margin on the right to put the legend fig.subplots_adjust(right=0.65 if ncols == 2 else 0.8) ax.set_prop_cycle(color=['r', 'g', 'b', 'c', 'm', 'y', 'g', 'b', 'c', 'm', 'y', 'r', 'b', 'c', 'm', 'y', 'r', 'g', 'c', 'm', 'y', 'r', 'g', 'b', ], linestyle=['-', '--', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':']) min_b = (numpy.min(self.schedule['JD']) - 2451545.0) if not lst else 0.0 max_b = (numpy.max(self.schedule['JD']) - 2451545.0) if not lst else 24.0 b = numpy.arange(min_b, max_b + bin_size, bin_size) # Creates a list of groups to plot. If use_groups=False, # this is just the list of targets. if not use_groups: groups = [target.name for target in self.targets] else: groups = groups or self.targets.get_groups() # Adds the ungrouped targets. if show_ungrouped: for target in self.targets: if len(target.groups) == 0: groups.append(target.name) for group in groups: # Cumulated group heights group_heights = numpy.zeros(len(b) - 1, dtype=numpy.float) group_target_tot_time = 0.0 # If we are not using groups or the "group" # name is that of an ungrouped target. if not use_groups or group in self.targets._names: targets = [group] else: targets = self.targets.get_group_targets(group, primary=use_primary_group) for tname in targets: t = self.targets.get_target(tname) tindex = [target.name for target in self.targets].index(tname) # plot each target tt = self.get_target_time(tname, observatory=observatory, lunation=lunation, return_lst=lst) if len(tt) == 0: continue if not lst: tt -= 2451545.0 heights, bins = numpy.histogram(tt, bins=b) heights = numpy.array(heights, dtype=float) heights = t.exptime t.min_exposures / 3600.0 ntiles = len(numpy.where(self.tiledb.tile_table['TargetIndex'].data == tindex)[0]) target_tot_time = ntiles * t.exptime * t.n_exposures / 3600. if skip_fast: completion = heights.cumsum() / target_tot_time if numpy.quantile(completion, 0.2) >= 1: continue group_heights += heights group_target_tot_time += target_tot_time # Only plot the heights if they are not zero. This prevents # targets that are not observed at an observatory to be displayed. if numpy.sum(group_heights) > 0: if cumulative is False: ax.plot(bins[:-1] + numpy.diff(bins) / 2, group_heights, label=group) else: ax.plot(bins[:-1] +	numpy.diff(bins)	numpy.diff
import numpy as np import json network_snrs = [] ln_bfs = [] for i in range(2000): try: with open(f'injections/{i}_memory/IMR_mem_inj_non_mem_rec_result.json') as f: res = json.load(f) except Exception as e: print(e) with open(f'injections/{i}_memory/pp_result.json') as f: pp_res = json.load(f) snrs_squared = [res['meta_data']['likelihood']['interferometers'][ifo]['optimal_SNR']**2 for ifo in ['H1', 'L1', 'V1']] network_snrs.append(np.sqrt(np.sum(snrs_squared))) ln_bfs.append(pp_res['memory_log_bf']) print(i) print() ln_bfs = np.array(ln_bfs) network_snrs = np.array(network_snrs) low_snr_indices = np.where(network_snrs < 15) high_snr_indices =	np.where(network_snrs > 15)	numpy.where
import cftime import numpy as np import pandas as pd import xarray as xr from xclim.indices import generic class TestSelectResampleOp: def test_month(self, q_series): q = q_series(np.arange(1000)) o = generic.select_resample_op(q, "count", freq="YS", month=3) np.testing.assert_array_equal(o, 31) def test_season_default(self, q_series): # Will use freq='YS', so count J, F and D of each year. q = q_series(np.arange(1000)) o = generic.select_resample_op(q, "min", season="DJF") assert o[0] == 0 assert o[1] == 366 def test_season(self, q_series): q = q_series(np.arange(1000)) o = generic.select_resample_op(q, "count", freq="AS-DEC", season="DJF") assert o[0] == 31 + 29 class TestThresholdCount: def test_simple(self, tas_series): ts = tas_series(np.arange(365)) out = generic.threshold_count(ts, "<", 50, "Y") np.testing.assert_array_equal(out, [50, 0]) class TestDomainCount: def test_simple(self, tas_series): ts = tas_series(np.arange(365)) out = generic.domain_count(ts, low=10, high=20, freq="Y") np.testing.assert_array_equal(out, [10, 0]) class TestDailyDownsampler: def test_std_calendar(self): # standard calendar # generate test DataArray time_std = pd.date_range("2000-01-01", "2000-12-31", freq="D") da_std = xr.DataArray(	np.arange(time_std.size)	numpy.arange
# # Copyright 2020 Logical Clocks AB # # 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 time from copy import deepcopy from abc import abstractmethod import numpy as np from maggy.optimizer.abstractoptimizer import AbstractOptimizer class BaseAsyncBO(AbstractOptimizer): """Base class for asynchronous bayesian optimization Do not initialize this class! only it's subclasses async bo Bayesian Optimization consists of a surrogat model to approximate the black-box function and an acquisition function to select the next hyperparameter configuration. In an asynchronous setting, we need to encourage diversity when maximizing the acquisition function to prevent redundant sampling. optimization direction all optimizations are converted to minimizations problems via the `get_metrics_dict()` and `get_metrics_array()` methods, where the metrics are negated in case of a maximization. In the `final_store` the original metric values are saved. pruner Pruners can be run as a subroutine of an optimizer. It's interface `pruner.pruning_routine()` is called at the beginning of the `get_suggestion()` method and returns the hparam config and budget for the trial. The `trial_id` of the newly created Trial object is reported back to the pruner via `pruner.report_trial()`. budget If a pruner was specified ( and hence a multi-fidelity optimization is conducted ), the budget is added as an additional hyperparameter to the hparam dict so it is passed to the map_fun. Note, that when fitting the surrogate model the "budget" key is obviously omitted. models The surrogate models for different budgets are saved in the `models` dict with the budgets as key. In case of a single fidelity optimization without a pruner or pruner with interim_results, The only model has the key `0`. If we fit multiple models, sample new hparam configs always from the largest model available (i.e. biggest budget) imputing busy trials (poss. shift to simple.py) the imputed metrics are calculated on the fly in `get_metrics_array(include_busy_locations=True, budget=`budget`)` for currently evaluating trials that were sampled from model with `budget` interim results In literature most surrogate models of BO algorithms are fit with the hyperparameter config and the final metric of a trial. Using additionally interim metrics of trials, i.e. use metrics that were trained for varying budgets makes it possible to use early stopping in BO. (Also it makes it possible to have only one model in multi fidelity bo instead of one model per fidelity) For each interim metric a hyperparameter config augumented with the budget the metric was generated with is added to the training data of the surrogate model .. math:: z_t = [x_t, n_t] ; y_t = y_{t,nt} When maximizing the acquisition function to sample the next hyperparameter config to evaluate, always augument with the max budget N .. math:: xt ← argmax acq([x, N]) """ def __init__( self, num_warmup_trials=15, random_fraction=0.33, interim_results=False, interim_results_interval=10, kwargs ): """ Attributes ---------- models (dict): The surrogate models for different budgets are saved in the `models` dict with the budgets as key. In case of a single fidelity optimization without a pruner. The only model has the key `0`. warm_up_configs(list[dict]): list of hparam configs used for warming up sampling_time_start (float): helper variable to calculate time needed for calculating next suggestion, i.e when sampling from model (`sample_type`=="model"). Calulating the time happens in `create_trial()` normalize_categorical (bool): If True, the encoded categorical hparam is also max-min normalized between 0 and 1 in searchspace.transform() :param num_warmup_trials: number of random trials at the beginning of experiment :type num_warmup_trials: int :param random_fraction: fraction of random samples, between [0,1] :type random_fraction: float :param interim_results: If True, use interim metrics from trials for fitting surrogate model. Else use final metrics only :type interim_results: bool :param interim_results_interval: Specifies which interim metrics are used (if interim_results==True) e.g. interval=10: the metric of every 10th epoch is used for fitting surrogate :type interim_results_interval: int """ super().__init__(kwargs) # configure warmup routine self.num_warmup_trials = num_warmup_trials self.warmup_sampling = "random" self.warmup_configs = [] # keeps track of warmup warmup configs allowed_warmup_sampling_methods = ["random"] if self.warmup_sampling not in allowed_warmup_sampling_methods: raise ValueError( "expected warmup_sampling to be in {}, got {}".format( allowed_warmup_sampling_methods, self.warmup_sampling ) ) # surrogate model related aruments self.models = {} # fitted model of the estimator self.random_fraction = random_fraction self.interim_results = interim_results self.interim_results_interval = interim_results_interval # helper variable to calculate time needed for calculating next suggestion self.sampling_time_start = 0.0 # If True, the encoded categorical hparam is also max-min normalized between 0 and 1 in searchspace.transform() self.normalize_categorical = True if self.name() == "TPE": self.normalize_categorical = False def initialize(self): # validate hparam types # at least one hparam needs to be continuous & no DISCRETE hparams cont = False for hparam in self.searchspace.items(): if hparam["type"] == self.searchspace.DISCRETE: raise ValueError( "This version of Bayesian Optimization does not support DISCRETE Hyperparameters yet, please encode {} as INTEGER".format( hparam["name"] ) ) if hparam["type"] in [self.searchspace.DOUBLE, self.searchspace.INTEGER]: cont = True if not cont: raise ValueError( "In this version of Bayesian Optimization at least one hparam has to be continuous (DOUBLE or INTEGER)" ) self.warmup_routine() self.init_model() def get_suggestion(self, trial=None): self._log("### start get_suggestion ###") self.sampling_time_start = time.time() if trial: self._log( "last finished trial: {} with params {}".format( trial.trial_id, trial.params ) ) else: self._log("no previous finished trial") # check if experiment has finished if self._experiment_finished(): return None # pruning routine if self.pruner: next_trial_info = self.pruner.pruning_routine() if next_trial_info == "IDLE": self._log( "Worker is IDLE and has to wait until a new trial can be scheduled" ) return "IDLE" elif next_trial_info is None: # experiment is finished self._log("Experiment has finished") return None elif next_trial_info["trial_id"]: # copy hparams of given promoted trial and start new trial with it parent_trial_id = next_trial_info["trial_id"] parent_trial_hparams = deepcopy( self.get_hparams_dict(trial_ids=parent_trial_id)[parent_trial_id] ) # update trial info dict and create new trial object next_trial = self.create_trial( hparams=parent_trial_hparams, sample_type="promoted", run_budget=next_trial_info["budget"], ) # report new trial id to pruner self.pruner.report_trial( original_trial_id=parent_trial_id, new_trial_id=next_trial.trial_id, ) self._log( "use hparams from promoted trial {}. \n start trial {}: {} \n".format( parent_trial_id, next_trial.trial_id, next_trial.params ) ) return next_trial else: # start sampling procedure with given budget run_budget = next_trial_info["budget"] if self.interim_results: model_budget = 0 else: model_budget = run_budget else: run_budget = 0 model_budget = 0 # check if there are still trials in the warmup buffer if self.warmup_configs: self._log("take sample from warmup buffer") next_trial_params = self.warmup_configs.pop() next_trial = self.create_trial( hparams=next_trial_params, sample_type="random", run_budget=run_budget, ) elif np.random.rand() < self.random_fraction: # random fraction applies, sample randomly hparams = self.searchspace.get_random_parameter_values(1)[0] next_trial = self.create_trial( hparams=hparams, sample_type="random", run_budget=run_budget ) self._log("sampled randomly: {}".format(hparams)) else: # update model if self.pruner and not self.interim_results: # skip model building if we already have a bigger model if max(list(self.models.keys()) + [-np.inf]) <= model_budget: self.update_model(model_budget) else: self.update_model(model_budget) if not self.models: # in case there is no model yet, sample randomly hparams = self.searchspace.get_random_parameter_values(1)[0] next_trial = self.create_trial( hparams=hparams, sample_type="random", run_budget=run_budget ) self._log("sampled randomly: {}".format(hparams)) else: if self.pruner and not self.interim_results: # sample from largest model available model_budget = max(self.models.keys()) # sample from model with model budget self._log( "start sampling routine from model with budget {}".format( model_budget ) ) hparams = self.sampling_routine(model_budget) next_trial = self.create_trial( hparams=hparams, sample_type="model", run_budget=run_budget, model_budget=model_budget, ) self._log( "sampled from model with budget {}: {}".format( model_budget, hparams ) ) # check if Trial with same hparams has already been created i = 0 while self.hparams_exist(trial=next_trial): self._log("sample randomly to encourage exploration") hparams = self.searchspace.get_random_parameter_values(1)[0] next_trial = self.create_trial( hparams=hparams, sample_type="random_forced", run_budget=run_budget ) i += 1 if i > 3: self._log( "not possible to sample new config. Stop Experiment (most/all configs have already been used)" ) return None # report new trial id to pruner if self.pruner: self.pruner.report_trial( original_trial_id=None, new_trial_id=next_trial.trial_id ) self._log( "start trial {}: {}, {} \n".format( next_trial.trial_id, next_trial.params, next_trial.info_dict ) ) return next_trial def finalize_experiment(self, trials): return @abstractmethod def init_model(self): """initializes the surrogate model of the gaussian process the model gets created with the right parameters, but is not fit with any data yet. the `base_model` will be cloned in `update_model` and fit with observation data """ raise NotImplementedError @abstractmethod def update_model(self, budget=0): """update surrogate model with new observations Use observations of finished trials + liars from busy trials to build model. Only build model when there are at least as many observations as hyperparameters :param budget: the budget for which model should be updated. Default is 0 If budget > 0 : multifidelity optimization. Only use observations that were run with `budget` for updateing model for that `budget`. One model exists per budget. If == 0: single fidelity optimization. Only one model exists that is fitted with all observations :type budget: int """ raise NotImplementedError @abstractmethod def sampling_routine(self, budget=0): """Samples new config from model This methods holds logic for: - maximizing acquisition function based on current model and observations - async logic: i.e. imputing busy_locations with a liar to encourage diversity in sampling :param budget: the budget from which model should be sampled. Default is 0 If budget > 0 : multifidelity optimization. Only use observations that were run with `budget` for updateing model for that `budget`. One model exists per budget. If == 0: single fidelity optimization. Only one model exists that is fitted with all observations :type budget: int :return: hyperparameter config that minimizes the acquisition function :rtype: dict """ raise NotImplementedError def warmup_routine(self): """implements logic for warming up bayesian optimization through random sampling by adding hparam configs to `warmup_config` list """ # generate warmup hparam configs if self.warmup_sampling == "random": self.warmup_configs = self.searchspace.get_random_parameter_values( self.num_warmup_trials ) else: raise NotImplementedError( "warmup sampling {} doesnt exist, use random".format( self.warmup_sampling ) ) def _experiment_finished(self): """checks if experiment is finished In normal BO, experiment has finished when specified amount of trials have run, in BOHB/ASHA when all iterations have been finished :return: True if experiment has finished, False else :rtype: bool """ if self.pruner: if self.pruner.finished(): return True elif len(self.final_store) >= self.num_trials: self._log( "Finished experiment, ran {}/{} trials".format( len(self.final_store), self.num_trials ) ) return True else: return False def get_busy_locations(self, budget=0): """returns hparams of currently evaluating trials Considers only trials that were sampled from model with specified budget This is a helper functions used when async strategy is `impute` """ if not self.include_busy_locations(): raise ValueError( "Only Optimizer GP with async_strategy == `impute` can include busy locations. Got Optimizer {} {}".format( self.name(), "async_strategy: " + self.async_strategy if self.name() == "GP" else "", ) ) hparams_busy = np.array( [ self.searchspace.dict_to_list(trial.params) for trial_id, trial in self.trial_store.items() if trial.info_dict["sample_type"] == "model" and trial.info_dict["model_budget"] == budget ] ) return hparams_busy def get_imputed_metrics(self, budget=0): """returns imputed metrics for currently evaluating trials Considers only trials that were sampled from model with specified budget This is a helper function, only used when async strategy is `impute` """ if not self.include_busy_locations(): raise ValueError( "Only Optimizer GP with async_strategy == `impute` can include busy locations. Got Optimizer {} {}".format( self.name(), "async_strategy: " + self.async_strategy if self.name() == "GP" else "", ) ) metrics_busy = np.empty(0, dtype=float) for trial_id, trial in self.trial_store.items(): if ( trial.info_dict["sample_type"] == "model" and trial.info_dict["model_budget"] == budget ): imputed_metric = self.impute_metric(trial.params, budget) metrics_busy = np.append(metrics_busy, imputed_metric) # add info about imputed metric to trial info dict if "imputed_metrics" in trial.info_dict.keys(): trial.info_dict["imputed_metrics"].append(imputed_metric) else: trial.info_dict["imputed_metrics"] = [imputed_metric] return metrics_busy def get_XY(self, budget=0, interim_results=False, interim_results_interval=10): """get transformed hparams and metrics for fitting surrogate :param budget: budget for which model should be build. Default is 0 If budget > 0 : One model exists per budget. Only use observations that were run with `budget` for updateing model for that `budget`. If == 0: Only one model exists that is fitted with all observations :type budget: int :param interim_results: If True interim results from metric history are used and hparams are augumented with budget. i.e. for every interim result have one observation with .. math:: z_t = [x_t, n_t] ; y_t = y_{t,nt} Else use final metrics only :type interim_results: bool :param interim_results_interval: Specifies the interval of the interim results being used, If interim_results==True e.g. if 10, use every metric of every 10th epoch :type interim_results_interval: int :return: Tuple of 2 arrays, the first containing hparams and metrics. There are four scenarios: * Hparams and Metrics of finalized trials shapes: (n_finalized_trials, n_hparams), (n_finalized_trials,) * Hparams and Metrics of finalized trials + hparams and imputed metrics of busy_locations (if async_strategy=="asnyc") shapes: (n_finalized_trials+n_busy_locations, n_hparams), (n_finalized_trials+n_busy_locations,) * Hparams (augumented with budget) and interim metrics of finalized trials shapes: (n_interim_results, n_hparams + 1), (n_interim_results,) Note that first and final metric of each trial are always used and that trials may be trained with different budgets * Hparams (augumented with budget) and interim metrics of finalized trials + hparams and imputed final metric for evaluating trials (if async_strategy=="asnyc") shapes: (n_interim_results+n_busy_locations, n_hparams + 1), (n_interim_results+n_busy_locations,) :rtype: (np.ndarray, np.ndarray) """ if not interim_results: # return final metrics only # get hparams and final metrics of finalized trials hparams = self.get_hparams_array(budget=budget) metrics = self.get_metrics_array(budget=budget, interim_metrics=False) # if async strategy is `impute` if self.include_busy_locations(): # get hparams and imputed metrics of evaluating trials hparams_busy = self.get_busy_locations(budget=budget) imputed_metrics = self.get_imputed_metrics(budget=budget) assert hparams_busy.shape[0] == imputed_metrics.shape[0], ( "Number of evaluating trials and imputed " "metrics needs to be equal, " "got n_busy_locations: {}, " "n_imputed_metrics: {}".format( hparams_busy.shape[0], imputed_metrics.shape[0] ) ) # append to hparams and metrics if len(hparams_busy) > 0: hparams = np.concatenate((hparams, hparams_busy)) metrics = np.concatenate((metrics, imputed_metrics)) # transform hparams # note that through transform, budget param gets ommited from hparams if it was existent (pruner) hparams_transform = np.apply_along_axis( self.searchspace.transform, 1, hparams, normalize_categorical=self.normalize_categorical, ) X = hparams_transform y = metrics assert X.shape[1] == len( self.searchspace.keys() ), "shape[1] of X needs to be equal to number of hparams" elif interim_results: # so far only supported for GP and no pruner # return interim results and hparams augumented with budget # return every nth interim result according to interim_results_interval. always return first and last result # get and transform hparams of all finalized trials hparams = self.get_hparams_array(budget=budget) hparams_transform = np.apply_along_axis( self.searchspace.transform, 1, hparams, normalize_categorical=self.normalize_categorical, ) # get full metric history for all finalized trials metrics = self.get_metrics_array( interim_metrics=True, budget=budget ) # array of metric history arrays # get indices of hparams/metrics to be used for each trial interim_result_indices = np.array( [ self.get_interim_result_idx( metric_history, interim_results_interval ) for metric_history in metrics ] ) # araay of list. each list represents the indices of the metrics to be used of that trial # only use every nth interim result of metric history. specified with interim_result_indices metrics_filtered = np.array( [ metrics[trial_idx][interim_result_indices[trial_idx]] for trial_idx in range(metrics.shape[0]) ] ) # flatten results so they can be used for fitting posterior # if arrays in yi_filtered do not have same length, e.g. some trials were early stopped or pruner was activated, # yi_filtered is ragged (yi_filtered.shape = (n_trials,)) and flatten does not work, use np.hstack(). # if all trials have been trained on max budget (yi_filtered.shape = (n_trials, n_filtered_results)) use flatten() since it is better for performance. if len(metrics_filtered.shape) == 2: metrics_flat = metrics_filtered.flatten() else: metrics_flat = np.hstack(metrics_filtered) # augument hparams with budget, i.e. z_t = [x_t, n_t] for every interim result max_budget = self.get_max_budget() n_hparams = len(self.searchspace.keys()) hparams_augumented = np.empty((0, n_hparams + 1)) # create one hparam config augumented with the corresponding normalized budget for every interim result # loop through trials for indices, trial_hparams in zip( interim_result_indices, hparams_transform ): # loop through interim results for idx in indices: # idx is budget normalized_budget = self.searchspace._normalize_integer( [0, max_budget - 1], idx ) augumented_trial_hparams = np.append( deepcopy(trial_hparams), normalized_budget ) hparams_augumented = np.vstack( (hparams_augumented, augumented_trial_hparams) ) # add evaluating trials if impute strategy, i.e. z = [x, max_budget] y = imputed_metric if self.include_busy_locations(): # get params and imputed metrics of evaluating trials hparams_busy = self.get_busy_locations(budget=budget) imputed_metrics = self.get_imputed_metrics(budget=budget) assert ( hparams_busy.shape[0] == imputed_metrics.shape[0] ), "Number of evaluating trials and imputed metrics needs to be equal, got n_busy_locations: {}, n_imputed_metrics: {}".format( hparams_busy.shape[0], imputed_metrics.shape[0] ) if len(hparams_busy) > 0: # transform hparams hp_trans = np.apply_along_axis( self.searchspace.transform, 1, hparams_busy, normalize_categorical=self.normalize_categorical, ) # augument with max budget (i.e. always 1 in normalized form) hp_aug = np.append( hp_trans, np.ones(hp_trans.shape[0]).reshape(-1, 1), 1 ) # append to hparams and metrics hparams_augumented = np.concatenate((hparams_augumented, hp_aug)) metrics_flat =	np.concatenate((metrics_flat, imputed_metrics))	numpy.concatenate
import numpy as np import numpy.linalg as la def barrier_move_not_acceptable_for_qp(P, q, x, residual, f_val, v, f_prime, d_residual, s, t, alpha): moved = x + s * v moved_val = t * (.5 * np.matmul(np.matmul(moved.T, P), moved) + np.matmul(q.T, moved)) \ - np.sum(np.log(residual + s * d_residual)) return moved_val >= f_val + alpha * s * f_prime def log_barrier_for_qp(P, q, A, b, alpha, beta, mu, iterations, tol=1e-3, eps=1e-6): t = 1 gap = np.inf m = A.shape[0] n = A.shape[1] x = np.zeros((n, 1)) gap_results = [] for iteration in range(iterations): residual = (b - np.matmul(A, x)).astype('f') f_val = t * (.5 * np.matmul(x.T, np.matmul(P, x)) + np.matmul(q.T, x)) - np.sum(np.log(residual)) f_grad = t * (np.matmul(P, x) + q) + np.matmul(A.T, np.reciprocal(residual)) f_hess = t * P + np.matmul(A.T, np.matmul(np.diag(np.reciprocal(np.square(residual))[:, 0]), A)) v = -1 * la.lstsq(f_hess, f_grad, rcond=None)[0] f_prime = np.matmul(f_grad.T, v) d_residual = -np.matmul(A, v) s = 1 while np.min(residual + s * d_residual) <= 0: s = beta while barrier_move_not_acceptable_for_qp(P, q, x, residual, f_val, v, f_prime, d_residual, s, t, alpha): s = beta if s == 0: break x += s * v if -f_prime < eps: gap = m / t if gap < tol: print('Iteration: %d - final gap: %f' % (iteration, gap)) gap_results.append(gap) break t = mu * t gap_results.append(gap) return gap_results def calculate_new_r(P, q, A, t_inv, step, z, dz, x, dx, s, ds): newz = z + step * dz newx = x + step * dx news = s + step * ds return np.concatenate((np.matmul(P, newx) + q + np.matmul(A.T, newz), newz * news - t_inv), axis=0) def interior_point_for_qp(P, q, A, b, alpha, beta, mu, iterations, tol=1e-3, eps=1e-6): m = A.shape[0] n = A.shape[1] x = np.zeros((n, 1)) s = (b - np.matmul(A, x)).astype('f') z = np.reciprocal(s) surrogates = [] dual_residual_norms = [] for iteration in range(iterations): gap = np.asscalar(np.matmul(s.T, z)) res =	np.matmul(P, x)	numpy.matmul
import numpy as np """ 备注：本示例中所有的matrix都使用的是array来体现实际上python中存在专门的matrix # 主要注意matrix和array的区别 # matrix 使用更加接近matlab的使用方式简单示例如下： B = np.matrix('1,2;3,4') print(BB) # 注意：若使用matrix，则直接表示矩阵乘法 # 而元素对应乘积则使用 multiply() 函数 """ def vector_dot(): a = np.array([1, 3, 5, 7, 9]) b = np.array([1, 2, 3, 4, 5]) # 对应元素相乘 print(a * b) # 对应元素2次幂 print(a ** 2) # dot product print(a.dot(b)) print(type(a)) print(a.dtype) print(a.shape) def array_matrix_sum(): d = np.arange(10) # 求和：针对数组 print(d.sum()) # 求最值 print(np.min(d)) # 两种方式结果一致 print(d.max()) # 累加 print(d.cumsum()) x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # 求和：针对矩阵 print(x.sum()) print(np.sum(x, axis=0)) # 列sum print(x.sum(axis=1)) # 行sum def array_matrix_slice(): # start,stop,num d = np.linspace(0, 90, 10) print(d) # start,stop,num,endpoint=False d = np.linspace(0, 100, 10, endpoint=False) print(d) # 切片 indices = [1, 3, -1] print(d[indices]) print(d[[1, 3, -1]]) # 外[]表示d的下标，内[]表示下标列表 print(d[:5]) # 按照条件切片 print(d[d >= 50]) # 按照条件查找元素下标 index = np.where(d >= 50) print(index[0]) print(d[index[0]]) e = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) print(e) # 矩阵切片方式 print(e[1:3, 1:3]) # 选取矩阵中某点 print(e[3, 2]) print(e[(3, 2)]) # 花式索引 --- array[[x],[y]] # 坐标对应(1,1),(1,2),(2,1),(2,2) # 最终将回去的所有点排成一个一维数组 print(e[[1, 1, 2, 2], [1, 2, 1, 2]]) # print(e[np.arange(1, 3), 1:3]) # 返回布尔值，（针对对应元素） # 该布尔值也可以用于索引切片 print(e % 2 == 0) print(e[e % 2 == 0]) def array_stack(): a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) b = np.array([0, 1, 0, -1]) c = np.array([0, 1, 0, -1]).reshape(2, 2) print('-------------堆叠--------------') # 垂直堆叠 r = np.vstack([a, b]) print(r) # 水平堆叠 r2 =	np.hstack([a, c])	numpy.hstack
"""Dark matter - electron scattering """ import numericalunits as nu import numpy as np from scipy.interpolate import RegularGridInterpolator, interp1d from scipy.integrate import quad, dblquad from scipy.stats import binom import wimprates as wr export, __all__ = wr.exporter() __all__ += ['dme_shells', 'l_to_letter', 'l_to_number'] # Load form factor and construct interpolators shell_data = wr.load_pickle('dme/dme_ionization_ff.pkl') for _shell, _sd in shell_data.items(): _sd['log10ffsquared_itp'] = RegularGridInterpolator( (_sd['lnks'], _sd['lnqs']), np.log10(_sd['ffsquared']), bounds_error=False, fill_value=-float('inf'),) dme_shells = [(5, 1), (5, 0), (4, 2), (4, 1), (4, 0)] l_to_number = dict(s=0, p=1, d=2, f=3) l_to_letter = {v: k for k, v in l_to_number.items()} @export def shell_str(n, l): if isinstance(l, str): return str(n) + l return str(n) + l_to_letter[l] @export def dme_ionization_ff(shell, e_er, q): """Return dark matter electron scattering ionization form factor Outside the parametrized range, the form factor is assumed 0 to give conservative results. :param shell: Name of atomic shell, e.g. '4p' Note not all shells are included in the data. :param e_er: Electronic recoil energy :param q: Momentun transfer """ if isinstance(shell, tuple): shell = shell_str(*shell) lnq =	np.log(q / (nu.me * nu.c0 * nu.alphaFS))	numpy.log
"""Module to provide functionality to import structures.""" # pylint: disable=no-self-use import io import datetime from collections import OrderedDict import numpy as np import ipywidgets as ipw from traitlets import Instance, Int, List, Unicode, Union, dlink, link, default, observe from sklearn.decomposition import PCA # ASE imports import ase from ase import Atom, Atoms from ase.data import chemical_symbols, covalent_radii # AiiDA imports from aiida.engine import calcfunction from aiida.orm import CalcFunctionNode, CalcJobNode, Data, QueryBuilder, Node, WorkChainNode from aiida.plugins import DataFactory # Local imports from .utils import get_ase_from_file from .viewers import StructureDataViewer from .data import LigandSelectorWidget CifData = DataFactory('cif') # pylint: disable=invalid-name StructureData = DataFactory('structure') # pylint: disable=invalid-name SYMBOL_RADIUS = {key: covalent_radii[i] for i, key in enumerate(chemical_symbols)} class StructureManagerWidget(ipw.VBox): '''Upload a structure and store it in AiiDA database. Attributes: structure(Atoms): trait that contains the selected structure. 'None' if no structure is selected. structure_node(StructureData, CifData): trait that contains AiiDA structure object node_class(str): trait that contains structure_node type (as string). ''' input_structure = Union([Instance(Atoms), Instance(Data)], allow_none=True) structure = Union([Instance(Atoms), Instance(Data)], allow_none=True) structure_node = Instance(Data, allow_none=True, read_only=True) node_class = Unicode() SUPPORTED_DATA_FORMATS = {'CifData': 'cif', 'StructureData': 'structure'} def __init__(self, importers, viewer=None, editors=None, storable=True, node_class=None, kwargs): """ Arguments: importers(list): list of tuples each containing the displayed name of importer and the importer object. Each object should contain 'structure' trait pointing to the imported structure. The trait will be linked to 'structure' trait of this class. storable(bool): Whether to provide Store button (together with Store format) node_class(str): AiiDA node class for storing the structure. Possible values: 'StructureData', 'CifData' or None (let the user decide). Note: If your workflows require a specific node class, better fix it here. """ # History of modifications self.history = [] # Undo functionality. btn_undo = ipw.Button(description='Undo', button_style='success') btn_undo.on_click(self.undo) self.structure_set_by_undo = False # To keep track of last inserted structure object self._inserted_structure = None # Structure viewer. if viewer: self.viewer = viewer else: self.viewer = StructureDataViewer(downloadable=False) dlink((self, 'structure'), (self.viewer, 'structure')) # Store button. self.btn_store = ipw.Button(description='Store in AiiDA', disabled=True) self.btn_store.on_click(self.store_structure) # Label and description that are stored along with the new structure. self.structure_label = ipw.Text(description='Label') self.structure_description = ipw.Text(description='Description') # Store format selector. data_format = ipw.RadioButtons(options=self.SUPPORTED_DATA_FORMATS, description='Data type:') link((data_format, 'label'), (self, 'node_class')) # Store button, store class selector, description. store_and_description = [self.btn_store] if storable else [] if node_class is None: store_and_description.append(data_format) elif node_class in self.SUPPORTED_DATA_FORMATS: self.node_class = node_class else: raise ValueError("Unknown data format '{}'. Options: {}".format(node_class, list(self.SUPPORTED_DATA_FORMATS.keys()))) self.output = ipw.HTML('') children = [ self._structure_importers(importers), self.viewer, ipw.HBox(store_and_description + [self.structure_label, self.structure_description]) ] structure_editors = self._structure_editors(editors) if structure_editors: structure_editors = ipw.VBox([btn_undo, structure_editors]) accordion = ipw.Accordion([structure_editors]) accordion.selected_index = None accordion.set_title(0, 'Edit Structure') children += [accordion] super().__init__(children=children + [self.output], kwargs) def _structure_importers(self, importers): """Preparing structure importers.""" if not importers: raise ValueError("The parameter importers should contain a list (or tuple) of " "importers, got a falsy object.") # If there is only one importer - no need to make tabs. if len(importers) == 1: # Assigning a function which will be called when importer provides a structure. dlink((importers[0], 'structure'), (self, 'input_structure')) return importers[0] # Otherwise making one tab per importer. importers_tab = ipw.Tab() importers_tab.children = [i for i in importers] # One importer per tab. for i, importer in enumerate(importers): # Labeling tabs. importers_tab.set_title(i, importer.title) dlink((importer, 'structure'), (self, 'input_structure')) return importers_tab def _structure_editors(self, editors): """Preparing structure editors.""" if editors and len(editors) == 1: link((editors[0], 'structure'), (self, 'structure')) if editors[0].has_trait('selection'): link((editors[0], 'selection'), (self.viewer, 'selection')) if editors[0].has_trait('camera_orientation'): dlink((self.viewer._viewer, '_camera_orientation'), (editors[0], 'camera_orientation')) # pylint: disable=protected-access return editors[0] # If more than one editor was defined. if editors: editors_tab = ipw.Tab() editors_tab.children = tuple(editors) for i, editor in enumerate(editors): editors_tab.set_title(i, editor.title) link((editor, 'structure'), (self, 'structure')) if editor.has_trait('selection'): link((editor, 'selection'), (self.viewer, 'selection')) if editor.has_trait('camera_orientation'): dlink((self.viewer._viewer, '_camera_orientation'), (editor, 'camera_orientation')) # pylint: disable=protected-access return editors_tab return None def store_structure(self, _=None): """Stores the structure in AiiDA database.""" if self.structure_node is None: return if self.structure_node.is_stored: self.output.value = "Already stored in AiiDA [{}], skipping...".format(self.structure_node) return self.btn_store.disabled = True self.structure_node.label = self.structure_label.value self.structure_label.disabled = True self.structure_node.description = self.structure_description.value self.structure_description.disabled = True if isinstance(self.input_structure, Data) and self.input_structure.is_stored: # Make a link between self.input_structure and self.structure_node @calcfunction def user_modifications(source_structure): # pylint: disable=unused-argument return self.structure_node structure_node = user_modifications(self.input_structure) else: structure_node = self.structure_node.store() self.output.value = "Stored in AiiDA [{}]".format(structure_node) def undo(self, _): """Undo modifications.""" self.structure_set_by_undo = True if self.history: self.history = self.history[:-1] if self.history: self.structure = self.history[-1] else: self.input_structure = None self.structure_set_by_undo = False @staticmethod @default('node_class') def _default_node_class(): return 'StructureData' @observe('node_class') def _change_structure_node(self, _=None): with self.hold_trait_notifications(): self._sync_structure_node() def _sync_structure_node(self): """Synchronize the structure_node trait using the currently provided info.""" if len(self.history) > 1: # There are some modifications, so converting from ASE. structure_node = self._convert_to_structure_node(self.structure) else: structure_node = self._convert_to_structure_node(self.input_structure) self.set_trait('structure_node', structure_node) def _convert_to_structure_node(self, structure): """Convert structure of any type to the StructureNode object.""" if structure is None: return None StructureNode = DataFactory(self.SUPPORTED_DATA_FORMATS[self.node_class]) # pylint: disable=invalid-name # If the input_structure trait is set to Atoms object, structure node must be created from it. if isinstance(structure, Atoms): return StructureNode(ase=structure) # If the input_structure trait is set to AiiDA node, check what type if isinstance(structure, Data): # Transform the structure to the StructureNode if needed. if isinstance(structure, StructureNode): return structure # Using self.structure, as it was already converted to the ASE Atoms object. return StructureNode(ase=self.structure) @observe('structure_node') def _observe_structure_node(self, change): """Modify structure label and description when a new structure is provided.""" struct = change['new'] if struct is None: self.btn_store.disabled = True self.structure_label.value = '' self.structure_label.disabled = True self.structure_description.value = '' self.structure_description.disabled = True return if struct.is_stored: self.btn_store.disabled = True self.structure_label.value = struct.label self.structure_label.disabled = True self.structure_description.value = struct.description self.structure_description.disabled = True else: self.btn_store.disabled = False self.structure_label.value = self.structure.get_chemical_formula() self.structure_label.disabled = False self.structure_description.value = '' self.structure_description.disabled = False @observe('input_structure') def _observe_input_structure(self, change): """Returns ASE atoms object and sets structure_node trait.""" # If the `input_structure` trait is set to Atoms object, then the `structure` trait should be set to it as well. self.history = [] if isinstance(change['new'], Atoms): self.structure = change['new'] # If the `input_structure` trait is set to AiiDA node, then the `structure` trait should # be converted to an ASE Atoms object. elif isinstance(change['new'], CifData): # Special treatement of the CifData object str_io = io.StringIO(change['new'].get_content()) self.structure = ase.io.read(str_io, format='cif', reader='ase', store_tags=True) elif isinstance(change['new'], StructureData): self.structure = change['new'].get_ase() else: self.structure = None @observe('structure') def _structure_changed(self, change=None): """Perform some operations that depend on the value of `structure` trait. This function enables/disables `btn_store` widget if structure is provided/set to None. Also, the function sets `structure_node` trait to the selected node type. """ if not self.structure_set_by_undo: self.history.append(change['new']) # If structure trait was set to None, structure_node should become None as well. if self.structure is None: self.set_trait('structure_node', None) self.btn_store.disabled = True return self.btn_store.disabled = False with self.hold_trait_notifications(): self._sync_structure_node() class StructureUploadWidget(ipw.VBox): """Class that allows to upload structures from user's computer.""" structure = Union([Instance(Atoms), Instance(Data)], allow_none=True) def __init__(self, title='', description="Upload Structure"): self.title = title self.file_upload = ipw.FileUpload(description=description, multiple=False, layout={'width': 'initial'}) supported_formats = ipw.HTML( """<a href="https://wiki.fysik.dtu.dk/ase/_modules/ase/io/formats.html" target="_blank"> Supported structure formats </a>""") self.file_upload.observe(self._on_file_upload, names='value') super().__init__(children=[self.file_upload, supported_formats]) def _validate_and_fix_ase_cell(self, ase_structure, vacuum_ang=10.0): """ Checks if the ase Atoms object has a cell set, otherwise sets it to bounding box plus specified "vacuum" space """ cell = ase_structure.cell if (np.linalg.norm(cell[0]) < 0.1 or np.linalg.norm(cell[1]) < 0.1 or np.linalg.norm(cell[2]) < 0.1): # if any of the cell vectors is too short, consider it faulty # set cell as bounding box + vacuum_ang bbox =	np.ptp(ase_structure.positions, axis=0)	numpy.ptp
# -- coding: utf-8 -- """ Created on Thu May 7 09:12:56 2015 @author: anderson """ # importing modules from __future__ import division import numpy as np import matplotlib.pyplot as plt import scipy.signal as sig from . import adjust_spines def plot_single_hfo(hfo, envelope = False, xlim =[-1,1], cutoff = None, v = True, axes = None, figure_size = (15,10),dpi=600,saveplot = None): """ Function to plot single Spike Parameters ---------- hfo: HFOObj HFO object to plot envelope: boolean True (default) - plot envelope of filtered signal figure_size: tuple (5,5) (default) - Size of figure, tuple of integers with width, height in inches dpi: int 600 - DPI resolution """ if axes == None: # Creating the figure fig = plt.figure(figsize=figure_size,dpi=dpi) ax1 = fig.add_subplot(311) ax2 = fig.add_subplot(312) ax3 = fig.add_subplot(313) else: ax1 = axes[0] ax2 = axes[1] ax3 = axes[2] # number of points npoints = hfo.waveform.shape[0] time_v = np.linspace(-1,1,npoints,endpoint=True) # creating the axes ax1.plot(time_v,hfo.waveform[:,0],'b') ax1.plot(time_v[hfo.start_idx:hfo.end_idx],hfo.waveform[hfo.start_idx:hfo.end_idx,0],'k') adjust_spines(ax1, ['left']) ax1.set_xlim(xlim) filt = hfo.waveform[:,1] ax2.plot(time_v,filt) ax2.plot(time_v[hfo.start_idx:hfo.end_idx],filt[hfo.start_idx:hfo.end_idx],'k') if envelope: env = hfo.waveform[:,2] ax4 = ax2.twinx() ax4.plot(time_v,env,'g') adjust_spines(ax2, ['left', 'bottom']) ax2.set_xlim(xlim) hfo.spectrum.plot(cutoff = cutoff, v = v, ax = ax3) ax3.set_title('peak freq = ' + str(hfo.spectrum.peak_freq)) adjust_spines(ax3, ['left', 'bottom']) if saveplot != None: if type(saveplot) == str: plt.savefig(saveplot, bbox_inches='tight') else: raise Exception('saveplot should be a string') plt.draw() def plot_mean_hfo(evlist,color='blue', xlim =[-1,1], figure_size=(10,10),dpi=600,saveplot = None): """ Function to plot cluster of HFOs Parameters ---------- evlist: EventList EventList object to plot color: str Color of plot figure_size: tuple (5,5) (default) - Size of figure, tuple of integers with width, height in inches dpi: int 600 - DPI resolution """ f = plt.figure(figsize=figure_size,dpi=dpi) raw = np.array([]) # creating a empty array filt = np.array([]) # creating a empty array pxx = np.array([]) # creating a empty array nwave, a = evlist[0].waveform.shape time_v = np.linspace(-1,1,nwave,endpoint=True) npw, = evlist[0].spectrum.nPxx.shape F = evlist[0].spectrum.F for hfo in evlist: raw = np.append(raw, hfo.waveform[:,0]) #ax1.plot(time_v,hfo.waveform[:,0],lw=.5) filt = np.append(filt, hfo.waveform[:,1]) #ax2.plot(time_v,hfo.waveform[:,1],lw=.5) pxx =	np.append(pxx, hfo.spectrum.nPxx)	numpy.append
import numpy as np import tensorflow as tf from tensorflow.contrib import rnn def _print_success_message(): print('Tests Passed') def test_create_lookup_tables(create_lookup_tables): with tf.Graph().as_default(): test_text = ''' Moe_Szyslak Moe's Tavern Where the elite meet to drink Bart_Simpson Eh yeah hello is Mike there Last name Rotch Moe_Szyslak Hold on I'll check <NAME>ch Mike Rotch Hey has anybody seen Mike Rotch lately Moe_Szyslak Listen you little puke One of these days I'm gonna catch you and I'm gonna carve my name on your back with an ice pick Moe_Szyslak Whats the matter Homer You're not your normal effervescent self Homer_Simpson I got my problems Moe Give me another one Moe_Szyslak Homer hey you should not drink to forget your problems Barney_Gumble Yeah you should only drink to enhance your social skills''' test_text = test_text.lower() test_text = test_text.split() vocab_to_int, int_to_vocab = create_lookup_tables(test_text) # Check types assert isinstance(vocab_to_int, dict),\ 'vocab_to_int is not a dictionary.' assert isinstance(int_to_vocab, dict),\ 'int_to_vocab is not a dictionary.' # Compare lengths of dicts assert len(vocab_to_int) == len(int_to_vocab),\ 'Length of vocab_to_int and int_to_vocab don\'t match. ' \ 'vocab_to_int is length {}. int_to_vocab is length {}'.format(len(vocab_to_int), len(int_to_vocab)) # Make sure the dicts have the same words vocab_to_int_word_set = set(vocab_to_int.keys()) int_to_vocab_word_set = set(int_to_vocab.values()) assert not (vocab_to_int_word_set - int_to_vocab_word_set),\ 'vocab_to_int and int_to_vocab don\'t have the same words.' \ '{} found in vocab_to_int, but not in int_to_vocab'.format(vocab_to_int_word_set - int_to_vocab_word_set) assert not (int_to_vocab_word_set - vocab_to_int_word_set),\ 'vocab_to_int and int_to_vocab don\'t have the same words.' \ '{} found in int_to_vocab, but not in vocab_to_int'.format(int_to_vocab_word_set - vocab_to_int_word_set) # Make sure the dicts have the same word ids vocab_to_int_word_id_set = set(vocab_to_int.values()) int_to_vocab_word_id_set = set(int_to_vocab.keys()) assert not (vocab_to_int_word_id_set - int_to_vocab_word_id_set),\ 'vocab_to_int and int_to_vocab don\'t contain the same word ids.' \ '{} found in vocab_to_int, but not in int_to_vocab'.format(vocab_to_int_word_id_set - int_to_vocab_word_id_set) assert not (int_to_vocab_word_id_set - vocab_to_int_word_id_set),\ 'vocab_to_int and int_to_vocab don\'t contain the same word ids.' \ '{} found in int_to_vocab, but not in vocab_to_int'.format(int_to_vocab_word_id_set - vocab_to_int_word_id_set) # Make sure the dicts make the same lookup missmatches = [(word, id, id, int_to_vocab[id]) for word, id in vocab_to_int.items() if int_to_vocab[id] != word] assert not missmatches,\ 'Found {} missmatche(s). First missmatch: vocab_to_int[{}] = {} and int_to_vocab[{}] = {}'.format( len(missmatches), missmatches[0]) assert len(vocab_to_int) > len(set(test_text))/2,\ 'The length of vocab seems too small. Found a length of {}'.format(len(vocab_to_int)) _print_success_message() def test_get_batches(get_batches): with tf.Graph().as_default(): test_batch_size = 128 test_seq_length = 5 test_int_text = list(range(1000test_seq_length)) batches = get_batches(test_int_text, test_batch_size, test_seq_length) # Check type assert isinstance(batches, np.ndarray),\ 'Batches is not a Numpy array' # Check shape assert batches.shape == (7, 2, 128, 5),\ 'Batches returned wrong shape. Found {}'.format(batches.shape) for x in range(batches.shape[2]): assert np.array_equal(batches[0,0,x], np.array(range(x * 35, x * 35 + batches.shape[3]))),\ 'Batches returned wrong contents. For example, input sequence {} in the first batch was {}'.format(x, batches[0,0,x]) assert np.array_equal(batches[0,1,x], np.array(range(x * 35 + 1, x * 35 + 1 + batches.shape[3]))),\ 'Batches returned wrong contents. For example, target sequence {} in the first batch was {}'.format(x, batches[0,1,x]) last_seq_target = (test_batch_size-1) * 35 + 31 last_seq = np.array(range(last_seq_target, last_seq_target+ batches.shape[3])) last_seq[-1] = batches[0,0,0,0] assert	np.array_equal(batches[-1,1,-1], last_seq)	numpy.array_equal
import numpy as np from .utils import log_nowarn, squared_distance_matrix from .checks import _check_size, _check_labels def hgda_train(X, Y, priors=None): """Train a heteroscedastic GDA classifier. Parameters ---------- X : ndarray, shape (m, n) training features. Y : ndarray, shape (m,) training labels with values in {0, ..., k - 1}. priors : ndarray, shape (k,) Prior probabilities for the classes (if None they get estimated from Y). Returns ------- means : ndarray, shape (k, n) class mean vectors. invcovs : ndarray, shape (k, n, n) inverse of the class covariance matrices. priors : ndarray, shape (k,) class prior probabilities. """ _check_size("mn, m, k?", X, Y, priors) Y = _check_labels(Y) k = Y.max() + 1 m, n = X.shape means = np.empty((k, n)) invcovs = np.empty((k, n, n)) if priors is None: priors = np.bincount(Y) / m for c in range(k): means[c, :] = X[Y == c, :].mean(0) cov = np.cov(X[Y == c, :].T) invcovs[c, :, :] = np.linalg.inv(cov) return means, invcovs, priors def hgda_inference(X, means, invcovs, priors): """Heteroscedastic GDA inference. Parameters ---------- X : ndarray, shape (m, n) input features (one row per feature vector). means : ndarray, shape (k, n) class mean vectors. invcovs : ndarray, shape (k, n, n) inverse of the class covariance matrices. priors : ndarray, shape (k,) class prior probabilities. Returns ------- ndarray, shape (m,) predicted labels (one per feature vector). ndarray, shape (m, k) scores assigned to each class. """ _check_size("mn, kn, knn, k", X, means, invcovs, priors) m, n = X.shape k = means.shape[0] scores = np.empty((m, k)) for c in range(k): det = np.linalg.det(invcovs[c, :, :]) diff = X - means[c, :] q = ((diff @ invcovs[c, :, :]) * diff).sum(1) scores[:, c] = 0.5 * q - 0.5 * np.log(det) - log_nowarn(priors[c]) labels = np.argmin(scores, 1) return labels, -scores def ogda_train(X, Y, priors=None): """Train a omoscedastic GDA classifier. Parameters ---------- X : ndarray, shape (m, n) training features. Y : ndarray, shape (m,) training labels with values in {0, ..., k - 1}. priors : ndarray, shape (k,) Prior probabilities for the classes (if None they get estimated from Y). Returns ------- W : ndarray, shape (n, k) weight vectors, each row representing a different class. b : ndarray, shape (k,) vector of biases. """ _check_size("mn, m, k?", X, Y, priors) Y = _check_labels(Y) k = Y.max() + 1 m, n = X.shape means = np.empty((k, n)) cov = np.zeros((n, n)) if priors is None: priors =	np.bincount(Y)	numpy.bincount
import numpy as np from numpy.linalg import inv # Kalman Filter Class class KalmanFilter: """ Simple Kalman filter """ def __init__(self, XY, B=np.array([0]), M=np.array([0])): stateMatrix = np.zeros((4, 1), np.float32) # [x, y, delta_x, delta_y] if XY != 0: stateMatrix = np.array([[XY[0]], [XY[1]], [0], [0]], np.float32) # np.array([XY[0], XY[1],0,0],np.float32) estimateCovariance = np.eye(stateMatrix.shape[0]) transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) processNoiseCov = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) * 0.001 measurementStateMatrix = np.zeros((2, 1), np.float32) observationMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32) measurementNoiseCov = np.array([[1, 0], [0, 1]], np.float32) * 13 X=stateMatrix P=estimateCovariance F=transitionMatrix Q=processNoiseCov Z=measurementStateMatrix H=observationMatrix R=measurementNoiseCov """ Initialise the filter Args: X: State estimate P: Estimate covariance F: State transition model B: Control matrix M: Control vector Q: Process noise covariance Z: Measurement of the state X H: Observation model R: Observation noise covariance """ self.X = X self.P = P self.F = F self.B = B self.M = M self.Q = Q self.Z = Z self.H = H self.R = R def predict(self): """ Predict the future state Args: self.X: State estimate self.P: Estimate covariance self.B: Control matrix self.M: Control vector Returns: updated self.X """ # Project the state ahead self.X = self.F @ self.X + self.B @ self.M self.P = self.F @ self.P @ self.F.T + self.Q return self.X def correct(self, Z): """ Update the Kalman Filter from a measurement Args: self.X: State estimate self.P: Estimate covariance Z: State measurement Returns: updated X """ K = self.P @ self.H.T @ inv(self.H @ self.P @ self.H.T + self.R) self.X += K @ (Z - self.H @ self.X) self.P = self.P - K @ self.H @ self.P return self.X """ needed variables to instantly initialize kalman with just parsing X,Y variables (x,y) """ def init_kalman(XY): kalman = None stateMatrix = np.zeros((4, 1), np.float32) # [x, y, delta_x, delta_y] if XY != 0: stateMatrix = np.array([[XY[0]], [XY[1]],[0],[0]],np.float32) # np.array([XY[0], XY[1],0,0],np.float32) estimateCovariance = np.eye(stateMatrix.shape[0]) transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) processNoiseCov = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) * 0.001 measurementStateMatrix = np.zeros((2, 1), np.float32) observationMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32) measurementNoiseCov =	np.array([[1, 0], [0, 1]], np.float32)	numpy.array
# Copyright (c) 2016 by <NAME> and the other collaborators on GitHub at # https://github.com/rmjarvis/Piff All rights reserved. # # Piff is free software: Redistribution and use in source and binary forms # with or without modification, are permitted provided that the following # conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the disclaimer given in the accompanying LICENSE # file. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the disclaimer given in the documentation # and/or other materials provided with the distribution. """ .. module:: interp """ from __future__ import print_function import numpy as np import scipy.linalg import galsim import warnings from .interp import Interp from .star import Star, StarFit class BasisInterp(Interp): r"""An Interp class that works whenever the interpolating functions are linear sums of basis functions. Does things the "slow way" to be stable to degenerate fits to individual stars, instead of fitting to parameter sets produced by single stars. First time coding this we will assume that each element of the PSF parameter vector p is a linear combination of the same set of basis functions across the focal plane, .. math:: p_i = \sum_{j} q_{ij} K_j(u,v,other stellar params). The property degenerate_points is set to True to indicate that this interpolator uses the alpha/beta quadratic form of chisq for each sample, rather than assuming that a best-fit parameter vector is available at every sample. Internally we'll store the interpolation coefficients in a 2d array of dimensions (nparams, nbases) Note: This is an abstract base class. The concrete class you probably want to use is BasisPolynomial. """ def __init__(self): self.degenerate_points = True # This Interpolator uses chisq quadratic forms self.use_qr = False # The default. May be overridden by subclasses. self.q = None def initialize(self, stars, logger=None): """Initialize both the interpolator to some state prefatory to any solve iterations and initialize the stars for use with this interpolator. This class will initialize everything to have constant PSF parameter vector taken from the first Star in the list. :param stars: A list of Star instances to use to initialize. :param logger: A logger object for logging debug info. [default: None] :returns: A new list of Stars which have their parameters initialized. """ c = np.mean([s.fit.params for s in stars], axis=0) self.q = c[:,np.newaxis] * self.constant(1.)[np.newaxis,:] stars = self.interpolateList(stars) return stars def basis(self, star): """Return 1d array of polynomial basis values for this star :param star: A Star instance :returns: 1d numpy array with values of u^i v^j for 0<i+j<=order """ raise NotImplementedError("Cannot call `basis` for abstract base class BasisInterp. " "You probably want to use BasisPolynomial.") def constant(self, value=1.): """Return 1d array of coefficients that represent a polynomial with constant value. :param value: The value to use as the constant term. [default: 1.] :returns: 1d numpy array with values of u^i v^j for 0<i+j<=order """ raise NotImplementedError("Cannot call `constant` for abstract base class BasisInterp. " "You probably want to use BasisPolynomial.") def solve(self, stars, logger=None): """Solve for the interpolation coefficients given some data. The StarFit element of each Star in the list is assumed to hold valid alpha and beta members specifying depending of chisq on differential changes to its parameter vector. :param stars: A list of Star instances to interpolate between :param logger: A logger object for logging debug info. [default: None] """ logger = galsim.config.LoggerWrapper(logger) if self.q is None: raise RuntimeError("Attempt to solve() before initialize() of BasisInterp") # The inputs to this function are for each star i the design equation is # # A_i p = b_i # # The parameters at each stars location are modeled as # # p_i = q . K_i(u_i,v_i,...) # # where K is the basis vector for the particular location of this star and q is # our 2d array of fitting parameters. There is a row in q for each element of p, # and a column in q for each element in K. # # Each star then gives us nparam equations, which become rows of our full design matrix. # Consider the first equation for one star: # # A[0,:] p = b[0] # A[0,:] q K = b[0] # # Each element in the A[0,:]T KT outer product is the coefficient of a single element q_mn. # So we can rewrite this as # # (A[0,:,np.newaxis] * K[np.newaxis,:]).flatten() * q.flatten() = b[0] # # Thus, the equation we want for our big design matrix has rows with # # (A_i[j,:,np.newaxis] * K_i[np.newaxis,:]).flatten() # # and the b vector has elements # # b_i[j] # # Now, the typical usage of this class is such that the size of A is # # (nstars * npixels x nparam * nbasis) # # Typical numbers are: # # nstars ~ 100 # npixels ~ 500 # nparam ~ 400 # nbasis ~ 6 # # So the size of A is ~ 50,000 x 2,400, which for double precision is about 1 GB. # Considering that a particular CCD in a dense field might have up to 10 times this many # stars, this is a very steep memory demand for this function. # # Therefore, we make the default behavior be to construct AT A and solve AT A dq = AT b. # But we have an option to use QR decomposition of A instead, which may have stability # advantages, since the condition of AT A is the square of the condition of A, so the # QR method often has fewer numerical problems than the direct method for large matrices. # It just requires a lot of memory. logger = galsim.config.LoggerWrapper(logger) if self.use_qr: self._solve_qr(stars, logger) else: self._solve_direct(stars, logger) def _solve_qr(self, stars, logger): """The implementation of solve() when use_qr = True. """ # First, build up one chunk for each star. We'll stack them later. A_chunks = [] b_chunks = [] for s in stars: # Get the basis function values at this star K = self.basis(s) b_chunks.append(s.fit.b) A_chunks.append((s.fit.A[:,:,np.newaxis] * K[np.newaxis,:]).reshape( s.fit.A.shape[0], s.fit.A.shape[1] * len(K))) # Now stack the chunks into a single A and b. A = np.vstack(A_chunks) b = np.concatenate(b_chunks) if A.shape[0] < A.shape[1]: raise RuntimeError("Too few constraints for solution. (Probably too few stars)") # Note: The following snippet is the straightforward way to do this using the # scipy qr function. However, it generates the full Q matrix, which is slow. # Using the lapack functions directly is slightly more obfuscated, but faster. #Q,R = scipy.linalg.qr(A, mode='economic', overwrite_a=True) #dq = Q.T.dot(b) #dq = scipy.linalg.solve_triangular(R, dq, overwrite_b=True, check_finite=False) # This computes A -> Q R, where QR are stored in a single matrix along with an # ancillary tau matrix that helps define the Householder matrices used to build # the real Q. QR, tau, work, info = scipy.linalg.lapack.dgeqrf(A) m = A.shape[1] # Check the diagonal values of the R matrix. The following calculation isn't a # real condition number, since the regular QR decomposition isn't actually rank # revealing. However, we only get problems with the normal QR decomposition if # this number is very small, in which case we should switch to a QRP decomposition. abs_Rdiag = np.abs(np.diag(QR)) cond = np.min(abs_Rdiag) / np.max(abs_Rdiag) if cond < 1.e-12: # Note: this calculation is much slower, but it is safe to use even for # singular inputs, so it will always produce a valid answer. logger.info('Nominal condition is %s (min, max = %s, %s)', cond, np.min(abs_Rdiag), np.max(abs_Rdiag)) logger.info('Switching to QRP solution') QR, P, tau, work, info = scipy.linalg.lapack.dgeqp3(A, overwrite_a=True) P[:] -= 1 # Switch to python 0-based indexing. abs_Rdiag = np.abs(np.diag(QR)) cond = np.min(abs_Rdiag) / np.max(abs_Rdiag) logger.info('Condition for QRP is %s (min, max = %s, %s)', cond, np.min(abs_Rdiag), np.max(abs_Rdiag)) # Skip any rows of R that have essentially 0 on the diagonal. k = np.sum(abs_Rdiag > 1.e-15 * np.max(abs_Rdiag)) logger.debug('k = %d, m = %d',k,m) else: P = None k = m # The next steps are the same regardless of whether we pivoted or not. # This computes y = Q.T b dq, work, info = scipy.linalg.lapack.dormqr('L', 'T', QR, tau, b, work) # Cut dq down to the first m elements, since it is still the size of b here. dq = dq[:m] # Solve R dq = y (in place) scipy.linalg.lapack.dtrtrs(QR[:k,:k], dq[:k], overwrite_b=True) if P is not None: # Apply the permuation if we have one. dq1 = dq dq1[k:m] = 0. dq =	np.empty(m)	numpy.empty
from __future__ import division # Python 2 users only from __future__ import print_function __doc__ = """ numpy utility functions used for tSNE modelling""" import numpy as np def Hbeta_vec(distances, betas): """ Function that computes the Gaussian kernel values given a vector of squared Euclidean distances, and the precision of the Gaussian kernel. The function also computes the perplexity of the distribution. From Parametric t-SNE for matlab at https://lvdmaaten.github.io/tsne/ Parameters ---------- distances: 2-d array_like, (N,N) Square matrix of distances between data points betas: 1-d array_like, (N,) Vector of precisions of the Gaussian kernel. beta = (2 sigma*2)^-1 Returns ------- H: 1-d array_like, (N,) Entropy of each point p_matr: 2-d array_like, (N,N) array of probability values The scalar formula for p_matr is: p_matr = np.exp(-D beta) / sum(np.exp(-D * beta)) This funcion is vectorized and calculates the full P matrix """ beta_matr = (betas[:,np.newaxis] * np.ones_like(distances)) p_matr = np.exp(-distances * beta_matr) sumP = np.sum(p_matr, axis=1) H = np.log(sumP) + (betas * np.sum(distances * p_matr, axis=1)) / sumP p_matr = p_matr / (sumP[:,np.newaxis]np.ones_like(p_matr)) return H, p_matr def Hbeta_scalar(distances, beta): """ Function that computes the Gaussian kernel values given a vector of squared Euclidean distances, and the precision of the Gaussian kernel. The function also computes the perplexity of the distribution. From Parametric t-SNE for matlab at https://lvdmaaten.github.io/tsne/ Parameters ---------- distances: 1-d array_like, (N,) Distance between the current data point and all others beta: float Precision of the Gaussian kernel. beta = (2 sigma2)^-1 Returns ------- H: float Entropy p_matr: 1-d array_like, (N,) array of probability values p_matr = np.exp(-D beta) / sum(np.exp(-D * beta)) """ p_matr = np.exp(-distances * beta) sumP = np.sum(p_matr) H =	np.log(sumP)	numpy.log
import numpy as np from sklearn.model_selection import train_test_split import catboost class CatBoostClassifier: def __init__(self, params): """ Catboost Classifier wrapper """ default = {'verbose': 0, 'n_estimators': 1000, 'allow_writing_files': False} for k, v in default.items(): if k not in params.keys(): params[k] = v self.default = default self.params = params self.model = catboost.CatBoostClassifier(params) self.hasPredictProba = True self.classes_ = None self.trained = False self.callbacks = None self.verbose = 0 self.early_stopping_rounds = 100 if 'early_stopping_rounds' in params.keys(): self.early_stopping_rounds = params.pop('early_stopping_rounds') if 'verbose' in params.keys(): self.verbose = params.pop('verbose') self.set_params(params) self._estimator_type = 'classifier' def set_params(self, params): for k, v in self.default.items(): if k not in params.keys(): params[k] = v self.model.set_params(params) return self def get_params(self, args): return self.model.get_params(**args) def fit(self, X, y): # Split data train_x, test_x, train_y, test_y = train_test_split(X, y, stratify=y, test_size=0.1) # Set Attributes self.classes_ =	np.unique(y)	numpy.unique
""" Parallax fitting and computation of distances """ import os import warnings import collections from bisect import bisect_left import h5py import numpy as np import scipy.stats from scipy.interpolate import interp1d from astropy.coordinates import SkyCoord from healpy import ang2pix from dustmaps.sfd import SFDQuery from dustmaps.bayestar import BayestarWebQuery from astropy.utils.exceptions import AstropyWarning import basta.utils_distances as udist import basta.constants as cnsts import basta.stats as stats import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt # Don't print Astropy warnings (catch error caused by mock'ing astropy in Sphinx) try: warnings.filterwarnings("ignore", category=AstropyWarning) except AssertionError: pass try: from basta._dustpath import __dustpath__ except ModuleNotFoundError: print("\nCannot find path to dustmaps. Did you run 'setup.py'?\n") raise def LOS_reddening(distanceparams): """ Returns color excess E(B-V) for a line of sight using a pre-downloaded 3D extinction map provided by Green et al. 2015/2018 - see http://argonaut.skymaps.info/. The extinction map is only computed for distance modulus between :math:`4 < m-M < 19` in units of magnitude. Parameters ---------- distanceparams : dictionary Dictionary with distance parameters Returns ------- EBV : function excess color function """ if "EBV" in distanceparams: return lambda x: np.asarray( np.random.normal( distanceparams["EBV"][1], distanceparams["EBV"][2] - distanceparams["EBV"][1], size=[ len(i) if isinstance(i, collections.Iterable) else 1 for i in [x] ][0], ) ) frame = distanceparams["dustframe"] # Convert to galactic coordinates if frame == "icrs": ra = distanceparams["RA"] dec = distanceparams["DEC"] c = SkyCoord(ra=ra, dec=dec, frame="icrs", unit="deg") elif frame == "galactic": lon = distanceparams["lon"] lat = distanceparams["lat"] c = SkyCoord(l=lon, b=lat, frame="galactic", unit="deg") else: raise ValueError("Unknown dust map frame for computing reddening!") # Load extinction data cube pathmap = os.path.join(__dustpath__, "bayestar/bayestar2019.h5") dcube = h5py.File(pathmap, "r") # Distance modulus bins bin_edges = dcube["/pixel_info"].attrs["DM_bin_edges"] dmbin = bin_edges + (bin_edges[1] - bin_edges[0]) / 2.0 # If webquery fails use local copy of dustmap try: bayestar = BayestarWebQuery(version="bayestar2019") Egr_samples = bayestar(c, mode="samples") except Exception: # contains positional info pinfo = dcube["/pixel_info"][:] nsides = np.unique(dcube["/pixel_info"][:]["nside"]) # Convert coordinates to galactic frame lon = c.galactic.l.deg lat = c.galactic.b.deg # Convert l,b[deg] to theta,phi[rad] theta = np.pi / 2.0 - lat * np.pi / 180.0 phi = lon * np.pi / 180.0 # To check if we are within the maps coordinates Egr_samples = np.array([np.nan]) # Run through nsides for ncont in reversed(nsides): healpixNside = ang2pix(ncont, theta, phi, nest=True) indNside = np.where(np.asarray([x[0] for x in pinfo]) == ncont)[0] dcubepixNside = [x[1] for x in pinfo[indNside]] kNside = int(bisect_left(dcubepixNside, healpixNside)) + indNside[0] if healpixNside == dcubepixNside[kNside - indNside[0]]: index = kNside Egr_samples = dcube["/samples"][index] break # If coordinates outside dust map, use Schegel if np.isnan(Egr_samples).any(): print("WARNING: Coordinates outside dust map boundaries!") print("Default to Schegel 1998 dust map") sfd = SFDQuery() EBV_fun = lambda x: np.full_like(x, sfd(c)) return EBV_fun Egr_med, Egr_err = [], [] for i in range(len(dmbin)): Egr_med.append(np.nanmedian(Egr_samples[:, i])) Egr_err.append(np.nanstd(Egr_samples[:, i])) Egr_med_fun = interp1d( dmbin, Egr_med, bounds_error=False, fill_value=(0, np.max(Egr_med)) ) Egr_err_fun = interp1d( dmbin, Egr_err, bounds_error=False, fill_value=np.max(Egr_err) ) dcube.close() def EBV_fun(dm): Egr = np.asarray(np.random.normal(Egr_med_fun(dm), Egr_err_fun(dm))) EBV = cnsts.extinction.Conv_Bayestar * Egr return EBV return EBV_fun def get_EBV(dist, LOS_EBV, debug=False, outfilename=""): """ Estimate E(B-V) by drawing distances from a normal parallax distribution with EDSD prior. Parameters ----- dist : array The drawn distances LOS_EBV : func EBV function. debug : bool, optional Debug flag. If True, this function outputs two plots, one of distance modulus vs. E(B-V) and a histogram of the E(B-V). outfilename : str, optional Name of directory of where to put plots outputted if debug is True. Returns ------- EBVs : array E(B-V) at distances """ dmod = 5 * np.log10(dist / 10) EBVs = LOS_EBV(dmod) if debug: plt.figure() plt.plot(dmod, EBVs, ".") plt.xlabel("dmod") plt.ylabel("E(B-V)") plt.savefig(outfilename + "_DEBUG_dmod_EBVs.png") plt.close() return EBVs def get_absorption(EBV, fitparams, filt): """ Compute extinction coefficient Rzeta for band zeta. Using parameterized law from Casagrande & VandenBerg 2014. Valid for: logg = 4.1 Teff = 5250 - 7000K Fe/H = -2.0 - 0.25 a/Fe = -0.4 - 0.4 Assume nominal reddening law with RV=3.1. In a band zeta, Azeta = RzetaE(B-V). Parameters ---------- EBV : array E(B-V) values fitparams : dict The fitting params in inputparams. filt : str Name of the given filter Returns ------- REBV : array Extinction coefficient times E(B-V) """ N = len(EBV) table = cnsts.extinction.R i_filter = table["Filter"] == filt if not any(i_filter) or table["RZ_mean"][i_filter] == 0: print("WARNING: Unknown extinction coefficient for filter: " + filt) print(" Using reddening law coefficient R = 0.") return np.zeros(N) metal = "MeH" if "MeH" in fitparams else "FeH" if "Teff" not in fitparams or metal not in fitparams: R = np.ones_like(EBV) * table["RZ_mean"][i_filter].item() else: Teff_val, Teff_err = fitparams["Teff"] metal_val, metal_err = fitparams[metal] Teff = np.random.normal(Teff_val, Teff_err, size=N) FeH = np.random.normal(metal_val, metal_err, size=N) a0 = table["a0"][i_filter].item() a1 = table["a1"][i_filter].item() a2 = table["a2"][i_filter].item() a3 = table["a3"][i_filter].item() T4 = 1e-4 * Teff R = a0 + T4 * (a1 + a2 * T4) + a3 * FeH return R * EBV def add_absolute_magnitudes( inputparams, n=1000, k=1000, outfilename="", debug=False, use_gaussian_priors=False ): """ Convert apparent magnitudes to absolute magnitudes using the distance Extinction E(B-V) is estimated based on Green et al. (2015) dust map. Extinction is converted to reddening using Casagrande & VandenBerg 2014. The converted colors and magnitudes are added to fitsparams. Parameters ---------- inputparams : dict Inputparams used in BASTA run. n : int Number of samples from parallax range k : int Number of samples from apparent magnitude range. outfilename : str, optional Name of directory of where to put plots outputted if debug is True. debug : bool, optional Debug flag. If True, debugging plots will be outputted. use_gaussian_priors : bool, optional If True, gaussian priors will be used for apparent magnitude in the distance computation. Returns ------- inputparams : dict Modified version of inputparams including absolute magnitudes. """ if "parallax" not in inputparams["fitparams"]: return inputparams print("\nPreparing distance/parallax/magnitude input ...", flush=True) qs = [0.158655, 0.5, 0.841345] fitparams = inputparams["fitparams"] distanceparams = inputparams["distanceparams"] if use_gaussian_priors: inputparams["fitparams"][filt] = [val, std] return inputparams # Get apparent magnitudes from input data mobs = distanceparams["m"] mobs_err = distanceparams["m_err"] if len(mobs.keys()) == 0: raise ValueError("No filters were given") # Convert the inputted parallax in mas to as plxobs = fitparams["parallax"][0] * 1e-3 plxobs_err = fitparams["parallax"][1] * 1e-3 L = udist.EDSD(None, None) * 1e3 fitparams.pop("parallax") # Sample distances more densely around the mode of the distance distribution # See Bailer-Jones 2015, Eq 19 coeffs = [1 / L, -2, plxobs / (plxobs_err2), -1 / (plxobs_err2)] roots = np.roots(coeffs) if np.sum((np.isreal(roots))) == 1: (mode,) = np.real(roots[np.isreal(roots)]) else: assert np.sum((np.isreal(roots))) == 3 if plxobs >= 0: mode = np.amin(np.real(roots[np.isreal(roots)])) else: (mode,) = np.real(roots[roots > 0]) # By sampling linearly in quantiles, the probablity mass is equal for the samples bla = scipy.stats.norm.cdf(0, loc=mode, scale=1000) + 0.01 dist = scipy.stats.norm.ppf( np.linspace(bla, 0.96, n - n // 2), loc=mode, scale=1000 ) # We also want to sample across the entire range. lindist = 10 ** np.linspace(-0.4, 4.4, n // 2) assert np.all(np.isfinite(dist)) assert np.all(dist > 0) dist = np.concatenate([dist, lindist]) dist = np.sort(dist) lldist = udist.compute_distlikelihoods( dist, plxobs, plxobs_err, L, outfilename=outfilename, debug=debug ) dists = np.repeat(dist, k) lldists = np.repeat(lldist, k) # Get EBV values LOS_EBV = LOS_reddening(distanceparams) EBV = get_EBV(dist, LOS_EBV, debug=debug, outfilename=outfilename) EBVs = np.repeat(EBV, k) distanceparams["As"] = {} llabsms_joined = np.zeros(n * k) for filt in mobs.keys(): # Sample apparent magnitudes over the entire parameter range if filt in cnsts.distanceranges.filters: m = np.linspace( cnsts.distanceranges.filters[filt]["min"], cnsts.distanceranges.filters[filt]["max"], k - k // 2, ) else: m = np.linspace(-10, 25, k - k // 2) m = np.concatenate( [ m, scipy.stats.norm.ppf( np.linspace(0.04, 0.96, k // 2), loc=mobs[filt], scale=mobs_err[filt], ), ] ) m = np.sort(m) llm = udist.compute_mslikelihoods(m, mobs[filt], mobs_err[filt]) ms = np.tile(m, n) llms = np.tile(llm, n) assert len(dists) == len(ms) == n * k A = get_absorption(EBV, fitparams, filt) As = np.repeat(A, k) absms = udist.compute_absmag(ms, dists, As) # Construct likelihood distribution llabsms = llms + lldists llabsms_joined += llabsms llabsms -=	np.amax(llabsms)	numpy.amax
#Differential Photometry script written in April 2019 by SKB, MP, KWD for WIYN 0.9m HDI data #This script calculates photometry and differential photometry for all stars in an image and takes target positions to pull out differential photometry of target stars. Auto calculates comparison stars based on lowest percentile of variability of stars in the image. # Script is run through a shell jupyter notebook script. #Initially created by <NAME> as a juypter notebook 2018 #Turned into modular form by <NAME> April 2019 #Modified by <NAME>, <NAME>, <NAME> April 2019 # python 2/3 compatibility from __future__ import print_function # numerical python import numpy as np # file management tools import glob import os # good module for timing tests import time # plotting stuff import matplotlib.pyplot as plt import matplotlib.cm as cm # ability to read/write fits files from astropy.io import fits # fancy image combination technique from astropy.stats import sigma_clip # median absolute deviation: for photometry from astropy.stats import mad_std # photometric utilities from photutils import DAOStarFinder,aperture_photometry, CircularAperture, CircularAnnulus, Background2D, MedianBackground # periodograms from astropy.stats import LombScargle from regions import read_ds9, write_ds9 from astropy.wcs import WCS import warnings import pandas as pd from astropy.coordinates import SkyCoord from astropy import units as u from astropy.visualization import ZScaleInterval import numpy.ma as ma warnings.filterwarnings("ignore") np.set_printoptions(suppress=True) def construct_astrometry(hdr_wcs): ''' construct_astrometry make the pixel to RA/Dec conversion (and back) from the header of an astrometry.net return inputs ------------------------------ hdr_wcs : header with astrometry information, typically from astrometry.net returns ------------------------------ w : the WCS instance ''' # initialize the World Coordinate System w = WCS(naxis=2) # specify the pixel to RA/Dec conversion w.wcs.ctype = ["RA---TAN", "DEC--TAN"] w.wcs.cd = np.array([[hdr_wcs['CD1_1'],hdr_wcs['CD1_2']],[hdr_wcs['CD2_1'],hdr_wcs['CD2_2']]]) w.wcs.crpix = [hdr_wcs['CRPIX1'], hdr_wcs['CRPIX2']] w.wcs.crval = [hdr_wcs['CRVAL1'],hdr_wcs['CRVAL2']] w.wcs.cunit = [hdr_wcs['CUNIT1'],hdr_wcs['CUNIT2']] w.wcs.latpole = hdr_wcs['LATPOLE'] #w.wcs.lonpole = hdr_wcs['LONPOLE'] w.wcs.theta0 = hdr_wcs['LONPOLE'] w.wcs.equinox = hdr_wcs['EQUINOX'] # calculate the RA/Dec to pixel conversion w.wcs.fix() w.wcs.cdfix() w.wcs.set() # return the instance return w def StarFind(imname, FWHM, nsigma): ''' StarFind find all stars in a .fits image inputs ---------- imname: name of .fits image to open. FWHM: fwhm of stars in field nsigma: number of sigma above background above which to select sources. (~3 to 4 is a good estimate) outputs -------- xpos: x positions of sources ypos: y positions of sources nstars: number of stars found in image ''' #open image im,hdr=fits.getdata(imname, header=True) im = np.array(im).astype('float') #determine background bkg_sigma = mad_std(im) print('begin: DAOStarFinder') daofind = DAOStarFinder(fwhm=FWHM, threshold=nsigmabkg_sigma, exclude_border=True) sources = daofind(im) #x and y positions xpos = sources['xcentroid'] ypos = sources['ycentroid'] #number of stars found nstars = len(xpos) print('found ' + str(nstars) + ' stars') return xpos, ypos, nstars def makeApertures(xpos, ypos, aprad,skybuff, skywidth): ''' makeApertures makes a master list of apertures and the annuli inputs --------- xpos: list - x positions of stars in image ypos: list - y positions of stars in image aprad: float - aperture radius skybuff: float - sky annulus inner radius skywidth: float - sky annulus outer radius outputs -------- apertures: list - list of aperture positions and radius annulus_apertures: list - list of annuli positions and radius see: https://photutils.readthedocs.io/en/stable/api/photutils.CircularAperture.html#photutils.CircularAperture for more details ''' # make the master list of apertures apertures = CircularAperture((xpos, ypos), r=aprad) annulus_apertures = CircularAnnulus((xpos, ypos), r_in=aprad+skybuff, r_out=aprad+skybuff+skywidth) apers = [apertures, annulus_apertures] return apertures, annulus_apertures def apertureArea(apertures): ''' returns the area of the aperture''' return apertures.area() ### should be apertures def backgroundArea(back_aperture): '''returns the area of the annuli''' return back_aperture.area() ### should be annulus_apertures def doPhotometry(imglist, xpos, ypos, aprad, skybuff, skywidth,timekey='MJD-OBS',verbose=1): ''' doPhotomoetry determine the flux for each star from aperture photometry inputs ------- imglist: list - list of .fits images xpos, ypos: lists - lists of x and y positions of stars aprad, skybuff, skywidth: floats - aperture, sky annuli inner, sky annuli outer radii outputs ------- Times: list - time stamps of each observation from the .fits header Photometry: list - aperture photometry flux values found at each xpos, ypos position ''' #number of images nimages = len(imglist) nstars = len(xpos) print('Found {} images'.format(nimages)) #create lists for timestamps and flux values Times = np.zeros(nimages) Photometry = np.zeros((nimages,nstars)) print('making apertures') #make the apertures around each star apertures, annulus_apertures = makeApertures(xpos, ypos, aprad, skybuff, skywidth) #plot apertures plt.figure(figsize=(12,12)) interval = ZScaleInterval() vmin, vmax = interval.get_limits(fits.getdata(imglist[0])) plt.imshow(fits.getdata(imglist[0]), vmin=vmin,vmax=vmax, origin='lower') apertures.plot(color='white', lw=2) #annulus_apertures.plot(color='red', lw=2) plt.title('apertures') plt.show() #determine area of apertures area_of_ap = apertureArea(apertures) #determine area of annuli area_of_background = backgroundArea(annulus_apertures) checknum = np.linspace(0,nimages,10).astype(int) #go through each image and run aperture photometry for ind in np.arange(nimages): if ((ind in checknum) & (verbose==1)): print('running aperture photometry on image: ', ind ) if (verbose>1): print('running aperture photometry on image: ', ind ) #open image data_image, hdr = fits.getdata(imglist[ind], header=True) #find time stamp and append to list Times[ind] = hdr[timekey] #do photometry phot_table = aperture_photometry(data_image, (apertures,annulus_apertures)) #determine flux: (aperture flux) - [(area of aperture * annuli flux)/area of background ] flux0 = np.array(phot_table['aperture_sum_0']) - (area_of_ap/area_of_background)np.array(phot_table['aperture_sum_1']) #append to list Photometry[ind,:] = flux0 return Times,Photometry def doPhotometryError(imglist,xpos, ypos,aprad, skybuff, skywidth, flux0, GAIN=1.3, manual = False, kwargs): ''' doPhotometryError determine error in photometry from background noise two options: - use sigma clipping and use whole background - manually input background box positions as kwargs inputs -------- imglist: list - list of .fits images xpos, ypos: lists - lists of x and y positions of stars aprad, skybuff, skywidth: floats - aperture, sky annuli inner, sky annuli outer radii flux0: list - aperture photometry found from doPhotometry() function GAIN: float - average gain manual: boolean - switch between manually inputting box (True) or using sigma clipping (False) if True -- must have kwargs manual = False is default kwargs kwargs[xboxcorner]: float - x edge of box in pixel coords kwargs[yboxcorner]: float - y edge of box in pixel coords kwargs[boxsize]: float - size of box in pixel coords ''' # find number of images in list nimages = len(imglist) nstars = len(xpos) print('Found {} images'.format(nimages)) #make apertures apertures, annulus_apertures = makeApertures(xpos, ypos, aprad, skybuff, skywidth) #find areas of apertures and annuli area_of_ap = apertureArea(apertures) area_of_background = backgroundArea(annulus_apertures) checknum = np.linspace(0,nimages,10).astype(int) #find error in photometry ePhotometry = np.zeros((nimages,nstars)) for ind in np.arange(nimages): #open images im = fits.getdata(imglist[ind]) if ind in checknum: print('running error analysis on image ', ind) #determine variance in background if manual == True: #manual method -- choose back size skyvar = np.std(im[kwargs['xboxcorner']:kwargs['xboxcorner']+kwargs['boxsize'],kwargs['yboxcorner']:kwargs['yboxcorner']+kwargs['boxsize']])2. err1 = skyvar(area_of_ap)*2./(kwargs['boxsize']kwargs['boxsize']) # uncertainty in mean sky brightness if manual == False: #automatic method -- use sigma clipping filtered_data = sigma_clip(im, sigma=3) skyvar = np.std(filtered_data)*2. err1 = skyvar(area_of_ap)**2./(	np.shape(im[0])	numpy.shape
import numpy as np import os import sample_loader import pathlib import glob import re import sys sys.path.append("../base/CropSample") sys.path.append("../base/IntegrateColumnIntoGrid") import CropSample import IntegrateColumnIntoGrid from scipy import ndimage import JSONHelper def crop_from_volume(s0, x, y, z): sdf = np.array([], dtype=np.float32) pdf =	np.array([], dtype=np.float32)	numpy.array
from luminaire.model.base_model import BaseModel, BaseModelObject, BaseModelHyperParams from luminaire.exploration.data_exploration import DataExploration from luminaire.model.model_utils import LADHolidays from typing import Dict, Tuple import pandas as pd import warnings warnings.filterwarnings('ignore') class LADStructuralHyperParams(BaseModelHyperParams): """ Exception class for Luminaire structural anomaly detection model. :param bool include_holidays_exog: whether to include holidays as exogenous variables in the regression. Holidays are defined in :class:`~model.model_utils.LADHolidays` :param int p: Order for the AR component of the model. :param int q: Order for the MA component of the model. :param bool is_log_transformed: A flag to specify whether to take a log transform of the input data. If the data contain negatives, is_log_transformed is ignored even though it is set to True. :param int max_ft_freq: The maximum frequency order for the Fourier transformation. """ def __init__(self, include_holidays_exog=True, p=2, q=2, is_log_transformed=True, max_ft_freq=3): super(LADStructuralHyperParams, self).__init__( model_name="LADStructuralModel", include_holidays_exog=include_holidays_exog, p=p, q=q, is_log_transformed=is_log_transformed, max_ft_freq=max_ft_freq, ) class LADStructuralError(Exception): """ Exception class for Luminaire structural anomaly detection model. """ def __init__(self, message): message = f'LAD structural failed! Error: {message}' super(LADStructuralError, self).__init__(message) class LADStructuralModel(BaseModel): """ A LAD structural time series model. :param dict hyper_params: Hyper parameters for Luminaire structural modeling. See :class:`luminaire.model.lad_structural.LADStructuralHyperParams` for detailed information. :param str freq: The frequency of the time-series. A `Pandas offset`_ such as 'D', 'H', or 'M'. Luminaire currently supports the following pandas frequency types: 'H', 'D', 'W', 'W-SUN', 'W-MON', 'W-TUE', 'W-WED', 'W-THU', 'W-FRI', 'W-SAT'. :param int min_ts_length: The minimum required length of the time series for training. :param int max_ts_length: The maximum required length of the time series for training. The input time series will be truncated if the length is greater than this value. :param float min_ts_mean: Minimum average values in the most recent window of the time series. This optional parameter can be used to avoid over-alerting from noisy low volume time series. :param int min_ts_mean_window: Size of the most recent window to calculate min_ts_mean. .. Note :: This class should be used to manually configure the structural model. Exact configuration parameters can be found in `luminaire.model.lad_structural.LADStructuralHyperParams`. Optimal configuration can be obtained by using Luminaire hyperparameter optimization. .. _statsmodels docs: http://www.statsmodels.org/stable/generated/statsmodels.tsa.arima_model.ARIMA.html .. _Pandas offset: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects >>> hyper = {"include_holidays_exog": 0, "is_log_transformed": 1, "max_ft_freq": 2, "p": 5, "q": 1} lad_struct_model = LADStructuralModel(hyper_params=hyper, freq='D') >>> lad_struct_model <luminaire.model.lad_structural.LADStructuralModel object at 0x103efe320> """ __version__ = "2.0" _target_metric = 'raw' _imputed_metric = 'interpolated' _sig_level = 0.10 _sig_level_extreme = 0.001 def __init__(self, hyper_params: LADStructuralHyperParams().params or None, freq, min_ts_length=None, max_ts_length=None, min_ts_mean=None, min_ts_mean_window=None, *kwargs): self.hyper_params = hyper_params max_scoring_length_dict = { 'H': 48, 'D': 10, 'W': 8, 'W-SUN': 8, 'W-MON': 8, 'W-TUE': 8, 'W-WED': 8, 'W-THU': 8, 'W-FRI': 8, 'W-SAT': 8, 'M': 24, 'MS': 24, } self.max_scoring_length = max_scoring_length_dict.get(freq) fit_diagnostic_lag_dict = { 'H': 144 2, 'D': 7 * 4, 'W': 12, 'W-SUN': 12, 'W-MON': 12, 'W-TUE': 12, 'W-WED': 12, 'W-THU': 12, 'W-FRI': 12, 'W-SAT': 12, 'M': 24, 'MS': 24, } self._fit_diagnostic_lag = fit_diagnostic_lag_dict.get(freq) super(LADStructuralModel, self).__init__(freq=freq, min_ts_mean=min_ts_mean, min_ts_mean_window=min_ts_mean_window, min_ts_length=min_ts_length, max_ts_length=max_ts_length, hyper_params, kwargs) @classmethod def _signals(cls, idx, m, n): """ This function computes the sinusoids given the significant frequencies :param list idx: A list containing the significant frequency indices obtained from the spectral density plot in fourier_extp() :param int m: Specifying the current frequency :param int n: Specifying the length of the time series :return: A numpy array containing the sinusoids corresponding to the significant frequencies """ import numpy as np signal = [] # Generating all the frequencies from a time series of length n fs =	np.fft.fftfreq(n)	numpy.fft.fftfreq
import numpy as np import pytest import torch from probflow.distributions import Bernoulli from probflow.models import Model from probflow.parameters import CenteredParameter def is_close(a, b, tol=1e-5): return np.abs(a - b) < tol def test_CenteredParameter_all_1d(): """Tests probflow.parameters.CenteredParameter w/ center_by=all + 1D""" # Create the parameter param = CenteredParameter(5) # posterior_mean should return mean sample1 = param.posterior_mean() sample2 = param.posterior_mean() assert sample1.ndim == 2 assert sample2.ndim == 2 assert sample1.shape[0] == 5 assert sample2.shape[0] == 5 assert sample1.shape[1] == 1 assert sample2.shape[1] == 1 assert np.all(sample1 == sample2) # mean should be 0! (that's the point of a centered parameter!) assert is_close(np.mean(sample1), 0) # posterior_sample should return samples sample1 = param.posterior_sample() sample2 = param.posterior_sample() assert sample1.ndim == 2 assert sample2.ndim == 2 assert sample1.shape[0] == 5 assert sample1.shape[1] == 1 assert sample2.shape[0] == 5 assert sample2.shape[1] == 1 assert np.all(sample1 != sample2) # mean should be 0! (that's the point of a centered parameter!) assert is_close(np.mean(sample1), 0) # posterior_sample should be able to return multiple samples sample1 = param.posterior_sample(10) sample2 = param.posterior_sample(10) assert sample1.ndim == 3 assert sample2.ndim == 3 assert sample1.shape[0] == 10 assert sample1.shape[1] == 5 assert sample1.shape[2] == 1 assert sample2.shape[0] == 10 assert sample2.shape[1] == 5 assert sample2.shape[2] == 1 assert np.all(sample1 != sample2) # mean should be 0! assert np.all(np.abs(np.mean(sample1, axis=1)) < 1e-5) def test_CenteredParameter_all_2d(): """Tests probflow.parameters.CenteredParameter w/ center_by=all + 2D""" # Shouldn't allow >2 dims with pytest.raises(ValueError): param = CenteredParameter([5, 6, 7]) # Create the parameter param = CenteredParameter([5, 6]) # posterior_mean should return mean sample1 = param.posterior_mean() sample2 = param.posterior_mean() assert sample1.ndim == 2 assert sample2.ndim == 2 assert sample1.shape[0] == 5 assert sample2.shape[0] == 5 assert sample1.shape[1] == 6 assert sample2.shape[1] == 6 assert np.all(sample1 == sample2) # mean should be 0! (that's the point of a centered parameter!) assert is_close(np.mean(sample1), 0) # posterior_sample should return samples sample1 = param.posterior_sample() sample2 = param.posterior_sample() assert sample1.ndim == 2 assert sample2.ndim == 2 assert sample1.shape[0] == 5 assert sample1.shape[1] == 6 assert sample2.shape[0] == 5 assert sample2.shape[1] == 6 assert np.all(sample1 != sample2) # mean should be 0! assert is_close(np.mean(sample1), 0) # posterior_sample should be able to return multiple samples sample1 = param.posterior_sample(10) sample2 = param.posterior_sample(10) assert sample1.ndim == 3 assert sample2.ndim == 3 assert sample1.shape[0] == 10 assert sample1.shape[1] == 5 assert sample1.shape[2] == 6 assert sample2.shape[0] == 10 assert sample2.shape[1] == 5 assert sample2.shape[2] == 6 assert np.all(sample1 != sample2) # mean should be 0! assert np.all(np.abs(np.mean(sample1.reshape((10, -1)), axis=1)) < 1e-5) def test_CenteredParameter_column(): """Tests probflow.parameters.CenteredParameter w/ center_by=column + 2D""" # Create the parameter param = CenteredParameter([5, 6], center_by="column") # posterior_mean should return mean sample1 = param.posterior_mean() sample2 = param.posterior_mean() assert sample1.ndim == 2 assert sample2.ndim == 2 assert sample1.shape[0] == 5 assert sample2.shape[0] == 5 assert sample1.shape[1] == 6 assert sample2.shape[1] == 6 assert	np.all(sample1 == sample2)	numpy.all
""" Base classes and functions for experiments. """ import numpy as np import torch import torch.nn.functional as F import torch.utils.data import matplotlib.pyplot as plt from IPython.display import display, clear_output from ipywidgets import Output from collections import defaultdict from tqdm import tqdm import logging import warnings import time from etn import coordinates, networks, transformers class Model(object): def __init__(self, tfs=[], coords=coordinates.identity_grid, net=None, equivariant=True, downsample=1, tf_opts={}, net_opts={}, seed=None, load_path=None, loglevel='INFO'): """ Model base class. """ # configure logging numeric_level = getattr(logging, loglevel.upper(), None) if not isinstance(numeric_level, int): raise ValueError('Invalid log level: %s' % loglevel) logging.basicConfig(level=numeric_level) logging.info(str(self)) if load_path is not None: logging.info('Loading model from file: %s -- using saved model configuration' % load_path) spec = torch.load(load_path) tfs = spec['tfs'] coords = spec['coords'] net = spec['net'] equivariant = spec['equivariant'] downsample = spec['downsample'] tf_opts = spec['tf_opts'] net_opts = spec['net_opts'] seed = spec['seed'] if net is None: raise ValueError('net parameter must be specified') if seed is not None: torch.manual_seed(seed) torch.cuda.manual_seed(seed) np.random.seed(seed) # build transformer sequence if len(tfs) > 0: pose_module = networks.EquivariantPosePredictor if equivariant else networks.DirectPosePredictor tfs = [getattr(transformers, tf) if type(tf) is str else tf for tf in tfs] seq = transformers.TransformerSequence([tf(pose_module, tf_opts) for tf in tfs]) #seq = transformers.TransformerParallel([tf(pose_module, tf_opts) for tf in tfs]) logging.info('Transformers: %s' % ' -> '.join([tf.__name__ for tf in tfs])) logging.info('Pose module: %s' % pose_module.__name__) else: seq = None # get coordinate function if given as a string if type(coords) is str: if hasattr(coordinates, coords): coords = getattr(coordinates, coords) elif hasattr(coordinates, coords + '_grid'): coords = getattr(coordinates, coords + '_grid') else: raise ValueError('Invalid coordinate system: ' + coords) logging.info('Coordinate transformation before classification: %s' % coords.__name__) # define network if type(net) is str: net = getattr(networks, net) network = net(net_opts) logging.info('Classifier architecture: %s' % net.__name__) self.tfs = tfs self.coords = coords self.downsample = downsample self.net = net self.equivariant = equivariant self.tf_opts = tf_opts self.net_opts = net_opts self.seed = seed self.model = self._build_model(net=network, transformer=seq, coords=coords, downsample=downsample) logging.info('Net opts: %s' % str(net_opts)) logging.info('Transformer opts: %s' % str(tf_opts)) if load_path is not None: self.model.load_state_dict(spec['state_dict']) def _build_model(self, net, transformer, coords, downsample): return networks.TransformerCNN( net=net, transformer=transformer, coords=coords, downsample=downsample) def _save(self, path, kwargs): spec = { 'tfs': [tf.__name__ for tf in self.tfs], 'coords': self.coords.__name__, 'net': self.net.__name__, 'equivariant': self.equivariant, 'downsample': self.downsample, 'tf_opts': self.tf_opts, 'net_opts': self.net_opts, 'seed': self.seed, 'state_dict': self.model.state_dict(), } spec.update(kwargs) torch.save(spec, path) def _load_dataset(self, path, num_examples=None): # override in subclasses to handle custom preprocessing / different data formats return Dataset(path=path, num_examples=num_examples) def train(self, num_epochs=300, num_examples=None, batch_size=128, valid_batch_size=100, train_path=None, valid_path=None, train_dataset_opts={}, valid_dataset_opts={}, optimizer='Adam', optimizer_opts={'amsgrad': True, 'lr': 2e-3, 'weight_decay': 0.}, lr_schedule={'step_size': 1, 'gamma': 0.99}, save_path=None, show_plot=False, device='cuda:0'): """Train the model.""" if save_path is not None: logging.info('Saving model with lowest validation error to %s' % save_path) else: warnings.warn('save_path not specified: model will not be saved') # load training and validation data if train_path is None: raise ValueError('train_path must be specified') if valid_path is None: raise ValueError('valid_path must be specified') logging.info('Loading training data from %s' % train_path) train_loader = torch.utils.data.DataLoader( self._load_dataset( path=train_path, num_examples=num_examples, train_dataset_opts), shuffle=True, batch_size=batch_size, drop_last=True) logging.info('Loading validation data from %s' % valid_path) valid_loader = torch.utils.data.DataLoader( self._load_dataset( valid_path, valid_dataset_opts), shuffle=False, batch_size=valid_batch_size, drop_last=False) self.model.to(device) optim = getattr(torch.optim, optimizer)(self.model.parameters(), optimizer_opts) scheduler = torch.optim.lr_scheduler.StepLR(optim, lr_schedule) if show_plot: plotter = Plotter(show_plot=True) plotter.show() best_err = float('inf') start_time = time.time() for i in range(num_epochs): # train for one epoch logging.info('Training epoch %d' % (i+1)) train_losses = self._train(optim, scheduler, train_loader, device) # evaluate on validation set logging.info('Evaluating model on validation set') valid_loss, valid_err = self._test(valid_loader, device) logging.info('Validation loss = %.2e, validation error = %.4f' % (valid_loss, valid_err)) # save model with lowest validation error seen so far if (save_path is not None) and (valid_err < best_err): logging.info('Saving model with better validation error: %.2e (previously %.2e)' % (valid_err, best_err)) best_err = valid_err self._save(save_path, epoch=i+1, valid_err=valid_err) # update plot if show_plot: plotter.update(train_loss=train_losses, valid_loss=valid_loss, valid_err=valid_err) logging.info('Finished training in %.1f s' % (time.time() - start_time)) return self def test(self, batch_size=100, test_path=None, test_dataset_opts={}, device='cuda:0'): """Test the model.""" if test_path is None: raise ValueError('test_path must be specified') logging.info('loading test data from %s' % test_path) loader = torch.utils.data.DataLoader( self._load_dataset( test_path, test_dataset_opts), shuffle=False, batch_size=batch_size, drop_last=False) self.model.to(device) loss, err_rate = self._test(loader, device) logging.info('Test loss = %.2e' % loss) logging.info('Test error = %.4f' % err_rate) return loss, err_rate def predict(self, input, device='cuda:0', tf_output=False): """Predict a distribution over labels for a single example.""" self.model.eval() self.model.to(device) x = input.to(device) if x.dim() == 3: x = x.unsqueeze(0) with torch.no_grad(): out = self.model(x, tf_output=tf_output) logits = out[0] if tf_output else out probs = F.softmax(logits.squeeze(0), dim=-1) if tf_output: return probs, out[1] else: return probs def _train(self, optim, scheduler, loader, device): self.model.train() losses = [] for x, y in tqdm(loader): x, y = x.to(device), y.to(device) logits = self.model(x) loss = F.cross_entropy(logits, y) optim.zero_grad() loss.backward() optim.step() losses.append(loss.item()) scheduler.step() return losses def _test(self, loader, device): self.model.eval() total_loss = 0 total_err = 0 count = 0 for x, y in tqdm(loader): x, y = x.to(device), y.to(device) count += x.shape[0] with torch.no_grad(): logits = self.model(x) yhat = torch.argmax(logits, dim=-1) total_err += (y != yhat).sum().item() total_loss += F.cross_entropy(logits, y, reduction='sum').item() loss = total_loss / count err_rate = total_err / count return loss, err_rate class Dataset(torch.utils.data.Dataset): def __init__(self, path, num_examples=None, normalization=None): self.path = path self.normalization = normalization self.data, self.targets = torch.load(self.path) if self.data.dim() == 3: self.data = self.data.unsqueeze(1) # singleton channel dimension if num_examples is not None: self.data = self.data[:num_examples] self.targets = self.targets[:num_examples].type(torch.long) if normalization is not None: mean, std = normalization mean = torch.Tensor(mean).view(1, -1, 1, 1) std = torch.Tensor(std).view(1, -1, 1, 1) self.data.add_(-mean).div_(std) def __getitem__(self, index): return self.data[index], self.targets[index] def __len__(self): return len(self.data) class Plotter(object): def __init__(self, id_string='', width=12, height=2.5, show_plot=True): """A dynamic plotting widget for tracking training progress in notebooks.""" self.id_string = id_string self.width = width self.height = height self.output = Output() self.metrics = defaultdict(list) self.show_plot = show_plot def update(self, **metrics): for k, v in metrics.items(): if type(v) is list: self.metrics[k] += v else: self.metrics[k].append(v) self.output.clear_output(wait=True) with self.output: if self.show_plot: self.plot() plt.show() maxlen = max(map(len, self.metrics.keys())) print(self.id_string) for k, v in self.metrics.items(): print(('%' + str(maxlen) + 's') % k, '\| current = %.2e' % v[-1], '\| max = %.2e (iter %4d)' % (np.max(v), np.argmax(v)), '\| min = %.2e (iter %4d)' % (np.min(v), np.argmin(v))) def show(self): display(self.output) def progress_string(self): s = self.id_string + '\n' maxlen = max(map(len, self.metrics.keys())) for k, v in self.metrics.items(): s += ''.join([('%' + str(maxlen) + 's') % k, '\| current = %.2e' % v[-1], '\| max = %.2e (iter %4d)' % (np.max(v),	np.argmax(v)	numpy.argmax
from hazel.chromosphere import Hazel_atmosphere from hazel.photosphere import SIR_atmosphere from hazel.parametric import Parametric_atmosphere from hazel.stray import Straylight_atmosphere from hazel.configuration import Configuration from hazel.io import Generic_output_file from collections import OrderedDict from hazel.codes import hazel_code, sir_code from hazel.spectrum import Spectrum from hazel.transforms import transformed_to_physical, physical_to_transformed, jacobian_transformation import hazel.util import numpy as np import copy import os from pathlib import Path import scipy.stats import scipy.special import scipy.signal import scipy.linalg import scipy.optimize import warnings import logging import sys __all__ = ['Model'] class Model(object): def __init__(self, config=None, working_mode='synthesis', verbose=0, debug=False, rank=0, randomization=None, root=''): np.random.seed(123) if (rank != 0): return self.photospheres = [] self.chromospheres = [] self.chromospheres_order = [] self.atmospheres = {} self.order_atmospheres = [] self.straylight = [] self.parametric = [] self.spectrum = [] self.configuration = None self.n_cycles = 1 self.spectrum = {} self.topologies = [] self.straylights = [] self.working_mode = working_mode self.pixel = 0 self.debug = debug self.use_analytical_RF_if_possible = False self.nlte_available = False self.use_nlte = False self.root = root self.epsilon = 1e-2 self.svd_tolerance = 1e-8 self.step_limiter_inversion = 1.0 self.backtracking = 'brent' self.verbose = verbose self.logger = logging.getLogger("model") self.logger.setLevel(logging.DEBUG) self.logger.handlers = [] ch = logging.StreamHandler() # formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') formatter = logging.Formatter('%(asctime)s - %(message)s') ch.setFormatter(formatter) self.logger.addHandler(ch) # Set randomization if (randomization is None): self.n_randomization = 1 else: self.n_randomization = randomization if (self.verbose >= 1): self.logger.info('Hazel2 v1.0') if ('torch' in sys.modules and 'torch_geometric' in sys.modules): if (self.verbose >= 1): self.logger.info('PyTorch and PyTorch Geometric found. NLTE for Ca II is available') self.nlte_available = True if (config is not None): if (self.verbose >= 1): self.logger.info('Using configuration from file : {0}'.format(config)) self.configuration = Configuration(config) self.use_configuration(self.configuration.config_dict) # Initialize pyhazel hazel_code._init() def __getstate__(self): d = self.__dict__.copy() if 'logger' in d: d['logger'] = d['logger'].name return d def __setstate__(self, d): if 'logger' in d: d['logger'] = logging.getLogger(d['logger']) self.__dict__.update(d) def __str__(self): tmp = '' for l, par in self.__dict__.items(): if (l != 'LINES'): tmp += '{0}: {1}\n'.format(l, par) return tmp def use_configuration(self, config_dict): """ Use a configuration file Parameters ---------- config_dict : dict Dictionary containing all the options from the configuration file previously read Returns ------- None """ # Deal with the spectral regions tmp = config_dict['spectral regions'] # Output file self.output_file = config_dict['working mode']['output file'] # Backtracking mode if ('backtracking' in config_dict['working mode']): self.backtracking = config_dict['working mode']['backtracking'] else: self.backtracking = 'brent' if (self.verbose >= 1): self.logger.info('Backtracking mode : {0}'.format(self.backtracking)) # Working mode # self.working_mode = config_dict['working mode']['action'] # Add spectral regions for key, value in config_dict['spectral regions'].items(): self.add_spectral(value) # Set number of cycles if present if (self.working_mode == 'inversion'): if ('number of cycles' in config_dict['working mode']): if (config_dict['working mode']['number of cycles'] != 'None'): self.n_cycles = int(config_dict['working mode']['number of cycles']) if (self.verbose >= 1): self.logger.info('Using {0} cycles'.format(self.n_cycles)) # Use analytical RFs if possible if ('analytical rf if possible' in config_dict['working mode']): if (config_dict['working mode']['analytical rf if possible'] != 'None'): self.use_analytical_RF_if_possible = hazel.util.tobool(config_dict['working mode']['analytical rf if possible']) else: self.use_analytical_RF_if_possible = False else: self.use_analytical_RF_if_possible = False if (self.verbose >= 1): self.logger.info('Using analytical RFs if possible : {0}'.format(self.use_analytical_RF_if_possible)) # Set number of maximum iterations if ('maximum iterations' in config_dict['working mode']): if (config_dict['working mode']['number of cycles'] != 'None'): self.max_iterations = int(config_dict['working mode']['maximum iterations']) else: self.max_iterations = 10 else: self.max_iterations = 10 if (self.verbose >= 1): self.logger.info('Using {0} max. iterations'.format(self.max_iterations)) # Randomization if (self.verbose >= 1): if (self.n_randomization == 1): self.logger.info('Not using randomizations') else: self.logger.info('Using a maximum of {0} randomizations'.format(self.n_randomization)) # Set number of maximum iterations if ('relative error' in config_dict['working mode']): if (config_dict['working mode']['relative error'] != 'None'): self.relative_error = float(config_dict['working mode']['relative error']) if (self.verbose >= 1): self.logger.info('Stopping when relative error is below {0}'.format(self.relative_error)) else: self.relative_error = 1e-4 else: self.relative_error = 1e-4 # Save all cycles if ('save all cycles' not in config_dict['working mode']): self.save_all_cycles = False else: self.save_all_cycles = hazel.util.tobool(config_dict['working mode']['save all cycles']) if (self.verbose >= 1): self.logger.info('Saving all cycles : {0}'.format(self.save_all_cycles)) # Deal with the atmospheres tmp = config_dict['atmospheres'] self.atmospheres = {} if (self.verbose >= 1): self.logger.info('Adding atmospheres') for key, value in tmp.items(): if ('photosphere' in key): if (self.verbose >=1): self.logger.info(' - New available photosphere : {0}'.format(value['name'])) self.add_photosphere(value) if ('chromosphere' in key): if (self.verbose >= 1): self.logger.info(' - New available chromosphere : {0}'.format(value['name'])) self.add_chromosphere(value) if ('parametric' in key): if (self.verbose >= 1): self.logger.info(' - New available parametric : {0}'.format(value['name'])) self.add_parametric(value) if ('straylight' in key): if (self.verbose >= 1): self.logger.info(' - New available straylight : {0}'.format(value['name'])) self.add_straylight(value) self.setup() def setup(self): """ Setup the model for synthesis/inversion. This setup includes adding the topologies, removing unused atmospheres, reading the number of cycles for the inversion and some sanity checks Parameters ---------- None Returns ------- None """ # Adding topologies if (self.verbose >= 1): self.logger.info("Adding topologies") for value in self.topologies: self.add_topology(value) # Remove unused atmospheres defined in the configuration file and not in the topology if (self.verbose >= 1): self.logger.info("Removing unused atmospheres") self.remove_unused_atmosphere() # Calculate indices for atmospheres index_chromosphere = 1 index_photosphere = 1 self.n_photospheres = 0 self.n_chromospheres = 0 for k, v in self.atmospheres.items(): if (v.type == 'photosphere'): v.index = index_photosphere index_photosphere += 1 self.n_photospheres += 1 if (v.type == 'chromosphere'): v.index = index_chromosphere index_chromosphere += 1 self.n_chromospheres += 1 # Use analytical RFs if only photospheres are defined if (self.n_chromospheres == 0 and self.use_analytical_RF_if_possible): self.use_analytical_RF = True if (self.verbose >= 1): self.logger.info('Using analytical RFs : {0}'.format(self.use_analytical_RF)) else: self.use_analytical_RF = False # Check that number of pixels is the same for all atmospheric files if in synthesis mode if (self.working_mode == 'synthesis'): n_pixels = [v.n_pixel for k, v in self.atmospheres.items()] all_equal = all(x == n_pixels[0] for x in n_pixels) if (not all_equal): for k, v in self.atmospheres.items(): self.logger.info('{0} -> {1}'.format(k, v.n_pixel)) raise Exception("Files with model atmospheres do not contain the same number of pixels") else: if (self.verbose >= 1): self.logger.info('Number of pixels to read : {0}'.format(n_pixels[0])) self.n_pixels = n_pixels[0] if (self.working_mode == 'inversion'): n_pixels = [v.n_pixel for k, v in self.spectrum.items()] all_equal = all(x == n_pixels[0] for x in n_pixels) if (not all_equal): for k, v in self.spectrum.items(): self.logger.info('{0} -> {1}'.format(k, v.n_pixel)) raise Exception("Files with spectral regions do not contain the same number of pixels") else: if (self.verbose >= 1): self.logger.info('Number of pixels to invert : {0}'.format(n_pixels[0])) self.n_pixels = n_pixels[0] # Check that the number of pixels from all observations (in case of inversion) is the same # Check also if they are equal to those of the models # n_pixels = [v.n_pixel for k, v in self.atmospheres.items()] # all_equal = all(x == n_pixels[0] for x in n_pixels) # Check that the number of cycles is the same for all atmospheres (in case of inversion) if (self.working_mode == 'inversion'): cycles = [] for k, v in self.atmospheres.items(): for k2, v2 in v.cycles.items(): if (v2 is not None): cycles.append(len(v2)) all_equal = all(x == cycles[0] for x in cycles) if (not all_equal): raise Exception("Number of cycles in the nodes of active atmospheres is not always the same") else: if (self.n_cycles is None): self.n_cycles = cycles[0] # if (self.working_mode == 'inversion'): # cycles = [] # for tmp in ['I', 'Q', 'U', 'V']: # if ( cycles.append # for k, v in self.atmospheres.items(): # for k2, v2 in v.cycles.items(): # cycles.append(len(v2)) # all_equal = all(x == cycles[0] for x in cycles) # if (not all_equal): # raise Exception("Number of cycles in the nodes of active atmospheres is not always the same") # else: # if (self.n_cycles is None): # self.n_cycles = cycles[0] filename = os.path.join(os.path.dirname(__file__),'data/LINEAS') ff = open(filename, 'r') self.LINES = ff.readlines() ff.close() self.init_sir() for k, v in self.spectrum.items(): v.allocate_info_cycles(n_cycles=self.n_cycles) for k, v in self.atmospheres.items(): v.allocate_info_cycles(n_cycles=self.n_cycles) # Count total number of free parameters if (self.working_mode == 'inversion'): self.n_free_parameters = 0 for k, v in self.atmospheres.items(): for k2, v2 in v.cycles.items(): if (v2 is not None): self.n_free_parameters += max(hazel.util.onlyint(v2[0:self.n_cycles+1])) if (self.verbose >= 1): self.logger.info('Total number of free parameters in all cycles : {0}'.format(self.n_free_parameters)) def open_output(self): self.output_handler = Generic_output_file(self.output_file) self.output_handler.open(self) def close_output(self): self.output_handler.close() def write_output(self, randomization=0): if (self.working_mode == 'synthesis'): self.flatten_parameters_to_reference(cycle=0) self.output_handler.write(self, pixel=0, randomization=randomization) def add_spectral(self, spectral): """ Programmatically add a spectral region Parameters ---------- spectral : dict Dictionary containing the following data 'Name', 'Wavelength', 'Topology', 'Weights Stokes', 'Wavelength file', 'Wavelength weight file', 'Observations file', 'Mask file' Returns ------- None """ # Make sure that all keys of the input dictionary are in lower case # This is irrelevant if a configuration file is used because this has been # already done value = hazel.util.lower_dict_keys(spectral) if (self.verbose >= 1): self.logger.info('Adding spectral region {0}'.format(value['name'])) if ('wavelength file' not in value): value['wavelength file'] = None elif (value['wavelength file'] == 'None'): value['wavelength file'] = None if ('wavelength weight file' not in value): value['wavelength weight file'] = None elif (value['wavelength weight file'] == 'None'): value['wavelength weight file'] = None if ('observations file' not in value): value['observations file'] = None elif (value['observations file'] == 'None'): value['observations file'] = None if ('stokes weights' not in value): value['stokes weights'] = None elif (value['stokes weights'] == 'None'): value['stokes weights'] = None if ('mask file' not in value): value['mask file'] = None elif (value['mask file'] == 'None'): value['mask file'] = None if ('los' not in value): value['los'] = None elif (value['los'] == 'None'): value['los'] = None for tmp in ['i', 'q', 'u', 'v']: if ('weights stokes {0}'.format(tmp) not in value): value['weights stokes {0}'.format(tmp)] = [None]10 elif (value['weights stokes {0}'.format(tmp)] == 'None'): value['weights stokes {0}'.format(tmp)] = [None]10 if ('boundary condition' not in value): value['boundary condition'] = None elif (value['boundary condition'] == 'None'): value['boundary condition'] = None if ('instrumental profile' not in value): value['instrumental profile'] = None elif (value['instrumental profile'] == 'None'): value['instrumental profile'] = None # Wavelength file is not present if (value['wavelength file'] is None): # If the wavelength is defined if ('wavelength' in value): axis = value['wavelength'] wvl = np.linspace(float(axis[0]), float(axis[1]), int(axis[2])) wvl_lr = None if (self.verbose >= 1): self.logger.info(' - Using wavelength axis from {0} to {1} with {2} steps'.format(float(axis[0]), float(axis[1]), int(axis[2]))) else: raise Exception('Wavelength range is not defined. Please, use "Wavelength" or "Wavelength file"') else: # If both observed and synthetic wavelength points are given if ('wavelength' in value): axis = value['wavelength'] if (len(axis) != 3): raise Exception("Wavelength range is not given in the format: lower, upper, steps") wvl = np.linspace(float(axis[0]), float(axis[1]), int(axis[2])) if (self.verbose >= 1): self.logger.info(' - Using wavelength axis from {0} to {1} with {2} steps'.format(float(axis[0]), float(axis[1]), int(axis[2]))) self.logger.info(' - Reading wavelength axis from {0}'.format(value['wavelength file'])) wvl_lr = np.loadtxt(self.root + value['wavelength file']) else: if (self.verbose >= 1): self.logger.info(' - Reading wavelength axis from {0}'.format(value['wavelength file'])) wvl = np.loadtxt(self.root + value['wavelength file']) wvl_lr = None if (value['wavelength weight file'] is None): if (self.verbose >= 1 and self.working_mode == 'inversion'): self.logger.info(' - Setting all wavelength weights to 1') weights = np.ones((4,len(wvl))) else: if (self.verbose >= 1): self.logger.info(' - Reading wavelength weights from {0}'.format(value['wavelength weight file'])) weights = np.loadtxt(self.root + value['wavelength weight file'], skiprows=1).T # Observations file not present if (value['observations file'] is None): if (self.working_mode == 'inversion'): raise Exception("Inversion mode without observations is not allowed.") obs_file = None else: if (self.verbose >= 1): self.logger.info(' - Using observations from {0}'.format(value['observations file'])) obs_file = value['observations file'] if (value['mask file'] is None): mask_file = None if (self.verbose >= 1): self.logger.info(' - No mask for pixels') else: if (self.verbose >= 1): self.logger.info(' - Using mask from {0}'.format(value['mask file'])) mask_file = value['mask file'] if (value['instrumental profile'] is None): if (self.verbose >= 1): self.logger.info(' - No instrumental profile') else: if (self.verbose >= 1): self.logger.info(' - Instrumental profile : {0}'.format(value['instrumental profile'])) # if (value['straylight file'] is None): # if (self.verbose >= 1): # self.logger.info(' - Not using straylight') # stray_file = None # else: # if (self.verbose >= 1): # self.logger.info(' - Using straylight from {0}'.format(value['straylight file'])) # stray_file = value['straylight file'] if (value['los'] is None): if (self.working_mode == 'synthesis'): raise Exception("You need to provide the LOS for spectral region {0}".format(value['name'])) los = None else: los = np.array(value['los']).astype('float64') if (self.verbose >= 1): self.logger.info(' - Using LOS {0}'.format(value['los'])) if (value['boundary condition'] is None): if (self.verbose >= 1): self.logger.info(' - Using default boundary conditions [1,0,0,0] in spectral region {0} or read from file. Check carefully!'.format(value['name'])) boundary = np.array([1.0,0.0,0.0,0.0]) self.normalization = 'on-disk' else: boundary = np.array(value['boundary condition']).astype('float64') if (boundary[0] == 0.0): if (self.verbose >= 1): self.logger.info(' - Using off-limb normalization (peak intensity)') if (self.verbose >= 1): self.logger.info(' - Using boundary condition {0}'.format(value['boundary condition'])) stokes_weights = [] for st in ['i', 'q', 'u', 'v']: tmp = hazel.util.tofloat(value['weights stokes {0}'.format(st)]) tmp = [i if i is not None else 1.0 for i in tmp] stokes_weights.append(tmp) stokes_weights = np.array(stokes_weights) self.spectrum[value['name']] = Spectrum(wvl=wvl, weights=weights, observed_file=obs_file, name=value['name'], stokes_weights=stokes_weights, los=los, boundary=boundary, mask_file=mask_file, instrumental_profile=value['instrumental profile'], root=self.root, wvl_lr=wvl_lr) self.topologies.append(value['topology']) def add_photosphere(self, atmosphere): """ Programmatically add a photosphere Parameters ---------- atmosphere : dict Dictionary containing the following data 'Name', 'Spectral region', 'Height', 'Line', 'Wavelength', 'Reference atmospheric model', 'Ranges', 'Nodes' Returns ------- None """ # Make sure that all keys of the input dictionary are in lower case # This is irrelevant if a configuration file is used because this has been # already done atm = hazel.util.lower_dict_keys(atmosphere) self.atmospheres[atm['name']] = SIR_atmosphere(working_mode=self.working_mode, name=atm['name'], verbose=self.verbose) lines = [int(k) for k in list(atm['spectral lines'])] # If NLTE is available because PyTorch and PyTorch Geom are available # check whether the line is needed in NLTE or not if self.nlte_available: if ('nlte' not in atm): self.atmospheres[atm['name']].nlte = False else: self.atmospheres[atm['name']].nlte = hazel.util.tobool(atm['nlte']) if (self.verbose >= 1): self.logger.info(" * Line in NLTE if available") else: self.atmospheres[atm['name']].nlte = False if ('wavelength' not in atm): atm['wavelength'] = None elif (atm['wavelength'] == 'None'): atm['wavelength'] = None if (atm['wavelength'] is not None): wvl_range = [float(k) for k in atm['wavelength']] else: wvl_range = [np.min(self.spectrum[atm['spectral region']].wavelength_axis), np.max(self.spectrum[atm['spectral region']].wavelength_axis)] if ('reference frame' in atm): if ('line-of-sight' in atm['reference frame']): self.atmospheres[atm['name']].reference_frame = 'line-of-sight' if ('vertical' in atm['reference frame']): raise Exception('Magnetic fields in photospheres are always in the line-of-sight reference frame.') else: self.atmospheres[atm['name']].reference_frame = 'line-of-sight' if (self.verbose >= 1): self.logger.info(" * Adding line : {0}".format(lines)) self.logger.info(" * Magnetic field reference frame : {0}".format(self.atmospheres[atm['name']].reference_frame)) self.atmospheres[atm['name']].add_active_line(lines=lines, spectrum=self.spectrum[atm['spectral region']], wvl_range=np.array(wvl_range), verbose=self.verbose) if (self.atmospheres[atm['name']].graphnet_nlte is not None): self.set_nlte(True) if ('ranges' in atm): for k, v in atm['ranges'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].ranges[k2] = None else: self.atmospheres[atm['name']].ranges[k2] = hazel.util.tofloat(v) for k2, v2 in self.atmospheres[atm['name']].parameters.items(): self.atmospheres[atm['name']].regularization[k2] = None if ('regularization' in atm): for k, v in atm['regularization'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].regularization[k2] = None else: self.atmospheres[atm['name']].regularization[k2] = v if ('reference atmospheric model' in atm): my_file = Path(self.root + atm['reference atmospheric model']) if (not my_file.exists()): raise FileExistsError("Input file {0} for atmosphere {1} does not exist.".format(my_file, atm['name'])) self.atmospheres[atm['name']].load_reference_model(self.root + atm['reference atmospheric model'], self.verbose) if (self.atmospheres[atm['name']].model_type == '3d'): self.atmospheres[atm['name']].n_pixel = self.atmospheres[atm['name']].model_handler.get_npixel() if ('nodes' in atm): for k, v in atm['nodes'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): self.atmospheres[atm['name']].cycles[k2] = hazel.util.toint(v) if ('temperature change to recompute departure coefficients' in atm): self.atmospheres[atm['name']].t_change_departure = float(atm['temperature change to recompute departure coefficients']) else: self.atmospheres[atm['name']].t_change_departure = 0.0 def add_chromosphere(self, atmosphere): """ Programmatically add a chromosphere Parameters ---------- atmosphere : dict Dictionary containing the following data 'Name', 'Spectral region', 'Height', 'Line', 'Wavelength', 'Reference atmospheric model', 'Ranges', 'Nodes' Returns ------- None """ # Make sure that all keys of the input dictionary are in lower case # This is irrelevant if a configuration file is used because this has been # already done atm = hazel.util.lower_dict_keys(atmosphere) self.atmospheres[atm['name']] = Hazel_atmosphere(working_mode=self.working_mode, name=atm['name']) if ('wavelength' not in atm): atm['wavelength'] = None elif (atm['wavelength'] == 'None'): atm['wavelength'] = None if (atm['wavelength'] is not None): wvl_range = [float(k) for k in atm['wavelength']] else: wvl_range = [np.min(self.spectrum[atm['spectral region']].wavelength_axis), np.max(self.spectrum[atm['spectral region']].wavelength_axis)] self.atmospheres[atm['name']].add_active_line(line=atm['line'], spectrum=self.spectrum[atm['spectral region']], wvl_range=np.array(wvl_range)) if ('reference frame' in atm): if (atm['reference frame'] == 'line-of-sight'): self.atmospheres[atm['name']].reference_frame = 'line-of-sight' if (atm['reference frame'] == 'vertical'): self.atmospheres[atm['name']].reference_frame = 'vertical' else: self.atmospheres[atm['name']].reference_frame = 'vertical' if (self.verbose >= 1): self.logger.info(" * Adding line : {0}".format(atm['line'])) self.logger.info(" * Magnetic field reference frame : {0}".format(self.atmospheres[atm['name']].reference_frame)) if ('ranges' in atm): for k, v in atm['ranges'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].ranges[k2] = None else: self.atmospheres[atm['name']].ranges[k2] = hazel.util.tofloat(v) for k2, v2 in self.atmospheres[atm['name']].parameters.items(): self.atmospheres[atm['name']].regularization[k2] = None if ('regularization' in atm): for k, v in atm['regularization'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].regularization[k2] = None else: self.atmospheres[atm['name']].regularization[k2] = v if ('coordinates for magnetic field vector' in atm): if (atm['coordinates for magnetic field vector'] == 'cartesian'): self.atmospheres[atm['name']].coordinates_B = 'cartesian' if (atm['coordinates for magnetic field vector'] == 'spherical'): self.atmospheres[atm['name']].coordinates_B = 'spherical' else: self.atmospheres[atm['name']].coordinates_B = 'cartesian' self.atmospheres[atm['name']].select_coordinate_system() if (self.verbose >= 1): self.logger.info(" * Magnetic field coordinates system : {0}".format(self.atmospheres[atm['name']].coordinates_B)) if ('reference atmospheric model' in atm): my_file = Path(self.root + atm['reference atmospheric model']) if (not my_file.exists()): raise FileExistsError("Input file {0} for atmosphere {1} does not exist.".format(my_file, atm['name'])) self.atmospheres[atm['name']].load_reference_model(self.root + atm['reference atmospheric model'], self.verbose) if (self.atmospheres[atm['name']].model_type == '3d'): self.atmospheres[atm['name']].n_pixel = self.atmospheres[atm['name']].model_handler.get_npixel() # Set values of parameters self.atmospheres[atm['name']].height = float(atm['height']) if ('nodes' in atm): for k, v in atm['nodes'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): self.atmospheres[atm['name']].cycles[k2] = hazel.util.toint(v) def add_parametric(self, atmosphere): """ Programmatically add a parametric atmosphere Parameters ---------- atmosphere : dict Dictionary containing the following data 'Name', 'Spectral region', 'Wavelength', 'Reference atmospheric model', 'Type', 'Ranges', 'Nodes' Returns ------- None """ # Make sure that all keys of the input dictionary are in lower case # This is irrelevant if a configuration file is used because this has been # already done atm = hazel.util.lower_dict_keys(atmosphere) self.atmospheres[atm['name']] = Parametric_atmosphere(working_mode=self.working_mode) if ('wavelength' not in atm): atm['wavelength'] = None elif (atm['wavelength'] == 'None'): atm['wavelength'] = None if (atm['wavelength'] is not None): wvl_range = [float(k) for k in atm['wavelength']] else: wvl_range = [np.min(self.spectrum[atm['spectral region']].wavelength_axis), np.max(self.spectrum[atm['spectral region']].wavelength_axis)] self.atmospheres[atm['name']].add_active_line(spectrum=self.spectrum[atm['spectral region']], wvl_range=np.array(wvl_range)) if ('ranges' in atm): for k, v in atm['ranges'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].ranges[k2] = None else: self.atmospheres[atm['name']].ranges[k2] = hazel.util.tofloat(v) if ('reference atmospheric model' in atm): my_file = Path(self.root + atm['reference atmospheric model']) if (not my_file.exists()): raise FileExistsError("Input file {0} for atmosphere {1} does not exist.".format(my_file, atm['name'])) self.atmospheres[atm['name']].load_reference_model(self.root + atm['reference atmospheric model'], self.verbose) if (self.atmospheres[atm['name']].model_type == '3d'): self.atmospheres[atm['name']].n_pixel = self.atmospheres[atm['name']].model_handler.get_npixel() # Set values of parameters if ('nodes' in atm): for k, v in atm['nodes'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): self.atmospheres[atm['name']].cycles[k2] = hazel.util.toint(v) for k2, v2 in self.atmospheres[atm['name']].parameters.items(): self.atmospheres[atm['name']].regularization[k2] = None if ('regularization' in atm): for k, v in atm['regularization'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].regularization[k2] = None else: self.atmospheres[atm['name']].regularization[k2] = v def add_straylight(self, atmosphere): """ Programmatically add a straylight atmosphere Parameters ---------- atmosphere : dict Dictionary containing the following data 'Name', 'Spectral region', 'Reference atmospheric model', 'Ranges', 'Nodes' Returns ------- None """ # Make sure that all keys of the input dictionary are in lower case # This is irrelevant if a configuration file is used because this has been # already done atm = hazel.util.lower_dict_keys(atmosphere) self.atmospheres[atm['name']] = Straylight_atmosphere(working_mode=self.working_mode) if ('wavelength' not in atm): atm['wavelength'] = None elif (atm['wavelength'] == 'None'): atm['wavelength'] = None if (atm['wavelength'] is not None): wvl_range = [float(k) for k in atm['wavelength']] else: wvl_range = [np.min(self.spectrum[atm['spectral region']].wavelength_axis), np.max(self.spectrum[atm['spectral region']].wavelength_axis)] self.atmospheres[atm['name']].add_active_line(spectrum=self.spectrum[atm['spectral region']], wvl_range=np.array(wvl_range)) if ('ranges' in atm): for k, v in atm['ranges'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].ranges[k2] = None else: self.atmospheres[atm['name']].ranges[k2] = hazel.util.tofloat(v) my_file = Path(self.root + atm['reference atmospheric model']) if (not my_file.exists()): raise FileExistsError("Input file {0} for atmosphere {1} does not exist.".format(my_file, atm['name'])) if ('reference atmospheric model' in atm): self.atmospheres[atm['name']].load_reference_model(self.root + atm['reference atmospheric model'], self.verbose) if (self.atmospheres[atm['name']].model_type == '3d'): self.atmospheres[atm['name']].n_pixel = self.atmospheres[atm['name']].model_handler.get_npixel() # Set values of parameters if ('nodes' in atm): for k, v in atm['nodes'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): self.atmospheres[atm['name']].cycles[k2] = hazel.util.toint(v) for k2, v2 in self.atmospheres[atm['name']].parameters.items(): self.atmospheres[atm['name']].regularization[k2] = None if ('regularization' in atm): for k, v in atm['regularization'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].regularization[k2] = None else: self.atmospheres[atm['name']].regularization[k2] = v def remove_unused_atmosphere(self): """ Remove unused atmospheres Parameters ---------- None Returns ------- None """ to_remove = [] for k, v in self.atmospheres.items(): if (not v.active): to_remove.append(k) if (self.verbose >= 1): self.logger.info(' - Atmosphere {0} deleted.'.format(k)) for k in to_remove: self.atmospheres.pop(k) def init_sir_external(self): """ Initialize SIR for this synthesis Parameters ---------- None Returns ------- None """ for k, v in self.atmospheres.items(): if (v.type == 'photosphere'): f = open('lte.grid', 'w') f.write("IMPORTANT: a) All items must be separated by commas. \n") f.write(" b) The first six characters of the last line \n") f.write(" in the header (if any) must contain the symbol --- \n") f.write("\n") f.write("Line and blends indices : Initial lambda Step Final lambda \n") f.write("(in this order) (mA) (mA) (mA) \n") f.write("-----------------------------------------------------------------------\n") ind_low = (np.abs(v.spectrum.wavelength_axis - v.wvl_range_lambda[0])).argmin() ind_top = (np.abs(v.spectrum.wavelength_axis - v.wvl_range_lambda[1])).argmin() low = v.spectrum.wavelength_axis[ind_low] top = v.spectrum.wavelength_axis[ind_top] # TODO delta = (v.spectrum.wavelength_axis[1] - v.spectrum.wavelength_axis[0]) filename = os.path.join(os.path.dirname(__file__),'data/LINEAS') ff = open(filename, 'r') flines = ff.readlines() ff.close() for i in range(len(v.lines)): for l in flines: tmp = l.split() index = int(tmp[0].split('=')[0]) if (index == v.lines[0]): wvl = float(tmp[2]) f.write("{0} : {1}, {2}, {3}\n".format(str(v.lines)[1:-1], 1e3(low-wvl), 1e3delta, 1e3(top-wvl))) f.close() v.n_lambda = sir_code.init_externalfile(v.index, filename) def init_sir(self): """ Initialize SIR for this synthesis. This version does not make use of any external file, which might be not safe when running in MPI mode. Parameters ---------- None Returns ------- None """ lines = [] n_lines = 0 elements = {'H':1,'HE':2,'LI':3,'BE':4,'B':5,'C':6,'N':7,'O':8,'F':9,'NE':10, 'NA':11,'MG':12,'AL':13,'SI':14,'P':15,'S':16,'CL':17,'AR':18,'K':19,'CA':20,'SC':21,'TI':22,'V':23,'CR':24, 'MN':25,'FE':26,'CO':27,'NI':28,'CU':29,'ZN':30,'GA':31,'GE':32,'AS':33,'SE':34,'BR':35,'KR':36, 'RB':37,'SR':38,'Y':39,'ZR':40,'NB':41,'MO':42,'TC':43,'RU':44,'RH':45,'PD':46,'AG':47,'CD':48,'IN':49, 'SN':50,'SB':51,'TE':52,'I':53,'XE':54,'CS':55,'BA':56,'LA':57,'CE':58,'PR':59,'ND':60,'PM':61, 'SM':62,'EU':63,'GD':64,'TB':65,'DY':66,'HO':67,'ER':68,'TM':69,'YB':70,'LU':71,'HF':72,'TA':73,'W':74, 'RE':75,'OS':76,'IR':77,'PT':78,'AU':79,'HG':80,'TL':81,'PB':82,'BI':83,'PO':84,'AT':85,'RN':86, 'FR':87,'RA':88,'AC':89,'TH':90,'PA':91,'U':92} states = {'S': 0, 'P': 1, 'D': 2, 'F': 3, 'G': 4, 'H': 5, 'I': 6} for k, v in self.atmospheres.items(): if (v.type == 'photosphere'): n_lines += 1 ind_low = (np.abs(v.spectrum.wavelength_axis - v.wvl_range_lambda[0])).argmin() ind_top = (np.abs(v.spectrum.wavelength_axis - v.wvl_range_lambda[1])).argmin() low = v.spectrum.wavelength_axis[ind_low] top = v.spectrum.wavelength_axis[ind_top] # TODO delta = (v.spectrum.wavelength_axis[1] - v.spectrum.wavelength_axis[0]) nblend = len(v.lines) lines = np.zeros(len(v.lines), dtype=np.intc) atom = np.zeros(len(v.lines), dtype=np.intc) istage = np.zeros(len(v.lines), dtype=np.intc) wvl = np.zeros(len(v.lines)) zeff = np.zeros(len(v.lines)) energy = np.zeros(len(v.lines)) loggf = np.zeros(len(v.lines)) mult1 = np.zeros(len(v.lines), dtype=np.intc) mult2 = np.zeros(len(v.lines), dtype=np.intc) design1 = np.zeros(len(v.lines), dtype=np.intc) design2 = np.zeros(len(v.lines), dtype=np.intc) tam1 = np.zeros(len(v.lines)) tam2 = np.zeros(len(v.lines)) alfa = np.zeros(len(v.lines)) sigma = np.zeros(len(v.lines)) for i in range(len(v.lines)): lines[i] = v.lines[i] for l in self.LINES: tmp = l.split() index = int(tmp[0].split('=')[0]) if (index == v.lines[i]): atom[i] = elements[tmp[0].split('=')[1]] istage[i] = tmp[1] wvl[i] = float(tmp[2]) zeff[i] = float(tmp[3]) energy[i] = float(tmp[4]) loggf[i] = float(tmp[5]) mult1[i] = int(tmp[6][:-1]) mult2[i] = int(tmp[8][:-1]) design1[i] = states[tmp[6][-1]] design2[i] = states[tmp[8][-1]] tam1[i] = float(tmp[7].split('-')[0]) tam2[i] = float(tmp[9].split('-')[0]) if (len(tmp) == 12): alfa[i] = float(tmp[-2]) sigma[i] = float(tmp[-1]) else: alfa[i] = 0.0 sigma[i] = 0.0 lambda0 = 1e3(low-wvl[0]) lambda1 = 1e3(top-wvl[0]) n_steps = ind_top - ind_low + 1 v.n_lambda = n_steps sir_code.init(v.index, nblend, lines, atom, istage, wvl, zeff, energy, loggf, mult1, mult2, design1, design2, tam1, tam2, alfa, sigma, lambda0, lambda1, n_steps) def exit_hazel(self): for k, v in self.atmospheres.items(): if (v.type == 'chromosphere'): hazel_code.exit(v.index) def add_topology(self, atmosphere_order): """ Add a new topology Parameters ---------- topology : str Topology Returns ------- None """ # Transform the order to a list of lists if (self.verbose >= 1): self.logger.info(' - {0}'.format(atmosphere_order)) vertical_order = atmosphere_order.split('->') order = [] for k in vertical_order: name = k.strip().replace('(','').replace(')','').split('+') name = [k.strip() for k in name] tmp = [] for n in name: tmp.append(n) self.atmospheres[n].active = True order.append(tmp) order_flat = [item for sublist in order for item in sublist] # Check that straylight components, if any, are not at the last position for atm in order_flat[:-1]: if (self.atmospheres[atm].type == 'straylight'): raise Exception("Straylight components can only be at the last position of a topology.") self.order_atmospheres.append(order) # Check that there are no two photospheres linked with -> # because they do not make any sense n_photospheres_linked = [] for atmospheres in self.order_atmospheres: for order in atmospheres: for k, atm in enumerate(order): if (self.atmospheres[atm].type == 'photosphere'): n_photospheres_linked.append(k) if (len(n_photospheres_linked) != len(set(n_photospheres_linked))): raise Exception("There are several photospheres linked with ->. This is not allowed.") def normalize_ff(self): """ Normalize all filling factors so that they add to one to avoid later problems. We use a softmax function to make sure they all add to one and can be unconstrained ff_i = exp(x_i) / sum(exp(x_i)) Parameters ---------- None Returns ------- None """ for atmospheres in self.order_atmospheres: for order in atmospheres: total_ff = 0.0 for atm in order: if (self.atmospheres[atm].type != 'straylight'): if (self.working_mode == 'inversion'): self.atmospheres[atm].parameters['ff'], self.atmospheres[atm].ranges['ff'][0], self.atmospheres[atm].ranges['ff'][1] ff = transformed_to_physical(self.atmospheres[atm].parameters['ff'], self.atmospheres[atm].ranges['ff'][0], self.atmospheres[atm].ranges['ff'][1]) else: ff = transformed_to_physical(self.atmospheres[atm].parameters['ff'], -0.00001, 1.00001) total_ff += ff for atm in order: if (self.atmospheres[atm].type != 'straylight'): if (self.working_mode == 'inversion'): ff = transformed_to_physical(self.atmospheres[atm].parameters['ff'], self.atmospheres[atm].ranges['ff'][0], self.atmospheres[atm].ranges['ff'][1]) self.atmospheres[atm].parameters['ff'] = ff / total_ff self.atmospheres[atm].parameters['ff'] = physical_to_transformed(self.atmospheres[atm].parameters['ff'], self.atmospheres[atm].ranges['ff'][0], self.atmospheres[atm].ranges['ff'][1]) else: ff = transformed_to_physical(self.atmospheres[atm].parameters['ff'], -0.00001, 1.00001) self.atmospheres[atm].parameters['ff'] = ff / total_ff self.atmospheres[atm].parameters['ff'] = physical_to_transformed(self.atmospheres[atm].parameters['ff'], -0.00001, 1.00001) def synthesize_spectral_region(self, spectral_region, perturbation=False): """ Synthesize all atmospheres for a single spectral region and normalize to the continuum of the quiet Sun at disk center Parameters ---------- spectral_region : str Spectral region to synthesize perturbation : bool Set to True if you are synthesizing with a perturbation. In this case, the synthesis is saved in spectrum.stokes_perturbed instead of spectrum.stokes Returns ------- None """ stokes = None stokes_out = None # Loop over all atmospheres for i, atmospheres in enumerate(self.order_atmospheres): for n, order in enumerate(atmospheres): for k, atm in enumerate(order): if (self.atmospheres[atm].spectrum.name == spectral_region): # Update the boundary condition only for the first atmosphere if several are sharing ff if (n > 0 and k == 0): ind_low, ind_top = self.atmospheres[atm].wvl_range if (perturbation): stokes_out = self.atmospheres[atm].spectrum.stokes_perturbed[:, ind_low:ind_top] hazel.util.i0_allen(self.atmospheres[atm].spectrum.wavelength_axis[ind_low:ind_top], 1.0)[None,:] else: stokes_out = self.atmospheres[atm].spectrum.stokes[:, ind_low:ind_top] * hazel.util.i0_allen(self.atmospheres[atm].spectrum.wavelength_axis[ind_low:ind_top], 1.0)[None,:] if (self.atmospheres[atm].type == 'straylight'): stokes, error = self.atmospheres[atm].synthesize(nlte=self.use_nlte) if (error == 1): raise stokes += (1.0 - self.atmospheres[atm].parameters['ff']) * stokes_out else: if (k == 0): if (self.use_analytical_RF): stokes, self.rf_analytical, error = self.atmospheres[atm].synthesize(stokes_out, returnRF=True, nlte=self.use_nlte) else: stokes, error = self.atmospheres[atm].synthesize(stokes_out, nlte=self.use_nlte) else: tmp, error = self.atmospheres[atm].synthesize(stokes_out, nlte=self.use_nlte) stokes += tmp ind_low, ind_top = self.atmospheres[atm].wvl_range mean_wvl = np.mean(self.atmospheres[atm].spectrum.wavelength_axis[ind_low:ind_top]) i0 = hazel.util.i0_allen(mean_wvl, 1.0) # Divide by i0 if (self.use_analytical_RF): for k, v in self.rf_analytical.items(): if (k != 'ff'): v /= i0 if (perturbation): self.atmospheres[atm].spectrum.stokes_perturbed[:, ind_low:ind_top] = stokes / i0#[None,:] else: self.atmospheres[atm].spectrum.stokes[:, ind_low:ind_top] = stokes / i0#[None,:] def set_nlte(self, option): """ Set calculation of Ca II 8542 A to NLTE Parameters ---------- option : bool Set to True to use NLTE, False to use LTE """ self.use_nlte = option if (self.verbose >= 1): self.logger.info('Setting NLTE for Ca II 8542 A to {0}'.format(self.use_nlte)) def synthesize(self, perturbation=False): """ Synthesize all atmospheres Parameters ---------- perturbation : bool Set to True if you are synthesizing with a perturbation. In this case, the synthesis is saved in spectrum.stokes_perturbed instead of spectrum.stokes Returns ------- None """ if (self.working_mode == 'inversion'): self.normalize_ff() for k, v in self.spectrum.items(): self.synthesize_spectral_region(k, perturbation=perturbation) if (v.normalization == 'off-limb'): if (perturbation): v.stokes_perturbed /= np.max(v.stokes_perturbed[0,:]) else: v.stokes /= np.max(v.stokes[0,:]) if (v.psf_spectral is not None): for i in range(4): if (perturbation): v.stokes_perturbed[i,:] = scipy.signal.convolve(v.stokes_perturbed[i,:], v.psf_spectral, mode='same', method='auto') else: v.stokes[i,:] = scipy.signal.convolve(v.stokes[i,:], v.psf_spectral, mode='same', method='auto') if (v.interpolate_to_lr): for i in range(4): if (perturbation): v.stokes_perturbed_lr[i,:] = np.interp(v.wavelength_axis_lr, v.wavelength_axis, v.stokes_perturbed[i,:]) else: v.stokes_lr[i,:] = np.interp(v.wavelength_axis_lr, v.wavelength_axis, v.stokes[i,:]) def find_active_parameters(self, cycle): """ Find all active parameters in all active atmospheres in the current cycle Parameters ---------- cycle : int Cycle to consider Returns ------- None """ pars = [] coupled = [] self.nodes = [] left = 0 right = 0 for atmospheres in self.order_atmospheres: for n, order in enumerate(atmospheres): for k, atm in enumerate(order): for l, par in self.atmospheres[atm].cycles.items(): if (par is not None): if (hazel.util.isint(par[cycle])): if (par[cycle] > 0): # [Atmosphere name, n_nodes, nodes, value, range] self.atmospheres[atm].nodes[l] = np.zeros(par[cycle]) self.atmospheres[atm].n_nodes[l] = par[cycle] right += par[cycle] n_lambda = len(self.atmospheres[atm].spectrum.wavelength_axis) tmp = {'atm': atm, 'n_nodes': par[cycle], 'parameter': l, 'ranges': self.atmospheres[atm].ranges[l], 'delta': self.atmospheres[atm].epsilon[l], 'left': left, 'right': right, 'regularization': self.atmospheres[atm].regularization[l], 'coupled': False} self.nodes.append(self.atmospheres[atm].nodes[l]) left = copy.copy(right) pars.append(tmp) else: self.atmospheres[atm].nodes[l] = 0.0 self.atmospheres[atm].n_nodes[l] = 0 else: n_lambda = len(self.atmospheres[atm].spectrum.wavelength_axis) tmp = {'atm': atm, 'n_nodes': par[cycle], 'parameter': l, 'coupled': True} coupled.append(tmp) self.active_meta = pars self.coupled_meta = coupled if (not self.nodes): raise Exception("No parameters to invert in cycle {0}. Please add them or reduce the number of cycles. ".format(cycle)) self.nodes =	np.concatenate(self.nodes)	numpy.concatenate
""" ======================================= The :mod:`mpi_array.locale_test` Module ======================================= Module defining :mod:`mpi_array.locale` unit-tests. Execute as:: python -m mpi_array.locale_test Classes ======= .. autosummary:: :toctree: generated/ :template: autosummary/inherits_TestCase_class.rst WinLndarrayTest - Tests for :obj:`mpi_array.locale.win_lndarray`. LndarrayTest - Tests for :obj:`mpi_array.locale.lndarray`. LndarrayProxyTest - Tests for :obj:`mpi_array.locale.LndarrayProxy`. """ from __future__ import absolute_import from array_split.split import shape_factors as _shape_factors import mpi4py.MPI as _mpi import numpy as _np # noqa: E402,F401 from . import locale as _locale from .license import license as _license, copyright as _copyright, version as _version from . import unittest as _unittest from . import logging as _logging # noqa: E402,F401 from .comms import create_distribution, LT_PROCESS, LT_NODE from .distribution import IndexingExtent, LocaleExtent from .distribution import GlobaleExtent __author__ = "<NAME>" __license__ = _license() __copyright__ = _copyright() __version__ = _version() class WinLndarrayTest(_unittest.TestCase): """ Tests for :obj:`mpi_array.locale.win_lndarray`. """ def test_construct_with_invalid_comm(self): """ Tests that ValueError is raised for invalid communicator argument passed to :obj:`mpi_array.locale.win_lndarray` constructor. """ comm = None self.assertRaises(ValueError, _locale.win_lndarray, shape=(100,), comm=comm) comm = _mpi.COMM_NULL self.assertRaises(ValueError, _locale.win_lndarray, shape=(100,), comm=comm) def test_construct(self): """ Tests for :obj:`mpi_array.locale.win_lndarray` construction. """ comm = _mpi.COMM_SELF ary = _locale.win_lndarray(shape=(10, 10, 10), dtype="int32", comm=comm) self.assertTrue(ary.comm is comm) self.assertTrue(ary.win is not None) comm = _mpi.COMM_WORLD if (_mpi.VERSION >= 3) and (comm.size > 1): comm = comm.Split_type(_mpi.COMM_TYPE_SHARED, key=comm.rank) if comm.size > 1: ary = _locale.win_lndarray(shape=(comm.size, 10, 10), dtype="int32", comm=comm) self.assertTrue(ary.comm is comm) self.assertTrue(ary.win is not None) ary[comm.rank] = comm.rank + 1 comm.barrier() for r in range(0, comm.size): self.assertTrue(_np.all(ary[r] == (r + 1))) class LndarrayTest(_unittest.TestCase): """ :obj:`unittest.TestCase` for :obj:`mpi_array.locale.lndarray`. """ def test_construct(self): """ Tests :meth:`mpi_array.locale.lndarray.__new__`. """ gshape = (11, 13, 51) with _locale.ones(shape=gshape, dtype="int16") as lary: slary = lary.lndarray # MPI windows own buffer data self.assertTrue(slary.flags.carray) self.assertFalse(slary.flags.owndata) self.assertFalse(slary.base.flags.owndata) self.assertTrue(slary.base.flags.carray) lshape = slary.shape bad_lshape = list(lshape) bad_lshape[-1] += 1 self.assertRaises( ValueError, _locale.lndarray, shape=lshape ) def test_view(self): """ Tests :meth:`mpi_array.locale.lndarray.__getitem__`. """ gshape = (11, 13, 51) with _locale.ones(shape=gshape, dtype="int16") as lary: slary = lary.lndarray # MPI windows own buffer data self.assertTrue(slary.flags.carray) self.assertFalse(slary.flags.owndata) self.assertFalse(slary.base.flags.owndata) self.assertTrue(slary.base.flags.carray) v = lary[0:slary.shape[0] // 2, 0:slary.shape[1] // 2, 0:slary.shape[2] // 2] self.assertTrue(isinstance(v, _locale.lndarray)) self.assertFalse(v.flags.owndata) self.assertFalse( ((v.size > 0) and (v.shape[0] > 1) and (v.shape[1] > 1)) and v.flags.carray, "v.size=%s, v.shape=%s, v.flags.carray=%s" % (v.size, v.shape, v.flags.carray) ) self.assertTrue(isinstance(v.base, _locale.lndarray)) self.assertFalse(v.base.flags.owndata) self.assertTrue(v.base.flags.carray) self.assertFalse(isinstance(v.base.base, _locale.lndarray)) self.assertTrue(isinstance(v.base.base, _np.ndarray)) self.assertFalse(v.base.base.flags.owndata) self.assertTrue(v.base.base.flags.carray) def test_numpy_sum(self): """ Test :func:`numpy.sum` reduction using a :obj:`mpi_array.locale.lndarray` as argument. """ gshape = (50, 50, 50) comms_and_distrib = create_distribution(gshape) with \ _locale.ones(shape=gshape, comms_and_distrib=comms_and_distrib, dtype="int32") \ as lary: slary = lary.lndarray l_sum = _np.sum(lary.rank_view_n) self.assertFalse(l_sum.flags.owndata) self.assertTrue(l_sum.base.flags.owndata) rank_logger = _logging.get_rank_logger(__name__ + self.id()) rank_logger.info("type(slary)=%s", type(slary)) rank_logger.info("type(slary.base)=%s", type(slary.base)) rank_logger.info("type(l_sum)=%s", type(l_sum)) rank_logger.info("type(l_sum.base)=%s", type(l_sum.base)) g_sum = comms_and_distrib.locale_comms.peer_comm.allreduce(l_sum, op=_mpi.SUM) self.assertEqual(_np.product(gshape), g_sum) class LndarrayProxyTest(_unittest.TestCase): """ :obj:`unittest.TestCase` for :obj:`mpi_array.locale.LndarrayProxy`. """ def test_construct_arg_checking(self): """ Test for :meth:`mpi_array.locale.LndarrayProxy.__new__`. """ self.assertRaises( ValueError, _locale.LndarrayProxy, shape=None, locale_extent=None, dtype="int64" ) gshape = (10, 11, 12, 13, 5) bad_lshape = gshape bad_lshape = (bad_lshape[0] - 1,) + bad_lshape[1:] self.assertRaises( ValueError, _locale.LndarrayProxy, shape=bad_lshape, locale_extent=LocaleExtent( peer_rank=_mpi.COMM_WORLD.rank, inter_locale_rank=_mpi.COMM_WORLD.rank, globale_extent=GlobaleExtent(stop=gshape), start=(0, 0, 0, 0, 0), stop=gshape ) ) def test_fill(self): """ Test for :meth:`mpi_array.locale.LndarrayProxy.fill`. """ comms_and_distrib = create_distribution(shape=(24, 35, 14, 7)) with \ _locale.empty( shape=(24, 35, 14, 7), comms_and_distrib=comms_and_distrib, dtype="int64" ) as lary: rank_val = comms_and_distrib.locale_comms.peer_comm.rank + 1 lary.fill(rank_val) self.assertTrue(_np.all(lary[lary.rank_view_slice_n] == rank_val)) def test_get_and_set_item(self): """ """ comms_and_distrib = create_distribution(shape=(24, 35, 14, 7)) with \ _locale.empty( shape=(24, 35, 14, 7), comms_and_distrib=comms_and_distrib, dtype="int64" ) as lary: rank_val = comms_and_distrib.locale_comms.peer_comm.rank + 1 lary[lary.rank_view_slice_n] = rank_val self.assertSequenceEqual( list(IndexingExtent(lary.rank_view_slice_n).shape), list(lary[lary.rank_view_slice_n].shape) ) self.assertTrue(_np.all(lary[lary.rank_view_slice_n] == rank_val)) self.assertSequenceEqual( list(IndexingExtent(lary.rank_view_slice_h).shape), list(lary[lary.rank_view_slice_h].shape) ) def test_empty_shared_1d(self): """ Test for :func:`_locale.empty` and :func:`_locale.empty_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape) with _locale.empty(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) self.assertSequenceEqual( list(lshape), list(IndexingExtent(lary.intra_partition.rank_view_slice_n).shape) ) with _locale.empty_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) self.assertSequenceEqual( list(lshape), list(IndexingExtent(lary1.intra_partition.rank_view_slice_n).shape) ) ary = _locale.empty_like(_np.zeros(lshape, dtype="int64")) self.assertEqual(_np.dtype("int64"), ary.dtype) self.assertSequenceEqual( list(lshape), list(ary.shape) ) def test_empty_non_shared_1d(self): """ Test for :func:`_locale.empty` and :func:`_locale.empty_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape, locale_type=LT_PROCESS) with _locale.empty(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) self.assertSequenceEqual(list(lshape), list(lary.shape)) self.assertSequenceEqual( list(lshape), list(IndexingExtent(lary.intra_partition.rank_view_slice_n).shape) ) with _locale.empty_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) self.assertSequenceEqual(list(lshape), list(lary1.shape)) self.assertSequenceEqual( list(lshape), list(IndexingExtent(lary1.intra_partition.rank_view_slice_n).shape) ) def test_zeros_shared_1d(self): """ Test for :func:`_locale.zeros` and :func:`_locale.zeros_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape) with _locale.zeros(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary == 0)) with _locale.zeros_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == 0)) def test_zeros_non_shared_1d(self): """ Test for :func:`_locale.zeros` and :func:`_locale.zeros_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape, locale_type=LT_PROCESS) with _locale.zeros(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary == 0)) with _locale.zeros_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == 0)) def test_ones_shared_1d(self): """ Test for :func:`_locale.ones` and :func:`_locale.ones_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape) with _locale.ones(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary == 1)) with _locale.ones_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == 1)) def test_ones_non_shared_1d(self): """ Test for :func:`_locale.ones` and :func:`_locale.ones_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape, locale_type=LT_PROCESS) with _locale.ones(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary == 1)) with _locale.ones_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == 1)) def test_copy_shared_1d(self): """ Test for :func:`_locale.copy`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape) with _locale.ones(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) lary.rank_view_n[...] = cand.locale_comms.peer_comm.rank with _locale.copy(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == lary)) def test_copy_non_shared_1d(self): """ Test for :func:`_locale.copy`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape, locale_type=LT_PROCESS) with _locale.ones(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) lary.rank_view_n[...] = cand.locale_comms.peer_comm.rank with _locale.copy(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == lary)) def do_test_views_2d(self, halo=0): """ Test for :meth:`mpi_array.locale.LndarrayProxy.rank_view_n` and :meth:`mpi_array.locale.LndarrayProxy.rank_view_h`. """ lshape = _np.array((4, 3), dtype="int64") gshape = lshape * _shape_factors(_mpi.COMM_WORLD.size, lshape.size)[::-1] cands = \ [ create_distribution(shape=gshape, locale_type=LT_NODE, halo=halo), create_distribution(shape=gshape, locale_type=LT_PROCESS, halo=halo) ] for cand in cands: with _locale.ones(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) rank_logger = _logging.get_rank_logger(self.id(), comm=cand.locale_comms.peer_comm) rank_logger.info( ( "\n========================================================\n" + "locale_extent = %s\n" + "rank_view_slice_n = %s\n" + "rank_view_slice_h = %s\n" + "rank_view_relative_slice_n = %s\n" + "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^" ) % ( lary.locale_extent, lary.intra_partition.rank_view_slice_n, lary.intra_partition.rank_view_slice_h, lary.intra_partition.rank_view_relative_slice_n, ) ) if cand.locale_comms.intra_locale_comm.rank == 0: lary.view_h[...] = -1 cand.locale_comms.intra_locale_comm.barrier() lary.rank_view_n[...] = cand.locale_comms.peer_comm.rank cand.locale_comms.intra_locale_comm.barrier() if	_np.any(lary.halo > 0)	numpy.any
# Copyright 2020 BULL SAS All rights reserved """This module provides the different tests for the resampling policies. """ import unittest import numpy as np from bbo.noise_reduction.resampling_policies import ( ResamplingPolicy, SimpleResampling, DynamicResampling, DynamicResamplingParametric, DynamicResamplingNonParametric, DynamicResamplingNonParametric2, ) def resampling_schedule(nbr_it): return 1 / np.log(1 + nbr_it) class TestResampling(unittest.TestCase): """Tests the proper implementation of the parent resampling class. """ def test_parent_class(self): """Test the initialization of the Resampling parent class. """ resampling_policy = ResamplingPolicy() def test_not_implemented(self): """Test that a not implemented error is raised when calling the resample method""" resampling_policy = ResamplingPolicy() with self.assertRaises(NotImplementedError): resampling_policy.resample(history={}) class TestSimpleResampling(unittest.TestCase): """Tests the SimpleResampling class. """ def test_simple_resampling_resample(self): """Tests that the parameter are properly resampled when the last parameter should be resampled. """ history = { "fitness": np.array([10, 5, 4, 2, 15, 20]), "parameters": np.array([[1, 2], [2, 3], [1, 3], [4, 3], [2, 1], [2, 1]]), } resampler = SimpleResampling(nbr_resamples=3) self.assertTrue(resampler.resample(history)) def test_simple_resampling_no_resample(self): """Tests that the parameter are properly resampled when the last parameter should not be resampled. """ history = { "fitness": np.array([10, 5, 4, 2, 15, 20]), "parameters": np.array([[1, 2], [2, 3], [1, 3], [2, 1], [2, 1], [2, 1]]), } resampler = SimpleResampling(nbr_resamples=3) self.assertFalse(resampler.resample(history)) def test_simple_resampling_no_sequential(self): """Tests that when the samples are not sequentially ordered, the resampling still happens properly. """ history = { "fitness": np.array([10, 5, 4, 2, 15, 20]), "parameters": np.array([[1, 2], [2, 3], [1, 3], [4, 3], [2, 1], [2, 1]]), } resampler = SimpleResampling(nbr_resamples=2) self.assertFalse(resampler.resample(history)) class TestDynamicResampling(unittest.TestCase): """Tests the abstract class of dynamic resampling. """ def test_initialization(self): """Tests the proper initialization of the class. """ dynamic_resampling = DynamicResampling(percentage=0.5) def test_resampling_schedule_None(self): """Tests the good definition of the resampling schedule when set to None. """ dynamic_resampling = DynamicResampling(percentage=0.5) self.assertEqual(0.5, dynamic_resampling.resampling_schedule(1)) def test_resampling_schedule(self): """Tests the good definition of the resampling schedule. """ dynamic_resampling = DynamicResampling( percentage=0.5, resampling_schedule=resampling_schedule ) self.assertEqual(0.5 / np.log(2), dynamic_resampling.resampling_schedule(1)) def test_allow_resampling_schedule_None(self): """Tests that the allow resampling schedule behaves as expected when set to None. """ dynamic_resampling = DynamicResampling(percentage=0.5) self.assertEqual(1, dynamic_resampling.allow_resampling_schedule(1)) def test_allow_resampling_schedule(self): """Tests that the allow resampling schedule behaves as expected when set to a value. """ dynamic_resampling = DynamicResampling( percentage=0.5, allow_resampling_start=5, allow_resampling_schedule=resampling_schedule, ) self.assertEqual(5 / np.log(2), dynamic_resampling.allow_resampling_schedule(1)) class TestDynamicResamplingParametric(unittest.TestCase): """Tests that the DynamicResamplingParametric class behaves as expected. """ def test_error_percentage(self): """Tests that dynamic resampling raises an error when the percentage is below 0. """ with self.assertRaises(ValueError): DynamicResamplingParametric(percentage=-0.1) def test_dynamic_resampling_formula(self): """ Tests that the computation performs what is expected. """ test_dynamic_resampling = DynamicResamplingParametric(0.2) history = { "fitness": np.array([10, 5, 4, 14, 15, 16]), "parameters":	np.array([[1, 2], [2, 3], [1, 3], [2, 1], [2, 1], [2, 1]])	numpy.array
import argparse import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import pdb import os from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau from tensorflow.keras.layers import Input, Dense, Conv2D, BatchNormalization, MaxPool1D, Flatten from tensorflow.keras.models import Model, Sequential from tensorflow.keras import regularizers from tensorflow.keras import optimizers from tensorflow.keras.utils import to_categorical from tensorflow.keras.models import load_model def get_argparser(): parser = argparse.ArgumentParser() ### CNNs parser.add_argument("--units", default=16, type=int, help="Number of units in hidden layers of the DNN") parser.add_argument("--layers", default=2, type=int, help="Number of layers in the DNN") parser.add_argument("--reg", default=0.1, type=float, help="Regularization weight of the DNN") parser.add_argument("--data_type", default='rgb', type=str, help="Which data to evaluate on: (ir, rgb, cover)") return parser if __name__ == '__main__': parser = get_argparser() args = parser.parse_args() data_type = args.data_type ######################## # Load and process data ######################## layers_arr = [1, 2, 3, 4] units_arr = [4, 8, 16, 32, 64] reg_arr = [0.001, 0.005, 0.01, 0.05, 0.1] plot_obj_train = np.zeros((len(layers_arr), len(units_arr), len(reg_arr))) plot_obj_val = np.zeros((len(layers_arr), len(units_arr), len(reg_arr))) for layers_idx in range(len(layers_arr)): for units_idx in range(len(units_arr)): for reg_idx in range(len(reg_arr)): layers = layers_arr[layers_idx] units = units_arr[units_idx] regularizer = reg_arr[reg_idx] kernel = 5 model_name = f'CNN10-layers{layers}-units{units}-kernel{kernel}-reg{regularizer}-{data_type}' loss_obj = np.loadtxt(f'output-data/train-history-{model_name}.csv', delimiter=',') print(f'For model {model_name} Best train accuracy is {np.max(loss_obj[2])}') print(f'For model {model_name} Best val accuracy is {np.max(loss_obj[3])}') plot_obj_train[layers_idx, units_idx, reg_idx] = np.max(loss_obj[2]) plot_obj_val[layers_idx, units_idx, reg_idx] = np.max(loss_obj[3]) # Pick one regularization for ri in range(len(reg_arr)): reg_amt = reg_arr[ri] cut_plot_obj = plot_obj_train[:, :, ri] fig, ax = plt.subplots(1, 1, figsize=(10, 6)) width = 0.15 n_col = len(layers_arr) ax.set_title(f'Hyperparameter Search for {data_type.upper()} models', fontsize=24) ax.set_ylim([0, 1.05]) ax.bar(np.arange(n_col) - 2 * width, cut_plot_obj[:, 0], width, label='4 filters') ax.bar(np.arange(n_col) - 1 * width, cut_plot_obj[:, 1], width, label='8 filters') ax.bar(np.arange(n_col) + 0 * width, cut_plot_obj[:, 2], width, label='16 filters') ax.bar(np.arange(n_col) + 1 * width, cut_plot_obj[:, 3], width, label='32 filters') ax.bar(np.arange(n_col) + 2 * width, cut_plot_obj[:, 4], width, label='64 filters') ax.set_axisbelow(True) ax.grid(True, which='major', axis='y', linestyle = '--') ax.legend(loc='best', ncol=3, fontsize=20) ax.set_xticks(np.arange(n_col)) ax.set_xticklabels(layers_arr, fontsize=16) ax.set_xlabel('Layers', fontsize=24) ax.set_ylabel('Training Accuracy', fontsize=24) fig.tight_layout(rect=[0, 0, 1, 1]) plt.savefig(f'plot-hyperparam-{data_type}-train-{reg_amt}.pdf') #################################################### cut_plot_obj = plot_obj_val[:, :, 4] fig, ax = plt.subplots(1, 1, figsize=(10, 6)) width = 0.15 n_col = len(layers_arr) ax.set_title(f'Hyperparameter Search for {data_type.upper()} models', fontsize=24) ax.set_ylim([0, 1.05]) ax.bar(	np.arange(n_col)	numpy.arange
#! /usr/bin/env python # coding=utf-8 # Copyright (c) 2019 Uber Technologies, Inc. # # 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 numpy as np from scipy.signal import lfilter from scipy.signal.windows import get_window def _pre_emphasize_data(data, emphasize_value=0.97): filter_window = np.asarray([1, -emphasize_value]) pre_emphasized_data = lfilter(filter_window, 1, data) return pre_emphasized_data def get_length_in_samp(sampling_rate_in_hz, length_in_s): return int(sampling_rate_in_hz * length_in_s) def get_group_delay(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type): X_stft_transform = _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type=window_type) Y_stft_transform = _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type=window_type, data_transformation='group_delay') X_stft_transform_real = np.real(X_stft_transform) X_stft_transform_imag = np.imag(X_stft_transform) Y_stft_transform_real = np.real(Y_stft_transform) Y_stft_transform_imag = np.imag(Y_stft_transform) nominator = np.multiply(X_stft_transform_real, Y_stft_transform_real) + np.multiply( X_stft_transform_imag, Y_stft_transform_imag) denominator = np.square(np.abs(X_stft_transform)) group_delay = np.divide(nominator, denominator + 1e-10) assert not np.isnan( group_delay).any(), 'There are NaN values in group delay' return np.transpose(group_delay) def get_phase_stft_magnitude(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type): stft = _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type=window_type) abs_stft = np.abs(stft) phase = np.angle(stft) stft_phase = np.concatenate((phase, abs_stft), axis=1) return np.transpose(stft_phase) def get_stft_magnitude(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type): stft = _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type=window_type) stft_magnitude = np.abs(stft) return np.transpose(stft_magnitude) ################################################################################ # The following code for FBank is adapted from jameslyons/python_speech_features # MIT licensed implementation # https://github.com/jameslyons/python_speech_features/blob/40c590269b57c64a8c1f1ddaaff2162008d1850c/python_speech_features/base.py#L84################################################################################ ################################################################################ def get_fbank(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type, num_filter_bands): stft = _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type=window_type, zero_mean_offset=True) stft_power = np.abs(stft) ** 2 upper_limit_freq = int(sampling_rate_in_hz / 2) upper_limit_mel = _convert_hz_to_mel(upper_limit_freq) lower_limit_mel = 0 list_mel_points = np.linspace(lower_limit_mel, upper_limit_mel, num_filter_bands + 2) mel_fbank_matrix = _get_mel_fbank_matrix(list_mel_points, num_filter_bands, num_fft_points, sampling_rate_in_hz) mel_fbank_feature = np.dot(stft_power, np.transpose(mel_fbank_matrix)) log_mel_fbank_feature = np.log(mel_fbank_feature + 1.0e-10) return np.transpose(log_mel_fbank_feature) def _get_mel_fbank_matrix(list_mel_points, num_filter_bands, num_fft_points, sampling_rate_in_hz): num_ess_fft_points = get_non_symmetric_length(num_fft_points) freq_scale = (num_fft_points + 1) / sampling_rate_in_hz freq_bins_on_mel_scale = np.floor( freq_scale * _convert_mel_to_hz(list_mel_points)) mel_scaled_fbank = np.zeros((num_filter_bands, num_ess_fft_points), dtype=np.float32) for filt_idx in range(num_filter_bands): start_bin_freq = freq_bins_on_mel_scale[filt_idx] middle_bin_freq = freq_bins_on_mel_scale[filt_idx + 1] end_bin_freq = freq_bins_on_mel_scale[filt_idx + 2] mel_scaled_fbank[filt_idx] = _create_triangular_filter(start_bin_freq, middle_bin_freq, end_bin_freq, num_ess_fft_points) return mel_scaled_fbank def _create_triangular_filter(start_bin_freq, middle_bin_freq, end_bin_freq, num_ess_fft_points): filter_window = np.zeros(num_ess_fft_points, dtype=np.float32) filt_support_begin = middle_bin_freq - start_bin_freq filt_support_end = end_bin_freq - middle_bin_freq for freq in range(int(start_bin_freq), int(middle_bin_freq)): filter_window[freq] = (freq - start_bin_freq) / filt_support_begin for freq in range(int(middle_bin_freq), int(end_bin_freq)): filter_window[freq] = (end_bin_freq - freq) / filt_support_end return filter_window def _convert_hz_to_mel(hz): return 2595.0 * np.log10(1 + hz / 700.0) def _convert_mel_to_hz(mel): return 700.0 * (10 ** (mel / 2595.0) - 1) def _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type, data_transformation=None, zero_mean_offset=False): pre_emphasized_data = _pre_emphasize_data(raw_data) stft = _short_time_fourier_transform(pre_emphasized_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type, data_transformation, zero_mean_offset) non_symmetric_stft = get_non_symmetric_data(stft) return non_symmetric_stft def _short_time_fourier_transform(data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type, data_transformation=None, zero_mean_offset=False): window_length_in_samp = get_length_in_samp(window_length_in_s, sampling_rate_in_hz) window_shift_in_samp = get_length_in_samp(window_shift_in_s, sampling_rate_in_hz) preprocessed_data_matrix = _preprocess_to_padded_matrix(data, window_length_in_samp, window_shift_in_samp, zero_mean_offset=zero_mean_offset) weighted_data_matrix = _weight_data_matrix( preprocessed_data_matrix, window_type, data_transformation=data_transformation ) fft = np.fft.fft(weighted_data_matrix, n=num_fft_points) return fft def _preprocess_to_padded_matrix(data, window_length_in_samp, window_shift_in_samp, zero_mean_offset=False): num_input = data.shape[0] num_output = get_num_output_padded_to_fit_input(num_input, window_length_in_samp, window_shift_in_samp) zero_padded_matrix = np.zeros((num_output, window_length_in_samp), dtype=np.float) for num_output_idx in range(num_output): start_idx = window_shift_in_samp * num_output_idx is_last_output = num_output_idx == num_output - 1 end_idx = start_idx + window_length_in_samp if not is_last_output else num_input end_padded_idx = window_length_in_samp if not is_last_output else end_idx - start_idx window_data = data[start_idx:end_idx] if zero_mean_offset: window_data = window_data -	np.mean(window_data)	numpy.mean
import numpy as np from nsgt.fft import rfftp, irfftp, fftp, ifftp import unittest class TestFFT(unittest.TestCase): def __init__(self, methodName, n=10000): super(TestFFT, self).__init__(methodName) self.n = n def test_rfft(self): seq = np.random.random(self.n) ft = rfftp() a = ft(seq) b = np.fft.rfft(seq) self.assertTrue(np.allclose(a, b)) def test_irfft(self): seq = np.random.random(self.n)+np.random.random(self.n)1.j outn = (self.n-1)2 + np.random.randint(0,2) # even or odd output size ft = irfftp() a = ft(seq, outn=outn) b = np.fft.irfft(seq, n=outn) self.assertTrue(np.allclose(a, b)) def test_fft(self): seq = np.random.random(self.n) ft = fftp() a = ft(seq) b = np.fft.fft(seq) self.assertTrue(np.allclose(a, b)) def test_ifft(self): seq = np.random.random(self.n)+np.random.random(self.n)*1.j ft = ifftp() a = ft(seq) b =	np.fft.ifft(seq)	numpy.fft.ifft
"""Krylov-subspaces model order reduction techniques """ import numpy as np import scipy.linalg as sclalg import sharpy.linear.src.libss as libss import time import sharpy.utils.settings as settings import sharpy.utils.cout_utils as cout import sharpy.utils.rom_interface as rom_interface import sharpy.utils.h5utils as h5 import sharpy.rom.utils.krylovutils as krylovutils import warnings as warn @rom_interface.rom class Krylov(rom_interface.BaseRom): """ Model Order Reduction Methods for Single Input Single Output (SISO) and MIMO Linear Time-Invariant (LTI) Systems using moment matching (Krylov Methods). Examples: General calling sequences for different systems SISO single point interpolation: >>> algorithm = 'one_sided_arnoldi' >>> interpolation_point = np.array([0.0]) >>> krylov_r = 4 >>> >>> rom = Krylov() >>> rom.initialise(sharpy_data, FullOrderModelSS) >>> rom.run(algorithm, krylov_r, interpolation_point) 2 by 2 MIMO with tangential, multipoint interpolation: >>> algorithm = 'dual_rational_arnoldi' >>> interpolation_point = np.array([0.0, 1.0j]) >>> krylov_r = 4 >>> right_vector = np.block([[1, 0], [0, 1]]) >>> left_vector = right_vector >>> >>> rom = Krylov() >>> rom.initialise(sharpy_data, FullOrderModelSS) >>> rom.run(algorithm, krylov_r, interpolation_point, right_vector, left_vector) 2 by 2 MIMO multipoint interpolation: >>> algorithm = 'mimo_rational_arnoldi' >>> interpolation_point = np.array([0.0]) >>> krylov_r = 4 >>> >>> rom = Krylov() >>> rom.initialise(sharpy_data, FullOrderModelSS) >>> rom.run(algorithm, krylov_r, interpolation_point) """ rom_id = 'Krylov' settings_types = dict() settings_default = dict() settings_description = dict() settings_options = dict() settings_types['print_info'] = 'bool' settings_default['print_info'] = True settings_description['print_info'] = 'Write ROM information to screen and log' settings_types['frequency'] = 'list(complex)' settings_default['frequency'] = [0] settings_description['frequency'] = 'Interpolation points in the continuous time complex plane [rad/s]' settings_types['algorithm'] = 'str' settings_default['algorithm'] = '' settings_description['algorithm'] = 'Krylov reduction method algorithm' settings_types['r'] = 'int' settings_default['r'] = 1 settings_description['r'] = 'Moments to match at the interpolation points' settings_types['single_side'] = 'str' settings_default['single_side'] = '' settings_description['single_side'] = 'Construct the rom using a single side. Leave blank (or empty string) for both.' settings_options['single_side'] = ['controllability', 'observability'] settings_types['tangent_input_file'] = 'str' settings_default['tangent_input_file'] = '' settings_description['tangent_input_file'] = 'Filepath to .h5 file containing tangent interpolation vectors' settings_types['restart_arnoldi'] = 'bool' settings_default['restart_arnoldi'] = False settings_description['restart_arnoldi'] = 'Restart Arnoldi iteration with r-=1 if ROM is unstable' settings_table = settings.SettingsTable() __doc__ += settings_table.generate(settings_types, settings_default, settings_description, settings_options) supported_methods = ('one_sided_arnoldi', 'two_sided_arnoldi', 'dual_rational_arnoldi', 'mimo_rational_arnoldi', 'mimo_block_arnoldi') def __init__(self): self.settings = dict() self.frequency = None self.algorithm = None self.ss = None self.r = 1 self.V = None self.H = None self.W = None self.ssrom = None self.sstype = None self.nfreq = None self.restart_arnoldi = None self.stable = None self.cpu_summary = dict() self.eigenvalue_table = None def initialise(self, in_settings=None): if in_settings is not None: self.settings = in_settings settings.to_custom_types(self.settings, self.settings_types, self.settings_default, self.settings_options) try: if self.settings['print_info']: cout.cout_wrap('Initialising Krylov Model Order Reduction') except ValueError: pass self.algorithm = self.settings['algorithm'] if self.algorithm not in self.supported_methods: raise NotImplementedError('Algorithm %s not recognised, check for spelling or it' 'could be that is not yet implemented' % self.algorithm) self.frequency = np.array(self.settings['frequency']) self.r = self.settings['r'].value self.restart_arnoldi = self.settings['restart_arnoldi'].value try: self.nfreq = self.frequency.shape[0] except AttributeError: self.nfreq = 1 def run(self, ss): """ Performs Model Order Reduction employing Krylov space projection methods. Supported methods include: ========================= ==================== ========================================================== Algorithm Interpolation Points Systems ========================= ==================== ========================================================== ``one_sided_arnoldi`` 1 SISO Systems ``two_sided_arnoldi`` 1 SISO Systems ``dual_rational_arnoldi`` K SISO systems and Tangential interpolation for MIMO systems ``mimo_rational_arnoldi`` K MIMO systems. Uses vector-wise construction (more robust) ``mimo_block_arnoldi`` K MIMO systems. Uses block Arnoldi methods (more efficient) ========================= ==================== ========================================================== Args: ss (sharpy.linear.src.libss.ss): State space to reduce Returns: (libss.ss): Reduced state space system """ self.ss = ss if self.settings['print_info']: cout.cout_wrap('Model Order Reduction in progress...') self.print_header() if self.ss.dt is None: self.sstype = 'ct' else: self.sstype = 'dt' self.frequency = np.exp(self.frequency * ss.dt) t0 = time.time() Ar, Br, Cr = self.__getattribute__(self.algorithm)(self.frequency, self.r) self.ssrom = libss.ss(Ar, Br, Cr, self.ss.D, self.ss.dt) self.stable = self.check_stability(restart_arnoldi=self.restart_arnoldi) if not self.stable: pass warn.warn('Reduced Order Model Unstable') # Under development # TL, TR = self.restart() # Wtr, Vr = self.restart() # TL, TR = self.stable_realisation() # self.ssrom = libss.ss(TL.T.dot(Ar.dot(TR)), TL.T.dot(Br), Cr.dot(TR), self.ss.D, self.ss.dt) # self.ssrom = libss.ss(Wtr.dot(self.ssrom.A.dot(Vr)), Wtr.dot(self.ssrom.B), self.ssrom.C.dot(Vr), self.ss.D, self.ss.dt) # self.stable = self.check_stability(restart_arnoldi=self.restart_arnoldi) t_rom = time.time() - t0 self.cpu_summary['run'] = t_rom if self.settings['print_info']: cout.cout_wrap('System reduced from order %d to ' % self.ss.states) cout.cout_wrap('\tn = %d states' % self.ssrom.states, 1) cout.cout_wrap('...Completed Model Order Reduction in %.2f s' % t_rom) return self.ssrom def print_header(self): cout.cout_wrap('Moment Matching Krylov Model Reduction') cout.cout_wrap('\tConstruction Algorithm:') cout.cout_wrap('\t\t%s' % self.algorithm, 1) cout.cout_wrap('\tInterpolation points:') if self.frequency.dtype == complex: cout.cout_wrap(self.nfreq * '\t\tsigma = %4f + %4fj [rad/s]\n' %tuple(self.frequency.view(float)), 1) else: cout.cout_wrap(self.nfreq * '\t\tsigma = %4f [rad/s]\n' % self.frequency, 1) cout.cout_wrap('\tKrylov order:') cout.cout_wrap('\t\tr = %d' % self.r, 1) def one_sided_arnoldi(self, frequency, r): r""" One-sided Arnoldi method expansion about a single interpolation point, :math:`\sigma`. The projection matrix :math:`\mathbf{V}` is constructed using an order :math:`r` Krylov space. The space for a single finite interpolation point known as a Pade approximation is described by: .. math:: \text{range}(\textbf{V}) = \mathcal{K}_r((\sigma\mathbf{I}_n - \mathbf{A})^{-1}, (\sigma\mathbf{I}_n - \mathbf{A})^{-1}\mathbf{b}) In the case of an interpolation about infinity, the problem is known as partial realisation and the Krylov space is .. math:: \text{range}(\textbf{V}) = \mathcal{K}_r(\mathbf{A}, \mathbf{b}) The resulting orthogonal projection leads to the following reduced order system: .. math:: \hat{\Sigma} : \left(\begin{array}{c\|c} \hat{A} & \hat{B} \\ \hline \hat{C} & {D}\end{array}\right) \text{with } \begin{cases}\hat{A}=V^TAV\in\mathbb{R}^{k\times k},\,\\ \hat{B}=V^TB\in\mathbb{R}^{k\times m},\,\\ \hat{C}=CV\in\mathbb{R}^{p\times k},\,\\ \hat{D}=D\in\mathbb{R}^{p\times m}\end{cases} Args: frequency (complex): Interpolation point :math:`\sigma \in \mathbb{C}` r (int): Number of moments to match. Equivalent to Krylov space order and order of the ROM. Returns: tuple: The reduced order model matrices: :math:`\mathbf{A}_r`, :math:`\mathbf{B}_r` and :math:`\mathbf{C}_r` """ A = self.ss.A B = self.ss.B C = self.ss.C nx = A.shape[0] if frequency != np.inf and frequency is not None: lu_A = krylovutils.lu_factor(frequency, A) V = krylovutils.construct_krylov(r, lu_A, B, 'Pade', 'b') else: V = krylovutils.construct_krylov(r, A, B, 'partial_realisation', 'b') # Reduced state space model Ar = V.T.dot(A.dot(V)) Br = V.T.dot(B) Cr = C.dot(V) self.V = V self.W = V return Ar, Br, Cr def two_sided_arnoldi(self, frequency, r): r""" Two-sided projection with a single interpolation point following the Arnoldi procedure. Very similar to the one-sided method available, but it adds the projection :math:`\mathbf{W}` built using the Krylov space for the :math:`\mathbf{c}` vector: .. math:: \mathcal{K}_r((\sigma\mathbf{I}_n - \mathbf{A})^{-T}, (\sigma\mathbf{I}_n - \mathbf{A})^{-T}\mathbf{c}^T)\subseteq\mathcal{W}=\text{range}(\mathbf{W}) The oblique projection :math:`\mathbf{VW}^T` matches twice as many moments as the single sided projection. The resulting system takes the form: .. math:: \hat{\Sigma} : \left(\begin{array}{c\|c} \hat{A} & \hat{B} \\ \hline \hat{C} & {D}\end{array}\right) \text{with } \begin{cases}\hat{A}=W^TAV\in\mathbb{R}^{k\times k},\,\\ \hat{B}=W^TB\in\mathbb{R}^{k\times m},\,\\ \hat{C}=CV\in\mathbb{R}^{p\times k},\,\\ \hat{D}=D\in\mathbb{R}^{p\times m}\end{cases} Args: frequency (complex): Interpolation point :math:`\sigma \in \mathbb{C}` r (int): Number of moments to match on each side. The resulting ROM will be of order :math:`2r`. Returns: tuple: The reduced order model matrices: :math:`\mathbf{A}_r`, :math:`\mathbf{B}_r` and :math:`\mathbf{C}_r`. """ A = self.ss.A B = self.ss.B C = self.ss.C nx = A.shape[0] if frequency != np.inf and frequency is not None: lu_A = krylovutils.lu_factor(frequency, A) V = krylovutils.construct_krylov(r, lu_A, B, 'Pade', 'b') W = krylovutils.construct_krylov(r, lu_A, C.T, 'Pade', 'c') else: V = krylovutils.construct_krylov(r, A, B, 'partial_realisation', 'b') W = krylovutils.construct_krylov(r, A, C.T, 'partial_realisation', 'c') T = W.T.dot(V) Tinv = sclalg.inv(T) reduction_checks(T, Tinv) self.W = W self.V = V # Reduced state space model Ar = W.T.dot(self.ss.A.dot(V.dot(Tinv))) Br = W.T.dot(self.ss.B) Cr = self.ss.C.dot(V.dot(Tinv)) return Ar, Br, Cr def real_rational_arnoldi(self, frequency, r): """ When employing complex frequencies, the projection matrix can be normalised to be real Following Algorithm 1b in Lee(2006) Args: frequency: r: Returns: """ raise NotImplementedError('Real valued rational Arnoldi Method Work in progress - use mimo_rational_arnoldi') ### Not working, having trouble with the last column of H. need to investigate the background behind the creation of H and see hwat can be done A = self.ss.A B = self.ss.B C = self.ss.C nx = A.shape[0] nfreq = frequency.shape[0] # Columns of matrix v v_ncols = 2 * np.sum(r) # Output projection matrices V = np.zeros((nx, v_ncols), dtype=float) H = np.zeros((v_ncols, v_ncols), dtype=float) res = np.zeros((nx,v_ncols+2), dtype=float) # lu_A = krylovutils.lu_factor(frequency[0] * np.eye(nx) - A) v_res = sclalg.lu_solve(lu_A, B) H[0, 0] = np.linalg.norm(v_res) V[:, 0] = v_res.real / H[0, 0] k = 0 for i in range(nfreq): for j in range(r[i]): # k = 2(ir[i] + j) print("i = %g\t j = %g\t k = %g" % (i, j, k)) # res[:, k] = np.imag(v_res) # if k > 0: # res[:, k-1] = np.real(v_res) # # # Working on the last finished column i.e. k-1 only when k>0 # if k > 0: # for t in range(k): # H[t, k-1] = V[:, t].T.dot(res[:, k-1]) # res[:, k-1] -= res[:, k-1] - H[t, k-1] * V[:, t] # # H[k, k-1] = np.linalg.norm(res[:, k-1]) # V[:, k] = res[:, k-1] / H[k, k-1] # # # Normalise working column k # for t in range(k+1): # H[t, k] = V[:, t].T.dot(res[:, k]) # res[:, k] -= H[t, k] * V[:, t] # # # Subdiagonal term # H[k+1, k] = np.linalg.norm(res[:, k]) # V[:, k + 1] = res[:, k] / np.linalg.norm(res[:, k]) # # if j == r[i] - 1 and i < nfreq - 1: # lu_A = krylovutils.lu_factor(frequency[i+1] * np.eye(nx) - A) # v_res = sclalg.lu_solve(lu_A, B) # else: # v_res = - sclalg.lu_solve(lu_A, V[:, k+1]) if k == 0: V[:, 0] = v_res.real / np.linalg.norm(v_res.real) else: res[:, k] = np.imag(v_res) res[:, k-1] = np.real(v_res) for t in range(k): H[t, k-1] = np.linalg.norm(res[:, k-1]) res[:, k-1] -= H[t, k-1]V[:, t] H[k, k-1] = np.linalg.norm(res[:, k-1]) V[:, k] = res[:, k-1] / H[k, k-1] if k == 0: H[0, 0] = V[:, 0].T.dot(v_res.imag) res[:, 0] -= H[0, 0] V[:, 0] else: for t in range(k+1): H[t, k] = V[:, t].T.dot(res[:, k]) res[:, k] -= H[t, k] * V[:, t] H[k+1, k] = np.linalg.norm(res[:, k]) V[:, k+1] = res[:, k] / H[k+1, k] if j == r[i] - 1 and i < nfreq - 1: lu_A = krylovutils.lu_factor(frequency[i+1], A) v_res = sclalg.lu_solve(lu_A, B) else: v_res = - sclalg.lu_solve(lu_A, V[:, k+1]) k += 2 # Add last column of H print(k) res[:, k-1] = - sclalg.lu_solve(lu_A, V[:, k-1]) for t in range(k-1): H[t, k-1] = V[:, t].T.dot(res[:, k-1]) res[:, k-1] -= H[t, k-1]*V[:, t] self.V = V self.H = H Ar = V.T.dot(A.dot(V)) Br = V.T.dot(B) Cr = C.dot(V) return Ar, Br, Cr def dual_rational_arnoldi(self, frequency, r): r""" Dual Rational Arnoli Interpolation for SISO sytems [1] and MIMO systems through tangential interpolation [2]. Effectively the same as the two_sided_arnoldi and the resulting V matrices for each interpolation point are concatenated .. math:: \bigcup\limits_{k = 1}^K\mathcal{K}_{b_k}((\sigma_i\mathbf{I}_n - \mathbf{A})^{-1}, (\sigma_i\mathbf{I}_n - \mathbf{A})^{-1}\mathbf{b})\subseteq\mathcal{V}&=\text{range}(\mathbf{V}) \\ \bigcup\limits_{k = 1}^K\mathcal{K}_{c_k}((\sigma_i\mathbf{I}_n - \mathbf{A})^{-T}, (\sigma_i\mathbf{I}_n - \mathbf{A})^{-T}\mathbf{c}^T)\subseteq\mathcal{Z}&=\text{range}(\mathbf{Z}) For MIMO systems, tangential interpolation is used through the right and left tangential direction vectors :math:`\mathbf{r}_i` and :math:`\mathbf{l}_i`. .. math:: \bigcup\limits_{k = 1}^K\mathcal{K}_{b_k}((\sigma_i\mathbf{I}_n - \mathbf{A})^{-1}, (\sigma_i\mathbf{I}_n - \mathbf{A})^{-1}\mathbf{Br}_i)\subseteq\mathcal{V}&=\text{range}(\mathbf{V}) \\ \bigcup\limits_{k = 1}^K\mathcal{K}_{c_k}((\sigma_i\mathbf{I}_n - \mathbf{A})^{-T}, (\sigma_i\mathbf{I}_n - \mathbf{A})^{-T}\mathbf{C}^T\mathbf{l}_i)\subseteq\mathcal{Z}&=\text{range}(\mathbf{Z}) Args: frequency (np.ndarray): Array containing the interpolation points :math:`\sigma = \{\sigma_1, \dots, \sigma_K\}\in\mathbb{C}` r (int): Krylov space order :math:`b_k` and :math:`c_k`. At the moment, different orders for the controllability and observability constructions are not supported. right_tangent (np.ndarray): Matrix containing the right tangential direction interpolation vector for each interpolation point in column form, i.e. :math:`\mathbf{r}\in\mathbb{R}^{m \times K}`. left_tangent (np.ndarray): Matrix containing the left tangential direction interpolation vector for each interpolation point in column form, i.e. :math:`\mathbf{l}\in\mathbb{R}^{p \times K}`. Returns: tuple: The reduced order model matrices: :math:`\mathbf{A}_r`, :math:`\mathbf{B}_r` and :math:`\mathbf{C}_r`. References: [1] Grimme [2] Gallivan """ A = self.ss.A B = self.ss.B C = self.ss.C nx = self.ss.states nu = self.ss.inputs ny = self.ss.outputs B.shape = (nx, nu) if nu != 1: left_tangent, right_tangent, rc, ro, fc, fo = self.load_tangent_vectors() assert right_tangent is not None and left_tangent is not None, 'Missing interpolation vectors for MIMO case' else: fc =	np.array(frequency)	numpy.array
""" Testing for the approximate neighbor search using Locality Sensitive Hashing Forest module (sklearn.neighbors.LSHForest). """ # Author: <NAME>, <NAME> import numpy as np import scipy.sparse as sp from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_array_less from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_not_equal from sklearn.utils.testing import assert_warns_message from sklearn.utils.testing import ignore_warnings from sklearn.metrics.pairwise import pairwise_distances from sklearn.neighbors import LSHForest from sklearn.neighbors import NearestNeighbors def test_neighbors_accuracy_with_n_candidates(): # Checks whether accuracy increases as `n_candidates` increases. n_candidates_values = np.array([.1, 50, 500]) n_samples = 100 n_features = 10 n_iter = 10 n_points = 5 rng = np.random.RandomState(42) accuracies = np.zeros(n_candidates_values.shape[0], dtype=float) X = rng.rand(n_samples, n_features) for i, n_candidates in enumerate(n_candidates_values): lshf = LSHForest(n_candidates=n_candidates) ignore_warnings(lshf.fit)(X) for j in range(n_iter): query = X[rng.randint(0, n_samples)].reshape(1, -1) neighbors = lshf.kneighbors(query, n_neighbors=n_points, return_distance=False) distances = pairwise_distances(query, X, metric='cosine') ranks = np.argsort(distances)[0, :n_points] intersection = np.intersect1d(ranks, neighbors).shape[0] ratio = intersection / float(n_points) accuracies[i] = accuracies[i] + ratio accuracies[i] = accuracies[i] / float(n_iter) # Sorted accuracies should be equal to original accuracies assert_true(np.all(np.diff(accuracies) >= 0), msg="Accuracies are not non-decreasing.") # Highest accuracy should be strictly greater than the lowest assert_true(np.ptp(accuracies) > 0, msg="Highest accuracy is not strictly greater than lowest.") def test_neighbors_accuracy_with_n_estimators(): # Checks whether accuracy increases as `n_estimators` increases. n_estimators = np.array([1, 10, 100]) n_samples = 100 n_features = 10 n_iter = 10 n_points = 5 rng = np.random.RandomState(42) accuracies = np.zeros(n_estimators.shape[0], dtype=float) X = rng.rand(n_samples, n_features) for i, t in enumerate(n_estimators): lshf = LSHForest(n_candidates=500, n_estimators=t) ignore_warnings(lshf.fit)(X) for j in range(n_iter): query = X[rng.randint(0, n_samples)].reshape(1, -1) neighbors = lshf.kneighbors(query, n_neighbors=n_points, return_distance=False) distances = pairwise_distances(query, X, metric='cosine') ranks = np.argsort(distances)[0, :n_points] intersection = np.intersect1d(ranks, neighbors).shape[0] ratio = intersection / float(n_points) accuracies[i] = accuracies[i] + ratio accuracies[i] = accuracies[i] / float(n_iter) # Sorted accuracies should be equal to original accuracies assert_true(np.all(np.diff(accuracies) >= 0), msg="Accuracies are not non-decreasing.") # Highest accuracy should be strictly greater than the lowest assert_true(np.ptp(accuracies) > 0, msg="Highest accuracy is not strictly greater than lowest.") @ignore_warnings def test_kneighbors(): # Checks whether desired number of neighbors are returned. # It is guaranteed to return the requested number of neighbors # if `min_hash_match` is set to 0. Returned distances should be # in ascending order. n_samples = 12 n_features = 2 n_iter = 10 rng = np.random.RandomState(42) X = rng.rand(n_samples, n_features) lshf = LSHForest(min_hash_match=0) # Test unfitted estimator assert_raises(ValueError, lshf.kneighbors, X[0]) ignore_warnings(lshf.fit)(X) for i in range(n_iter): n_neighbors = rng.randint(0, n_samples) query = X[rng.randint(0, n_samples)].reshape(1, -1) neighbors = lshf.kneighbors(query, n_neighbors=n_neighbors, return_distance=False) # Desired number of neighbors should be returned. assert_equal(neighbors.shape[1], n_neighbors) # Multiple points n_queries = 5 queries = X[rng.randint(0, n_samples, n_queries)] distances, neighbors = lshf.kneighbors(queries, n_neighbors=1, return_distance=True) assert_equal(neighbors.shape[0], n_queries) assert_equal(distances.shape[0], n_queries) # Test only neighbors neighbors = lshf.kneighbors(queries, n_neighbors=1, return_distance=False) assert_equal(neighbors.shape[0], n_queries) # Test random point(not in the data set) query = rng.randn(n_features).reshape(1, -1) lshf.kneighbors(query, n_neighbors=1, return_distance=False) # Test n_neighbors at initialization neighbors = lshf.kneighbors(query, return_distance=False) assert_equal(neighbors.shape[1], 5) # Test `neighbors` has an integer dtype assert_true(neighbors.dtype.kind == 'i', msg="neighbors are not in integer dtype.") def test_radius_neighbors(): # Checks whether Returned distances are less than `radius` # At least one point should be returned when the `radius` is set # to mean distance from the considering point to other points in # the database. # Moreover, this test compares the radius neighbors of LSHForest # with the `sklearn.neighbors.NearestNeighbors`. n_samples = 12 n_features = 2 n_iter = 10 rng = np.random.RandomState(42) X = rng.rand(n_samples, n_features) lshf = LSHForest() # Test unfitted estimator assert_raises(ValueError, lshf.radius_neighbors, X[0]) ignore_warnings(lshf.fit)(X) for i in range(n_iter): # Select a random point in the dataset as the query query = X[rng.randint(0, n_samples)].reshape(1, -1) # At least one neighbor should be returned when the radius is the # mean distance from the query to the points of the dataset. mean_dist = np.mean(pairwise_distances(query, X, metric='cosine')) neighbors = lshf.radius_neighbors(query, radius=mean_dist, return_distance=False) assert_equal(neighbors.shape, (1,)) assert_equal(neighbors.dtype, object) assert_greater(neighbors[0].shape[0], 0) # All distances to points in the results of the radius query should # be less than mean_dist distances, neighbors = lshf.radius_neighbors(query, radius=mean_dist, return_distance=True) assert_array_less(distances[0], mean_dist) # Multiple points n_queries = 5 queries = X[rng.randint(0, n_samples, n_queries)] distances, neighbors = lshf.radius_neighbors(queries, return_distance=True) # dists and inds should not be 1D arrays or arrays of variable lengths # hence the use of the object dtype. assert_equal(distances.shape, (n_queries,)) assert_equal(distances.dtype, object) assert_equal(neighbors.shape, (n_queries,)) assert_equal(neighbors.dtype, object) # Compare with exact neighbor search query = X[rng.randint(0, n_samples)].reshape(1, -1) mean_dist = np.mean(pairwise_distances(query, X, metric='cosine')) nbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X) distances_exact, _ = nbrs.radius_neighbors(query, radius=mean_dist) distances_approx, _ = lshf.radius_neighbors(query, radius=mean_dist) # Radius-based queries do not sort the result points and the order # depends on the method, the random_state and the dataset order. Therefore # we need to sort the results ourselves before performing any comparison. sorted_dists_exact = np.sort(distances_exact[0]) sorted_dists_approx = np.sort(distances_approx[0]) # Distances to exact neighbors are less than or equal to approximate # counterparts as the approximate radius query might have missed some # closer neighbors. assert_true(np.all(np.less_equal(sorted_dists_exact, sorted_dists_approx))) @ignore_warnings def test_radius_neighbors_boundary_handling(): X = [[0.999, 0.001], [0.5, 0.5], [0, 1.], [-1., 0.001]] n_points = len(X) # Build an exact nearest neighbors model as reference model to ensure # consistency between exact and approximate methods nnbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X) # Build a LSHForest model with hyperparameter values that always guarantee # exact results on this toy dataset. lsfh = LSHForest(min_hash_match=0, n_candidates=n_points).fit(X) # define a query aligned with the first axis query = [[1., 0.]] # Compute the exact cosine distances of the query to the four points of # the dataset dists = pairwise_distances(query, X, metric='cosine').ravel() # The first point is almost aligned with the query (very small angle), # the cosine distance should therefore be almost null: assert_almost_equal(dists[0], 0, decimal=5) # The second point form an angle of 45 degrees to the query vector assert_almost_equal(dists[1], 1 - np.cos(np.pi / 4)) # The third point is orthogonal from the query vector hence at a distance # exactly one: assert_almost_equal(dists[2], 1) # The last point is almost colinear but with opposite sign to the query # therefore it has a cosine 'distance' very close to the maximum possible # value of 2. assert_almost_equal(dists[3], 2, decimal=5) # If we query with a radius of one, all the samples except the last sample # should be included in the results. This means that the third sample # is lying on the boundary of the radius query: exact_dists, exact_idx = nnbrs.radius_neighbors(query, radius=1) approx_dists, approx_idx = lsfh.radius_neighbors(query, radius=1) assert_array_equal(np.sort(exact_idx[0]), [0, 1, 2]) assert_array_equal(np.sort(approx_idx[0]), [0, 1, 2]) assert_array_almost_equal(	np.sort(exact_dists[0])	numpy.sort
import numpy as np from utils import getMI class Node(): def __init__(self, nType, idx): ###################################################################################### ## description: ## ## the base class of the basic nodes (in/out-put) in LogicNet ## ## parameters: ## ## nType: node type (in/out/lut), also serves as the prefix of the node name ## ## idx: index of the node in the net (2-tuple) ## ## members: ## ## val: a numpy array of 0/1 values of the node after inference ## ## name: the node name ## ## idx: index of the node in the net (2-tuple) ## ## fis: a list of fanin nodes ## ###################################################################################### self.val = None self.name = nType + str(idx[0]) + '_' + str(idx[1]) self.idx = idx self.fis = [] # clears all fanins def disconnectAll(self): self.fis.clear() # adds a node to the fanin list def connect(self, fanin): self.fis.append(fanin) # sets the values of the 'input' node in a net def setVal(self, data): self.val = data # returns the values of the node def getVal(self): return self.val # retrieves and sets the values of the an 'output' node from its single fanin node def eval(self): assert len(self.fis) == 1 self.val = self.fis[0].getVal() # returns the node name def getName(self): return self.name # returns the mutual information between the correct labels and the node's values def getMI(self, labels): assert self.val is not None return getMI(self.val, labels) class LUT(Node): def __init__(self, idx, k=6): ###################################################################################### ## description: ## ## the class of the look-up-tables in LogicNet, inheritance of the "Node" ## ## parameters: ## ## idx: index of the node in the net (2-tuple) ## ## k: number of inputs of the LUT ## ## members: ## ## k: number of inputs of the LUT ## ## arr: the numpy array the stores the function of the LUT ## ###################################################################################### super().__init__('lut', idx) self.k = k shp = (2,) * k self.arr = (np.random.rand(shp) < 0.5).astype(np.int8) # the magic method __getitem__ for LUT # for a lut object n, and a binary k-tuple X=(x_1, ..., x_k), the method returns n(X) # usage: (2 types of indexing supported) # 1. u[X] (with tuple) 2. u[x_12^(k-1) + ... + x_k] (with int) def __getitem__(self, key): if type(key) == int: return self.arr.flat[key] elif type(key) == tuple: return self.arr[key] else: print('Illegal indexing!!') assert False # the magic method __setitem__ for LUT # the method set n(X) to the given binary value v # usage: (2 types of indexing supported) # 1. u[X] = v 2. u[x_12^(k-1) + ... + x_k] = v def __setitem__(self, key, value): if type(key) == int: self.arr.flat[key] = value elif type(key) == tuple: self.arr[key] = value else: print('Illegal indexing!!') assert False def __prepInVal__(self): inVals = [nd.getVal() for nd in self.fis] inVals = np.array(inVals, dtype=np.int8).transpose() return inVals def __setRandOut__(self, cnt, randId): for i in randId: x = 0 for j in range(self.k): mask = 1 << j n = i ^ mask if cnt.flat[n] > 0: x += 1 elif cnt.flat[n] < 0: x -= 1 if x > 0: self[i] = 1 elif x < 0: self[i] = 0 # trains the LUT to fit the labels def train(self, labels): cnt = np.zeros(self.arr.shape, dtype=np.int32) inVals = self.__prepInVal__() assert len(inVals) == len(labels) for inVal, lab in zip(inVals, labels): inVal = tuple(inVal) cnt[inVal] += lab 2 - 1 #if lab == 1: cnt[inVal] += 1 #elif lab == 0: cnt[inVal] -= 1 #else: assert False randId = [] for i in range(2self.k): if cnt.flat[i] > 0: self[i] = 1 elif cnt.flat[i] < 0: self[i] = 0 else: randId.append(i) self.__setRandOut__(cnt, randId) self.eval() # evalutes the values of the LUT during inference def eval(self): inVals = self.__prepInVal__() self.val = np.array([self[tuple(inVal)] for inVal in inVals], dtype=np.int8) # converts the information of the LUT to strings def toStrings(self): names = [fi.getName() for fi in self.fis] + [self.name] pats = [] fmt = '0{}b'.format(str(self.k)) for i in range(2self.k): if self[i] == 1: pats.append(format(i, fmt) + ' 1') if len(pats) == 2**self.k: # const 1 names, pats = [self.name], ['1'] elif len(pats) == 0: # const 0 names = [self.name] return names, pats # trains the LUT to mimic a neuron according to the input weights def trainFromNN(self, W, B): assert len(W) == self.k sigmoid = lambda x: 1 / (1 + np.exp(-x)) toBin = lambda x: np.array(list(	np.binary_repr(x, self.k)	numpy.binary_repr
from pathlib import Path from timeit import default_timer as timer import h5py import numpy as np import torch from methods.utils.data_utilities import (_segment_index, load_dcase_format, to_metrics2020_format) from torch.utils.data import Dataset, Sampler from tqdm import tqdm from utils.common import int16_samples_to_float32 class UserDataset(Dataset): """ User defined datset """ def __init__(self, args, cfg, dataset, dataset_type='train', overlap=''): """ Args: args: input args cfg: configurations dataset: dataset used dataset_type: 'train' \| 'valid' \| 'dev_test' \| 'eval_test' overlap: '1' \| '2' """ super().__init__() self.dataset_type = dataset_type self.read_into_mem = args.read_into_mem self.sample_rate = cfg['data']['sample_rate'] self.clip_length = dataset.clip_length self.label_resolution = dataset.label_resolution self.frame_length = int(self.clip_length / self.label_resolution) self.label_interp_ratio = int(self.label_resolution * self.sample_rate / cfg['data']['hop_length']) # Chunklen and hoplen and segmentation. Since all of the clips are 60s long, it only segments once here data = np.zeros((1, self.clip_length * self.sample_rate)) if 'train' in self.dataset_type: chunklen = int(cfg['data']['train_chunklen_sec'] * self.sample_rate) hoplen = int(cfg['data']['train_hoplen_sec'] * self.sample_rate) self.segmented_indexes, self.segmented_pad_width = _segment_index(data, chunklen, hoplen) elif self.dataset_type in ['valid', 'dev_test', 'eval_test']: chunklen = int(cfg['data']['test_chunklen_sec'] * self.sample_rate) hoplen = int(cfg['data']['test_hoplen_sec'] * self.sample_rate) self.segmented_indexes, self.segmented_pad_width = _segment_index(data, chunklen, hoplen, last_frame_always_paddding=True) self.num_segments = len(self.segmented_indexes) # Data and meta path fold_str_idx = dataset.fold_str_index ov_str_idx = dataset.ov_str_index data_sr_folder_name = '{}fs'.format(self.sample_rate) main_data_dir = Path(cfg['hdf5_dir']).joinpath(cfg['dataset']).joinpath('data').joinpath(data_sr_folder_name) dev_data_dir = main_data_dir.joinpath('dev').joinpath(cfg['data']['type']) eval_data_dir = main_data_dir.joinpath('eval').joinpath(cfg['data']['type']) main_meta_dir = Path(cfg['hdf5_dir']).joinpath(cfg['dataset']).joinpath('meta') dev_meta_dir = main_meta_dir.joinpath('dev') eval_meta_dir = main_meta_dir.joinpath('eval') if self.dataset_type == 'train': data_dirs = [dev_data_dir] self.meta_dir = dev_meta_dir train_fold = [int(fold.strip()) for fold in str(cfg['training']['train_fold']).split(',')] ov_set = str(cfg['training']['overlap']) if not overlap else overlap self.paths_list = [path for data_dir in data_dirs for path in sorted(data_dir.glob('.h5')) \ if int(path.stem[fold_str_idx]) in train_fold and path.stem[ov_str_idx] in ov_set \ and not path.name.startswith('.')] elif self.dataset_type == 'valid': if cfg['training']['valid_fold'] != 'eval': data_dirs = [dev_data_dir] self.meta_dir = dev_meta_dir valid_fold = [int(fold.strip()) for fold in str(cfg['training']['valid_fold']).split(',')] ov_set = str(cfg['training']['overlap']) if not overlap else overlap self.paths_list = [path for data_dir in data_dirs for path in sorted(data_dir.glob('.h5')) \ if int(path.stem[fold_str_idx]) in valid_fold and path.stem[ov_str_idx] in ov_set \ and not path.name.startswith('.')] ori_meta_dir = Path(cfg['dataset_dir']).joinpath('metadata_dev') else: data_dirs = [eval_data_dir] self.meta_dir = eval_meta_dir ov_set = str(cfg['training']['overlap']) if not overlap else overlap self.paths_list = [path for data_dir in data_dirs for path in sorted(data_dir.glob('.h5')) \ if not path.name.startswith('.')] ori_meta_dir = Path(cfg['dataset_dir']).joinpath('metadata_eval') frame_begin_index = 0 self.valid_gt_sed_metrics2019 = [] self.valid_gt_doa_metrics2019 = [] self.valid_gt_dcaseformat = {} for path in self.paths_list: ori_meta_path = ori_meta_dir.joinpath(path.stem + '.csv') output_dict, sed_metrics2019, doa_metrics2019 = \ load_dcase_format(ori_meta_path, frame_begin_index=frame_begin_index, frame_length=self.frame_length, num_classes=len(dataset.label_set)) self.valid_gt_dcaseformat.update(output_dict) self.valid_gt_sed_metrics2019.append(sed_metrics2019) self.valid_gt_doa_metrics2019.append(doa_metrics2019) frame_begin_index += self.frame_length self.valid_gt_sed_metrics2019 = np.concatenate(self.valid_gt_sed_metrics2019, axis=0) self.valid_gt_doa_metrics2019 = np.concatenate(self.valid_gt_doa_metrics2019, axis=0) self.gt_metrics2020_dict = to_metrics2020_format(self.valid_gt_dcaseformat, self.valid_gt_sed_metrics2019.shape[0], label_resolution=self.label_resolution) elif self.dataset_type == 'dev_test': data_dirs = [dev_data_dir] self.meta_dir = dev_meta_dir dev_test_fold = [int(fold.strip()) for fold in str(cfg['inference']['test_fold']).split(',')] ov_set = str(cfg['inference']['overlap']) if not overlap else overlap self.paths_list = [path for data_dir in data_dirs for path in sorted(data_dir.glob('.h5')) \ if int(path.stem[fold_str_idx]) in dev_test_fold and path.stem[ov_str_idx] in ov_set \ and not path.name.startswith('.')] elif self.dataset_type == 'eval_test': data_dirs = [eval_data_dir] self.meta_dir = eval_meta_dir self.paths_list = [path for data_dir in data_dirs for path in sorted(data_dir.glob('.h5')) \ if not path.name.startswith('.')] self.paths_list = [Path(str(path) + '%' + str(n)) for path in self.paths_list for n in range(self.num_segments)] # Read into memory if self.read_into_mem: load_begin_time = timer() print('Start to load dataset: {}, ov={}......\n'.format(self.dataset_type + ' set', ov_set)) iterator = tqdm(self.paths_list, total=len(self.paths_list), unit='clips') self.dataset_list = [] for path in iterator: fn, n_segment = path.stem, int(path.name.split('%')[1]) data_path = Path(str(path).split('%')[0]) index_begin = self.segmented_indexes[n_segment][0] index_end = self.segmented_indexes[n_segment][1] pad_width_before = self.segmented_pad_width[n_segment][0] pad_width_after = self.segmented_pad_width[n_segment][1] with h5py.File(data_path, 'r') as hf: x = int16_samples_to_float32(hf['waveform'][:, index_begin: index_end]) pad_width = ((0, 0), (pad_width_before, pad_width_after)) x = np.pad(x, pad_width, mode='constant') if 'test' not in self.dataset_type: ov = fn[-1] index_begin_label = int(index_begin / (self.sample_rate self.label_resolution)) index_end_label = int(index_end / (self.sample_rate * self.label_resolution)) # pad_width_before_label = int(pad_width_before / (self.sample_rate * self.label_resolution)) pad_width_after_label = int(pad_width_after / (self.sample_rate * self.label_resolution)) meta_path = self.meta_dir.joinpath(fn + '.h5') with h5py.File(meta_path, 'r') as hf: sed_label = hf['sed_label'][index_begin_label: index_end_label, ...] doa_label = hf['doa_label'][index_begin_label: index_end_label, ...] # NOTE: this is Catesian coordinates if pad_width_after_label != 0: sed_label_new = np.zeros((pad_width_after_label, 2, 14)) doa_label_new = np.zeros((pad_width_after_label, 2, 3)) sed_label = np.concatenate((sed_label, sed_label_new), axis=0) doa_label = np.concatenate((doa_label, doa_label_new), axis=0) self.dataset_list.append({ 'filename': fn, 'n_segment': n_segment, 'ov': ov, 'waveform': x, 'sed_label': sed_label, 'doa_label': doa_label }) else: self.dataset_list.append({ 'filename': fn, 'n_segment': n_segment, 'waveform': x }) iterator.close() print('Loading dataset time: {:.3f}\n'.format(timer()-load_begin_time)) def __len__(self): """Get length of the dataset """ return len(self.paths_list) def __getitem__(self, idx): """ Read features from the dataset """ if self.read_into_mem: data_dict = self.dataset_list[idx] fn = data_dict['filename'] n_segment = data_dict['n_segment'] x = data_dict['waveform'] if 'test' not in self.dataset_type: ov = data_dict['ov'] sed_label = data_dict['sed_label'] doa_label = data_dict['doa_label'] else: path = self.paths_list[idx] fn, n_segment = path.stem, int(path.name.split('%')[1]) data_path = Path(str(path).split('%')[0]) index_begin = self.segmented_indexes[n_segment][0] index_end = self.segmented_indexes[n_segment][1] pad_width_before = self.segmented_pad_width[n_segment][0] pad_width_after = self.segmented_pad_width[n_segment][1] with h5py.File(data_path, 'r') as hf: x = int16_samples_to_float32(hf['waveform'][:, index_begin: index_end]) pad_width = ((0, 0), (pad_width_before, pad_width_after)) x = np.pad(x, pad_width, mode='constant') if 'test' not in self.dataset_type: ov = fn[-1] index_begin_label = int(index_begin / (self.sample_rate * self.label_resolution)) index_end_label = int(index_end / (self.sample_rate * self.label_resolution)) # pad_width_before_label = int(pad_width_before / (self.sample_rate * self.label_resolution)) pad_width_after_label = int(pad_width_after / (self.sample_rate * self.label_resolution)) meta_path = self.meta_dir.joinpath(fn + '.h5') with h5py.File(meta_path, 'r') as hf: sed_label = hf['sed_label'][index_begin_label: index_end_label, ...] doa_label = hf['doa_label'][index_begin_label: index_end_label, ...] # NOTE: this is Catesian coordinates if pad_width_after_label != 0: sed_label_new = np.zeros((pad_width_after_label, 2, 14)) doa_label_new = np.zeros((pad_width_after_label, 2, 3)) sed_label = np.concatenate((sed_label, sed_label_new), axis=0) doa_label = np.concatenate((doa_label, doa_label_new), axis=0) if 'test' not in self.dataset_type: sample = { 'filename': fn, 'n_segment': n_segment, 'ov': ov, 'waveform': x, 'sed_label': sed_label, 'doa_label': doa_label } else: sample = { 'filename': fn, 'n_segment': n_segment, 'waveform': x } return sample class UserBatchSampler(Sampler): """User defined batch sampler. Only for train set. """ def __init__(self, clip_num, batch_size, seed=2020): self.clip_num = clip_num self.batch_size = batch_size self.random_state = np.random.RandomState(seed) self.indexes =	np.arange(self.clip_num)	numpy.arange
from rpn.generation import compute_deltas import numpy as np def make_corners(regions): """Transforms boxes from center format to corners""" x0, y0, x1, y1 = np.transpose(regions) ws = x1 - x0 // 2 hs = y1 - y0 // 2 return np.transpose([x0 + ws, y0 + hs, 2 * ws, 2 * hs]) def compute_iou(regions, gt_boxes): """IOU metric computation. Differs from rpn version as there is no place for precomputation Also returns indices instead of respective gt boxes as classes are chosen with their help""" regions_areas = regions[:, 2] * regions[:, 3] x0, y0, x1, y1 = make_corners(regions) ious = [] for gt_box in gt_boxes: gt_x0, gt_y0, gt_x1, gt_y1 = gt_box gt_area = (gt_x1 - gt_x0) * (gt_y1 - gt_y0) int_x0 = np.maximum(gt_x0, x0) int_y0 = np.maximum(gt_y0, y0) int_x1 =	np.minimum(gt_x1, x1)	numpy.minimum
# -- coding: utf-8 -- """ Copyright (c) 2019 <NAME> pySME is a Python script to run R SME package (https://cran.r-project.org/web/packages/sme/index.html). SME package generates smoothing-splines mixed-effects models from metabolomics data. This script follows methodology given by Berk et al. (2011) and utilizes bootstrapping to approximate p-values. Running this script requires R with SME package installed. """ import os import numpy as np from scipy import interpolate import pandas as pd import matplotlib.pyplot as plt from matplotlib import cm from rpy2.robjects.packages import importr from rpy2.robjects import pandas2ri import statsmodels.stats.multitest as smm import time import copy import smeutils smePack = importr('sme', lib_loc="C:/Users/user/Documents/R/win-library/3.6") statsPack = importr('stats') # Settings ==================================================================== # Input files info = pd.read_csv('./sme_info.csv') data = pd.read_csv('./sme_data.csv') info_arr = np.array(info) data_fid = np.array(data.columns) data_arr = np.array(data) selIdx = np.arange(len(data_fid)) # Parameters RUN = True N = 12 # Number of subjects t_n = 4 # Number of time points iplN = 100 # Number of interpolated time points n_bootstrap = 500 # Number of bootstrap sampling selIdx = selIdx[:] # List of metabolites to analyze relative = False # Scale data to initial values correctOutlier = False SAVE = False USEMEAN = True # SME Parameters ctra = "AICc" # Criteria init_l_mc = 1e-8 # Initial lambda_mu init_l_vc = 1e-8 # Initial lambda_v init_l_mt = 5e-8 # Initial lambda_mu init_l_vt = 5e-8 # Initial lambda_v maxIter = 100000 # Maximum iteration deltaEM = 1e-3 # Threshold for expetation maximization deltaNM = 1e-3 # Threshold for nelder mead normalizeTime = True seed = 1234 # RNG seed showFig = False # Flag to plot figures figSize = (20,16) # Size of figures plotLegend = False # Flag to plot legend colorMap = 'viridis' # kwarg for colormap plotSMEMeanOnly = False # Only plot SME mean trace mergePlot = True # Merge multiple plots plotHeatmap = False # Plot heatmap comparing two data groups t = np.array([1,3,5,7]) iplT = np.linspace(1, 7, iplN) iplTIdx = np.empty(t_n) for i in range(t_n): iplTIdx[i] = np.where(iplT == t[i])[0] iplTIdx = iplTIdx.astype(int) sel = np.array([data_fid[selIdx]]).flatten() #============================================================================== np.random.seed(seed) # Set seed #============================================================================== if relative: data = smeutils.normalizeData(data, N, t_n, data_fid) #============================================================================== t0 = time.time() fulldataRaw = pd.concat([info,data], axis=1) fulldataRaw = fulldataRaw.astype('float64') fulldata = copy.deepcopy(fulldataRaw) fulldata = fulldata.drop(fulldata.index[16]) # ind 5 has an outlier if correctOutlier: fulldata = smeutils.correctOutlier(fulldata, sel, t, t_n) # Initialize ================================================================== grp0_f = fulldata[(fulldata.grp == 0)]['ind'] grp1_f = fulldata[(fulldata.grp == 1)]['ind'] grp0 = np.unique(fulldata[(fulldata.grp == 0)]['ind']) grp1 = np.unique(fulldata[(fulldata.grp == 1)]['ind']) pandas2ri.activate() fd_ri = pandas2ri.py2ri(fulldata) fd_rigrp0 = fd_ri.rx(fd_ri.rx2("grp").ro == 0, True) fd_rigrp1 = fd_ri.rx(fd_ri.rx2("grp").ro == 1, True) fd_rigrp0tme = fd_rigrp0.rx2("tme") fd_rigrp0ind = fd_rigrp0.rx2("ind") fd_rigrp1tme = fd_rigrp1.rx2("tme") fd_rigrp1ind = fd_rigrp1.rx2("ind") ys0mu = np.empty((len(sel), iplN)) ys1mu = np.empty((len(sel), iplN)) ys0vHat = np.empty((len(sel), len(grp0), iplN)) ys1vHat = np.empty((len(sel), len(grp1), iplN)) l2 = np.empty(len(sel)) se = np.empty(len(sel)) se0 = np.empty((len(sel), len(grp0))) se1 = np.empty((len(sel), len(grp1))) sem = np.empty(len(sel)) tval = np.empty(len(sel)) ys0v = np.empty((len(sel), len(grp0), t_n)) ys1v = np.empty((len(sel), len(grp1), t_n)) ys0eta = np.empty((len(sel), len(grp0), t_n)) ys1eta = np.empty((len(sel), len(grp1), t_n)) ys0mubs = np.empty((n_bootstrap, len(sel), iplN)) ys1mubs = np.empty((n_bootstrap, len(sel), iplN)) ys0vHatbs = np.empty((n_bootstrap, len(sel), len(grp0), iplN)) ys1vHatbs = np.empty((n_bootstrap, len(sel), len(grp1), iplN)) l2bs = np.empty((n_bootstrap, len(sel))) sebs = np.empty((n_bootstrap, len(sel))) se0bs = np.empty((n_bootstrap, len(sel), len(grp0))) se1bs = np.empty((n_bootstrap, len(sel), len(grp1))) sembs = np.empty((n_bootstrap, len(sel))) tvalbs = np.empty((n_bootstrap, len(sel))) pval = np.empty(len(sel)) t1 = time.time() print(t1 - t0) # SME ========================================================================= if RUN: for m_i in range(len(sel)): fd_rigrp0obj = fd_rigrp0.rx2(sel[m_i]) fd_rigrp1obj = fd_rigrp1.rx2(sel[m_i]) fit0 = smePack.sme(fd_rigrp0obj, fd_rigrp0tme, fd_rigrp0ind, criteria=ctra, maxIter=maxIter, deltaEM=deltaEM, deltaNM=deltaNM, initial_lambda_mu=init_l_mc, initial_lambda_v=init_l_mc, normalizeTime=normalizeTime) fit1 = smePack.sme(fd_rigrp1obj, fd_rigrp1tme, fd_rigrp1ind, criteria=ctra, maxIter=maxIter, deltaEM=deltaEM, deltaNM=deltaNM, initial_lambda_mu=init_l_mt, initial_lambda_v=init_l_vt, normalizeTime=normalizeTime) fit0coef = np.array(fit0.rx2('coefficients')) fit1coef = np.array(fit1.rx2('coefficients')) spl0mu = interpolate.CubicSpline(t, fit0coef[0], bc_type='natural') ys0mu[m_i] = spl0mu(iplT) spl1mu = interpolate.CubicSpline(t, fit1coef[0], bc_type='natural') ys1mu[m_i] = spl1mu(iplT) l2[m_i] = np.sqrt(np.trapz(np.square(ys0mu[m_i] - ys1mu[m_i]), x=iplT)) for g0 in range(len(grp0)): spl0 = interpolate.CubicSpline(t, fit0coef[g0 + 1] + fit0coef[0], bc_type='natural') ys0vHat[m_i][g0] = spl0(iplT) ys0v[m_i][g0] = ys0mu[m_i][iplTIdx] - ys0vHat[m_i][g0][iplTIdx] ys0eta[m_i][g0] = fulldataRaw.loc[fulldataRaw.ind == grp0[g0], sel[m_i]] - ys0vHat[m_i][g0][iplTIdx] se0[m_i][g0] = np.trapz(np.square(ys0mu[m_i] - ys0vHat[m_i][g0]), x=iplT) for g1 in range(len(grp1)): spl1 = interpolate.CubicSpline(t, fit1coef[g1 + 1] + fit1coef[0], bc_type='natural') ys1vHat[m_i][g1] = spl1(iplT) ys1v[m_i][g1] = ys1mu[m_i][iplTIdx] - ys1vHat[m_i][g1][iplTIdx] ys1eta[m_i][g1] = fulldataRaw.loc[fulldataRaw.ind == grp1[g1], sel[m_i]] - ys1vHat[m_i][g1][iplTIdx] se1[m_i][g1] = np.trapz(np.square(ys1mu[m_i] - ys1vHat[m_i][g1]), x=iplT) se[m_i] = np.sqrt(np.mean(se0[m_i])/len(grp0) + np.mean(se1[m_i])/len(grp1)) sem = 0. tval = np.divide(l2, se + sem) ys0vFlat = ys0v.reshape((ys0v.shape[0], -1)) ys0etaFlat = ys0eta.reshape((ys0eta.shape[0], -1)) ys0etaFlat = np.delete(ys0etaFlat, 13, 1) # ind 5 has an outlier ys1vFlat = ys1v.reshape((ys1v.shape[0], -1)) ys1etaFlat = ys1eta.reshape((ys1eta.shape[0], -1)) t2 = time.time() print(t2 - t1) # Bootstrapping =============================================================== fulldataS = [] for bcount in range(n_bootstrap): print("Bootstrap run: " + str(bcount)) fulldataC = copy.deepcopy(fulldataRaw) for m_i in range(len(sel)): if USEMEAN: for Di in range(N): ysmuMean = (ys0mu[m_i][iplTIdx] + ys1mu[m_i][iplTIdx])/2 if Di in grp0: fulldataC[sel[m_i]][np.arange(0,t_nN,N)+Di] = (ysmuMean + np.random.choice(ys0vFlat[m_i], size=t_n) + np.random.choice(ys0etaFlat[m_i], size=t_n)) else: fulldataC[sel[m_i]][np.arange(0,t_nN,N)+Di] = (ysmuMean + np.random.choice(ys1vFlat[m_i], size=t_n) + np.random.choice(ys1etaFlat[m_i], size=t_n)) else: ct_rand = np.random.rand() for Di in range(N): if ct_rand < 0.5: if Di in grp0: fulldataC[sel[m_i]][np.arange(0,t_nN,N)+Di] = (ys0mu[m_i][iplTIdx] + np.random.choice(ys0vFlat[m_i], size=t_n) + np.random.choice(ys0etaFlat[m_i], size=t_n)) else: fulldataC[sel[m_i]][np.arange(0,t_nN,N)+Di] = (ys0mu[m_i][iplTIdx] +	np.random.choice(ys1vFlat[m_i], size=t_n)	numpy.random.choice
# 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]) 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(160, 'R 3 m :H', transformations) space_groups[160] = sg space_groups['R 3 m :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([0,0,1]) 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)) 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([1,2,7]) trans_den = N.array([3,3,6]) 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([1,2,7]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,7]) trans_den = N.array([3,3,6]) transformations.append((rot, 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([2,1,5]) trans_den = N.array([3,3,6]) 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([2,1,5]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,5]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(161, 'R 3 c :H', transformations) space_groups[161] = sg space_groups['R 3 c :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)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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(162, 'P -3 1 m', transformations) space_groups[162] = 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([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)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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(163, 'P -3 1 c', transformations) space_groups[163] = 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([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(164, 'P -3 m 1', transformations) space_groups[164] = 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([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)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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(165, 'P -3 c 1', transformations) space_groups[165] = 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([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([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([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)) 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(166, 'R -3 m :H', transformations) space_groups[166] = sg space_groups['R -3 m :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([0,0,1]) 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)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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)) 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([1,2,7]) trans_den = N.array([3,3,6]) 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([1,2,7]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,7]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_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([1,2,1]) trans_den = N.array([3,3,6]) 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([1,2,1]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,1]) trans_den = N.array([3,3,6]) transformations.append((rot, 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([2,1,5]) trans_den = N.array([3,3,6]) 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([2,1,5]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,5]) trans_den = N.array([3,3,6]) transformations.append((rot, 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([3,3,6]) 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([3,3,6]) transformations.append((rot, 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([3,3,6]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(167, 'R -3 c :H', transformations) space_groups[167] = sg space_groups['R -3 c :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([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,-1,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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)) sg = SpaceGroup(168, 'P 6', transformations) space_groups[168] = sg space_groups['P 6'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,1]) trans_den = N.array([1,1,6]) 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([0,0,5]) trans_den = N.array([1,1,6]) 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([-1,0,0,0,-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(169, 'P 61', transformations) space_groups[169] = sg space_groups['P 61'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,5]) trans_den = N.array([1,1,6]) 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([1,1,6]) 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([-1,0,0,0,-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(170, 'P 65', transformations) space_groups[170] = sg space_groups['P 65'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,1]) trans_den = N.array([1,1,3]) 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([0,0,2]) trans_den = N.array([1,1,3]) 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([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([-1,0,0,0,-1,0,0,0,1]) rot.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(171, 'P 62', transformations) space_groups[171] = sg space_groups['P 62'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,2]) trans_den = N.array([1,1,3]) 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([0,0,1]) trans_den = N.array([1,1,3]) 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([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([-1,0,0,0,-1,0,0,0,1]) rot.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(172, 'P 64', transformations) space_groups[172] = sg space_groups['P 64'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(173, 'P 63', transformations) space_groups[173] = sg space_groups['P 63'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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)) sg = SpaceGroup(174, 'P -6', transformations) space_groups[174] = sg space_groups['P -6'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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,-1,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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)) sg = SpaceGroup(175, 'P 6/m', transformations) space_groups[175] = sg space_groups['P 6/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,-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,-1,1,0,0,0,1]) 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,-1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,1]) 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([-1,1,0,-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,-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,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(176, 'P 63/m', transformations) space_groups[176] = sg space_groups['P 63/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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([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([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_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(177, 'P 6 2 2', transformations) space_groups[177] = sg space_groups['P 6 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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,6]) 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([0,0,5]) trans_den = N.array([1,1,6]) 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([0,0,0]) 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([0,0,2]) trans_den = N.array([1,1,3]) 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([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([1,1,3]) 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([0,0,5]) trans_den = N.array([1,1,6]) 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([1,1,6]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(178, 'P 61 2 2', transformations) space_groups[178] = sg space_groups['P 61 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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,5]) trans_den = N.array([1,1,6]) 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([1,1,6]) 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([0,0,0]) 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([0,0,1]) trans_den = N.array([1,1,3]) 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([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([0,0,2]) 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([0,0,1]) trans_den = N.array([1,1,6]) 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([0,0,5]) trans_den = N.array([1,1,6]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(179, 'P 65 2 2', transformations) space_groups[179] = sg space_groups['P 65 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,-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,-1,1,0,0,0,1]) 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,-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([0,0,0]) 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([0,0,1]) trans_den = N.array([1,1,3]) 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([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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([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([0,0,0]) 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([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(180, 'P 62 2 2', transformations) space_groups[180] = sg space_groups['P 62 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,-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,-1,1,0,0,0,1]) 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,-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([0,0,0]) 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([0,0,2]) trans_den = N.array([1,1,3]) 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([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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([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([0,0,0]) 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([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(181, 'P 64 2 2', transformations) space_groups[181] = sg space_groups['P 64 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,-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,-1,1,0,0,0,1]) 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,-1,0,0,0,1]) rot.shape = (3, 3) trans_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([-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)) 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(182, 'P 63 2 2', transformations) space_groups[182] = sg space_groups['P 63 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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([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([-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([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_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(183, 'P 6 m m', transformations) space_groups[183] = sg space_groups['P 6 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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([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([-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)) 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(184, 'P 6 c c', transformations) space_groups[184] = sg space_groups['P 6 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([1,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([0,0,1]) 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)) 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([0,0,0]) 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(185, 'P 63 c m', transformations) space_groups[185] = sg space_groups['P 63 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,-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,-1,1,0,0,0,1]) 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,-1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,1]) 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([0,0,0]) 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([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(186, 'P 63 m c', transformations) space_groups[186] = sg space_groups['P 63 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([-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,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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)) 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([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.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(187, 'P -6 m 2', transformations) space_groups[187] = sg space_groups['P -6 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([-1,1,0,-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,-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,-1,0,0,0,1]) rot.shape = (3, 3) trans_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)) 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([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)) sg = SpaceGroup(188, 'P -6 c 2', transformations) space_groups[188] = sg space_groups['P -6 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([-1,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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([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([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_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(189, 'P -6 2 m', transformations) space_groups[189] = sg space_groups['P -6 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([-1,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([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,-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(190, 'P -6 2 c', transformations) space_groups[190] = sg space_groups['P -6 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([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,-1,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_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(191, 'P 6/m m m', transformations) space_groups[191] = sg space_groups['P 6/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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([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([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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([-1,1,0,0,1,0,0,0,-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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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,-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(192, 'P 6/m c c', transformations) space_groups[192] = sg space_groups['P 6/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([1,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([-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([0,0,0]) 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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([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([0,0,0]) 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(193, 'P 63/m c m', transformations) space_groups[193] = sg space_groups['P 63/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,-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,-1,1,0,0,0,1]) 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,-1,0,0,0,1]) rot.shape = (3, 3) trans_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([-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)) 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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([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)) 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(194, 'P 63/m m c', transformations) space_groups[194] = sg space_groups['P 63/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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.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(195, 'P 2 3', transformations) space_groups[195] = sg space_groups['P 2 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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(196, 'F 2 3', transformations) space_groups[196] = sg space_groups['F 2 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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(197, 'I 2 3', transformations) space_groups[197] = sg space_groups['I 2 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(198, 'P 21 3', transformations) space_groups[198] = sg space_groups['P 21 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([1,0,0]) 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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(199, 'I 21 3', transformations) space_groups[199] = sg space_groups['I 21 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.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(200, 'P m -3', transformations) space_groups[200] = sg space_groups['P m -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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) 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)) sg = SpaceGroup(201, 'P n -3 :2', transformations) space_groups[201] = sg space_groups['P n -3 :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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([0,0,-1,-1,0,0,0,-1,0]) 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,-1,0,0,0,-1,-1,0,0]) 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([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) 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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([0,0,-1,-1,0,0,0,-1,0]) 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,-1,0,0,0,-1,-1,0,0]) 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([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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(202, 'F m -3', transformations) space_groups[202] = sg space_groups['F m -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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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([0,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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([0,-1,0,0,0,1,1,0,0]) 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([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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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([0,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,0,-1,-1,0,0,0,-1,0]) 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,-1,0,0,0,-1,-1,0,0]) 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([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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([0,-1,0,0,0,1,1,0,0]) 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([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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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([0,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,0,-1,-1,0,0,0,-1,0]) 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,-1,0,0,0,-1,-1,0,0]) 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([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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([0,-1,0,0,0,1,1,0,0]) 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([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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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([0,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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([0,-1,0,0,0,1,1,0,0]) 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([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(203, 'F d -3 :2', transformations) space_groups[203] = sg space_groups['F d -3 :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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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(204, 'I m -3', transformations) space_groups[204] = sg space_groups['I m -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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(205, 'P a -3', transformations) space_groups[205] = sg space_groups['P a -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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([1,0,0]) 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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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([-1,0,0]) 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(206, 'I a -3', transformations) space_groups[206] = sg space_groups['I a -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([1,0,0,0,0,-1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,0,1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,-1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,-1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,-1,0,-1,0]) rot.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(207, 'P 4 3 2', transformations) space_groups[207] = sg space_groups['P 4 3 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([1,0,0,0,0,-1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,0,1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,1,0,1,0,0]) rot.shape = (3, 3) 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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([0,0,1,0,-1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,-1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,-1,0,-1,0]) rot.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(208, 'P 42 3 2', transformations) space_groups[208] = sg space_groups['P 42 3 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([1,0,0,0,0,-1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,0,1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,-1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,-1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,-1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_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([1,0,0,0,0,-1,0,1,0]) 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([1,0,0,0,0,1,0,-1,0]) 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([0,0,1,0,1,0,-1,0,0]) 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([0,0,-1,0,1,0,1,0,0]) 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([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([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,-1,0,1,0,0]) 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([0,0,-1,0,-1,0,-1,0,0]) 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([-1,0,0,0,0,1,0,1,0]) 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([-1,0,0,0,0,-1,0,-1,0]) 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([1,0,0,0,0,-1,0,1,0]) 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([1,0,0,0,0,1,0,-1,0]) 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([0,0,1,0,1,0,-1,0,0]) 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([0,0,-1,0,1,0,1,0,0]) 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([1,0,1]) 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([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,-1,0,1,0,0]) 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([0,0,-1,0,-1,0,-1,0,0]) 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([-1,0,0,0,0,1,0,1,0]) 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([-1,0,0,0,0,-1,0,-1,0]) 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([1,0,0,0,0,-1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,0,1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,1,0,1,0,0]) rot.shape = (3, 3) 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)) rot = N.array([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([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([0,0,1,0,-1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,-1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,-1,0,-1,0]) rot.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(209, 'F 4 3 2', transformations) space_groups[209] = sg space_groups['F 4 3 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([1,0,0,0,0,-1,0,1,0]) 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([1,0,0,0,0,1,0,-1,0]) 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([0,0,1,0,1,0,-1,0,0]) 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([0,0,-1,0,1,0,1,0,0]) 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([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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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([0,0,1,0,-1,0,1,0,0]) 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([0,0,-1,0,-1,0,-1,0,0]) 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([-1,0,0,0,0,1,0,1,0]) 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([-1,0,0,0,0,-1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den =	N.array([4,4,4])	numpy.array
from __future__ import print_function, absolute_import import numpy as np import vtk from collections import defaultdict from itertools import chain from six import itervalues from vtk.util.numpy_support import numpy_to_vtk def _basic_elem(elem, cell_type, ugrid, nid_dict): nids = [nid_dict[nid] for nid in elem.node_ids] ids = vtk.vtkIdList() ids.SetNumberOfIds(len(nids)) for i in range(len(nids)): ids.SetId(i, nids[i]) ugrid.InsertNextCell(cell_type, ids) return elem.eid def _grid(grid, cell_type, ugrid, nid_dict): nids = [nid_dict[nid] for nid in [grid.nid]] ids = vtk.vtkIdList() ids.SetNumberOfIds(len(nids)) for i in range(len(nids)): ids.SetId(i, nids[i]) ugrid.InsertNextCell(cell_type, ids) return grid.nid def _rbe2(elem, cell_type, ugrid, nid_dict): independent = elem.gn dependent = elem.Gmi nids = [] for i in range(len(dependent)): nids.append(nid_dict[independent]) nids.append(nid_dict[dependent[i]]) ids = vtk.vtkIdList() ids.SetNumberOfIds(len(nids)) for i in range(len(nids)): ids.SetId(i, nids[i]) ugrid.InsertNextCell(cell_type, ids) return elem.eid def _rbe3(elem, cell_type, ugrid, nid_dict): independent = elem.refgrid _dependent = elem.Gijs dependent = [] for dep in _dependent: if isinstance(dep, list): dependent.extend(dep) else: dependent.append(dep) nids = [] for i in range(len(dependent)): nids.append(nid_dict[independent]) nids.append(nid_dict[dependent[i]]) ids = vtk.vtkIdList() ids.SetNumberOfIds(len(nids)) for i in range(len(nids)): ids.SetId(i, nids[i]) ugrid.InsertNextCell(cell_type, ids) return elem.eid _category_list = ['grid', 'element', 'mpc', 'force', 'disp', 'cord'] _nastran_to_vtk = { 'GRID': (vtk.VTK_VERTEX, _grid, 'grid'), 'CQUAD4': (vtk.VTK_QUAD, _basic_elem, 'element'), 'CTRIA3': (vtk.VTK_TRIANGLE, _basic_elem, 'element'), 'CBEAM': (vtk.VTK_LINE, _basic_elem, 'element'), 'CBUSH': (vtk.VTK_LINE, _basic_elem, 'element'), 'CBAR': (vtk.VTK_LINE, _basic_elem, 'element'), 'RBE2': (vtk.VTK_POLY_LINE, _rbe2, 'mpc'), 'RBE3': (vtk.VTK_POLY_LINE, _rbe3, 'mpc') } _card_types = {} i = 0 for key in sorted(_nastran_to_vtk.keys()): _card_types[key] = i i += 1 class UGrid(vtk.vtkUnstructuredGrid): category_list = _category_list category_dict = {_category_list[i]: i for i in range(len(_category_list))} nastran_to_vtk = _nastran_to_vtk card_types = _card_types @classmethod def make_from_bdf(cls, bdf): # type: (BDF) -> UGrid nid_list = [] nid_dict = {} node_pos = np.empty((len(bdf.nodes), 3), dtype=np.float64) i = 0 for node in itervalues(bdf.nodes): node_pos[i] = node.get_position() nid = node.nid nid_list.append(nid) nid_dict[nid] = i i += 1 _points = vtk.vtkPoints() _points.SetData(numpy_to_vtk(node_pos)) points = vtk.vtkPoints() points.DeepCopy(_points) cells = [] cell_types = [] cell_count = 0 elem_types = [] eid_list = [] _nastran_to_vtk = cls.nastran_to_vtk bdf_data_to_plot = chain( itervalues(bdf.nodes), itervalues(bdf.elements), itervalues(bdf.rigid_elements) ) category_list = [] category_dict = cls.category_dict ugrid = vtk.vtkUnstructuredGrid() ugrid.SetPoints(points) for elem in bdf_data_to_plot: elem_type = elem.type cell_type, add_method, category = _nastran_to_vtk.get(elem_type, (None, None, None)) if cell_type is None: continue cell_types.append(cell_type) elem_types.append(elem_type) eid = add_method(elem, cell_type, ugrid, nid_dict) # returns element/grid id eid_list.append(eid) category_list.append(category_dict[category]) cell_count += 1 id_array = numpy_to_vtk(np.array(cells, dtype=np.int64), deep=1, array_type=vtk.VTK_ID_TYPE) vtk_cells = vtk.vtkCellArray() vtk_cells.SetCells(cell_count, id_array) cell_types = np.array(cell_types, 'B') vtk_cell_types = numpy_to_vtk(cell_types) cell_locations = np.array([i for i in range(cell_count)]) vtk_cell_locations = numpy_to_vtk(cell_locations, deep=1, array_type=vtk.VTK_ID_TYPE) vtk_cell_locations.SetName('index') ugrid.GetCellData().AddArray(vtk_cell_locations) _elem_types = vtk.vtkIdTypeArray() _elem_types.SetName('element_type') _elem_types.SetNumberOfValues(len(elem_types)) card_types = cls.card_types for i in range(len(elem_types)): _elem_types.SetValue(i, card_types[elem_types[i]]) ugrid.GetCellData().AddArray(_elem_types) _cat = numpy_to_vtk(np.array(category_list, dtype=np.int64), deep=1, array_type=vtk.VTK_ID_TYPE) _cat.SetName('category') ugrid.GetCellData().AddArray(_cat) id_array = numpy_to_vtk(	np.array(eid_list, dtype=np.int64)	numpy.array
""" Independent Model Ensemble of binary classifiers with resulting probabilities normalized across all classes. Assumes all classes are independent and are not related by any class hierarchy. """ import numpy as np from mainmodel.mainmodel import MainModel from classifiers.binary.optbinary import OptimalBinaryClassifier class OvAModel(MainModel): def __init__(self, data_args): """ Initialize Independent Model - call init method of parent class """ self.name = "OVA Classifier" # Use output of Binary Classifier model for OvAModel - just need to # normalize those probabilities. if 'init_from_binary' in data_args.keys(): self.init_model = data_args['init_from_binary'] else: self.init_model = None super(OvAModel, self).__init__(data_args) def run_cfv(self, start_time): """ Overwrite MainModel function to see if results from BinaryModel were passed in """ if self.init_model is not None: # Normalize probabilities per row (Except last column which is label) print("Using Binary Model results.") self.class_labels = self.init_model.class_labels self.class_counts = self.init_model.class_counts self.num_folds = self.init_model.num_folds self.X = self.init_model.X self.y = self.init_model.y self.results = self.init_model.results a = np.copy(self.init_model.results) new_results = [] for index in range(self.init_model.num_folds): trial_results = [] for row_index, row in enumerate(a[index]): num_classes = len(self.init_model.class_labels) prob_sum = 0 # compute normalizing sum for class_index in range(num_classes): prob_sum += row[class_index] new_probs = [] for class_index in range(num_classes): new_probs.append(row[class_index] / prob_sum) new_probs.append(row[num_classes]) # Add label trial_results.append(new_probs) new_results.append(	np.array(trial_results, dtype='object')	numpy.array
""" Created on April, 2019 @author: <NAME> Toolkit functions used for processing training data. Cite: <NAME>, et al. "Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation." Advances in Neural Information Processing Systems. 2018. """ import numpy as np from scipy.spatial import ConvexHull import re import cv2 import pickle import json from copy import deepcopy def sample_pnts_from_obj(data, n_pnts = 5000, mode = 'uniform'): # sample points on each object mesh. flags = data.keys() all_pnts = data['v'][:,:3] area_list = np.array(calculate_face_area(data)) distribution = area_list/np.sum(area_list) # sample points the probability depends on the face area new_pnts = [] if mode == 'random': random_face_ids = np.random.choice(len(data['f']), n_pnts, replace=True, p=distribution) random_face_ids, sample_counts = np.unique(random_face_ids, return_counts=True) for face_id, sample_count in zip(random_face_ids, sample_counts): face = data['f'][face_id] vid_in_face = [int(item.split('/')[0]) for item in face] weights = np.diff(np.sort(np.vstack( [np.zeros((1, sample_count)), np.random.uniform(0, 1, size=(len(vid_in_face) - 1, sample_count)),	np.ones((1, sample_count))	numpy.ones
####### ## 1. Dicom to PNG # In[] import SimpleITK as sitk import os from skimage import transform,exposure,io import numpy as np import cv2 os.environ["CUDA_VISIBLE_DEVICES"]="0" def windowing(img, center, width): low = center - width/2 high = center + width/2 img = (img - low)/width img[img<0.]=0. img[img>1.]=1. return img def dicom2png(src_Path, dst_Path, imshape): #print(src_Path) if(not os.path.isdir(src_Path)): return if(not os.path.isdir(dst_Path)): return for filename in os.listdir(src_Path): if(filename[-4:] != ".dcm"): continue filepath = src_Path + "/" + filename if(os.path.isdir(filepath)): continue print(filepath) img = sitk.ReadImage(filepath) img = sitk.GetArrayFromImage(img).astype("int16") shape = img.shape img = exposure.equalize_hist(img) img = img[0,:,:] if(imshape is not None): img = transform.resize(img, imshape) # #img = windowing(img, 8000, 3000) img = img255 img = np.asarray(img, dtype = "int16") img = np.expand_dims(img, -1) cv2.imwrite(dst_Path +"/"+ filename[:-4]+"_" + str(shape[1]) + "_" + str(shape[2]) +".png", img) folder = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_0_Original" dst = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_1_BasicData" imshape = (2048,2048) for lower in os.listdir(folder) : if (not os.path.isdir(folder + "/" + lower)): continue if( not os.path.isdir(dst + "/" + lower)): os.mkdir(dst+ "/" + lower) dicom2png( folder + "/" + lower, dst+ "/" + lower, imshape) ################################################################################### ## 2. Lung Mask # In[] ################################################################################## ################################################################################# ## 3. Lung Mask Crop # In[] import cv2 import numpy as np import os from operator import eq import random import matplotlib.pyplot as plt from skimage import io import shutil import csv os.environ["CUDA_VISIBLE_DEVICES"]="0" folder = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_1_BasicData/Imgs" dst = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_2_ImgCropped" LungMaskPath = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_1_BasicData/Masks" csvfile = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_2_ImgCropped/metadata.csv" for lower in os.listdir(folder) : if (not os.path.isdir(folder + "/" + lower)): continue if( not os.path.isdir(dst + "/" + lower)): os.mkdir(dst+ "/" + lower) for file in os.listdir(folder + "/" + lower) : print(file) LungMask = cv2.imread(LungMaskPath + "/" + lower + "/" + file, 0) _, LungMask = cv2.threshold(LungMask, 127, 255, cv2.THRESH_BINARY) _, contours, _ = cv2.findContours(LungMask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) rects = [] for cnt in contours: rects.append(cv2.boundingRect(cnt)) tcomx = 10 tcomy = 10 bcomx = 10 bcomy = 10 top_x, top_y, bottom_x, bottom_y = 0, 0 ,0, 0 rects.sort() top_x = min([x for (x, y, w, h) in rects]) - tcomx #26 top_y = min([y for (x, y, w, h) in rects]) - tcomy #26 bottom_x = max([x+w for (x, y, w, h) in rects]) + bcomx #234 bottom_y = max([y+h for (x, y, w, h) in rects]) + bcomy #227 #print(top_x, top_y, bottom_x, bottom_y) if(top_x <=0 ) : top_x = tcomx if(top_y <=0 ) : top_y = tcomy if(bottom_x >= 1024 ) : bottom_x = 1024 - tcomx if(bottom_y >= 1024 ) : bottom_y = 1024 - tcomy Img = cv2.imread(folder + "/" + lower + "/" + file, 0) ImgCrop = Img[top_y2:bottom_y2, top_x2:bottom_x2] ImgCrop = cv2.resize(ImgCrop, (1024,1024)) cv2.imwrite(dst+ "/" + lower + "/" + file, ImgCrop) f = open(csvfile, 'a', encoding = "utf-8", newline='') f_writer = csv.writer(f) strline = [] strline.append(file) strline.append(str(top_y2)) strline.append(str(bottom_y2)) strline.append(str(top_x2)) strline.append(str(bottom_x*2)) f_writer.writerow(strline) f.close() #################################################################################################################### ################################################################################### ## 4. Line Detection # In[] ################################################################################## ################################################################################### ## 5. Mask recur # In[] import os os.environ["CUDA_VISIBLE_DEVICES"]="0" import cv2 import csv import numpy as np from skimage.morphology import skeletonize Classlist = ["Aortic Knob", "Lt Lower CB", "Pulmonary Conus", "Rt Lower CB", "Rt Upper CB", "DAO" , "Carina" , "LAA"] filePath = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_3_LineMask" dstPath = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_4_LineMask_Recur" csvfile = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_2_ImgCropped/metadata.csv" for clss in Classlist : clsfilepath = filePath + "/" + clss if(not os.path.isdir(dstPath+"/" + clss) ): os.mkdir(dstPath+"/" + clss) for file in os.listdir(clsfilepath): mask = cv2.imread(clsfilepath + "/" + file, 0) mask = np.asarray(mask) f = open(csvfile, 'r', encoding = "utf-8", newline='') f_reader = csv.reader(f) xtop = yleft = xbottom = yright = 0 for row in f_reader: if (row[0] == file): xtop = int(row[1]) yleft = int(row[3]) xbottom = int(row[2]) yright = int(row[4]) break f.close() if xtop == 0 or yleft == 0 or xbottom == 0 or yright == 0: continue #print(file) #print(xbottom, xtop, yright, yleft) mask_ = cv2.resize(mask, (yright- yleft, xbottom - xtop)) mask_ =	np.asarray(mask_)	numpy.asarray
"""Kernels for Gaussian process regression and classification. The kernels in this module allow kernel-engineering, i.e., they can be combined via the "+" and "" operators or be exponentiated with a scalar via "". These sum and product expressions can also contain scalar values, which are automatically converted to a constant kernel. All kernels allow (analytic) gradient-based hyperparameter optimization. The space of hyperparameters can be specified by giving lower und upper boundaries for the value of each hyperparameter (the search space is thus rectangular). Instead of specifying bounds, hyperparameters can also be declared to be "fixed", which causes these hyperparameters to be excluded from optimization. """ # Author: <NAME> <<EMAIL>> # License: BSD 3 clause # Note: this module is strongly inspired by the kernel module of the george # package. from abc import ABCMeta, abstractmethod from collections import namedtuple import math from inspect import signature import warnings import numpy as np from scipy.special import kv, gamma from scipy.spatial.distance import pdist, cdist, squareform from ..metrics.pairwise import pairwise_kernels from ..base import clone def _check_length_scale(X, length_scale): length_scale = np.squeeze(length_scale).astype(float) if np.ndim(length_scale) > 1: raise ValueError("length_scale cannot be of dimension greater than 1") if np.ndim(length_scale) == 1 and X.shape[1] != length_scale.shape[0]: raise ValueError("Anisotropic kernel must have the same number of " "dimensions as data (%d!=%d)" % (length_scale.shape[0], X.shape[1])) return length_scale class Hyperparameter(namedtuple('Hyperparameter', ('name', 'value_type', 'bounds', 'n_elements', 'fixed'))): """A kernel hyperparameter's specification in form of a namedtuple. .. versionadded:: 0.18 Attributes ---------- name : string The name of the hyperparameter. Note that a kernel using a hyperparameter with name "x" must have the attributes self.x and self.x_bounds value_type : string The type of the hyperparameter. Currently, only "numeric" hyperparameters are supported. bounds : pair of floats >= 0 or "fixed" The lower and upper bound on the parameter. If n_elements>1, a pair of 1d array with n_elements each may be given alternatively. If the string "fixed" is passed as bounds, the hyperparameter's value cannot be changed. n_elements : int, default=1 The number of elements of the hyperparameter value. Defaults to 1, which corresponds to a scalar hyperparameter. n_elements > 1 corresponds to a hyperparameter which is vector-valued, such as, e.g., anisotropic length-scales. fixed : bool, default: None Whether the value of this hyperparameter is fixed, i.e., cannot be changed during hyperparameter tuning. If None is passed, the "fixed" is derived based on the given bounds. """ # A raw namedtuple is very memory efficient as it packs the attributes # in a struct to get rid of the __dict__ of attributes in particular it # does not copy the string for the keys on each instance. # By deriving a namedtuple class just to introduce the __init__ method we # would also reintroduce the __dict__ on the instance. By telling the # Python interpreter that this subclass uses static __slots__ instead of # dynamic attributes. Furthermore we don't need any additional slot in the # subclass so we set __slots__ to the empty tuple. __slots__ = () def __new__(cls, name, value_type, bounds, n_elements=1, fixed=None): if not isinstance(bounds, str) or bounds != "fixed": bounds = np.atleast_2d(bounds) if n_elements > 1: # vector-valued parameter if bounds.shape[0] == 1: bounds = np.repeat(bounds, n_elements, 0) elif bounds.shape[0] != n_elements: raise ValueError("Bounds on %s should have either 1 or " "%d dimensions. Given are %d" % (name, n_elements, bounds.shape[0])) if fixed is None: fixed = isinstance(bounds, str) and bounds == "fixed" return super(Hyperparameter, cls).__new__( cls, name, value_type, bounds, n_elements, fixed) # This is mainly a testing utility to check that two hyperparameters # are equal. def __eq__(self, other): return (self.name == other.name and self.value_type == other.value_type and np.all(self.bounds == other.bounds) and self.n_elements == other.n_elements and self.fixed == other.fixed) class Kernel(metaclass=ABCMeta): """Base class for all kernels. .. versionadded:: 0.18 """ def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ params = dict() # introspect the constructor arguments to find the model parameters # to represent cls = self.__class__ init = getattr(cls.__init__, 'deprecated_original', cls.__init__) init_sign = signature(init) args, varargs = [], [] for parameter in init_sign.parameters.values(): if (parameter.kind != parameter.VAR_KEYWORD and parameter.name != 'self'): args.append(parameter.name) if parameter.kind == parameter.VAR_POSITIONAL: varargs.append(parameter.name) if len(varargs) != 0: raise RuntimeError("scikit-learn kernels should always " "specify their parameters in the signature" " of their __init__ (no varargs)." " %s doesn't follow this convention." % (cls, )) for arg in args: try: value = getattr(self, arg) except AttributeError: warnings.warn('From version 0.24, get_params will raise an ' 'AttributeError if a parameter cannot be ' 'retrieved as an instance attribute. Previously ' 'it would return None.', FutureWarning) value = None params[arg] = value return params def set_params(self, params): """Set the parameters of this kernel. The method works on simple kernels as well as on nested kernels. The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object. Returns ------- self """ if not params: # Simple optimisation to gain speed (inspect is slow) return self valid_params = self.get_params(deep=True) for key, value in params.items(): split = key.split('__', 1) if len(split) > 1: # nested objects case name, sub_name = split if name not in valid_params: raise ValueError('Invalid parameter %s for kernel %s. ' 'Check the list of available parameters ' 'with `kernel.get_params().keys()`.' % (name, self)) sub_object = valid_params[name] sub_object.set_params({sub_name: value}) else: # simple objects case if key not in valid_params: raise ValueError('Invalid parameter %s for kernel %s. ' 'Check the list of available parameters ' 'with `kernel.get_params().keys()`.' % (key, self.__class__.__name__)) setattr(self, key, value) return self def clone_with_theta(self, theta): """Returns a clone of self with given hyperparameters theta. Parameters ---------- theta : array, shape (n_dims,) The hyperparameters """ cloned = clone(self) cloned.theta = theta return cloned @property def n_dims(self): """Returns the number of non-fixed hyperparameters of the kernel.""" return self.theta.shape[0] @property def hyperparameters(self): """Returns a list of all hyperparameter specifications.""" r = [getattr(self, attr) for attr in dir(self) if attr.startswith("hyperparameter_")] return r @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ theta = [] params = self.get_params() for hyperparameter in self.hyperparameters: if not hyperparameter.fixed: theta.append(params[hyperparameter.name]) if len(theta) > 0: return np.log(np.hstack(theta)) else: return np.array([]) @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ params = self.get_params() i = 0 for hyperparameter in self.hyperparameters: if hyperparameter.fixed: continue if hyperparameter.n_elements > 1: # vector-valued parameter params[hyperparameter.name] = np.exp( theta[i:i + hyperparameter.n_elements]) i += hyperparameter.n_elements else: params[hyperparameter.name] = np.exp(theta[i]) i += 1 if i != len(theta): raise ValueError("theta has not the correct number of entries." " Should be %d; given are %d" % (i, len(theta))) self.set_params(params) @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : array, shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ bounds = [hyperparameter.bounds for hyperparameter in self.hyperparameters if not hyperparameter.fixed] if len(bounds) > 0: return np.log(np.vstack(bounds)) else: return np.array([]) def __add__(self, b): if not isinstance(b, Kernel): return Sum(self, ConstantKernel(b)) return Sum(self, b) def __radd__(self, b): if not isinstance(b, Kernel): return Sum(ConstantKernel(b), self) return Sum(b, self) def __mul__(self, b): if not isinstance(b, Kernel): return Product(self, ConstantKernel(b)) return Product(self, b) def __rmul__(self, b): if not isinstance(b, Kernel): return Product(ConstantKernel(b), self) return Product(b, self) def __pow__(self, b): return Exponentiation(self, b) def __eq__(self, b): if type(self) != type(b): return False params_a = self.get_params() params_b = b.get_params() for key in set(list(params_a.keys()) + list(params_b.keys())): if np.any(params_a.get(key, None) != params_b.get(key, None)): return False return True def __repr__(self): return "{0}({1})".format(self.__class__.__name__, ", ".join(map("{0:.3g}".format, self.theta))) @abstractmethod def __call__(self, X, Y=None, eval_gradient=False): """Evaluate the kernel.""" @abstractmethod def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ @abstractmethod def is_stationary(self): """Returns whether the kernel is stationary. """ class NormalizedKernelMixin: """Mixin for kernels which are normalized: k(X, X)=1. .. versionadded:: 0.18 """ def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return np.ones(X.shape[0]) class StationaryKernelMixin: """Mixin for kernels which are stationary: k(X, Y)= f(X-Y). .. versionadded:: 0.18 """ def is_stationary(self): """Returns whether the kernel is stationary. """ return True class CompoundKernel(Kernel): """Kernel which is composed of a set of other kernels. .. versionadded:: 0.18 Parameters ---------- kernels : list of Kernel objects The other kernels """ def __init__(self, kernels): self.kernels = kernels def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ return dict(kernels=self.kernels) @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ return np.hstack([kernel.theta for kernel in self.kernels]) @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ k_dims = self.k1.n_dims for i, kernel in enumerate(self.kernels): kernel.theta = theta[i k_dims:(i + 1) * k_dims] @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : array, shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ return np.vstack([kernel.bounds for kernel in self.kernels]) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Note that this compound kernel returns the results of all simple kernel stacked along an additional axis. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Returns ------- K : array, shape (n_samples_X, n_samples_Y, n_kernels) Kernel k(X, Y) K_gradient : array, shape (n_samples_X, n_samples_X, n_dims, n_kernels) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if eval_gradient: K = [] K_grad = [] for kernel in self.kernels: K_single, K_grad_single = kernel(X, Y, eval_gradient) K.append(K_single) K_grad.append(K_grad_single[..., np.newaxis]) return np.dstack(K), np.concatenate(K_grad, 3) else: return np.dstack([kernel(X, Y, eval_gradient) for kernel in self.kernels]) def __eq__(self, b): if type(self) != type(b) or len(self.kernels) != len(b.kernels): return False return np.all([self.kernels[i] == b.kernels[i] for i in range(len(self.kernels))]) def is_stationary(self): """Returns whether the kernel is stationary. """ return np.all([kernel.is_stationary() for kernel in self.kernels]) def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X, n_kernels) Diagonal of kernel k(X, X) """ return np.vstack([kernel.diag(X) for kernel in self.kernels]).T class KernelOperator(Kernel): """Base class for all kernel operators. .. versionadded:: 0.18 """ def __init__(self, k1, k2): self.k1 = k1 self.k2 = k2 def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ params = dict(k1=self.k1, k2=self.k2) if deep: deep_items = self.k1.get_params().items() params.update(('k1__' + k, val) for k, val in deep_items) deep_items = self.k2.get_params().items() params.update(('k2__' + k, val) for k, val in deep_items) return params @property def hyperparameters(self): """Returns a list of all hyperparameter.""" r = [Hyperparameter("k1__" + hyperparameter.name, hyperparameter.value_type, hyperparameter.bounds, hyperparameter.n_elements) for hyperparameter in self.k1.hyperparameters] for hyperparameter in self.k2.hyperparameters: r.append(Hyperparameter("k2__" + hyperparameter.name, hyperparameter.value_type, hyperparameter.bounds, hyperparameter.n_elements)) return r @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ return np.append(self.k1.theta, self.k2.theta) @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ k1_dims = self.k1.n_dims self.k1.theta = theta[:k1_dims] self.k2.theta = theta[k1_dims:] @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : array, shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ if self.k1.bounds.size == 0: return self.k2.bounds if self.k2.bounds.size == 0: return self.k1.bounds return np.vstack((self.k1.bounds, self.k2.bounds)) def __eq__(self, b): if type(self) != type(b): return False return (self.k1 == b.k1 and self.k2 == b.k2) \ or (self.k1 == b.k2 and self.k2 == b.k1) def is_stationary(self): """Returns whether the kernel is stationary. """ return self.k1.is_stationary() and self.k2.is_stationary() class Sum(KernelOperator): """Sum-kernel k1 + k2 of two kernels k1 and k2. The resulting kernel is defined as k_sum(X, Y) = k1(X, Y) + k2(X, Y) .. versionadded:: 0.18 Parameters ---------- k1 : Kernel object The first base-kernel of the sum-kernel k2 : Kernel object The second base-kernel of the sum-kernel """ def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Returns ------- K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if eval_gradient: K1, K1_gradient = self.k1(X, Y, eval_gradient=True) K2, K2_gradient = self.k2(X, Y, eval_gradient=True) return K1 + K2, np.dstack((K1_gradient, K2_gradient)) else: return self.k1(X, Y) + self.k2(X, Y) def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return self.k1.diag(X) + self.k2.diag(X) def __repr__(self): return "{0} + {1}".format(self.k1, self.k2) class Product(KernelOperator): """Product-kernel k1 * k2 of two kernels k1 and k2. The resulting kernel is defined as k_prod(X, Y) = k1(X, Y) * k2(X, Y) .. versionadded:: 0.18 Parameters ---------- k1 : Kernel object The first base-kernel of the product-kernel k2 : Kernel object The second base-kernel of the product-kernel """ def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Returns ------- K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if eval_gradient: K1, K1_gradient = self.k1(X, Y, eval_gradient=True) K2, K2_gradient = self.k2(X, Y, eval_gradient=True) return K1 * K2, np.dstack((K1_gradient * K2[:, :, np.newaxis], K2_gradient * K1[:, :, np.newaxis])) else: return self.k1(X, Y) * self.k2(X, Y) def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return self.k1.diag(X) * self.k2.diag(X) def __repr__(self): return "{0} * {1}".format(self.k1, self.k2) class Exponentiation(Kernel): """Exponentiate kernel by given exponent. The resulting kernel is defined as k_exp(X, Y) = k(X, Y) ** exponent .. versionadded:: 0.18 Parameters ---------- kernel : Kernel object The base kernel exponent : float The exponent for the base kernel """ def __init__(self, kernel, exponent): self.kernel = kernel self.exponent = exponent def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ params = dict(kernel=self.kernel, exponent=self.exponent) if deep: deep_items = self.kernel.get_params().items() params.update(('kernel__' + k, val) for k, val in deep_items) return params @property def hyperparameters(self): """Returns a list of all hyperparameter.""" r = [] for hyperparameter in self.kernel.hyperparameters: r.append(Hyperparameter("kernel__" + hyperparameter.name, hyperparameter.value_type, hyperparameter.bounds, hyperparameter.n_elements)) return r @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ return self.kernel.theta @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ self.kernel.theta = theta @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : array, shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ return self.kernel.bounds def __eq__(self, b): if type(self) != type(b): return False return (self.kernel == b.kernel and self.exponent == b.exponent) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Returns ------- K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if eval_gradient: K, K_gradient = self.kernel(X, Y, eval_gradient=True) K_gradient = \ self.exponent K[:, :, np.newaxis] (self.exponent - 1) return K self.exponent, K_gradient else: K = self.kernel(X, Y, eval_gradient=False) return K self.exponent def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return self.kernel.diag(X) self.exponent def __repr__(self): return "{0} {1}".format(self.kernel, self.exponent) def is_stationary(self): """Returns whether the kernel is stationary. """ return self.kernel.is_stationary() class ConstantKernel(StationaryKernelMixin, Kernel): """Constant kernel. Can be used as part of a product-kernel where it scales the magnitude of the other factor (kernel) or as part of a sum-kernel, where it modifies the mean of the Gaussian process. k(x_1, x_2) = constant_value for all x_1, x_2 .. versionadded:: 0.18 Parameters ---------- constant_value : float, default: 1.0 The constant value which defines the covariance: k(x_1, x_2) = constant_value constant_value_bounds : pair of floats >= 0, default: (1e-5, 1e5) The lower and upper bound on constant_value """ def __init__(self, constant_value=1.0, constant_value_bounds=(1e-5, 1e5)): self.constant_value = constant_value self.constant_value_bounds = constant_value_bounds @property def hyperparameter_constant_value(self): return Hyperparameter( "constant_value", "numeric", self.constant_value_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Only supported when Y is None. Returns ------- K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ X = np.atleast_2d(X) if Y is None: Y = X elif eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") K = np.full((X.shape[0], Y.shape[0]), self.constant_value, dtype=np.array(self.constant_value).dtype) if eval_gradient: if not self.hyperparameter_constant_value.fixed: return (K, np.full((X.shape[0], X.shape[0], 1), self.constant_value, dtype=np.array(self.constant_value).dtype)) else: return K, np.empty((X.shape[0], X.shape[0], 0)) else: return K def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return np.full(X.shape[0], self.constant_value, dtype=np.array(self.constant_value).dtype) def __repr__(self): return "{0:.3g}2".format(np.sqrt(self.constant_value)) class WhiteKernel(StationaryKernelMixin, Kernel): """White kernel. The main use-case of this kernel is as part of a sum-kernel where it explains the noise of the signal as independently and identically normally-distributed. The parameter noise_level equals the variance of this noise. k(x_1, x_2) = noise_level if x_1 == x_2 else 0 .. versionadded:: 0.18 Parameters ---------- noise_level : float, default: 1.0 Parameter controlling the noise level (variance) noise_level_bounds : pair of floats >= 0, default: (1e-5, 1e5) The lower and upper bound on noise_level """ def __init__(self, noise_level=1.0, noise_level_bounds=(1e-5, 1e5)): self.noise_level = noise_level self.noise_level_bounds = noise_level_bounds @property def hyperparameter_noise_level(self): return Hyperparameter( "noise_level", "numeric", self.noise_level_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Only supported when Y is None. Returns ------- K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ X = np.atleast_2d(X) if Y is not None and eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") if Y is None: K = self.noise_level *	np.eye(X.shape[0])	numpy.eye
from abc import * import os import numpy as np import time import tensorflow as tf from keras.datasets import mnist import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, Activation, Flatten, Reshape from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D from keras.layers import LeakyReLU, Dropout, ReLU from keras.layers import BatchNormalization from keras.optimizers import Adam, RMSprop IMGDIR = './imgs' PLOTIMGROW=5 PLOTIMGCOL=5 class ElapsedTimer(object): def __init__(self): self.start = time.time() def elapsed(self, sec): if sec < 60: return "{} sec".format(sec) elif sec < 3600: return "{} min".format(sec / 60) else: return "{} hr".format(sec / 3600) def elapsed_time(self): print("Elapsed: {} ".format(self.elapsed(time.time() - self.start))) class GanBase(object): def __init__(self, row=28, col=28, chn=1): self.img_row = row self.img_col = col self.img_chn = chn self.latent_size = 100 self.hidden_size = 256 self.img_size = row * col * chn self.D = self.build_discriminator() # discriminator self.G = self.build_generator() # generator self.DM = self.build_discriminator_model() # discriminator loss self.AM = self.build_adversarial_model() # generator loss @abstractmethod def build_discriminator(self): pass @abstractmethod def build_generator(self): pass @abstractmethod def build_discriminator_model(self): pass @abstractmethod def build_adversarial_model(self): pass class DcGan(GanBase): def build_discriminator(self): seq = Sequential() depth = self.latent_size dropout = 0.4 # In: 28 x 28 x 1, depth = 1 # Out: 14 x 14 x 1, depth=64 input_shape = (self.img_row, self.img_col, self.img_chn) seq.add(Conv2D(depth1, 5, strides=2, input_shape=input_shape, padding='same')) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dropout(dropout)) seq.add(Conv2D(depth2, 5, strides=2, padding='same')) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dropout(dropout)) seq.add(Conv2D(depth4, 5, strides=2, padding='same')) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dropout(dropout)) seq.add(Conv2D(depth8, 5, strides=1, padding='same')) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dropout(dropout)) # Out: 1-dim probability seq.add(Flatten()) seq.add(Dense(1)) seq.add(Activation('sigmoid')) seq.summary() return seq def build_generator(self): seq = Sequential() dropout = 0.4 depth = self.latent_size * 4 dim = 7 # In: 100 # Out: dim x dim x depth seq.add(Dense(dimdimdepth, input_dim=self.latent_size)) seq.add(BatchNormalization(momentum=0.9)) seq.add(Activation('relu')) seq.add(Reshape((dim, dim, depth))) seq.add(Dropout(dropout)) # In: dim x dim x depth # Out: 2dim x 2dim x depth/2 seq.add(UpSampling2D()) seq.add(Conv2DTranspose(int(depth/2), 5, padding='same')) seq.add(BatchNormalization(momentum=0.9)) seq.add(Activation('relu')) seq.add(UpSampling2D()) seq.add(Conv2DTranspose(int(depth/4), 5, padding='same')) seq.add(BatchNormalization(momentum=0.9)) seq.add(Activation('relu')) seq.add(Conv2DTranspose(int(depth/8), 5, padding='same')) seq.add(BatchNormalization(momentum=0.9)) seq.add(Activation('relu')) # Out: 28 x 28 x 1 grayscale image [0.0,1.0] per pix seq.add(Conv2DTranspose(1, 5, padding='same')) seq.add(Activation('sigmoid')) seq.summary() return seq def build_discriminator_model(self): optimizer = RMSprop(lr=0.0002, decay=6e-8) seq = Sequential() seq.add(self.D) seq.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) return seq def build_adversarial_model(self): optimizer = RMSprop(lr=0.0001, decay=3e-8) seq = Sequential() seq.add(self.G) seq.add(self.D) seq.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) return seq class BasicGan(GanBase): def build_discriminator(self): input_shape = (self.img_row, self.img_col, self.img_chn) seq = Sequential() seq.add(Flatten(input_shape=input_shape)) seq.add(Dense(self.hidden_size)) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dense(self.hidden_size)) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dense(1)) seq.add(Activation('sigmoid')) #seq.summary() return seq def build_generator(self): output_shape = (self.img_row, self.img_col, self.img_chn) seq = Sequential() seq.add(Dense(self.hidden_size, input_dim=self.latent_size)) seq.add(ReLU()) seq.add(Dense(self.hidden_size)) seq.add(ReLU()) seq.add(Dense(self.img_size)) seq.add(Activation('tanh')) seq.add(Reshape(output_shape)) #seq.summary() return seq def build_discriminator_model(self): seq = Sequential() seq.add(self.D) seq.compile(optimizer=Adam(lr=0.0002), loss='binary_crossentropy', metrics=['accuracy']) seq.summary() return seq def build_adversarial_model(self): self.D.trainable = False seq = Sequential() seq.add(self.G) seq.add(self.D) seq.compile(optimizer=Adam(lr=0.0002), loss='binary_crossentropy', metrics=['accuracy']) seq.summary() return seq class MnistGan(object): def __init__(self): self.dcgan = BasicGan() #import pdb; pdb.set_trace() (self.x_train, _), (_, _) = mnist.load_data() self.x_train = self.x_train / 127.5 - 1. self.x_train = np.expand_dims(self.x_train, axis=3) def train(self, epoch_size=200, batch_size=128, save_interval=1): real_labels = np.ones([batch_size, 1]) fake_labels = np.zeros([batch_size, 1]) comp_labels = np.concatenate((real_labels, fake_labels)) for i in range(epoch_size): real_imgs = self.x_train[np.random.randint(0, self.x_train.shape[0], size=batch_size), :, :, :] fake_imgs = np.random.uniform(-1.0, 1.0, size=[batch_size, self.dcgan.latent_size]) fake_imgs = self.dcgan.G.predict(fake_imgs) comp_imgs = np.concatenate((real_imgs, fake_imgs)) # ============================================================ # # Train the discriminator # # ============================================================ # d_loss = self.dcgan.DM.train_on_batch(comp_imgs, comp_labels) # ============================================================ # # Train the generator # # ============================================================ # fake_imgs = np.random.uniform(-1.0, 1.0, size=[batch_size, self.dcgan.latent_size]) a_loss = self.dcgan.AM.train_on_batch(fake_imgs, real_labels) msg = "%04d: [D loss: %f, acc: %f]" % (i, d_loss[0], d_loss[1]) msg = "%s [A loss: %f, acc: %f]" % (msg, a_loss[0], a_loss[1]) print(msg) if i % save_interval == 0: fake_img =	np.random.uniform(-1, 1, size=[PLOTIMGROW*PLOTIMGCOL, self.dcgan.latent_size])	numpy.random.uniform
import numpy as np from dataloaders.datasets.blending_masks import blending_masks def test1(): ''' x x x x x x xyxyxy z z z z z z ''' masks = blending_masks( np.array([[ (0,0), (0,2) ]], dtype=np.int32), size=3) np.testing.assert_almost_equal( masks, np.array([[ [ [1., 1., 0.5], [1., 1., 0.5], [1., 1., 0.5], ], [ [0.5, 1., 1.], [0.5, 1., 1.], [0.5, 1., 1.], ] ]]) ) def test2(): ''' x x x x x x xyxyxy z z z z z z ''' masks = blending_masks(np.array([[(0,0)], [(2,0)]], dtype=np.int32), size=3) np.testing.assert_almost_equal( masks, np.array([ [[ [1., 1., 1.], [1., 1., 1.], [0.5, 0.5, 0.5], ]], [[ [0.5, 0.5, 0.5], [1., 1., 1.], [1., 1., 1.], ]] ]) ) def test3(): ''' x x x x x x xyxyxy z z z z z z ''' masks = blending_masks(np.array( [ [(0,0)], [(2,0)], [(4,0)] ], dtype=np.int32), size=3) np.testing.assert_almost_equal( masks, np.array([[ [ [1., 1., 0.5], [1., 1., 0.5], [1., 1., 0.5], ], [ [0.5, 1., 0.5], [0.5, 1., 0.5], [0.5, 1., 0.5], ], [ [0.5, 1., 1.], [0.5, 1., 1.], [0.5, 1., 1.], ] ]]) ) def test3(): offsets = np.array([ [(0, 0), (0, 75)], [(60, 0), (60, 75)], [(120, 0), (120,75)] ], dtype=np.int32) size = 100 masks = blending_masks(offsets, size=size) width = np.max(offsets[:,:,0]) + size height = np.max(offsets[:,:,1]) + size pic = np.zeros((width, height), dtype=np.float32) for x in range(offsets.shape[0]): for y in range(offsets.shape[1]): offx, offy = offsets[x,y] pic[offx:offx+size,offy:offy+size] += masks[x,y] np.testing.assert_almost_equal(np.min(pic), 1.) np.testing.assert_almost_equal(	np.max(pic)	numpy.max
from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import namedtuple from copy import copy, deepcopy from datetime import datetime, timedelta from textwrap import dedent import pytest from distutils.version import LooseVersion import numpy as np import pytz import pandas as pd from xarray import Variable, IndexVariable, Coordinate, Dataset from xarray.core import indexing from xarray.core.variable import as_variable, as_compatible_data from xarray.core.indexing import PandasIndexAdapter, LazilyIndexedArray from xarray.core.pycompat import PY3, OrderedDict from xarray.core.common import full_like, zeros_like, ones_like from . import TestCase, source_ndarray, requires_dask class VariableSubclassTestCases(object): def test_properties(self): data = 0.5 * np.arange(10) v = self.cls(['time'], data, {'foo': 'bar'}) self.assertEqual(v.dims, ('time',)) self.assertArrayEqual(v.values, data) self.assertEqual(v.dtype, float) self.assertEqual(v.shape, (10,)) self.assertEqual(v.size, 10) self.assertEqual(v.sizes, {'time': 10}) self.assertEqual(v.nbytes, 80) self.assertEqual(v.ndim, 1) self.assertEqual(len(v), 10) self.assertEqual(v.attrs, {'foo': u'bar'}) def test_attrs(self): v = self.cls(['time'], 0.5 * np.arange(10)) self.assertEqual(v.attrs, {}) attrs = {'foo': 'bar'} v.attrs = attrs self.assertEqual(v.attrs, attrs) self.assertIsInstance(v.attrs, OrderedDict) v.attrs['foo'] = 'baz' self.assertEqual(v.attrs['foo'], 'baz') def test_getitem_dict(self): v = self.cls(['x'], np.random.randn(5)) actual = v[{'x': 0}] expected = v[0] self.assertVariableIdentical(expected, actual) def _assertIndexedLikeNDArray(self, variable, expected_value0, expected_dtype=None): """Given a 1-dimensional variable, verify that the variable is indexed like a numpy.ndarray. """ self.assertEqual(variable[0].shape, ()) self.assertEqual(variable[0].ndim, 0) self.assertEqual(variable[0].size, 1) # test identity self.assertTrue(variable.equals(variable.copy())) self.assertTrue(variable.identical(variable.copy())) # check value is equal for both ndarray and Variable self.assertEqual(variable.values[0], expected_value0) self.assertEqual(variable[0].values, expected_value0) # check type or dtype is consistent for both ndarray and Variable if expected_dtype is None: # check output type instead of array dtype self.assertEqual(type(variable.values[0]), type(expected_value0)) self.assertEqual(type(variable[0].values), type(expected_value0)) elif expected_dtype is not False: self.assertEqual(variable.values[0].dtype, expected_dtype) self.assertEqual(variable[0].values.dtype, expected_dtype) def test_index_0d_int(self): for value, dtype in [(0, np.int_), (np.int32(0), np.int32)]: x = self.cls(['x'], [value]) self._assertIndexedLikeNDArray(x, value, dtype) def test_index_0d_float(self): for value, dtype in [(0.5, np.float_), (np.float32(0.5), np.float32)]: x = self.cls(['x'], [value]) self._assertIndexedLikeNDArray(x, value, dtype) def test_index_0d_string(self): for value, dtype in [('foo', np.dtype('U3' if PY3 else 'S3')), (u'foo', np.dtype('U3'))]: x = self.cls(['x'], [value]) self._assertIndexedLikeNDArray(x, value, dtype) def test_index_0d_datetime(self): d = datetime(2000, 1, 1) x = self.cls(['x'], [d]) self._assertIndexedLikeNDArray(x, np.datetime64(d)) x = self.cls(['x'], [np.datetime64(d)]) self._assertIndexedLikeNDArray(x, np.datetime64(d), 'datetime64[ns]') x = self.cls(['x'], pd.DatetimeIndex([d])) self._assertIndexedLikeNDArray(x, np.datetime64(d), 'datetime64[ns]') def test_index_0d_timedelta64(self): td = timedelta(hours=1) x = self.cls(['x'], [np.timedelta64(td)]) self._assertIndexedLikeNDArray(x, np.timedelta64(td), 'timedelta64[ns]') x = self.cls(['x'], pd.to_timedelta([td])) self._assertIndexedLikeNDArray(x, np.timedelta64(td), 'timedelta64[ns]') def test_index_0d_not_a_time(self): d = np.datetime64('NaT', 'ns') x = self.cls(['x'], [d]) self._assertIndexedLikeNDArray(x, d) def test_index_0d_object(self): class HashableItemWrapper(object): def __init__(self, item): self.item = item def __eq__(self, other): return self.item == other.item def __hash__(self): return hash(self.item) def __repr__(self): return '%s(item=%r)' % (type(self).__name__, self.item) item = HashableItemWrapper((1, 2, 3)) x = self.cls('x', [item]) self._assertIndexedLikeNDArray(x, item, expected_dtype=False) def test_0d_object_array_with_list(self): listarray = np.empty((1,), dtype=object) listarray[0] = [1, 2, 3] x = self.cls('x', listarray) assert x.data == listarray assert x[0].data == listarray.squeeze() assert x.squeeze().data == listarray.squeeze() def test_index_and_concat_datetime(self): # regression test for #125 date_range = pd.date_range('2011-09-01', periods=10) for dates in [date_range, date_range.values, date_range.to_pydatetime()]: expected = self.cls('t', dates) for times in [[expected[i] for i in range(10)], [expected[i:(i + 1)] for i in range(10)], [expected[[i]] for i in range(10)]]: actual = Variable.concat(times, 't') self.assertEqual(expected.dtype, actual.dtype) self.assertArrayEqual(expected, actual) def test_0d_time_data(self): # regression test for #105 x = self.cls('time', pd.date_range('2000-01-01', periods=5)) expected = np.datetime64('2000-01-01T00Z', 'ns') self.assertEqual(x[0].values, expected) def test_datetime64_conversion(self): times = pd.date_range('2000-01-01', periods=3) for values, preserve_source in [ (times, True), (times.values, True), (times.values.astype('datetime64[s]'), False), (times.to_pydatetime(), False), ]: v = self.cls(['t'], values) self.assertEqual(v.dtype, np.dtype('datetime64[ns]')) self.assertArrayEqual(v.values, times.values) self.assertEqual(v.values.dtype, np.dtype('datetime64[ns]')) same_source = source_ndarray(v.values) is source_ndarray(values) assert preserve_source == same_source def test_timedelta64_conversion(self): times = pd.timedelta_range(start=0, periods=3) for values, preserve_source in [ (times, True), (times.values, True), (times.values.astype('timedelta64[s]'), False), (times.to_pytimedelta(), False), ]: v = self.cls(['t'], values) self.assertEqual(v.dtype, np.dtype('timedelta64[ns]')) self.assertArrayEqual(v.values, times.values) self.assertEqual(v.values.dtype, np.dtype('timedelta64[ns]')) same_source = source_ndarray(v.values) is source_ndarray(values) assert preserve_source == same_source def test_object_conversion(self): data = np.arange(5).astype(str).astype(object) actual = self.cls('x', data) self.assertEqual(actual.dtype, data.dtype) def test_pandas_data(self): v = self.cls(['x'], pd.Series([0, 1, 2], index=[3, 2, 1])) self.assertVariableIdentical(v, v[[0, 1, 2]]) v = self.cls(['x'], pd.Index([0, 1, 2])) self.assertEqual(v[0].values, v.values[0]) def test_pandas_period_index(self): v = self.cls(['x'], pd.period_range(start='2000', periods=20, freq='B')) self.assertEqual(v[0], pd.Period('2000', freq='B')) assert "Period('2000-01-03', 'B')" in repr(v) def test_1d_math(self): x = 1.0 * np.arange(5) y = np.ones(5) # should we need `.to_base_variable()`? # probably a break that `+v` changes type? v = self.cls(['x'], x) base_v = v.to_base_variable() # unary ops self.assertVariableIdentical(base_v, +v) self.assertVariableIdentical(base_v, abs(v)) self.assertArrayEqual((-v).values, -x) # binary ops with numbers self.assertVariableIdentical(base_v, v + 0) self.assertVariableIdentical(base_v, 0 + v) self.assertVariableIdentical(base_v, v * 1) self.assertArrayEqual((v > 2).values, x > 2) self.assertArrayEqual((0 == v).values, 0 == x) self.assertArrayEqual((v - 1).values, x - 1) self.assertArrayEqual((1 - v).values, 1 - x) # binary ops with numpy arrays self.assertArrayEqual((v * x).values, x ** 2) self.assertArrayEqual((x * v).values, x ** 2) self.assertArrayEqual(v - y, v - 1) self.assertArrayEqual(y - v, 1 - v) # verify attributes are dropped v2 = self.cls(['x'], x, {'units': 'meters'}) self.assertVariableIdentical(base_v, +v2) # binary ops with all variables self.assertArrayEqual(v + v, 2 * v) w = self.cls(['x'], y, {'foo': 'bar'}) self.assertVariableIdentical(v + w, self.cls(['x'], x + y).to_base_variable()) self.assertArrayEqual((v * w).values, x * y) # something complicated self.assertArrayEqual((v ** 2 * w - 1 + x).values, x ** 2 * y - 1 + x) # make sure dtype is preserved (for Index objects) self.assertEqual(float, (+v).dtype) self.assertEqual(float, (+v).values.dtype) self.assertEqual(float, (0 + v).dtype) self.assertEqual(float, (0 + v).values.dtype) # check types of returned data self.assertIsInstance(+v, Variable) self.assertNotIsInstance(+v, IndexVariable) self.assertIsInstance(0 + v, Variable) self.assertNotIsInstance(0 + v, IndexVariable) def test_1d_reduce(self): x = np.arange(5) v = self.cls(['x'], x) actual = v.sum() expected = Variable((), 10) self.assertVariableIdentical(expected, actual) self.assertIs(type(actual), Variable) def test_array_interface(self): x = np.arange(5) v = self.cls(['x'], x) self.assertArrayEqual(np.asarray(v), x) # test patched in methods self.assertArrayEqual(v.astype(float), x.astype(float)) # think this is a break, that argsort changes the type self.assertVariableIdentical(v.argsort(), v.to_base_variable()) self.assertVariableIdentical(v.clip(2, 3), self.cls('x', x.clip(2, 3)).to_base_variable()) # test ufuncs self.assertVariableIdentical(np.sin(v), self.cls(['x'], np.sin(x)).to_base_variable()) self.assertIsInstance(np.sin(v), Variable) self.assertNotIsInstance(np.sin(v), IndexVariable) def example_1d_objects(self): for data in [range(3), 0.5 * np.arange(3), 0.5 * np.arange(3, dtype=np.float32), pd.date_range('2000-01-01', periods=3), np.array(['a', 'b', 'c'], dtype=object)]: yield (self.cls('x', data), data) def test___array__(self): for v, data in self.example_1d_objects(): self.assertArrayEqual(v.values, np.asarray(data)) self.assertArrayEqual(np.asarray(v), np.asarray(data)) self.assertEqual(v[0].values, np.asarray(data)[0]) self.assertEqual(np.asarray(v[0]), np.asarray(data)[0]) def test_equals_all_dtypes(self): for v, _ in self.example_1d_objects(): v2 = v.copy() self.assertTrue(v.equals(v2)) self.assertTrue(v.identical(v2)) self.assertTrue(v.no_conflicts(v2)) self.assertTrue(v[0].equals(v2[0])) self.assertTrue(v[0].identical(v2[0])) self.assertTrue(v[0].no_conflicts(v2[0])) self.assertTrue(v[:2].equals(v2[:2])) self.assertTrue(v[:2].identical(v2[:2])) self.assertTrue(v[:2].no_conflicts(v2[:2])) def test_eq_all_dtypes(self): # ensure that we don't choke on comparisons for which numpy returns # scalars expected = Variable('x', 3 * [False]) for v, _ in self.example_1d_objects(): actual = 'z' == v self.assertVariableIdentical(expected, actual) actual = ~('z' != v) self.assertVariableIdentical(expected, actual) def test_encoding_preserved(self): expected = self.cls('x', range(3), {'foo': 1}, {'bar': 2}) for actual in [expected.T, expected[...], expected.squeeze(), expected.isel(x=slice(None)), expected.set_dims({'x': 3}), expected.copy(deep=True), expected.copy(deep=False)]: self.assertVariableIdentical(expected.to_base_variable(), actual.to_base_variable()) self.assertEqual(expected.encoding, actual.encoding) def test_concat(self): x = np.arange(5) y = np.arange(5, 10) v = self.cls(['a'], x) w = self.cls(['a'], y) self.assertVariableIdentical(Variable(['b', 'a'], np.array([x, y])), Variable.concat([v, w], 'b')) self.assertVariableIdentical(Variable(['b', 'a'], np.array([x, y])), Variable.concat((v, w), 'b')) self.assertVariableIdentical(Variable(['b', 'a'], np.array([x, y])), Variable.concat((v, w), 'b')) with self.assertRaisesRegexp(ValueError, 'inconsistent dimensions'): Variable.concat([v, Variable(['c'], y)], 'b') # test indexers actual = Variable.concat( [v, w], positions=[np.arange(0, 10, 2), np.arange(1, 10, 2)], dim='a') expected = Variable('a', np.array([x, y]).ravel(order='F')) self.assertVariableIdentical(expected, actual) # test concatenating along a dimension v = Variable(['time', 'x'], np.random.random((10, 8))) self.assertVariableIdentical(v, Variable.concat([v[:5], v[5:]], 'time')) self.assertVariableIdentical(v, Variable.concat([v[:5], v[5:6], v[6:]], 'time')) self.assertVariableIdentical(v, Variable.concat([v[:1], v[1:]], 'time')) # test dimension order self.assertVariableIdentical(v, Variable.concat([v[:, :5], v[:, 5:]], 'x')) with self.assertRaisesRegexp(ValueError, 'all input arrays must have'): Variable.concat([v[:, 0], v[:, 1:]], 'x') def test_concat_attrs(self): # different or conflicting attributes should be removed v = self.cls('a', np.arange(5), {'foo': 'bar'}) w = self.cls('a', np.ones(5)) expected = self.cls('a', np.concatenate([np.arange(5), np.ones(5)])).to_base_variable() self.assertVariableIdentical(expected, Variable.concat([v, w], 'a')) w.attrs['foo'] = 2 self.assertVariableIdentical(expected, Variable.concat([v, w], 'a')) w.attrs['foo'] = 'bar' expected.attrs['foo'] = 'bar' self.assertVariableIdentical(expected, Variable.concat([v, w], 'a')) def test_concat_fixed_len_str(self): # regression test for #217 for kind in ['S', 'U']: x = self.cls('animal', np.array(['horse'], dtype=kind)) y = self.cls('animal', np.array(['aardvark'], dtype=kind)) actual = Variable.concat([x, y], 'animal') expected = Variable( 'animal', np.array(['horse', 'aardvark'], dtype=kind)) self.assertVariableEqual(expected, actual) def test_concat_number_strings(self): # regression test for #305 a = self.cls('x', ['0', '1', '2']) b = self.cls('x', ['3', '4']) actual = Variable.concat([a, b], dim='x') expected = Variable('x', np.arange(5).astype(str).astype(object)) self.assertVariableIdentical(expected, actual) self.assertEqual(expected.dtype, object) self.assertEqual(type(expected.values[0]), str) def test_copy(self): v = self.cls('x', 0.5 * np.arange(10), {'foo': 'bar'}) for deep in [True, False]: w = v.copy(deep=deep) self.assertIs(type(v), type(w)) self.assertVariableIdentical(v, w) self.assertEqual(v.dtype, w.dtype) if self.cls is Variable: if deep: self.assertIsNot(source_ndarray(v.values), source_ndarray(w.values)) else: self.assertIs(source_ndarray(v.values), source_ndarray(w.values)) self.assertVariableIdentical(v, copy(v)) def test_copy_index(self): midx = pd.MultiIndex.from_product([['a', 'b'], [1, 2], [-1, -2]], names=('one', 'two', 'three')) v = self.cls('x', midx) for deep in [True, False]: w = v.copy(deep=deep) self.assertIsInstance(w._data, PandasIndexAdapter) self.assertIsInstance(w.to_index(), pd.MultiIndex) self.assertArrayEqual(v._data.array, w._data.array) def test_real_and_imag(self): v = self.cls('x', np.arange(3) - 1j * np.arange(3), {'foo': 'bar'}) expected_re = self.cls('x', np.arange(3), {'foo': 'bar'}) self.assertVariableIdentical(v.real, expected_re) expected_im = self.cls('x', -np.arange(3), {'foo': 'bar'}) self.assertVariableIdentical(v.imag, expected_im) expected_abs = self.cls('x', np.sqrt(2 * np.arange(3) ** 2)).to_base_variable() self.assertVariableAllClose(abs(v), expected_abs) def test_aggregate_complex(self): # should skip NaNs v = self.cls('x', [1, 2j, np.nan]) expected = Variable((), 0.5 + 1j) self.assertVariableAllClose(v.mean(), expected) def test_pandas_cateogrical_dtype(self): data = pd.Categorical(np.arange(10, dtype='int64')) v = self.cls('x', data) print(v) # should not error assert v.dtype == 'int64' def test_pandas_datetime64_with_tz(self): data = pd.date_range(start='2000-01-01', tz=pytz.timezone('America/New_York'), periods=10, freq='1h') v = self.cls('x', data) print(v) # should not error if 'America/New_York' in str(data.dtype): # pandas is new enough that it has datetime64 with timezone dtype assert v.dtype == 'object' def test_multiindex(self): idx = pd.MultiIndex.from_product([list('abc'), [0, 1]]) v = self.cls('x', idx) self.assertVariableIdentical(Variable((), ('a', 0)), v[0]) self.assertVariableIdentical(v, v[:]) def test_load(self): array = self.cls('x', np.arange(5)) orig_data = array._data copied = array.copy(deep=True) array.load() assert type(array._data) is type(orig_data) assert type(copied._data) is type(orig_data) self.assertVariableIdentical(array, copied) class TestVariable(TestCase, VariableSubclassTestCases): cls = staticmethod(Variable) def setUp(self): self.d = np.random.random((10, 3)).astype(np.float64) def test_data_and_values(self): v = Variable(['time', 'x'], self.d) self.assertArrayEqual(v.data, self.d) self.assertArrayEqual(v.values, self.d) self.assertIs(source_ndarray(v.values), self.d) with self.assertRaises(ValueError): # wrong size v.values = np.random.random(5) d2 = np.random.random((10, 3)) v.values = d2 self.assertIs(source_ndarray(v.values), d2) d3 = np.random.random((10, 3)) v.data = d3 self.assertIs(source_ndarray(v.data), d3) def test_numpy_same_methods(self): v = Variable([], np.float32(0.0)) self.assertEqual(v.item(), 0) self.assertIs(type(v.item()), float) v = IndexVariable('x', np.arange(5)) self.assertEqual(2, v.searchsorted(2)) def test_datetime64_conversion_scalar(self): expected = np.datetime64('2000-01-01T00:00:00Z', 'ns') for values in [ np.datetime64('2000-01-01T00Z'), pd.Timestamp('2000-01-01T00'), datetime(2000, 1, 1), ]: v = Variable([], values) self.assertEqual(v.dtype, np.dtype('datetime64[ns]')) self.assertEqual(v.values, expected) self.assertEqual(v.values.dtype, np.dtype('datetime64[ns]')) def test_timedelta64_conversion_scalar(self): expected = np.timedelta64(24 * 60 * 60 * 10 9, 'ns') for values in [ np.timedelta64(1, 'D'), pd.Timedelta('1 day'), timedelta(days=1), ]: v = Variable([], values) self.assertEqual(v.dtype, np.dtype('timedelta64[ns]')) self.assertEqual(v.values, expected) self.assertEqual(v.values.dtype, np.dtype('timedelta64[ns]')) def test_0d_str(self): v = Variable([], u'foo') self.assertEqual(v.dtype, np.dtype('U3')) self.assertEqual(v.values, 'foo') v = Variable([], np.string_('foo')) self.assertEqual(v.dtype, np.dtype('S3')) self.assertEqual(v.values, bytes('foo', 'ascii') if PY3 else 'foo') def test_0d_datetime(self): v = Variable([], pd.Timestamp('2000-01-01')) self.assertEqual(v.dtype, np.dtype('datetime64[ns]')) self.assertEqual(v.values, np.datetime64('2000-01-01T00Z', 'ns')) def test_0d_timedelta(self): for td in [pd.to_timedelta('1s'), np.timedelta64(1, 's')]: v = Variable([], td) self.assertEqual(v.dtype, np.dtype('timedelta64[ns]')) self.assertEqual(v.values, np.timedelta64(10 9, 'ns')) def test_equals_and_identical(self): d = np.random.rand(10, 3) d[0, 0] = np.nan v1 = Variable(('dim1', 'dim2'), data=d, attrs={'att1': 3, 'att2': [1, 2, 3]}) v2 = Variable(('dim1', 'dim2'), data=d, attrs={'att1': 3, 'att2': [1, 2, 3]}) self.assertTrue(v1.equals(v2)) self.assertTrue(v1.identical(v2)) v3 = Variable(('dim1', 'dim3'), data=d) self.assertFalse(v1.equals(v3)) v4 = Variable(('dim1', 'dim2'), data=d) self.assertTrue(v1.equals(v4)) self.assertFalse(v1.identical(v4)) v5 = deepcopy(v1) v5.values[:] = np.random.rand(10, 3) self.assertFalse(v1.equals(v5)) self.assertFalse(v1.equals(None)) self.assertFalse(v1.equals(d)) self.assertFalse(v1.identical(None)) self.assertFalse(v1.identical(d)) def test_broadcast_equals(self): v1 = Variable((), np.nan) v2 = Variable(('x'), [np.nan, np.nan]) self.assertTrue(v1.broadcast_equals(v2)) self.assertFalse(v1.equals(v2)) self.assertFalse(v1.identical(v2)) v3 = Variable(('x'), [np.nan]) self.assertTrue(v1.broadcast_equals(v3)) self.assertFalse(v1.equals(v3)) self.assertFalse(v1.identical(v3)) self.assertFalse(v1.broadcast_equals(None)) v4 = Variable(('x'), [np.nan] * 3) self.assertFalse(v2.broadcast_equals(v4)) def test_no_conflicts(self): v1 = Variable(('x'), [1, 2, np.nan, np.nan]) v2 = Variable(('x'), [np.nan, 2, 3, np.nan]) self.assertTrue(v1.no_conflicts(v2)) self.assertFalse(v1.equals(v2)) self.assertFalse(v1.broadcast_equals(v2)) self.assertFalse(v1.identical(v2)) self.assertFalse(v1.no_conflicts(None)) v3 = Variable(('y'), [np.nan, 2, 3, np.nan]) self.assertFalse(v3.no_conflicts(v1)) d = np.array([1, 2, np.nan, np.nan]) self.assertFalse(v1.no_conflicts(d)) self.assertFalse(v2.no_conflicts(d)) v4 = Variable(('w', 'x'), [d]) self.assertTrue(v1.no_conflicts(v4)) def test_as_variable(self): data = np.arange(10) expected = Variable('x', data) self.assertVariableIdentical(expected, as_variable(expected)) ds = Dataset({'x': expected}) self.assertVariableIdentical(expected, as_variable(ds['x']).to_base_variable()) self.assertNotIsInstance(ds['x'], Variable) self.assertIsInstance(as_variable(ds['x']), Variable) FakeVariable = namedtuple('FakeVariable', 'values dims') fake_xarray = FakeVariable(expected.values, expected.dims) self.assertVariableIdentical(expected, as_variable(fake_xarray)) xarray_tuple = (expected.dims, expected.values) self.assertVariableIdentical(expected, as_variable(xarray_tuple)) with self.assertRaisesRegexp(TypeError, 'tuples to convert'): as_variable(tuple(data)) with self.assertRaisesRegexp( TypeError, 'without an explicit list of dimensions'): as_variable(data) actual = as_variable(data, name='x') self.assertVariableIdentical(expected.to_index_variable(), actual) actual = as_variable(0) expected = Variable([], 0) self.assertVariableIdentical(expected, actual) def test_repr(self): v = Variable(['time', 'x'], [[1, 2, 3], [4, 5, 6]], {'foo': 'bar'}) expected = dedent(""" <xarray.Variable (time: 2, x: 3)> array([[1, 2, 3], [4, 5, 6]]) Attributes: foo: bar """).strip() self.assertEqual(expected, repr(v)) def test_repr_lazy_data(self): v = Variable('x', LazilyIndexedArray(np.arange(2e5))) self.assertIn('200000 values with dtype', repr(v)) self.assertIsInstance(v._data, LazilyIndexedArray) def test_items(self): data = np.random.random((10, 11)) v = Variable(['x', 'y'], data) # test slicing self.assertVariableIdentical(v, v[:]) self.assertVariableIdentical(v, v[...]) self.assertVariableIdentical(Variable(['y'], data[0]), v[0]) self.assertVariableIdentical(Variable(['x'], data[:, 0]), v[:, 0]) self.assertVariableIdentical(Variable(['x', 'y'], data[:3, :2]), v[:3, :2]) # test array indexing x = Variable(['x'], np.arange(10)) y = Variable(['y'], np.arange(11)) self.assertVariableIdentical(v, v[x.values]) self.assertVariableIdentical(v, v[x]) self.assertVariableIdentical(v[:3], v[x < 3]) self.assertVariableIdentical(v[:, 3:], v[:, y >= 3]) self.assertVariableIdentical(v[:3, 3:], v[x < 3, y >= 3]) self.assertVariableIdentical(v[:3, :2], v[x[:3], y[:2]]) self.assertVariableIdentical(v[:3, :2], v[range(3), range(2)]) # test iteration for n, item in enumerate(v): self.assertVariableIdentical(Variable(['y'], data[n]), item) with self.assertRaisesRegexp(TypeError, 'iteration over a 0-d'): iter(Variable([], 0)) # test setting v.values[:] = 0 self.assertTrue(np.all(v.values == 0)) # test orthogonal setting v[range(10), range(11)] = 1 self.assertArrayEqual(v.values, np.ones((10, 11))) def test_isel(self): v = Variable(['time', 'x'], self.d) self.assertVariableIdentical(v.isel(time=slice(None)), v) self.assertVariableIdentical(v.isel(time=0), v[0]) self.assertVariableIdentical(v.isel(time=slice(0, 3)), v[:3]) self.assertVariableIdentical(v.isel(x=0), v[:, 0]) with self.assertRaisesRegexp(ValueError, 'do not exist'): v.isel(not_a_dim=0) def test_index_0d_numpy_string(self): # regression test to verify our work around for indexing 0d strings v = Variable([], np.string_('asdf')) self.assertVariableIdentical(v[()], v) v = Variable([], np.unicode_(u'asdf')) self.assertVariableIdentical(v[()], v) def test_indexing_0d_unicode(self): # regression test for GH568 actual = Variable(('x'), [u'tmax'])[0][()] expected = Variable((), u'tmax') self.assertVariableIdentical(actual, expected) def test_shift(self): v = Variable('x', [1, 2, 3, 4, 5]) self.assertVariableIdentical(v, v.shift(x=0)) self.assertIsNot(v, v.shift(x=0)) expected = Variable('x', [np.nan, 1, 2, 3, 4]) self.assertVariableIdentical(expected, v.shift(x=1)) expected = Variable('x', [np.nan, np.nan, 1, 2, 3]) self.assertVariableIdentical(expected, v.shift(x=2)) expected = Variable('x', [2, 3, 4, 5, np.nan]) self.assertVariableIdentical(expected, v.shift(x=-1)) expected = Variable('x', [np.nan] * 5) self.assertVariableIdentical(expected, v.shift(x=5)) self.assertVariableIdentical(expected, v.shift(x=6)) with self.assertRaisesRegexp(ValueError, 'dimension'): v.shift(z=0) v = Variable('x', [1, 2, 3, 4, 5], {'foo': 'bar'}) self.assertVariableIdentical(v, v.shift(x=0)) expected = Variable('x', [np.nan, 1, 2, 3, 4], {'foo': 'bar'}) self.assertVariableIdentical(expected, v.shift(x=1)) def test_shift2d(self): v = Variable(('x', 'y'), [[1, 2], [3, 4]]) expected = Variable(('x', 'y'), [[np.nan, np.nan], [np.nan, 1]]) self.assertVariableIdentical(expected, v.shift(x=1, y=1)) def test_roll(self): v = Variable('x', [1, 2, 3, 4, 5]) self.assertVariableIdentical(v, v.roll(x=0)) self.assertIsNot(v, v.roll(x=0)) expected = Variable('x', [5, 1, 2, 3, 4]) self.assertVariableIdentical(expected, v.roll(x=1)) self.assertVariableIdentical(expected, v.roll(x=-4)) self.assertVariableIdentical(expected, v.roll(x=6)) expected = Variable('x', [4, 5, 1, 2, 3]) self.assertVariableIdentical(expected, v.roll(x=2)) self.assertVariableIdentical(expected, v.roll(x=-3)) with self.assertRaisesRegexp(ValueError, 'dimension'): v.roll(z=0) def test_roll_consistency(self): v = Variable(('x', 'y'), np.random.randn(5, 6)) for axis, dim in [(0, 'x'), (1, 'y')]: for shift in [-3, 0, 1, 7, 11]: expected = np.roll(v.values, shift, axis=axis) actual = v.roll(*{dim: shift}).values self.assertArrayEqual(expected, actual) def test_transpose(self): v = Variable(['time', 'x'], self.d) v2 = Variable(['x', 'time'], self.d.T) self.assertVariableIdentical(v, v2.transpose()) self.assertVariableIdentical(v.transpose(), v.T) x = np.random.randn(2, 3, 4, 5) w = Variable(['a', 'b', 'c', 'd'], x) w2 = Variable(['d', 'b', 'c', 'a'], np.einsum('abcd->dbca', x)) self.assertEqual(w2.shape, (5, 3, 4, 2)) self.assertVariableIdentical(w2, w.transpose('d', 'b', 'c', 'a')) self.assertVariableIdentical(w, w2.transpose('a', 'b', 'c', 'd')) w3 = Variable(['b', 'c', 'd', 'a'], np.einsum('abcd->bcda', x)) self.assertVariableIdentical(w, w3.transpose('a', 'b', 'c', 'd')) def test_transpose_0d(self): for value in [ 3.5, ('a', 1), np.datetime64('2000-01-01'), np.timedelta64(1, 'h'), None, object(), ]: variable = Variable([], value) actual = variable.transpose() assert actual.identical(variable) def test_squeeze(self): v = Variable(['x', 'y'], [[1]]) self.assertVariableIdentical(Variable([], 1), v.squeeze()) self.assertVariableIdentical(Variable(['y'], [1]), v.squeeze('x')) self.assertVariableIdentical(Variable(['y'], [1]), v.squeeze(['x'])) self.assertVariableIdentical(Variable(['x'], [1]), v.squeeze('y')) self.assertVariableIdentical(Variable([], 1), v.squeeze(['x', 'y'])) v = Variable(['x', 'y'], [[1, 2]]) self.assertVariableIdentical(Variable(['y'], [1, 2]), v.squeeze()) self.assertVariableIdentical(Variable(['y'], [1, 2]), v.squeeze('x')) with self.assertRaisesRegexp(ValueError, 'cannot select a dimension'): v.squeeze('y') def test_get_axis_num(self): v = Variable(['x', 'y', 'z'], np.random.randn(2, 3, 4)) self.assertEqual(v.get_axis_num('x'), 0) self.assertEqual(v.get_axis_num(['x']), (0,)) self.assertEqual(v.get_axis_num(['x', 'y']), (0, 1)) self.assertEqual(v.get_axis_num(['z', 'y', 'x']), (2, 1, 0)) with self.assertRaisesRegexp(ValueError, 'not found in array dim'): v.get_axis_num('foobar') def test_set_dims(self): v = Variable(['x'], [0, 1]) actual = v.set_dims(['x', 'y']) expected = Variable(['x', 'y'], [[0], [1]]) self.assertVariableIdentical(actual, expected) actual = v.set_dims(['y', 'x']) self.assertVariableIdentical(actual, expected.T) actual = v.set_dims(OrderedDict([('x', 2), ('y', 2)])) expected = Variable(['x', 'y'], [[0, 0], [1, 1]]) self.assertVariableIdentical(actual, expected) v = Variable(['foo'], [0, 1]) actual = v.set_dims('foo') expected = v self.assertVariableIdentical(actual, expected) with self.assertRaisesRegexp(ValueError, 'must be a superset'): v.set_dims(['z']) def test_set_dims_object_dtype(self): v = Variable([], ('a', 1)) actual = v.set_dims(('x',), (3,)) exp_values = np.empty((3,), dtype=object) for i in range(3): exp_values[i] = ('a', 1) expected = Variable(['x'], exp_values) assert actual.identical(expected) def test_stack(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]], {'foo': 'bar'}) actual = v.stack(z=('x', 'y')) expected = Variable('z', [0, 1, 2, 3], v.attrs) self.assertVariableIdentical(actual, expected) actual = v.stack(z=('x',)) expected = Variable(('y', 'z'), v.data.T, v.attrs) self.assertVariableIdentical(actual, expected) actual = v.stack(z=(),) self.assertVariableIdentical(actual, v) actual = v.stack(X=('x',), Y=('y',)).transpose('X', 'Y') expected = Variable(('X', 'Y'), v.data, v.attrs) self.assertVariableIdentical(actual, expected) def test_stack_errors(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]], {'foo': 'bar'}) with self.assertRaisesRegexp(ValueError, 'invalid existing dim'): v.stack(z=('x1',)) with self.assertRaisesRegexp(ValueError, 'cannot create a new dim'): v.stack(x=('x',)) def test_unstack(self): v = Variable('z', [0, 1, 2, 3], {'foo': 'bar'}) actual = v.unstack(z=OrderedDict([('x', 2), ('y', 2)])) expected = Variable(('x', 'y'), [[0, 1], [2, 3]], v.attrs) self.assertVariableIdentical(actual, expected) actual = v.unstack(z=OrderedDict([('x', 4), ('y', 1)])) expected = Variable(('x', 'y'), [[0], [1], [2], [3]], v.attrs) self.assertVariableIdentical(actual, expected) actual = v.unstack(z=OrderedDict([('x', 4)])) expected = Variable('x', [0, 1, 2, 3], v.attrs) self.assertVariableIdentical(actual, expected) def test_unstack_errors(self): v = Variable('z', [0, 1, 2, 3]) with self.assertRaisesRegexp(ValueError, 'invalid existing dim'): v.unstack(foo={'x': 4}) with self.assertRaisesRegexp(ValueError, 'cannot create a new dim'): v.stack(z=('z',)) with self.assertRaisesRegexp(ValueError, 'the product of the new dim'): v.unstack(z={'x': 5}) def test_unstack_2d(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]]) actual = v.unstack(y={'z': 2}) expected = Variable(['x', 'z'], v.data) self.assertVariableIdentical(actual, expected) actual = v.unstack(x={'z': 2}) expected = Variable(['y', 'z'], v.data.T) self.assertVariableIdentical(actual, expected) def test_stack_unstack_consistency(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]]) actual = (v.stack(z=('x', 'y')) .unstack(z=OrderedDict([('x', 2), ('y', 2)]))) self.assertVariableIdentical(actual, v) def test_broadcasting_math(self): x = np.random.randn(2, 3) v = Variable(['a', 'b'], x) # 1d to 2d broadcasting self.assertVariableIdentical( v v, Variable(['a', 'b'], np.einsum('ab,ab->ab', x, x))) self.assertVariableIdentical( v * v[0], Variable(['a', 'b'], np.einsum('ab,b->ab', x, x[0]))) self.assertVariableIdentical( v[0] * v, Variable(['b', 'a'],	np.einsum('b,ab->ba', x[0], x)	numpy.einsum
import tensorflow as tf from tensorflow.python.framework import ops import sys import os import matplotlib.pyplot as plt import random from mpl_toolkits.mplot3d import Axes3D BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) devox_module = tf.load_op_library(os.path.join(BASE_DIR, 'tf_devoxelize.so')) sys.path.append('../voxelization') from tf_vox import group_voxel, voxelize, avg_voxel sys.path.append('../../utils') from tf_devox import trilinear_devoxelize import tf_util from plyfile import PlyData, PlyElement sys.path.append(os.path.join(BASE_DIR, '..', 'tf_ops/pc_distance')) import tf_nn_distance tf.enable_eager_execution() def get_cd_loss(pred, pc): """ pred: BxNx3, label: BxN, """ dists_forward,_,dists_backward,_ = tf_nn_distance.nn_distance(pred, pc) # loss = tf.reduce_mean(dists_forward+dists_backward) loss = (tf.reduce_mean(tf.sqrt(dists_forward)) + tf.reduce_mean(tf.sqrt(dists_backward)))/2 return loss def write_ply(tensor, name): np.savetxt(name, np.squeeze(tensor.numpy().transpose(0, 2, 1))) len = tensor.numpy().shape[2] file = os.path.join('.', name) f = open(file, "r+") lines = [line.lstrip().rstrip().replace(' ', ' ') for line in f] vertex_nums, face_nums, _ = lines[1].split() f = open(file, "w+") f.seek(0) head = "ply\nformat ascii 1.0\ncomment VCGLIB generated\nelement vertex " + '2048' + "\nproperty float x\nproperty float y\nproperty float z\nelement face " + str(0) + "\nproperty list uchar int vertex_indices\nend_header\n" f.write(head) for line in lines[:]: f.write(line + "\n") f.close() if __name__ == '__main__': import numpy as np import time for file in os.listdir('../../data'): file = os.path.join('../../data',file) plydata = PlyData.read(file) a_data = [] for i in range(plydata['vertex'].count): line = [plydata['vertex']['x'][i], plydata['vertex']['y'][i], plydata['vertex']['z'][i]] a_data.append(line) pc =	np.array(a_data)	numpy.array
import numpy as np import statistics import matplotlib.pyplot as plt import seaborn as sns np_normal_dis = np.random.normal(5, 0.5, 100) print(np_normal_dis.min()) print(np_normal_dis.max()) print(np_normal_dis.mean()) # print(np_normal_dis.median()) # not working print(np_normal_dis.std()) two_dimension_array = np.array([(1,2,3), [4,5,6]]) print(two_dimension_array) print('Max row:', np.amax(two_dimension_array, axis=0)) print('Max column:', np.amax(two_dimension_array, axis=1)) print('Min column:', np.amin(two_dimension_array, axis=0)) print('Min column:', np.amin(two_dimension_array, axis=1)) a = [1, 2, 3] print('Tile:', np.tile(a, 2)) # just repeat a list print('Repeat:', np.repeat(a, 2)) # sort repeat print(np.random.random()) # between 0 - 1 r = np.random.random(size=[2, 3]) # 2 row 3 column print(r) print(	np.random.choice(['a', 'e', 'i', 'o', 'u'], size=10)	numpy.random.choice
#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Sat Oct 14 21:31:56 2017 @author: Franz """ import scipy.signal import numpy as np import scipy.io as so import os.path import re import matplotlib.pylab as plt import h5py import matplotlib.patches as patches import numpy.random as rand import seaborn as sns import pandas as pd from functools import reduce import random import pdb class Mouse : def __init__(self, idf, list=None, typ='') : self.recordings = [] self.recordings.append(list) self.typ = typ self.idf = idf def add(self, rec) : self.recordings.append(rec) def __len__(self) : return len(self.recordings) def __repr__(self) : return ", ".join(self.recordings) ### PROCESSING OF RECORDING DATA ############################################## def load_stateidx(ppath, name, ann_name=''): """ load the sleep state file of recording (folder) $ppath/$name @Return: M,K sequence of sleep states, sequence of 0'1 and 1's indicating non- and annotated states """ ddir = os.path.join(ppath, name) ppath, name = os.path.split(ddir) if ann_name == '': ann_name = name sfile = os.path.join(ppath, name, 'remidx_' + ann_name + '.txt') f = open(sfile, 'r') lines = f.readlines() f.close() n = 0 for l in lines: if re.match('\d', l): n += 1 M = np.zeros(n, dtype='int') K = np.zeros(n, dtype='int') i = 0 for l in lines : if re.search('^\s+$', l) : continue if re.search('\s#', l) : continue if re.match('\d+\s+-?\d+', l) : a = re.split('\s+', l) M[i] = int(a[0]) K[i] = int(a[1]) i += 1 return M,K def load_recordings(ppath, rec_file) : """ load_recordings(ppath, rec_file) load recording listing with syntax: [E\|C] \s+ recording_name #COMMENT @RETURN: (list of controls, lis of experiments) """ exp_list = [] ctr_list = [] rfile = os.path.join(ppath, rec_file) f = open(rfile, newline=None) lines = f.readlines() f.close() for l in lines : if re.search('^\s+$', l) : continue if re.search('^\s#', l) : continue a = re.split('\s+', l) if re.search('E', a[0]) : exp_list.append(a[1]) if re.search('C', a[0]) : ctr_list.append(a[1]) return ctr_list, exp_list def load_dose_recordings(ppath, rec_file): """ load recording list with following syntax: A line is either control or experiments; Control recordings look like: C \s recording_name Experimental recordings also come with an additional dose parameter (allowing for comparison of multiple doses with controls) E \s recording_name \s dose_1 E \s recording_name \s dose_2 """ rfile = os.path.join(ppath, rec_file) f = open(rfile, newline=None) lines = f.readlines() f.close() # first get all potential doses doses = {} ctr_list = [] for l in lines : if re.search('^\s+$', l): continue if re.search('^\s#', l): continue a = re.split('\s+', l) if re.search('E', a[0]): if a[2] in doses: doses[a[2]].append(a[1]) else: doses[a[2]] = [a[1]] if re.search('C', a[0]): ctr_list.append(a[1]) return ctr_list, doses def get_snr(ppath, name): """ read and return sampling rate (SR) from file $ppath/$name/info.txt """ fid = open(os.path.join(ppath, name, 'info.txt'), newline=None) lines = fid.readlines() fid.close() values = [] for l in lines : a = re.search("^" + 'SR' + ":" + "\s+(.)", l) if a : values.append(a.group(1)) return float(values[0]) def get_infoparam(ifile, field): """ NOTE: field is a single string and the function does not check for the type of the values for field. In fact, it just returns the string following field """ fid = open(ifile, newline=None) lines = fid.readlines() fid.close() values = [] for l in lines : a = re.search("^" + field + ":" + "\s+(.)", l) if a : values.append(a.group(1)) return values def add_infoparam(ifile, field, vals): """ :param ifile: info file :param field: Parameters specifier, e.g. 'SR' :param vals: list with parameters """ fid = open(ifile, 'a') vals = [str(s) for s in vals] param = " ".join(vals) fid.write('%s:\t%s' % (field, param)) fid.write(os.linesep) fid.close() def laser_start_end(laser, SR=1525.88, intval=5): """laser_start_end(ppath, name) print start and end index of laser stimulation trains: For example, if you was stimulated for 2min every 20 min with 20 Hz, return the start and end index of the each 2min stimulation period (train) returns the tuple (istart, iend), both indices are inclusive, i.e. part of the sequence @Param: laser - laser, vector of 0s and 1s intval - minimum time separation [s] between two laser trains @Return: (istart, iend) - tuple of two np.arrays with laser start and end indices """ idx = np.where(laser > 0.5)[0] if len(idx) == 0 : return ([], []) idx2 = np.nonzero(np.diff(idx)(1./SR) > intval)[0] istart = np.hstack([idx[0], idx[idx2+1]]) iend = np.hstack([idx[idx2], idx[-1]]) return (istart, iend) def load_laser(ppath, name): """ load laser from recording ppath/name @RETURN: @laser, vector of 0's and 1's """ # laser might be .mat or h5py file # perhaps we could find a better way of testing that file = os.path.join(ppath, name, 'laser_'+name+'.mat') try: laser = np.array(h5py.File(file,'r').get('laser')) except: laser = so.loadmat(file)['laser'] return np.squeeze(laser) def laser_protocol(ppath, name): """ What was the stimulation frequency and the inter-stimulation interval for recording $ppath/$name? @Return: iinter-stimulation intervals, avg. inter-stimulation interval, frequency """ laser = load_laser(ppath, name) SR = get_snr(ppath, name) # first get inter-stimulation interval (istart, iend) = laser_start_end(laser, SR) intv = np.diff(np.array(istart/float(SR))) d = intv/60.0 print("The laser was turned on in average every %.2f min," % (np.mean(d))) print("with a min. interval of %.2f min and max. interval of %.2f min." % (np.min(d), np.max(d))) print("Laser stimulation lasted for %f s." % (np.mean(np.array(iend/float(SR)-istart/float(SR)).mean()))) # print laser start times print("Start time of each laser trial:") j=1 for t in istart: print("trial %d: %.2f" % (j, (t / float(SR)) / 60)) j += 1 # for each laser stimulation interval, check laser stimulation frequency dt = 1/float(SR) freq = [] laser_up = [] laser_down = [] for (i,j) in zip(istart, iend): part = laser[i:j+1] (a,b) = laser_start_end(part, SR, 0.005) dur = (j-i+1)dt freq.append(len(a) / dur) up_dur = (b-a+1)dt1000 down_dur = (a[1:]-b[0:-1]-1)dt1000 laser_up.append(np.mean(up_dur)) laser_down.append(np.mean(down_dur)) print(os.linesep + "Laser stimulation freq. was %.2f Hz," % np.mean(np.array(freq))) print("with laser up and down duration of %.2f and %.2f ms." % (np.mean(np.array(laser_up)), np.mean(np.array(laser_down)))) return d, np.mean(d), np.mean(np.array(freq)) def swap_eeg(ppath, rec, ch='EEG'): """ swap EEG and EEG2 or EMG with EMG2 if $ch='EMG' """ if ch == 'EEG': name = 'EEG' else: name = ch EEG = so.loadmat(os.path.join(ppath, rec, name+'.mat'))[name] EEG2 = so.loadmat(os.path.join(ppath, rec, name+'2.mat'))[name + '2'] tmp = EEG EEG = EEG2 EEG2 = tmp file_eeg1 = os.path.join(ppath, rec, '%s.mat' % name) file_eeg2 = os.path.join(ppath, rec, '%s2.mat' % name) so.savemat(file_eeg1, {name : EEG}) so.savemat(file_eeg2, {name+'2' : EEG2}) def eeg_conversion(ppath, rec, conv_factor=0.195): """ multiply all EEG and EMG channels with the given conversion factor and write the conversion factor as parameter (conversion:) into the info file. Only if there's no conversion factor in the info file specified, the conversion will be executed :param ppath: base filder :param rec: recording :param conv_factor: conversion factor :return: n/s """ ifile = os.path.join(ppath, rec, 'info.txt') conv = get_infoparam(ifile, 'conversion') if len(conv) > 0: print("found conversion: parameter in info file") print("returning: no conversion necessary!!!") return else: files = os.listdir(os.path.join(ppath, rec)) files = [f for f in files if re.match('^EEG', f)] for f in files: name = re.split('\.', f)[0] EEG = so.loadmat(os.path.join(ppath, rec, name+'.mat'), squeeze_me=True)[name] if EEG[0].dtype == 'int16': EEG = EEG conv_factor file_eeg = os.path.join(ppath, rec, '%s.mat' % name) print(file_eeg) so.savemat(file_eeg, {name: EEG}) else: print('Wrong datatype! probably already converted; returning...') return files = os.listdir(os.path.join(ppath, rec)) files = [f for f in files if re.match('^EMG', f)] for f in files: name = re.split('\.', f)[0] EMG = so.loadmat(os.path.join(ppath, rec, name+'.mat'), squeeze_me=True)[name] if EMG[0].dtype == 'int16': EMG = EMG * conv_factor file_emg = os.path.join(ppath, rec, '%s.mat' % name) print(file_emg) so.savemat(file_emg, {name: EMG}) else: print('Wrong datatype! probably already converted; returning...') return add_infoparam(ifile, 'conversion', [conv_factor]) calculate_spectrum(ppath, rec) ### DEPRICATED ############################################ def video_pulse_detection(ppath, rec, SR=1000, iv = 0.01): """ return index of each video frame onset ppath/rec - recording @Optional SR - sampling rate of EEG(!) recording iv - minimum time inverval (in seconds) between two frames @Return index of each video frame onset """ V = np.squeeze(so.loadmat(os.path.join(ppath, rec, 'videotime_' + rec + '.mat'))['video']) TS = np.arange(0, len(V)) # indices where there's a jump in the signal t = TS[np.where(V<0.5)]; if len(t) == 0: idx = [] return idx # time points where the interval between jumps is longer than iv t2 = np.where(np.diff(t)(1.0/SR)>=iv)[0] idx = np.concatenate(([t[0]],t[t2+1])) return idx # SIGNAL PROCESSING ########################################################### def my_lpfilter(x, w0, N=4): """ create a lowpass Butterworth filter with a cutoff of w0 the Nyquist rate. The nice thing about this filter is that is has zero-phase distortion. A conventional lowpass filter would introduce a phase lag. w0 - filter cutoff; value between 0 and 1, where 1 corresponds to nyquist frequency. So if you want a filter with cutoff at x Hz, the corresponding w0 value is given by w0 = 2 * x / sampling_rate N - order of filter @Return: low-pass filtered signal See also my hp_filter, or my_bpfilter """ from scipy import signal b,a = signal.butter(N, w0) y = signal.filtfilt(b,a, x) return y def my_hpfilter(x, w0, N=4): """ create an N-th order highpass Butterworth filter with cutoff frequency w0 * sampling_rate/2 """ from scipy import signal # use scipy.signal.firwin to generate filter #taps = signal.firwin(numtaps, w0, pass_zero=False) #y = signal.lfilter(taps, 1.0, x) b,a = signal.butter(N, w0, 'high') y = signal.filtfilt(b,a, x, padlen = x.shape[0]-1) return y def my_bpfilter(x, w0, w1, N=4,bf=True): """ create N-th order bandpass Butterworth filter with corner frequencies w0sampling_rate/2 and w1sampling_rate/2 """ #from scipy import signal #taps = signal.firwin(numtaps, w0, pass_zero=False) #y = signal.lfilter(taps, 1.0, x) #return y from scipy import signal b,a = signal.butter(N, [w0, w1], 'bandpass') if bf: y = signal.filtfilt(b,a, x) else: y = signal.lfilter(b,a, x) return y def my_notchfilter(x, sr=1000, band=5, freq=60, ripple=10, order=3, filter_type='butter'): from scipy.signal import iirfilter,lfilter fs = sr nyq = fs/2.0 low = freq - band/2.0 high = freq + band/2.0 low = low/nyq high = high/nyq b, a = iirfilter(order, [low, high], rp=ripple, btype='bandstop', analog=False, ftype=filter_type) filtered_data = lfilter(b, a, x) return filtered_data def downsample_vec(x, nbin): """ y = downsample_vec(x, nbin) downsample the vector x by replacing nbin consecutive \ bin by their mean \ @RETURN: the downsampled vector """ n_down = int(np.floor(len(x) / nbin)) x = x[0:n_downnbin] x_down = np.zeros((n_down,)) # 0 1 2 \| 3 4 5 \| 6 7 8 for i in range(nbin) : idx = list(range(i, int(n_downnbin), int(nbin))) x_down += x[idx] return x_down / nbin def smooth_data(x, sig): """ y = smooth_data(x, sig) smooth data vector @x with gaussian kernel with standard deviation $sig """ sig = float(sig) if sig == 0.0: return x # gaussian: gauss = lambda x, sig : (1/(signp.sqrt(2.np.pi)))np.exp(-(xx)/(2.sigsig)) bound = 1.0/10000 L = 10. p = gauss(L, sig) while (p > bound): L = L+10 p = gauss(L, sig) #F = map(lambda x: gauss((x, sig)), np.arange(-L, L+1.)) # py3: F = [gauss(x, sig) for x in np.arange(-L, L+1.)] F = F / np.sum(F) return scipy.signal.fftconvolve(x, F, 'same') def power_spectrum(data, length, dt): """ scipy's implementation of Welch's method using hanning window to estimate the power spectrum The function returns power density with units V2/Hz see also https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.welch.html The label on the y-axis should say PSD [V2/Hz] @Parameters data - time series; float vector! length - length of hanning window, even integer! @Return: power density, frequencies The function returns power density in units V^2 / Hz Note that np.var(data) ~ np.sum(power density) * (frequencies[1]-frequencies[0]) """ f, pxx = scipy.signal.welch(data, fs=1.0/dt, window='hanning', nperseg=int(length), noverlap=int(length/2)) return pxx, f def spectral_density(data, length, nfft, dt): """ calculate the spectrogram for the time series given by data with time resolution dt The powerspectrum for each window of length $length is computed using Welch's method. The windows for the powerspectrum calculation are half-overlapping. If length contains 5s of data, then the first windows goes from 0s to 5s, the second window from 2.5 to 7.5s, ... The last window ends at ceil(len(data)/length)5s Another example, assume we have 13 s of data, with 5 s windows, the the powerdensity is calculated for the following time windows: 0 -- 5, 2.5 -- 7.5, 5 -- 10, 7.5 -- 12.5, 10 -- 15 In total there are thus 2ceil(13/5)-1 = 5 windows The last window starts at 23-2 (5/2) = 10 s Note: the returned time axis starts at time point goes from 0 to 10s in 2.5s steps @Parameters: data - time series length - window length of data used to calculate powerspectrum. Note that the time resolution of the spectrogram is length/2 nfft - size of the window used to calculate the powerspectrum. determines the frequency resolution. @Return: Powspectrum, frequencies, time axis """ n = len(data) k = int(np.ceil((1.0n)/length)) data = np.concatenate((data, np.zeros((lengthk-n,)))) fdt = lengthdt/2 # time step for spectrogram t = np.arange(0, fdt(2k-2)+fdt/2.0, fdt) # frequency axis of spectrogram f = np.linspace(0, 1, int(np.ceil(nfft/2.0))+1) (0.5/dt) # the power spectrum is calculated for 2k-1 time points Pow = np.zeros((len(f), k2-1)) j = 0 for i in range(0, k-2+1): w1=data[(lengthi):(i+1)length] w2=data[lengthi+int(length/2):(i+1)length+int(length/2)] Pow[:,j] = power_spectrum(w1, nfft, dt)[0] Pow[:,j+1] = power_spectrum(w2, nfft, dt)[0] j += 2 # last time point Pow[:,j],f = power_spectrum(data[length(k-1):klength], nfft, dt) return Pow, f, t def calculate_spectrum(ppath, name, fres=0.5): """ calculate EEG and EMG spectrogram used for sleep stage detection. Function assumes that data vectors EEG.mat and EMG.mat exist in recording folder ppath/name; these are used to calculate the powerspectrum fres - resolution of frequency axis all data saved in "true" mat files :return EEG Spectrogram, EMG Spectrogram, frequency axis, time axis """ SR = get_snr(ppath, name) swin = round(SR)5 fft_win = round(swin/5) # approximate number of data points per second if (fres == 1.0) or (fres == 1): fft_win = int(fft_win) elif fres == 0.5: fft_win = 2int(fft_win) else: print("Resolution %f not allowed; please use either 1 or 0.5" % fres) (peeg2, pemg2) = (False, False) # Calculate EEG spectrogram EEG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EEG.mat'))['EEG']) Pxx, f, t = spectral_density(EEG, int(swin), int(fft_win), 1/SR) if os.path.isfile(os.path.join(ppath, name, 'EEG2.mat')): peeg2 = True EEG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EEG2.mat'))['EEG2']) Pxx2, f, t = spectral_density(EEG, int(swin), int(fft_win), 1/SR) #save the stuff to a .mat file spfile = os.path.join(ppath, name, 'sp_' + name + '.mat') if peeg2 == True: so.savemat(spfile, {'SP':Pxx, 'SP2':Pxx2, 'freq':f, 'dt':t[1]-t[0],'t':t}) else: so.savemat(spfile, {'SP':Pxx, 'freq':f, 'dt':t[1]-t[0],'t':t}) # Calculate EMG spectrogram EMG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EMG.mat'))['EMG']) Qxx, f, t = spectral_density(EMG, int(swin), int(fft_win), 1/SR) if os.path.isfile(os.path.join(ppath, name, 'EMG2.mat')): pemg2 = True EMG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EMG2.mat'))['EMG2']) Qxx2, f, t = spectral_density(EMG, int(swin), int(fft_win), 1/SR) # save the stuff to .mat file spfile = os.path.join(ppath, name, 'msp_' + name + '.mat') if pemg2 == True: so.savemat(spfile, {'mSP':Qxx, 'mSP2':Qxx2, 'freq':f, 'dt':t[1]-t[0],'t':t}) else: so.savemat(spfile, {'mSP':Qxx, 'freq':f, 'dt':t[1]-t[0],'t':t}) return Pxx, Qxx, f, t def whiten_spectrogram(ppath, name, fmax=50): """ experimental :param ppath: :param name: :param fmax: :return: """ P = so.loadmat(os.path.join(ppath, name, 'sp_' + name + '.mat'), squeeze_me=True) SPE = P['SP'] freq = P['freq'] ifreq = np.where(freq <= fmax)[0] SPE = SPE[ifreq,:] nfilt = 5 filt = np.ones((nfilt, nfilt)) filt = np.divide(filt, filt.sum()) #SPE = scipy.signal.convolve2d(SPE, filt, boundary='symm', mode='same') m = np.mean(SPE,axis=1) SPE -= np.tile(m, (SPE.shape[1], 1)).T SPE = SPE.T C = np.dot(SPE.T, SPE) [evals, L] = np.linalg.eigh(C) idx = np.argsort(evals) D = np.diag(np.sqrt(evals[idx])) L = L[:,idx] W = np.dot(L, np.dot(np.linalg.inv(D),np.dot(L.T,SPE.T))) nfilt = 2 filt = np.ones((nfilt,nfilt)) filt = np.divide(filt, filt.sum()) W = scipy.signal.convolve2d(W, filt, boundary='symm', mode='same') return W, D, L def normalize_spectrogram(ppath, name, fmax=0, band=[], vm=5, pplot=True, sptype='', filt_dim=[]): """ Normalize EEG spectrogram by deviding each frequency band by its average value. :param ppath, name: base folder, recording name :param fmax: maximum frequency; frequency axis of spectrogram goes from 0 to fmax if fmax=0, use complete frequency axis :param band: list or tuple, define lower and upper range of a frequency band, if pplot=True, plot band, along with spectrogram; if band=[], disregard :param vm: color range for plotting spectrogram :pplot: if True, plot spectrogram along with power band :sptype: if sptype='fine' plot 'special' spectrogram, save under sp_fine_$name.mat; otherwise plot 'normal' spectrogram sp_$name.mat :filt_dim: list or tuple; the two values define the dimensions of box filter used to filter the normalized spectrogram; if filt_dim=[], then no filtering :return SPE, t, freq: normalized spectrogram (np.array), time axis, frequency axis """ if (len(sptype) == 0) or (sptype=='std'): P = so.loadmat(os.path.join(ppath, name, 'sp_' + name + '.mat'), squeeze_me=True) elif sptype == 'fine': P = so.loadmat(os.path.join(ppath, name, 'sp_fine_' + name + '.mat'), squeeze_me=True) SPE = P['SP'] freq = P['freq'] t = P['t'] if fmax > 0: ifreq = np.where(freq <= fmax)[0] else: ifreq = np.arange(0, len(freq)) freq = freq[ifreq] nfilt = 4 filt = np.ones((nfilt,nfilt)) filt = np.divide(filt, filt.sum()) SPE = SPE[ifreq,:] # before #SPE = SPE[ifreq] #W = scipy.signal.convolve2d(SPE, filt, boundary='symm', mode='same') #sp_mean = W.mean(axis=1) sp_mean = SPE.mean(axis=1) SPE = np.divide(SPE, np.tile(sp_mean, (SPE.shape[1], 1)).T) if len(filt_dim) > 0: filt = np.ones(filt_dim) filt = np.divide(filt, filt.sum()) SPE = scipy.signal.convolve2d(SPE, filt, boundary='symm', mode='same') # get high gamma peaks if len(band) > 0: iband = np.where((freq >= band[0]) & (freq <= band[-1]))[0] pow_band = SPE[iband,:].mean(axis=0) thr = pow_band.mean() + pow_band.std() idx = np.where(pow_band > thr)[0] # plot normalized spectrogram, along with band if pplot: plt.ion() plt.figure() if len(band) > 0: med = np.median(SPE.mean(axis=0)) ax1 = plt.subplot(211) plt.pcolormesh(t, freq, SPE, vmin=0, vmax=vmmed, cmap='jet') plt.subplot(212, sharex=ax1) plt.plot(t,SPE[iband,:].mean(axis=0)) plt.plot(t[idx], pow_band[idx], '.') plt.draw() return SPE, t, freq[ifreq] def recursive_spectrogram(ppath, name, sf=0.3, alpha=0.3, pplot=True): """ calculate EEG/EMG spectrogram in a way that can be implemented by a closed-loop system. The spectrogram is temporally filtered using a recursive implementation of a lowpass filter @Parameters: ppath/name - mouse EEG recording sf - smoothing factor along frequency axis alpha - temporal lowpass filter time constant pplot - if pplot==True, plot figure @Return: SE, SM - EEG, EMG spectrogram """ EEG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EEG.mat'))['EEG']) EMG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EMG.mat'))['EMG']) len_eeg = len(EEG) fdt = 2.5 SR = get_snr(ppath, name) # we calculate the powerspectrum for 5s windows swin = int(np.round(SR) 5.0) # but we sample new data each 2.5 s swinh = int(swin/2.0) fft_win = int(swin / 5.0) # number of 2.5s long samples spoints = int(np.floor(len_eeg / swinh)) SE = np.zeros((int(fft_win/2+1), spoints)) SM = np.zeros((int(fft_win/2+1), spoints)) print("Starting calculating spectrogram for %s..." % name) for i in range(2, spoints): # we take the last two swinh windows (the new 2.5 s long sample and the one from # the last iteration) x = EEG[(i-2)swinh:iswinh] [p, f] = power_spectrum(x.astype('float'), fft_win, 1.0/SR) p = smooth_data(p, sf) # recursive low pass filtering of spectrogram: # the current state is an estimate of the current sample and the previous state SE[:,i] = alphap + (1-alpha) SE[:,i-1] # and the same of EMG x = EMG[(i-2)swinh:iswinh] [p, f] = power_spectrum(x.astype('float'), fft_win, 1.0/SR) p = smooth_data(p, sf) SM[:,i] = alphap + (1-alpha) SM[:,i-1] if pplot: # plot EEG spectrogram t = np.arange(0, SM.shape[1])fdt plt.figure() ax1 = plt.subplot(211) im = np.where((f>=0) & (f<=30))[0] med = np.median(SE.max(axis=0)) ax1.imshow(np.flipud(SE[im,:]), vmin=0, vmax=med2) plt.xticks(()) ix = list(range(0, 30, 10)) fi = f[im][::-1] plt.yticks(ix, list(map(int, fi[ix]))) box_off(ax1) plt.axis('tight') plt.ylabel('Freq (Hz)') # plot EMG amplitude ax2 = plt.subplot(212) im = np.where((f>=10) & (f<100))[0] df = np.mean(np.diff(f)) # amplitude is the square root of the integral ax2.plot(t, np.sqrt(SM[im,:].sum(axis=0)df)/1000.0) plt.xlim((0, t[-1])) plt.ylabel('EMG Ampl (mV)') plt.xlabel('Time (s)') box_off(ax2) plt.show(block=False) return SE, SM, f def recursive_sleepstate_rem(ppath, recordings, sf=0.3, alpha=0.3, past_mu=0.2, std_thdelta = 1.5, past_len=120, sdt=2.5, psave=False, xemg=False): """ predict a REM period only based on EEG/EMG history; the same algorithm is also used for closed-loop REM sleep manipulation. The algorithm uses for REM sleep detection a threshold on delta power, EMG power, and theta/delta power. For theta/delta I use two thresholds: A hard (larger) threshold and a soft (lower) threshold. Initially, theta/delta has to cross the hard threshold to initiate a REM period. Then, as long as, theta/delta is above the soft threshold (and EMG power stays low) REM sleep continues. @Parameters: ppath base folder with recordings recordings list of recordings sf smoothing factor for each powerspectrum alpha smoothing factor along time dimension past_mu percentage (0 .. 1) of brain states that are allowed to have EMG power larger than threshold during the last $past_len seconds past_len window to calculate $past_mu std_thdelta the hard theta/delta threshold is given by, mean(theta/delta) + $std_thdelta std(theta/delta) sdt time bin for brain sttate, typically 2.5s psave if True, save threshold parameters to file. """ idf = re.split('_', recordings[0])[0] # 02/05/2020 changed from int to float: past_len = float(np.round(past_len/sdt)) # calculate spectrogram (SE, SM) = ([],[]) for rec in recordings: A,B, freq = recursive_spectrogram(ppath, rec, sf=sf, alpha=alpha) SE.append(A) SM.append(B) # fuse lists SE and SM SE = np.squeeze(reduce(lambda x,y: np.concatenate((x,y)), SE)) if not xemg: SM = np.squeeze(reduce(lambda x,y: np.concatenate((x,y)), SM)) else: SM = SE # EEG, EMG bands ntbins = SE.shape[1] r_delta = [0.5, 4] r_theta = [5,12] # EMG band r_mu = [300, 500] i_delta = np.where((freq >= r_delta[0]) & (freq <= r_delta[1]))[0] i_theta = np.where((freq >= r_theta[0]) & (freq <= r_theta[1]))[0] i_mu = np.where((freq >= r_mu[0]) & (freq <= r_mu[1]))[0] pow_delta = np.sum(SE[i_delta,:], axis=0) pow_theta = np.sum(SE[i_theta,:], axis=0) pow_mu = np.sum(SM[i_mu,:], axis=0) # theta/delta th_delta = np.divide(pow_theta, pow_delta) thr_th_delta1 = np.nanmean(th_delta) + std_thdeltanp.nanstd(th_delta) thr_th_delta2 = np.nanmean(th_delta) + 0.0np.nanstd(th_delta) thr_delta = pow_delta.mean() thr_mu = pow_mu.mean() + 0.5*	np.nanstd(pow_mu)	numpy.nanstd
''' Authors: <NAME>, <NAME>, <NAME> Email ID: <EMAIL>, <EMAIL>, <EMAIL> ''' import keras import tensorflow as tf from keras.models import Sequential from keras.models import Model #from tensorflow.keras import layers #from tensorflow.keras import optimizers from keras.layers import Dense from keras.layers import LSTM from keras.layers import Activation from sklearn.neighbors import KernelDensity from keras.layers import Masking from keras.layers import Input from keras.layers import Concatenate from keras import optimizers from scipy.stats import spearmanr from scipy import stats from statistics import mean import copy import mlflow import seaborn as sns import numpy as np from matplotlib import pyplot as plt from numpy import genfromtxt from sklearn.utils import shuffle import csv import random import math import sklearn import mlflow import mlflow.keras from sklearn.metrics import mean_squared_error from matplotlib import pyplot as plt import os import glob import multiprocessing as mp from keras.callbacks import EarlyStopping from keras.callbacks import ModelCheckpoint import matplotlib.cm import matplotlib import argparse from sklearn.neighbors import KNeighborsRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.tree import DecisionTreeRegressor from sklearn.svm import SVR from sklearn.kernel_ridge import KernelRidge from xgboost import XGBRegressor from sklearn.neighbors import RadiusNeighborsRegressor from xgboost import XGBRFRegressor os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' numLatency = 118 embeddingsFile = "onnxEmbeddings.csv" lat = [] maxVal = 0 matplotlib.use('Agg') def parse_latency(file): global lat data = np.genfromtxt(file, delimiter=',') latency = np.mean(data, axis=1) latency = latency[:numLatency] lat.append(latency) #latency = latency/np.amax(latency) return latency def parse_features(): Features = [] maxLayer = 0 maxFlops = 0 maxChannel = 0 maxDim = 224 maxKernel = 7 maxPadding = 3 with open(embeddingsFile, newline='') as f: reader = csv.reader(f) data = list(reader) for i in range(len(data)): temp = [data[i][j * 13:(j + 1) * 13] for j in range((len(data[i]) + 12) // 13 )] maxLayer = max(maxLayer, len(temp)) for j in range(len(temp)): maxFlops=max(maxFlops, float(temp[j][12])) maxChannel = max(maxChannel, int(temp[j][7])) maxChannel = max(maxChannel, int(temp[j][8])) Features.append(temp) numpyFeatures = np.ones((len(Features), maxLayer, 13)) numpyFeatures = numpyFeatures-1 for i in range(len(Features)): temp = Features[i] for j in range(len(temp)): for k in range(len(temp[j])): numpyFeatures[i][j][k] = temp[j][k] if k == 5 or k == 6: numpyFeatures[i][j][k] = numpyFeatures[i][j][k]/maxDim elif k == 7 or k == 8: numpyFeatures[i][j][k] = numpyFeatures[i][j][k]/maxChannel elif k == 9: numpyFeatures[i][j][k] = numpyFeatures[i][j][k]/maxKernel elif k == 12: numpyFeatures[i][j][k] = numpyFeatures[i][j][k]/maxFlops return numpyFeatures, maxLayer def learn_xgb_model(hardware, maxLayer, lat_mean, features, featuresShape, splitPercentage=0.99, shuffleFeatures=True): numSample = len(lat_mean) features = features[:numSample] if shuffleFeatures == True: features, lat_mean = shuffle(features,lat_mean) trainf = features[:int(splitPercentagelen(features))] trainy = lat_mean[:int(splitPercentagelen(features))] testf = features[int(splitPercentagelen(features)):] testy = lat_mean[int(splitPercentagelen(features)):] print("================= Dataset Stage ==============") print(trainf.shape, trainy.shape, testf.shape, testy.shape) trainf = np.reshape(trainf, (trainf.shape[0], trainf.shape[1]trainf.shape[2])) testf = np.reshape(testf, (testf.shape[0], testf.shape[1]testf.shape[2])) model = XGBRegressor() model.fit(trainf, trainy) trainPredict = model.predict(trainf) testPredict = model.predict(testf) trainScore = math.sqrt(mean_squared_error(trainy, trainPredict)) testScore = math.sqrt(mean_squared_error(testy, testPredict)) ### Train Model characteristics r2_score = sklearn.metrics.r2_score(trainy, trainPredict) s_coefficient, pvalue = spearmanr(trainy, trainPredict) writeToFile('Train Score: %f RMSE' % (trainScore)) writeToFile("The R^2 Value for %s: %f"%(hardware, r2_score)) writeToFile("The Spearnman Coefficient and p-value for %s: %f and %f"%(hardware, s_coefficient, pvalue)) plt.figure() plt.xlabel("Actual Latency (in ms)") plt.ylabel("Predicted Latency (in ms)") sns.scatterplot(trainy, trainPredict) plt.savefig(args.name+'/plots/'+hardware+'_'+args.learning_type+'_'+str(splitPercentage)+'_train.png') r2_score = sklearn.metrics.r2_score(testy, testPredict) s_coefficient, pvalue = spearmanr(testy, testPredict) writeToFile('Test Score: %f RMSE' % (testScore)) writeToFile("The R^2 Value for %s: %f"%(hardware, r2_score)) writeToFile("The Spearnman Coefficient and p-value for %s: %f and %f"%(hardware, s_coefficient, pvalue)) plt.figure() plt.xlabel("Actual Latency (in ms)") plt.ylabel("Predicted Latency (in ms)") sns.scatterplot(testy, testPredict) plt.savefig(args.name+'/plots/'+hardware+"_"+args.learning_type+'_'+str(1-splitPercentage)+'_test.png') return model def learn_xgb_model_collab(hardware, maxLayer, lat_mean, features, featuresShape, splitPercentage=0.99, shuffleFeatures=True): print('Learning' + hardware) numSample = len(lat_mean) features = features[:numSample] if shuffleFeatures == True: features, lat_mean = shuffle(features,lat_mean) testf = features testy = lat_mean testf = np.reshape(testf, (testf.shape[0], testf.shape[1]testf.shape[2])) results = [] index = [] for i in range(10, numSample): trainf = features[:i] trainy = lat_mean[:i] # print("================= Dataset Stage ==============") # print(trainf.shape, trainy.shape, testf.shape, testy.shape) trainf = np.reshape(trainf, (trainf.shape[0], trainf.shape[1]trainf.shape[2])) model = XGBRegressor() model.fit(trainf, trainy) testPredict = model.predict(testf) testScore = math.sqrt(mean_squared_error(testy, testPredict)) r2_score = sklearn.metrics.r2_score(testy, testPredict) s_coefficient, pvalue = spearmanr(testy, testPredict) results.append(r2_score) index.append(i) matplotlib.rcParams['figure.dpi'] = 500 plt.figure() plt.xlabel("Number of Datapoints") plt.ylabel("Average R^2") sns.lineplot(index, results) plt.savefig(args.name+'/plots/'+hardware+'_indiLearn.png') f = open(args.name+'/meta/plotdata.txt', a) s1 = ','.join(map(str, index)) s2 = ','.join(map(str, results)) f.write(hardware+'\n'+s1+'\n'+s2+'\n') f.close() def learn_lstm_model(hardware, maxLayer, lat_mean, features, featuresShape): numSample = len(lat_mean) features = features[:numSample] features, lat_mean = shuffle(features,lat_mean) trainf = features[:int(0.99len(features))] trainy = lat_mean[:int(0.99len(features))] #testf = features[:int(1.0len(features))] #testy = lat_mean[:int(1.0len(features))] testf = features[int(0.99len(features)):] testy = lat_mean[int(0.99len(features)):] print("================= Dataset Stage ==============") print(trainf.shape, trainy.shape, testf.shape, testy.shape) #mlflow.keras.autolog() #Create an LSTM model model=Sequential() model.add(Masking(mask_value=-1,input_shape=(maxLayer, featuresShape))) model.add(LSTM(20, activation='relu')) model.add(Dense(1, name = 'fc')) opt = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False) #initial_learning_rate = 0.01 # lr_schedule = optimizers.schedules.ExponentialDecay(initial_learning_rate, #opt = optimizers.SGD(learning_rate = initial_learning_rate) model.compile(loss='mean_squared_error', optimizer=opt, metrics=[keras.metrics.MeanAbsolutePercentageError()]) model.summary() #filepath="checkpoint-{loss:.5f}-{val_loss:.5f}-{val_mean_absolute_percentage_error}.hdf5" filepath=args.name+'/models/model.hdf5' #checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')#montor can be val_loss or loss checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')#montor can be val_loss or loss es = EarlyStopping(monitor='loss', mode='min', verbose=1, patience=50) val = model.fit(trainf, trainy, epochs=250, batch_size=512, verbose=1, callbacks=[es, checkpoint]) #val = model.fit(trainf, trainy, epochs=250, batch_size=512, verbose=1, callbacks=[es, checkpoint], validation_data=(testf, testy)) model.load_weights(filepath) trainPredict = model.predict(trainf) testPredict = model.predict(testf) trainScore = math.sqrt(mean_squared_error(trainy, trainPredict)) writeToFile('Train Score: %f RMSE' % (trainScore)) testScore = math.sqrt(mean_squared_error(testy, testPredict)) ### Train Model characteristics r2_score = sklearn.metrics.r2_score(trainy, trainPredict) s_coefficient, pvalue = spearmanr(trainy, trainPredict) writeToFile('Train Score: %f RMSE' % (trainScore)) writeToFile("The R^2 Value for %s: %f"%(hardware, r2_score)) writeToFile("The Spearnman Coefficient and p-value for %s: %f and %f"%(hardware, s_coefficient, pvalue)) plt.figure() plt.xlabel("Actual Latency (in ms)") plt.ylabel("Predicted Latency (in ms)") sns.scatterplot(trainy, trainPredict[:,0]) #plt.title(hardware+' R2: '+str(r2_score)+' SpearVal: '+str(s_coefficient)) plt.savefig(args.name+'/plots/'+hardware+"_"+args.learning_type+'_train.png') r2_score = sklearn.metrics.r2_score(testy, testPredict) s_coefficient, pvalue = spearmanr(testy, testPredict) writeToFile('Test Score: %f RMSE' % (testScore)) writeToFile("The R^2 Value for %s: %f"%(hardware, r2_score)) writeToFile("The Spearnman Coefficient and p-value for %s: %f and %f"%(hardware, s_coefficient, pvalue)) plt.figure() plt.xlabel("Actual Latency (in ms)") plt.ylabel("Predicted Latency (in ms)") sns.scatterplot(testy, testPredict[:,0]) #plt.title(hardware+' R2: '+str(r2_score)+' SpearVal: '+str(s_coefficient)) plt.savefig(args.name+'/plots/'+hardware+"_"+args.learning_type+'_test.png') ### Adding Other Regressors extractor = Model(outputs=model.get_layer('fc').input, inputs=model.input) extractor.summary() knn = KNeighborsRegressor() trainPredict = extractor.predict(trainf) testPredict = extractor.predict(testf) randForest = RandomForestRegressor() decisionTree = DecisionTreeRegressor() svr = SVR() kernelrdidge = KernelRidge() xgb = XGBRegressor() xgbrf = XGBRFRegressor() modellist = [ ('knn', knn), ('randomForest', randForest), ('dTree', decisionTree), ('svr', svr), ('kerenlrdige', kernelrdidge), ('xgb', xgb), ('xgbrf', xgbrf) ] for name, model_lowB in modellist: model_lowB.fit(trainPredict, trainy) modeltestPred = model_lowB.predict(testPredict) testScore = math.sqrt(mean_squared_error(testy, modeltestPred)) r2_score = sklearn.metrics.r2_score(testy, modeltestPred) s_coefficient, pvalue = spearmanr(testy, modeltestPred) writeToFile('Test Score with %s : %f RMSE' % (name, testScore)) writeToFile("The R^2 Value with %s for %s: %f"%(hardware, name, r2_score)) writeToFile("The Spearnman Coefficient and p-value for %s with %s : %f and %f"%(hardware, name, s_coefficient, pvalue)) plt.figure() plt.xlabel("Actual Latency (in ms)") plt.ylabel("Predicted Latency (in ms)") sns.scatterplot(testy, modeltestPred) #plt.title(name + hardware+' R2: '+str(r2_score)+' SpearVal: '+str(s_coefficient)) plt.savefig(args.name+'/plots/'+hardware+args.learning_type+'_'+name+'.png') return (model, modellist, extractor) ''' This function takes in the dictionary of hardware_names to its maxLayer, latency and features map net_dict[key][2] - refers to the network features for a hardware and net_dict[key][1] - refers to the latency for that hardware 1. First determine the mean and std of the latencies for each hardware in the dictionary 2. Sample from the distribution - i.e. from Mu-8sigma to Mu+2sigma, at each parts of the distribution, find all indices that intersect in all the hardwares considered here. For ex., if network no. 2374 falls between mu-1sigma and mu for all the hardware devices in the dictionary, then add 2374 to the representation set for all the hardware 3. Find maxSamples such networks that become the golden representation of the hardware 4. Return the list of lists of maxSamples network representation for all hardwares and also the indices of the representation networks 5. The indices will be used by any hardware not on the list to make and append it's representation TODO: Not using max samples for now - change ''' def sample_hwrepresentation(net_dict, maxSamples): mean_lat = [] sd_lat = [] final_indices = [] #Determining the Mean and Standard Deviation of Latencies for key in net_dict: net_dict[key][2] = net_dict[key][2][:numLatency,:,:] #Not required actually.. Simply doing net_dict[key][1] = net_dict[key][1][:numLatency] print(np.mean(net_dict[key][1]), np.std(net_dict[key][1])) mean_lat.append(np.mean(net_dict[key][1])) sd_lat.append(np.std(net_dict[key][1])) for i in range(-2,8): #This range might not be enough -- the range should be more generic when hardware increases index_where = [] index = 0 for key in net_dict: index_where.append(np.where(np.logical_and(net_dict[key][1] > mean_lat[index]+isd_lat[index], net_dict[key][1] <= mean_lat[index]+(i+1)sd_lat[index]))) index += 1 for j in range(len(index_where)): index_where[0] = np.intersect1d(index_where[0], index_where[j]) final_intersection = index_where[0] if len(final_intersection) >= 4: loop_index = 4 else: loop_index = len(final_intersection) hw_features_cncat = [] for j in range(loop_index): final_indices.append(final_intersection[j]) print("The final indices size is %f"%(len(final_indices))) for key in net_dict: hw_features_per_device = [] for j in range(len(final_indices)): hw_features_per_device.append(net_dict[key][1][final_indices[j]]) net_dict[key][1] = np.delete(net_dict[key][1], final_indices, axis=0) net_dict[key][2] = np.delete(net_dict[key][2], final_indices, axis=0) hw_features_cncat.append(hw_features_per_device) print(len(final_indices), net_dict[key][2].shape) return final_indices, hw_features_cncat def random_indices(maxSamples): rand_indices = [] for i in range(maxSamples): rand_indices.append(random.randint(0,numLatency-1)) return rand_indices ''' Function which computes total MACs of each network and samples maxSamples indices from it based on FLOPS. ''' def flopsBasedIndices(maxSamples): with open('../DiverseRandNetworkGenerator/Embeddings.csv') as f: reader = csv.reader(f) data = list(reader) totalFLOPSList = np.zeros(len(data)) for i in range(len(data)): temp = [data[i][j * 13:(j + 1) * 13] for j in range((len(data[i]) + 12) // 13 )] for j in range(len(temp)): totalFLOPSList[i]+=int(temp[j][12]) mean = np.mean(totalFLOPSList) sd = np.std(totalFLOPSList) def random_sampling(net_dict, rand_indices, maxSamples): for key in net_dict: net_dict[key][2] = net_dict[key][2][:numLatency,:,:] net_dict[key][1] = net_dict[key][1][:numLatency] hw_features_cncat = [] #rand_indices = [] #final_indices = [] #for i in range(maxSamples): # rand_indices.append(random.randint(0,5000)) for key in net_dict: hw_features_per_device = [] for j in range(maxSamples): hw_features_per_device.append(net_dict[key][1][rand_indices[j]]) hw_features_cncat.append(hw_features_per_device) #If this is not done separately, the code will break for key in net_dict: net_dict[key][1] =	np.delete(net_dict[key][1], rand_indices, axis=0)	numpy.delete
import numpy as np class ProblemTest: ''' Parametes object. Effectively represents each problem that we want to submit to the solver ''' def __init__(self): self.N = 100 self.M = 1000 self.K_pol = 3 self.K_cus = 0 self.U = 200 self.T = 1 self.u_min, self.u_max = -10, 10 self.initial_condition = 0 self.sigma = 2 self.transition_function_deterministic = lambda n, x, u: x + u * self.dt self.transition_function_stochastic = lambda n, x, u: np.sqrt(self.dt) * self.sigma * np.random.randn(self.M) self.terminal_condition_fnc = lambda x: x 2 self.running_reward = lambda x, u: x 2 + u ** 2 self.transition_function = lambda n, x, u: self.transition_function_deterministic(n, x, u) + \ self.transition_function_stochastic(n, x, u) self.generate_training_points = lambda x: np.random.randn(self.M) self.dt = self.T / self.N self.K = self.K_pol + self.K_cus self.custom_basis =	np.array([])	numpy.array
import os import sys sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../') import argparse import matplotlib.pyplot as plt import numpy as np from datasetapi.voc_dataset.voc_data_processing import generate_wh_xyminmax_list from datasetapi.coco_dataset.coco_data_processing import get_coco_wh_xyminmax, generate_calibset import random random.seed(2021) # ids_or_names = ["abn1", # "abn2", # "abn3", # "abn4", # "abn5", # "abn6", # "abn7", # "abn8", # "abn9", # "abn10"] def plot_wh(wh_list, calib_wh_list, ids_or_names=None, plot_idx=None): """ :param wh_list: w & h list of training set :param calib_wh_list: w & h list of calibration set :param ids_or_names: category or id list :param plot_idx: which category or id will be plot :return: """ r = lambda: random.randint(0,255) colors=[] for i in range(len(ids_or_names) + 1): color = ('#%02X%02X%02X' % (r(),r(),r())) colors.append(color) cls_list = [] plot_w_list = [] plot_h_list = [] calib_cls_list = [] calib_plot_w_list = [] calib_plot_h_list = [] for i in range(len(ids_or_names)): cls_list.append([]) plot_w_list.append([]) plot_h_list.append([]) calib_cls_list.append([]) calib_plot_w_list.append([]) calib_plot_h_list.append([]) for wh in wh_list: cls_list[ids_or_names.index(wh[0])].append(wh[0]) # cls_name_or_id = wh[0] plot_w_list[ids_or_names.index(wh[0])].append(wh[1]) # w = wh[1] plot_h_list[ids_or_names.index(wh[0])].append(wh[2]) # h = wh[2] plot_list = [cls_list, plot_w_list, plot_h_list] for calib_wh in calib_wh_list: # calib_cls_name = calib_wh[0] calib_cls_list[ids_or_names.index(calib_wh[0])].append(calib_wh[0]) calib_plot_w_list[ids_or_names.index(calib_wh[0])].append(calib_wh[1]) # w calib_plot_h_list[ids_or_names.index(calib_wh[0])].append(calib_wh[2]) # h calib_plot_list = [calib_cls_list, calib_plot_w_list, calib_plot_h_list] area = np.pi * 2**2 if plot_idx is None: for i in range(len(plot_list[0])): cls = plot_list[0][i][0] x = plot_list[1][i] # w y = plot_list[2][i] # h plt.scatter(x, y, s=area, c=colors[ids_or_names.index(cls)], alpha=0.1, label=cls) calib_cls = calib_plot_list[0][i][0] calib_x = calib_plot_list[1][i] # w calib_y = calib_plot_list[2][i] # h plt.scatter(calib_x, calib_y, s=area, marker='x', c=colors[ids_or_names.index(calib_cls)], alpha=0.5) else: cls = plot_list[0][plot_idx][0] x = plot_list[1][plot_idx] # w y = plot_list[2][plot_idx] # h calib_cls = calib_plot_list[0][plot_idx][0] calib_x = calib_plot_list[1][plot_idx] # w calib_y = calib_plot_list[2][plot_idx] # h if type(cls) is str: plt.scatter(x, y, s=area, c=colors[ids_or_names.index(cls)], alpha=0.1, label='trainset_cls:'+cls) plt.scatter(calib_x, calib_y, marker='x', c=colors[ids_or_names.index(calib_cls) + 1], alpha=0.3, label='calib_cls:'+calib_cls) elif type(cls) is int: plt.scatter(x, y, s=area, c=colors[ids_or_names.index(cls)], alpha=0.1, label='trainset_cls:'+str(cls)) plt.scatter(calib_x, calib_y, s=area, marker='x', c=colors[ids_or_names.index(calib_cls) + 1], alpha=0.3, label='calib_cls:'+str(calib_cls)) line_color = colors[ids_or_names.index(calib_cls) + 1] plt.plot([np.min(calib_x),np.max(calib_x)], [np.min(calib_y),	np.min(calib_y)	numpy.min
import numpy as np from scipy import interpolate from sklearn.model_selection import KFold def evaluate(distances, labels, nrof_folds=10): thresholds = np.arange(0, 4, 0.01) tpr, fpr, accuracy, best_thresholds = calculate_roc(thresholds, distances, labels, nrof_folds=nrof_folds) thresholds = np.arange(0, 4, 0.001) val, val_std, far = calculate_val(thresholds, distances, labels, 1e-3, nrof_folds=nrof_folds) return tpr, fpr, accuracy, val, val_std, far, best_thresholds def calculate_roc(thresholds, distances, labels, nrof_folds=10): nrof_pairs = min(len(labels), len(distances)) nrof_thresholds = len(thresholds) k_fold = KFold(n_splits=nrof_folds, shuffle=False) tprs = np.zeros((nrof_folds,nrof_thresholds)) fprs = np.zeros((nrof_folds,nrof_thresholds)) accuracy = np.zeros((nrof_folds)) indices = np.arange(nrof_pairs) for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): # Find the best threshold for the fold acc_train = np.zeros((nrof_thresholds)) for threshold_idx, threshold in enumerate(thresholds): _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, distances[train_set], labels[train_set]) best_threshold_index = np.argmax(acc_train) for threshold_idx, threshold in enumerate(thresholds): tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, distances[test_set], labels[test_set]) _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], distances[test_set], labels[test_set]) tpr = np.mean(tprs,0) fpr = np.mean(fprs,0) return tpr, fpr, accuracy, thresholds[best_threshold_index] def calculate_accuracy(threshold, dist, actual_issame): predict_issame = np.less(dist, threshold) tp = np.sum(np.logical_and(predict_issame, actual_issame)) fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame))) fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame)) tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn) fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn) acc = float(tp+tn)/dist.size return tpr, fpr, acc def calculate_val(thresholds, distances, labels, far_target=1e-3, nrof_folds=10): nrof_pairs = min(len(labels), len(distances)) nrof_thresholds = len(thresholds) k_fold = KFold(n_splits=nrof_folds, shuffle=False) val = np.zeros(nrof_folds) far = np.zeros(nrof_folds) indices = np.arange(nrof_pairs) for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): far_train = np.zeros(nrof_thresholds) for threshold_idx, threshold in enumerate(thresholds): _, far_train[threshold_idx] = calculate_val_far(threshold, distances[train_set], labels[train_set]) if np.max(far_train)>=far_target: f = interpolate.interp1d(far_train, thresholds, kind='slinear') threshold = f(far_target) else: threshold = 0.0 val[fold_idx], far[fold_idx] = calculate_val_far(threshold, distances[test_set], labels[test_set]) val_mean =	np.mean(val)	numpy.mean
#!/usr/bin/env python # Copyright 2021 # author: <NAME> <<EMAIL>> # # 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 numpy as np from scipy.stats import ttest_ind import netCDF4 as nc import pickle import os from PIL import Image as PIL_Image import sys import shutil import glob import datetime import time import calendar from numpy import genfromtxt from scipy.optimize import curve_fit from scipy.cluster.vq import kmeans,vq from scipy.interpolate import interpn, interp1d from math import e as e_constant import math import matplotlib.dates as mdates from mpl_toolkits.mplot3d import Axes3D import matplotlib.cm as cm from matplotlib.collections import LineCollection from matplotlib.ticker import (MultipleLocator, NullFormatter, ScalarFormatter) from matplotlib.colors import ListedColormap, BoundaryNorm import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation import matplotlib import warnings warnings.filterwarnings("ignore") plt.style.use('classic') # font size # font_size = 14 # matplotlib.rc('font', *{'family': 'serif', 'serif': ['Arial'], 'size': font_size}) # matplotlib.rc('font', weight='bold') p_progress_writing = False SMALL_SIZE = 8 MEDIUM_SIZE = 10 BIGGER_SIZE = 12 plt.rc('font', size=SMALL_SIZE) # controls default text sizes plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title time_format = '%d-%m-%Y_%H:%M' time_format_khan = '%Y%m%d.0%H' time_format_mod = '%Y-%m-%d_%H:%M:%S' time_format_twolines = '%H:%M\n%d-%m-%Y' time_format_twolines_noYear_noMin_intMonth = '%H\n%d-%m' time_format_twolines_noYear = '%H:%M\n%d-%b' time_format_twolines_noYear_noMin = '%H\n%d-%b' time_format_date = '%Y-%m-%d' time_format_time = '%H:%M:%S' time_format_parsivel = '%Y%m%d%H%M' time_format_parsivel_seconds = '%Y%m%d%H%M%S' time_str_formats = [ time_format, time_format_mod, time_format_twolines, time_format_twolines_noYear, time_format_date, time_format_time, time_format_parsivel ] default_cm = cm.jet cm_vir = cm.viridis listed_cm_colors_list = ['silver', 'red', 'green', 'yellow', 'blue', 'black'] listed_cm = ListedColormap(listed_cm_colors_list, 'indexed') colorbar_tick_labels_list_cloud_phase = ['Clear', 'Water', 'SLW', 'Mixed', 'Ice', 'Unknown'] listed_cm_colors_list_cloud_phase = ['white', 'red', 'green', 'yellow', 'blue', 'purple'] listed_cm_cloud_phase = ListedColormap(listed_cm_colors_list_cloud_phase, 'indexed') avogadros_ = 6.022140857E+23 # molecules/mol gas_const = 83144.598 # cm3 mbar k-1 mol-1 gas_const_2 = 8.3144621 # J mol-1 K-1 gas_const_water = 461 # J kg-1 K-1 gas_const_dry = 287 # J kg-1 K-1 boltzmann_ = gas_const / avogadros_ # cm3 mbar / k molecules gravity_ = 9.80665 # m/s poisson_ = 2/7 # for dry air (k) latent_heat_v = 2.501E+6 # J/kg latent_heat_f = 3.337E+5 # J/kg latent_heat_s = 2.834E+6 # J/kg heat_capacity__Cp = 1005.7 # J kg-1 K-1 dry air heat_capacity__Cv = 719 # J kg-1 K-1 water vapor Rs_da = 287.05 # Specific gas const for dry air, J kg^{-1} K^{-1} Rs_v = 461.51 # Specific gas const for water vapour, J kg^{-1} K^{-1} Cp_da = 1004.6 # Specific heat at constant pressure for dry air Cv_da = 719. # Specific heat at constant volume for dry air Cp_v = 1870. # Specific heat at constant pressure for water vapour Cv_v = 1410. # Specific heat at constant volume for water vapour Cp_lw = 4218 # Specific heat at constant pressure for liquid water Epsilon = 0.622 # Epsilon=Rs_da/Rs_v; The ratio of the gas constants degCtoK = 273.15 # Temperature offset between K and C (deg C) rho_w = 1000. # Liquid Water density kg m^{-3} grav = 9.80665 # Gravity, m s^{-2} Lv = 2.5e6 # Latent Heat of vaporisation boltzmann = 5.67e-8 # Stefan-Boltzmann constant mv = 18.0153e-3 # Mean molar mass of water vapor(kg/mol) m_a = 28.9644e-3 # Mean molar mass of air(kg/mol) Rstar_a = 8.31432 # Universal gas constant for air (N m /(mol K)) path_output = '/g/data/k10/la6753/' # Misc class Object_create(object): pass def list_files_recursive(path_, filter_str=None): # create list of raw spectra files file_list = [] # r=root, d=directories, f = files if filter_str is None: for r, d, f in os.walk(path_): for file in f: file_list.append(os.path.join(r, file)) else: for r, d, f in os.walk(path_): for file in f: if filter_str in file: file_list.append(os.path.join(r, file)) return file_list def list_files(path_, filter_str=''): file_list = sorted(glob.glob(str(path_ + filter_str))) return file_list def coincidence(arr_1,arr_2): # only coincidences check_ = arr_1 * arr_2 check_[check_ == check_] = 1 arr_1_checked = arr_1 * check_ arr_2_checked = arr_2 * check_ return arr_1_checked[~np.isnan(arr_1_checked)], arr_2_checked[~np.isnan(arr_2_checked)] def array_2d_fill_gaps_by_interpolation_linear(array_): rows_ = array_.shape[0] cols_ = array_.shape[1] output_array_X = np.zeros((rows_, cols_), dtype=float) output_array_Y = np.zeros((rows_, cols_), dtype=float) row_sum = np.sum(array_, axis=1) col_index = np.arange(array_.shape[1]) col_sum = np.sum(array_, axis=0) row_index = np.arange(array_.shape[0]) for r_ in range(array_.shape[0]): if row_sum[r_] != row_sum[r_]: # get X direction interpolation coin_out = coincidence(col_index, array_[r_, :]) output_array_X[r_, :][np.isnan(array_[r_, :])] = np.interp( col_index[np.isnan(array_[r_, :])], coin_out[0], coin_out[1]) for c_ in range(array_.shape[1]): if col_sum[c_] != col_sum[c_]: # get Y direction interpolation coin_out = coincidence(row_index, array_[:, c_]) output_array_Y[:, c_][np.isnan(array_[:, c_])] = np.interp( row_index[np.isnan(array_[:, c_])], coin_out[0], coin_out[1]) output_array = np.array(array_) output_array[np.isnan(array_)] = 0 return output_array + ((output_array_X + output_array_Y)/2) def array_2d_fill_gaps_by_interpolation_cubic(array_): rows_ = array_.shape[0] cols_ = array_.shape[1] output_array_X = np.zeros((rows_, cols_), dtype=float) output_array_Y = np.zeros((rows_, cols_), dtype=float) row_sum = np.sum(array_, axis=1) col_index = np.arange(array_.shape[1]) col_sum = np.sum(array_, axis=0) row_index = np.arange(array_.shape[0]) for r_ in range(array_.shape[0]): if row_sum[r_] != row_sum[r_]: # get X direction interpolation coin_out = coincidence(col_index, array_[r_, :]) interp_function = interp1d(coin_out[0], coin_out[1], kind='cubic') output_array_X[r_, :][np.isnan(array_[r_, :])] = interp_function(col_index[np.isnan(array_[r_, :])]) for c_ in range(array_.shape[1]): if col_sum[c_] != col_sum[c_]: # get Y direction interpolation coin_out = coincidence(row_index, array_[:, c_]) interp_function = interp1d(coin_out[0], coin_out[1], kind='cubic') output_array_Y[:, c_][np.isnan(array_[:, c_])] = interp_function(row_index[np.isnan(array_[:, c_])]) output_array = np.array(array_) output_array[np.isnan(array_)] = 0 return output_array + ((output_array_X + output_array_Y)/2) def combine_2_time_series(time_1_reference, data_1, time_2, data_2, forced_time_step=None, forced_start_time=None, forced_stop_time=None, cumulative_var_1=False, cumulative_var_2=False): """ takes two data sets with respective time series, and outputs the coincident stamps from both data sets It does this by using mean_discrete() for both sets with the same start stamp and averaging time, the averaging time is the forced_time_step :param time_1_reference: 1D array, same units as time_2, this series will define the returned time step reference :param data_1: can be 1D or 2D array, first dimention most be same as time_1 :param time_2: 1D array, same units as time_1 :param data_2: can be 1D or 2D array, first dimention most be same as time_2 :param window_: optional, if 0 (default) the values at time_1 and time_2 most match exactly, else, the match can be +- window_ :param forced_time_step: if not none, the median of the differential of the time_1_reference will be used :param forced_start_time: if not none, the returned series will start at this time stamp :param forced_stop_time: if not none, the returned series will stop at this time stamp :param cumulative_var_1: True is you want the variable to be accumulated instead of means, only of 1D data :param cumulative_var_2: True is you want the variable to be accumulated instead of means, only of 1D data :return: Index_averaged_1: 1D array, smallest coincident time, without time stamp gaps :return: Values_mean_1: same shape as data_1 both according to Index_averaged_1 times :return: Values_mean_2: same shape as data_2 both according to Index_averaged_1 times """ # define forced_time_step if forced_time_step is None: forced_time_step = np.median(np.diff(time_1_reference)) # find time period if forced_start_time is None: first_time_stamp = max(np.nanmin(time_1_reference), np.nanmin(time_2)) else: first_time_stamp = forced_start_time if forced_stop_time is None: last_time_stamp = min(np.nanmax(time_1_reference), np.nanmax(time_2)) else: last_time_stamp = forced_stop_time # do the averaging print('starting averaging of data 1') if cumulative_var_1: Index_averaged_1, Values_mean_1 = mean_discrete(time_1_reference, data_1, forced_time_step, first_time_stamp, last_index=last_time_stamp, cumulative_parameter_indx=0) else: Index_averaged_1, Values_mean_1 = mean_discrete(time_1_reference, data_1, forced_time_step, first_time_stamp, last_index=last_time_stamp) print('starting averaging of data 2') if cumulative_var_2: Index_averaged_2, Values_mean_2 = mean_discrete(time_2, data_2, forced_time_step, first_time_stamp, last_index=last_time_stamp, cumulative_parameter_indx=0) else: Index_averaged_2, Values_mean_2 = mean_discrete(time_2, data_2, forced_time_step, first_time_stamp, last_index=last_time_stamp) # check that averaged indexes are the same if np.nansum(np.abs(Index_averaged_1 - Index_averaged_2)) != 0: print('error during averaging of series, times do no match ????') return None, None, None # return the combined, trimmed data return Index_averaged_1, Values_mean_1, Values_mean_2 def split_str_chunks(s, n): """Produce `n`-character chunks from `s`.""" out_list = [] for start in range(0, len(s), n): out_list.append(s[start:start+n]) return out_list def coincidence_multi(array_list): # only coincidences parameters_list = array_list check_ = parameters_list[0] for param_ in parameters_list[1:]: check_ = check_ * param_ check_[check_ == check_] = 1 new_arr_list = [] for param_ in parameters_list: new_arr_list.append(param_ * check_) check_ = check_ * param_ # delete empty rows_ list_list = [] for param_ in parameters_list: t_list = [] for i in range(check_.shape[0]): if check_[i] == check_[i]: t_list.append(param_[i]) list_list.append(t_list) # concatenate ar_list = [] for ii in range(len(parameters_list)): ar_list.append(np.array(list_list[ii])) return ar_list def coincidence_zero(arr_1,arr_2): # only coincidences check_ = arr_1 * arr_2 # delete empty rows_ list_1 = [] list_2 = [] for i in range(check_.shape[0]): if check_[i] != 0: list_1.append(arr_1[i]) list_2.append(arr_2[i]) return np.array(list_1),np.array(list_2) def discriminate(X_, Y_, Z_, value_disc_list, discrmnt_invert_bin = False): if discrmnt_invert_bin: Z_mask = np.ones(Z_.shape[0]) Z_mask[Z_ > value_disc_list[0]] = np.nan Z_mask[Z_ >= value_disc_list[1]] = 1 Y_new = Y_ * Z_mask X_new = X_ * Z_mask else: Z_mask = np.ones(Z_.shape[0]) Z_mask[Z_ < value_disc_list[0]] = np.nan Z_mask[Z_ > value_disc_list[1]] = np.nan Y_new = Y_ * Z_mask X_new = X_ * Z_mask return X_new, Y_new def add_ratio_to_values(header_, values_, nominator_index, denominator_index, ratio_name, normalization_value=1.): nominator_data = values_[:,nominator_index] denominator_data = values_[:,denominator_index] ratio_ = normalization_value * nominator_data / denominator_data values_new = np.column_stack((values_,ratio_)) header_new = np.append(header_,ratio_name) return header_new, values_new def bin_data(x_val_org,y_val_org, start_bin_edge=0, bin_size=1, min_bin_population=1): # get coincidences only x_val,y_val = coincidence(x_val_org,y_val_org) # combine x and y in matrix M_ = np.column_stack((x_val,y_val)) # checking if always ascending to increase efficiency always_ascending = 1 for x in range(x_val.shape[0]-1): if x_val[x]==x_val[x] and x_val[x+1]==x_val[x+1]: if x_val[x+1] < x_val[x]: always_ascending = 0 if always_ascending == 0: M_sorted = M_[M_[:,0].argsort()] # sort by first column M_ = M_sorted # convert data to list of bins y_binned = [] x_binned = [] last_row = 0 last_row_temp = last_row while start_bin_edge <= np.nanmax(x_val): y_val_list = [] for row_ in range(last_row, M_.shape[0]): if start_bin_edge <= M_[row_, 0] < start_bin_edge + bin_size: if M_[row_, 1] == M_[row_, 1]: y_val_list.append(M_[row_, 1]) last_row_temp = row_ if M_[row_, 0] >= start_bin_edge + bin_size: last_row_temp = row_ break x_binned.append(start_bin_edge) if len(y_val_list) >= min_bin_population: y_binned.append(y_val_list) else: y_binned.append([]) start_bin_edge += bin_size last_row = last_row_temp # add series if bin_size >= 1: x_binned_int = np.array(x_binned, dtype=int) else: x_binned_int = x_binned return x_binned_int, y_binned def shiftedColorMap(cmap, midpoint=0.5, name='shiftedcmap'): cdict = { 'red': [], 'green': [], 'blue': [], 'alpha': [] } # regular index to compute the colors reg_index = np.linspace(0, 1, 257) # shifted index to match the data shift_index = np.hstack([ np.linspace(0.0, midpoint, 128, endpoint=False), np.linspace(midpoint, 1.0, 129, endpoint=True) ]) for ri, si in zip(reg_index, shift_index): r, g, b, a = cmap(ri) cdict['red'].append((si, r, r)) cdict['green'].append((si, g, g)) cdict['blue'].append((si, b, b)) cdict['alpha'].append((si, a, a)) newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict) plt.register_cmap(cmap=newcmap) return newcmap def student_t_test(arr_1, arr_2): return ttest_ind(arr_1, arr_2, nan_policy='omit') def k_means_clusters(array_, cluster_number, forced_centers=None): if forced_centers is None: centers_, x = kmeans(array_,cluster_number) data_id, x = vq(array_, centers_) return centers_, data_id else: data_id, x = vq(array_, forced_centers) return forced_centers, data_id def grid_(x, y, z, resX=100, resY=100): "Convert 3 column data to matplotlib grid" xi = np.linspace(min(x), max(x), resX) yi = np.linspace(min(y), max(y), resY) Z = matplotlib.mlab.griddata(x, y, z, xi, yi) X, Y = np.meshgrid(xi, yi) return X, Y, Z def find_max_index_2d_array(array_): return np.unravel_index(np.argmax(array_, axis=None), array_.shape) def find_min_index_2d_array(array_): return np.unravel_index(np.argmin(array_, axis=None), array_.shape) def find_max_index_1d_array(array_): return np.argmax(array_, axis=None) def find_min_index_1d_array(array_): return np.argmin(array_, axis=None) def time_series_interpolate_discrete(Index_, Values_, index_step, first_index, position_=0., last_index=None): """ this will average values from Values_ that are between Index_[n:n+avr_size) :param Index_: n by 1 numpy array to look for position, :param Values_: n by m numpy array, values to be averaged :param index_step: in same units as Index_ :param first_index: is the first discrete index on new arrays. :param position_: will determine where is the stamp located; 0 = beginning, .5 = mid, 1 = top (optional, default = 0) :param last_index: in case you want to force the returned series to some fixed period/length :return: Index_averaged, Values_averaged """ # checking if always ascending to increase efficiency always_ascending = 1 for x in range(Index_.shape[0]-1): if Index_[x]==Index_[x] and Index_[x+1]==Index_[x+1]: if Index_[x+1] < Index_[x]: always_ascending = 0 if always_ascending == 0: MM_ = np.column_stack((Index_,Values_)) MM_sorted = MM_[MM_[:,0].argsort()] # sort by first column Index_ = MM_sorted[:,0] Values_ = MM_sorted[:,1:] # error checking! if Index_.shape[0] != Values_.shape[0]: print('error during shape check! Index_.shape[0] != Values_.shape[0]') return None, None if Index_[-1] < first_index: print('error during shape check! Index_[-1] < first_index') return None, None # initialize output matrices if last_index is None: final_index = np.nanmax(Index_) else: final_index = last_index total_averaged_rows = int((final_index-first_index) / index_step) + 1 if len(Values_.shape) == 1: Values_mean = np.zeros(total_averaged_rows) Values_mean[:] = np.nan else: Values_mean = np.zeros((total_averaged_rows,Values_.shape[1])) Values_mean[:,:] = np.nan Index_interp = np.zeros(total_averaged_rows) for r_ in range(total_averaged_rows): Index_interp[r_] = first_index + (r_ * index_step) Index_interp -= (position_ * index_step) Values_interp = np.interp(Index_interp, Index_, Values_) Index_interp = Index_interp + (position_ * index_step) return Index_interp, Values_interp def array_2D_sort_ascending_by_column(array_, column_=0): array_sorted = array_[array_[:, column_].argsort()] return array_sorted def get_ax_range(ax): x_1 = ax.axis()[0] x_2 = ax.axis()[1] y_1 = ax.axis()[2] y_2 = ax.axis()[3] return x_1, x_2, y_1, y_2 def get_array_perimeter_only(array_): return np.concatenate([array_[0, :-1], array_[:-1, -1], array_[-1, ::-1], array_[-2:0:-1, 0]]) # WRF def wrf_var_search(wrf_nc_file, description_str): description_str_lower = description_str.lower() for var_ in sorted(wrf_nc_file.variables): try: if description_str_lower in wrf_nc_file.variables[var_].description.lower(): print(var_, '\|', wrf_nc_file.variables[var_].description) except: pass def create_virtual_sonde_from_wrf(sonde_dict, filelist_wrf_output, wrf_filename_time_format = 'wrfout_d03_%Y-%m-%d_%H_%M_%S'): # create time array filelist_wrf_output_noPath = [] for filename_ in filelist_wrf_output: filelist_wrf_output_noPath.append(filename_.split('/')[-1]) wrf_time_file_list = np.array(time_str_to_seconds(filelist_wrf_output_noPath, wrf_filename_time_format)) # create lat and lon arrays wrf_domain_file = nc.Dataset(filelist_wrf_output[0]) # p(sorted(wrf_domain_file.variables)) # wrf_vars = sorted(wrf_domain_file.variables) # for i_ in range(len(wrf_vars)): # try: # print(wrf_vars[i_], '\t\t', wrf_domain_file.variables[wrf_vars[i_]].description) # except: # print(wrf_vars[i_]) wrf_lat = wrf_domain_file.variables['XLAT'][0, :, :].filled(np.nan) wrf_lon = wrf_domain_file.variables['XLONG'][0, :, :].filled(np.nan) wrf_lat_U = wrf_domain_file.variables['XLAT_U'][0, :, :].filled(np.nan) wrf_lon_U = wrf_domain_file.variables['XLONG_U'][0, :, :].filled(np.nan) wrf_lat_V = wrf_domain_file.variables['XLAT_V'][0, :, :].filled(np.nan) wrf_lon_V = wrf_domain_file.variables['XLONG_V'][0, :, :].filled(np.nan) wrf_domain_file.close() # load sonde's profile sonde_hght = sonde_dict['hght'] # m ASL sonde_pres = sonde_dict['pres'] # hPa sonde_time = sonde_dict['time'] # seconds since epoc sonde_lati = sonde_dict['lati'] # degrees sonde_long = sonde_dict['long'] # degrees # create output lists of virtual sonde list_p__ = [] list_hgh = [] list_th_ = [] list_th0 = [] list_qv_ = [] list_U__ = [] list_V__ = [] list_tim = [] list_lat = [] list_lon = [] wrf_point_abs_address_old = 0 # loop thru real sonde's points for t_ in range(sonde_hght.shape[0]): p_progress_bar(t_, sonde_hght.shape[0]) point_hght = sonde_hght[t_] point_pres = sonde_pres[t_] point_time = sonde_time[t_] point_lati = sonde_lati[t_] point_long = sonde_long[t_] # find closest cell via lat, lon index_tuple = find_index_from_lat_lon_2D_arrays(wrf_lat,wrf_lon, point_lati,point_long) index_tuple_U = find_index_from_lat_lon_2D_arrays(wrf_lat_U,wrf_lon_U, point_lati,point_long) index_tuple_V = find_index_from_lat_lon_2D_arrays(wrf_lat_V,wrf_lon_V, point_lati,point_long) # find closest file via time file_index = time_to_row_sec(wrf_time_file_list, point_time) # open wrf file wrf_domain_file = nc.Dataset(filelist_wrf_output[file_index]) # get pressure array from wrf wrf_press = (wrf_domain_file.variables['PB'][0, :, index_tuple[0], index_tuple[1]].data + wrf_domain_file.variables['P'][0, :, index_tuple[0], index_tuple[1]].data) / 100 # hPa # find closest model layer via pressure layer_index = find_min_index_1d_array(np.abs(wrf_press - point_pres)) # define point absolute address and check if it is a new point wrf_point_abs_address_new = (index_tuple[0], index_tuple[1], file_index, layer_index) if wrf_point_abs_address_new != wrf_point_abs_address_old: wrf_point_abs_address_old = wrf_point_abs_address_new # get wrf data index_tuple_full = (0, layer_index, index_tuple[0], index_tuple[1]) index_tuple_full_U = (0, layer_index, index_tuple_U[0], index_tuple_U[1]) index_tuple_full_V = (0, layer_index, index_tuple_V[0], index_tuple_V[1]) # save to arrays list_p__.append(float(wrf_press[layer_index])) list_hgh.append(float(point_hght)) list_th_.append(float(wrf_domain_file.variables['T'][index_tuple_full])) list_th0.append(float(wrf_domain_file.variables['T00'][0])) list_qv_.append(float(wrf_domain_file.variables['QVAPOR'][index_tuple_full])) list_U__.append(float(wrf_domain_file.variables['U'][index_tuple_full_U])) list_V__.append(float(wrf_domain_file.variables['V'][index_tuple_full_V])) list_tim.append(float(wrf_time_file_list[file_index])) list_lat.append(float(wrf_lat[index_tuple[0], index_tuple[1]])) list_lon.append(float(wrf_lon[index_tuple[0], index_tuple[1]])) wrf_domain_file.close() # convert lists to arrays array_p__ = np.array(list_p__) array_hgh = np.array(list_hgh) array_th_ = np.array(list_th_) array_th0 = np.array(list_th0) array_qv_ = np.array(list_qv_) array_U__ = np.array(list_U__) array_V__ = np.array(list_V__) array_tim = np.array(list_tim) array_lat = np.array(list_lat) array_lon = np.array(list_lon) # calculate derivative variables wrf_temp_K = calculate_temperature_from_potential_temperature(array_th_ + array_th0, array_p__) wrf_temp_C = kelvin_to_celsius(wrf_temp_K) wrf_e = MixR2VaporPress(array_qv_, array_p__100) wrf_td_C = DewPoint(wrf_e) wrf_td_C[wrf_td_C > wrf_temp_C] = wrf_temp_C[wrf_td_C > wrf_temp_C] wrf_RH = calculate_RH_from_QV_T_P(array_qv_, wrf_temp_K, array_p__100) wrf_WD, wrf_WS = cart_to_polar(array_V__, array_U__) wrf_WD_met = wrf_WD + 180 wrf_WD_met[wrf_WD_met >= 360] = wrf_WD_met[wrf_WD_met >= 360] - 360 wrf_WS_knots = ws_ms_to_knots(wrf_WS) # create virtual sonde dict wrf_sonde_dict = {} wrf_sonde_dict['hght'] = array_hgh wrf_sonde_dict['pres'] = array_p__ wrf_sonde_dict['temp'] = wrf_temp_C wrf_sonde_dict['dwpt'] = wrf_td_C wrf_sonde_dict['sknt'] = wrf_WS_knots wrf_sonde_dict['drct'] = wrf_WD_met wrf_sonde_dict['relh'] = wrf_RH wrf_sonde_dict['time'] = array_tim wrf_sonde_dict['lati'] = array_lat wrf_sonde_dict['long'] = array_lon return wrf_sonde_dict def wrf_get_temp_K(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_press = (wrf_domain_file.variables['PB'][0, :, :, :].data + wrf_domain_file.variables['P'][0, :, :, :].data) / 100 # hPa wrf_theta = (wrf_domain_file.variables['T'][0, :, :, :].data + wrf_domain_file.variables['T00'][0].data) # K wrf_temp_K = calculate_temperature_from_potential_temperature(wrf_theta, wrf_press) if original_arg_type_str: wrf_domain_file.close() return wrf_temp_K def wrf_get_press_hPa(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_press = (wrf_domain_file.variables['PB'][0, :, :, :].data + wrf_domain_file.variables['P'][0, :, :, :].data) / 100 # hPa if original_arg_type_str: wrf_domain_file.close() return wrf_press def wrf_get_height_m(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_height = (wrf_domain_file.variables['PH'][0,:-1,:,:].data + wrf_domain_file.variables['PHB'][0,:-1,:,:].data) / gravity_ if original_arg_type_str: wrf_domain_file.close() return wrf_height def wrf_get_terrain_height_m(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_height = (wrf_domain_file.variables['PH'][0,0,:,:].data + wrf_domain_file.variables['PHB'][0,0,:,:].data) / gravity_ if original_arg_type_str: wrf_domain_file.close() return wrf_height def wrf_get_water_vapor_mixing_ratio(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_QVAPOR = wrf_domain_file.variables['QVAPOR'][0,:,:,:].data if original_arg_type_str: wrf_domain_file.close() return wrf_QVAPOR def wrf_get_cloud_water_mixing_ratio(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_QCLOUD = wrf_domain_file.variables['QCLOUD'][0,:,:,:].data if original_arg_type_str: wrf_domain_file.close() return wrf_QCLOUD def wrf_get_ice_mixing_ratio(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_QICE = wrf_domain_file.variables['QICE'][0,:,:,:].data if original_arg_type_str: wrf_domain_file.close() return wrf_QICE def wrf_get_lat_lon(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_lat = wrf_domain_file.variables['XLAT'][0, :, :].filled(np.nan) wrf_lon = wrf_domain_file.variables['XLONG'][0, :, :].filled(np.nan) if original_arg_type_str: wrf_domain_file.close() return wrf_lat, wrf_lon def wrf_rename_files_fix_time_format(filename_original_list, original_character=':', replacement_character='_'): for i_, filename_ in enumerate(filename_original_list): p_progress_bar(i_, len(filename_original_list)) new_filename = filename_.replace(original_character,replacement_character) os.rename(filename_, new_filename) # meteorology def calculate_saturation_vapor_pressure_wexler(T_array_K): # result in mb (hPa) G0 = -0.29912729E+4 G1 = -0.60170128E+4 G2 = 0.1887643854E+2 G3 = -0.28354721E-1 G4 = 0.17838301E-4 G5 = -0.84150417E-9 G6 = 0.44412543E-12 G7 = 0.2858487E+1 e_s = np.exp((G0 * (T_array_K ** -2)) + (G1 * (T_array_K ** -1)) + G2 + (G3 * T_array_K) + (G4 * (T_array_K ** 2)) + (G5 * (T_array_K ** 3)) + (G6 * (T_array_K ** 4)) + (G7 * np.log(T_array_K))) return e_s * 0.01 def calculate_saturation_mixing_ratio(P_array_mb, T_array_K): e_s = calculate_saturation_vapor_pressure_wexler(T_array_K) q_s = 621.97 * (e_s / (P_array_mb - e_s)) return q_s def calculate_potential_temperature(T_array_K, P_array_hPa): potential_temp = T_array_K * ((1000 / P_array_hPa) ** poisson_) return potential_temp def calculate_equivalent_potential_temperature(T_array_K, P_array_hPa, R_array_kg_over_kg): P_o = 1000 T_e = T_array_K + (latent_heat_v * R_array_kg_over_kg / heat_capacity__Cp) theta_e = T_e * ((P_o/P_array_hPa)*poisson_) return theta_e def calculate_temperature_from_potential_temperature(theta_array_K, P_array_hPa): temperature_ = theta_array_K ( (P_array_hPa/1000) ** poisson_ ) return temperature_ def calculate_mountain_height_from_sonde(sonde_dict): """ calculates H_hat from given values of u_array, v_array, T_array, effective_height, rh_array, q_array, p_array """ # Set initial conditions height = 1000 # metres # define arrays WS_array = ws_knots_to_ms(sonde_dict['SKNT']) U_array, V_array = polar_to_cart(sonde_dict['DRCT'], WS_array) T_array = celsius_to_kelvin(sonde_dict['TEMP']) RH_array = sonde_dict['RELH'] P_array = sonde_dict['PRES'] Z_array = sonde_dict['HGHT'] Q_array = sonde_dict['MIXR']/1000 TH_array = sonde_dict['THTA'] # calculated arrays q_s = calculate_saturation_mixing_ratio(P_array, T_array) e_ = gas_const_dry / gas_const_water # gradients d_ln_TH = np.gradient(np.log(TH_array)) d_z = np.gradient(Z_array) d_q_s = np.gradient(q_s) # Dry Brunt - Vaisala N_dry = gravity_ * d_ln_TH / d_z N_dry[RH_array >= 90] = 0 # Moist Brunt - Vaisala term_1_1 = 1 + ( latent_heat_v * q_s / (gas_const_dry * T_array) ) term_1_2 = 1 + ( e_ * (latent_heat_v*2) q_s / (heat_capacity__Cp * gas_const_dry * (T_array*2) ) ) term_2_1 = d_ln_TH / d_z term_2_2 = latent_heat_v / (heat_capacity__Cp T_array) term_2_3 = d_q_s / d_z term_3 = d_q_s / d_z # should be d_q_w but sonde data has no cloud water data N_moist = gravity_ * ( (term_1_1 / term_1_2) * (term_2_1 + ( term_2_2 * term_2_3) ) - term_3 ) N_moist[RH_array < 90] = 0 # define output array N_2 = (N_dry + N_moist)*2 H_hat_2 = N_2 (height2) / (U_array2) return H_hat_2 def calculate_mountain_height_from_era5(era5_pressures_filename, era5_surface_filename, point_lat, point_lon, return_arrays=False, u_wind_mode='u', range_line_degrees=None, time_start_str_YYYYmmDDHHMM='',time_stop_str_YYYYmmDDHHMM='', reference_height=1000, return_debug_arrays=False): """ calculates H_hat from given values of u_array, v_array, T_array, effective_height, rh_array, q_array, p_array u_wind_mode: can be u, wind_speed, normal_to_range. If normal_to_range, then range_line most not be none if range_line_degrees is not None, u_wind_mode will automatically be set to normal_to_range range_line_degrees: degress (decimals) from north, clockwise, of the mountain range line. """ # load files era5_sur = nc.Dataset(era5_surface_filename, 'r') era5_pre = nc.Dataset(era5_pressures_filename, 'r') # check if times are the same for both files dif_sum = np.sum(np.abs(era5_pre.variables['time'][:] - era5_sur.variables['time'][:])) if dif_sum > 0: print('Error, times in selected files are not the same') return # check if lat lon are the same for both files dif_sum = np.sum(np.abs(era5_pre.variables['latitude'][:] - era5_sur.variables['latitude'][:])) dif_sum = dif_sum + np.sum(np.abs(era5_pre.variables['longitude'][:] - era5_sur.variables['longitude'][:])) if dif_sum > 0: print('Error, latitude or longitude in selected files are not the same') return # find lat lon index lat_index, lon_index = find_index_from_lat_lon(era5_sur.variables['latitude'][:], era5_sur.variables['longitude'][:], [point_lat], [point_lon]) lat_index = lat_index[0] lon_index = lon_index[0] # copy arrays time_array = time_era5_to_seconds(np.array(era5_sur.variables['time'][:])) r_1 = 0 r_2 = -1 if time_start_str_YYYYmmDDHHMM != '': r_1 = time_to_row_str(time_array, time_start_str_YYYYmmDDHHMM) if time_stop_str_YYYYmmDDHHMM != '': r_2 = time_to_row_str(time_array, time_stop_str_YYYYmmDDHHMM) time_array = time_array[r_1:r_2] sp_array = np.array(era5_sur.variables['sp'][r_1:r_2, lat_index, lon_index]) / 100 # hPa P_array = np.array(era5_pre.variables['level'][:]) # hPa if range_line_degrees is not None: WD_, WS_ = cart_to_polar(np.array(era5_pre.variables['v'][r_1:r_2,:,lat_index,lon_index]).flatten(), np.array(era5_pre.variables['u'][r_1:r_2,:,lat_index,lon_index]).flatten()) WD_delta = WD_ - range_line_degrees range_normal_component = WS_ * np.sin(np.deg2rad(WD_delta)) U_array = range_normal_component.reshape((sp_array.shape[0], P_array.shape[0])) else: if u_wind_mode == 'u': U_array = np.array(era5_pre.variables['u'][r_1:r_2,:,lat_index,lon_index]) else: U_array = np.sqrt(np.array(era5_pre.variables['v'][r_1:r_2,:,lat_index,lon_index]) 2 + np.array(era5_pre.variables['u'][r_1:r_2,:,lat_index,lon_index]) 2) T_array = np.array(era5_pre.variables['t'][r_1:r_2, :, lat_index, lon_index]) Q_L_array = np.array(era5_pre.variables['crwc'][r_1:r_2, :, lat_index, lon_index]) RH_array = np.array(era5_pre.variables['r'][r_1:r_2, :, lat_index, lon_index]) Z_array = np.array(era5_pre.variables['z'][r_1:r_2, :, lat_index, lon_index]) / gravity_ # calculate arrays TH_array = np.zeros((time_array.shape[0], P_array.shape[0]), dtype=float) for t_ in range(time_array.shape[0]): TH_array[t_,:] = calculate_potential_temperature(T_array[t_,:], P_array[:]) # calculated arrays q_s = calculate_saturation_mixing_ratio(P_array, T_array) e_ = gas_const_dry / gas_const_water # create output dict H_hat_2 = {} # loop tru time stamps for t_ in range(time_array.shape[0]): p_progress_bar(t_,time_array.shape[0]) # find surface pressure at this time stamp surface_p = sp_array[t_] # find pressure at 1000 meters pressure_1000m = np.interp(reference_height, Z_array[t_, :], P_array) pressure_1000m_index = np.argmin(np.abs(P_array - pressure_1000m)) # find extrapolations ql_0 = np.interp(np.log(surface_p), np.log(P_array), Q_L_array[t_, :]) z__0 = np.interp(np.log(surface_p), np.log(P_array), Z_array[t_, :]) th_0 = np.interp(np.log(surface_p), np.log(P_array), TH_array[t_, :]) qs_0 = np.interp(np.log(surface_p), np.log(P_array), q_s[t_, :]) t__1000 = np.interp(reference_height, Z_array[t_, :], T_array[t_, :]) u__1000 = np.interp(reference_height, Z_array[t_, :], U_array[t_, :]) ql_1000 = np.interp(reference_height, Z_array[t_, :], Q_L_array[t_, :]) z__1000 = reference_height th_1000 = np.interp(reference_height, Z_array[t_, :], TH_array[t_, :]) qs_1000 = np.interp(reference_height, Z_array[t_, :], q_s[t_, :]) # gradients d_ln_TH = np.log(th_1000) - np.log(th_0) d_z = z__1000 - z__0 d_q_s = qs_1000 - qs_0 d_q_w = (d_q_s) + (ql_1000 - ql_0) # Brunt - Vaisala if np.max(RH_array[t_, pressure_1000m_index:])>= 90: # Moist term_1_1 = 1 + ( latent_heat_v * qs_1000 / (gas_const_dry * t__1000) ) term_1_2 = 1 + ( e_ * (latent_heat_v*2) qs_1000 / (heat_capacity__Cp * gas_const_dry * (t__1000*2) ) ) term_2_1 = d_ln_TH / d_z term_2_2 = latent_heat_v / (heat_capacity__Cp t__1000) term_2_3 = d_q_s / d_z term_3 = d_q_w / d_z N_2 = gravity_ * ( (term_1_1 / term_1_2) * (term_2_1 + ( term_2_2 * term_2_3) ) - term_3 ) else: # Dry N_2 = gravity_ * d_ln_TH / d_z # populate each time stamp H_hat_2[time_array[t_]] = N_2 * (reference_height 2) / (u__1000 2) era5_sur.close() era5_pre.close() if return_arrays: H_hat_2_time = sorted(H_hat_2.keys()) H_hat_2_time = np.array(H_hat_2_time) H_hat_2_vals = np.zeros(H_hat_2_time.shape[0], dtype=float) for r_ in range(H_hat_2_time.shape[0]): H_hat_2_vals[r_] = H_hat_2[H_hat_2_time[r_]] if return_debug_arrays: return H_hat_2_time, H_hat_2_vals, N_2, u__1000 ** 2 else: return H_hat_2_time, H_hat_2_vals else: return H_hat_2 def calculate_mountain_height_from_WRF(filename_SP, filename_PR, filename_UU, filename_VV, filename_TH, filename_QR, filename_QV, filename_PH, return_arrays=False, u_wind_mode='u', range_line_degrees=None, reference_height=1000): """ calculates H_hat from WRF point output text files u_wind_mode: can be u, wind_speed, normal_to_range. If normal_to_range, then range_line most not be none if range_line_degrees is not None, u_wind_mode will automatically be set to normal_to_range range_line_degrees: degress (decimals) from north, clockwise, of the mountain range line. :param filename_SP: fullpath filename of surface pressure file :param filename_PR: fullpath filename of pressure file :param filename_UU: fullpath filename of u wind file :param filename_VV: fullpath filename of v wind file :param filename_TH: fullpath filename of potential temperature file :param filename_QR: fullpath filename of rain water mixing ratio file :param filename_QV: fullpath filename of Water vapor mixing ratio file :param filename_PH: fullpath filename of geopotential height file :param return_arrays: if true, will return also brunt vaisalla and wind component squared :param u_wind_mode: can be u, wind_speed, normal_to_range. If normal_to_range, then range_line most not be none :param range_line_degrees: if not None, u_wind_mode will automatically be set to normal_to_range :param reference_height: mean height of mountain range :return: H_hat_2 """ # load arrays from text SP_array = genfromtxt(filename_SP, dtype=float, skip_header=1)[:,9] / 100 # hPa PR_array = genfromtxt(filename_PR, dtype=float, skip_header=1)[:,1:] / 100 # hPa UU_array = genfromtxt(filename_UU, dtype=float, skip_header=1)[:,1:] VV_array = genfromtxt(filename_VV, dtype=float, skip_header=1)[:,1:] TH_array = genfromtxt(filename_TH, dtype=float, skip_header=1)[:,1:] QR_array = genfromtxt(filename_QR, dtype=float, skip_header=1)[:,1:] QV_array = genfromtxt(filename_QV, dtype=float, skip_header=1)[:,1:] Z_array = genfromtxt(filename_PH, dtype=float, skip_header=1)[:,1:] # already in meters # calculate arrays if range_line_degrees is not None: WD_, WS_ = cart_to_polar(UU_array.flatten(), VV_array.flatten()) WD_delta = WD_ - range_line_degrees range_normal_component = WS_ * np.sin(np.deg2rad(WD_delta)) U_array = range_normal_component.reshape((UU_array.shape[0], UU_array.shape[1])) else: if u_wind_mode == 'u': U_array = UU_array else: U_array = np.sqrt(UU_array 2 + VV_array 2) T_array = calculate_temperature_from_potential_temperature(TH_array, PR_array) RH_array = calculate_RH_from_QV_T_P(QV_array, T_array, PR_array100) q_s = calculate_saturation_mixing_ratio(PR_array, T_array) e_ = gas_const_dry / gas_const_water # create output array H_hat_2 = np.zeros(PR_array.shape[0], dtype=float) # loop tru time stamps for r_ in range(PR_array.shape[0]): p_progress_bar(r_, PR_array.shape[0]) # find surface pressure at this time stamp surface_p = SP_array[r_] # find pressure at 1000 meters pressure_1000m = np.interp(reference_height, Z_array[r_, :], PR_array[r_, :]) pressure_1000m_index = np.argmin(np.abs(PR_array[r_, :] - pressure_1000m)) # find extrapolations ql_0 = np.interp(np.log(surface_p), np.log(PR_array[r_, :]), QR_array[r_, :]) z__0 = np.interp(np.log(surface_p), np.log(PR_array[r_, :]), Z_array[r_, :]) th_0 = np.interp(np.log(surface_p), np.log(PR_array[r_, :]), TH_array[r_, :]) qs_0 = np.interp(np.log(surface_p), np.log(PR_array[r_, :]), q_s[r_, :]) t__1000 = np.interp(reference_height, Z_array[r_, :], T_array[r_, :]) u__1000 = np.interp(reference_height, Z_array[r_, :], U_array[r_, :]) ql_1000 = np.interp(reference_height, Z_array[r_, :], QR_array[r_, :]) z__1000 = reference_height th_1000 = np.interp(reference_height, Z_array[r_, :], TH_array[r_, :]) qs_1000 = np.interp(reference_height, Z_array[r_, :], q_s[r_, :]) # gradients d_ln_TH = np.log(th_1000) - np.log(th_0) d_z = z__1000 - z__0 d_q_s = qs_1000 - qs_0 d_q_w = (d_q_s) + (ql_1000 - ql_0) # Brunt - Vaisala if np.max(RH_array[r_, pressure_1000m_index:])>= 90: # Moist term_1_1 = 1 + ( latent_heat_v qs_1000 / (gas_const_dry * t__1000) ) term_1_2 = 1 + ( e_ * (latent_heat_v*2) qs_1000 / (heat_capacity__Cp * gas_const_dry * (t__1000*2) ) ) term_2_1 = d_ln_TH / d_z term_2_2 = latent_heat_v / (heat_capacity__Cp t__1000) term_2_3 = d_q_s / d_z term_3 = d_q_w / d_z N_2 = gravity_ * ( (term_1_1 / term_1_2) * (term_2_1 + ( term_2_2 * term_2_3) ) - term_3 ) else: # Dry N_2 = gravity_ * d_ln_TH / d_z # populate each time stamp H_hat_2[r_] = N_2 * (reference_height 2) / (u__1000 2) if return_arrays: return H_hat_2, N_2, u__1000 ** 2 else: return H_hat_2 def calculate_dewpoint_from_T_RH(T_, RH_): """ from Magnus formula, using Bolton's constants :param T_: ambient temperature [Celsius] :param RH_: relative humidity :return: Td_ dew point temperature [celsius] """ a = 6.112 b = 17.67 c = 243.5 y_ = np.log(RH_/100) + ((bT_)/(c+T_)) Td_ = (c y_) / (b - y_) return Td_ def calculate_RH_from_QV_T_P(arr_qvapor, arr_temp_K, arr_press_Pa): tv_ = 6.11 * e_constant*((2500000/461) ((1/273) - (1/arr_temp_K))) pv_ = arr_qvapor * (arr_press_Pa/100) / (arr_qvapor + 0.622) return np.array(100 * pv_ / tv_) def calculate_profile_input_for_cluster_analysis_from_ERA5(p_profile, t_profile, td_profile, q_profile, u_profile, v_profile, h_profile, surface_p): """ takes data from ERA5 for only one time stamp for all pressure levels from 250 to 1000 hPa :param p_profile: in hPa :param t_profile: in Celsius :param td_profile: in Celsius :param q_profile: in kg/kg :param u_profile: in m/s :param v_profile: in m/s :param h_profile: in m :param surface_p: in hPa :return: surface_p, qv_, qu_, tw_, sh_, tt_ """ # trim profiles from surface to top # find which levels should be included levels_total = 0 for i_ in range(p_profile.shape[0]): if p_profile[i_] > surface_p: break levels_total += 1 ####################################### find extrapolations surface_t = np.interp(np.log(surface_p), np.log(p_profile), t_profile) surface_td = np.interp(np.log(surface_p), np.log(p_profile), td_profile) surface_q = np.interp(np.log(surface_p), np.log(p_profile), q_profile) surface_u = np.interp(np.log(surface_p), np.log(p_profile), u_profile) surface_v = np.interp(np.log(surface_p), np.log(p_profile), v_profile) surface_h = np.interp(np.log(surface_p), np.log(p_profile), h_profile) # create temp arrays T_array = np.zeros(levels_total + 1, dtype=float) Td_array = np.zeros(levels_total + 1, dtype=float) Q_array = np.zeros(levels_total + 1, dtype=float) U_array = np.zeros(levels_total + 1, dtype=float) V_array = np.zeros(levels_total + 1, dtype=float) H_array = np.zeros(levels_total + 1, dtype=float) P_array = np.zeros(levels_total + 1, dtype=float) T_array[:levels_total] = t_profile[:levels_total] Td_array[:levels_total] = td_profile[:levels_total] Q_array[:levels_total] = q_profile[:levels_total] U_array[:levels_total] = u_profile[:levels_total] V_array[:levels_total] = v_profile[:levels_total] H_array[:levels_total] = h_profile[:levels_total] P_array[:levels_total] = p_profile[:levels_total] T_array[-1] = surface_t Td_array[-1] = surface_td Q_array[-1] = surface_q U_array[-1] = surface_u V_array[-1] = surface_v H_array[-1] = surface_h P_array[-1] = surface_p ###################################### r_850 = np.argmin(np.abs(P_array - 850)) r_500 = np.argmin(np.abs(P_array - 500)) dp_ = np.abs(np.gradient(P_array)) tt_ = (T_array[r_850] - (2 * T_array[r_500]) + Td_array[r_850]) qu_ = np.sum(Q_array * U_array * dp_) / gravity_ qv_ = np.sum(Q_array * V_array * dp_) / gravity_ tw_ = np.sum(Q_array * dp_) / gravity_ del_u = U_array[r_850] - U_array[r_500] del_v = V_array[r_850] - V_array[r_500] del_z = H_array[r_850] - H_array[r_500] sh_ = ((del_u / del_z) 2 + (del_v / del_z) 2) ** 0.5 return surface_p, qv_, qu_, tw_, sh_, tt_ def barometric_equation(presb_pa, tempb_k, deltah_m, Gamma=-0.0065): """The barometric equation models the change in pressure with height in the atmosphere. INPUTS: presb_k (pa): The base pressure tempb_k (K): The base temperature deltah_m (m): The height differential between the base height and the desired height Gamma [=-0.0065]: The atmospheric lapse rate OUTPUTS pres (pa): Pressure at the requested level REFERENCE: http://en.wikipedia.org/wiki/Barometric_formula """ return presb_pa * \ (tempb_k/(tempb_k+Gammadeltah_m))(gravm_a/(Rstar_aGamma)) def barometric_equation_inv(heightb_m, tempb_k, presb_pa, prest_pa, Gamma=-0.0065): """The barometric equation models the change in pressure with height in the atmosphere. This function returns altitude given initial pressure and base altitude, and pressure change. INPUTS: heightb_m (m): presb_pa (pa): The base pressure tempb_k (K) : The base temperature deltap_pa (m): The pressure differential between the base height and the desired height Gamma [=-0.0065]: The atmospheric lapse rate OUTPUTS heightt_m REFERENCE: http://en.wikipedia.org/wiki/Barometric_formula """ return heightb_m + \ tempb_k ((presb_pa/prest_pa)*(Rstar_aGamma/(gravm_a))-1) / Gamma def Theta(tempk, pres, pref=100000.): """Potential Temperature INPUTS: tempk (K) pres (Pa) pref: Reference pressure (default 100000 Pa) OUTPUTS: Theta (K) Source: Wikipedia Prints a warning if a pressure value below 2000 Pa input, to ensure that the units were input correctly. """ try: minpres = min(pres) except TypeError: minpres = pres if minpres < 2000: print("WARNING: P<2000 Pa; did you input a value in hPa?") return tempk (pref/pres)*(Rs_da/Cp_da) def TempK(theta, pres, pref=100000.): """Inverts Theta function.""" try: minpres = min(pres) except TypeError: minpres = pres if minpres < 2000: print("WARNING: P<2000 Pa; did you input a value in hPa?") return theta (pres/pref)*(Rs_da/Cp_da) def ThetaE(tempk, pres, e): """Calculate Equivalent Potential Temperature for lowest model level (or surface) INPUTS: tempk: Temperature [K] pres: Pressure [Pa] e: Water vapour partial pressure [Pa] OUTPUTS: theta_e: equivalent potential temperature References: Eq. (9.40) from Holton (2004) Eq. (22) from Bolton (1980) <NAME> and <NAME> (2013), 'Land-Ocean Warming Contrast over a Wide Range of Climates: Convective Quasi-Equilibrium Theory and Idealized Simulations', J. Climate """ # tempc tempc = tempk - degCtoK # Calculate theta theta = Theta(tempk, pres) # T_lcl formula needs RH es = VaporPressure(tempc) RH = 100. e / es # theta_e needs q (water vapour mixing ratio) qv = MixRatio(e, pres) # Calculate the temp at the Lifting Condensation Level T_lcl = ((tempk-55)2840 / (2840-(np.log(RH/100)(tempk-55)))) + 55 # print "T_lcl :%.3f"%T_lcl # DEBUG STUFF #### theta_l = tempk * \ (100000./(pres-e))*(Rs_da/Cp_da)(tempk/T_lcl)*(0.28qv) # print "theta_L: %.3f"%theta_l # Calculate ThetaE theta_e = theta_l * np.exp((Lv * qv) / (Cp_da * T_lcl)) return theta_e def ThetaE_Bolton(tempk, pres, e, pref=100000.): """Theta_E following Bolton (1980) INPUTS: tempk: Temperature [K] pres: Pressure [Pa] e: Water vapour partial pressure [Pa] See http://en.wikipedia.org/wiki/Equivalent_potential_temperature """ # Preliminary: T = tempk qv = MixRatio(e, pres) Td = DewPoint(e) + degCtoK kappa_d = Rs_da / Cp_da # Calculate TL (temp [K] at LCL): TL = 56 + ((Td-56.)-1+(np.log(T/Td)/800.))(-1) # print "TL: %.3f"%TL # Calculate Theta_L: thetaL = T * (pref/(pres-e))*kappa_d(T/TL)*(0.28qv) # print "theta_L: %.3f"%thetaL # put it all together to get ThetaE thetaE = thetaL * np.exp((3036./TL-0.78)qv(1+0.448qv)) return thetaE def ThetaV(tempk, pres, e): """Virtual Potential Temperature INPUTS tempk (K) pres (Pa) e: Water vapour pressure (Pa) (Optional) OUTPUTS theta_v : Virtual potential temperature """ mixr = MixRatio(e, pres) theta = Theta(tempk, pres) return theta (1+mixr/Epsilon) / (1+mixr) def GammaW(tempk, pres): """Function to calculate the moist adiabatic lapse rate (deg C/Pa) based on the environmental temperature and pressure. INPUTS: tempk (K) pres (Pa) RH (%) RETURNS: GammaW: The moist adiabatic lapse rate (Deg C/Pa) REFERENCE: http://glossary.ametsoc.org/wiki/Moist-adiabatic_lapse_rate (Note that I multiply by 1/(gravrho) to give MALR in deg/Pa) """ tempc = tempk-degCtoK es = VaporPressure(tempc) ws = MixRatio(es, pres) # tempv=VirtualTempFromMixR(tempk,ws) tempv = VirtualTemp(tempk, pres, es) latent = Latentc(tempc) Rho = pres / (Rs_datempv) # This is the previous implementation: # A=1.0+latentws/(Rs_datempk) # B=1.0+Epsilonlatentlatentws/(Cp_daRs_datempktempk) # Gamma=(A/B)/(Cp_daRho) # This is algebraically identical but a little clearer: A = -1. (1.0+latentws/(Rs_datempk)) B = Rho * (Cp_da+Epsilonlatentlatentws/(Rs_datempktempk)) Gamma = A / B return Gamma def DensHumid(tempk, pres, e): """Density of moist air. This is a bit more explicit and less confusing than the method below. INPUTS: tempk: Temperature (K) pres: static pressure (Pa) mixr: mixing ratio (kg/kg) OUTPUTS: rho_air (kg/m^3) SOURCE: http://en.wikipedia.org/wiki/Density_of_air """ pres_da = pres - e rho_da = pres_da / (Rs_da tempk) rho_wv = e/(Rs_v * tempk) return rho_da + rho_wv def Density(tempk, pres, mixr): """Density of moist air INPUTS: tempk: Temperature (K) pres: static pressure (Pa) mixr: mixing ratio (kg/kg) OUTPUTS: rho_air (kg/m^3) """ virtualT = VirtualTempFromMixR(tempk, mixr) return pres / (Rs_da * virtualT) def VirtualTemp(tempk, pres, e): """Virtual Temperature INPUTS: tempk: Temperature (K) e: vapour pressure (Pa) p: static pressure (Pa) OUTPUTS: tempv: Virtual temperature (K) SOURCE: hmmmm (Wikipedia).""" tempvk = tempk / (1-(e/pres)(1-Epsilon)) return tempvk def VirtualTempFromMixR(tempk, mixr): """Virtual Temperature INPUTS: tempk: Temperature (K) mixr: Mixing Ratio (kg/kg) OUTPUTS: tempv: Virtual temperature (K) SOURCE: hmmmm (Wikipedia). This is an approximation based on a m """ return tempk (1.0+0.6mixr) def Latentc(tempc): """Latent heat of condensation (vapourisation) INPUTS: tempc (C) OUTPUTS: L_w (J/kg) SOURCE: http://en.wikipedia.org/wiki/Latent_heat#Latent_heat_for_condensation_of_water """ return 1000 (2500.8 - 2.36tempc + 0.0016tempc*2 - 0.00006tempc*3) def VaporPressure(tempc, phase="liquid"): """Water vapor pressure over liquid water or ice. INPUTS: tempc: (C) OR dwpt (C), if SATURATION vapour pressure is desired. phase: ['liquid'],'ice'. If 'liquid', do simple dew point. If 'ice', return saturation vapour pressure as follows: Tc>=0: es = es_liquid Tc <0: es = es_ice RETURNS: e_sat (Pa) SOURCE: http://cires.colorado.edu/~voemel/vp.html (#2: CIMO guide (WMO 2008), modified to return values in Pa) This formulation is chosen because of its appealing simplicity, but it performs very well with respect to the reference forms at temperatures above -40 C. At some point I'll implement Goff-Gratch (from the same resource). """ over_liquid = 6.112 np.exp(17.67tempc/(tempc+243.12))100. over_ice = 6.112 * np.exp(22.46tempc/(tempc+272.62))100. # return where(tempc<0,over_ice,over_liquid) if phase == "liquid": # return 6.112exp(17.67tempc/(tempc+243.12))100. return over_liquid elif phase == "ice": # return 6.112exp(22.46tempc/(tempc+272.62))100. return np.where(tempc < 0, over_ice, over_liquid) else: raise NotImplementedError def SatVap(dwpt, phase="liquid"): """This function is deprecated, return ouput from VaporPres""" print("WARNING: This function is deprecated, please use VaporPressure()" + " instead, with dwpt as argument") return VaporPressure(dwpt, phase) def MixRatio(e, p): """Mixing ratio of water vapour INPUTS e (Pa) Water vapor pressure p (Pa) Ambient pressure RETURNS qv (kg kg^-1) Water vapor mixing ratio` """ return Epsilon * e / (p - e) def MixR2VaporPress(qv, p): """Return Vapor Pressure given Mixing Ratio and Pressure INPUTS qv (kg kg^-1) Water vapor mixing ratio` p (Pa) Ambient pressure RETURNS e (Pa) Water vapor pressure """ return qv * p / (Epsilon + qv) def DewPoint(e): """ Use Bolton's (1980, MWR, p1047) formulae to find tdew. INPUTS: e (Pa) Water Vapor Pressure OUTPUTS: Td (C) """ ln_ratio = np.log(e/611.2) Td = ((17.67-ln_ratio)degCtoK+243.5ln_ratio)/(17.67-ln_ratio) return Td - degCtoK def WetBulb(tempc, RH): """Stull (2011): Wet-Bulb Temperature from Relative Humidity and Air Temperature. INPUTS: tempc (C) RH (%) OUTPUTS: tempwb (C) """ Tw = tempc * np.arctan(0.151977(RH+8.313659)0.5) + \ np.arctan(tempc+RH) - np.arctan(RH-1.676331) + \ 0.00391838RH*1.5np.arctan(0.023101RH) - \ 4.686035 return Tw # unit conversions def convert_unit_and_save_data_ppb_ugm3(filename_, station_name): # https://uk-air.defra.gov.uk/assets/documents/reports/cat06/0502160851_Conversion_Factors_Between_ppb_and.pdf # http://www2.dmu.dk/AtmosphericEnvironment/Expost/database/docs/PPM_conversion.pdf parameters_unit_scaling = {'11' : 1.96, # O3 '10' : 1.25, # NO '9' : 1.88, # NO2 '16' : 2.62, # SO2 '8' : 1.15} # CO new_unit_name = '[$\mu$g/m$^3$]' parameter_name_mod = {'9' : 'NO$_2$', '11' : 'O$_3$', '12' : 'PM$_1$$_0$', '13' : 'PM$_2$$_.$$_5$', '7' : 'CO$_2$', '16' : 'SO$_2$', } # station_name = 'QF_01' data_array = open_csv_file(filename_) current_header = data_array[0,:] new_header = np.array(current_header) v_current = np.array(data_array[1:,:],dtype=float) v_new = np.array(v_current) for keys_ in parameters_unit_scaling.keys(): v_new[:, int(keys_)] = v_current[:, int(keys_)] parameters_unit_scaling[str(keys_)] # add station name suffix for i_ in range(5,22): if str(i_) in parameter_name_mod.keys(): parameter_name = parameter_name_mod[str(i_)] else: parameter_name = current_header[i_].split('_')[0] if str(i_) in parameters_unit_scaling.keys(): parameter_unit = new_unit_name else: parameter_unit = current_header[i_].split('_')[1] new_header[i_] = station_name + '_' + parameter_name + '_' + parameter_unit data_array[1:,:] = v_new data_array[0,:] = new_header filename_new = filename_.split('\\')[-1].split('.')[0] + '_unit_converted.csv' current_filename_without_path = filename_.split('\\')[-1] current_filename_path = filename_[:-len(current_filename_without_path)] numpy_save_txt(current_filename_path + filename_new, data_array) print('done!') def save_data_with_unit_conversion_ppb_ugm3(file_list_path): file_list = sorted(glob.glob(str(file_list_path + '\\' + '.csv'))) # https://uk-air.defra.gov.uk/assets/documents/reports/cat06/0502160851_Conversion_Factors_Between_ppb_and.pdf # http://www2.dmu.dk/AtmosphericEnvironment/Expost/database/docs/PPM_conversion.pdf parameters_unit_scaling = {'12' : 1.96, # O3 '13' : 1.25, # NO '14' : 1.88, # NO2 '15' : 2.62, # SO2 '16' : 1.15} # CO parameters_new_names = ['YYYY', # 0 'MM', # 1 'DD', # 2 'HH', # 3 'mm', # 4 'Day of the week', # 5 'WD degrees', # 6 'WS m/s', # 7 'Temp Celsius', # 8 'RH %', # 9 'SR W/m2', # 10 'ATP mbar', # 11 'O3 ug/m3', # 12 'NO ug/m3', # 13 'NO2 ug/m3', # 14 'SO2 ug/m3', # 15 'CO mg/m3', # 16 'CO2 ppm', # 17 'PM10 ug/m3', # 18 'PM2.5 ug/m3', # 19 'THC ppm', # 20 'Rain mm', # 21 'Ox ppb', # 22 'NOx ppb'] # 23 for month_ in range(1,13): print(month_) filename_old = file_list[month_ -1] data_array = open_csv_file(file_list[month_ -1]) v_ppb = np.array(data_array[1:,:],dtype=float) v_ug_m3 = np.array(v_ppb) for keys_ in parameters_unit_scaling.keys(): v_ug_m3[:, int(keys_)] = v_ppb[:, int(keys_)] parameters_unit_scaling[str(keys_)] data_array[0, :] = parameters_new_names data_array[1:,:] = v_ug_m3 filename_new = filename_old.split('\\')[-1].split('.')[0] + '_ugm3.csv' numpy_save_txt(file_list_path + '\\' + filename_new, data_array) print('done!') def RH_to_abs_conc(arr_RH,arr_T): a_ = 1-(373.15/arr_T) c_1 = 13.3185 c_2 = -1.97 c_3 = -.6445 c_4 = -.1299 Po_H2O = 1013.25 * e_constant ** ((c_1 * (a_*1)) + (c_2 (a_*2)) + (c_3 (a_*3)) + (c_4 (a_*4)) ) # mbar return (arr_RH Po_H2O) / (100 * boltzmann_ * arr_T) def Mixing_Ratio_to_molecules_per_cm3(arr_MR, ATP_mbar, Temp_C): arr_temp = Temp_C + 273.15 # kelvin arr_Molec_per_cm3 = arr_MR * ( ATP_mbar / ( boltzmann_ * arr_temp ) ) # molecules / cm3 return arr_Molec_per_cm3 def molecules_per_cm3_to_Mixing_Ratio(arr_Molec_per_cm3, ATP_mbar, Temp_C): arr_temp = Temp_C + 273.15 # kelvin arr_MR = (arr_Molec_per_cm3 * boltzmann_ * arr_temp) / ATP_mbar return arr_MR def ws_knots_to_ms(arr_): return arr_ * .514444 def ws_ms_to_knots(arr_): return arr_ / .514444 def kelvin_to_celsius(arr_temp_k): return arr_temp_k - 273.15 def celsius_to_kelvin(arr_temp_c): return arr_temp_c + 273.15 # geo reference def find_index_from_lat_lon(series_lat, series_lon, point_lat_list, point_lon_list): lat_index_list = [] lon_index_list = [] # mask arrays lat_m = series_lat lon_m = series_lon if np.sum(lat_m) != np.sum(lat_m) or np.sum(lon_m) != np.sum(lon_m): lat_m = np.ma.masked_where(np.isnan(lat_m), lat_m) lat_m = np.ma.masked_where(np.isinf(lat_m), lat_m) lon_m = np.ma.masked_where(np.isnan(lon_m), lon_m) lon_m = np.ma.masked_where(np.isinf(lon_m), lon_m) if type(point_lat_list) == tuple or type(point_lat_list) == list: for lat_ in point_lat_list: lat_index_list.append(np.argmin(np.abs(lat_m - lat_))) for lon_ in point_lon_list: lon_index_list.append(np.argmin(np.abs(lon_m - lon_))) else: lat_index_list = np.argmin(np.abs(lat_m - point_lat_list)) lon_index_list = np.argmin(np.abs(lon_m - point_lon_list)) return lat_index_list, lon_index_list def find_index_from_lat_lon_2D_arrays(lat_arr, lon_arr, point_lat, point_lon): lat_del_arr = lat_arr - point_lat lon_del_arr = lon_arr - point_lon dist_arr = ( lat_del_arr2 + lon_del_arr2 )0.5 return find_min_index_2d_array(dist_arr) def find_index_from_lat_lon_1D_arrays(lat_arr, lon_arr, point_lat, point_lon): lat_del_arr = lat_arr - point_lat lon_del_arr = lon_arr - point_lon dist_arr = ( lat_del_arr2 + lon_del_arr2 )0.5 return find_min_index_1d_array(dist_arr) def distance_array_lat_lon_2D_arrays_degrees(lat_arr, lon_arr, point_lat, point_lon): lat_del_arr = lat_arr - point_lat lon_del_arr = lon_arr - point_lon return ( lat_del_arr2 + lon_del_arr2 )*0.5 def meter_per_degrees(lat_point): lat_mean_rad = np.deg2rad(np.abs(lat_point)) m_per_deg_lat = 111132.954 - 559.822 np.cos(2 * lat_mean_rad) + 1.175 * np.cos(4 * lat_mean_rad) m_per_deg_lon = 111132.954 * np.cos(lat_mean_rad) return np.abs(m_per_deg_lat), np.abs(m_per_deg_lon) def degrees_per_meter(lat_point): m_per_deg_lat, m_per_deg_lon = meter_per_degrees(lat_point) return 1/m_per_deg_lat, 1/m_per_deg_lon def distance_array_lat_lon_2D_arrays_degress_to_meters(lat_arr, lon_arr, point_lat, point_lon): m_per_deg_lat, m_per_deg_lon = meter_per_degrees(	np.nanmean(lat_arr)	numpy.nanmean
import math import warnings import numpy as np from scipy import stats from statsmodels.tsa import stattools from scipy.interpolate import interp1d from scipy.stats import chi2 from .Base import Base from .features.irregular_autoregressive import IAR_phi, CIAR_phiR_beta from .features.structure_function import SF_ML_amplitude, SF_ML_gamma from .features.damped_random_walk import GP_DRW_sigma, GP_DRW_tau from .features.harmonics import Harmonics from .features.periods import Period_fit_v2, PeriodPowerRate, PeriodLS_v2 from .features.conditional_autoregressive import CAR_mean, CAR_sigma, CAR_tau class Amplitude(Base): """Half the difference between the maximum and the minimum magnitude""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] n = len(magnitude) sorted_mag = np.sort(magnitude) return (np.median(sorted_mag[int(-math.ceil(0.05 * n)):]) - np.median(sorted_mag[0:int(math.ceil(0.05 * n))])) / 2.0 class Rcs(Base): """Range of cumulative sum""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sigma = np.std(magnitude) N = len(magnitude) m = np.mean(magnitude) s = np.cumsum(magnitude - m) * 1.0 / (N * sigma) R = np.max(s) - np.min(s) return R class StetsonK(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'error'] def fit(self, data): magnitude = data[0] error = data[2] n = len(magnitude) mean_mag = (np.sum(magnitude/(errorerror))/np.sum(1.0 / (error error))) sigmap = (np.sqrt(n * 1.0 / (n - 1)) * (magnitude - mean_mag) / error) k = (1 / np.sqrt(n * 1.0) * np.sum(np.abs(sigmap)) / np.sqrt(np.sum(sigmap 2))) return k class Meanvariance(Base): """variability index""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] return np.std(magnitude) / np.mean(magnitude) class Autocor_length(Base): def __init__(self, shared_data, lags=100): super().__init__(shared_data) self.Data = ['magnitude'] self.nlags = lags def fit(self, data): magnitude = data[0] ac = stattools.acf(magnitude, nlags=self.nlags, fft=False) k = next((index for index, value in enumerate(ac) if value < np.exp(-1)), None) while k is None: if self.nlags > len(magnitude): warnings.warn('Setting autocorrelation length as light curve length') return len(magnitude) self.nlags = self.nlags + 100 ac = stattools.acf(magnitude, nlags=self.nlags, fft=False) k = next((index for index, value in enumerate(ac) if value < np.exp(-1)), None) return k class SlottedA_length(Base): def __init__(self, shared_data, T=-99): """ lc: MACHO lightcurve in a pandas DataFrame k: lag (default: 1) T: tau (slot size in days. default: 4) """ super().__init__(shared_data) self.Data = ['magnitude', 'time'] SlottedA_length.SAC = [] self.T = T def slotted_autocorrelation(self, data, time, T, K, second_round=False, K1=100): slots = np.zeros((K, 1)) i = 1 # make time start from 0 time = time - np.min(time) # subtract mean from mag values m = np.mean(data) data = data - m prod = np.zeros((K, 1)) pairs = np.subtract.outer(time, time) pairs[np.tril_indices_from(pairs)] = 10000000 ks = np.int64(np.floor(np.abs(pairs) / T + 0.5)) # We calculate the slotted autocorrelation for k=0 separately idx = np.where(ks == 0) prod[0] = ((sum(data 2) + sum(data[idx[0]] * data[idx[1]])) / (len(idx[0]) + len(data))) slots[0] = 0 # We calculate it for the rest of the ks if second_round is False: for k in np.arange(1, K): idx = np.where(ks == k) if len(idx[0]) != 0: prod[k] = sum(data[idx[0]] * data[idx[1]]) / (len(idx[0])) slots[i] = k i = i + 1 else: prod[k] = np.infty else: for k in np.arange(K1, K): idx = np.where(ks == k) if len(idx[0]) != 0: prod[k] = sum(data[idx[0]] * data[idx[1]]) / (len(idx[0])) slots[i - 1] = k i = i + 1 else: prod[k] = np.infty np.trim_zeros(prod, trim='b') slots = np.trim_zeros(slots, trim='b') return prod / prod[0], np.int64(slots).flatten() def fit(self, data): magnitude = data[0] time = data[1] N = len(time) if self.T == -99: deltaT = time[1:] - time[:-1] sorted_deltaT = np.sort(deltaT) self.T = sorted_deltaT[int(N * 0.05)+1] K = 100 [SAC, slots] = self.slotted_autocorrelation(magnitude, time, self.T, K) SAC2 = SAC[slots] SlottedA_length.autocor_vector = SAC2 k = next((index for index, value in enumerate(SAC2) if value < np.exp(-1)), None) while k is None: K = K+K if K > (np.max(time) - np.min(time)) / self.T: break else: [SAC, slots] = self.slotted_autocorrelation(magnitude, time, self.T, K, second_round=True, K1=K/2) SAC2 = SAC[slots] k = next((index for index, value in enumerate(SAC2) if value < np.exp(-1)), None) return slots[k] * self.T def getAtt(self): return SlottedA_length.autocor_vector class StetsonK_AC(SlottedA_length): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'error'] def fit(self, data): try: a = StetsonK_AC(self.shared_data) autocor_vector = a.getAtt() N_autocor = len(autocor_vector) sigmap = (np.sqrt(N_autocor * 1.0 / (N_autocor - 1)) * (autocor_vector - np.mean(autocor_vector)) / np.std(autocor_vector)) K = (1 / np.sqrt(N_autocor * 1.0) * np.sum(np.abs(sigmap)) / np.sqrt(np.sum(sigmap ** 2))) return K except: print("error: please run SlottedA_length first to generate values for StetsonK_AC ") class StetsonL(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'error', 'magnitude2', 'error2'] def fit(self, data): aligned_magnitude = data[4] aligned_magnitude2 = data[5] aligned_error = data[7] aligned_error2 = data[8] N = len(aligned_magnitude) mean_mag = (np.sum(aligned_magnitude/(aligned_erroraligned_error)) / np.sum(1.0 / (aligned_error aligned_error))) mean_mag2 = (np.sum(aligned_magnitude2/(aligned_error2aligned_error2)) / np.sum(1.0 / (aligned_error2 aligned_error2))) sigmap = (np.sqrt(N * 1.0 / (N - 1)) * (aligned_magnitude[:N] - mean_mag) / aligned_error) sigmaq = (np.sqrt(N * 1.0 / (N - 1)) * (aligned_magnitude2[:N] - mean_mag2) / aligned_error2) sigma_i = sigmap * sigmaq J = (1.0 / len(sigma_i) * np.sum(np.sign(sigma_i) * np.sqrt(np.abs(sigma_i)))) K = (1 / np.sqrt(N * 1.0) * np.sum(np.abs(sigma_i)) / np.sqrt(np.sum(sigma_i ** 2))) return J * K / 0.798 class Con(Base): """Index introduced for selection of variable starts from OGLE database. To calculate Con, we counted the number of three consecutive measurements that are out of 2sigma range, and normalized by N-2 Pavlos not happy """ def __init__(self, shared_data, consecutiveStar=3): super().__init__(shared_data) self.Data = ['magnitude'] self.consecutiveStar = consecutiveStar def fit(self, data): magnitude = data[0] N = len(magnitude) if N < self.consecutiveStar: return 0 sigma = np.std(magnitude) m = np.mean(magnitude) count = 0 for i in range(N - self.consecutiveStar + 1): flag = 0 for j in range(self.consecutiveStar): if (magnitude[i + j] > m + 2 * sigma or magnitude[i + j] < m - 2 * sigma): flag = 1 else: flag = 0 break if flag: count = count + 1 return count * 1.0 / (N - self.consecutiveStar + 1) class Color(Base): """Average color for each MACHO lightcurve mean(B1) - mean(B2) """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'magnitude2'] def fit(self, data): magnitude = data[0] magnitude2 = data[3] return np.mean(magnitude) - np.mean(magnitude2) class Beyond1Std(Base): """Percentage of points beyond one st. dev. from the weighted (by photometric errors) mean """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'error'] def fit(self, data): magnitude = data[0] error = data[2] n = len(magnitude) weighted_mean = np.average(magnitude, weights=1 / error 2) # Standard deviation with respect to the weighted mean var = sum((magnitude - weighted_mean) 2) std = np.sqrt((1.0 / (n - 1)) * var) count = np.sum(np.logical_or(magnitude > weighted_mean + std, magnitude < weighted_mean - std)) return float(count) / n class SmallKurtosis(Base): """Small sample kurtosis of the magnitudes. See http://www.xycoon.com/peakedness_small_sample_test_1.htm """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] n = len(magnitude) mean = np.mean(magnitude) std = np.std(magnitude) S = sum(((magnitude - mean) / std) ** 4) c1 = float(n * (n + 1)) / ((n - 1) * (n - 2) * (n - 3)) c2 = float(3 * (n - 1) ** 2) / ((n - 2) * (n - 3)) return c1 * S - c2 class Std(Base): """Standard deviation of the magnitudes""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] return np.std(magnitude) class Skew(Base): """Skewness of the magnitudes""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] return stats.skew(magnitude) class StetsonJ(Base): """Stetson (1996) variability index, a robust standard deviation""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'error', 'magnitude2', 'error2'] #lc fields are [data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd] def fit(self, data): aligned_magnitude = data[4] aligned_magnitude2 = data[5] aligned_error = data[7] aligned_error2 = data[8] N = len(aligned_magnitude) mean_mag = (np.sum(aligned_magnitude/(aligned_erroraligned_error)) / np.sum(1.0 / (aligned_error aligned_error))) mean_mag2 = (np.sum(aligned_magnitude2 / (aligned_error2aligned_error2)) / np.sum(1.0 / (aligned_error2 aligned_error2))) sigmap = (np.sqrt(N * 1.0 / (N - 1)) * (aligned_magnitude[:N] - mean_mag) / aligned_error) sigmaq = (np.sqrt(N * 1.0 / (N - 1)) * (aligned_magnitude2[:N] - mean_mag2) / aligned_error2) sigma_i = sigmap * sigmaq J = (1.0 / len(sigma_i) * np.sum(np.sign(sigma_i) * np.sqrt(np.abs(sigma_i)))) return J class MaxSlope(Base): """ Examining successive (time-sorted) magnitudes, the maximal first difference (value of delta magnitude over delta time) """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time'] def fit(self, data): magnitude = data[0] time = data[1] slope = np.abs(magnitude[1:] - magnitude[:-1]) / (time[1:] - time[:-1]) np.max(slope) return np.max(slope) class MedianAbsDev(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] median = np.median(magnitude) devs = (abs(magnitude - median)) return np.median(devs) class MedianBRP(Base): """Median buffer range percentage Fraction (<= 1) of photometric points within amplitude/10 of the median magnitude """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] median = np.median(magnitude) amplitude = (np.max(magnitude) - np.min(magnitude)) / 10 n = len(magnitude) count = np.sum(np.logical_and(magnitude < median + amplitude, magnitude > median - amplitude)) return float(count) / n class PairSlopeTrend(Base): """ Considering the last 30 (time-sorted) measurements of source magnitude, the fraction of increasing first differences minus the fraction of decreasing first differences. """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] data_last = magnitude[-30:] return (float(len(np.where(np.diff(data_last) > 0)[0]) - len(np.where(np.diff(data_last) <= 0)[0])) / 30) class FluxPercentileRatioMid20(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_60_index = math.ceil(0.60 * lc_length) F_40_index = math.ceil(0.40 * lc_length) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.ceil(0.95 * lc_length) F_40_60 = sorted_data[F_60_index] - sorted_data[F_40_index] F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] F_mid20 = F_40_60 / F_5_95 return F_mid20 class FluxPercentileRatioMid35(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_325_index = math.ceil(0.325 * lc_length) F_675_index = math.ceil(0.675 * lc_length) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.ceil(0.95 * lc_length) F_325_675 = sorted_data[F_675_index] - sorted_data[F_325_index] F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] F_mid35 = F_325_675 / F_5_95 return F_mid35 class FluxPercentileRatioMid50(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_25_index = math.ceil(0.25 * lc_length) F_75_index = math.ceil(0.75 * lc_length) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.ceil(0.95 * lc_length) F_25_75 = sorted_data[F_75_index] - sorted_data[F_25_index] F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] F_mid50 = F_25_75 / F_5_95 return F_mid50 class FluxPercentileRatioMid65(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_175_index = math.ceil(0.175 * lc_length) F_825_index = math.ceil(0.825 * lc_length) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.ceil(0.95 * lc_length) F_175_825 = sorted_data[F_825_index] - sorted_data[F_175_index] F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] F_mid65 = F_175_825 / F_5_95 return F_mid65 class FluxPercentileRatioMid80(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_10_index = math.ceil(0.10 * lc_length) F_90_index = math.ceil(0.90 * lc_length) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.floor(0.95 * lc_length) F_10_90 = sorted_data[F_90_index] - sorted_data[F_10_index] F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] F_mid80 = F_10_90 / F_5_95 return F_mid80 class PercentDifferenceFluxPercentile(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] median_data = np.median(magnitude) sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.ceil(0.95 * lc_length) F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] percent_difference = F_5_95 / median_data return percent_difference class PercentAmplitude(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] median_data = np.median(magnitude) distance_median = np.abs(magnitude - median_data) max_distance = np.max(distance_median) percent_amplitude = max_distance / median_data return percent_amplitude class LinearTrend(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time'] def fit(self, data): magnitude = data[0] time = data[1] regression_slope = stats.linregress(time, magnitude)[0] return regression_slope class Eta_color(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'magnitude2'] def fit(self, data): aligned_magnitude = data[4] aligned_magnitude2 = data[5] aligned_time = data[6] B_Rdata = aligned_magnitude - aligned_magnitude2 w = 1.0 / np.power(aligned_time[1:] - aligned_time[:-1], 2) sigma2 = np.var(B_Rdata) S1 = sum(w * (B_Rdata[1:] - B_Rdata[:-1]) ** 2) S2 = sum(w) eta_B_R = (1 / sigma2) * (S1 / S2) return eta_B_R class Eta_e(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time'] def fit(self, data): magnitude = data[0] time = data[1] w = 1.0 / np.power(np.subtract(time[1:], time[:-1]), 2) sigma2 = np.var(magnitude) S1 = np.sum(w * np.power(np.subtract(magnitude[1:], magnitude[:-1]), 2)) S2 = np.sum(w) eta_e = (1 / sigma2) * (S1 / S2) return eta_e class Mean(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] B_mean = np.mean(magnitude) return B_mean class Q31(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] percentiles = np.percentile(magnitude, (25, 75)) return percentiles[1] - percentiles[0] class Q31_color(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'magnitude2'] def fit(self, data): aligned_magnitude = data[4] aligned_magnitude2 = data[5] N = len(aligned_magnitude) b_r = aligned_magnitude[:N] - aligned_magnitude2[:N] percentiles = np.percentile(b_r, (25, 75)) return percentiles[1] - percentiles[0] class AndersonDarling(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] ander = stats.anderson(magnitude)[0] return 1 / (1.0 + np.exp(-10 * (ander - 0.3))) class Psi_CS_v2(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time'] def fit(self, data): try: magnitude = data[0] time = data[1] new_time_v2 = self.shared_data['new_time_v2'] folded_data = magnitude[np.argsort(new_time_v2)] sigma = np.std(folded_data) N = len(folded_data) m = np.mean(folded_data) s = np.cumsum(folded_data - m) * 1.0 / (N * sigma) R = np.max(s) -	np.min(s)	numpy.min
#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Mon Apr 17 14:45:00 2017 @author: shenda class order: ['A', 'N', 'O', '~'] """ import numpy as np from sklearn import ensemble from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from OptF import OptF from copy import deepcopy import xgboost as xgb import ReadData import random from collections import Counter class MyXGB(object): """ bottom basic classifier, a warpper for xgboost the proba order is ['N', 'A', 'O', '~'] """ def __init__(self, n_estimators=5000, max_depth=10, subsample=0.85, colsample_bytree=0.85, min_child_weight=4, num_round = 500): self.param = {'learning_rate':0.1, 'eta':0.1, 'silent':1, 'objective':'multi:softprob', 'num_class': 4} self.bst = None self.num_round = num_round self.pred = None my_seed = random.randint(0, 1000) self.param['n_estimators'] = n_estimators self.param['max_depth'] = max_depth self.param['subsample'] = subsample self.param['colsample_bytree'] = colsample_bytree self.param['min_child_weight'] = min_child_weight # self.param['random_state'] = my_seed self.param['seed'] = my_seed self.param['n_jobs'] = -1 print(self.param.items()) print(self.num_round) def fit(self, train_data, train_label): train_label = ReadData.Label2Index(train_label) dtrain = xgb.DMatrix(train_data, label=train_label) self.bst = xgb.train(self.param, dtrain, num_boost_round=self.num_round) def predict_prob(self, test_data): dtest = xgb.DMatrix(test_data) self.pred = self.bst.predict(dtest) return self.pred def predict(self, test_data): pred_prob = self.predict_prob(test_data) pred_num = np.argmax(pred_prob, axis=1) pred = ReadData.Index2Label(pred_num) return pred def get_importance(self): return self.bst.get_score(importance_type='gain') def plot_importance(self): xgb.plot_importance(self.bst) class MyLR(object): """ Top level classifier, a warpper for Logistic Regression """ def __init__(self): self.clf = LogisticRegression() def fit(self, train_qrs_data, train_qrs_label): train_data =np.array(train_qrs_data) train_label = train_qrs_label self.clf.fit(train_data, train_label) def predict(self, test_qrs_data): test_data = np.array(test_qrs_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_qrs_data): test_qrs_data = np.array(test_qrs_data) if test_qrs_data.ndim == 1: test_qrs_data = np.expand_dims(np.array(test_qrs_data), axis=0) test_data = np.array(test_qrs_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_qrs_data) return self.clf.predict_proba(test_data) class MyKNN(object): """ bottom basic support unequal length vector """ def __init__(self, n_neighbors=3): self.n_neighbors = n_neighbors self.train_data = None self.train_label = None self.labels = ['N', 'A', 'O', '~'] # self.thresh = [0.5, 0.3, 0.2, ] def fit(self, train_data, train_label): self.train_data = np.array(train_data) self.train_label = np.array(train_label) def dist(self, vec1, vec2): res = 0.0 if len(vec1) <= len(vec2): vec1 = np.r_[vec1, np.zeros(len(vec2)-len(vec1))] else: vec2 = np.r_[vec2, np.zeros(len(vec1)-len(vec2))] dist_num = np.linalg.norm(vec1 - vec2) return dist_num def predict_prob(self, test_data): test_data = np.array(test_data) pred = [] for i in test_data: tmp_dist_list = [] tmp_pred = [] for j in self.train_data: tmp_dist_list.append(self.dist(i, j)) pred_n_neighbors = self.train_label[np.argsort(tmp_dist_list)[:self.n_neighbors]] pred_counter = Counter(pred_n_neighbors) # print(pred_counter) for ii in self.labels: tmp_pred.append(pred_counter[ii]) pred.append(tmp_pred) return pred def predict(self, test_data): pred = self.predict_prob(test_data) pred_label = [] for i in pred: pred_label.append(self.labels[np.argsort(i)[-1]]) return pred_label class MyGBDT(object): """ bottom basic a warpper for GradientBoostingClassifier """ def __init__(self): self.clf = ensemble.GradientBoostingClassifier() def fit(self, train_data, train_label): train_data =np.array(train_data) self.clf.fit(train_data, train_label) def predict(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) test_data = np.array(test_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_data) return self.clf.predict_proba(test_data) class MyExtraTrees(object): """ bottom basic a warpper for ExtraTreesClassifier """ def __init__(self): self.clf = ensemble.ExtraTreesClassifier(n_estimators=100) def fit(self, train_data, train_label): train_data =np.array(train_data) self.clf.fit(train_data, train_label) def predict(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) test_data = np.array(test_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_data) return self.clf.predict_proba(test_data) class MyAdaBoost(object): """ bottom basic a warpper for AdaBoostClassifier """ def __init__(self): self.clf = ensemble.AdaBoostClassifier(n_estimators=100, learning_rate=0.1) def fit(self, train_data, train_label): train_data =np.array(train_data) self.clf.fit(train_data, train_label) def predict(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) test_data = np.array(test_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_data) return self.clf.predict_proba(test_data) class MyRF(object): """ bottom basic a warpper for Random Forest """ def __init__(self): self.clf = ensemble.RandomForestClassifier( n_estimators=1000, n_jobs=-1) def fit(self, train_data, train_label): train_data =np.array(train_data) self.clf.fit(train_data, train_label) def predict(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) test_data = np.array(test_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_data) return self.clf.predict_proba(test_data) class MyOptF(object): """ bottom basic classifier, a warpper for Opt F-score """ def __init__(self, alpha=0.5, epochs=10): self.clf = OptF(alpha, epochs) def fit(self, train_data, train_label): self.clf.fit(train_data, deepcopy(train_label)) def predict(self, test_data): return list(self.clf.predict(test_data)) def predict_prob(self, test_data): return self.clf.predict_prob(test_data) class RF(object): """ Top level classifier, a warpper for Random Forest use long_feature and qrs_feature seperatedly, thus no use any more deprecated """ def __init__(self): self.clf = ensemble.RandomForestClassifier() def fit(self, train_long_data, train_long_label, train_qrs_data, train_qrs_label): train_data = np.c_[np.array(train_long_data), np.array(train_qrs_data)] train_label = train_long_label self.clf.fit(train_data, train_label) def predict(self, test_long_data, test_qrs_data): test_data = np.c_[np.array(test_long_data), np.array(test_qrs_data)] if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_long_data, test_qrs_data): test_long_data = np.array(test_long_data) test_qrs_data = np.array(test_qrs_data) if test_long_data.ndim == 1 or test_qrs_data.ndim == 1: test_long_data = np.expand_dims(np.array(test_long_data), axis=0) test_qrs_data = np.expand_dims(np.array(test_qrs_data), axis=0) test_data = np.c_[np.array(test_long_data), np.array(test_qrs_data)] else: test_data = np.c_[np.array(test_long_data), np.array(test_qrs_data)] return list(list(self.clf.predict_proba(test_data))[0]) class RFSimp(object): """ Top level classifier, a warpper for Random Forest use long/qrs feature deprecated """ def __init__(self): self.clf = ensemble.RandomForestClassifier() def fit(self, train_qrs_data, train_qrs_label): train_data =np.array(train_qrs_data) train_label = train_qrs_label self.clf.fit(train_data, train_label) def predict(self, test_qrs_data): test_data = np.array(test_qrs_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_qrs_data): test_qrs_data = np.array(test_qrs_data) if test_qrs_data.ndim == 1: test_qrs_data = np.expand_dims(np.array(test_qrs_data), axis=0) test_data = np.array(test_qrs_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_qrs_data) return self.clf.predict_proba(test_data) class LR(object): """ Top level classifier, a warpper for Logistic Regression deprecated """ def __init__(self): self.clf = LogisticRegression() def fit(self, train_long_data, train_long_label, train_qrs_data, train_qrs_label): train_data = np.c_[np.array(train_long_data), np.array(train_qrs_data)] train_label = train_long_label self.clf.fit(train_data, train_label) def predict(self, test_long_data, test_qrs_data): test_data = np.c_[np.array(test_long_data), np.array(test_qrs_data)] if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_long_data, test_qrs_data): test_long_data = np.array(test_long_data) test_qrs_data = np.array(test_qrs_data) if test_long_data.ndim == 1 or test_qrs_data.ndim == 1: test_long_data = np.expand_dims(np.array(test_long_data), axis=0) test_qrs_data = np.expand_dims(np.array(test_qrs_data), axis=0) test_data = np.c_[np.array(test_long_data), np.array(test_qrs_data)] else: test_data = np.c_[np.array(test_long_data),	np.array(test_qrs_data)	numpy.array
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # 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 paddle from paddle import ParamAttr import paddle.nn as nn import paddle.nn.functional as F from paddle.nn.initializer import Normal, Constant from paddlex.ppdet.core.workspace import register from paddlex.ppdet.modeling import bbox_utils from paddlex.ppdet.modeling.proposal_generator.target_layer import RBoxAssigner import numpy as np class S2ANetAnchorGenerator(nn.Layer): """ AnchorGenerator by paddle """ def __init__(self, base_size, scales, ratios, scale_major=True, ctr=None): super(S2ANetAnchorGenerator, self).__init__() self.base_size = base_size self.scales = paddle.to_tensor(scales) self.ratios = paddle.to_tensor(ratios) self.scale_major = scale_major self.ctr = ctr self.base_anchors = self.gen_base_anchors() @property def num_base_anchors(self): return self.base_anchors.shape[0] def gen_base_anchors(self): w = self.base_size h = self.base_size if self.ctr is None: x_ctr = 0.5 * (w - 1) y_ctr = 0.5 * (h - 1) else: x_ctr, y_ctr = self.ctr h_ratios = paddle.sqrt(self.ratios) w_ratios = 1 / h_ratios if self.scale_major: ws = (w * w_ratios[:] * self.scales[:]).reshape([-1]) hs = (h * h_ratios[:] * self.scales[:]).reshape([-1]) else: ws = (w * self.scales[:] * w_ratios[:]).reshape([-1]) hs = (h * self.scales[:] * h_ratios[:]).reshape([-1]) base_anchors = paddle.stack( [ x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1), x_ctr + 0.5 * (ws - 1), y_ctr + 0.5 * (hs - 1) ], axis=-1) base_anchors = paddle.round(base_anchors) return base_anchors def _meshgrid(self, x, y, row_major=True): yy, xx = paddle.meshgrid(x, y) yy = yy.reshape([-1]) xx = xx.reshape([-1]) if row_major: return xx, yy else: return yy, xx def forward(self, featmap_size, stride=16): # featmap_sizestride project it to original area base_anchors = self.base_anchors feat_h = featmap_size[0] feat_w = featmap_size[1] shift_x = paddle.arange(0, feat_w, 1, 'int32') stride shift_y = paddle.arange(0, feat_h, 1, 'int32') * stride shift_xx, shift_yy = self._meshgrid(shift_x, shift_y) shifts = paddle.stack([shift_xx, shift_yy, shift_xx, shift_yy], axis=-1) all_anchors = base_anchors[:, :] + shifts[:, :] all_anchors = all_anchors.reshape([feat_h * feat_w, 4]) return all_anchors def valid_flags(self, featmap_size, valid_size): feat_h, feat_w = featmap_size valid_h, valid_w = valid_size assert valid_h <= feat_h and valid_w <= feat_w valid_x = paddle.zeros([feat_w], dtype='uint8') valid_y = paddle.zeros([feat_h], dtype='uint8') valid_x[:valid_w] = 1 valid_y[:valid_h] = 1 valid_xx, valid_yy = self._meshgrid(valid_x, valid_y) valid = valid_xx & valid_yy valid = valid[:, None].expand( [valid.size(0), self.num_base_anchors]).reshape([-1]) return valid class AlignConv(nn.Layer): def __init__(self, in_channels, out_channels, kernel_size=3, groups=1): super(AlignConv, self).__init__() self.kernel_size = kernel_size self.align_conv = paddle.vision.ops.DeformConv2D( in_channels, out_channels, kernel_size=self.kernel_size, padding=(self.kernel_size - 1) // 2, groups=groups, weight_attr=ParamAttr(initializer=Normal(0, 0.01)), bias_attr=None) @paddle.no_grad() def get_offset(self, anchors, featmap_size, stride): """ Args: anchors: [M,5] xc,yc,w,h,angle featmap_size: (feat_h, feat_w) stride: 8 Returns: """ anchors = paddle.reshape(anchors, [-1, 5]) # (NA,5) dtype = anchors.dtype feat_h, feat_w = featmap_size pad = (self.kernel_size - 1) // 2 idx = paddle.arange(-pad, pad + 1, dtype=dtype) yy, xx = paddle.meshgrid(idx, idx) xx = paddle.reshape(xx, [-1]) yy = paddle.reshape(yy, [-1]) # get sampling locations of default conv xc = paddle.arange(0, feat_w, dtype=dtype) yc = paddle.arange(0, feat_h, dtype=dtype) yc, xc = paddle.meshgrid(yc, xc) xc = paddle.reshape(xc, [-1, 1]) yc = paddle.reshape(yc, [-1, 1]) x_conv = xc + xx y_conv = yc + yy # get sampling locations of anchors # x_ctr, y_ctr, w, h, a = np.unbind(anchors, dim=1) x_ctr = anchors[:, 0] y_ctr = anchors[:, 1] w = anchors[:, 2] h = anchors[:, 3] a = anchors[:, 4] x_ctr = paddle.reshape(x_ctr, [x_ctr.shape[0], 1]) y_ctr = paddle.reshape(y_ctr, [y_ctr.shape[0], 1]) w = paddle.reshape(w, [w.shape[0], 1]) h = paddle.reshape(h, [h.shape[0], 1]) a = paddle.reshape(a, [a.shape[0], 1]) x_ctr = x_ctr / stride y_ctr = y_ctr / stride w_s = w / stride h_s = h / stride cos, sin = paddle.cos(a), paddle.sin(a) dw, dh = w_s / self.kernel_size, h_s / self.kernel_size x, y = dw * xx, dh * yy xr = cos * x - sin * y yr = sin * x + cos * y x_anchor, y_anchor = xr + x_ctr, yr + y_ctr # get offset filed offset_x = x_anchor - x_conv offset_y = y_anchor - y_conv # x, y in anchors is opposite in image coordinates, # so we stack them with y, x other than x, y offset = paddle.stack([offset_y, offset_x], axis=-1) # NA,ksks2 # [NA, ks, ks, 2] --> [NA, ksks2] offset = paddle.reshape(offset, [offset.shape[0], -1]) # [NA, ksks2] --> [ksks2, NA] offset = paddle.transpose(offset, [1, 0]) # [NA, ksks2] --> [1, ksks2, H, W] offset = paddle.reshape(offset, [1, -1, feat_h, feat_w]) return offset def forward(self, x, refine_anchors, stride): featmap_size = (x.shape[2], x.shape[3]) offset = self.get_offset(refine_anchors, featmap_size, stride) x = F.relu(self.align_conv(x, offset)) return x @register class S2ANetHead(nn.Layer): """ S2Anet head Args: stacked_convs (int): number of stacked_convs feat_in (int): input channels of feat feat_out (int): output channels of feat num_classes (int): num_classes anchor_strides (list): stride of anchors anchor_scales (list): scale of anchors anchor_ratios (list): ratios of anchors target_means (list): target_means target_stds (list): target_stds align_conv_type (str): align_conv_type ['Conv', 'AlignConv'] align_conv_size (int): kernel size of align_conv use_sigmoid_cls (bool): use sigmoid_cls or not reg_loss_weight (list): loss weight for regression """ __shared__ = ['num_classes'] __inject__ = ['anchor_assign'] def __init__(self, stacked_convs=2, feat_in=256, feat_out=256, num_classes=15, anchor_strides=[8, 16, 32, 64, 128], anchor_scales=[4], anchor_ratios=[1.0], target_means=0.0, target_stds=1.0, align_conv_type='AlignConv', align_conv_size=3, use_sigmoid_cls=True, anchor_assign=RBoxAssigner().__dict__, reg_loss_weight=[1.0, 1.0, 1.0, 1.0, 1.0], cls_loss_weight=[1.0, 1.0]): super(S2ANetHead, self).__init__() self.stacked_convs = stacked_convs self.feat_in = feat_in self.feat_out = feat_out self.anchor_list = None self.anchor_scales = anchor_scales self.anchor_ratios = anchor_ratios self.anchor_strides = anchor_strides self.anchor_base_sizes = list(anchor_strides) self.target_means = target_means self.target_stds = target_stds assert align_conv_type in ['AlignConv', 'Conv', 'DCN'] self.align_conv_type = align_conv_type self.align_conv_size = align_conv_size self.use_sigmoid_cls = use_sigmoid_cls self.cls_out_channels = num_classes if self.use_sigmoid_cls else 1 self.sampling = False self.anchor_assign = anchor_assign self.reg_loss_weight = reg_loss_weight self.cls_loss_weight = cls_loss_weight self.s2anet_head_out = None # anchor self.anchor_generators = [] for anchor_base in self.anchor_base_sizes: self.anchor_generators.append( S2ANetAnchorGenerator(anchor_base, anchor_scales, anchor_ratios)) self.anchor_generators = paddle.nn.LayerList(self.anchor_generators) self.add_sublayer('s2anet_anchor_gen', self.anchor_generators) self.fam_cls_convs = nn.Sequential() self.fam_reg_convs = nn.Sequential() for i in range(self.stacked_convs): chan_in = self.feat_in if i == 0 else self.feat_out self.fam_cls_convs.add_sublayer( 'fam_cls_conv_{}'.format(i), nn.Conv2D( in_channels=chan_in, out_channels=self.feat_out, kernel_size=3, padding=1, weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)), bias_attr=ParamAttr(initializer=Constant(0)))) self.fam_cls_convs.add_sublayer('fam_cls_conv_{}_act'.format(i), nn.ReLU()) self.fam_reg_convs.add_sublayer( 'fam_reg_conv_{}'.format(i), nn.Conv2D( in_channels=chan_in, out_channels=self.feat_out, kernel_size=3, padding=1, weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)), bias_attr=ParamAttr(initializer=Constant(0)))) self.fam_reg_convs.add_sublayer('fam_reg_conv_{}_act'.format(i), nn.ReLU()) self.fam_reg = nn.Conv2D( self.feat_out, 5, 1, weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)), bias_attr=ParamAttr(initializer=Constant(0))) prior_prob = 0.01 bias_init = float(-	np.log((1 - prior_prob) / prior_prob)	numpy.log
#!/usr/bin/env pytest import numpy as np import pytest import siglib as sl @pytest.mark.parametrize( "x,frame_length,frame_step,pad,pad_value,expected", ( (np.arange(10), 5, 5, True, 0j, np.arange(10, dtype=np.complex).reshape(2, 5)), (np.arange(10), 5, 5, False, 0j, np.arange(10, dtype=np.complex).reshape(2, 5)), ), ) def test_frame(x, frame_length, frame_step, pad, pad_value, expected): result = sl.frame(x, frame_length, frame_step, pad=pad, pad_value=pad_value) np.testing.assert_equal(result, expected) @pytest.mark.parametrize( "x,ntaps,expected", ( (np.zeros(10), 5, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), (np.arange(10), 5, [4, 4, 4, 4, 4, 5, 6, 7, 8, 9]), ), ) def test_closing(x, ntaps, expected): result = sl.closing(x, ntaps) expected = np.array(expected, dtype=np.complex) np.testing.assert_equal(result, expected) @pytest.mark.parametrize( "x,ntaps,expected", ( (np.zeros(10), 5, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), (np.arange(10), 5, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), ), ) def test_opening(x, ntaps, expected): result = sl.opening(x, ntaps) expected = np.array(expected, dtype=np.complex) np.testing.assert_equal(result, expected) @pytest.mark.parametrize( "x,idx,ntaps,expected", ((np.arange(10 ** 2), np.array([45.567]), 5, [44.96565413]),), ) def test_resample(x, idx, ntaps, expected): result = sl.resample(x, idx, ntaps) np.testing.assert_allclose(result, expected, rtol=1e-9) @pytest.mark.parametrize( "x,delay,pad,pad_value,expected", ( ( [1 + 3j, 4 + 2j, 5 + 6j, 1 + 0j], 1, True, 1 + 0j, [10 - 10j, 32 + 14j, 5.0 - 6j, 1.0 + 0j], ), ( [1 + 3j, 4 + 2j, 5 + 6j, 1 + 0j], 1, False, 1 + 0j, [10 - 10j, 32 + 14j, 5.0 - 6j], ), ), ) def test_dcm(x, delay, pad, pad_value, expected): x =	np.array(x)	numpy.array
""" Simplified transforms.py (c) Delirium Digital Limited 2020 Written by <NAME> """ import cv2 import numpy as np import math # from scipy.spatial.transform import Rotation as R import tensorflow as tf import logging logger = logging.getLogger(__name__) def resize_image(data_numpy, x_scale, y_scale): return cv2.resize(data_numpy, None, fx=x_scale, fy=y_scale, interpolation=cv2.INTER_LINEAR) def rotate_image(image, angle): """ Rotate image around center by angle (degrees) """ (h, w) = image.shape[:2] center = (w / 2, h / 2) scale = 1.0 M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h)) return rotated def scale_keypoints(keypoints, x_scale=1, y_scale=1, z_scale=1): """ Scale keypoints based on x and y scales (useful for image crop and resize) """ scaled_keypoints = [] scale_matrix = np.array([x_scale, y_scale, z_scale]) for k in keypoints: scaled_keypoints.append(k * scale_matrix) return np.array(scaled_keypoints) def center_keypoints(keypoints, center_x, center_y): """ Center keypoints TODO: Is this needed? """ centered_keypoints = [] for keypoint in keypoints: kp = [center_x + keypoint[0], center_y - keypoint[1], keypoint[2]] centered_keypoints.append(kp) return np.array(centered_keypoints) def offset_keypoints(keypoints, x_offset=0, y_offset=0, z_offset=0): keypoints[:, 0] += x_offset keypoints[:, 1] += y_offset keypoints[:, 2] += z_offset return keypoints def flip_keypoints(keypoints): """ Flipped keypoints horizontally (x = -x) """ for i, k in enumerate(keypoints): keypoints[i, 0] = -keypoints[i, 0] return keypoints def rotate(x, y, xo, yo, theta): """rotate x,y around xo,yo by theta (rad)""" xr = math.cos(theta) * (x - xo) - math.sin(theta) * (y - yo) + xo yr = math.sin(theta) * (x - xo) + math.cos(theta) * (y - yo) + yo return [xr, yr] def rotate_keypoints(keypoints, origin, angle): """ Rotate keypoints Doesn't impact keypoints_vis as by this point the image is square (TODO: Validate this is true!) :param keypoints: :param angle: Angle in degrees (to align with rotate_image and opencv angles) :param origin: point to rotate around :return keypoints: """ rads = -np.deg2rad(angle) rotated_keypoints = [] for keypoint in keypoints: kp_r = rotate(keypoint[0], keypoint[1], origin[0], origin[1], rads) rotated_keypoints.append(kp_r) return rotated_keypoints def flip_back(output_flipped, matched_parts): """ output_flipped: numpy.ndarray(batch_size, num_keypoints, height, width) """ assert output_flipped.ndim == 4, \ 'output_flipped should be [batch_size, num_keypoints, height, width]' output_flipped = output_flipped[:, :, :, ::-1] for pair in matched_parts: tmp = output_flipped[:, pair[0], :, :].copy() output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] output_flipped[:, pair[1], :, :] = tmp return output_flipped # From https://github.com/nsantavas/Attention-A-Lightweight-2D-Hand-Pose-Estimation-Approach/blob/2136e93586f0d0eb40518f47d2b9f78c6220ea33/model_train.py#L62 def augment_image(image): """ Augment dataset with random brightness, saturation, contrast, and image quality. Then it is casted to bfloat16 and normalized """ image = tf.cast(image, tf.uint8) image = tf.image.random_jpeg_quality(image, min_jpeg_quality=70, max_jpeg_quality=100) image = tf.cast(image, tf.float32) image = tf.image.random_brightness(image, max_delta=25 / 255) image = tf.image.random_saturation(image, lower=0.3, upper=1.7) image = tf.image.random_contrast(image, lower=0.3, upper=1.7) image = tf.cast(image, tf.float32) return image # ------------------------------------------------------------------------------ # Code below from https://github.com/microsoft/human-pose-estimation.pytorch/blob/master/lib/utils/transforms.py # # Copyright (c) Microsoft # Licensed under the MIT License. # Written by <NAME> (<EMAIL>) # ------------------------------------------------------------------------------ def fliplr_joints(joints, joints_vis, width, matched_parts): """ flip coords """ # Flip horizontal joints[:, 0] = width - joints[:, 0] - 1 # Change left-right parts for pair in matched_parts: joints[pair[0], :], joints[pair[1], :] = \ joints[pair[1], :], joints[pair[0], :].copy() joints_vis[pair[0]], joints_vis[pair[1]] = \ joints_vis[pair[1]], joints_vis[pair[0]].copy() _joints_vis = np.transpose(np.array([joints_vis, joints_vis, joints_vis]), axes=[1, 0]) return joints*_joints_vis, joints_vis def get_affine_transform(center, scale, rot, output_size, shift=	np.array([0, 0], dtype=np.float32)	numpy.array
""" Project ------- """ from typing import Any from typing import Callable from typing import Dict from typing import List from typing import Optional from typing import Tuple from typing import Union import numpy as np import scipy.sparse as sp # type: ignore from anndata import AnnData # type: ignore import metacells.parameters as pr import metacells.utilities as ut __all__ = [ "renormalize_query_by_atlas", "project_query_onto_atlas", "find_systematic_genes", "project_atlas_to_query", "find_biased_genes", "compute_query_projection", ] @ut.logged() @ut.timed_call() def renormalize_query_by_atlas( # pylint: disable=too-many-statements,too-many-branches what: str = "__x__", , adata: AnnData, qdata: AnnData, var_annotations: Dict[str, Any], layers: Dict[str, Any], varp_annotations: Dict[str, Any], ) -> Optional[AnnData]: """ Add an ``ATLASNORM`` pseudo-gene to query metacells data to compensate for the query having filtered out many genes. This renormalizes the gene fractions in the query to fit the atlas in case the query has aggressive filtered a significant amount of genes. Input* Annotated query ``qdata`` and atlas ``adata``, where the observations are cells and the variables are genes, where ``X`` is a per-variable-per-observation matrix or the name of a per-variable-per-observation annotation containing such a matrix. Returns None if no normalization is needed (or possible). Otherwise, a copy of the query metacells data, with an additional variable (gene) called ``ATLASNORM`` to the query data, such that the total number of UMIs for each query metacells is as expected given the total number of UMIs of the genes common to the query and the atlas. This is skipped if the query and the atlas have exactly the same list of genes, or if if the query already contains a high number of genes missing from the atlas so that the total number of UMIs for the query metacells is already at least the expected based on the common genes. Computation Parameters 1. Computes how many UMIs should be added to each query metacell so that its (total UMIs / total common gene UMIs) would be the same as the (total atlas UMIs / total atlas common UMIs). If this is zero (or negative), stop. 2. Add an ``ATLASNORM`` pseudo-gene to the query with the above amount of UMIs. For each per-variable (gene) observation, add the value specified in ``var_annotations``, whose list of keys must cover the set of per-variable annotations in the query data. For each per-observation-per-variable layer, add the value specified in ``layers``, whose list of keys must cover the existing layers. For each per-variable-per-variable annotation, add the value specified in ``varp_annotations``. """ for name in qdata.var.keys(): if "\|" not in name and name not in var_annotations.keys(): raise RuntimeError(f"missing default value for variable annotation {name}") for name in qdata.layers.keys(): if name not in layers.keys(): raise RuntimeError(f"missing default value for layer {name}") for name in qdata.varp.keys(): if name not in varp_annotations.keys(): raise RuntimeError(f"missing default value for variable-variable {name}") if list(qdata.var_names) == list(adata.var_names): return None query_genes_list = list(qdata.var_names) atlas_genes_list = list(adata.var_names) common_genes_list = list(sorted(set(qdata.var_names) & set(adata.var_names))) query_gene_indices = np.array([query_genes_list.index(gene) for gene in common_genes_list]) atlas_gene_indices = np.array([atlas_genes_list.index(gene) for gene in common_genes_list]) common_qdata = ut.slice(qdata, name=".common", vars=query_gene_indices) common_adata = ut.slice(adata, name=".common", vars=atlas_gene_indices) assert list(common_qdata.var_names) == list(common_adata.var_names) atlas_total_umis_per_metacell = ut.get_o_numpy(adata, what, sum=True) atlas_common_umis_per_metacell = ut.get_o_numpy(common_adata, what, sum=True) atlas_total_umis = np.sum(atlas_total_umis_per_metacell) atlas_common_umis = np.sum(atlas_common_umis_per_metacell) atlas_disjoint_umis_fraction = atlas_total_umis / atlas_common_umis - 1.0 ut.log_calc("atlas_total_umis", atlas_total_umis) ut.log_calc("atlas_common_umis", atlas_common_umis) ut.log_calc("atlas_disjoint_umis_fraction", atlas_disjoint_umis_fraction) query_total_umis_per_metacell = ut.get_o_numpy(qdata, what, sum=True) query_common_umis_per_metacell = ut.get_o_numpy(common_qdata, what, sum=True) query_total_umis = np.sum(query_total_umis_per_metacell) query_common_umis =	np.sum(query_common_umis_per_metacell)	numpy.sum
import os import sys import yaml import json import time import argparse import numpy as np import pickle import matplotlib.pyplot as plt from torch.utils.data import DataLoader import src.utils import src.dataset import src.evaluation if __name__ == "__main__": # config file parser = argparse.ArgumentParser(description="Test linear model.") parser.add_argument('--config', type=str, default="config_linear_test.yaml") args = parser.parse_args() ### END CONFIG ### ### PATHS & CONFIG project_root = os.getcwd() data_root = os.path.join(project_root, "datasets/maad") exp_root = os.path.join(project_root, "experiments") config_root = os.path.join(project_root, "config") # config config_path = os.path.join(config_root, args.config) with open(config_path, "r") as fin: config = yaml.load(fin, Loader=yaml.FullLoader) # data data_path_test = os.path.join(data_root, config["dataset"]["set"]) # experiment path method = config["model"]["type"] run_name = method date_time = src.utils.get_current_time() run_name = date_time + "_" + run_name exp_dir = os.path.join(exp_root, run_name) if not os.path.exists(exp_dir): os.makedirs(exp_dir) # create evaluation directory eval_dir = "eval_" + src.utils.get_current_time() eval_path = os.path.join(exp_dir, eval_dir) if not os.path.isdir(eval_path): os.makedirs(eval_path) ### DATA dset_test = src.dataset.MAADDataset(data_path_test, obs_len=config["model"]["obs_len"], adj_type="identity") loader_test = DataLoader(dset_test, batch_size=1, shuffle=False, num_workers=1) ### PREDICTION print("\nPredicting...") pred_start = time.time() prediction_data = {} step = 0 for cnt, batch in enumerate(loader_test): step += 1 # get data obs_traj, obs_traj_rel, frame_ids, seq_ids, labels, V_obs, A_obs = batch # prepare data obs_traj = obs_traj.numpy()[0] # its anyway batch size = 1 frame_ids = frame_ids.numpy()[0].tolist() seq_ids = seq_ids.numpy()[0] labels = labels.numpy()[0] # init linear trajectory linear_traj = np.zeros(obs_traj.shape) N = obs_traj.shape[0] # model each agent individually for i in range(N): # get agent trajectory agent_traj = obs_traj[i] # trajectory features start_pos = agent_traj[:, 0] end_pos = agent_traj[:, -1] n_ts = agent_traj.shape[1] if method == "cvm": # CVM velocity = agent_traj[:, 1] - agent_traj[:, 0] approx_agent_traj = np.zeros(agent_traj.shape) + velocity[:, np.newaxis] approx_agent_traj[:, 0] = start_pos approx_agent_traj = np.cumsum(approx_agent_traj, axis=1) elif method == "lti": # LTI x_interp = np.linspace(start_pos[0], end_pos[0], n_ts) y_interp = np.linspace(start_pos[1], end_pos[1], n_ts) approx_agent_traj = np.zeros(agent_traj.shape) approx_agent_traj[0] = x_interp approx_agent_traj[1] = y_interp else: sys.exit("Unknown model type {}. Abort!".format(method)) # add to matrix linear_traj[i] = approx_agent_traj if True: plt.plot(agent_traj[0], agent_traj[1]) plt.plot(approx_agent_traj[0], approx_agent_traj[1]) # prepare data for dict export obs_traj = np.transpose(obs_traj, (2, 0, 1)) linear_traj = np.transpose(linear_traj, (2, 0, 1)) frame_ids = np.transpose(frame_ids, (2, 0, 1)) labels =	np.transpose(labels, (2, 0, 1))	numpy.transpose
import numpy as np import xarray as xr import matplotlib.pyplot as plt import cartopy.crs as ccrs import cartopy.feature as cfeature from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER def format_lon_lat(ax, proj, lon_min, lon_max, lat_min, lat_max, title=''): from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter #from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER ax.set_extent([lon_min, lon_max, lat_min, lat_max], proj) # Add coastline ax.coastlines('50m') # Modify the title ax.set_title(title) # Set lon labels lon_labels =	np.arange(lon_min, lon_max + 1, 10)	numpy.arange
""" Copyright 2018 IBM Corporation 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. """ from gensim.utils import simple_preprocess from gensim.models import Word2Vec from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity import numpy as np class CustomParVec(): ''' Custom Paragraph Vector. Each paragraph(or sentence) is the sum of each of its word's Word2Vec vector representation scaled by that word's tf-idf. ''' def __init__(self, word_sentence_list, workers = 2, dimensions = 100, min_word_count = 2, context = 5, downsampling = 0, tfidf = True, pre_trained = None, supervised_docs = None): ''' Args: word_sentence_list (list[list[str]]) : List of lists of words that make up a paragraph(or sentence). workers (int) : Number of threads to run in parallel (Default = 2). dimensions (int) : Vector dimensionality (Default = 100). min_word_count (int) : Minimum word count (Default = 2). context (int) : Context window size (Default = 5). downsampling (int) : Downsample setting for frequent words (Default = 0). tfidf (bool) : Specify whether or not to use idf in scaling (Default = True). pre_trained (Word2Vec) : Use a pre-trained Word2Vec model (Default = None). supervised_docs (list[str]) : List of sentences from some "ground truth" (Default = None). ''' self.dimensions = dimensions # Set the number of dimension if not pre_trained: self.word2vec_model = Word2Vec(word_sentence_list, workers=workers, \ size=self.dimensions, min_count = min_word_count, \ window = context, sample = downsampling) self.word2vec_model.init_sims(replace=True) # used for memory efficiency else: self.word2vec_model = pre_trained self.sentences = [' '.join(words) for words in word_sentence_list] # Keep a list of the full sentences themselves. self.tf_idf_obj = TfidfVectorizer(use_idf = tfidf) # Create TfidfVectorizer object self.tf_idf_obj.fit(self.sentences) # Transform and fit tf-idf to all sentences(could be paragraphs) self.tf_idf = self.tf_idf_obj.transform(self.sentences) self.word_index = self.tf_idf_obj.get_feature_names() # Keep track of words by index for lookups if supervised_docs: self.extra_tf_idf_obj = TfidfVectorizer(use_idf = tfidf) # Create TfidfVectorizer object self.extra_tf_idf_obj.fit(supervised_docs) # Transform and fit tf-idf to all sentences(could be paragraphs) self.extra_tf_idf = self.extra_tf_idf_obj.transform(supervised_docs) self.extra_word_index = self.extra_tf_idf_obj.get_feature_names() # Keep track of words by index for lookups else: self.extra_tf_idf_obj = None def learnVectors(self): ''' Create a vector representation of every paragraph(or sentence) in the initial data provided. Yields: numpy.ndarray: Next numpy array representing the paragraph (or sentence). ''' rows, cols = self.tf_idf.nonzero() # Get the rows and column indices of non zero tf-idf values curr_line = 0 curr_vec = np.zeros(self.dimensions) for row, col in zip(rows, cols): if curr_line == row: # Check that the current word belongs to the same paragraph (or sentence). try: # Infer the vector of the current word by scaling the word's word2vec vector by its tf-idf value. # Add that inferred vector to the current vector representing the current paragraph. curr_vec += (self.word2vec_model[(self.word_index[col])] * self.tf_idf[row, col]) except: continue else: # If we are on the next paragraph, yield the current vector and reset it. yield(curr_vec) curr_line = row curr_vec = np.zeros(self.dimensions) try: curr_vec = self.word2vec_model[(self.word_index[col])] * self.tf_idf[row, col] except: continue def train(self): self.vectors = list(self.learnVectors()) def getMostSimilar(self, sentence, top_n = 10, threshold = 0.5, sentences = None, vectors = None): ''' Given a new sentence, find the closest top_n elements Args: sentence(string) : Text we want to find most similar to. top_n (int) : Total number of most similar tuples we want returned (Default value is 5). threshold (float) : Minimum Cosine Distance to be returned sentences (list[string]) : List of sentences to be compared to vectors[list[numpy nd array]] : Vector embedding of sentences Returns: list[(float, string)]: A list of (cosine similarity, sentence) tuples of size top_n closest to the input sentence. ''' inferred_vector = self.inferVector(sentence) if sentences and vectors: corpus = sentences vecs = vectors else: corpus = self.sentences vecs = self.vectors cos_similarities = np.ravel(cosine_similarity(inferred_vector.reshape(1,-1), vecs)) most_similar = np.argpartition(-cos_similarities, top_n)[:top_n] return [(cos_similarities[sentence_index], corpus[sentence_index]) for sentence_index in most_similar if cos_similarities[sentence_index] >= threshold] def inferVector(self, line): if self.extra_tf_idf_obj: return self.inferVector2(line) return self.inferVector1(line) def inferVector1(self, line): ''' Given a new line, infer a custom vector representation using the corpus tfidf. Args: line : new sentence to be inferred Returns: numpy.ndarray : vector representation of the line ''' line = ' '.join(simple_preprocess(line)) # pre-process the line line_tf_idf = self.tf_idf_obj.transform([line]) # infer the tf-idf values for the words in the line rows, cols = line_tf_idf.nonzero() new_vec = np.zeros(self.dimensions) # Apply the same sentence to vector conversion as above. for col in cols: try: new_vec += (self.word2vec_model[(self.word_index[col])] * line_tf_idf[0, col]) except: continue return	np.asarray(new_vec)	numpy.asarray
import os.path import numpy as np class MnistNShot: def __init__(self, root, batchsz, n_way, k_shot, k_query, imgsz): """ Different from mnistNShot, the :param root: :param batchsz: task num :param n_way: :param k_shot: :param k_qry: :param imgsz: """ self.resize = imgsz if not os.path.isfile(os.path.join(root, 'mnist.npy')): # TODO : May add formatting npy file here raise FileNotFoundError("Must have the npy file first") else: # if data.npy exists, just load it. self.x = np.load(os.path.join(root, 'mnist.npy')) print('load from mnist.npy.') # TODO: can not shuffle here, we must keep training and test set distinct! # mnist 10 classes self.x_train, self.x_test = self.x[:10], self.x[0:] # self.normalization() # normalization manually self.x_train /= 255. self.x_test /= 255. self.batchsz = batchsz self.n_cls = self.x.shape[0] # 1623 self.n_way = n_way # n way self.k_shot = k_shot # k shot self.k_query = k_query # k query # must less than number of images per class # assert (k_shot + k_query) <= 20 # save pointer of current read batch in total cache self.indexes = {"train": 0, "test": 0} self.datasets = {"train": self.x_train, "test": self.x_test} # original data cached print("DB: train", self.x_train.shape, "test", self.x_test.shape) self.datasets_cache = {"train": self.load_data_cache(self.datasets["train"]), # current epoch data cached "test": self.load_data_cache(self.datasets["test"])} def normalization(self): """ Normalizes our data, to have a mean of 0 and sdt of 1 """ # TODO: you can set mean and std here self.mean =	np.mean(self.x_train)	numpy.mean
import glob, os, time, sys import numpy as np import scipy.linalg as sl, scipy.stats, scipy.special class OutlierGibbs(object): """Gibbs-based pulsar-timing outlier analysis. Based on: Article by <NAME>: "Robust and Accurate Inference via a Mixture of Gaussian and Student's t Errors", https://doi.org/10.1080/10618600.2018.1537925 arXiv:1707.03057. Article by <NAME>: "Controlling Outlier Contamination In Multimessenger Time-domain Searches For Supermasssive Binary Black Holes", (In prep. 2021) Code from https://github.com/jellis18/gibbs_student_t Authors: <NAME>, <NAME>, <NAME> Example usage: > gibbs = OutlierGibbs(pta, model='mixture', vary_df=True, theta_prior='beta', vary_alpha=True) > params = np.array([p.sample() for p in gibbs.params]).flatten() > gibbs.sample(params, outdir='./outlier/', niter=10000, resume=False) > poutlier = np.mean(gibbs.poutchain, axis = 0) # Gives marginalized outlier probability of each TOA """ def __init__(self, pta, model='mixture', m=0.01, tdf=4, vary_df=True, theta_prior='beta', alpha=1e10, vary_alpha=True, pspin=None): """ Parameters ----------- pta : object instance of a pta object for a single pulsar model : str type of outlier model [default = mixture of Gaussian and Student's t] tdf : int degrees of freedom for Student's t outlier distribution [default = 4] m : float a-priori proportion of observations that are outliers [default = 0.01] vary_df : boolean vary the Student's t degrees of freedom [default = True] theta_prior : str prior outlier probability [default = beta distribution] alpha : float relative width of outlier to inlier distribution [default = 1e10] vary_alpha : boolean vary the relative outlier to inlier width [default = True] pspin : float pulsar spin period for vvh17 model, arXiv:1609.02144 [default = None] """ self.pta = pta if np.any(['basis_ecorr' in key for key in self.pta._signal_dict.keys()]): pass else: print('ERROR: Gibbs outlier analysis must use basis_ecorr, not kernel ecorr') # a-priori proportion of observations that are outliers self.mp = m # a-priori outlier probability distribution self.theta_prior = theta_prior # spin period self.pspin = pspin # vary t-distribution d.o.f self.vary_df = vary_df # vary alpha self.vary_alpha = vary_alpha # For now assume one pulsar self._residuals = self.pta.get_residuals()[0] # which likelihood model self._lmodel = model # auxiliary variable stuff xs = [p.sample() for p in pta.params] self._b = np.zeros(self.pta.get_basis(xs)[0].shape[1]) # for caching self.TNT = None self.d = None # outlier detection variables self._pout = np.zeros_like(self._residuals) self._z = np.zeros_like(self._residuals) if not vary_alpha: self._alpha = np.ones_like(self._residuals) * alpha else: self._alpha = np.ones_like(self._residuals) self._theta = self.mp self.tdf = tdf if model in ['t', 'mixture', 'vvh17']: self._z = np.ones_like(self._residuals) @property def params(self): ret = [] for param in self.pta.params: ret.append(param) return ret def map_params(self, xs): return {par.name: x for par, x in zip(self.params, xs)} def get_hyper_param_indices(self): ind = [] for ct, par in enumerate(self.params): if 'ecorr' in par.name or 'log10_A' in par.name or 'gamma' in par.name: ind.append(ct) return np.array(ind) def get_white_noise_indices(self): ind = [] for ct, par in enumerate(self.params): if 'efac' in par.name or 'equad' in par.name: ind.append(ct) return np.array(ind) def update_hyper_params(self, xs): # get hyper parameter indices hind = self.get_hyper_param_indices() # get initial log-likelihood and log-prior lnlike0, lnprior0 = self.get_lnlikelihood(xs), self.get_lnprior(xs) xnew = xs.copy() for ii in range(10): # standard gaussian jump (this allows for different step sizes) q = xnew.copy() sigmas = 0.05 * len(hind) probs = [0.1, 0.15, 0.5, 0.15, 0.1] sizes = [0.1, 0.5, 1.0, 3.0, 10.0] scale = np.random.choice(sizes, p=probs) par = np.random.choice(hind, size=1) # only one hyper param at a time q[par] += np.random.randn(len(q[par])) * sigmas * scale # get log-like and log prior at new position lnlike1, lnprior1 = self.get_lnlikelihood(q), self.get_lnprior(q) # metropolis step diff = (lnlike1 + lnprior1) - (lnlike0 + lnprior0) if diff > np.log(np.random.rand()): xnew = q lnlike0 = lnlike1 lnprior0 = lnprior1 else: xnew = xnew return xnew def update_white_params(self, xs): # get white noise parameter indices wind = self.get_white_noise_indices() xnew = xs.copy() lnlike0, lnprior0 = self.get_lnlikelihood_white(xnew), self.get_lnprior(xnew) for ii in range(20): # standard gaussian jump (this allows for different step sizes) q = xnew.copy() sigmas = 0.05 * len(wind) probs = [0.1, 0.15, 0.5, 0.15, 0.1] sizes = [0.1, 0.5, 1.0, 3.0, 10.0] scale = np.random.choice(sizes, p=probs) par = np.random.choice(wind, size=1) q[par] += np.random.randn(len(q[par])) * sigmas * scale # get log-like and log prior at new position lnlike1, lnprior1 = self.get_lnlikelihood_white(q), self.get_lnprior(q) # metropolis step diff = (lnlike1 + lnprior1) - (lnlike0 + lnprior0) if diff > np.log(np.random.rand()): xnew = q lnlike0 = lnlike1 lnprior0 = lnprior1 else: xnew = xnew return xnew def update_b(self, xs): # map parameter vector params = self.map_params(xs) # start likelihood calculations loglike = 0 # get auxiliaries Nvec = self._alpha*self._z self.pta.get_ndiag(params)[0] phiinv = self.pta.get_phiinv(params, logdet=False)[0] residuals = self._residuals T = self.pta.get_basis(params)[0] if self.TNT is None and self.d is None: self.TNT = np.dot(T.T, T / Nvec[:,None]) self.d = np.dot(T.T, residuals/Nvec) #d = self.pta.get_TNr(params)[0] #TNT = self.pta.get_TNT(params)[0] # Red noise piece Sigma = self.TNT + np.diag(phiinv) try: u, s, _ = sl.svd(Sigma) mn = np.dot(u, np.dot(u.T, self.d)/s) Li = u * np.sqrt(1/s) except np.linalg.LinAlgError: Q, R = sl.qr(Sigma) Sigi = sl.solve(R, Q.T) mn = np.dot(Sigi, self.d) u, s, _ = sl.svd(Sigi) Li = u * np.sqrt(1/s) b = mn + np.dot(Li, np.random.randn(Li.shape[0])) return b def update_theta(self, xs): if self._lmodel in ['t', 'gaussian']: return self._theta elif self._lmodel in ['mixture', 'vvh17']: n = len(self._residuals) if self.theta_prior == 'beta': mk = n * self.mp k1mm = n * (1-self.mp) else: mk, k1mm = 1.0, 1.0 # from Tak, <NAME> (2018): k = sample size, m = 0.01 ret = scipy.stats.beta.rvs(np.sum(self._z) + mk, n - np.sum(self._z) + k1mm) return ret def update_z(self, xs): # map parameters params = self.map_params(xs) if self._lmodel in ['t', 'gaussian']: return self._z elif self._lmodel in ['mixture', 'vvh17']: Nvec0 = self.pta.get_ndiag(params)[0] Tmat = self.pta.get_basis(params)[0] Nvec = self._alpha * Nvec0 theta_mean = np.dot(Tmat, self._b) top = self._theta * scipy.stats.norm.pdf(self._residuals, loc=theta_mean, scale=np.sqrt(Nvec)) if self._lmodel == 'vvh17': top = self._theta / self.pspin bot = top + (1-self._theta) * scipy.stats.norm.pdf(self._residuals, loc=theta_mean, scale=np.sqrt(Nvec0)) q = top / bot q[np.isnan(q)] = 1 self._pout = q return scipy.stats.binom.rvs(1, list(map(lambda x: min(x, 1), q))) def update_alpha(self, xs): # map parameters params = self.map_params(xs) # equation 12 of Tak, <NAME> if np.sum(self._z) >= 1 and self.vary_alpha: Nvec0 = self.pta.get_ndiag(params)[0] Tmat = self.pta.get_basis(params)[0] theta_mean = np.dot(Tmat, self._b) top = ((self._residuals - theta_mean)*2 self._z / Nvec0 + self.tdf) / 2 bot = scipy.stats.gamma.rvs((self._z + self.tdf) / 2) return top / bot else: return self._alpha def update_df(self, xs): if self.vary_df: # 1. evaluate the log conditional posterior of df for 1, 2, ..., 30. log_den_df = np.array(list(map(self.get_lnlikelihood_df, np.arange(1,31)))) # 2. normalize the probabilities den_df = np.exp(log_den_df - log_den_df.max()) den_df /= den_df.sum() # 3. sample one of values (1, 2, ..., 30) according to the probabilities df = np.random.choice(np.arange(1, 31), p=den_df) return df else: return self.tdf def get_lnlikelihood_white(self, xs): # map parameters params = self.map_params(xs) matrix = self.pta.get_ndiag(params)[0] # Nvec and Tmat Nvec = self._alpha*self._z matrix Tmat = self.pta.get_basis(params)[0] # whitened residuals mn = np.dot(Tmat, self._b) yred = self._residuals - mn # log determinant of N logdet_N = np.sum(np.log(Nvec)) # triple product in likelihood function rNr = np.sum(yred*2/Nvec) # first component of likelihood function loglike = -0.5 (logdet_N + rNr) return loglike def get_lnlikelihood(self, xs): # map parameter vector params = self.map_params(xs) # start likelihood calculations loglike = 0 # get auxiliaries Nvec = self._alpha*self._z self.pta.get_ndiag(params)[0] phiinv, logdet_phi = self.pta.get_phiinv(params, logdet=True)[0] residuals = self._residuals T = self.pta.get_basis(params)[0] if self.TNT is None and self.d is None: self.TNT = np.dot(T.T, T / Nvec[:,None]) self.d = np.dot(T.T, residuals/Nvec) # log determinant of N logdet_N = np.sum(np.log(Nvec)) # triple product in likelihood function rNr = np.sum(residuals*2/Nvec) # first component of likelihood function loglike += -0.5 (logdet_N + rNr) # Red noise piece Sigma = self.TNT + np.diag(phiinv) try: cf = sl.cho_factor(Sigma) expval = sl.cho_solve(cf, self.d) except np.linalg.LinAlgError: return -np.inf logdet_sigma = np.sum(2 * np.log(np.diag(cf[0]))) loglike += 0.5 * (np.dot(self.d, expval) - logdet_sigma - logdet_phi) return loglike def get_lnlikelihood_df(self, df): n = len(self._residuals) ll = -(df/2) * np.sum(np.log(self._alpha)+1/self._alpha) + \ n * (df/2) * np.log(df/2) - n*scipy.special.gammaln(df/2) return ll def get_lnprior(self, xs): return sum(p.get_logpdf(x) for p, x in zip(self.params, xs)) def sample(self, xs, outdir='./', niter=10000, resume=False): print(f'Creating chain directory: {outdir}') os.system(f'mkdir -p {outdir}') self.chain = np.zeros((niter, len(xs))) self.bchain = np.zeros((niter, len(self._b))) self.thetachain = np.zeros(niter) self.zchain = np.zeros((niter, len(self._residuals))) self.alphachain = np.zeros((niter, len(self._residuals))) self.poutchain = np.zeros((niter, len(self._residuals))) self.dfchain = np.zeros(niter) self.iter = 0 startLength = 0 xnew = xs if resume: print('Resuming from previous run...') # read in previous chains tmp_chains = [] tmp_chains.append(np.loadtxt(f'{outdir}/chain.txt')) tmp_chains.append(np.loadtxt(f'{outdir}/bchain.txt')) tmp_chains.append(np.loadtxt(f'{outdir}/thetachain.txt')) tmp_chains.append(np.loadtxt(f'{outdir}/zchain.txt')) tmp_chains.append(np.loadtxt(f'{outdir}/alphachain.txt')) tmp_chains.append(np.loadtxt(f'{outdir}/poutchain.txt')) tmp_chains.append(	np.loadtxt(f'{outdir}/dfchain.txt')	numpy.loadtxt
# -- coding: utf-8 -- """Clark_Whitehead_HW5_Neural_Network_final.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1c1Doh3Fy7Hv0paTgVngGdJeH6vg0vJX1 # Neural Network In this assignment you will build an artificial neural network _from scratch_, meaning without modern machine learning packages (e.g. scikit-learn, tensorflow, and pytorch). You should use numpy, and can use other standard python libraries if you like. ## Part 1: Neural Network with Stochastic Gradient Descent (4 points) Define a class NeuralNetwork that implements an artificial neural network with a single hidden layer. The hidden layer should have non-linear activation function (e.g. Rectificed Linear Units, ReLU), and the output should have a Softmax activation function. Use the template provided. The hard part of this is the train method, which requires computing lots of gradients. See the [notes](https://laulima.hawaii.edu/access/content/group/MAN.90549.202130/Notes_6__Introduction_to_Neural_Networks.pdf) on Laulima to see the equations for calculating these analytically. Translating these equations into code is non-trivial. The backpropagation algorithm is a dynamic programming algorithm that computes the gradients layer by layer, and can be written very elegantly in terms of matrix manipulations (a couple lines of numpy code). _Reminder: Do NOT copy and paste code from the internet. Write your own code._ ## Part 2: Apply Your Model to Fashion Dataset (3 points) We will test the model on the Fashion MNIST dataset. This is a 10-class classification task designed to be similar to the MNIST digit recognition dataset. The classes are different items of clothing (shoes, shirts, handbags, etc.) instead of digits. Here is an [introduction](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) and [github page](https://github.com/zalandoresearch/fashion-mnist). 1. Demonstrate overfitting in your network by plotting the training and test set losses vs. epoch. An epoch is one iteration through the training set; in SGD this means one weight update for each training example. You can use a smaller dataset so that you overfit faster, but clearly state how many examples are in your train and test sets. 2. Optimize the hyperparameters (learning rate and number of hidden neurons) of the neural network. Because the test dataset is fairly large (10k examples), you don't need to split off a separate validation set for this analysis. Report the best performance (test accuracy) and the best hyperparameters. 3. Visualize the 10 test examples with the largest loss. ## Part 3: Better and Faster: Mini-Batch SGD (3 points) Implement mini-batch gradient descent in your NeuralNetwork train method. This is much more efficient to update the weights on batches of training data, e.g. 100 examples at a time, which serves two purposes: (1) each update is a better, less-noisy estimate of the true gradient, and (2) the matrix multiplications can be parallelized for an almost-linear speedup with multiple cores or a GPU (by default, numpy should automatically use multiple CPUs for matrix multiplications). This requires implementing the forward and backpropagation computations efficiently, using matrix multiplications rather than for loops. """ import numpy as np from sklearn.metrics import accuracy_score # Download Fashion MNIST Dataset import gzip import os from urllib.request import urlretrieve import numpy as np import matplotlib.pyplot as plt def fashion_mnist(): """ Download compressed Fashion MNIST data to local directory, and unpack data into numpy arrays. Return (train_images, train_labels, test_images, test_labels). Args: None Returns: Tuple of (train_images, train_labels, test_images, test_labels), each a matrix. Rows are examples. Columns of images are pixel values. Columns of labels are a onehot encoding of the correct class. """ url = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/' files = ['train-images-idx3-ubyte.gz', 'train-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz', 't10k-labels-idx1-ubyte.gz'] path = './' # Download data to current directory. os.makedirs(path, exist_ok=True) # Create path if it doesn't exist. # Download any missing files for file in files: if file not in os.listdir(path): urlretrieve(url + file, os.path.join(path, file)) print("Downloaded %s to %s" % (file, path)) def _images(path): """Return images loaded locally.""" with gzip.open(path) as f: # First 16 bytes are magic_number, n_imgs, n_rows, n_cols pixels = np.frombuffer(f.read(), 'B', offset=16) return pixels.reshape(-1, 784).astype('float32') / 255 def _labels(path): """Return labels loaded locally.""" with gzip.open(path) as f: # First 8 bytes are magic_number, n_labels integer_labels = np.frombuffer(f.read(), 'B', offset=8) def _onehot(integer_labels): """Return matrix whose rows are onehot encodings of integers.""" n_rows = len(integer_labels) n_cols = integer_labels.max() + 1 onehot = np.zeros((n_rows, n_cols), dtype='uint8') onehot[np.arange(n_rows), integer_labels] = 1 return onehot return _onehot(integer_labels) train_images = _images(os.path.join(path, files[0])) train_labels = _labels(os.path.join(path, files[1])) test_images = _images(os.path.join(path, files[2])) test_labels = _labels(os.path.join(path, files[3])) return train_images, train_labels, test_images, test_labels train_images, train_labels, test_images, test_labels = fashion_mnist() # Plot examples from dataset. plt.figure(1, figsize=(14,6)) for i in range(10): plt.subplot(1,10, i+1) plt.imshow(train_images[i,:].reshape(28,28), cmap='bone') plt.title(f'Label: {train_labels[i].argmax()}') plt.xticks([]) plt.yticks([]) print(test_images.shape) train_images_1000 = train_images[0:1000, :] train_labels_1000 = train_labels[0:1000, :] train_images_100 = train_images[0:100, :] train_labels_100 = train_labels[0:100, :] test_images_1000 = test_images[0:1000, :] test_labels_1000 = test_labels[0:1000, :] test_images_100 = test_images[0:100, :] test_labels_100 = test_labels[0:100, :] train_images_10 = train_images[0:10, :] train_labels_10 = train_labels[0:10, :] train_images_1 = train_images[0:1, :] train_labels_1 = train_labels[0:1, :] print(train_images_10.shape) #x_train shape = 11x4 x_train = np.array([[1.2, 4.3, 2.1, 1.9], [6.2, 8.3, 5.1, 9.9], [2.3, 4.3, 3.1, 0.9], [4.1, 4.4, 1.1, 0.3], [6.1, 7.1, 8.1, 9.1], [1.0, 2.0, 1.0, 1.0], [5.1, 5.1, 5.1, 5.1], [1.8, 4.0, 3.9, 2.7], [4.4, 0.8, 1.9, 2.7], [6.9, 8.8, 5.7, 7.1]]) #y_train shape = 1x11 y_train = np.array([[1,0], [0,1], [1,0], [1,0], [0,1], [1,0], [0,1], [1,0], [1,0], [0,1]]) # Part 1: Defining the neural network. class NeuralNetwork(): def __init__(self, inputs, hidden, outputs): """ Initialize a simple neural network with a single hidden layer. This method randomly initializes the parameters of the model, saving them as private variables. Each layer is parameterized by a weight matrix and a bias vector; a useful trick is store the weights and biases for a layer together, as a single matrix. Args: inputs: int, input dimension hidden: int, number of hidden neurons outputs: int, number of output neurons Returns: None """ # Initialize the weights and biases of the neural network as private variables. # Store a weight matrix for each layer. self.hidden = hidden self.train_acc = [] self.test_acc = [] self.train_loss = [] self.test_loss = [] self.w1 = 2 * np.random.rand(inputs, hidden) - 1 self.w2 = 2 * np.random.rand(hidden, outputs) - 1 def loss(self, y_true, y_pred): """ Compute categorical cross-entropy loss function. Sum loss contributions over the outputs (axis=1), but average over the examples (axis=0) Args: y_true: NxD numpy array with N examples and D outputs (one-hot labels). y_pred: NxD numpy array with N examples and D outputs (probabilities). Returns: loss: array of length N representing loss for each example. """ # WRITE ME loss = np.sum(y_pred * y_true, axis=1) #multiply the one_hot from y_true by it's predicted value loss = -np.log(loss) #take neg log of the predicted value loss = np.mean(loss) #take mean of loss from whole batch return loss def softmax(self, input): normalization = input - np.max(input, axis=1, keepdims=True) #subtract max value from all #values in order to keep exp from exploding exp = np.exp(input) #take e to the x where x=inputs for all z before softmax output = exp / np.sum(exp, axis=1, keepdims=True) #divide all exp by sum so their combined total = 1 output = np.clip(output, 1e-7, 1 - 1e-7) #clip in case there is a 0 or a 1. Necessary for loss so we don't take log of 0 # output = np.sum(output * targets, axis=1, keepdims=1) return output def relu(self, input): output = np.maximum(0, input) return output def predict(self, X): """ Make predictions on inputs X. Args: X: NxM numpy array where n-th row is an input. Returns: y_pred: NxD array where n-th row is vector of probabilities. """ # WRITE ME self.h = np.dot(X, self.w1) self.h = self.relu(self.h) output = np.dot(self.h, self.w2) y_pred = self.softmax(output) return y_pred def evaluate(self, X, y): """ Make predictions and compute loss. Args: X: NxM numpy array where n-th row is an input. y: NxD numpy array with N examples and D outputs (one-hot labels). Returns: loss: array of length N representing loss for each example. """ # WRITE ME y_pred = self.predict(X) y_true = y loss = self.loss(y_pred, y_true) return loss def train(self, X, y, X_test, y_test, max_epochs, lr=0.0001): """ Train the neural network using stochastic gradient descent. Args: X: NxM numpy array where n-th row is an input. y: NxD numpy array with N examples and D outputs (one-hot labels). lr: scalar learning rate. Use small value for debugging. max_epochs: int, each epoch is one iteration through the training data. Returns: None """ # WRITE ME for i in range(max_epochs): y_pred = self.predict(X) self.train_loss.append(self.loss(y, y_pred)) acc_pred = np.where(y_pred > 0.5, 1, 0) self.train_acc.append(accuracy_score(acc_pred, y)) #derivatives dl_dz = y_pred - y dl_dw2 = np.dot(self.h.T, dl_dz) dh_dzh = self.h > 0 dl_dzh =	np.dot(dl_dz, self.w2.T)	numpy.dot
# -- coding: utf-8 -- # run in py3 !! import os os.environ["CUDA_VISIBLE_DEVICES"] = "1"; import tensorflow as tf config = tf.ConfigProto() # config.gpu_options.per_process_gpu_memory_fraction=0.5 config.gpu_options.allow_growth = True tf.Session(config=config) import numpy as np from sklearn import preprocessing import tensorflow as tf import time import matplotlib as mpl mpl.use('Agg') import matplotlib.pyplot as plt from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import pandas as pd from keras import backend as K import keras.layers.convolutional as conv from keras.layers import merge from keras.wrappers.scikit_learn import KerasRegressor from keras import utils from keras.layers.pooling import MaxPooling1D, MaxPooling2D from keras.layers import pooling from keras.models import Sequential, Model from keras.regularizers import l1, l2 from keras import layers from keras.layers import Dense, Dropout, Activation, Flatten, Input, Convolution1D, Convolution2D, LSTM from keras.optimizers import SGD, RMSprop from keras.layers.normalization import BatchNormalization from keras import initializers from keras.callbacks import EarlyStopping from keras import callbacks from keras import backend as K from keras.utils import to_categorical from keras.callbacks import EarlyStopping, ModelCheckpoint, Callback from keras.models import Model from keras import initializers, layers from keras.optimizers import SGD, Adadelta, Adam from keras.regularizers import l1, l2 from keras import regularizers import sys sys.path.append('.') from hist_figure import his_figures if len(sys.argv) > 1: prefix = sys.argv[1] else: prefix = time.time() DATAPATH = '5fold/' RESULT_PATH = './results/' feature_num = 25 batch_num = 2 # batch_size = 32 batch_size = 512 SEQ_LENGTH = 20 STATEFUL = False scaler = None # tmp, for fit_transform # id,usage,date,com_date,week,month,year # com_date,date,id,month,usage,week,year def get_data(path_to_dataset='df_dh.csv', sequence_length=20, stateful=False, issplit=True): fold_index = 1 ### dtypes = {'sub': 'float', 'super': 'float', 'error': 'float', 'com_date': 'int', 'week': 'str', 'month': 'str', 'year': 'str', 'numbers': 'int', 'log': 'float', 'id': 'str', 'usage': 'float'} parse_dates = ['date'] print(path_to_dataset) df = pd.read_csv(DATAPATH + path_to_dataset, header=0, dtype=dtypes, parse_dates=parse_dates, encoding="utf-8") # print(path_to_dataset) print(df.columns) df = df[df['error'] >= 0] # df_test = pd.read_csv(DATAPATH+"test"+str(fold_index)+".csv", header = 0, dtype=dtypes, parse_dates=parse_dates,encoding="utf-8") def helper(x): split = list(map(int, x.strip('[').strip(']').split(','))) d = {} for counter, value in enumerate(split): k = str(len(split)) + "-" + str(counter) d[k] = value return d # df_train_temp = df_train['week'].apply(helper).apply(pd.Series) df_week = df['week'].apply(helper).apply(pd.Series).as_matrix() # 7 df_month = df['month'].apply(helper).apply(pd.Series).as_matrix() # 12 df_year = df['year'].apply(helper).apply(pd.Series).as_matrix() # 3 df_empty = df[['super', 'com_date', 'error', 'numbers']].copy() # print(df_empty) df_super = df_empty.ix[:, [0]] df_com_date = df_empty.ix[:, [1]] df_error = df_empty.ix[:, [2]] df_numbers = df_empty.ix[:, [3]] X_train_ = np.column_stack((df_super, df_com_date, df_numbers, df_week, df_month)) Y_train_ = df_error.as_matrix() ss_x = preprocessing.MaxAbsScaler() ss_y = preprocessing.MaxAbsScaler() global scaler scaler = ss_y # ss_x = preprocessing.StandardScaler() array_new = ss_x.fit_transform(df_empty.ix[:, [0]]) df_super = pd.DataFrame(array_new) array_new = ss_x.fit_transform(df_empty.ix[:, [1]]) df_com_date = pd.DataFrame(array_new) array_new = ss_x.fit_transform(df_empty.ix[:, [3]]) df_numbers = pd.DataFrame(array_new) array_new = ss_y.fit_transform(df_empty.ix[:, [2]]) df_error = pd.DataFrame(array_new) df_week = ss_x.fit_transform(df_week) df_week = pd.DataFrame(df_week) df_month = ss_x.fit_transform(df_month) df_month = pd.DataFrame(df_month) X_train =	np.column_stack((df_super, df_com_date, df_numbers, df_week, df_month))	numpy.column_stack
import caffe from skimage import color, io import cv2 import numpy as np import glog as log import os import tifffile import time def load_network(prototxt='models/test.prototxt', caffemodel='models/trn_iter_15000.caffemodel', gpu_num=0): caffe.set_mode_gpu() caffe.set_device(gpu_num) log.info('DCNN Weights: %s' % caffemodel) log.info('DCNN Model Definition: %s' % prototxt) return caffe.Net(prototxt, caffemodel, caffe.TEST) # fac = 2 ^ (number of times image is downsampled in the network) def prepare_input(img_in, fac=8, extra_pad=0, pad_mode='reflect', read_img=True): if read_img: log.info('Reading %s' % img_in) img_in = cv2.imread(img_in) if img_in.ndim == 2: # force color conversion img_in = np.repeat(img_in[:,:,np.newaxis],3,axis=2) # Pad H,W to make them multiples of fac H=img_in.shape[0] W=img_in.shape[1] padH = (fac - (H % fac)) % fac padW = (fac - (W % fac)) % fac if padH>0 or padW>0 or extra_pad>0: img_in = np.pad(img_in,pad_width=((extra_pad,padH+extra_pad),(extra_pad,padW+extra_pad),(0,0)),mode=pad_mode) img_in=img_in.astype(np.float32) return (img_in, H, W) # Forward Pass def forward_once(net, img_in, H, W): t=time.time() # HWC to CHW img_in=	np.transpose(img_in,[2,0,1])	numpy.transpose
from torch.utils.data import DataLoader, Dataset, Sampler from pathlib import Path from collections import defaultdict import json import random from multiprocessing import Pool import h5py import pickle import math from tqdm import tqdm import torch import numpy as np import csv from copy import deepcopy from torch.utils.data.distributed import DistributedSampler from transformers import T5TokenizerFast, BartTokenizer from tokenization import VLT5TokenizerFast, VLT5Tokenizer project_dir = Path(__file__).resolve().parent.parent # VLT5 workspace_dir = project_dir.parent dataset_dir = workspace_dir.joinpath('datasets/').resolve() vcr_dir = dataset_dir.joinpath('VCR') vcr_img_dir = vcr_dir.joinpath('vcr1images') vcr_feature_dir = vcr_dir.joinpath('features') class VCRFineTuneDataset(Dataset): def __init__(self, split='train', raw_dataset=None, rank=-1, topk=-1, verbose=True, args=None, mode='train'): super().__init__() self.topk = topk self.verbose = verbose self.args = args self.mode = mode # Loading datasets to data self.split = split self.sources = split.split(',') if self.verbose: print('Data sources: ', self.sources) if 't5' in self.args.backbone: if self.args.use_vision: self.tokenizer = VLT5TokenizerFast.from_pretrained( args.backbone, max_length=self.args.max_text_length, do_lower_case=self.args.do_lower_case) else: self.tokenizer = T5TokenizerFast.from_pretrained( args.backbone, max_length=self.args.max_text_length, do_lower_case=self.args.do_lower_case) elif 'bart' in self.args.backbone: self.tokenizer = BartTokenizer.from_pretrained( args.backbone, # max_length=self.args.max_text_length, do_lower_case=self.args.do_lower_case) additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \ [f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)] special_tokens_dict = {'additional_special_tokens': additional_special_tokens} num_added_toks = self.tokenizer.add_special_tokens(special_tokens_dict) self.img_ids_to_source = {} data_info_dicts = [] for source in self.sources: data_info_path = dataset_dir.joinpath(f'VCR/{source}.jsonl') with open(data_info_path) as f: _data_info_dicts = [json.loads(s) for s in f] for _d in _data_info_dicts: self.img_ids_to_source[_d['img_id']] = source _d['source'] = source data_info_dicts.extend(_data_info_dicts) if self.verbose: print(f"Loaded {len(_data_info_dicts)} data from", source) data = data_info_dicts self.rank = rank if self.topk > 0: data = data[:self.topk] if self.verbose: print(f"Use only {self.topk} data") self.data = data if self.verbose: print("# all sentences:", len(self.data)) self.n_boxes = args.n_boxes self.source_to_h5 = { 'train': vcr_feature_dir.joinpath(f'train_boxes36.h5'), 'val': vcr_feature_dir.joinpath(f'val_boxes36.h5'), 'test': vcr_feature_dir.joinpath(f'test_boxes36.h5'), 'train_GT': vcr_feature_dir.joinpath(f'train_boxes_GT.h5'), 'val_GT': vcr_feature_dir.joinpath(f'val_boxes_GT.h5'), 'test_GT': vcr_feature_dir.joinpath(f'test_boxes_GT.h5'), } def __len__(self): return len(self.data) def __getitem__(self, idx): out_dict = {} out_dict['args'] = self.args datum = self.data[idx] uid = f"{datum['img_id']}_{datum['question_number']}" out_dict['uid'] = uid test = 'test' in datum['annot_id'] out_dict['is_test'] = test ###### Image ###### assert self.args.use_vision img_id = datum['img_id'] out_dict['img_id'] = img_id img_path = vcr_img_dir.joinpath(datum['img_fn']) # assert img_path.exists() out_dict['img_path'] = img_path source = self.img_ids_to_source[img_id] f = self.source_to_h5[source] f_GT = self.source_to_h5[f'{source}_GT'] if isinstance(f, Path): f = h5py.File(f, 'r') self.source_to_h5[source] = f if isinstance(f_GT, Path): f_GT = h5py.File(f_GT, 'r') self.source_to_h5[f'{source}_GT'] = f_GT img_h = f[f'{img_id}/img_h'][()] img_w = f[f'{img_id}/img_w'][()] gt_boxes = f_GT[f'{img_id}/boxes'][()] # (x1, y1, x2, y2) n_gt_boxes = min(len(gt_boxes), 36) gt_boxes = gt_boxes[:n_gt_boxes] n_pred_boxes = 36 - n_gt_boxes pred_boxes = f[f'{img_id}/boxes'][:n_pred_boxes] boxes = np.concatenate([gt_boxes, pred_boxes], axis=0) # Normalize the boxes (to 0 ~ 1) boxes[:, (0, 2)] /= img_w boxes[:, (1, 3)] /= img_h np.testing.assert_array_less(boxes, 1+1e-5)	np.testing.assert_array_less(-boxes, 0+1e-5)	numpy.testing.assert_array_less
import numpy as np import pandas as pd import pytest from scipy import stats from locan import LocData from locan.analysis import BlinkStatistics from locan.analysis.blinking import _blink_statistics, _DistributionFits def test__blink_statistics_0(): # frame with on and off periods up to three frames and starting with one-frame on-period. frames = np.array([0, 4, 6, 7, 8, 12, 13]) results = _blink_statistics(frames, memory=0, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert np.array_equal(results["on_periods"], [1, 1, 3, 2]) assert np.array_equal(results["off_periods"], [3, 1, 3]) assert np.array_equal(results["on_periods_frame"], [0, 4, 6, 12]) assert np.array_equal(results["off_periods_frame"], [1, 5, 9]) assert all( [ np.array_equal(one, two) for one, two in zip( results["on_periods_indices"], [[0], [1], [2, 3, 4], [5, 6]] ) ] ) results = _blink_statistics(frames, memory=1, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert np.array_equal(results["on_periods"], [1, 5, 2]) assert np.array_equal(results["off_periods"], [3, 3]) assert np.array_equal(results["on_periods_frame"], [0, 4, 12]) assert np.array_equal(results["off_periods_frame"], [1, 9]) assert all( [ np.array_equal(one, two) for one, two in zip( results["on_periods_indices"], [[0], [1, 2, 3, 4], [5, 6]] ) ] ) results = _blink_statistics(frames, memory=10, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert np.array_equal(results["on_periods"], [14]) assert np.array_equal(results["off_periods"], []) assert np.array_equal(results["on_periods_frame"], [0]) assert np.array_equal(results["off_periods_frame"], []) assert all( [ np.array_equal(one, two) for one, two in zip(results["on_periods_indices"], [[0, 1, 2, 3, 4, 5, 6]]) ] ) def test__blink_statistics_1(): # frame with on and off periods up to three frames and starting with two-frame on-period. frames = np.array([0, 1, 3, 6, 7, 8, 12, 13]) results = _blink_statistics(frames, memory=0, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert np.array_equal(results["on_periods"], [2, 1, 3, 2]) assert np.array_equal(results["off_periods"], [1, 2, 3]) assert np.array_equal(results["on_periods_frame"], [0, 3, 6, 12]) assert np.array_equal(results["off_periods_frame"], [2, 4, 9]) assert all( [ np.array_equal(one, two) for one, two in zip( results["on_periods_indices"], [[0, 1], [2], [3, 4, 5], [6, 7]] ) ] ) results = _blink_statistics(frames, memory=1, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert np.array_equal(results["on_periods"], [4, 3, 2]) assert np.array_equal(results["off_periods"], [2, 3]) assert np.array_equal(results["on_periods_frame"], [0, 6, 12]) assert np.array_equal(results["off_periods_frame"], [4, 9]) assert all( [ np.array_equal(one, two) for one, two in zip( results["on_periods_indices"], [[0, 1, 2], [3, 4, 5], [6, 7]] ) ] ) results = _blink_statistics(frames, memory=10, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert	np.array_equal(results["on_periods"], [14])	numpy.array_equal
""" Experiment runner for the model with knowledge graph attached to interaction data """ from __future__ import division from __future__ import print_function import argparse import datetime import time import tensorflow as tf import numpy as np import scipy.sparse as sp import sys import json from preprocessing import create_trainvaltest_split, \ sparse_to_tuple, preprocess_user_item_features, globally_normalize_bipartite_adjacency, \ load_data_monti, load_official_trainvaltest_split, normalize_features from model import RecommenderGAE, RecommenderSideInfoGAE from utils import construct_feed_dict def run(user_features, movie_features, learning_rate=0.01, epochs=500, hidden=[500, 75], feat_hidden=64, accumulation='sum', dropout=0.7, num_basis_functions=2, features=False, symmetric=True, testing=True): """accumulation can be sum or stack""" # Set random seed # seed = 123 # use only for unit testing seed = int(time.time()) np.random.seed(seed) tf.set_random_seed(seed) tf.reset_default_graph() # Settings # ap = argparse.ArgumentParser() # # ap.add_argument("-d", "--dataset", type=str, default="ml_100k", # # choices=['ml_100k', 'ml_1m', 'ml_10m', 'douban', 'yahoo_music', 'flixster'], # # help="Dataset string.") # ap.add_argument("-lr", "--learning_rate", type=float, default=0.01, # help="Learning rate") # ap.add_argument("-e", "--epochs", type=int, default=2500, # help="Number training epochs") # ap.add_argument("-hi", "--hidden", type=int, nargs=2, default=[500, 75], # help="Number hidden units in 1st and 2nd layer") # ap.add_argument("-fhi", "--feat_hidden", type=int, default=64, # help="Number hidden units in the dense layer for features") # ap.add_argument("-ac", "--accumulation", type=str, default="sum", choices=['sum', 'stack'], # help="Accumulation function: sum or stack.") # ap.add_argument("-do", "--dropout", type=float, default=0.7, # help="Dropout fraction") # ap.add_argument("-nb", "--num_basis_functions", type=int, default=2, # help="Number of basis functions for Mixture Model GCN.") # ap.add_argument("-ds", "--data_seed", type=int, default=1234, # help="""Seed used to shuffle data in data_utils, taken from cf-nade (1234, 2341, 3412, 4123, 1324). # Only used for ml_1m and ml_10m datasets. """) # ap.add_argument("-sdir", "--summaries_dir", type=str, default='logs/' + str(datetime.datetime.now()).replace(' ', '_'), # help="Directory for saving tensorflow summaries.") # # Boolean flags # fp = ap.add_mutually_exclusive_group(required=False) # fp.add_argument('-nsym', '--norm_symmetric', dest='norm_symmetric', # help="Option to turn on symmetric global normalization", action='store_true') # fp.add_argument('-nleft', '--norm_left', dest='norm_symmetric', # help="Option to turn on left global normalization", action='store_false') # ap.set_defaults(norm_symmetric=True) # fp = ap.add_mutually_exclusive_group(required=False) # fp.add_argument('-f', '--features', dest='features', # help="Whether to use features (1) or not (0)", action='store_true') # fp.add_argument('-no_f', '--no_features', dest='features', # help="Whether to use features (1) or not (0)", action='store_false') # ap.set_defaults(features=False) # fp = ap.add_mutually_exclusive_group(required=False) # fp.add_argument('-ws', '--write_summary', dest='write_summary', # help="Option to turn on summary writing", action='store_true') # fp.add_argument('-no_ws', '--no_write_summary', dest='write_summary', # help="Option to turn off summary writing", action='store_false') # ap.set_defaults(write_summary=False) # fp = ap.add_mutually_exclusive_group(required=False) # fp.add_argument('-t', '--testing', dest='testing', # help="Option to turn on test set evaluation", action='store_true') # fp.add_argument('-v', '--validation', dest='testing', # help="Option to only use validation set evaluation", action='store_false') # ap.set_defaults(testing=False) # args = vars(ap.parse_args()) # print('Settings:') # print(args, '\n') # Define parameters DATASET = 'ml_100k' DATASEED = 1234 NB_EPOCH = epochs DO = dropout HIDDEN = hidden FEATHIDDEN = feat_hidden BASES = num_basis_functions LR = learning_rate WRITESUMMARY = False SUMMARIESDIR = 'logs/' + str(datetime.datetime.now()).replace(' ', '_') FEATURES = features SYM = symmetric TESTING = testing ACCUM = accumulation SELFCONNECTIONS = False SPLITFROMFILE = True VERBOSE = True NUMCLASSES = 5 # Splitting dataset in training, validation and test set print("Using official MovieLens dataset split u1.base/u1.test with 20% validation set size...") u_features = user_features v_features = movie_features _, _, adj_train, train_labels, train_u_indices, train_v_indices, \ val_labels, val_u_indices, val_v_indices, test_labels, \ test_u_indices, test_v_indices, class_values = load_official_trainvaltest_split('ml_100k', TESTING) num_users, num_items = adj_train.shape num_side_features = 0 # feature loading if not FEATURES: u_features = sp.identity(num_users, format='csr') v_features = sp.identity(num_items, format='csr') u_features, v_features = preprocess_user_item_features(u_features, v_features) elif FEATURES and u_features is not None and v_features is not None: # use features as side information and node_id's as node input features print("Normalizing feature vectors...") u_features_side = normalize_features(u_features) v_features_side = normalize_features(v_features) u_features_side, v_features_side = preprocess_user_item_features(u_features_side, v_features_side) u_features_side = np.array(u_features_side.todense(), dtype=np.float32) v_features_side = np.array(v_features_side.todense(), dtype=np.float32) num_side_features = u_features_side.shape[1] # node id's for node input features id_csr_v = sp.identity(num_items, format='csr') id_csr_u = sp.identity(num_users, format='csr') u_features, v_features = preprocess_user_item_features(id_csr_u, id_csr_v) else: raise ValueError('Features flag is set to true but no features are loaded from dataset ' + DATASET) # global normalization support = [] support_t = [] adj_train_int = sp.csr_matrix(adj_train, dtype=np.int32) for i in range(NUMCLASSES): # build individual binary rating matrices (supports) for each rating support_unnormalized = sp.csr_matrix(adj_train_int == i + 1, dtype=np.float32) if support_unnormalized.nnz == 0 and DATASET != 'yahoo_music': # yahoo music has dataset split with not all ratings types present in training set. # this produces empty adjacency matrices for these ratings. sys.exit('ERROR: normalized bipartite adjacency matrix has only zero entries!!!!!') support_unnormalized_transpose = support_unnormalized.T support.append(support_unnormalized) support_t.append(support_unnormalized_transpose) support = globally_normalize_bipartite_adjacency(support, symmetric=SYM) support_t = globally_normalize_bipartite_adjacency(support_t, symmetric=SYM) if SELFCONNECTIONS: support.append(sp.identity(u_features.shape[0], format='csr')) support_t.append(sp.identity(v_features.shape[0], format='csr')) num_support = len(support) support = sp.hstack(support, format='csr') support_t = sp.hstack(support_t, format='csr') if ACCUM == 'stack': div = HIDDEN[0] // num_support if HIDDEN[0] % num_support != 0: print("""\nWARNING: HIDDEN[0] (=%d) of stack layer is adjusted to %d such that it can be evenly split in %d splits.\n""" % (HIDDEN[0], num_support * div, num_support)) HIDDEN[0] = num_support * div # Collect all user and item nodes for test set test_u = list(set(test_u_indices)) test_v = list(set(test_v_indices)) test_u_dict = {n: i for i, n in enumerate(test_u)} test_v_dict = {n: i for i, n in enumerate(test_v)} test_u_indices = np.array([test_u_dict[o] for o in test_u_indices]) test_v_indices =	np.array([test_v_dict[o] for o in test_v_indices])	numpy.array
import os import sys import imp import numpy as np import warnings warnings.filterwarnings("ignore") # Test for Torch def torch(test_models, model_path, img_path): results_o, results_d, op_sets = dict(), dict(), dict() from PIL import Image import torch import torchvision.models as models from torchvision import transforms from torch.autograd import Variable # Torch to IR from ox.pytorch.pytorch_parser import PytorchParser for model in test_models: if 'inception' in model: image_size = 299 else: image_size = 224 image = Image.open(img_path) transformation = transforms.Compose([ transforms.Resize((image_size, image_size)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) image_tensor = transformation(image).float() image_tensor = image_tensor.unsqueeze_(0) x = Variable(image_tensor) inputshape = [3, image_size, image_size] arch_filename = os.path.join(model_path, 'PyTorch', model+'.pth') # test model if 'resnet50' in model: model_eval = models.resnet50() elif 'inception' in model: from models.torch import inception model_eval = inception.inceptionresnetv2(pretrained=False) elif 'shufflenet' in model: from models.torch import shufflenet model_eval = shufflenet.shufflenet() elif 'fcn' in model: from models.torch import fcn model_eval = fcn.FCNs() elif 'lstm' in model: from models.torch import lstm model_eval = lstm.Lstm() model_eval.eval() predict = model_eval(x).data.numpy() preds = np.squeeze(predict) print('\033[1;31;40m') print(' Result of', model, ': ', np.argmax(preds)) print('\033[0m') results_o[model] = preds torch.save(model_eval, arch_filename) # convert IR_filename = os.path.join(model_path, 'IR', model+'_torch') parser = PytorchParser(arch_filename, inputshape) ops = parser.run(IR_filename) op_sets[model] = ops del parser del PytorchParser # IR to Torch from ox.pytorch.pytorch_emitter import PytorchEmitter for model in test_models: if 'inception' in model: image_size = 299 else: image_size = 224 image = Image.open(img_path) transformation = transforms.Compose([ transforms.Resize((image_size, image_size)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) image_tensor = transformation(image).float() image_tensor = image_tensor.unsqueeze_(0) x = Variable(image_tensor) inputshape = [3, image_size, image_size] arch_filename = os.path.join(model_path, 'IR', model+'_torch.pb') weight_filename = os.path.join(model_path, 'IR', model+'_torch.npy') converted_file = os.path.join(model_path, 'PyTorch', model+'_ox') emitter = PytorchEmitter((arch_filename, weight_filename)) emitter.run(converted_file + '.py', converted_file + '.npy', 'test') model_converted = imp.load_source('PytorchModel', converted_file + '.py').KitModel(converted_file + '.npy') model_converted.eval() predict = model_converted(x).data.numpy() preds = np.squeeze(predict) print('\033[1;31;40m') print(' Result of ', model+'_ox : ', np.argmax(preds)) print('\033[0m') results_d[model] = np.mean(np.power(results_o[model] - preds, 2)) del emitter del PytorchEmitter return results_d, op_sets # Test for Tensorflow def tensorflow(test_models, model_path, img_path): results_o, results_d, op_sets = dict(), dict(), dict() import tensorflow as tf from PIL import Image image = Image.open(img_path) # Tensorflow to IR from ox.tensorflow.tensorflow_parser import TensorflowParser for model in test_models: arch_filename = os.path.join(model_path, 'tensorflow', model, model+'.ckpt.meta') weight_filename = os.path.join(model_path, 'tensorflow', model, model+'.ckpt') # test model if 'resnet50' in model: img = np.array(image.resize((299, 299), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import resnet50 preds = resnet50.test(x, model_path) elif 'inception' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import inception_v3 preds = inception_v3.test(x, model_path) elif 'shufflenet' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import shufflenet preds = shufflenet.test(x, model_path) elif 'fcn' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import fcn preds = fcn.test(x, model_path) elif 'lstm' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import lstm preds = lstm.test(x, model_path) print('\033[1;31;40m') print(' Result of', model, ': ', np.argmax(preds)) print('\033[0m') preds = np.squeeze(preds) if 'fcn' in model: preds = np.array(preds).astype(np.int32) results_o[model] = preds import tensorflow.contrib.keras as keras keras.backend.clear_session() # parser IR_filename = os.path.join(model_path, 'IR', model+'_tf') parser = TensorflowParser(arch_filename, weight_filename, ["OX_output"]) ops = parser.run(IR_filename) op_sets[model] = ops del parser del TensorflowParser # IR to Tensorflow from ox.tensorflow.tensorflow_emitter import TensorflowEmitter for model in test_models: arch_filename = os.path.join(model_path, 'IR', model+'_tf.pb') weight_filename = os.path.join(model_path, 'IR', model+'_tf.npy') converted_file = os.path.join(model_path, 'tensorflow', model, model+'_ox') emitter = TensorflowEmitter((arch_filename, weight_filename)) emitter.run(converted_file + '.py', None, 'test') # test model if 'resnet' in model: img = image.resize((299, 299), Image.ANTIALIAS) else: img = image.resize((224, 224), Image.ANTIALIAS) img = np.array(img) x = np.expand_dims(img, axis=0) if 'lstm' in model: x = np.reshape(x, (-1, 224 * 224 * 3)) model_converted = imp.load_source('TFModel', converted_file + '.py').KitModel(weight_filename) input_tf, model_tf = model_converted with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) predict = sess.run(model_tf, feed_dict = {input_tf : x}) del model_converted del sys.modules['TFModel'] preds = np.squeeze(predict) if 'fcn' in model: preds =	np.array(preds)	numpy.array
import warnings import numpy as np import quaternionic import pytest # Array methods def test_new(array): q = array(1, 2, 3, 4) assert q.dtype == np.float assert q.shape == (4,) assert q.w == 1.0 and q.x == 2.0 and q.y == 3.0 and q.z == 4.0 q = array([1, 2, 3, 4]) assert q.dtype == np.float assert q.shape == (4,) assert q.w == 1.0 and q.x == 2.0 and q.y == 3.0 and q.z == 4.0 q = array([[1, 2, 3, 4]]) assert q.dtype == np.float assert q.shape == (1, 4) assert q.w == 1.0 and q.x == 2.0 and q.y == 3.0 and q.z == 4.0 with pytest.raises(ValueError): array(np.array(3.14)) with pytest.raises(ValueError): array(np.array([])) with pytest.raises(ValueError): array(np.random.rand(4, 3)) def test_getitem(array): q = array(np.random.normal(size=(17, 3, 4))) p = q[1:-1] assert isinstance(p, array) assert p.shape == (q.shape[0]-2,) + q.shape[1:] assert np.array_equal(p.ndarray, q.ndarray[1:-1]) with pytest.raises(ValueError): q[..., 1:3] def test_array_finalize(array): q = array([1, 2, 3, 4]) with pytest.raises(ValueError): q[1:3] def test_repr(array): q = array(np.random.normal(size=(17, 3, 4))) assert repr(q) == 'quaternionic.' + repr(q.ndarray) def test_str(array): q = array(np.random.normal(size=(17, 3, 4))) assert str(q) == str(q.ndarray) def test_ones_like(Qs): z = np.ones_like(Qs) assert np.all(z.ndarray[:, 1:] == 0) assert	np.all(z.ndarray[:, 0] == 1)	numpy.all
import pandas as pd import numpy as np import os from sim.Bus import Bus from sim.Route import Route from sim.Busstop import Bus_stop from sim.Passenger import Passenger import matplotlib.pyplot as plt pd.options.mode.chained_assignment = None def getBusRoute(data): my_path = os.path.abspath(os.path.dirname(__file__)) path = my_path + "/data/" + data + "/" _path_trips = path + 'trips.txt' _path_st = path + 'stop_times.txt' trips = pd.DataFrame(pd.read_csv(_path_trips)) stop_times = pd.DataFrame(pd.read_csv(_path_st)) stop_times.dropna(subset=['arrival_time'], inplace=True) bus_routes = {} trip_ids = set(stop_times['trip_id']) try: service_id = trips.iloc[np.random.randint(0, trips.shape[0])]['service_id'] trips = trips[trips['service_id'] == service_id] except: pass # each route_id may correspond to multiple trip_id for trip_id in trip_ids: # A completely same route indicates the same shape_id in trip file, but this field is not 100% provided by opendata try: if 'shape_id' in trips.columns: route_id = str(trips[trips['trip_id'] == trip_id].iloc[0]['shape_id']) block_id = '' dir = '' else: route_id = str(trips[trips['trip_id'] == trip_id].iloc[0]['route_id']) block_id = str(trips[trips['trip_id'] == trip_id].iloc[0]['block_id']) dir = str(trips[trips['trip_id'] == trip_id].iloc[0]['trip_headsign']) except: continue # Identifies a set of dates when service is available for one or more routes. trip = stop_times[stop_times['trip_id'] == trip_id] try: trip['arrival_time'] = pd.to_datetime(trip['arrival_time'], format='%H:%M:%S') except: trip['arrival_time'] = pd.to_datetime(trip['arrival_time'], format="%Y-%m-%d %H:%M:%S") trip = trip.sort_values(by='arrival_time') trip_dist = trip.iloc[:]['shape_dist_traveled'].to_list() if len(trip_dist) <= 0 or np.isnan(trip_dist[0]): continue schedule = ((trip.iloc[:]['arrival_time'].dt.hour * 60 + trip.iloc[:]['arrival_time'].dt.minute) * 60 + trip.iloc[:]['arrival_time'].dt.second).to_list() if len(schedule) <= 2 or np.isnan(schedule[0]): continue b = Bus(id=trip_id, route_id=route_id, stop_list=trip.iloc[:]['stop_id'].to_list(), dispatch_time=schedule[0], block_id=block_id, dir=dir) b.left_stop = [] b.speed = (trip_dist[1] - trip_dist[0]) / (schedule[1] - schedule[0]) b.c_speed = b.speed for i in range(len(trip_dist)): if str(b.stop_list[i]) in b.stop_dist: b.left_stop.append(str(b.stop_list[i]) + '_' + str(i)) b.stop_dist[str(b.stop_list[i]) + '_' + str(i)] = trip_dist[i] b.schedule[str(b.stop_list[i]) + '_' + str(i)] = schedule[i] else: b.left_stop.append(str(b.stop_list[i])) b.stop_dist[str(b.stop_list[i])] = trip_dist[i] b.schedule[str(b.stop_list[i])] = schedule[i] b.stop_list = b.left_stop[:] b.set() if route_id in bus_routes: bus_routes[route_id].append(b) else: bus_routes[route_id] = [b] # Do not consider the route with only 1 trip bus_routes_ = {} for k, v in bus_routes.items(): if len(v) > 1: bus_routes_[k] = v return bus_routes_ def getStopList(data, read=0): my_path = os.path.abspath(os.path.dirname(__file__)) path = my_path + "/data/" + data + "/" _path_stops = path + 'stops.txt' _path_st = path + 'stop_times.txt' _path_trips = path + 'trips.txt' stops = pd.DataFrame(pd.read_csv(_path_stops)) stop_times = pd.DataFrame(pd.read_csv(_path_st)) trips = pd.DataFrame(pd.read_csv(_path_trips)) stop_list = {} select_stops = pd.merge(stops, stop_times, on=['stop_id'], how='left') select_stops = select_stops.sort_values(by='shape_dist_traveled', ascending=False) select_stops = select_stops.drop_duplicates(subset='stop_id', keep="first").sort_values(by='shape_dist_traveled', ascending=True) for i in range(select_stops.shape[0]): stop = Bus_stop(id=str(select_stops.iloc[i]['stop_id']), lat=select_stops.iloc[i]['stop_lat'], lon=select_stops.iloc[i]['stop_lon']) stop.loc = select_stops.iloc[i]['shape_dist_traveled'] try: stop.next_stop = str(select_stops.iloc[i + 1]['stop_id']) except: stop.next_stop = None stop_list[str(select_stops.iloc[i]['stop_id'])] = stop _path_demand = path + 'demand.csv' pax_num = 0 try: demand = pd.DataFrame(pd.read_csv(_path_demand)) except: print('No available demand file') return stop_list, 0 try: demand['Ride_Start_Time'] = pd.to_datetime(demand['Ride_Start_Time'], format='%H:%M:%S') except: demand['Ride_Start_Time'] = pd.to_datetime(demand['Ride_Start_Time'], format="%Y-%m-%d %H:%M:%S") demand['Ride_Start_Time_sec'] = (demand.iloc[:]['Ride_Start_Time'].dt.hour * 60 + demand.iloc[:][ 'Ride_Start_Time'].dt.minute) * 60 + demand.iloc[:]['Ride_Start_Time'].dt.second demand.dropna(subset=['ALIGHTING_STOP_STN'], inplace=True) demand = demand[demand.ALIGHTING_STOP_STN != demand.BOARDING_STOP_STN] demand = demand.sort_values(by='Ride_Start_Time_sec') for stop_id, stop in stop_list.items(): demand_by_stop = demand[demand['BOARDING_STOP_STN'] == int(stop_id)] # macro demand setting if read == 0: t = 0 while t < 24: d = demand_by_stop[(demand_by_stop['Ride_Start_Time_sec'] >= t * 3600) & ( demand_by_stop['Ride_Start_Time_sec'] < (t + 1) * 3600)] stop.dyna_arr_rate.append(d.shape[0] / 3600.) for dest_id in stop_list.keys(): od = d[demand['ALIGHTING_STOP_STN'] == int(dest_id)] if od.empty: continue if dest_id not in stop.dest: stop.dest[dest_id] = [0 for _ in range(24)] stop.dest[dest_id][t] = od.shape[0] / 3600. t += 1 else: # micro demand setting for i in range(demand_by_stop.shape[0]): pax = Passenger(id=demand_by_stop.iloc[i]['TripID'], origin=stop_id, plan_board_time=float(demand_by_stop.iloc[i]['Ride_Start_Time_sec'])) pax.dest = str(int(demand_by_stop.iloc[i]['ALIGHTING_STOP_STN'])) pax.realcost = float(demand_by_stop.iloc[i]['Ride_Time']) * 60. pax.route = str(demand_by_stop.iloc[i]['Srvc_Number']) + '_' + str( int(demand_by_stop.iloc[i]['Direction'])) stop.pax[pax.id] = pax pax_num += 1 return stop_list, pax_num def demand_analysis(engine=None): if engine is not None: stop_list = list(engine.busstop_list.keys()) stop_hash = {} i = 0 for p in stop_list: stop_hash[p] = i i += 1 # output data for stack area graph demand = [] for t in range(24): d = np.zeros(len(stop_list)) for s in stop_list: for pid, pax in engine.busstop_list[s].pax.items(): if int((pax.plan_board_time - 0) / 3600) == t: d[stop_hash[s]] += 1 demand.append(d) df = pd.DataFrame(demand, columns=[str(i) for i in range(len(stop_list))]) df.to_csv('demand.csv') return def sim_validate(engine, data): actual_onboard = [] sim_onboard = [] sim_travel_cost = [] actual_travel_cost = [] for pid, pax in engine.pax_list.items(): actual_onboard.append(pax.plan_board_time) sim_onboard.append(pax.onboard_time) sim_travel_cost.append(abs(pax.onboard_time - pax.alight_time)) actual_travel_cost.append(pax.realcost) actual_onboard = np.array(actual_onboard) sim_onboard = np.array(sim_onboard) actual_travel_cost = np.array(actual_travel_cost) sim_travel_cost = np.array(sim_travel_cost) print('Boarding RMSE:%g' % (np.sqrt(np.mean((actual_onboard - sim_onboard) 2)))) print('Travel RMSE:%g' % (np.sqrt(np.mean((actual_travel_cost - sim_travel_cost) 2)))) sim_comp = pd.DataFrame() sim_comp['actual_onboard'] = actual_onboard sim_comp['sim_onboard'] = sim_onboard sim_comp['sim_travel_cost'] = sim_travel_cost sim_comp['actual_travel_cost'] = actual_travel_cost sim_comp.to_csv('G:\\mcgill\\MAS\\gtfs_testbed\\result\\sim_comp' + str(data) + '.csv') print('ok') def visualize_pax(engine): for pax_id, pax in engine.pax_list.items(): if pax.onboard_time < 999999999: plt.plot([int(pax_id), int(pax_id)], [pax.arr_time, pax.onboard_time]) plt.show() def train_result_track(eng, ep, qloss_log, ploss_log, log, name='', seed=0): reward_bus_wise = [] reward_bus_wisep1 = [] reward_bus_wisep2 = [] rs = [] wait_cost = log['wait_cost'] travel_cost = log['travel_cost'] delay = log['delay'] hold_cost = log['hold_cost'] headways_var = log['headways_var'] headways_mean = log['headways_mean'] AOD = log["AOD"] for bid, r in eng.reward_signal.items(): if len(r) > 0: # .bus_list[bid].forward_bus!=None and engine.bus_list[bid].backward_bus!=None : reward_bus_wise.append(np.mean(r)) rs += r reward_bus_wisep1.append(np.mean(eng.reward_signalp1[bid])) reward_bus_wisep2.append(np.mean(eng.reward_signalp2[bid])) if ep % 1 == 0: train_log = pd.DataFrame() train_log['bunching'] = [log['bunching']] train_log['ploss'] = [np.mean(ploss_log)] train_log['qloss'] = [np.mean(qloss_log)] train_log['reward'] = [np.mean(reward_bus_wise)] train_log['reward1'] = [np.mean(reward_bus_wisep1)] train_log['reward2'] = [np.mean(reward_bus_wisep2)] train_log['avg_hold'] = np.mean(hold_cost) train_log['action'] = np.mean(np.array(eng.action_record)) train_log['wait'] = [np.mean(wait_cost)] train_log['travel'] = [np.mean(travel_cost)] train_log['delay'] = [np.mean(delay)] train_log['AOD'] = AOD for k, v in headways_mean.items(): train_log['headway_mean' + str(k)] = [np.mean(v)] for k, v in headways_var.items(): train_log['headway_var' + str(k)] = [np.mean(v)] res = pd.DataFrame() res['stw'] = log['stw'] res['sto'] = log['sto'] res['sth'] = log['sth'] print( 'Episode: %g \| reward: %g \| reward_var: %g \| reward1: %g \| reward2: %g \| ploss: %g \| qloss: %g \|\n wait ' 'cost: %g \| travel cost: %g \| max hold :%g\| min hold :%g\| avg hold :%g \| var hold :%g' % ( ep - 1, np.mean(reward_bus_wise),	np.var(rs)	numpy.var
import numpy as np def braille(): return { 'a' : np.array([[1, 0], [0, 0], [0, 0]], dtype=bool), 'b' : np.array([[1, 0], [1, 0], [0, 0]], dtype=bool), 'c' : np.array([[1, 1], [0, 0], [0, 0]], dtype=bool), 'd' : np.array([[1, 1], [0, 1], [0, 0]], dtype=bool), 'e' : np.array([[1, 0], [0, 1], [0, 0]], dtype=bool), 'f' : np.array([[1, 1], [1, 0], [0, 0]], dtype=bool), 'g' : np.array([[1, 1], [1, 1], [0, 0]], dtype=bool), 'h' : np.array([[1, 0], [1, 1], [0, 0]], dtype=bool), 'i' : np.array([[0, 1], [1, 0], [0, 0]], dtype=bool), 'j' : np.array([[0, 1], [1, 1], [0, 0]], dtype=bool), 'k' : np.array([[1, 0], [0, 0], [1, 0]], dtype=bool), 'l' : np.array([[1, 0], [1, 0], [1, 0]], dtype=bool), 'm' : np.array([[1, 1], [0, 0], [1, 0]], dtype=bool), 'n' : np.array([[1, 1], [0, 1], [1, 0]], dtype=bool), 'o' : np.array([[1, 0], [0, 1], [1, 0]], dtype=bool), 'p' : np.array([[1, 1], [1, 0], [1, 0]], dtype=bool), 'q' : np.array([[1, 1], [1, 1], [1, 0]], dtype=bool), 'r' : np.array([[1, 0], [1, 1], [1, 0]], dtype=bool), 's' : np.array([[0, 1], [1, 0], [1, 0]], dtype=bool), 't' : np.array([[0, 1], [1, 1], [1, 0]], dtype=bool), 'u' : np.array([[1, 0], [0, 0], [1, 1]], dtype=bool), 'v' : np.array([[1, 0], [1, 0], [1, 1]], dtype=bool), 'w' : np.array([[0, 1], [1, 1], [0, 1]], dtype=bool), 'x' : np.array([[1, 1], [0, 0], [1, 1]], dtype=bool), 'y' : np.array([[1, 1], [0, 1], [1, 1]], dtype=bool), 'z' : np.array([[1, 0], [0, 1], [1, 1]], dtype=bool), '1' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 0], [0, 0], [0, 0]], dtype=bool)], '2' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 0], [1, 0], [0, 0]], dtype=bool)], '3' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 1], [0, 0], [0, 0]], dtype=bool)], '4' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 1], [1, 0], [0, 0]], dtype=bool)], '5' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 0], [0, 1], [0, 0]], dtype=bool)], '6' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 1], [1, 0], [0, 0]], dtype=bool)], '7' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 1], [1, 1], [0, 0]], dtype=bool)], '8' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 0], [1, 1], [0, 0]], dtype=bool)], '9' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[0, 1], [1, 0], [0, 0]], dtype=bool)], '0' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool),	np.array([[0, 1], [1, 1], [0, 0]], dtype=bool)	numpy.array
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([])	numpy.array
import numpy as np class SECS: """Spherical Elementary Current System (SECS). The algorithm is implemented directly in spherical coordinates from the equations of the 1999 Amm & Viljanen paper [1]_. Parameters ---------- sec_df_loc : ndarray (nsec x 3 [lat, lon, r]) The latitude, longiutde, and radius of the divergence free (df) SEC locations. sec_cf_loc : ndarray (nsec x 3 [lat, lon, r]) The latitude, longiutde, and radius of the curl free (cf) SEC locations. References ---------- .. [1] <NAME>., and <NAME>. "Ionospheric disturbance magnetic field continuation from the ground to the ionosphere using spherical elementary current systems." Earth, Planets and Space 51.6 (1999): 431-440. doi:10.1186/BF03352247 """ def __init__(self, sec_df_loc=None, sec_cf_loc=None): if sec_df_loc is None and sec_cf_loc is None: raise ValueError("Must initialize the object with SEC locations") self.sec_df_loc = sec_df_loc self.sec_cf_loc = sec_cf_loc if self.sec_df_loc is not None: self.sec_df_loc = np.asarray(sec_df_loc) if self.sec_df_loc.shape[-1] != 3: raise ValueError("SEC DF locations must have 3 columns (lat, lon, r)") if self.sec_df_loc.ndim == 1: # Add an empty dimension if only one SEC location is passed in self.sec_df_loc = self.sec_df_loc[np.newaxis, ...] if self.sec_cf_loc is not None: self.sec_cf_loc = np.asarray(sec_cf_loc) if self.sec_cf_loc.shape[-1] != 3: raise ValueError("SEC CF locations must have 3 columns (lat, lon, r)") if self.sec_cf_loc.ndim == 1: # Add an empty dimension if only one SEC location is passed in self.sec_cf_loc = self.sec_cf_loc[np.newaxis, ...] # Storage of the scaling factors self.sec_amps = None self.sec_amps_var = None @property def has_df(self): """Whether this system has any divergence free currents.""" return self.sec_df_loc is not None @property def has_cf(self): """Whether this system has any curl free currents.""" return self.sec_cf_loc is not None @property def nsec(self): """The number of elementary currents in this system.""" nsec = 0 if self.has_df: nsec += len(self.sec_df_loc) if self.has_cf: nsec += len(self.sec_cf_loc) return nsec def fit(self, obs_loc, obs_B, obs_std=None, epsilon=0.05): """Fits the SECS to the given observations. Given a number of observation locations and measurements, this function fits the SEC system to them. It uses singular value decomposition (SVD) to fit the SEC amplitudes with the `epsilon` parameter used to regularize the solution. Parameters ---------- obs_locs : ndarray (nobs x 3 [lat, lon, r]) Contains latitude, longitude, and radius of the observation locations (place where the measurements are made) obs_B: ndarray (ntimes x nobs x 3 [Bx, By, Bz]) An array containing the measured/observed B-fields. obs_std : ndarray (ntimes x nobs x 3 [varX, varY, varZ]), optional Standard error of vector components at each observation location. This can be used to weight different observations more/less heavily. An infinite value eliminates the observation from the fit. Default: ones(nobs x 3) equal weights epsilon : float Value used to regularize/smooth the SECS amplitudes. Multiplied by the largest singular value obtained from SVD. Default: 0.05 """ if obs_loc.shape[-1] != 3: raise ValueError("Observation locations must have 3 columns (lat, lon, r)") if obs_B.ndim == 2: # Just a single snapshot given, so expand the dimensionality obs_B = obs_B[np.newaxis, ...] # Assume unit standard error of all measurements if obs_std is None: obs_std = np.ones(obs_B.shape) ntimes = len(obs_B) # Calculate the transfer functions T_obs = self._calc_T(obs_loc) # Store the fit sec_amps in the object self.sec_amps = np.empty((ntimes, self.nsec)) self.sec_amps_var = np.empty((ntimes, self.nsec)) # Calculate the singular value decomposition (SVD) # NOTE: T_obs has shape (nobs, 3, nsec), we reshape it # to (nobs3, nsec); obs_std has shape (ntimes, nobs, 3), # we reshape it to (ntimes, nobs3), then loop over ntimes # to solve using (potentially) time-dependent observation # standard errors to weight the observations for i in range(ntimes): # Only (re-)calculate SVD when necessary if i == 0 or not np.all(obs_std[i] == obs_std[i-1]): # Weight T_obs with obs_std svd_in = (T_obs.reshape(-1, self.nsec) / obs_std[i].ravel()[:, np.newaxis]) # Find singular value decompostion U, S, Vh = np.linalg.svd(svd_in, full_matrices=False) # Eliminate singular values less than epsilon by setting their # reciprocal to zero (setting S to infinity firsts avoids # divide-by-zero warings) S[S < epsilon * S.max()] = np.inf W = 1./S # Update VWU if obs_std changed VWU = Vh.T @ (np.diag(W) @ U.T) # Solve for SEC amplitudes and error variances # shape: ntimes x nsec self.sec_amps[i, :] = (VWU @ (obs_B[i] / obs_std[i]).reshape(-1).T).T # Maybe we want the variance of the predictions sometime later...? # shape: ntimes x nsec valid = np.isfinite(obs_std[i].reshape(-1)) self.sec_amps_var[i, :] = np.sum( (VWU[:,valid] * obs_std[i].reshape(-1)[valid])*2, axis=1) return self def fit_unit_currents(self): """Sets all SECs to a unit current amplitude.""" self.sec_amps = np.ones((1, self.nsec)) return self def predict(self, pred_loc, J=False): """Calculate the predicted magnetic field or currents. After a set of observations has been fit to this system we can predict the magnetic fields or currents at any other location. This function uses those fit amplitudes to predict at the requested locations. Parameters ---------- pred_loc: ndarray (npred x 3 [lat, lon, r]) An array containing the locations where the predictions are desired. J: boolean Whether to predict currents (J=True) or magnetic fields (J=False) Default: False (magnetic field prediction) Returns ------- ndarray (ntimes x npred x 3 [lat, lon, r]) The predicted values calculated from the current amplitudes that were fit to this system. """ if pred_loc.shape[-1] != 3: raise ValueError("Prediction locations must have 3 columns (lat, lon, r)") if self.sec_amps is None: raise ValueError("There are no currents associated with the SECs," + "you need to call .fit() first to fit to some observations.") # T_pred shape=(npred x 3 x nsec) # sec_amps shape=(nsec x ntimes) if J: # Predicting currents T_pred = self._calc_J(pred_loc) else: # Predicting magnetic fields T_pred = self._calc_T(pred_loc) # NOTE: dot product is slow on multi-dimensional arrays (i.e. > 2 dimensions) # Therefore this is implemented as tensordot, and the arguments are # arranged to eliminate needs of transposing things later. # The dot product is done over the SEC locations, so the final output # is of shape: (ntimes x npred x 3) return np.squeeze(np.tensordot(self.sec_amps, T_pred, (1, 2))) def predict_B(self, pred_loc): """Calculate the predicted magnetic fields. After a set of observations has been fit to this system we can predict the magnetic fields or currents at any other location. This function uses those fit amplitudes to predict at the requested locations. Parameters ---------- pred_loc: ndarray (npred x 3 [lat, lon, r]) An array containing the locations where the predictions are desired. Returns ------- ndarray (ntimes x npred x 3 [lat, lon, r]) The predicted values calculated from the current amplitudes that were fit to this system. """ return self.predict(pred_loc) def predict_J(self, pred_loc): """Calculate the predicted currents. After a set of observations has been fit to this system we can predict the magnetic fields or currents at any other location. This function uses those fit amplitudes to predict at the requested locations. Parameters ---------- pred_loc: ndarray (npred x 3 [lat, lon, r]) An array containing the locations where the predictions are desired. Returns ------- ndarray (ntimes x npred x 3 [lat, lon, r]) The predicted values calculated from the current amplitudes that were fit to this system. """ return self.predict(pred_loc, J=True) def _calc_T(self, obs_loc): """Calculates the T transfer matrix. The magnetic field transfer matrix to go from SEC locations to observation locations. It assumes unit current amplitudes that will then be scaled with the proper amplitudes later. """ if self.has_df: T = T_df(obs_loc=obs_loc, sec_loc=self.sec_df_loc) if self.has_cf: T1 = T_cf(obs_loc=obs_loc, sec_loc=self.sec_cf_loc) # df is already present in T if self.has_df: T = np.concatenate([T, T1], axis=2) else: T = T1 return T def _calc_J(self, obs_loc): """Calculates the J transfer matrix. The current transfer matrix to go from SEC locations to observation locations. It assumes unit current amplitudes that will then be scaled with the proper amplitudes later. """ if self.has_df: J = J_df(obs_loc=obs_loc, sec_loc=self.sec_df_loc) if self.has_cf: J1 = J_cf(obs_loc=obs_loc, sec_loc=self.sec_cf_loc) # df is already present in T if self.has_df: J = np.concatenate([J, J1], axis=2) else: J = J1 return J def T_df(obs_loc, sec_loc): """Calculates the divergence free magnetic field transfer function. The transfer function goes from SEC location to observation location and assumes unit current SECs at the given locations. Parameters ---------- obs_loc : ndarray (nobs, 3 [lat, lon, r]) The locations of the observation points. sec_loc : ndarray (nsec, 3 [lat, lon, r]) The locations of the SEC points. Returns ------- ndarray (nobs, 3, nsec) The T transfer matrix. """ nobs = len(obs_loc) nsec = len(sec_loc) obs_r = obs_loc[:, 2][:, np.newaxis] sec_r = sec_loc[:, 2][np.newaxis, :] theta = calc_angular_distance(obs_loc[:, :2], sec_loc[:, :2]) alpha = calc_bearing(obs_loc[:, :2], sec_loc[:, :2]) # magnetic permeability mu0 = 4np.pi1e-7 # simplify calculations by storing this ratio x = obs_r/sec_r sin_theta = np.sin(theta) cos_theta = np.cos(theta) factor = 1./np.sqrt(1 - 2xcos_theta + x2) # Amm & Viljanen: Equation 9 Br = mu0/(4np.piobs_r) (factor - 1) # Amm & Viljanen: Equation 10 (transformed to try and eliminate trig operations and # divide by zeros) Btheta = -mu0/(4np.piobs_r) * (factor(x - cos_theta) + cos_theta) # If sin(theta) == 0: Btheta = 0 # There is a possible 0/0 in the expansion when sec_loc == obs_loc Btheta = np.divide(Btheta, sin_theta, out=np.zeros_like(sin_theta), where=sin_theta != 0) # When observation points radii are outside of the sec locations under_locs = sec_r < obs_r # NOTE: If any SECs are below observations the math will be done on all points. # This could be updated to only work on the locations where this condition # occurs, but would make the code messier, with minimal performance gain # except for very large matrices. if np.any(under_locs): # Flipped from previous case x = sec_r/obs_r # Amm & Viljanen: Equation A.7 Br2 = mu0x/(4np.piobs_r) * (1./np.sqrt(1 - 2xcos_theta + x*2) - 1) # Amm & Viljanen: Equation A.8 Btheta2 = - mu0 / (4np.piobs_r) ((obs_r-sec_rcos_theta) / np.sqrt(obs_r2 - 2obs_rsec_rcos_theta + sec_r**2) - 1) Btheta2 = np.divide(Btheta2, sin_theta, out=np.zeros_like(sin_theta), where=sin_theta != 0) # Update only the locations where secs are under observations Btheta[under_locs] = Btheta2[under_locs] Br[under_locs] = Br2[under_locs] # Transform back to Bx, By, Bz at each local point T =	np.empty((nobs, 3, nsec))	numpy.empty
""" Procedures needed for variable importance estimation. Created on Thu Dec 8 09:11:57 2020. @author: MLechner # -- coding: utf-8 -- """ from concurrent import futures import math import numpy as np import ray from mcf import mcf_forest_functions as mcf_forest from mcf import mcf_data_functions as mcf_data from mcf import general_purpose as gp from mcf import general_purpose_system_files as gp_sys from mcf import general_purpose_mcf as gp_mcf def variable_importance(indatei, forest, v_dict, v_x_type, v_x_values, c_dictin, x_name_mcf, regrf=False): """Compute variable importance measure. Parameters ---------- indatei: Str. forest : List of list. Estimated forest. v_dict : DICT. Variables. v_x_type : List of Int. v_x_values : List of Int. c_dictin : DICT. Parameters x_name_mcf : List. Variable names from MCF procedure regrf : Bool. Returns ------- vim : Dictionary. Variable importance measures and names of variable Procedure: a) Predict Total of OOB of estimated forest_est (should already be there) b) For each variable, randomize one or groups of covariates c) recompute OOB-MSE with the ys of these more noisy variables Use multiprocessing in new oob prediction in the same way as in forest Building Outer loop over variables and group of variables Inner loop: Loop over trees Initially take out the indices of leaf 0 [16]--> OOB data For every observation determine it terminal leaf - save index together with number of terminal leaf For all terminal leaves compute OOB prediction """ if c_dictin['with_output'] and c_dictin['verbose']: print('\nVariable importance measures (OOB data)') print('\nSingle variables') (x_name, _, _, c_dict, _, data_np, y_i, y_nn_i, x_i, _, _, d_i, w_i, _ ) = mcf_data.prepare_data_for_forest(indatei, v_dict, v_x_type, v_x_values, c_dictin, regrf=regrf) no_of_vars = len(x_name) partner_k = determine_partner_k(x_name) # Loop over all variables to get respective OOB values of MSE if x_name != x_name_mcf: raise Exception('Wrong order of variable names', x_name, x_name_mcf) number_of_oobs = 1 + no_of_vars oob_values = [None] * number_of_oobs if c_dict['no_parallel'] < 1.5: maxworkers = 1 else: if c_dict['mp_automatic']: maxworkers = gp_mcf.find_no_of_workers(c_dict['no_parallel'], c_dict['sys_share']) else: maxworkers = c_dict['no_parallel'] if c_dict['with_output'] and c_dict['verbose']: print('Number of parallel processes: ', maxworkers) if c_dict['mp_with_ray'] and maxworkers > 1: if c_dict['mem_object_store_2'] is None: ray.init(num_cpus=maxworkers, include_dashboard=False) else: ray.init(num_cpus=maxworkers, include_dashboard=False, object_store_memory=c_dict['mem_object_store_2']) if c_dict['with_output'] and c_dict['verbose']: print("Size of Ray Object Store: ", round(c_dict['mem_object_store_2']/(10241024)), " MB") data_np_ref = ray.put(data_np) forest_ref = ray.put(forest) if (c_dict['mp_type_vim'] == 2 and not c_dict['mp_with_ray']) or ( maxworkers == 1): for jdx in range(number_of_oobs): oob_values[jdx], _ = get_oob_mcf( data_np, y_i, y_nn_i, x_i, d_i, w_i, c_dict, jdx, True, [], forest, False, regrf, partner_k[jdx]) if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(jdx+1, number_of_oobs) else: # Fast but needs a lot of memory because it copied a lot maxworkers = min(maxworkers, number_of_oobs) if c_dict['mp_with_ray']: tasks = [ray_get_oob_mcf.remote( data_np_ref, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, True, [], forest_ref, True, regrf, partner_k[idx]) for idx in range(number_of_oobs)] still_running = list(tasks) jdx = 0 while len(still_running) > 0: finished, still_running = ray.wait(still_running) finished_res = ray.get(finished) for res in finished_res: iix = res[1] oob_values[iix] = res[0] if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(jdx+1, number_of_oobs) jdx += 1 else: with futures.ProcessPoolExecutor(max_workers=maxworkers) as fpp: ret_fut = {fpp.submit( get_oob_mcf, data_np, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, True, [], forest, True, regrf, partner_k[idx]): idx for idx in range(number_of_oobs)} for jdx, frv in enumerate(futures.as_completed(ret_fut)): results_fut = frv.result() del ret_fut[frv] del frv iix = results_fut[1] oob_values[iix] = results_fut[0] if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(jdx+1, number_of_oobs) oob_values = np.array(oob_values) if regrf: oob_values = oob_values.reshape(-1) mse_ref = oob_values[0] # reference value vim = vim_print(mse_ref, oob_values[1:], x_name, 0, c_dict['with_output'], True, partner_k) # Variables are grouped no_g, no_m_g = number_of_groups_vi(no_of_vars) partner_k = None if no_g > 0: if c_dict['with_output']: print('\nGroups of variables') ind_groups = vim_grouping(vim, no_g) n_g = len(ind_groups) oob_values = [None] n_g if c_dict['mp_with_ray'] and maxworkers > 1: tasks = [ray_get_oob_mcf.remote( data_np_ref, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, False, ind_groups, forest_ref, True, regrf, partner_k) for idx in range(n_g)] still_running = list(tasks) idx = 0 while len(still_running) > 0: finished, still_running = ray.wait(still_running) finished_res = ray.get(finished) for res in finished_res: iix = res[1] oob_values[iix] = res[0] if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(idx+1, n_g) idx += 1 else: for idx in range(n_g): oob_values[idx], _ = get_oob_mcf( data_np, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, False, ind_groups, forest, False, regrf, partner_k) if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(idx+1, n_g) if regrf: oob_values = np.array(oob_values) oob_values = oob_values.reshape(-1) vim_g = vim_print(mse_ref, np.array(oob_values), x_name, ind_groups, c_dict['with_output'], False) else: vim_g = None # Groups are accumulated from worst to best if no_m_g > 0: if c_dict['with_output']: print('\nMerged groups of variables') ind_groups = vim_grouping(vim_g, no_m_g, True) n_g = len(ind_groups) oob_values = [None] * n_g if c_dict['mp_with_ray'] and maxworkers > 1: tasks = [ray_get_oob_mcf.remote( data_np_ref, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, False, ind_groups, forest_ref, True, regrf, partner_k) for idx in range(n_g)] still_running = list(tasks) idx = 0 while len(still_running) > 0: finished, still_running = ray.wait(still_running) finished_res = ray.get(finished) for res in finished_res: iix = res[1] oob_values[iix] = res[0] if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(idx+1, n_g) idx += 1 else: for idx in range(n_g): oob_values[idx], _ = get_oob_mcf( data_np, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, False, ind_groups, forest, False, regrf, partner_k) if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(idx+1, n_g) if regrf: oob_values = np.array(oob_values) oob_values = oob_values.reshape(-1) vim_mg = vim_print(mse_ref, np.array(oob_values), x_name, ind_groups, c_dict['with_output'], False) else: vim_mg = None if c_dict['mp_with_ray']: del finished, still_running, data_np_ref, forest_ref, tasks ray.shutdown() return vim, vim_g, vim_mg, x_name def number_of_groups_vi(no_x_names): """Determine no of groups for groupwise variable importance measure. Parameters ---------- no_x_names :INT. No of variables considered in analysis. Returns ------- groups : INT. merged_groups : INT. """ if no_x_names >= 100: groups = 20 merged_groups = 19 elif 20 <= no_x_names < 100: groups = 10 merged_groups = 9 elif 10 <= no_x_names < 20: groups = 5 merged_groups = 4 elif 4 <= no_x_names < 10: groups = 2 merged_groups = 0 else: groups = 0 merged_groups = 0 return groups, merged_groups def vim_grouping(vim, no_groups, accu=False): """Group variables according to their variable importance measure. Parameters ---------- vim : Tuple (Numpy array list of INT). Relative vim and index. no_g : INT. No of groups. accu : Bool. Build groups by accumulation. Default = False. Returns ------- ind_groups : List of list of INT. Grouping of indices. """ indices = vim[1] no_ind = len(indices) if not accu: group_size = no_ind / no_groups group_size_int = math.floor(group_size) one_more = 0 start_i = 0 ind_groups = [] for idx in range(no_groups): if accu: if idx < 2: ind_groups = [indices[0], indices[0] + indices[idx]] else: new_group = ind_groups[idx-1] + indices[idx] ind_groups.append(new_group) else: if idx == (no_groups - 1): end_i = no_ind - 1 else: end_i = start_i + group_size_int - 1 one_more += group_size - group_size_int if one_more >= 1: one_more -= 1 end_i += 1 ind_groups.append(indices[start_i:end_i+1]) start_i = end_i + 1 return ind_groups def vim_print(mse_ref, mse_values, x_name, ind_list=0, with_output=True, single=True, partner_k=None): """Print Variable importance measure and create sorted output. Parameters ---------- mse_ref : Numpy Float. Reference value of non-randomized x. mse_values : Numpy array. MSE's for randomly permuted x. x_name : List of strings. Variable names. ind_list : List of INT, optional. Variable positions. Default is 0. with_output : Boolean, optional. Default is True. single : Boolean, optional. The default is True. partner_k : List of None and Int or None. Index of variables that were jointly randomized. Default is None. Returns ------- vim: Tuple of Numpy array and list of lists. MSE sorted and sort index. """ if partner_k is not None: for idx, val in enumerate(partner_k): if val is not None: if (idx > (val-1)) and (idx > 0): mse_values[idx-1] = mse_values[val-1] mse = mse_values / np.array(mse_ref) * 100 var_indices = np.argsort(mse) var_indices = np.flip(var_indices) vim_sorted = mse[var_indices] if single: x_names_sorted =	np.array(x_name, copy=True)	numpy.array
#!/usr/bin.env/python # -- coding: utf-8 -- """ This module provides normalisation methods using landmark registration, first described with application to cytometry data by Hahne et al [1] with further expansion by Finak et al [2]. Landmark registration is implemented in the LandmarkReg class using Scikit-FDA. [1] <NAME>, <NAME>, <NAME>, <NAME>, Gascoyne RD, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>. Per-channel basis normalization methods for flow cytometry data. Cytometry A. 2010 Feb;77(2):121-31. doi: 10.1002/cyto.a.20823. PMID: 19899135; PMCID: PMC3648208. [2] <NAME>, <NAME>, <NAME>, et al. High-throughput flow cytometry data normalization for clinical trials. Cytometry A. 2014;85(3):277-286. doi:10.1002/cyto.a.22433 Copyright 2020 <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. """ from skfda.preprocessing.registration import landmark_registration_warping, landmark_shift_deltas from skfda.representation.grid import FDataGrid from sklearn.cluster import KMeans from detecta import detect_peaks from KDEpy import FFTKDE import matplotlib.pyplot as plt import pandas as pd import numpy as np __author__ = "<NAME>" __copyright__ = "Copyright 2020, cytopy" __credits__ = ["<NAME>", "<NAME>", "<NAME>", "<NAME>"] __license__ = "MIT" __version__ = "2.0.0" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Production" def peaks(y: np.ndarray, x: np.ndarray, kwargs): """ Detect peaks of some function, y, in the grid space, x. Parameters ---------- y: numpy.ndarray x: numpy.ndarray kwargs: Additional keyword arguments passed to detecta.detect_peaks function Returns ------- List """ p = detect_peaks(y, kwargs) return [x[i] for i in p] def filter_by_closest_centroid(x: np.ndarray, labels: np.ndarray, centroid: float): """ Filter peaks ('x') to keep only those closest to their nearest centroid (centroid of clustered peaks). Labels indicate where the peak originated from; either target sample (0) or reference (1). Parameters ---------- x: numpy.ndarray labels: numpy.ndarray centroid: float Returns ------- float, float Peaks closest to centroid in cluster 1, Peaks closest to centroid in cluster 2 """ x, labels = np.array(x), np.array(labels) y1 = x[np.where(labels == 0)] y2 = x[np.where(labels == 1)] if len(y1) > 1: y1 = y1[np.abs(y1 - centroid).argmin()] else: y1 = y1[0] if len(y2) > 1: y2 = y2[np.abs(y2 - centroid).argmin()] else: y2 = y2[0] return y1, y2 def cluster_landmarks(p: np.ndarray, plabels: np.ndarray): """ Cluster peaks (p). plabels indicate where the peak originated from; either target sample (0) or reference (1). The number of clusters, determined by KMeans clustering is equal to the number of peaks for the target sample. Parameters ---------- p: numpy.ndarray Peaks plabels: numpy.ndarray Peak labels Returns ------- numpy.ndarray, numpy.ndarray K Means labels for each peak, cluster centroids """ km = KMeans(n_clusters=len(np.where(np.array(plabels) == 0)[0]), random_state=42) km_labels = km.fit_predict(np.array(p).reshape(-1, 1)) centroids = km.cluster_centers_.reshape(-1) return km_labels, centroids def zero_entropy_clusters(km_labels: np.ndarray, plabels: np.ndarray, centroids: np.ndarray): """ Determine which clusters (if any) have zero entropy (only contains peaks from a single sample; either target or reference) Parameters ---------- km_labels: numpy.ndarray K means cluster labels plabels: numpy.ndarray Origin of the peak; either target (0) or reference (1) centroids: numpy.ndarray Cluster centroids Returns ------- List List of centroids for clusters with zero entropy """ zero_entropy = list() for i in	np.unique(km_labels)	numpy.unique
# Copyright 2016 The TensorFlow Authors All Rights Reserved. # # 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. # ============================================================================== """Various function to manipulate graphs for computing distances. """ import skimage.morphology import numpy as np import networkx as nx import itertools import logging import graph_tool as gt import graph_tool.topology import graph_tool.generation import src.utils as utils # Compute shortest path from all nodes to or from all source nodes def get_distance_node_list(gtG, source_nodes, direction, weights=None): gtG_ = gt.Graph(gtG) v = gtG_.add_vertex() if weights is not None: weights = gtG_.edge_properties[weights] for s in source_nodes: e = gtG_.add_edge(s, int(v)) if weights is not None: weights[e] = 0. if direction == 'to': dist = gt.topology.shortest_distance( gt.GraphView(gtG_, reversed=True), source=gtG_.vertex(int(v)), target=None, weights=weights) elif direction == 'from': dist = gt.topology.shortest_distance( gt.GraphView(gtG_, reversed=False), source=gtG_.vertex(int(v)), target=None, weights=weights) dist = np.array(dist.get_array()) dist = dist[:-1] if weights is None: dist = dist - 1 return dist # Functions for semantically labelling nodes in the traversal graph. def generate_lattice(sz_x, sz_y): """Generates a lattice with sz_x vertices along x and sz_y vertices along y direction Each of these vertices is step_size distance apart. Origin is at (0,0). """ g = gt.generation.lattice([sz_x, sz_y]) x, y = np.meshgrid(np.arange(sz_x), np.arange(sz_y)) x = np.reshape(x, [-1, 1]); y = np.reshape(y, [-1, 1]); nodes = np.concatenate((x, y), axis=1) return g, nodes def add_diagonal_edges(g, nodes, sz_x, sz_y, edge_len): offset = [sz_x + 1, sz_x - 1] for o in offset: s = np.arange(nodes.shape[0] - o - 1) t = s + o ind = np.all(np.abs(nodes[s, :] - nodes[t, :]) == np.array([[1, 1]]), axis=1) s = s[ind][:, np.newaxis] t = t[ind][:, np.newaxis] st = np.concatenate((s, t), axis=1) for i in range(st.shape[0]): e = g.add_edge(st[i, 0], st[i, 1], add_missing=False) g.ep['wts'][e] = edge_len def convert_traversible_to_graph(traversible, ff_cost=1., fo_cost=1., oo_cost=1., connectivity=4): assert (connectivity == 4 or connectivity == 8) sz_x = traversible.shape[1] sz_y = traversible.shape[0] g, nodes = generate_lattice(sz_x, sz_y) # Assign costs. edge_wts = g.new_edge_property('float') g.edge_properties['wts'] = edge_wts wts = np.ones(g.num_edges(), dtype=np.float32) edge_wts.get_array()[:] = wts if connectivity == 8: add_diagonal_edges(g, nodes, sz_x, sz_y, np.sqrt(2.)) se = np.array([[int(e.source()), int(e.target())] for e in g.edges()]) s_xy = nodes[se[:, 0]] t_xy = nodes[se[:, 1]] s_t = np.ravel_multi_index((s_xy[:, 1], s_xy[:, 0]), traversible.shape) t_t = np.ravel_multi_index((t_xy[:, 1], t_xy[:, 0]), traversible.shape) s_t = traversible.ravel()[s_t] t_t = traversible.ravel()[t_t] wts = np.zeros(g.num_edges(), dtype=np.float32) wts[np.logical_and(s_t == True, t_t == True)] = ff_cost wts[np.logical_and(s_t == False, t_t == False)] = oo_cost wts[np.logical_xor(s_t, t_t)] = fo_cost edge_wts = g.edge_properties['wts'] for i, e in enumerate(g.edges()): edge_wts[e] = edge_wts[e] * wts[i] # d = edge_wts.get_array()1. # edge_wts.get_array()[:] = dwts return g, nodes def label_nodes_with_class(nodes_xyt, class_maps, pix): """ Returns: class_maps__: one-hot class_map for each class. node_class_label: one-hot class_map for each class, nodes_xyt.shape[0] x n_classes """ # Assign each pixel to a node. selem = skimage.morphology.disk(pix) class_maps_ = class_maps * 1. for i in range(class_maps.shape[2]): class_maps_[:, :, i] = skimage.morphology.dilation(class_maps[:, :, i] * 1, selem) class_maps__ = np.argmax(class_maps_, axis=2) class_maps__[np.max(class_maps_, axis=2) == 0] = -1 # For each node pick out the label from this class map. x = np.round(nodes_xyt[:, [0]]).astype(np.int32) y = np.round(nodes_xyt[:, [1]]).astype(np.int32) ind = np.ravel_multi_index((y, x), class_maps__.shape) node_class_label = class_maps__.ravel()[ind][:, 0] # Convert to one hot versions. class_maps_one_hot = np.zeros(class_maps.shape, dtype=np.bool) node_class_label_one_hot = np.zeros((node_class_label.shape[0], class_maps.shape[2]), dtype=np.bool) for i in range(class_maps.shape[2]): class_maps_one_hot[:, :, i] = class_maps__ == i node_class_label_one_hot[:, i] = node_class_label == i return class_maps_one_hot, node_class_label_one_hot def label_nodes_with_class_geodesic(nodes_xyt, class_maps, pix, traversible, ff_cost=1., fo_cost=1., oo_cost=1., connectivity=4): """Labels nodes in nodes_xyt with class labels using geodesic distance as defined by traversible from class_maps. Inputs: nodes_xyt class_maps: counts for each class. pix: distance threshold to consider close enough to target. traversible: binary map of whether traversible or not. Output: labels: For each node in nodes_xyt returns a label of the class or -1 is unlabelled. """ g, nodes = convert_traversible_to_graph(traversible, ff_cost=ff_cost, fo_cost=fo_cost, oo_cost=oo_cost, connectivity=connectivity) class_dist = np.zeros_like(class_maps * 1.) n_classes = class_maps.shape[2] if False: # Assign each pixel to a class based on number of points. selem = skimage.morphology.disk(pix) class_maps_ = class_maps * 1. class_maps__ = np.argmax(class_maps_, axis=2) class_maps__[np.max(class_maps_, axis=2) == 0] = -1 # Label nodes with classes. for i in range(n_classes): # class_node_ids = np.where(class_maps__.ravel() == i)[0] class_node_ids = np.where(class_maps[:, :, i].ravel() > 0)[0] dist_i = get_distance_node_list(g, class_node_ids, 'to', weights='wts') class_dist[:, :, i] = np.reshape(dist_i, class_dist[:, :, i].shape) class_map_geodesic = (class_dist <= pix) class_map_geodesic = np.reshape(class_map_geodesic, [-1, n_classes]) # For each node pick out the label from this class map. x = np.round(nodes_xyt[:, [0]]).astype(np.int32) y = np.round(nodes_xyt[:, [1]]).astype(np.int32) ind = np.ravel_multi_index((y, x), class_dist[:, :, 0].shape) node_class_label = class_map_geodesic[ind[:, 0], :] class_map_geodesic = class_dist <= pix return class_map_geodesic, node_class_label def _get_next_nodes_undirected(n, sc, n_ori): nodes_to_add = [] nodes_to_validate = [] (p, q, r) = n nodes_to_add.append((n, (p, q, r), 0)) if n_ori == 4: for _ in [1, 2, 3, 4]: if _ == 1: v = (p - sc, q, r) elif _ == 2: v = (p + sc, q, r) elif _ == 3: v = (p, q - sc, r) elif _ == 4: v = (p, q + sc, r) nodes_to_validate.append((n, v, _)) return nodes_to_add, nodes_to_validate def _get_next_nodes(n, sc, n_ori): nodes_to_add = [] nodes_to_validate = [] (p, q, r) = n for r_, a_ in zip([-1, 0, 1], [1, 0, 2]): nodes_to_add.append((n, (p, q, np.mod(r + r_, n_ori)), a_)) if n_ori == 6: if r == 0: v = (p + sc, q, r) elif r == 1: v = (p + sc, q + sc, r) elif r == 2: v = (p, q + sc, r) elif r == 3: v = (p - sc, q, r) elif r == 4: v = (p - sc, q - sc, r) elif r == 5: v = (p, q - sc, r) elif n_ori == 4: if r == 0: v = (p + sc, q, r) elif r == 1: v = (p, q + sc, r) elif r == 2: v = (p - sc, q, r) elif r == 3: v = (p, q - sc, r) nodes_to_validate.append((n, v, 3)) return nodes_to_add, nodes_to_validate def generate_graph(valid_fn_vec=None, sc=1., n_ori=6, starting_location=(0, 0, 0), vis=False, directed=True): timer = utils.Timer() timer.tic() if directed: G = nx.DiGraph(directed=True) else: G = nx.Graph() G.add_node(starting_location) new_nodes = G.nodes() while len(new_nodes) != 0: nodes_to_add = [] nodes_to_validate = [] for n in new_nodes: if directed: na, nv = _get_next_nodes(n, sc, n_ori) else: na, nv = _get_next_nodes_undirected(n, sc, n_ori) nodes_to_add = nodes_to_add + na if valid_fn_vec is not None: nodes_to_validate = nodes_to_validate + nv else: node_to_add = nodes_to_add + nv # Validate nodes. vs = [_[1] for _ in nodes_to_validate] valids = valid_fn_vec(vs) for nva, valid in zip(nodes_to_validate, valids): if valid: nodes_to_add.append(nva) new_nodes = [] for n, v, a in nodes_to_add: if not G.has_node(v): new_nodes.append(v) G.add_edge(n, v, action=a) timer.toc(average=True, log_at=1, log_str='src.graph_utils.generate_graph') return (G) def vis_G(G, ax, vertex_color='r', edge_color='b', r=None): if edge_color is not None: for e in G.edges(): XYT = zip(e) x = XYT[-3] y = XYT[-2] t = XYT[-1] if r is None or t[0] == r: ax.plot(x, y, edge_color) if vertex_color is not None: XYT = zip(G.nodes()) x = XYT[-3] y = XYT[-2] t = XYT[-1] ax.plot(x, y, vertex_color + '.') def convert_to_graph_tool(G): timer = utils.Timer() timer.tic() gtG = gt.Graph(directed=G.is_directed()) gtG.ep['action'] = gtG.new_edge_property('int') nodes_list = G.nodes() nodes_array = np.array(nodes_list) nodes_id = np.zeros((nodes_array.shape[0],), dtype=np.int64) for i in range(nodes_array.shape[0]): v = gtG.add_vertex() nodes_id[i] = int(v) # d = {key: value for (key, value) in zip(nodes_list, nodes_id)} d = dict(itertools.izip(nodes_list, nodes_id)) for src, dst, data in G.edges_iter(data=True): e = gtG.add_edge(d[src], d[dst]) gtG.ep['action'][e] = data['action'] nodes_to_id = d timer.toc(average=True, log_at=1, log_str='src.graph_utils.convert_to_graph_tool') return gtG, nodes_array, nodes_to_id def _rejection_sampling(rng, sampling_d, target_d, bins, hardness, M): bin_ind = np.digitize(hardness, bins) - 1 i = 0 ratio = target_d[bin_ind] / (M * sampling_d[bin_ind]) while i < ratio.size and rng.rand() > ratio[i]: i = i + 1 return i def heuristic_fn_vec(n1, n2, n_ori, step_size): # n1 is a vector and n2 is a single point. dx = (n1[:, 0] - n2[0, 0]) / step_size dy = (n1[:, 1] - n2[0, 1]) / step_size dt = n1[:, 2] - n2[0, 2] dt = np.mod(dt, n_ori) dt = np.minimum(dt, n_ori - dt) if n_ori == 6: if dx * dy > 0: d = np.maximum(np.abs(dx), np.abs(dy)) else: d = np.abs(dy - dx) elif n_ori == 4: d = np.abs(dx) +	np.abs(dy)	numpy.abs
# -- coding: utf-8 -- """ Created on Wed Dec 22 15:40:32 2021 @author: <NAME> Script takes as input a dataset of the type "Spooled files_000x.tif", images from the Andor camera, assumes every odd number for x is a shot with the tweezer off (just MOT), while every even number is with tweezer on as well. Subtracts MOT from tweezer + MOT and averages over multiple attemps """ #%% Imports import numpy as np from PIL import Image import cv2 import matplotlib.pyplot as plt #%% Loading Data # Data location and define empty lists data_location = "U:/KAT1/Images/dipole/MOToffDetuningSweep/" file_name = "15ms_121mhz" file_prefix = "/Spooled files_" # File numbers, step is 2 because only odd/even iterations. Stop_number is the # amount of pixel fixels you made start_number = 4 stop_number = 40 step_number = 2 #%% Load even and odd files lists, corresponding to tweezer on or off def load_image_list(start, stop, step, data_location, file_prefix): iteration_list = [] # Load files for i in range(start, stop, step): im = Image.open(data_location + file_name + file_prefix + str(i).zfill(4) + ".tif") array =	np.array(im)	numpy.array
#!/usr/bin/python3 # -- coding: utf-8 -- ''' API ======================================================================== Connection point for all package utilities to be provided ''' import collections import numba import numpy as np import sys import time import types from bisect import bisect_left import matplotlib.pyplot as plt from . import GLOBALS from . import auxiliaries as aux from .auxiliaries import to_ndarray, wait from .errorfunctions import get_errorfunction, maxsumabs from . import models from . import reference as ref # Careful with this circular import # This global dictionary G is for passing some telemtery and debug arguments global G G = GLOBALS.dictionary _sqrtrange = (aux.sqrtrange_python, aux.sqrtrange_numba) fitsets = {'Poly10': models.Poly10} #%%═════════════════════════════════════════════════════════════════════ ## ROOT FINDING def interval(f, x1, y1, x2, y2, fit1): '''Returns the last x where f(x)<0''' is_debug = G['debug'] if is_debug: #─────────────────────────────────────────────────────┐ print(f'\t{x1=}\t{y1=}') print(f'\t{x2=}\t{y2=}') G['mid'], = G['ax_root'].plot(x1, y1,'.', color = 'blue') #──────────────────────────────────────────────────────────────────┘ # sqrtx1 = x10.5 # sqrtx2 = x20.5 while x2 - x1 > 2: # Arithmetic mean between linear estimate and half linest = x1 - y1 / (y2 - y1) * (x2 - x1) halfest = (x2 + x1) / 2 # sqrtest1 = sqrtx1 - y1 * (sqrtx2 - sqrtx1) / (y2 - y1) # sqrtest1 = sqrtest1sqrtest1 # sqrtest2 = int(x1 + (x2 - x1) / (y2 / y1 - 1)2) # x_mid = int((x2 + x1) / 2) x_mid = int((linest + halfest) / 2) if x_mid == x1: # To stop repetition in close cases x_mid += 1 elif x_mid == x2: x_mid -= 1 y_mid, fit2 = f(x_mid) if is_debug: #─────────────────────────────────────────────────┐ print(f'\t{x_mid=}\t{y_mid=}') G['mid'].set_xdata(x_mid) G['mid'].set_ydata(y_mid) #──────────────────────────────────────────────────────────────┘ if y_mid > 0: if is_debug: #─────────────────────────────────────────────┐ wait('\tError over tolerance\n') G['ax_root'].plot(x2, y2,'.', color = 'black') #──────────────────────────────────────────────────────────┘ x2, y2 = x_mid, y_mid # sqrtx2 = x_mid 0.5 if is_debug: #─────────────────────────────────────────────┐ G['xy2'].set_xdata(x2) G['xy2'].set_ydata(y2) #──────────────────────────────────────────────────────────┘ else: if is_debug: #─────────────────────────────────────────────┐ wait('\tError under tolerance\n') G['ax_root'].plot(x1, y1,'.', color = 'black') #──────────────────────────────────────────────────────────┘ x1, y1, fit1 = x_mid, y_mid, fit2 # sqrtx1 = x_mid * 0.5 if is_debug: #─────────────────────────────────────────────┐ G['xy1'].set_xdata(x1) G['xy1'].set_ydata(y1) #──────────────────────────────────────────────────────────┘ if x2 - x1 == 2: # Points have only one point in between y_mid, fit2 = f(x1+1) # Testing that point return (x1+1, fit2) if (y_mid <0) else (x1, fit1) # If under, give that fit else: return x1, fit1 #─────────────────────────────────────────────────────────────────────── def droot(f, y1, x2, limit): '''Finds the upper limit to interval ''' is_debug = G['debug'] x1 = 0 y2, fit2 = f(x2) fit1 = None if is_debug: #─────────────────────────────────────────────────────┐ G['xy1'], = G['ax_root'].plot(x1, y1,'g.') G['xy2'], = G['ax_root'].plot(x2, y2,'b.') #──────────────────────────────────────────────────────────────────┘ while y2 < 0: if is_debug: #─────────────────────────────────────────────────┐ wait('Calculating new attempt in droot\n') G['ax_root'].plot(x1, y1,'k.') #──────────────────────────────────────────────────────────────┘ x1, y1, fit1 = x2, y2, fit2 x2 = 2 x2 += 1 if is_debug: #─────────────────────────────────────────────────┐ print(f'{limit=}') print(f'{x1=}\t{y1=}') print(f'{x2=}\t{y2=}') G['xy1'].set_xdata(x1) G['xy1'].set_ydata(y1) G['xy2'].set_xdata(x2) #──────────────────────────────────────────────────────────────┘ if x2 >= limit: if is_debug: #─────────────────────────────────────────────┐ G['ax_root'].plot([limit, limit], [y1,0],'b.') #──────────────────────────────────────────────────────────┘ y2, fit2 = f(limit) if y2<0: if is_debug: #─────────────────────────────────────────┐ wait('End reached within tolerance\n') #──────────────────────────────────────────────────────┘ return limit, fit2 else: if is_debug: #─────────────────────────────────────────┐ wait('End reached outside tolerance\n') #──────────────────────────────────────────────────────┘ x2 = limit break y2, fit2 = f(x2) if is_debug: #─────────────────────────────────────────────────┐ G['ax_root'].plot(x1, y1,'k.') print(f'{x1=}\t{y1=}') print(f'{x2=}\t{y2=}') G['ax_root'].plot(x2, y2,'k.') G['xy2'].set_ydata(y2) #──────────────────────────────────────────────────────────────┘ if is_debug: #─────────────────────────────────────────────────────┐ G['xy2'].set_color('red') wait('Points for interval found\n') #──────────────────────────────────────────────────────────────────┘ return interval(f, x1, y1, x2, y2, fit1) #─────────────────────────────────────────────────────────────────────── # @numba.jit(nopython=True,cache=True) def n_lines(x: np.ndarray, y: np.ndarray, x0: float, y0: np.ndarray, tol: float ) -> float: '''Estimates number of lines required to fit within error tolerance''' if (length := len(x)) > 1: inds = _sqrtrange[0](length - 2) # indices so that x[-1] is not included res = (y[-1] - y0) / (x[-1] - x0)(x[inds] - x0).reshape([-1,1]) - (y[inds] - y0) reference = maxsumabs(res, tol) if reference < 0: reference = 0 return 0.5 * reference ** 0.5 + 1 else: return 1. #─────────────────────────────────────────────────────────────────────── def _get_f2zero(x, y, x0, y0, sqrtrange, f_fit, errorfunction): def f2zero(i: int) -> tuple: '''Function such that i is optimal when f2zero(i) = 0''' inds = sqrtrange(i) residuals, fit = f_fit(x[inds], y[inds], x0, y0) return errorfunction(residuals), fit return f2zero #─────────────────────────────────────────────────────────────────────── def _get_f2zero_debug(x, y, x0, y0, sqrtrange, f_fit, errorfunction): def f2zero_debug(i: int) -> tuple: '''Function such that i is optimal when f2zero(i) = 0''' inds = sqrtrange(i) residuals, fit = f_fit(x[inds], y[inds], x0, y0) if len(residuals) == 1: print(f'\t\t{residuals=}') print(f'\t\tstart = {G["start"]} end = {i + G["start"]} points = {i + 1}') print(f'\t\tx0\t{x0}\n\t\tx[0]\t{x[inds][0]}\n\t\tx[-1]\t{x[inds][-1]}\n\t\txstart = {G["x"][G["start"]]}') indices_all = np.arange(-1, i + 1) + G['start'] G['x_plot'] = G['x'][indices_all] G['y_plot'] = G['fyc'](fit, G['x_plot']) G['line_fit'].set_xdata(G['x_plot']) G['line_fit'].set_ydata(G['y_plot']) # print(f'{G["y_plot"].shape=}') # print(f'{G["y"][indices_all].shape=}') res_all = G['y_plot'] - G['y'][indices_all].flatten() print(f'\t\t{residuals.shape=}\n\t\t{res_all.shape=}') G['ax_res'].clear() G['ax_res'].grid() G['ax_res'].axhline(color = 'red', linestyle = '--') G['ax_res'].set_ylabel('Residual relative to tolerance') G['ax_res'].plot(indices_all - G['start'],	np.abs(res_all)	numpy.abs
import dxfgrabber import numpy as np import sys, os.path, inspect import re import pcbnew import bulge import pcbpoint from sets import Set import pdb # how close together to points need to be to each other to be considered # connected? thresh = 0.01 # when breaking and arc into segments, what's the max seg length we want. arc_line_length = 5.0 # I mostly depend on pcbpoint to deal with scaling issues. For radius below, # still need to scale. # the internal coorinate space of pcbnew is 10E-6 mm. (a millionth of a mm) # the coordinate 121550000 corresponds to 121.550000 SCALE = 1000000.0 class graphic_actions: def __init__(self, print_unhandled=False): self.print_unhandled = print_unhandled def line_action(self, start, end): if (self.print_unhandled): print("line: {} {}".format(start, end)) def circle_action(self, center, radius): if (self.print_unhandled): print("circle center: {} radius: {}".format(center, radius)) def arc_action(self, center, radius, start_angle, end_angle): if (self.print_unhandled): print("arc center: {} radius {} angles: {} {}".format(center, radius, start_angle, end_angle)) def poly_action(self, points): if (self.print_unhandled): print("poly: {}".format(points)) # here is some helper stuff # dxf arcs are different from pcbnew arcs # dxf arcs have a center point, radius and start/stop angles # pcbnew arcs have a center pointer, radius, and a start point, # angle (counter clockwise) def dxfarc2pcbarc(self, center, radius, start_angle, end_angle): # need negative angles because pcbnew flips over x axis start_angle, end_angle = (min(start_angle, end_angle), max(start_angle, end_angle)) return (center, start_angle-end_angle, # location of start of arc center.polar(radius, start_angle)) class segment_actions(graphic_actions): def __init__(self, board, layer, print_unhandled=False): graphic_actions.__init__(self, print_unhandled) self.board = board self.layer = layer def make_basic_seg(self): seg = pcbnew.DRAWSEGMENT(self.board) seg.SetLayer(self.layer) seg.SetShape(pcbnew.S_SEGMENT) self.board.Add(seg) return seg def line_action(self, start, end): seg = self.make_basic_seg() seg.SetShape(pcbnew.S_SEGMENT) seg.SetStart(pcbpoint.pcbpoint(start).wxpoint()) seg.SetEnd(pcbpoint.pcbpoint(end).wxpoint()) def circle_action(self, center, radius): seg = self.make_basic_seg() seg.SetShape(pcbnew.S_CIRCLE) seg.SetCenter(pcbpoint.pcbpoint(center).wxpoint()) # kicad has a goofy way of specifying circles. instead # of a radius, you give a point on the circle. The radius # can be computed from there. seg.SetEnd((pcbpoint.pcbpoint(center)+ pcbpoint.pcbpoint(radius, 0)).wxpoint() ) def arc_action(self, center, radius, start_angle, end_angle): # dxf arcs are different from pcbnew arcs # dxf arcs have a center point, radius and start/stop angles # pcbnew arcs have a center pointer, radius, and a start point, # angle (counter clockwise) seg = self.make_basic_seg() center, angle, arcstart = self.dxfarc2pcbarc(pcbpoint.pcbpoint(center), radius, start_angle, end_angle) seg.SetShape(pcbnew.S_ARC) seg.SetCenter(pcbpoint.pcbpoint(center).wxpoint()) # negative angle since y goes the wrong way. seg.SetAngle(angle10) seg.SetArcStart(pcbpoint.pcbpoint(arcstart).wxpoint()) def poly_action(self, points): seg = self.make_basic_seg() seg.SetShape(pcbnew.S_POLYGON) sps = seg.GetPolyShape() o = sps.NewOutline() for pt in points: ppt = pcbpoint.pcbpoint(pt).wxpoint() sps.Append(ppt.x, ppt.y) class zone_actions(graphic_actions): def __init__(self, board, net, layer, print_unhandled=False): graphic_actions.__init__(self, print_unhandled) self.board = board self.net = net self.layer = layer # poly is the only thing that really makes sense in # the zone context def poly_action(self, points): pcbpt = pcbpoint.pcbpoint(points[0]).wxpoint() zone_container = self.board.InsertArea(self.net.GetNet(), 0, self.layer, pcbpt.x, pcbpt.y, pcbnew.CPolyLine.DIAGONAL_EDGE) shape_poly_set = zone_container.Outline() shapeid = 0 for pt in points[1:]: pcbpt = pcbpoint.pcbpoint(pt).wxpoint() shape_poly_set.Append(pcbpt.x, pcbpt.y) zone_container.Hatch() class mounting_actions(graphic_actions): def __init__(self, board, footprint_mapping, flip=False, clearance=None, print_unhandled=False): graphic_actions.__init__(self, print_unhandled) self.footprint_mapping = footprint_mapping self.board = board self.flip = flip self.clearance = clearance def circle_action(self, center, radius): d = str(radius2) if d not in self.footprint_mapping: print("diameter {} not found in footprint mapping".format(d)) return fp = self.footprint_mapping[d] mod = pcbnew.InstantiateFootprint(fp[0], fp[1]) mod.SetPosition(pcbpoint.pcbpoint(center).wxpoint()) if (self.flip): mod.Flip(pcbpoint.pcbpoint(center).wxpoint()) if (self.clearance != None): for pad in mod.Pads(): pad.SetLocalClearance(self.clearance) self.board.Add(mod) # http://www.ariel.com.au/a/python-point-int-poly.html # determine if a point is inside a given polygon or not # Polygon is a list of (x,y) pairs. def point_inside_polygon(x,y,poly): n = len(poly) inside =False p1x,p1y = poly[0] for i in range(n+1): p2x,p2y = poly[i % n] if y > min(p1y,p2y): if y <= max(p1y,p2y): if x <= max(p1x,p2x): if p1y != p2y: xinters = (y-p1y)(p2x-p1x)/(p2y-p1y)+p1x if p1x == p2x or x <= xinters: inside = not inside p1x,p1y = p2x,p2y return inside def longest_angle_for_polygon(poly): prevpt = poly[-1] length = None retval = None for pt in poly: d = prevpt.distance(pt) if (length and (length>d)): prevpt = pt continue length = d retval = prevpt.angle(pt) prevpt = pt return retval def center_for_polygon(poly): # based on this: # https://en.wikipedia.org/wiki/Centroid#Centroid_of_a_polygon prev = poly[-1] x = 0.0 y = 0.0 area = 0.0 for cur in poly: x = x + (prev.x+cur.x)(prev.xcur.y - cur.xprev.y) y = y + (prev.y+cur.y)(prev.xcur.y - cur.xprev.y) area = area + prev.xcur.y - cur.xprev.y prev = cur area = area/2.0 x = x/6.0/area y = y/6.0/area return pcbpoint.pcbpoint(x,y,noscale=True) class orient_actions(graphic_actions): def __init__(self, board, modnames, print_unhandled=False): graphic_actions.__init__(self, print_unhandled) self.board = board self.modnames = Set(modnames) # I only care about poly because I want directionality (which a cirle doesn't have) # and I want to check for enclosing (which doesn't make sense for line, arc def poly_action(self, points): for mod in self.board.GetModules(): #modname = mod.GetFPID().GetLibItemName().c_str() #if (modname != "LED_5730"): # continue modname = mod.GetReference() if (modname not in self.modnames): continue pos = pcbpoint.pcbpoint(mod.GetPosition()) inside = point_inside_polygon(pos.x, pos.y, points) if (not inside): continue angle = longest_angle_for_polygon(points) if (angle>0): angle = angle - 180.0 mod.SetOrientation(angle10) mod.SetPosition(center_for_polygon(points).wxpoint()) class myarc: def __init__(self, center, radius, start_angle, end_angle): self.center = center = pcbpoint.pcbpoint(center) self.radius = radius self.start_angle = start_angle self.end_angle = end_angle self.start_point = center.polar(radius, start_angle) self.end_point = center.polar(radius, end_angle) self.other = Set() def reverse(self): self.start_angle, self.end_angle = (self.end_angle, self.start_angle) self.start_point, self.end_point = (self.end_point, self.start_point) def __str__(self): return "arc c{} r{} {},{} {},{}".format(self.center, self.radius, self.start_angle, self.end_angle, self.start_point, self.end_point) class myline: def __init__(self, start_point, end_point): self.start_point = pcbpoint.pcbpoint(start_point) self.end_point = pcbpoint.pcbpoint(end_point) self.other = Set() def reverse(self): self.start_point, self.end_point = (self.end_point, self.start_point) def __str__(self): return "line {} {}".format(self.start_point, self.end_point) def mydist(o1, o2): return min(o1.start_point.distance(o2.start_point), o1.start_point.distance(o2.end_point), o1.end_point.distance(o2.start_point), o1.end_point.distance(o2.end_point)) # it is possible for two polygons to meet at a point. This implementation # will destroy those. worry about it later. def remove_non_duals(e): if (len(e.other) == 2): return # if I have three lines joining in one spot. this doesn't deal # with the properly. I try to mitigate that by beginning with # lines that connect at only one edge first. others = e.other e.other = Set() for other in others: other.other.remove(e) remove_non_duals(other) def merge_arcs_and_lines(elts): # yes, this is a O(n^2) algorithm. scanline would be better # this is quicker to implement for e1 in elts: for e2 in elts: if (e1==e2): continue if mydist(e1, e2)<thresh: e1.other.add(e2) e2.other.add(e1) # this needs some work. if I have a line that connects to a poly, # I want to first lose that line and only then break triple connections. for e in elts: if (len(e.other) == 1): remove_non_duals(e) for e in elts: remove_non_duals(e) merged = [] for e in elts: if (len(e.other) != 2): continue members = [e] # after this pop, the e.other set will have one member other = e.other.pop() other.other.remove(e) while (other): if (other == e): break nextelt = other.other.pop() nextelt.other.remove(other) members.append(other) other = nextelt if (len(members) < 2): raise ValueError("There should be at least two members in this merged poly") prev = members[-1] # here, I'm about to reorder the point order of the member lines/arcs. I # want to end_point to match the next member. if ((members[0].start_point.distance(prev.end_point) > thresh) and (members[0].end_point.distance(prev.end_point) > thresh)): prev.reverse() for m in members: if (m.start_point.distance(prev.end_point) > thresh): m.reverse() if (m.start_point.distance(prev.end_point) > thresh): raise ValueError("expecting the start and end to match here {} {}".format(prev, m)) prev = m merged.append(members) return merged def break_curve(center, radius, start_angle, end_angle): retpts = [] center = pcbpoint.pcbpoint(center) # in this file, I generally use degrees (kicad uses degrees), # in this function, radians more convenient. start_radians, end_radians = (	np.deg2rad(start_angle)	numpy.deg2rad
# -- coding: utf-8 -- """LDFA-H: Latent Dynamic Factor Analysis of High-dimensional time-series This module implements the fitting algorithm of LDFA-H and the accessory functions to facilitate the associate analyses or inferences. Todo ---- * Correct function ``fit_Phi``. .. _[1] Bong et al. (2020). Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings. Submitted to NeurIPS2020. """ import time, sys, traceback import numpy as np from scipy import linalg import ldfa.optimize as core def _generate_lambda_glasso(bin_num, lambda_glasso, offset, lambda_diag=None): """Generate sparsity penalty matrix Lambda for a submatrix in Pi.""" lambda_glasso_out = np.full((bin_num, bin_num), -1) + (1+lambda_glasso) * \ (np.abs(np.arange(bin_num) - np.arange(bin_num)[:,np.newaxis]) <= offset) if lambda_diag: lambda_glasso_out[np.arange(bin_num), np.arange(bin_num)] = lambda_diag return lambda_glasso_out def _switch_back(Sigma, Phi_S, Gamma_T, Phi_T, beta): """Make the initial Phi_S positive definite.""" w, v = np.linalg.eig(Sigma) sqrtS = (vnp.sqrt(w)[...,None,:])@v.transpose(0,2,1) alpha = np.min(np.linalg.eigvals( sqrtS @ linalg.block_diag(Gamma_T) @ sqrtS),-1)/2 return (Sigma - alpha[:,None,None]linalg.block_diag(Phi_T), [P + (balpha)@b.T for P, b in zip(Phi_S, beta)]) def _make_PD(m, thres_eigen=1e-4): """Make a coariance PSD based on its eigen decomposition.""" s, v = np.linalg.eigh(m) if np.min(s)<=thres_eigennp.max(s): delta = thres_eigennp.max(s) - np.min(s) s = s + delta return ([email protected](s)@np.linalg.inv(v)) def _temporal_est(V_eps_T, ar_order): """Perform temporal estimate given V_T. .. _[1] <NAME>. and <NAME>. (2008). Regularized estimation of large covariance matrices. Ann. Statist., 36(1):199–227. """ num_time = V_eps_T.shape[0] resids = np.zeros(num_time) Amatrix = np.zeros([num_time, num_time]) resids[0] = V_eps_T[0,0] for i in np.arange(1, ar_order): Amatrix[i,:i] = np.linalg.pinv(V_eps_T[:i,:i]) @ V_eps_T[:i,i] resids[i] = V_eps_T[i,i] \ - V_eps_T[i,:i] @ np.linalg.pinv(V_eps_T[:i,:i]) @ V_eps_T[:i,i] for i in np.arange(ar_order, num_time): Amatrix[i,i-ar_order:i] = np.linalg.pinv(V_eps_T[i-ar_order:i,i-ar_order:i]) \ @ V_eps_T[i-ar_order:i,i] resids[i] = V_eps_T[i,i] \ - V_eps_T[i,i-ar_order:i] \ @ np.linalg.pinv(V_eps_T[i-ar_order:i,i-ar_order:i]) \ @ V_eps_T[i-ar_order:i,i] # invIA = np.linalg.pinv(np.eye(num_time) - Amatrix) # Psi_T_hat = invIA @ np.diag(resids) @ invIA.T # Gamma_T_hat = np.linalg.inv(Psi_T_hat) Gamma_T_hat = (np.eye(num_time)-Amatrix).T @ np.diag(1/resids) \ @ (np.eye(num_time)-Amatrix) Psi_T_hat = np.linalg.pinv(Gamma_T_hat) return Gamma_T_hat, Psi_T_hat def fit(data, num_f, lambda_cross, offset_cross, lambda_auto=None, offset_auto=None, lambda_aug=0, ths_ldfa=1e-2, max_ldfa=1000, ths_glasso=1e-8, max_glasso=1000, ths_lasso=1e-8, max_lasso=1000, params_init=dict(), make_PD=False, verbose=False): """The main function to perform multi-factor LDFA-H estimation. Parameters ---------- data: list of (N, p_k, T) ndarrays Observed data from K areas. Data from each area k consists of p_k-variate time-series over T time bins in N trials. num_f: int The number of factors. lambda_cross, lambda_auto: float The sparsity penalty parameter for the inverse cross-correlation and inverse auto-correlation matrix, respectively. The default value for lambda_auto is 0. offset_cross, offset_auto: int The bandwidth parameter for the inverse cross-correlation matrix and inverse auto-correlation matrix, respectively. The default value for offset_auto is the given value of offset_cross. ths_ldfa, ths_glasso, ths_lasso: float, optional The threshold values for deciding the convergence of the main iteration, the glasso iteration, and the lasso iteration, respectively. max_ldfa, max_glasso, max_lasso: int, optional The maximum number of iteration for the main iteration, the glasso iteration, and the lasso iteration, respectively. beta_init: list of (p_k, num_f) ndarrays, optional Custom initial values for beta. If not given, beta is initialized by CCA. make_PD: boolean, optional Switch for manual positive definitization. If data does not generate a positive definite estimate of the covariance matrix, ``make_PD = True`` helps with maintaining the matrix positive definite throughout the fitting algorithm. The default value is False for the sake of running time. verbose: boolean, optional Swith for vocal feedback throughout the fitting algorithm. The default value is False. Returns ------- Pi: (KT, KT) ndarray The estimated sparse inverse correlation matrix. Rho: (KT, KT) ndarray The estimated correlation matrix before sparsification. Note that Rho != Pi^{-1}. params: dict The dictionary of the estimated parameters. It provides with the estimation of Omega: (num_f, KT, KT) ndarray; Gamma_S: a list of (p_k, p_k) ndarrays for k = 1, ..., K; Gamma_T: a list of (T, T) ndarrays for k = 1, ..., K; beta: a list of (p_k, num_f) ndarrays for k = 1, ..., K; and mu: a list of (p_k, T) ndarrays for k = 1, ..., K. Examples -------- Pi, Rho, params =\ fit(data, num_f, lambda_cross, offset_cross, lambda_auto, offset_auto) .. _[1] Bong et al. (2020). Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings. Submitted to NeurIPS2020. """ dims = [dat.shape[1] for dat in data] num_time = data[0].shape[2] num_trial = data[0].shape[0] # get full_graph if lambda_auto is None: lambda_auto = lambda_cross if offset_auto is None: offset_auto = offset_cross lambda_glasso_auto = _generate_lambda_glasso(num_time, lambda_auto, offset_auto) lambda_glasso_cross = _generate_lambda_glasso(num_time, lambda_cross, offset_cross) lambda_glasso = np.array(np.block( [[lambda_glasso_auto if j==i else lambda_glasso_cross for j, _ in enumerate(data)] for i, _ in enumerate(data)])) # set mu mu= [np.mean(dat, 0) for dat in data] # initialization if all(key in params_init for key in ('Omega', 'mu', 'beta', 'Gamma_S', 'Gamma_T')): Omega = params_init['Omega']; mu = params_init['mu']; beta = params_init['beta'] Gamma_S = params_init['Gamma_S']; Gamma_T = params_init['Gamma_T'] Sigma = np.linalg.inv(Omega) sig = np.sqrt(np.diagonal(Sigma,0,1,2)) Rho = Sigma/sig[:,None,:]/sig[:,:,None] Pi = np.linalg.inv(Rho) else: if 'beta' in params_init: beta = [b.copy() for b in params['beta_init']] weight = [np.linalg.pinv(b) for b in beta] elif len(data)==2: # initialize beta by CCA S_xt = np.tensordot( np.concatenate([dat-m for dat, m in zip(data,mu)], 1), np.concatenate([dat-m for dat, m in zip(data,mu)], 1), axes=((0,2),(0,2)))/num_trial/num_time S_1 = S_xt[:dims[0],:dims[0]] S_12 = S_xt[:dims[0],dims[0]:] S_2 = S_xt[dims[0]:,dims[0]:] U_1 = linalg.inv(linalg.sqrtm(S_1)) U_2 = linalg.inv(linalg.sqrtm(S_2)) u, s, vh = np.linalg.svd(U_1 @ S_12 @ U_2) weight = [u[:,:num_f].T @ U_1, vh[:num_f] @ U_2] beta = [linalg.inv(U_1) @ u[:,:num_f], linalg.inv(U_2) @ vh[:num_f].T] else: print("Default initialization only supports 2 populations now.") raise weight = [wnp.sqrt(np.sum(b2, 0))[:,None] for w, b in zip(weight,beta)] beta = [b/np.sqrt(np.sum(b2, 0)) for b in beta] # initialization on other parameters m_z_x = np.concatenate([np.matmul(w, dat-m)[...,None,:] for m, w, dat in zip(mu, weight, data)], -2) V_z_x = np.zeros((num_f,len(dims),num_time)2) m_zk_x = m_z_x.transpose((2,0,1,3)) V_zk_x = np.diagonal(V_z_x,0,1,4).transpose(4,0,1,2,3) # mu = [np.mean(dat - b @ m, 0) # for dat, m, b in zip(data, m_zk_x, beta)] m_eps = [dat - b @ m1 - m2 for dat, m1, b, m2 in zip(data, m_zk_x, beta, mu)] v_eps = [(np.sum(np.square(m))(1+lambda_aug)/num_trial + np.trace(V.reshape(num_fnum_time,num_fnum_time))) # /(d-1) for m, V, d in zip(m_eps, V_zk_x, dims)] V_eps_S = [np.tensordot(m,m,axes=((0,2),(0,2)))(1+lambda_aug)/num_trial/v + [email protected](np.diagonal(V,0,1,3),-1)@b.T/v for m, V, v, b, d in zip(m_eps, V_zk_x, v_eps, beta, dims)] V_eps_T = [np.tensordot(m,np.linalg.pinv(V2)@m, axes=((0,1),(0,1))) (lambda_augnp.eye(num_time)+1)/d/num_trial + np.tensordot(V1,[email protected](V2)@b,axes=([0,2],[0,1]))/d for m, V1, V2, b, d in zip(m_eps, V_zk_x, V_eps_S, beta, dims)] sd_eps_T = [np.sqrt(np.diag(V)) for V in V_eps_T] R_eps_T = [V/sd/sd[:,None] for V, sd in zip(V_eps_T, sd_eps_T)] Phi_T = [Rsdsd[:,None] for sd, R in zip(sd_eps_T, R_eps_T)] Gamma_T = [np.linalg.inv(P) for P in Phi_T] V_zf = (np.diagonal(V_z_x,0,0,3).transpose((4,0,1,2,3)) + (m_z_x.reshape((-1,num_f,len(dims)num_time)).transpose((1,2,0)) @ m_z_x.reshape((-1,num_f,len(dims)num_time)).transpose((1,0,2)) (lambda_augnp.eye(2num_time)+1)/ num_trial)\ .reshape((num_f,len(dims),num_time,len(dims),num_time))) Sigma, Phi_S = _switch_back( V_zf.reshape(num_f,len(dims)num_time,len(dims)num_time), V_eps_S, Gamma_T, Phi_T, beta) if make_PD: Phi_S = [_make_PD(P) for P in Phi_S] for f in np.arange(num_f): Sigma[f] = _make_PD(Sigma[f]) Gamma_S = [np.linalg.inv(P) for P in Phi_S] sig = np.sqrt(np.diagonal(Sigma,0,1,2)) Rho = Sigma/sig[:,None,:]/sig[:,:,None] Pi =	np.linalg.inv(Rho)	numpy.linalg.inv
import numpy as np from index_mesh import IndexMesh from combine_cells import combine_cells from util import find_local_numbering class EMIMesh(IndexMesh): def __init__(self, Nx, Ny, Nz, N): super().__init__(Nx, Ny, Nz, N) def set_up_mesh(G): "Set up vectors containing the indices of the different types of nodes" # Read geometry Nx = G.Nx Ny = G.Ny Nz = G.Nz N = G.N mesh = EMIMesh(Nx, Ny, Nz, N) # x-direction ranges from west (w) to east (e) # y-direction ranges from south (s) to north (n) # z-direction ranges from low (l) to high (h) # Set up indices for outer boundary # Set up indices for outer boundary (corners) mesh.e_lsw = np.array([mesh[ 0, 0, 0]]) mesh.e_lse = np.array([mesh[Nx-1, 0, 0]]) mesh.e_lnw = np.array([mesh[ 0, Ny-1, 0]]) mesh.e_lne = np.array([mesh[Nx-1, Ny-1, 0]]) mesh.e_hsw = np.array([mesh[ 0, 0, Nz-1]]) mesh.e_hse = np.array([mesh[Nx-1, 0, Nz-1]]) mesh.e_hnw = np.array([mesh[ 0, Ny-1, Nz-1]]) mesh.e_hne = np.array([mesh[Nx-1, Ny-1, Nz-1]]) # Set up indices for outer boundary (lines) # x direction mesh.e_ls = mesh[ 1:Nx-1, 0, 0] mesh.e_ln = mesh[ 1:Nx-1, Ny-1, 0] mesh.e_hs = mesh[ 1:Nx-1, 0, Nz-1] mesh.e_hn = mesh[ 1:Nx-1, Ny-1, Nz-1] # y direction mesh.e_lw = mesh[ 0, 1:Ny-1, 0] mesh.e_le = mesh[ Nx-1, 1:Ny-1, 0] mesh.e_hw = mesh[ 0, 1:Ny-1, Nz-1] mesh.e_he = mesh[ Nx-1, 1:Ny-1, Nz-1] # z direction mesh.e_sw = mesh[ 0, 0, 1:Nz-1] mesh.e_se = mesh[ Nx-1, 0, 1:Nz-1] mesh.e_nw = mesh[ 0, Ny-1, 1:Nz-1] mesh.e_ne = mesh[ Nx-1, Ny-1, 1:Nz-1] # Set up indices for outer boundary (sides) mesh.e_l = mesh[1:-1, 1:-1, 0] mesh.e_h = mesh[1:-1, 1:-1, Nz-1] mesh.e_s = mesh[1:-1, 0, 1:-1] mesh.e_n = mesh[1:-1, Ny-1, 1:-1] mesh.e_w = mesh[0, 1:-1, 1:-1] mesh.e_e = mesh[Nx-1, 1:-1, 1:-1] # Set up indices for the cell mesh, cells = combine_cells(G, mesh) print("Combined cells") # Set up indices for the remaining extracellular cells vec = np.ones(N) index_sets_to_exclude_from_e = ( mesh.e_lsw, mesh.e_lse, mesh.e_lnw, mesh.e_lne, mesh.e_hsw, mesh.e_hse, mesh.e_hnw, mesh.e_hne, mesh.e_hw, mesh.e_he, mesh.e_hs, mesh.e_hn, mesh.e_lw, mesh.e_le, mesh.e_ls, mesh.e_ln, mesh.e_ne, mesh.e_sw, mesh.e_se, mesh.e_nw, mesh.e_w, mesh.e_e, mesh.e_s, mesh.e_n, mesh.e_h, mesh.e_l, mesh.m_lsw, mesh.m_lse, mesh.m_lnw, mesh.m_lne, mesh.m_hsw, mesh.m_hse, mesh.m_hnw, mesh.m_hne, mesh.m_hw, mesh.m_he, mesh.m_hs, mesh.m_hn, mesh.m_lw, mesh.m_le, mesh.m_ls, mesh.m_ln, mesh.m_ne, mesh.m_sw, mesh.m_se, mesh.m_nw, mesh.m_w, mesh.m_e, mesh.m_s, mesh.m_n, mesh.m_h, mesh.m_l, mesh.i_all ) for index_set in index_sets_to_exclude_from_e: vec[index_set] = 0 mesh.e = np.flatnonzero(vec) # Set up indices for the membrane potential mesh.v = np.concatenate([ mesh.m_lsw, mesh.m_lse, mesh.m_lnw, mesh.m_lne, mesh.m_hsw, mesh.m_hse, mesh.m_hnw, mesh.m_hne, mesh.m_hw, mesh.m_he, mesh.m_hs, mesh.m_hn, mesh.m_lw, mesh.m_le, mesh.m_ls, mesh.m_ln, mesh.m_ne, mesh.m_sw, mesh.m_se, mesh.m_nw, mesh.m_w, mesh.m_e, mesh.m_s, mesh.m_n, mesh.m_h, mesh.m_l ]) mesh.v.sort() # Set up indices for the gap junction mesh.w = np.unique(np.concatenate([mesh.gx_e, mesh.gy_n])) mesh.w.sort() # Set up indices for the nodes not in the intracellular domain mesh.not_i = np.concatenate([ mesh.e_lsw, mesh.e_lse, mesh.e_lnw, mesh.e_lne, mesh.e_hsw, mesh.e_hse, mesh.e_hnw, mesh.e_hne, mesh.e_hw, mesh.e_he, mesh.e_hs, mesh.e_hn, mesh.e_lw, mesh.e_le, mesh.e_ls, mesh.e_ln, mesh.e_ne, mesh.e_sw, mesh.e_se, mesh.e_nw, mesh.e_w, mesh.e_e, mesh.e_s, mesh.e_n, mesh.e_h, mesh.e_l, mesh.e, mesh.gx_he, mesh.gx_le, mesh.gx_se, mesh.gx_ne, mesh.gx_lse, mesh.gx_hse, mesh.gx_lne, mesh.gx_hne, mesh.gy_ne, mesh.gy_nw, mesh.gy_ln, mesh.gy_hn, mesh.gy_lnw, mesh.gy_hnw, mesh.gy_lne, mesh.gy_hne ]) mesh.not_i.sort() # Set up indices for the extracellular domain mesh.e_all = np.concatenate([ mesh.e_lsw, mesh.e_lse, mesh.e_lnw, mesh.e_lne, mesh.e_hsw, mesh.e_hse, mesh.e_hnw, mesh.e_hne, mesh.e_hw, mesh.e_he, mesh.e_hs, mesh.e_hn, mesh.e_lw, mesh.e_le, mesh.e_ls, mesh.e_ln, mesh.e_ne, mesh.e_sw, mesh.e_se, mesh.e_nw, mesh.e_w, mesh.e_e, mesh.e_s, mesh.e_n, mesh.e_h, mesh.e_l, mesh.m_lsw, mesh.m_lse, mesh.m_lnw, mesh.m_lne, mesh.m_hsw, mesh.m_hse, mesh.m_hnw, mesh.m_hne, mesh.m_hw, mesh.m_he, mesh.m_hs, mesh.m_hn, mesh.m_lw, mesh.m_le, mesh.m_ls, mesh.m_ln, mesh.m_ne, mesh.m_sw, mesh.m_se, mesh.m_nw, mesh.m_w, mesh.m_e, mesh.m_s, mesh.m_n, mesh.m_h, mesh.m_l, mesh.gx_he, mesh.gx_le, mesh.gx_se, mesh.gx_ne, mesh.gy_hn, mesh.gy_ln, mesh.gy_nw, mesh.gy_ne, mesh.gx_lse, mesh.gx_lne, mesh.gx_hse, mesh.gx_hne, mesh.gy_lnw, mesh.gy_lne, mesh.gy_hnw, mesh.gy_hne, mesh.e ]) mesh.e_all.sort() mesh.i_all = np.unique(np.concatenate([ mesh.v, mesh.w, mesh.i, mesh.i_lsw, mesh.i_lse, mesh.i_lnw, mesh.i_lne, mesh.i_hsw, mesh.i_hse, mesh.i_hnw, mesh.i_hne, mesh.i_hw, mesh.i_he, mesh.i_hs, mesh.i_hn, mesh.i_lw, mesh.i_le, mesh.i_ls, mesh.i_ln, mesh.i_ne, mesh.i_sw, mesh.i_se, mesh.i_nw, mesh.i_w, mesh.i_e, mesh.i_s, mesh.i_n, mesh.i_h, mesh.i_l, mesh.gx_e, mesh.gy_n, mesh.gxi_w, mesh.gxi_hw, mesh.gxi_lw, mesh.gxi_sw, mesh.gxi_nw, mesh.gyi_hs, mesh.gyi_ls, mesh.gyi_sw, mesh.gyi_se, mesh.gyi_s, mesh.gxi_lsw, mesh.gxi_lnw, mesh.gxi_hsw, mesh.gxi_hnw, mesh.gyi_lsw, mesh.gyi_lse, mesh.gyi_hsw, mesh.gyi_hse ])) mesh.i_all.sort() mesh.i_inner_all = np.unique(np.concatenate([ mesh.i, mesh.i_lsw, mesh.i_lse, mesh.i_lnw, mesh.i_lne, mesh.i_hsw, mesh.i_hse, mesh.i_hnw, mesh.i_hne, mesh.i_hw, mesh.i_he, mesh.i_hs, mesh.i_hn, mesh.i_lw, mesh.i_le, mesh.i_ls, mesh.i_ln, mesh.i_ne, mesh.i_sw, mesh.i_se, mesh.i_nw, mesh.i_w, mesh.i_e, mesh.i_s, mesh.i_n, mesh.i_l, mesh.i_h ])) mesh.i_inner_all.sort() # local numbering in mesh.v # cells[n].v should not overlap with cells[k].v, where k != n cells_v_idx = find_local_numbering(N, mesh.v, [cell.v for cell in cells]) for i, cell in enumerate(cells): cell.v_idx = cells_v_idx[i] del cells_v_idx # local numbering in e_all cells_v_of_e = find_local_numbering(N, mesh.e_all, [cell.v for cell in cells]) for i, cell in enumerate(cells): cell.v_of_e = cells_v_of_e[i] del cells_v_of_e # local numbering in i_all cells_v_of_i = find_local_numbering(N, mesh.i_all, [cell.v for cell in cells]) cells_gxw_of_i = find_local_numbering(N, mesh.i_all, [cell.gx_w for cell in cells]) cells_gxe_of_i = find_local_numbering(N, mesh.i_all, [cell.gx_e for cell in cells]) cells_gys_of_i = find_local_numbering(N, mesh.i_all, [cell.gy_s for cell in cells]) cells_gyn_of_i = find_local_numbering(N, mesh.i_all, [cell.gy_n for cell in cells]) for i, cell in enumerate(cells): cell.v_of_i = cells_v_of_i[i] cell.gxw_of_i = cells_gxw_of_i[i] cell.gxe_of_i = cells_gxe_of_i[i] cell.gys_of_i = cells_gys_of_i[i] cell.gyn_of_i = cells_gyn_of_i[i] del cells_v_of_i del cells_gxw_of_i del cells_gxe_of_i del cells_gys_of_i del cells_gyn_of_i # cells[n].c_all can overlap with cells[k].c_all, where k != n # solution: treat cell grid as checkered board so that direct neighbours are split in different sets. # Swipe over the white first, then the black. #cells[n].c_of_i cell_indices_white = [] cell_indices_black = [] for j in range(G.num_cells_y): for i in range(G.num_cells_x): cell_index = j*G.num_cells_x + i if (i + j) & 1 == 1: cell_indices_black.append(cell_index) else: cell_indices_white.append(cell_index) cells_c_of_i_white = find_local_numbering(N, mesh.i_all, [cells[i].c_all for i in cell_indices_white]) cells_c_of_i_black = find_local_numbering(N, mesh.i_all, [cells[i].c_all for i in cell_indices_black]) for i, cell_index in enumerate(cell_indices_white): cells[cell_index].c_of_i = cells_c_of_i_white[i] for i, cell_index in enumerate(cell_indices_black): cells[cell_index].c_of_i = cells_c_of_i_black[i] del cell_indices_white del cell_indices_black del cells_c_of_i_white del cells_c_of_i_black # local numbering in w cells_gxw_of_w = find_local_numbering(N, mesh.w, [cell.gx_w for cell in cells]) cells_gxe_of_w = find_local_numbering(N, mesh.w, [cell.gx_e for cell in cells]) cells_gys_of_w = find_local_numbering(N, mesh.w, [cell.gy_s for cell in cells]) cells_gyn_of_w = find_local_numbering(N, mesh.w, [cell.gy_n for cell in cells]) for i, cell in enumerate(cells): cell.gxw_of_w = cells_gxw_of_w[i] cell.gxe_of_w = cells_gxe_of_w[i] cell.gys_of_w = cells_gys_of_w[i] cell.gyn_of_w = cells_gyn_of_w[i] del cells_gxw_of_w del cells_gxe_of_w del cells_gys_of_w del cells_gyn_of_w # Inner membrane points mesh.v_inner = np.concatenate([mesh.m_l, mesh.m_h, mesh.m_s, mesh.m_n, mesh.m_e, mesh.m_w]) mesh.v_inner.sort() vec = np.zeros(N, dtype=np.int64) vec[mesh.v_inner] = 1 vec = vec[mesh.v] mesh.v_inner_of_v = np.flatnonzero(vec) # Set up way to extract membrane points from the intracellular and extracellular domains vec = np.zeros(N, dtype=np.int64) vec[mesh.v] = 1 vec_of_i = vec[mesh.i_all] vec_of_e = vec[mesh.e_all] mesh.v_of_i = np.flatnonzero(vec_of_i) mesh.v_of_e = np.flatnonzero(vec_of_e) # v inner vec = np.zeros(N, dtype=np.int64) vec[mesh.v_inner] = 1 vec_of_i = vec[mesh.i_all] vec_of_e = vec[mesh.e_all] mesh.v_inner_of_i = np.flatnonzero(vec_of_i) mesh.v_inner_of_e = np.flatnonzero(vec_of_e) # Set up way to extract intercalated disc points from the intracellular domain vec = np.zeros(N, dtype=np.int64) vec[mesh.w] = 1 vec_of_i = vec[mesh.i_all] mesh.w_of_i =	np.flatnonzero(vec_of_i)	numpy.flatnonzero
# Copyright 2018, FBPIC contributors # Authors: <NAME>, <NAME> # License: 3-Clause-BSD-LBNL """ This file is part of the Fourier-Bessel Particle-In-Cell code (FB-PIC) It defines a class for continuous particle injection with a moving window. """ import warnings import numpy as np from scipy.constants import c import sys, inspect class ContinuousInjector( object ): """ Class that stores a number of attributes that are needed for continuous injection by a moving window. """ def __init__(self, Npz, zmin, zmax, dz_particles, Npr, rmin, rmax, Nptheta, n, dens_func, ux_m, uy_m, uz_m, ux_th, uy_th, uz_th ): """ Initialize continuous injection Parameters ---------- See the docstring of the `Particles` object """ # Register properties of the injected plasma self.Npr = Npr self.rmin = rmin self.rmax = rmax self.Nptheta = Nptheta self.n = n self.dens_func = dens_func self.ux_m = ux_m self.uy_m = uy_m self.uz_m = uz_m self.ux_th = ux_th self.uy_th = uy_th self.uz_th = uz_th # Register spacing between evenly-spaced particles in z if Npz != 0: self.dz_particles = (zmax - zmin)/Npz else: # Fall back to the user-provided `dz_particles`. # Note: this is an optional argument of `Particles` and so # it is not always available. self.dz_particles = dz_particles # Register variables that define the positions # where the plasma is injected. self.v_end_plasma = c * uz_m / np.sqrt(1 + ux_m2 + uy_m2 + uz_m*2) # These variables are set by `initialize_injection_positions` self.nz_inject = None self.z_inject = None self.z_end_plasma = None def initialize_injection_positions( self, comm, v_moving_window, species_z, dt ): """ Initialize the positions that keep track of the injection of particles. This is automatically called at the beginning of `step`. Parameters ---------- comm: a BoundaryCommunicator object Contains information about grid MPI decomposition v_moving_window: float (in m/s) The speed of the moving window species_z: 1darray of float (in m) (One element per macroparticle) Used in order to infer the position of the end of the plasma dt: float (in s) Timestep of the simulation """ # The injection position is only initialized for the last proc if comm.rank != comm.size-1: return # Initialize the injection position only if it has not be initialized if self.z_inject is not None: return # Initialize plasma ahead* of the right physical # boundary of the box in the damping region (including the # injection area) so that after `exchange_period` iterations # (without adding new plasma), there will still be plasma # inside the physical domain and the damping region (without the # injection area). This ensures that there are never particles in the # rightmost guard region and that there are always particles inside # the damped region, where the field can be non-zero. New particles, # which are injected in the Injection region, do not see any fields. _, zmax_global_domain_with_damp = comm.get_zmin_zmax( local=False, with_damp=True, with_guard=False ) self.z_inject = zmax_global_domain_with_damp \ + (3-comm.n_inject)comm.dz \ + comm.exchange_perioddt(v_moving_window-self.v_end_plasma) self.nz_inject = 0 # Try to detect the position of the end of the plasma: # Find the maximal position of the continously-injected particles if len( species_z ) > 0: # Add half of the spacing between particles (the # injection function itself will add a half-spacing again) self.z_end_plasma = species_z.max() + 0.5self.dz_particles else: # Default value for empty species _, zmax_global_physical_domain = comm.get_zmin_zmax( local=False, with_damp=False, with_guard=False ) self.z_end_plasma = zmax_global_physical_domain # Check that the particle spacing has been properly calculated if self.dz_particles is None: raise ValueError( 'The simulation uses continuous injection of particles, \n' 'but was unable to calculate the spacing between particles.\n' 'This may be because you used the `Particles` API directly.\n' 'In this case, please pass the argument `dz_particles` \n' 'initializing the `Particles` object.') def reset_injection_positions( self ): """ Reset the variables that keep track of continuous injection to `None` This is typically called when restarting a simulation from a checkpoint """ self.nz_inject = None self.z_inject = None self.z_end_plasma = None def increment_injection_positions( self, v_moving_window, duration ): """ Update the positions between which the new particles will be generated, the next time when `generate_particles` is called. This function is automatically called when the moving window moves. Parameters ---------- v_moving_window: float (in m/s) The speed of the moving window duration: float (in seconds) The duration since the last time that the moving window moved. """ # Move the injection position self.z_inject += v_moving_window * duration # Take into account the motion of the end of the plasma self.z_end_plasma += self.v_end_plasma * duration # Increment the number of particle to add along z nz_new = int( (self.z_inject - self.z_end_plasma)/self.dz_particles ) self.nz_inject += nz_new # Increment the virtual position of the end of the plasma # (When `generate_particles` is called, then the plasma # is injected between z_end_plasma - nz_injectdz_particles # and z_end_plasma, and afterwards nz_inject is set to 0.) self.z_end_plasma += nz_new self.dz_particles def generate_particles( self, time ): """ Generate new particles at the right end of the plasma (i.e. between z_end_plasma - nz_injectdz and z_end_plasma) Parameters ---------- time: float (in second) The current physical time of the simulation """ # Create a temporary density function that takes into # account the fact that the plasma has moved if self.dens_func is not None: args = _check_dens_func_arguments( self.dens_func ) if args == ['z', 'r']: def dens_func(z, r): return self.dens_func( z - self.v_end_plasmatime, r ) elif args == ['x', 'y', 'z']: def dens_func(x, y, z): return self.dens_func( x, y, z - self.v_end_plasmatime ) else: dens_func = None # Create new particle cells # Determine the positions between which new particles will be created Npz = self.nz_inject zmax = self.z_end_plasma zmin = self.z_end_plasma - self.nz_injectself.dz_particles # Create the particles Ntot, x, y, z, ux, uy, uz, inv_gamma, w = generate_evenly_spaced( Npz, zmin, zmax, self.Npr, self.rmin, self.rmax, self.Nptheta, self.n, dens_func, self.ux_m, self.uy_m, self.uz_m, self.ux_th, self.uy_th, self.uz_th ) # Reset the number of particle cells to be created self.nz_inject = 0 return( Ntot, x, y, z, ux, uy, uz, inv_gamma, w ) # Utility functions # ----------------- def generate_evenly_spaced( Npz, zmin, zmax, Npr, rmin, rmax, Nptheta, n, dens_func, ux_m, uy_m, uz_m, ux_th, uy_th, uz_th ): """ Generate evenly-spaced particles, according to the density function `dens_func`, and with the momenta given by the `ux/y/z` arguments. Parameters ---------- See the docstring of the `Particles` object """ # Generate the particles and eliminate the ones that have zero weight ; # infer the number of particles Ntot if NpzNprNptheta > 0: # Get the 1d arrays of evenly-spaced positions for the particles dz = (zmax-zmin)1./Npz z_reg = zmin + dz( np.arange(Npz) + 0.5 ) dr = (rmax-rmin)1./Npr r_reg = rmin + dr( np.arange(Npr) + 0.5 ) dtheta = 2np.pi/Nptheta theta_reg = dtheta np.arange(Nptheta) # Get the corresponding particles positions # (copy=True is important here, since it allows to # change the angles individually) zp, rp, thetap = np.meshgrid( z_reg, r_reg, theta_reg, copy=True, indexing='ij' ) # Prevent the particles from being aligned along any direction unalign_angles( thetap, Npz, Npr, method='random' ) # Flatten them (This performs a memory copy) r = rp.flatten() x = r * np.cos( thetap.flatten() ) y = r * np.sin( thetap.flatten() ) z = zp.flatten() # Get the weights (i.e. charge of each macroparticle), which # are equal to the density times the volume r d\theta dr dz w = n * r * dthetadrdz # Modulate it by the density profile if dens_func is not None : args = _check_dens_func_arguments( dens_func ) if args == ['x', 'y', 'z']: w = dens_func( x=x, y=y, z=z ) elif args == ['z', 'r']: w = dens_func( z=z, r=r ) # Select the particles that have a non-zero weight selected = (w > 0) if np.any(w < 0): warnings.warn( 'The specified particle density returned negative densities.\n' 'No particles were generated in areas of negative density.\n' 'Please check the validity of the `dens_func`.') # Infer the number of particles and select them Ntot = int(selected.sum()) x = x[ selected ] y = y[ selected ] z = z[ selected ] w = w[ selected ] # Initialize the corresponding momenta uz = uz_m * np.ones(Ntot) + uz_th * np.random.normal(size=Ntot) ux = ux_m * np.ones(Ntot) + ux_th * np.random.normal(size=Ntot) uy = uy_m * np.ones(Ntot) + uy_th * np.random.normal(size=Ntot) inv_gamma = 1./np.sqrt( 1 + ux2 + uy2 + uz**2 ) # Return the particle arrays return( Ntot, x, y, z, ux, uy, uz, inv_gamma, w ) else: # No particles are initialized ; the arrays are still created Ntot = 0 return( Ntot, np.empty(0), np.empty(0), np.empty(0), np.empty(0), np.empty(0), np.empty(0), np.empty(0),	np.empty(0)	numpy.empty
# -- coding: utf-8 -- """ Created on Thu Nov 14 10:28:23 2019 @author: thele """ from . import measurement_functions from . import condition_functions import numpy as np class Investigation_stage(): def __init__(self,jump,measure,check,configs,timer,pygor=None): self.jump = jump self.measure = measure self.check = check self.configure_investigation_sequence(configs) self.inv_max = len(self.aquisition_functions) self.isdynamic = configs.get('cond_meas',[False]self.inv_max) self.stage_results = [] self.timer = timer self.pygor = pygor self.cond=list(range(1,1+self.inv_max)) self.conditional_idx_list = [] def configure_investigation_sequence(self,configs): seq_keys = configs["measurement_seq"] self.aquisition_functions = [] self.function_configs = [] self.cond_functions = [] for seq_key in seq_keys: afunc_name = configs[seq_key].get('func','do_nothing') cfunc_name = configs[seq_key].get('condition','check_nothing') self.aquisition_functions += [getattr(measurement_functions,afunc_name)] self.cond_functions += [getattr(condition_functions,cfunc_name)] self.function_configs += [configs[seq_key]] def do_extra_measure(self,params,minc,maxc,kwags): self.jump(params) self.timer.start() plunger_jump = lambda params:self.jump(params,True) kwags['pygor'] = self.pygor anchor_vals = self.check() results_full = {} results = [] all_resutls = [None]self.inv_max for i in range(self.inv_max): data = self.aquisition_functions[i](plunger_jump,self.measure,anchor_vals,self.function_configs[i],kwags) self.timer.logtime() check_result,continue_on,meta_info = self.cond_functions[i](data,minc,maxc,self.function_configs[i],kwags) if len(self.stage_results)>0: np.array(self.stage_results,dtype=np.float) past_results = np.array(self.stage_results,dtype=np.float)[:,i] continue_on = bool_cond(check_result,past_results[~np.isnan(past_results)],**self.isdynamic[i]) if isinstance(self.isdynamic[i],dict) else continue_on all_resutls[i] = check_result if isinstance(meta_info,dict): new_kwags = meta_info.get('kwags',None) if new_kwags is not None: kwags['last_check'] = new_kwags results += [[check_result,continue_on,meta_info,data]] if not continue_on: break self.stage_results += [all_resutls] self.timer.stop() results_full['extra_measure'] = results results_full['conditional_idx'] = self.cond[i] results_full['times'] = self.timer.times_list[-1] return results_full def bool_cond(score,past,min_thresh=0.0001,min_data=10,quantile=0.85): th_score = np.maximum(min_thresh,	np.quantile(past, quantile)	numpy.quantile
import numpy as np from scipy.signal import savgol_filter from qube.postprocess.dataset import Axis def create_name(name, suffix=None, prefix=None): elements = [] if prefix: elements.append(str(prefix)) elements.append(str(name)) if suffix: elements.append(str(suffix)) name = '_'.join(elements) return name def duplicate_dataset(dataset, suffix=None, prefix=None, custom_name=None): new_ds = dataset.copy(shallow_copy=False) if custom_name: name = custom_name else: name = dataset.name new_ds.name = create_name(name, suffix, prefix) return new_ds def remove_dim_in_axes(axes, dim=None): new_axes = [] if dim is not None: for si in axes: ax = si.copy(shallow_copy=False) if si.dim != dim and si.dim > dim: ax.dim = si.dim - 1 new_axes.append(ax) elif si.dim < dim: new_axes.append(ax) return new_axes def histogram1d(dataset, bins=10, range=None, normed=None, weights=None, density=None): ds = dataset.copy() ds.name = f'{ds.name}_hist1d' hist, bins = np.histogram( ds.value, bins=bins, range=range, normed=normed, weights=weights, density=density, ) bins = bins[:-1] # remove 1 point for ax.plot axis = Axis( name=ds.name, value=bins, unit=ds.unit, dim=0, ) ds.value = hist ds.unit = 'Counts' ds.axes = {axis.name: axis} return ds def take(dataset, indices, axis=None): ds = dataset.copy() ds.name = f'{ds.name}_take' ds.value = np.take(ds.value, indices=indices, axis=axis) old_axes = ds.get_axes(counters=False) ds.clear_axes() if axis is not None: for si in old_axes: ax = si.copy(shallow_copy=False) if si.dim == axis: ax.value =	np.take(ax.value, indices=indices)	numpy.take
from abc import abstractmethod import numpy as np import torch from codes.c_models.base_model import RNNModel from codes.d_agents.a0_base_agent import BaseAgent from codes.e_utils import replay_buffer from codes.e_utils.common_utils import float32_preprocessor from codes.e_utils.names import RLAlgorithmName class OffPolicyAgent(BaseAgent): """ Abstract Agent interface """ def __init__(self, worker_id, action_shape, params, device): super(OffPolicyAgent, self).__init__(worker_id, action_shape, params, device) if hasattr(self.params, "PER_PROPORTIONAL") and self.params.PER_PROPORTIONAL: self.buffer = replay_buffer.PrioritizedReplayBuffer( experience_source=None, buffer_size=self.params.REPLAY_BUFFER_SIZE, n_step=self.params.N_STEP, beta_start=0.4, beta_frames=self.params.MAX_GLOBAL_STEP ) elif hasattr(self.params, "PER_RANK_BASED") and self.params.PER_RANK_BASED: self.buffer = replay_buffer.RankBasedPrioritizedReplayBuffer( experience_source=None, buffer_size=self.params.REPLAY_BUFFER_SIZE, params=self.params, alpha=0.7, beta_start=0.5, beta_frames=self.params.MAX_GLOBAL_STEP ) else: self.buffer = replay_buffer.ExperienceReplayBuffer( experience_source=None, buffer_size=self.params.REPLAY_BUFFER_SIZE ) def train_off_policy(self, step_idx, local_step_idx): train_results = self.on_train(step_idx, local_step_idx) self.training_steps += 1 return train_results @abstractmethod def on_train(self, step_idx, local_step_idx): raise NotImplementedError def unpack_batch(self, batch): state, actions, rewards, dones, last_states, agent_states = [], [], [], [], [], [] if isinstance(self.model, RNNModel): actor_hidden_states = [] if self.params.RL_ALGORITHM in [RLAlgorithmName.DDPG_V0]: critic_hidden_states = [] critic_1_hidden_states = None critic_2_hidden_states = None elif self.params.RL_ALGORITHM in [RLAlgorithmName.TD3_V0]: critic_hidden_states = None critic_1_hidden_states = [] critic_2_hidden_states = [] else: raise ValueError() else: actor_hidden_states = critic_hidden_states = critic_1_hidden_states = critic_2_hidden_states = None for exp in batch: state.append(np.array(exp.state, copy=False)) actions.append(exp.action) rewards.append(exp.reward) dones.append(exp.last_state is None) if exp.last_state is None: last_states.append(exp.state) # the result will be masked anyway else: last_states.append(	np.array(exp.last_state, copy=False)	numpy.array

import trimesh import numpy as np import sys from matplotlib.cm import get_cmap as colormap import os from glob import glob import cv2 import shutil import json import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt sys.path.append('../') from evaluate import Renderer from vPlaneRecover.transforms import NYU40_COLORMAP from vPlaneRecover.datasets.scannet import load_scannet_label_mapping, load_scannet_nyu40_mapping from fit_plane.util import get_nyu_id2labl NYU_ID2LAEBL = get_nyu_id2labl() CLASS_W_LONGSTRIKE = set(['table', 'desk', 'chair', 'window','bookshelf','shelves']) NONE_PLANE_CLASS = set(['window', 'lamp', 'pillow', 'bag','curtain', 'shower curtain', 'toilet', 'bag', 'mirror', 'person', 'clothes', 'sink']) INVALID_ID = 16777216 // 100 - 1 # (255,255,255) refer to fit_plane code get_gtPlane_segmt.py PLANE_AREA_THRES = 0.1 PLANE_EDGE_RATIO_THRES = 0.1 PLANE_AREA_RATIO_THRES = 0.5 PLANE_SEC_EDGE_THRES = 0.2 N_DEPTH_THRES = 0.8 DEPTH_ABSDIFF_THRES = 0.1 DEPTH_RELDIFF_THRES = 0.05 N_VERT_THRES = 120 # same as MergedPlaneAreaThreshold = 120 in get_gtPlane_segmt.py DEPTH_SHIFT = 1000 width, height = 640, 480 zoom_x, zoom_y = width / 1296., height/ 968. def get_planeColor_map( n_plane): # get color _cmap = np.array(NYU40_COLORMAP[1:]) if n_plane - 40 > 0: cmap = (colormap('jet')(np.linspace(0, 1, n_plane - 40))[:, :3] * 255).astype(np.uint8) cmap = cmap[np.random.permutation(n_plane - 40), :] plane_color = np.concatenate([_cmap, cmap], axis=0) else: plane_color = _cmap return plane_color def color2plane(mesh): # convert color w.r.t. plane id vert_colors = mesh.visual.vertex_colors.view(np.ndarray) chan_0 = vert_colors[:, 2] chan_1 = vert_colors[:, 1] chan_2 = vert_colors[:, 0] plane_id = (chan_2 * 256 ** 2 + chan_1 * 256 + chan_0) // 100 - 1 # there is no (0,0,0) color in fitting mesh # mesh.vertex_attributes['plane_id'] = plane_id return plane_id def filter_plane_area_only(mesh): unique_id = np.unique(mesh.vertex_attributes['plane_ins']) face_color = mesh.visual.face_colors # filter them with face area again, we cannot compute bbox, because # hull precision error: Initial simplex is flat, because every piece is flat for i, cur_id in enumerate(unique_id): if cur_id == INVALID_ID: continue # use plane id select vertex vert_mask = mesh.vertex_attributes['plane_ins'] == cur_id trg_color = mesh.visual.vertex_colors[vert_mask][0] face_mask = face_color == trg_color sub_mesh = mesh.submesh(np.nonzero(face_mask.all(axis=1)))[0] surface_area = sub_mesh.area_faces.sum() if surface_area <= PLANE_AREA_THRES: mesh.visual.vertex_colors[vert_mask] = np.array([255, 255, 255, 255]).astype(np.uint8) return mesh def filter_plane_instance(mesh, semseg, plane_param, n_plane): unique_id = np.unique( mesh.vertex_attributes['plane_ins']) face_color = mesh.visual.face_colors verts = mesh.vertices add_addtion = 0 for cur_id in unique_id: if cur_id == INVALID_ID: continue # use plane id select vertex vert_mask = mesh.vertex_attributes['plane_ins'] == cur_id param = plane_param[cur_id:cur_id+1] # debug # mesh.visual.vertex_colors[vert_mask] = np.array([255, 0,0,255]) # load semseg id sem_id = np.unique(semseg[vert_mask]) assert len(sem_id>1) #use face color get submesh # ensure the plane vert >= 120 as state in fitting if vert_mask.sum() < N_VERT_THRES or NYU_ID2LAEBL[sem_id[0]] in NONE_PLANE_CLASS: mesh.visual.vertex_colors[vert_mask] = np.array([255, 255, 255, 255]).astype(np.uint8) continue trg_color = mesh.visual.vertex_colors[vert_mask][0] face_mask = face_color == trg_color sub_mesh = mesh.submesh(np.nonzero(face_mask.all(axis=1)))[0] # NOTE, some verts may do not have a face or have a face with different color with the verts color # we ask all verts become white first, and give the largest one meets the requirement their original color mesh.visual.vertex_colors[vert_mask] = np.array([255, 255, 255, 255]).astype(np.uint8) # if sem_id == 5: # mesh.visual.vertex_colors[vert_mask] = np.array([255, 0, 0, 255]).astype(np.uint8) # mesh.show() split_mesh = sub_mesh.split(only_watertight=False) # we cannot sort the mesh w.r.t. bbox first, because to compute bbox, there must be at least 4 verts, # but some split piece may not have verts_masks, surfaces, edge_ratios, sec_edges, area_ratios = [], [], [], [], [] for i, m in enumerate(split_mesh): # https://stackoverflow.com/questions/16210738/implementation-of-numpy-in1d-for-2d-arrays vmask = np.in1d(verts.view(dtype='f8,f8,f8').reshape(verts.shape[0]), m.vertices.view(dtype='f8,f8, f8').reshape(m.vertices.shape[0])) if vmask.sum() < N_VERT_THRES: continue # for each spart bbox = m.bounding_box_oriented # which is oriented bounding box, align with object instead of axis (so it is more tight) edges = bbox.primitive.extents max_edge, sec_edge = edges[edges.argsort()[-2:][::-1]] edge_ratio = sec_edge/max_edge # to remove single long thin surface bbox_area = max_edge * sec_edge surface_area = m.area_faces.sum() area_ratio = surface_area / bbox_area # to remove L, M, T shape surface # if surface_area > max_surface : verts_masks.append(vmask) surfaces.append(surface_area) edge_ratios.append(edge_ratio) sec_edges.append(sec_edge) area_ratios.append(area_ratio) n_assgined = -1 for mask, surface, edge_ratio, area_ratio, sec_edge in \ zip(verts_masks, surfaces, edge_ratios, area_ratios, sec_edges): if surface >= PLANE_AREA_THRES and mask.sum() > N_VERT_THRES \ and sec_edge>=PLANE_SEC_EDGE_THRES and edge_ratio >= PLANE_EDGE_RATIO_THRES: if NYU_ID2LAEBL[sem_id[0]] in CLASS_W_LONGSTRIKE: if area_ratio >= PLANE_AREA_RATIO_THRES: n_assgined += 1 else: n_assgined += 1 # mesh.visual.vertex_colors[mask] = trg_color #np.array([255, 0, 255, 255]).astype(np.uint8) # uncomment it if wish to split a large one into pieces to ensure connection # as one plane is splited into 2+, add new param to the result # if n_assgined > 0: # add_addtion += 1 # more_color = (n_plane + add_addtion) * 100 # new_color = np.array([more_color / (256 * 256), more_color / 256 % 256, more_color % 256, 255]) # plane_param = np.concatenate([plane_param, param]) # mesh.visual.vertex_colors[mask] = new_color # all part meet the standard will be preserved as the original one if n_assgined >= 0: mesh.visual.vertex_colors[mask] = trg_color # if n_assgined == -1 and sem_id == 5: # mesh.visual.vertex_colors[vert_mask] = np.array([255, 255, 255, 255]).astype(np.uint8) # print(cur_id, sem_id, ":", surface, PLANE_AREA_THRES, '/', edge_ratio, PLANE_EDGE_RATIO_THRES, '/', # area_ratio, PLANE_AREA_RATIO_THRES, '/', sec_edge, PLANE_SEC_EDGE_THRES) # # if sem_id == 5: # mesh.show() return mesh, plane_param def load_ply(src, data_pth, scene_name, label2id, scanNet2nyu): src_mesh_coarse = trimesh.load(src, process=False) # ============================== # load instance label agregated idx # offer the instance id, and all the over_segmt_pieceID belong to this instance # ============================== # each vertex in gt mesh indexs a seg group segIndices = \ json.load(open('{}/{}/{}_vh_clean_2.0.010000.segs.json'.format(data_pth, scene_name, scene_name), 'r'))[ 'segIndices'] # maps seg groups to instances segGroups = json.load(open('{}/{}/{}.aggregation.json'.format(data_pth, scene_name, scene_name), 'r'))['segGroups'] mapping = {ind: group['label'] for group in segGroups for ind in group['segments']} # get per vertex instance ids (0 is unknown, [1,...] are objects) n = len(segIndices) label_verts = np.zeros(n, dtype=np.long) for i in range(n): if segIndices[i] in mapping: label_verts[i] = scanNet2nyu[label2id[mapping[segIndices[i]]]] assert len(src_mesh_coarse.vertices) == n return src_mesh_coarse, label_verts def main(): train_pth = '/data/ScanNet/ScanNet_raw_data/scannet/scannetv2_train.txt' data_pth = '/data/ScanNet/ScanNet_raw_data/scannet/scans/' frm_err_pth = '/data/ScanNet/ScanNet_raw_data/video_plane_fitting/N_depth_thres=0.8/frm_err_pth/' if not os.path.isdir(frm_err_pth): os.makedirs((frm_err_pth)) train_scenes = [] with open(train_pth) as vf: for line in vf: tmp = line.strip() # if tmp[-2:] == '00': train_scenes.append(tmp) train_scenes.sort() # train_scenes = train_scenes[:3] # sum err, num_frame total_absDiff = np.zeros([len(train_scenes), 2]) total_absRel = np.zeros([len(train_scenes),2]) all_absDiff, all_absRel = [], [] for k, src in enumerate(train_scenes): print('process:', src) # read orignal face for depth render, plane fitting mesh have lots of holes between planes # Our plane fitting algorithm remove the face if the 3 verts have different plane ins id src_orginal_mesh = trimesh.load('{}/{}/{}_vh_clean_2.ply'.format(data_pth, src, src), process=False) # ========== render the depth and compare with gt depth ============ # if inconsistent #frame > N, remove the scene, because either the plane fitting or the gt pose is inaccurate # =================================================================== # load the gt_depth gt_depth_pth = '{}/{}/depth/*.png'.format(data_pth, src) depth_list = sorted(glob(gt_depth_pth), key= lambda x: int(os.path.basename(x)[:-4])) # load pose path gt_pose_pth = '{}/{}/pose/'.format(data_pth, src) # load intrinsic with open(os.path.join(data_pth, src, '%s.txt' % src)) as info_f: info = [line.rstrip().split(' = ') for line in info_f] info = {key: value for key, value in info} intrinsics = np.array([ [float(info['fx_color'])*zoom_x, 0., float(info['mx_color'])*zoom_x], [0., float(info['fy_color'])*zoom_y, float(info['my_color'])*zoom_y], [0., 0., 1.]]) # init render renderer = Renderer() mesh_opengl = renderer.mesh_opengl(src_orginal_mesh) # we use the mesh_viz as it has same face as original scannet mesh # compare gt depth with render depth # as we will use the rendered_depth to generate tsdf_mesh, the mesh will be prune naturally during that process sum_absDiff, sum_absRel, n_frm = 0, 0, 0 scene_stat = {} save_pth = frm_err_pth + '/' + src + '.npz' if os.path.isfile(save_pth): data =

np.load(save_pth)

numpy.load

import numpy as np from agents.common import * def generate_move_minimax( board: np.ndarray, player: BoardPiece, saved_state: Optional[SavedState] ) -> Tuple[PlayerAction, Optional[SavedState]]: """ generate move function employing minimax algorithm with/without alpha-beta pruning Parameters ----------- board: np.ndarray board reflecting current game state player: BoardPiece player for which the minimax algorithm is supposed to calculate an action Return ----------- action action for player, generated by minimax """ depth = 5 """change comment to use/not use alpha-beta pruning""" #action, _ = minimax(board, depth, player, True) action, _ = minimax_alpha_beta_pruning(board, depth, player, True, -np.inf, +np.inf) return action, saved_state def minimax_alpha_beta_pruning( board: np.ndarray, depth, player: BoardPiece, maximizing: bool, alpha, beta ): """ Minimax algorithm with alpha-beta pruning Parameters ----------- board: np.ndarray board reflecting current game state depth: int depth explored by the minimax algorithm player: BoardPiece player for which the minimax algorithm is supposed to calculate an action maximizing : Bool if True, result is maximized for the given player alpha alpha-value for pruning, if MAXIMIZING = True, should be initialized to -np.inf beta beta-value for pruning, if MAXIMIZING = True, should be initialized to +np.inf Return ----------- Tuple [action (best column), heuristic_value] """ opponent = PLAYER2 if player == PLAYER1 else PLAYER1 """Edge Case: (terminal node) AI or Player is winning with the next piece or the board is full (draw)""" terminal_state = check_end_state(board, player) if terminal_state == GameState.IS_WIN and maximizing: # Maximizing Player is Winning # print("winning") return -1, np.inf elif terminal_state == GameState.IS_WIN and maximizing == False: # Maximizing Player looses because Minimizing player wins # print("loosing") return -1, -np.inf elif terminal_state == GameState.IS_DRAW: # Game over because draw return -1, None if depth == 0: # case where depth = 0 value_for_depth0 = evaluate_board(board, player) return -1, value_for_depth0 valid_columns = get_valid_columns(board) best_column = np.random.choice(valid_columns, 1) """ recursive minimizing/maximizing case""" if maximizing: # maximizing for player value = -np.inf for column in valid_columns: c_board = apply_player_action(board, column, player, True) ### player _, new_score = minimax_alpha_beta_pruning(c_board, depth - 1, opponent, False, alpha, beta) ### maximizingTrue is False for next iter, because will be oppnent if new_score > value: value = new_score best_column = column alpha = max(alpha, new_score) if alpha >= beta: break ### beta cut-off return best_column, value else: # minimizing for opponent value = np.inf for column in valid_columns: c_board = apply_player_action(board, column, opponent, True) ### opponent is minimizing player _, new_score = minimax_alpha_beta_pruning(c_board, depth - 1, player, True, alpha, beta) ### maximizingTrue is True for next iter, because will be player if new_score < value: value = new_score best_column = column beta = min(beta, new_score) if alpha >= beta: break ### alpha cut off return best_column, value def minimax( board: np.ndarray, depth: int, player: BoardPiece, MAXIMIZING: bool ): """ Minimax algorithm Parameters ----------- board: np.ndarray board reflecting current game state depth: int depth explored by the minimax algorithm player: BoardPiece player for which the minimax algorithm is supposed to calculate an action MAXIMIZING : Bool if True, result is maximized for the given player Return ----------- Tuple [action (best column), heuristic_value] """ opponent = PLAYER2 if player == PLAYER1 else PLAYER1 """Edge Case: (terminal node) AI or Player is winning with the next piece or the board is full (draw)""" terminal_state = check_end_state(board, player) if terminal_state == GameState.IS_WIN and MAXIMIZING == True: # Maximizing Player is Winning # print("winning") return -1, np.inf elif terminal_state == GameState.IS_WIN and MAXIMIZING == False: # Maximizing Player looses because Minimizing player wins # print("loosing") return -1, -np.inf elif terminal_state == GameState.IS_DRAW: # Game over because draw return -1, None if depth == 0: # case where depth = 0 value_for_depth0 = evaluate_board(board, player) return -1, value_for_depth0 valid_columns = get_valid_columns(board) best_column = np.random.choice(valid_columns, 1) """ recursive minimizing/maximizing case""" if MAXIMIZING: # maximizing for player # print("maximizing works") value = -np.inf for column in valid_columns: c_board = apply_player_action(board, column, player, True) ### player _, new_score = minimax(c_board, depth - 1, opponent, False) ### maximizingTrue is False for next iter, because will be oppnent if new_score > value: value = new_score best_column = column # print("after column", column, "best column is", best_column) return best_column, value else: # minimizing for opponent # print("minimizing works") value = np.inf for column in valid_columns: c_board = apply_player_action(board, column, opponent, True) ### opponent is minimizing player _, new_score = minimax(c_board, depth - 1, player, True) ### maximizingTrue is True for next iter, because will be player if new_score < value: value = new_score best_column = column # print("after column", column, "best column is", best_column) return best_column, value def get_valid_columns(board: np.ndarray) -> np.ndarray: """ Function returning list of all columns with possible valid moves for current board Parameters ----------- board: np.ndarray board reflecting current game state Return ----------- valid_columns_array: array containing indices of all valid columns """ valid_columns_array = [] # initialize empty list for m, col in enumerate(board.T): for n, row in enumerate(col): if board.T[m, n] == 0: valid_columns_array.append(m) break return valid_columns_array def evaluate_window(window: np.ndarray, player: BoardPiece) -> int: """ evaluate a window (fraction of the board) passed into the function by func: evaluate_board in terms of player pieces, opponent pieces and empty slots Parameters ----------- window: array of shape (4,) player: BoardPiece Player for whom the window (i.e. the board) is evaluated Return ----------- window_score: int score for the respective window """ window_score = 0 opponent = PLAYER2 if player == PLAYER1 else PLAYER1 """Raise score if Player has 1 or more pieces in a window""" if sum(window == player) == 3 and sum(window == NO_PLAYER) == 1: window_score += 70 if sum(window == player) == 2 and sum(window == NO_PLAYER) == 2: window_score += 5 if sum(window == player) == 1 and sum(window == NO_PLAYER) == 3: window_score += 2 """penalty if opponent hast 1 or more pieces in a window with empty space next to it""" if sum(window == opponent) == 3 and sum(window == NO_PLAYER) == 1: window_score -= 70 if sum(window == opponent) == 2 and sum(window == NO_PLAYER) == 2: window_score -= 5 if sum(window == opponent) == 1 and sum(window == NO_PLAYER) == 3: window_score -= 2 return window_score def evaluate_board( board: np.ndarray, player: BoardPiece ) -> int: """ return a score reflecting the state of the board for the given player Parameters ----------- board: np.ndarray board reflecting current game state player: BoardPiece Player for whom the the board) is evaluated Return ----------- board_score: int score for the respective window """ WINDOW_LENGTH = 4 # Window length to be evaluated (=4 for connect 4) slide = WINDOW_LENGTH - 1 # reducing parameter based on window length when iterating over the board board_score = 0 # initialize board score to 0 n_rows, m_columns = board.shape # initialize board shape """ evaluate horizontally """ for r, row in enumerate(board): for c in range(m_columns - slide): window = row[c:c + WINDOW_LENGTH] board_score += evaluate_window(window, player) """ evaluate horizontally """ tboard =

np.transpose(board)

numpy.transpose

import numpy as np import matplotlib.pyplot as plt def line_intersect(p,q): P = np.matrix([[p[0][0],-1],[q[0][0],-1]]) c = -

np.matrix([[p[1][0]],[q[1][0]]])

numpy.matrix

import argparse import numpy as np from os import path import struct from internal import db_handling def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--sift_feature_dir', required=True) parser.add_argument('--query_txt_file', required=True) parser.add_argument('--database_file', required=True) args = parser.parse_args() return args def main(): args = parse_args() db = db_handling.COLMAPDatabase.connect(args.database_file) db.create_tables() with open(args.query_txt_file) as f: for line in f: name, _, h, w, fx, fy, cx, cy = line.split(' ') params = np.array([float(fx), float(fy), float(cx), float(cy)]) camera_id = db.add_camera(1, int(h), int(w), params) image_id = db.add_image(path.join('images', name), camera_id) featurefile = path.join(args.sift_feature_dir, path.splitext(name)[0] + '.sift') with open(featurefile, 'rb') as f: data = f.read() header = struct.unpack_from('iiiii', data, 0) _, _, num_points, num_entries, desc_size = header assert num_entries == 5 and desc_size == 128 offset = 20 keypoints = np.zeros((num_points, 2)) for i in range(num_points): point = struct.unpack_from('fffff', data, offset) offset += 20 keypoints[i, :] = np.array((point[1], point[0])) descriptors = np.zeros((num_points, desc_size)) for i in range(num_points): descriptor = struct.unpack_from('128B', data, offset) offset += desc_size descriptors[i, :] =

np.asarray(descriptor)

numpy.asarray

import numpy as np import pandas as pd import os import sys import inspect import unittest from joblib import load currentdir = os.path.dirname( os.path.abspath( inspect.getfile( inspect.currentframe()))) parentdir = os.path.dirname(currentdir) sys.path.insert(0, parentdir) from src.lr.text_processing.util import pre_process_nli_df from src.lr.text_processing.util import get_corpus from src.lr.text_processing.transformations.wordnet import get_noun_syn_dict from src.lr.text_processing.transformations.wordnet import p_h_transformation_syn_dict from src.lr.text_processing.transformations.wordnet import parallelize from src.lr.training.util import filter_df_by_label # data_path = parentdir + "/src/data/toy/train.csv" # assert os.path.exists(data_path) class SynTrans(unittest.TestCase): @classmethod def setUp(cls): folder = "toy" cls.n_cores = 2 train_path = parentdir + "/src/data/{}/train.csv".format(folder) dev_path = parentdir + "/src/data/{}/dev.csv".format(folder) veto_path = parentdir + "/src/data/{}/syn_veto.csv".format(folder) cls.syn_path = parentdir + "/src/data/{}/syn_noun.csv".format(folder) cls.train_path_mod = parentdir + "/src/data/{}/train_p_h_syn_noun.csv".format(folder) cls.dev_path_mod = parentdir + "/src/data/{}/dev_p_h_syn_noun.csv".format(folder) train = pd.read_csv(train_path) dev = pd.read_csv(dev_path) train = filter_df_by_label(train.dropna()).reset_index(drop=True) dev = filter_df_by_label(dev.dropna()).reset_index(drop=True) pre_process_nli_df(train) pre_process_nli_df(dev) cls.train = train cls.dev = dev cls.veto = pd.read_csv(veto_path).veto.values @classmethod def tearDown(cls): for path in [cls.train_path_mod, cls.dev_path_mod, cls.syn_path]: if os.path.exists(path): os.remove(path) def test_syn_transformation(self): # get syn dict syn_dict = get_noun_syn_dict(df=self.train, n_cores=self.n_cores, veto=self.veto) # removing possible verbs syn_dict = {k: syn_dict[k] for k in syn_dict if k[-3:] != "ing"} # saving to a dataframe key = sorted(syn_dict.keys()) value = [syn_dict[k] for k in key] syn_df = pd.DataFrame({"key": key, "value": value}) train_t = p_h_transformation_syn_dict(df=self.train, syn_dict=syn_dict) dev_t = p_h_transformation_syn_dict(df=self.dev, syn_dict=syn_dict) self.assertTrue(not np.any(self.train.premise == train_t.premise)) self.assertTrue(not np.any(self.train.hypothesis == train_t.hypothesis)) self.assertTrue(np.all(self.train.label == train_t.label)) self.assertTrue(not np.any(self.dev.premise == dev_t.premise)) self.assertTrue(not

np.any(self.dev.hypothesis == dev_t.hypothesis)

numpy.any

#!/usr/bin/env python3 """ Created on Wed Feb 12 10:44:59 2020 @author: <NAME> Skeleton modified from https://www.tensorflow.org/tutorials/customization/custom_training https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough Training of an RBM parametrization of the unitary matrix that diagonalises the extended HAMILTONIAN of harmonically driven quantum systems ==================== IMPORTANT NOTE ======================== ============================================================ """ ############### IMPORT WHAT WE NEED FROM THE OS/PYTHON DISTRIBUTION ##################### from __future__ import absolute_import, division, print_function, unicode_literals try: # %tensorflow_version only exists in Colab. get_ipython().run_line_magic('tensorflow_version', '2.x') except Exception: pass import tensorflow as tf import numpy as np import math as m #import matplotlib as mpl import matplotlib.pyplot as plt ############### IMPORT WHAT WE NEED FROM THIS PROJECT ##################### import model as Model import matrixmanipulation as mp import NN_model as NN_Model import Training as training ############### FULL RUN ################################ # 1. CALCULATE THE EXACT FLOQUET STATES # 2. CALCULATE THE RBM REPRESENTATION H = Model.Hamiltonian() N = 32 CentralFloquetE = np.zeros([N,2],dtype=np.float64) CentralFloquetEVec = np.zeros([N,H.dim,2],dtype=np.complex128) CentralFloquetE_RBM = np.zeros([N,2],dtype=np.float64) CentralFloquetEVec_RBM = np.zeros([N,H.dim,2],dtype=np.complex128) loss_list_RWA = tf.Variable([],dtype=tf.float64) N_training = 4 N_tr_ph = 2 N_tr_rho = 2 file = open('RBM_TrainingFloquet.dat','wb') optimizer = tf.keras.optimizers.Adam(learning_rate=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False,name='Adam') #optimizer = tf.keras.optimizers.SGD(learning_rate=0.1) #%% file_psi = open('absPsi_comparison_Floquet_3D.dat','w') for i in [4,16,31]:#[16,18]:#:,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]:#range(N): delta = 1.7 Omega = 10.0*i/N phase = np.arctan(1.0)/4.1341341 print(i,delta,Omega,phase) N_training = 256 H = Model.Hamiltonian(delta,Omega,phase) ###### FITTING A WAVEFUNCTION: ###### loss_Floquet,loss_Floquet_phase : fits the Floquet wavefunction ###### loss_RWA,loss_RWA_phase : fits the RWA wavefunction trained_parameters,loss_history = training.RMB_training_Psi(N_training,H, Model.loss_Floquet, Model.loss_Floquet_Phase) #trained_parameters,loss_history = training.RMB_training_Psi(N_training,H, # Model.loss_RWA, # Model.loss_RWA_Phase) model = Model.RBM_Model(delta,Omega,phase,trained_parameters) UF = Model.Unitary_Matrix(model) U_ = tf.transpose(tf.math.conj(UF))@model.H_TLS@UF if (UF.shape[1] > model.S): index_ = int(model.S*((UF.shape[1]/model.S -1))/2) else: index_ = 0 CentralFloquetEVec_RBM[i,:,0] = UF[:,index_ ].numpy() CentralFloquetEVec_RBM[i,:,1] = UF[:,index_+1].numpy() CentralFloquetE_RBM[i,0] = tf.math.real(U_[index_ , index_ ]).numpy() CentralFloquetE_RBM[i,1] = tf.math.real(U_[index_ + 1 , index_ + 1]).numpy() absPsi_comparison = np.zeros([UF.shape[0],11],dtype=np.double) absPsi_comparison[:,0] = np.linspace(0,UF.shape[0]-1,UF.shape[0],dtype=np.int) absPsi_comparison[:,1] = np.mod(np.linspace(0,UF.shape[0]-1,UF.shape[0],dtype=np.int),2) - 0.5 absPsi_comparison[:,2] = absPsi_comparison[:,0]//2 - 16 absPsi_comparison[:,3:5] = Model.Rect2Pol(UF[:,model.N_Floquet_UF*model.S:model.N_Floquet_UF*model.S+1]) absPsi_comparison[:,5:7] = Model.Rect2Pol(UF[:,model.N_Floquet_UF*model.S+1:model.N_Floquet_UF*model.S+2]) aux = np.zeros([66,1],dtype=np.complex) aux[:,0] = model.U_Floquet[:,0] absPsi_comparison[:,7:9] = Model.Rect2Pol(aux) aux[:,0] = model.U_Floquet[:,1] absPsi_comparison[:,9:11] = Model.Rect2Pol(aux) np.savetxt(file_psi,absPsi_comparison) file_psi.write("\n\n") np.savetxt(file_psi,loss_history) file_psi.write("\n\n") plt.plot(loss_history) plt.show() plt.plot(np.abs(UF[:,model.N_Floquet_UF*model.S:model.N_Floquet_UF*model.S+2])) plt.show() plt.plot(np.abs(model.U_Floquet[:,:])) plt.show() plt.imshow(np.abs(U_.numpy())) plt.show() file_psi.close() #%% for i in [16]:#:,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]:#range(N): delta = 1.7 Omega = 10.0*i/N phase = np.arctan(1.0)/4.1341341 N_training= 12000 trained_parameters,loss_history = training.RBM_training_FloquetStates(N_training,H) model = Model.RBM_Model(delta,Omega,phase,trained_parameters) UF = Model.Unitary_Matrix(model) U_ = tf.transpose(tf.math.conj(UF))@model.H_TLS@UF if (UF.shape[1] > model.S): index_ = int(model.S*((UF.shape[1]/model.S -1))/2) else: index_ = 0 CentralFloquetEVec_RBM[i,:,0] = UF[:,index_ ].numpy() CentralFloquetEVec_RBM[i,:,1] = UF[:,index_+1].numpy() CentralFloquetE_RBM[i,0] = tf.math.real(U_[index_ , index_ ]).numpy() CentralFloquetE_RBM[i,1] = tf.math.real(U_[index_ + 1 , index_ + 1]).numpy() plt.plot(np.abs(UF[:,model.N_Floquet_UF*model.S:model.N_Floquet_UF*model.S+2])) plt.show() plt.plot(np.abs(model.U_Floquet[:,:])) plt.show() plt.imshow(np.abs(U_.numpy())) plt.show() #%% ########## TRAINING AGAINST THE RWA ########################### ########## TO OBTAIN AN INITIAL SET RBM PARAMETERS ################ ########## THIS IS EQUIVALENT TO RECONSTRUCT A SET OF WAVE FUNCTIONS ###### for i in [14,16,18]:#:,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]:#range(N): delta = 1.7 Omega = 10.0*i/N phase = np.arctan(1.0)/4.1341341 print(i,delta,Omega,phase) N_training = 4 H = Model.Hamiltonian(delta,Omega,phase) model = Model.RBM_Model(delta,Omega,phase) CentralFloquetEVec[i,:,0] = model.U_Floquet[:,0].numpy() CentralFloquetEVec[i,:,1] = model.U_Floquet[:,1].numpy() CentralFloquetE[i,0] = tf.math.real(model.E_Floquet[0]).numpy() CentralFloquetE[i,1] = tf.math.real(model.E_Floquet[1]).numpy() optimizer = tf.keras.optimizers.Adam(learning_rate=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False,name='Adam') for j in range(N_training):#[0,1,2,3,5]: # Fit the norm for i_ in range(N_tr_rho): #loss_value, grads = Model.grad_fun(model,Model.loss_Psi_rho) loss_value, grads = Model.grad_fun(model,Model.loss_RWA) #loss_value, grads = Model.grad_fun(model,Model.loss_Floquet) loss_list_RWA = tf.concat([loss_list_RWA,[loss_value.numpy()]],axis=0) #optimizer.apply_gradients(zip(grads, model.trainable_variables)) optimizer.apply_gradients(zip(grads[0:3], model.trainable_variables[0:3])) # Fit the phase for i_ in range(N_tr_ph): #loss_value, grads = Model.grad_Phase(model,Model.loss_Psi_Phase) loss_value, grads = Model.grad_Phase(model,Model.loss_RWA_Phase) #loss_value, grads = Model.grad_Phase(model,Model.loss_Floquet_Phase) loss_list_RWA = tf.concat([loss_list_RWA,[loss_value.numpy()]],axis=0) optimizer.apply_gradients(zip(grads, model.trainable_variables[3:6])) UF = Model.Unitary_Matrix(model) #print("Hamiltonian in the dressed basis (RBM parametrisation): ") U_ = tf.transpose(tf.math.conj(UF))@model.H_TLS@UF #print(U_) if (UF.shape[1] > model.S): index_ = int(model.S*((UF.shape[1]/model.S -1))/2) else: index_ = 0 CentralFloquetEVec_RBM[i,:,0] = UF[:,index_ ].numpy() CentralFloquetEVec_RBM[i,:,1] = UF[:,index_+1].numpy() CentralFloquetE_RBM[i,0] = tf.math.real(U_[index_ , index_ ]).numpy() CentralFloquetE_RBM[i,1] = tf.math.real(U_[index_ + 1 , index_ + 1]).numpy() #################################################################### #################################################################### #################################################################### #################################################################### #################################################################### ####### FINDING THE FLOQUET OPERATOR TRAINING A RBM ############## #################################################################### optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False,name='Adam') #optimizer = tf.keras.optimizers.SGD(learning_rate=0.00001) loss_list = tf.Variable([],dtype=tf.float64) CentralFloquetE_RBM2 = np.zeros([N,2], dtype = np.float64) CentralFloquetEVec_RBM2 =

np.zeros([N,H.dim,2],dtype = np.complex128)

numpy.zeros

import numpy as np import scipy.special as ss import scipy.signal as ss2 import scipy from numpy import abs, sin, cos, real, exp, pi, sqrt def psi_s(z, x, beta): """ 2D longitudinal potential Eq. (23) from Ref[1] with no constant factor (e*beta**2/2/rho**2). Ref[1]: <NAME> and <NAME>, PRAB 23, 014402 (2020). Note that 'x' here corresponds to 'chi = x / rho' in the paper. """ #try: out = (cos(2 * alpha(z, x, beta)) - 1 / (1+x)) / ( kappa(z, x, beta) - beta * (1+x) * sin(2*alpha(z, x, beta))) #except ZeroDivisionError: # out = 0 # print(f"Oops! ZeroDivisionError at (z,x)= ({z:5.2f},{x:5.2f}). Returning 0.") return np.nan_to_num(out) def psi_x_where_x_equals_zero(z, dx, beta): """ Evaluate psi_x close to x = 0 This is a rough approximation of the singularity across x = 0 """ return (psi_x(z, -dx/2, beta) + psi_x(z, dx/2, beta))/2 @np.vectorize def ss_ellipf(phi, m): y = ss.ellipkinc(phi, m) # y = np.float(y) return y @np.vectorize def ss_ellipe(phi, m): y = ss.ellipeinc(phi, m) # y = np.float(y) return y def psi_x(z, x, beta): """ Eq.(24) from Ref[1] with argument zeta=0 and no constant factor e*beta**2/2/rho**2. Note that 'x' here corresponds to 'chi = x/rho', and 'z' here corresponds to 'xi = z/2/rho' in the paper. """ # z = np.float(z) # x = np.float(x) kap = kappa(z, x, beta) alp = alpha(z, x, beta) arg2 = -4 * (1+x) / x**2 try: T1 = (1/abs(x)/(1 + x) * ((2 + 2*x + x**2) * ss.ellipkinc(alp, arg2)- x**2 * ss.ellipeinc(alp, arg2))) D = kap**2 - beta**2 * (1 + x)**2 *

sin(2*alp)

numpy.sin

import numpy as np import os defargs = { 'VERBOSE':'TRUE', 'LOGFILE':'snap.logfile', 'TILECOSTTHRESH':500, 'MINREGIONSIZE':100, 'TILEEDGEWEIGHT':2.5, 'SCNDRYARCFLOWMAX':8} def Snaphu(C,**kwargs): #get the file names outfile = '' while outfile == '' or os.path.isfile(outfile): num = np.random.randint(1000000) outfile = '{:0d}.out'.format(num) infile = '{:0d}.in'.format(num) cfgfile = '{:0d}.cfg'.format(num) #save the config file f = open(cfgfile,'w') f.write('INFILE {:s}\n'.format(infile)) f.write('LINELENGTH {:d}\n'.format(C.shape[1])) f.write('OUTFILE {:s}\n'.format(outfile)) keys = list(defargs.keys()) for k in keys: v = kwargs.get(k,defargs[k]) if isinstance(v,str): o = k + ' {:s}\n'.format(v) elif isinstance(v,(np.int,np.int32,np.int64)): o = k + ' {:d}\n'.format(v) elif isinstance(v,(np.float,np.float32,np.float64)): o = k + ' {:f}\n'.format(v) f.write(o) f.close() #save the input data f = open(infile,'wb') C.flatten().astype('complex64').tofile(f) f.close() #run snaphu os.system('snaphu -f {:s}'.format(cfgfile)) #remove input file os.system('rm -v {:s}'.format(infile)) #read the output file f = open(outfile,'rb') c = np.fromfile(f,dtype='float32',count=C.size*2).reshape((C.shape[0]*2,C.shape[1])) f.close() #create output arrays of amplitude and phase ia = np.arange(C.shape[0])*2 ip =

np.arange(C.shape[0])

numpy.arange

import numpy as np import pandas as pd import joblib import tensorflow as tf import sys import functools import os import tensorflow.keras.backend as K from matplotlib import pyplot as plt # from IPython.display import clear_output from scipy.stats import gaussian_kde, binned_statistic as binstat from tensorflow.keras.preprocessing.sequence import pad_sequences from sklearn.model_selection import ShuffleSplit, GroupShuffleSplit from sklearn.preprocessing import MinMaxScaler, StandardScaler from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error, median_absolute_error from tensorflow.keras.losses import Loss from scipy.spatial.distance import jensenshannon as js class HuberLoss(Loss): """ Custom TensorFlow Loss subclass implementing the Huber loss. """ def __init__(self, threshold: float = 1): """ :param threshold: float The Huber threshold between L1 and L2 losses. """ super().__init__() self.threshold = threshold def call(self, y_true, y_pred): error = y_true - y_pred is_small_error = tf.abs(error) <= self.threshold small_error_loss = tf.square(error) / 2 big_error_loss = self.threshold * (tf.abs(error) - (0.5 * self.threshold)) return tf.where(is_small_error, small_error_loss, big_error_loss) def root_mean_squared_error(y, y_pred, sample_weight=None): """ Compute the root mean squared error metric. """ value = mean_squared_error(y, y_pred, sample_weight=sample_weight) return np.sqrt(value) def process_input_parameters(pars, min_folds_cv=5): """ Check the consistency of the input parameters and make modifications if necessary. :param pars: argparse.Namespace An argparse namespace object containing the input parameters. :param min_folds_cv: int The minimum number of folds required for K-fold cross-validation. :return: pars, argparse.Namespace The processed version of the input namespace object. """ if len(pars.lcdir) > 1: assert len(pars.wavebands) == len(pars.lcdir), "The number of items in lcdir must either be 1 or match " \ "the number of items in wavebands." assert len(pars.wavebands) == len(pars.lcfile_suffices), \ "The number of items in wavebands and lcfile_suffices must match." if not os.path.isdir(os.path.join(pars.rootdir, pars.outdir)): os.mkdir(os.path.join(pars.rootdir, pars.outdir)) pars.hparam_grid = np.array(pars.hpars) # Check if only the CPU is to be used: if pars.cpu: os.environ["CUDA_VISIBLE_DEVICES"] = "" # Join the list elements of pars.subset into a long string: if pars.subset: pars.subset = ' '.join(pars.subset) # Check the number of meta input features: if pars.meta_input is None: pars.n_meta = 0 else: pars.n_meta = len(pars.meta_input) if pars.nn_type == 'cnn': pars.n_channels = len(pars.wavebands) else: pars.n_channels = 2 * len(pars.wavebands) if pars.weighing_by_density: print("Density weighing is ON with cutoff {}".format(pars.weighing_by_density)) else: print("Density weighing is OFF.") print("Number of input channels: {}".format(pars.n_channels)) print("Number of meta features: {}".format(pars.n_meta)) if pars.train: pars.predict = False # We want to train a regression model. if pars.pick_fold is not None: for ii in pars.pick_fold: print(type(ii)) assert isinstance(ii, int) and 0 < ii <= pars.k_fold, \ "pick_fold must be > 0 AND <= k_fold integer" assert pars.k_fold >= min_folds_cv, \ "pick_fold requires k_fold >= {}".format(min_folds_cv) pars.refit = False if not pars.cross_validate: assert len(pars.hparam_grid) == 1, "Cannot do grid-search of hyper-parameters if cross_validate is False." pars.refit = True if pars.explicit_test_frac: assert pars.refit or pars.ensemble, \ "For the evaluation of the model on the test set, 'refit' or 'ensemble' must be set." if pars.optimize_lr: pars.n_epochs = 100 pars.decay = 0.0 pars.save_model = False pars.cross_validate = False pars.refit = True return pars def read_dataset(filename: str, columns: list = None, subset_expr: str = None, input_feature_names: list = None, trim_quantiles: list = None, qlo: float = 0.25, qhi: float = 0.75, plothist: bool = False, histfig: str = "hist.png", dropna_cols: list = None, comment: str = '#', dtype=None): """ Loads, trims, and exports dataset to numpy arrays. :param filename: str The name of the input file. :param columns: list of strings Passed to the usecols parameter of pandas.read_csv() :param subset_expr: str Expression for subsetting the input data, passed as the first parameter of pandas.DataFrame.query() :param input_feature_names: list of strings An optional subset of the usecols parameter, including the names of the columns to be returned as features. If None, all columns in usecols will be returned. :param trim_quantiles: list An optional subset of the usecols parameter, including the names of the columns to be threshold-rejected beyond the quantiles specified by qlo and qhi. If None, no quantile-trimming will be performed. :param qlo: float Lower quantile for threshold rejection. :param qhi: float Upper quantile for threshold rejection. :param plothist: bool If True, the histograms of the columns in usecols will be plotted before and, if performed, after quantile trimming. :param histfig: str The name of the output histogram figure file if plothist is True. :param dropna_cols: :param comment: :param dtype: :return: """ with open(filename) as f: header = f.readline() cols = header.strip('#').split() df = pd.read_csv(filename, names=cols, header=None, sep="\s+", usecols=columns, comment=comment, dtype=dtype) if dropna_cols is not None: df.dropna(inplace=True, subset=dropna_cols) ndata = len(df) print(df.head()) print("----------\n{} lines read from {}\n".format(ndata, filename)) df_orig = df # Apply threshold rejections: if subset_expr is not None: df = df.query(subset_expr) ndata = len(df) print("{} lines after threshold rejections\n".format(ndata)) # plot histogram for each column in original dataset if plothist: fig, ax = plt.subplots(figsize=(20, 10)) fig.clf() _ = pd.DataFrame.hist(df, bins=int(np.ceil(np.cbrt(ndata) * 2)), figsize=(20, 10), grid=False, color='red', ax=ax) plt.savefig(histfig) # omit data beyond specific quantiles [qlo, qhi] if trim_quantiles is not None: dfq = df[trim_quantiles] quantiles = pd.DataFrame.quantile(dfq, q=[qlo, qhi], axis=0, numeric_only=True, interpolation='linear') print("Values at [{},{}] quantiles to be applied for data trimming:".format(qlo, qhi)) print(quantiles.sum) mask = (dfq > dfq.quantile(qlo)) & (dfq < dfq.quantile(qhi)) # print(mask) mask = mask.all(axis=1) # print(mask.shape) df = pd.DataFrame.dropna(df[mask]) ndata = len(df) print("\n{} lines remained after quantile rejection.\n".format(ndata)) # plot histogram for each column in trimmed dataset if plothist: fig, ax = plt.subplots(figsize=(20, 10)) _ = pd.DataFrame.hist(df, bins=int(np.ceil(np.cbrt(ndata) * 2)), figsize=(20, 10), grid=False, color='green', ax=ax) fig.savefig("hist_trim.png", format="png") if input_feature_names is not None: return df.loc[:, input_feature_names], df_orig else: return df, df_orig def read_time_series_for_rnn(name_list, source_dir, nts, input_wavebands, ts_file_suffix, rootdir="", periods=None, max_phase=1.0, phase_shift=None, nbins=None): print("Reading time series...", file=sys.stderr) n_data = len(name_list) scaler = StandardScaler(copy=True, with_mean=True, with_std=False) X_list = list() times_dict = dict() mags_dict = dict() phases_dict = dict() if nbins is not None: print("Light curves will be binned to max. {0} points in [0, {1:.1f}].".format(nbins, max_phase)) for iband, waveband in enumerate(input_wavebands): X = np.zeros((n_data, nts, 2)) # Input shape required by an RNN: (batch_size, time_steps, features) phases = list() times = list() mags = list() if len(source_dir) > 1: directory = source_dir[iband] else: directory = source_dir[0] for ii, name in enumerate(name_list): print('Reading data for {}\r'.format(name), end="", file=sys.stderr) pp, mm = np.genfromtxt(os.path.join(rootdir, directory, name + ts_file_suffix[iband]), unpack=True, comments='#') phasemask = (pp < max_phase) pp = pp[phasemask] mm = mm[phasemask] if phase_shift is not None: pp = get_phases(1.0, pp, shift=phase_shift, all_positive=True) inds = np.argsort(pp) pp = pp[inds] mm = mm[inds] if nbins is not None: pp, mm = binlc(pp, mm, nbins=nbins, max_y=max_phase) if periods is not None: tt = pp * periods[ii] else: tt = pp # here we only subtract the mean: mm = scaler.fit_transform(mm.reshape(-1, 1)).flatten() times.append(tt) mags.append(mm) phases.append(pp) times_padded = pad_sequences(times, maxlen=nts, dtype='float64', padding='post', truncating='post', value=-1) mags_padded = pad_sequences(mags, maxlen=nts, dtype='float64', padding='post', truncating='post', value=-1) X[:, :, 0] = times_padded X[:, :, 1] = mags_padded X_list.append(X) times_dict[waveband] = times mags_dict[waveband] = mags phases_dict[waveband] = phases # Create final data matrix for the time series: X = np.concatenate(X_list, axis=2) print("") return X, times_dict, mags_dict, phases_dict def read_time_series_for_cnn(name_list, source_dir, nts, input_wavebands, ts_file_suffix, nuse=1, rootdir="", n_aug=None): nmags = int(nts / nuse) n_data = len(name_list) if n_aug is not None: assert isinstance(n_aug, int) and n_aug > 0, \ "n_aug must be a positive integer" dict_x_ts = dict() for waveband in input_wavebands: dict_x_ts[waveband] = np.zeros((n_data, nmags)) if n_aug is not None: dict_x_ts[waveband] = np.zeros((n_data * n_aug, nmags)) groups = np.zeros((n_data * n_aug)) dict_x_ts_scaled = dict() print("Reading time series...", file=sys.stderr) for ii, name in enumerate(name_list): print('Reading data for {}\r'.format(name), end="", file=sys.stderr) for iband, waveband in enumerate(input_wavebands): if len(source_dir) > 1: directory = source_dir[iband] else: directory = source_dir[0] if n_aug is None: phases, timeseries = np.genfromtxt(os.path.join(directory, name + ts_file_suffix[iband]), unpack=True, comments='#') phases = phases[0:nts] timeseries = timeseries[0:nts] dict_x_ts[waveband][ii][:] = timeseries[nuse - 1::nuse] groups = None else: tsinput = np.genfromtxt(os.path.join(directory, name + ts_file_suffix[iband]), unpack=False, comments='#') # check if there are n_aug+1 columns in the data matrix assert tsinput.shape[1] == n_aug + 1, \ "data matrix in " + os.path.join(directory, name + ts_file_suffix[iband]) + " has wrong shape" phases = tsinput[0:nts, 0] for jj in range(n_aug): timeseries = tsinput[0:nts, jj + 1] dict_x_ts[waveband][jj + ii * n_aug][:] = timeseries[nuse - 1::nuse] groups[jj + ii * n_aug] = ii phases = phases[nuse - 1::nuse] # Scale the time series to the [0,1] range scaler = MinMaxScaler(copy=True, feature_range=(0, 1)) ts_list = list() for ii, waveband in enumerate(input_wavebands): scaler.fit(dict_x_ts[waveband].T) dict_x_ts_scaled[waveband] = (scaler.transform(dict_x_ts[waveband].T)).T ts_list.append(np.expand_dims(dict_x_ts_scaled[waveband], axis=2)) # Create final data matrix for the time series: X = np.concatenate(ts_list, axis=2) print("") return X, dict_x_ts, dict_x_ts_scaled, phases, groups def cross_validate(model, folds: list, x_list: list or tuple, y, model_kwargs: dict = {}, compile_kwargs: dict = {}, initial_weights: list = None, sample_weight_fit=None, sample_weight_eval=None, ids=None, indices_to_scale: list or tuple = None, scaler=None, n_epochs: int = 1, batch_size: int = None, shuffle=True, verbose: int = 0, callbacks: list = [], metrics: list or tuple = None, log_training=True, log_prefix='', pick_fold: list or tuple = None, save_data=True, rootdir='.', filename_train='train.dat', filename_val='val.dat', strategy=None, n_devices=1, validation_freq=1, seed=1): # Initialize variables: histories = list() model_weights = list() scalers_folds = list() Y_train_collected = np.array([]) Y_val_collected = np.array([]) Y_train_pred_collected = np.array([]) Y_val_pred_collected = np.array([]) fitting_weights_train_collected = np.array([]) fitting_weights_val_collected = np.array([]) eval_weights_train_collected = np.array([]) eval_weights_val_collected = np.array([]) ids_train_collected = np.array([]) ids_val_collected = np.array([]) numcv_t = np.array([]) numcv_v = np.array([]) # callbacks.append(PrintLearningRate()) if ids is None: # create IDs by simply numbering the data ids = np.linspace(1, y.shape[0], y.shape[0]).astype(int) first_fold = True for i_cv, (train_index, val_index) in enumerate(folds): # if pick_fold is not None and pick_fold != i_cv + 1: if pick_fold is not None and i_cv + 1 not in pick_fold: continue # Create and compile the model: tf.keras.backend.clear_session() tf.random.set_seed(seed) if strategy is not None: # Apply distributed strategy on model if multiple devices are present: with strategy.scope(): model_ = model(**model_kwargs) else: model_ = model(**model_kwargs) if first_fold: first_fold = False model_.summary() if initial_weights is None: initial_weights = model_.get_weights() else: # Initialize model weights: model_.set_weights(initial_weights) if strategy is not None: with strategy.scope(): model_.compile(**compile_kwargs) else: model_.compile(**compile_kwargs) print("fold " + str(i_cv + 1) + "/" + str(len(folds))) print("n_train = {} ; n_val = {}".format(train_index.shape[0], val_index.shape[0])) if log_training: callbacks_fold = callbacks + [tf.keras.callbacks.CSVLogger( os.path.join(rootdir, log_prefix + f"_fold{i_cv + 1}.log"))] else: callbacks_fold = callbacks # -------------------------------------------------- # Split the arrays to training and validations sets: x_train_list = list() x_val_list = list() scalers = list() for i, x in enumerate(x_list): x_t, x_v = x[train_index], x[val_index] if indices_to_scale is not None and i in indices_to_scale: scaler.fit(x_t) x_t = scaler.transform(x_t) x_v = scaler.transform(x_v) scalers.append(scaler.copy()) x_train_list.append(x_t) x_val_list.append(x_v) y_train, y_val = y[train_index], y[val_index] if sample_weight_fit is not None: fitting_weights_train, fitting_weights_val = sample_weight_fit[train_index], sample_weight_fit[val_index] else: fitting_weights_train, fitting_weights_val = None, None if sample_weight_eval is not None: eval_weights_train, eval_weights_val = sample_weight_eval[train_index], sample_weight_eval[val_index] else: eval_weights_train, eval_weights_val = None, None ids_t, ids_v = ids[train_index], ids[val_index] # -------------------------------------------------- # Fit and evaluate the model for this fold: history = model_.fit(x=x_train_list, y=y_train, sample_weight=fitting_weights_train, epochs=n_epochs, initial_epoch=0, batch_size=batch_size, shuffle=shuffle, validation_data=(x_val_list, y_val, fitting_weights_val), verbose=verbose, callbacks=callbacks_fold, validation_freq=validation_freq) Y_train_pred = (model_.predict(x_train_list)).flatten() Y_val_pred = (model_.predict(x_val_list)).flatten() histories.append(history) model_weights.append(model_.get_weights()) scalers_folds.append(scalers.copy()) # -------------------------------------------------- # Append the values of this fold to those from the previous fold(s). Y_train_collected = np.hstack((Y_train_collected, y_train)) Y_val_collected = np.hstack((Y_val_collected, y_val)) Y_train_pred_collected = np.hstack((Y_train_pred_collected, Y_train_pred)) Y_val_pred_collected = np.hstack((Y_val_pred_collected, Y_val_pred)) if sample_weight_fit is not None: fitting_weights_train_collected = np.hstack((fitting_weights_train_collected, fitting_weights_train)) fitting_weights_val_collected =

np.hstack((fitting_weights_val_collected, fitting_weights_val))

numpy.hstack

# pylint: disable=E1101 import torch import torch.nn as nn from torch.utils.data import DataLoader, TensorDataset from sklearn.metrics import average_precision_score, confusion_matrix import numpy as np from physionet import PhysioNet, get_data_min_max, variable_time_collate_fn2 from sklearn import model_selection from sklearn import metrics from sklearn.metrics import precision_score, recall_score, f1_score from person_activity import PersonActivity def one_hot(y_): # Function to encode output labels from number indexes # e.g.: [[5], [0], [3]] --> [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]] y_ = y_.reshape(len(y_)) y_ = [int(x) for x in y_] n_values = np.max(y_) + 1 return np.eye(n_values)[np.array(y_, dtype=np.int32)] def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) def log_normal_pdf(x, mean, logvar, mask): const = torch.from_numpy(np.array([2. * np.pi])).float().to(x.device) const = torch.log(const) return -.5 * (const + logvar + (x - mean) ** 2. / torch.exp(logvar)) * mask def normal_kl(mu1, lv1, mu2, lv2): v1 = torch.exp(lv1) v2 = torch.exp(lv2) lstd1 = lv1 / 2. lstd2 = lv2 / 2. kl = lstd2 - lstd1 + ((v1 + (mu1 - mu2) ** 2.) / (2. * v2)) - .5 return kl def mean_squared_error(orig, pred, mask): error = (orig - pred) ** 2 error = error * mask return error.sum() / mask.sum() def normalize_masked_data(data, mask, att_min, att_max): # we don't want to divide by zero att_max[att_max == 0.] = 1. if (att_max != 0.).all(): data_norm = (data - att_min) / att_max else: raise Exception("Zero!") if torch.isnan(data_norm).any(): raise Exception("nans!") # set masked out elements back to zero data_norm[mask == 0] = 0 return data_norm, att_min, att_max def evaluate(dim, rec, dec, test_loader, args, num_sample=10, device="cuda"): mse, test_n = 0.0, 0.0 with torch.no_grad(): for test_batch in test_loader: test_batch = test_batch.to(device) observed_data, observed_mask, observed_tp = ( test_batch[:, :, :dim], test_batch[:, :, dim: 2 * dim], test_batch[:, :, -1], ) if args.sample_tp and args.sample_tp < 1: subsampled_data, subsampled_tp, subsampled_mask = subsample_timepoints( observed_data.clone(), observed_tp.clone(), observed_mask.clone(), args.sample_tp) else: subsampled_data, subsampled_tp, subsampled_mask = \ observed_data, observed_tp, observed_mask out = rec(torch.cat((subsampled_data, subsampled_mask), 2), subsampled_tp) qz0_mean, qz0_logvar = ( out[:, :, : args.latent_dim], out[:, :, args.latent_dim:], ) epsilon = torch.randn( num_sample, qz0_mean.shape[0], qz0_mean.shape[1], qz0_mean.shape[2] ).to(device) z0 = epsilon * torch.exp(0.5 * qz0_logvar) + qz0_mean z0 = z0.view(-1, qz0_mean.shape[1], qz0_mean.shape[2]) batch, seqlen = observed_tp.size() time_steps = ( observed_tp[None, :, :].repeat(num_sample, 1, 1).view(-1, seqlen) ) pred_x = dec(z0, time_steps) pred_x = pred_x.view(num_sample, -1, pred_x.shape[1], pred_x.shape[2]) pred_x = pred_x.mean(0) mse += mean_squared_error(observed_data, pred_x, observed_mask) * batch test_n += batch return mse / test_n def compute_losses(dim, dec_train_batch, qz0_mean, qz0_logvar, pred_x, args, device): observed_data, observed_mask \ = dec_train_batch[:, :, :dim], dec_train_batch[:, :, dim:2*dim] noise_std = args.std noise_std_ = torch.zeros(pred_x.size()).to(device) + noise_std noise_logvar = 2. * torch.log(noise_std_).to(device) logpx = log_normal_pdf(observed_data, pred_x, noise_logvar, observed_mask).sum(-1).sum(-1) pz0_mean = pz0_logvar = torch.zeros(qz0_mean.size()).to(device) analytic_kl = normal_kl(qz0_mean, qz0_logvar, pz0_mean, pz0_logvar).sum(-1).sum(-1) if args.norm: logpx /= observed_mask.sum(-1).sum(-1) analytic_kl /= observed_mask.sum(-1).sum(-1) return logpx, analytic_kl def evaluate_classifier(model, test_loader, dec=None, args=None, classifier=None, dim=41, device='cuda', reconst=False, num_sample=1, dataset='P12'): pred = [] true = [] test_loss = 0 for test_batch, label in test_loader: test_batch, label = test_batch.to(device), label.to(device) batch_len = test_batch.shape[0] observed_data, observed_mask, observed_tp \ = test_batch[:, :, :dim], test_batch[:, :, dim:2*dim], test_batch[:, :, -1] with torch.no_grad(): out = model( torch.cat((observed_data, observed_mask), 2), observed_tp) if reconst: qz0_mean, qz0_logvar = out[:, :, :args.latent_dim], out[:, :, args.latent_dim:] epsilon = torch.randn( num_sample, qz0_mean.shape[0], qz0_mean.shape[1], qz0_mean.shape[2]).to(device) z0 = epsilon * torch.exp(.5 * qz0_logvar) + qz0_mean z0 = z0.view(-1, qz0_mean.shape[1], qz0_mean.shape[2]) if args.classify_pertp: pred_x = dec(z0, observed_tp[None, :, :].repeat( num_sample, 1, 1).view(-1, observed_tp.shape[1])) out = classifier(pred_x) else: out = classifier(z0) if args.classify_pertp: N = label.size(-1) out = out.view(-1, N) label = label.view(-1, N) _, label = label.max(-1) test_loss += nn.CrossEntropyLoss()(out, label.long()).item() * batch_len * 50. else: label = label.unsqueeze(0).repeat_interleave( num_sample, 0).view(-1) test_loss += nn.CrossEntropyLoss()(out, label).item() * batch_len * num_sample pred.append(out.cpu().numpy()) true.append(label.cpu().numpy()) pred = np.concatenate(pred, 0) true = np.concatenate(true, 0) acc = np.mean(pred.argmax(1) == true) if dataset == 'P12' or dataset == 'P19' or dataset == 'eICU': auc = metrics.roc_auc_score(true, pred[:, 1]) if not args.classify_pertp else 0. aupr = average_precision_score(true, pred[:, 1]) if not args.classify_pertp else 0. return test_loss / pred.shape[0], acc, auc, aupr, None, None, None elif dataset == 'PAM': auc = metrics.roc_auc_score(one_hot(true), pred) if not args.classify_pertp else 0. aupr = average_precision_score(one_hot(true), pred) if not args.classify_pertp else 0. precision = precision_score(true, pred.argmax(1), average='macro', ) if not args.classify_pertp else 0. recall = recall_score(true, pred.argmax(1), average='macro', ) if not args.classify_pertp else 0. F1 = 2 * (precision * recall) / (precision + recall) if not args.classify_pertp else 0. return test_loss/pred.shape[0], acc, auc, aupr, precision, recall, F1 def random_sample(idx_0, idx_1, batch_size): """ Returns a balanced sample by randomly sampling without replacement. :param idx_0: indices of negative samples :param idx_1: indices of positive samples :param batch_size: batch size :return: indices of balanced batch of negative and positive samples """ idx0_batch = np.random.choice(idx_0, size=int(batch_size / 2), replace=False) idx1_batch = np.random.choice(idx_1, size=int(batch_size / 2), replace=False) idx = np.concatenate([idx0_batch, idx1_batch], axis=0) return idx def preprocess_P19(PT_dict, arr_outcomes, labels_ts): total = [] for i, patient in enumerate(PT_dict): length = patient['length'] record_id = patient['id'] tt = torch.squeeze(torch.tensor(patient['time'][:length]), 1) vals = torch.tensor(patient['arr'][:length, :], dtype=torch.float32) m = np.zeros(shape=patient['arr'][:length, :].shape) m[patient['arr'][:length, :].nonzero()] = 1 mask = torch.tensor(m, dtype=torch.float32) outcome = torch.tensor(arr_outcomes[i][0], dtype=torch.float32) total.append((record_id, tt, vals, mask, outcome)) ''' # calculate and save P19 statistics - age, gender, density scores (can be used for all algorithms) idx_under_65 = [] idx_over_65 = [] idx_male = [] idx_female = [] for i in range(len(PT_dict)): if total[i][0] == PT_dict[i]['id']: age, gender, _, _, _, _ = PT_dict[i]['extended_static'] if age > 0: if age < 65: idx_under_65.append(i) else: idx_over_65.append(i) if gender == 0: idx_female.append(i) if gender == 1: idx_male.append(i) np.save('P19_idx_under_65.npy', np.array(idx_under_65), allow_pickle=True) np.save('P19_idx_over_65.npy', np.array(idx_over_65), allow_pickle=True) np.save('P19_idx_male.npy', np.array(idx_male), allow_pickle=True) np.save('P19_idx_female.npy', np.array(idx_female), allow_pickle=True) # save density scores X_features = np.array([d['arr'] for d in PT_dict]) counts = np.count_nonzero(X_features, axis=(0, 1)) ascending_indices = np.argsort(counts) density_scores = counts / (X_features.shape[0] * 60) res = [[ind, density_scores[ind], labels_ts[:-1][ind]] for ind in ascending_indices] np.save('P19_density_scores.npy', res, allow_pickle=True) ''' return total def preprocess_eICU(PT_dict, arr_outcomes, labels_ts): total = [] for i, patient in enumerate(PT_dict): record_id = str(i) tt = torch.squeeze(torch.tensor(patient['time']), 1) vals = torch.tensor(patient['arr'], dtype=torch.float32) m = np.zeros(shape=patient['arr'].shape) m[patient['arr'].nonzero()] = 1 mask = torch.tensor(m, dtype=torch.float32) outcome = torch.tensor(arr_outcomes[i], dtype=torch.float32) total.append((record_id, tt, vals, mask, outcome)) ''' # calculate and save P19 statistics - gender, density scores (can be used for all algorithms) idx_male = [] idx_female = [] for i in range(len(PT_dict)): if total[i][0] == str(i): vec = PT_dict[i]['extended_static'] if vec[-3] > 0: idx_female.append(i) if vec[-4] > 0: idx_male.append(i) print('\nOnly 1.329/36.443 samples have gender data available.\n') np.save('eICU_idx_male.npy', np.array(idx_male), allow_pickle=True) np.save('eICU_idx_female.npy', np.array(idx_female), allow_pickle=True) # save density scores X_features = np.array([d['arr'] for d in PT_dict]) counts = np.count_nonzero(X_features, axis=(0, 1)) ascending_indices = np.argsort(counts) density_scores = counts / (X_features.shape[0] * 300) res = [[ind, density_scores[ind], labels_ts[ind]] for ind in ascending_indices] np.save('eICU_density_scores.npy', res, allow_pickle=True) ''' return total def preprocess_PAM(PT_dict, arr_outcomes): length = 600 total = [] for i, patient in enumerate(PT_dict): record_id = str(i) tt = torch.tensor(list(range(length))) vals = torch.tensor(patient, dtype=torch.float32) m = np.zeros(shape=patient.shape) m[patient.nonzero()] = 1 mask = torch.tensor(m, dtype=torch.float32) outcome = torch.tensor(arr_outcomes[i][0], dtype=torch.float32) total.append((record_id, tt, vals, mask, outcome)) return total def random_sample_8(ytrain, B, replace=False): """ Returns a balanced sample of tensors by randomly sampling without replacement. """ idx0_batch = np.random.choice(np.where(ytrain == 0)[0], size=int(B / 8), replace=replace) idx1_batch = np.random.choice(np.where(ytrain == 1)[0], size=int(B / 8), replace=replace) idx2_batch = np.random.choice(np.where(ytrain == 2)[0], size=int(B / 8), replace=replace) idx3_batch = np.random.choice(np.where(ytrain == 3)[0], size=int(B / 8), replace=replace) idx4_batch = np.random.choice(np.where(ytrain == 4)[0], size=int(B / 8), replace=replace) idx5_batch = np.random.choice(np.where(ytrain == 5)[0], size=int(B / 8), replace=replace) idx6_batch = np.random.choice(np.where(ytrain == 6)[0], size=int(B / 8), replace=replace) idx7_batch = np.random.choice(np.where(ytrain == 7)[0], size=int(B / 8), replace=replace) idx = np.concatenate([idx0_batch, idx1_batch, idx2_batch, idx3_batch, idx4_batch, idx5_batch, idx6_batch, idx7_batch], axis=0) return idx def get_data(args, dataset, device, q, upsampling_batch, split_type, feature_removal_level, missing_ratio, flag=1, reverse=False, predictive_label='mortality'): if dataset == 'P12': train_dataset_obj_1 = PhysioNet('data/physionet', train=True, quantization=q, download=True, n_samples=12000, device=device, set_letter='a') train_dataset_obj_2 = PhysioNet('data/physionet', train=True, quantization=q, download=True, n_samples=12000, device=device, set_letter='b') train_dataset_obj_3 = PhysioNet('data/physionet', train=True, quantization=q, download=True, n_samples=12000, device=device, set_letter='c') dataset_1 = train_dataset_obj_1[:len(train_dataset_obj_1)] dataset_2 = train_dataset_obj_2[:len(train_dataset_obj_2)] dataset_3 = train_dataset_obj_3[:len(train_dataset_obj_3)] total_dataset = dataset_1 + dataset_2 + dataset_3 if predictive_label == 'LoS': los_outcomes = np.load('../saved/LoS_y1_out.npy', allow_pickle=True) for i, tpl in enumerate(total_dataset): a, b, c, d, _ = tpl los_label = los_outcomes[i][0] los_label = torch.tensor(los_label, dtype=torch.float32) total_dataset[i] = (a, b, c, d, los_label) ''' # calculate and save statistics idx_under_65 = [] idx_over_65 = [] idx_male = [] idx_female = [] P_list = np.load('P_list.npy', allow_pickle=True) for i in range(len(P_list)): if total_dataset[i][0] == P_list[i]['id']: age, gender, _, _, _ = P_list[i]['static'] if age > 0: if age < 65: idx_under_65.append(i) else: idx_over_65.append(i) if gender == 0: idx_female.append(i) if gender == 1: idx_male.append(i) np.save('mtand_idx_under_65.npy', np.array(idx_under_65), allow_pickle=True) np.save('mtand_idx_over_65.npy', np.array(idx_over_65), allow_pickle=True) np.save('mtand_idx_male.npy', np.array(idx_male), allow_pickle=True) np.save('mtand_idx_female.npy', np.array(idx_female), allow_pickle=True) ''' elif dataset == 'P19': PT_dict = np.load('../../../P19data/processed_data/PT_dict_list_6.npy', allow_pickle=True) labels_ts = np.load('../../../P19data/processed_data/labels_ts.npy', allow_pickle=True) labels_demogr = np.load('../../../P19data/processed_data/labels_demogr.npy', allow_pickle=True) arr_outcomes = np.load('../../../P19data/processed_data/arr_outcomes_6.npy', allow_pickle=True) total_dataset = preprocess_P19(PT_dict, arr_outcomes, labels_ts) elif dataset == 'eICU': PT_dict = np.load('../../../eICUdata/processed_data/PTdict_list.npy', allow_pickle=True) labels_ts = np.load('../../../eICUdata/processed_data/eICU_ts_vars.npy', allow_pickle=True) labels_demogr = np.load('../../../eICUdata/processed_data/eICU_static_vars.npy', allow_pickle=True) arr_outcomes = np.load('../../../eICUdata/processed_data/arr_outcomes.npy', allow_pickle=True) total_dataset = preprocess_eICU(PT_dict, arr_outcomes, labels_ts) elif dataset == 'PAM': PT_dict = np.load('../../../PAMdata/processed_data/PTdict_list.npy', allow_pickle=True) arr_outcomes = np.load('../../../PAMdata/processed_data/arr_outcomes.npy', allow_pickle=True) total_dataset = preprocess_PAM(PT_dict, arr_outcomes) print('len(total_dataset):', len(total_dataset)) if split_type == 'random': # Shuffle and split train_data, test_data = model_selection.train_test_split(total_dataset, train_size=0.9, # 80% train, 10% validation, 10% test shuffle=True) elif split_type == 'age' or split_type == 'gender': if dataset == 'P12': prefix = 'mtand' elif dataset == 'P19': prefix = 'P19' elif dataset == 'eICU': # possible only with split_type == 'gender' prefix = 'eICU' if split_type == 'age': if dataset == 'eICU': print('\nCombination of eICU dataset and age split is not possible.\n') if reverse == False: idx_train = np.load('%s_idx_under_65.npy' % prefix, allow_pickle=True) idx_vt = np.load('%s_idx_over_65.npy' % prefix, allow_pickle=True) else: idx_train = np.load('%s_idx_over_65.npy' % prefix, allow_pickle=True) idx_vt = np.load('%s_idx_under_65.npy' % prefix, allow_pickle=True) elif split_type == 'gender': if reverse == False: idx_train = np.load('%s_idx_male.npy' % prefix, allow_pickle=True) idx_vt = np.load('%s_idx_female.npy' % prefix, allow_pickle=True) else: idx_train = np.load('%s_idx_female.npy' % prefix, allow_pickle=True) idx_vt = np.load('%s_idx_male.npy' % prefix, allow_pickle=True) np.random.shuffle(idx_train) np.random.shuffle(idx_vt) train_data = [total_dataset[i] for i in idx_train] test_data = [total_dataset[i] for i in idx_vt] record_id, tt, vals, mask, labels = train_data[0] input_dim = vals.size(-1) data_min, data_max = get_data_min_max(total_dataset, device) batch_size = 128 if flag: if args.classif: if split_type == 'random': train_data, val_data = model_selection.train_test_split(train_data, train_size=0.8889, shuffle=True) # 80% train, 10% validation, 10% test elif split_type == 'age' or split_type == 'gender': val_data, test_data = model_selection.train_test_split(test_data, train_size=0.5, shuffle=False) if dataset == 'P12': num_all_features = 36 elif dataset == 'P19': num_all_features = 34 elif dataset == 'eICU': num_all_features = 14 elif dataset == 'PAM': num_all_features = 17 num_missing_features = round(missing_ratio * num_all_features) if feature_removal_level == 'sample': for i, tpl in enumerate(val_data): idx = np.random.choice(num_all_features, num_missing_features, replace=False) _, _, values, _, _ = tpl tpl = list(tpl) values[:, idx] = torch.zeros(values.shape[0], num_missing_features) tpl[2] = values val_data[i] = tuple(tpl) for i, tpl in enumerate(test_data): idx = np.random.choice(num_all_features, num_missing_features, replace=False) _, _, values, _, _ = tpl tpl = list(tpl) values[:, idx] = torch.zeros(values.shape[0], num_missing_features) tpl[2] = values test_data[i] = tuple(tpl) elif feature_removal_level == 'set': if dataset == 'P12': dict_params = train_dataset_obj_1.params_dict density_scores_names = np.load('../saved/IG_density_scores_P12.npy', allow_pickle=True)[:, 1] idx = [dict_params[name] for name in density_scores_names[:num_missing_features]] elif dataset == 'P19': labels_ts = np.load('../../../P19data/processed_data/labels_ts.npy', allow_pickle=True) dict_params = {label: i for i, label in enumerate(labels_ts[:-1])} density_scores_names = np.load('../saved/IG_density_scores_P19.npy', allow_pickle=True)[:, 1] idx = [dict_params[name] for name in density_scores_names[:num_missing_features]] elif dataset == 'eICU': labels_ts = np.load('../../../eICUdata/processed_data/eICU_ts_vars.npy', allow_pickle=True) dict_params = {label: i for i, label in enumerate(labels_ts)} density_scores_names = np.load('../saved/IG_density_scores_eICU.npy', allow_pickle=True)[:, 1] idx = [dict_params[name] for name in density_scores_names[:num_missing_features]] elif dataset == 'PAM': density_scores_indices = np.load('../saved/IG_density_scores_PAM.npy', allow_pickle=True)[:, 0] idx = list(map(int, density_scores_indices[:num_missing_features])) for i, tpl in enumerate(val_data): _, _, values, _, _ = tpl tpl = list(tpl) values[:, idx] = torch.zeros(values.shape[0], num_missing_features) tpl[2] = values val_data[i] = tuple(tpl) for i, tpl in enumerate(test_data): _, _, values, _, _ = tpl tpl = list(tpl) values[:, idx] = torch.zeros(values.shape[0], num_missing_features) tpl[2] = values test_data[i] = tuple(tpl) if upsampling_batch: train_data_upsamled = [] true_labels = np.array(list(map(lambda x: float(x[7]), np.array(train_data)[:, 4]))) if dataset == 'P12' or dataset == 'P19' or dataset == 'eICU': # 2 classes idx_0 = np.where(true_labels == 0)[0] idx_1 = np.where(true_labels == 1)[0] for _ in range(len(true_labels) // batch_size): indices = random_sample(idx_0, idx_1, batch_size) for i in indices: train_data_upsamled.append(train_data[i]) elif dataset == 'PAM': # 8 classes for b in range(len(true_labels) // batch_size): indices = random_sample_8(true_labels, batch_size) for i in indices: train_data_upsamled.append(train_data[i]) train_data = train_data_upsamled test_data_combined = variable_time_collate_fn(test_data, device, classify=args.classif, data_min=data_min, data_max=data_max) train_data_combined = variable_time_collate_fn(train_data, device, classify=args.classif, data_min=data_min, data_max=data_max) val_data_combined = variable_time_collate_fn( val_data, device, classify=args.classif, data_min=data_min, data_max=data_max) print(train_data_combined[1].sum( ), val_data_combined[1].sum(), test_data_combined[1].sum()) print(train_data_combined[0].size(), train_data_combined[1].size(), val_data_combined[0].size(), val_data_combined[1].size(), test_data_combined[0].size(), test_data_combined[1].size()) train_data_combined = TensorDataset( train_data_combined[0], train_data_combined[1].long().squeeze()) val_data_combined = TensorDataset( val_data_combined[0], val_data_combined[1].long().squeeze()) test_data_combined = TensorDataset( test_data_combined[0], test_data_combined[1].long().squeeze()) else: train_data_combined = variable_time_collate_fn( train_data, device, classify=args.classif, data_min=data_min, data_max=data_max) train_dataloader = DataLoader( train_data_combined, batch_size=batch_size, shuffle=False) test_dataloader = DataLoader( test_data_combined, batch_size=batch_size, shuffle=False) else: train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=False, collate_fn=lambda batch: variable_time_collate_fn2(batch, args, device, data_type="train", data_min=data_min, data_max=data_max)) test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, collate_fn=lambda batch: variable_time_collate_fn2(batch, args, device, data_type="test", data_min=data_min, data_max=data_max)) data_objects = {"dataset_obj": {}, "train_dataloader": train_dataloader, "test_dataloader": test_dataloader, "input_dim": input_dim, "n_train_batches": len(train_dataloader), "n_test_batches": len(test_dataloader), "attr": {}, # optional "classif_per_tp": False, # optional "n_labels": 1} # optional if args.classif: val_dataloader = DataLoader( val_data_combined, batch_size=batch_size, shuffle=False) data_objects["val_dataloader"] = val_dataloader return data_objects def variable_time_collate_fn(batch, device=torch.device("cpu"), classify=False, activity=False, data_min=None, data_max=None): """ Expects a batch of time series data in the form of (record_id, tt, vals, mask, labels) where - record_id is a patient id - tt is a 1-dimensional tensor containing T time values of observations. - vals is a (T, D) tensor containing observed values for D variables. - mask is a (T, D) tensor containing 1 where values were observed and 0 otherwise. - labels is a list of labels for the current patient, if labels are available. Otherwise None. Returns: combined_tt: The union of all time observations. combined_vals: (M, T, D) tensor containing the observed values. combined_mask: (M, T, D) tensor containing 1 where values were observed and 0 otherwise. """ D = batch[0][2].shape[1] # number of labels N = batch[0][-1].shape[1] if activity else 1 len_tt = [ex[1].size(0) for ex in batch] maxlen = np.max(len_tt) enc_combined_tt = torch.zeros([len(batch), maxlen]).to(device) enc_combined_vals = torch.zeros([len(batch), maxlen, D]).to(device) enc_combined_mask = torch.zeros([len(batch), maxlen, D]).to(device) if classify: if activity: combined_labels = torch.zeros([len(batch), maxlen, N]).to(device) else: combined_labels = torch.zeros([len(batch), N]).to(device) for b, (record_id, tt, vals, mask, labels) in enumerate(batch): currlen = tt.size(0) enc_combined_tt[b, :currlen] = tt.to(device) enc_combined_vals[b, :currlen] = vals.to(device) enc_combined_mask[b, :currlen] = mask.to(device) if classify: if activity: combined_labels[b, :currlen] = labels.to(device) else: if labels is not None: combined_labels[b] = labels.to(device) if not activity: enc_combined_vals, _, _ = normalize_masked_data(enc_combined_vals, enc_combined_mask, att_min=data_min, att_max=data_max) if torch.max(enc_combined_tt) != 0.: enc_combined_tt = enc_combined_tt / torch.max(enc_combined_tt) combined_data = torch.cat( (enc_combined_vals, enc_combined_mask, enc_combined_tt.unsqueeze(-1)), 2) if classify: return combined_data, combined_labels else: return combined_data def get_activity_data(args, device): n_samples = min(10000, args.n) dataset_obj = PersonActivity('data/PersonActivity', download=True, n_samples=n_samples, device=device) print(dataset_obj) train_data, test_data = model_selection.train_test_split(dataset_obj, train_size=0.8, random_state=42, shuffle=True) record_id, tt, vals, mask, labels = train_data[0] input_dim = vals.size(-1) batch_size = min(min(len(dataset_obj), args.batch_size), args.n) test_data_combined = variable_time_collate_fn(test_data, device, classify=args.classif, activity=True) train_data, val_data = model_selection.train_test_split(train_data, train_size=0.8, random_state=11, shuffle=True) train_data_combined = variable_time_collate_fn( train_data, device, classify=args.classif, activity=True) val_data_combined = variable_time_collate_fn( val_data, device, classify=args.classif, activity=True) print(train_data_combined[1].sum( ), val_data_combined[1].sum(), test_data_combined[1].sum()) print(train_data_combined[0].size(), train_data_combined[1].size(), val_data_combined[0].size(), val_data_combined[1].size(), test_data_combined[0].size(), test_data_combined[1].size()) train_data_combined = TensorDataset( train_data_combined[0], train_data_combined[1].long()) val_data_combined = TensorDataset( val_data_combined[0], val_data_combined[1].long()) test_data_combined = TensorDataset( test_data_combined[0], test_data_combined[1].long()) train_dataloader = DataLoader( train_data_combined, batch_size=batch_size, shuffle=False) test_dataloader = DataLoader( test_data_combined, batch_size=batch_size, shuffle=False) val_dataloader = DataLoader( val_data_combined, batch_size=batch_size, shuffle=False) data_objects = {"train_dataloader": train_dataloader, "test_dataloader": test_dataloader, "val_dataloader": val_dataloader, "input_dim": input_dim, "n_train_batches": len(train_dataloader), "n_test_batches": len(test_dataloader), "classif_per_tp": False, # optional "n_labels": 1} # optional return data_objects def irregularly_sampled_data_gen(n=10, length=20, seed=0): np.random.seed(seed) obs_values, ground_truth, obs_times = [], [], [] for i in range(n): t1 = np.sort(np.random.uniform(low=0.0, high=1.0, size=length)) t2 = np.sort(np.random.uniform(low=0.0, high=1.0, size=length)) t3 = np.sort(np.random.uniform(low=0.0, high=1.0, size=length)) a = 10 * np.random.randn() b = 10 * np.random.rand() f1 = .8 * np.sin(20*(t1+a) + np.sin(20*(t1+a))) + \ 0.01 * np.random.randn() f2 = -.5 * np.sin(20*(t2+a + 20) + np.sin(20*(t2+a + 20)) ) + 0.01 * np.random.randn() f3 = np.sin(12*(t3+b)) + 0.01 * np.random.randn() obs_times.append(np.stack((t1, t2, t3), axis=0)) obs_values.append(np.stack((f1, f2, f3), axis=0)) t = np.linspace(0, 1, 100) fg1 = .8 * np.sin(20*(t+a) + np.sin(20*(t+a))) fg2 = -.5 * np.sin(20*(t+a + 20) + np.sin(20*(t+a + 20))) fg3 = np.sin(12*(t+b)) ground_truth.append(np.stack((fg1, fg2, fg3), axis=0)) return obs_values, ground_truth, obs_times def sine_wave_data_gen(args, seed=0): np.random.seed(seed) obs_values, ground_truth, obs_times = [], [], [] for _ in range(args.n): t = np.sort(np.random.choice(np.linspace( 0, 1., 101), size=args.length, replace=True)) b = 10 * np.random.rand() f = np.sin(12*(t+b)) + 0.1 * np.random.randn() obs_times.append(t) obs_values.append(f) tc = np.linspace(0, 1, 100) fg = np.sin(12*(tc + b)) ground_truth.append(fg) obs_values = np.array(obs_values) obs_times = np.array(obs_times) ground_truth = np.array(ground_truth) print(obs_values.shape, obs_times.shape, ground_truth.shape) mask = np.ones_like(obs_values) combined_data = np.concatenate((np.expand_dims(obs_values, axis=2), np.expand_dims( mask, axis=2), np.expand_dims(obs_times, axis=2)), axis=2) print(combined_data.shape) print(combined_data[0]) train_data, test_data = model_selection.train_test_split(combined_data, train_size=0.8, random_state=42, shuffle=True) print(train_data.shape, test_data.shape) train_dataloader = DataLoader(torch.from_numpy( train_data).float(), batch_size=args.batch_size, shuffle=False) test_dataloader = DataLoader(torch.from_numpy( test_data).float(), batch_size=args.batch_size, shuffle=False) data_objects = {"dataset_obj": combined_data, "train_dataloader": train_dataloader, "test_dataloader": test_dataloader, "input_dim": 1, "ground_truth": np.array(ground_truth)} return data_objects def kernel_smoother_data_gen(args, alpha=100., seed=0, ref_points=10): np.random.seed(seed) obs_values, ground_truth, obs_times = [], [], [] for _ in range(args.n): key_values = np.random.randn(ref_points) key_points = np.linspace(0, 1, ref_points) query_points = np.sort(np.random.choice( np.linspace(0, 1., 101), size=args.length, replace=True)) weights = np.exp(-alpha*(np.expand_dims(query_points, 1) -

np.expand_dims(key_points, 0)

numpy.expand_dims

from pixell import enmap, curvedsky, fft as enfft, sharp, wcsutils from enlib import array_ops, bench from soapack import interfaces as sints from optweight import alm_c_utils import numpy as np from scipy.interpolate import interp1d, RectBivariateSpline from scipy import ndimage import numba import healpy as hp from astropy.io import fits import yaml import pkgutil from concurrent import futures import multiprocessing import os import hashlib # Utility functions to support tiling classes and functions. Just keeping code organized so I don't get whelmed. # copied from soapack.interfaces def config_from_yaml_file(filename): """Returns a dictionary from a yaml file given by absolute filename. """ with open(filename) as f: config = yaml.safe_load(f) return config def config_from_yaml_resource(resource): """Returns a dictionary from a yaml file given by the resource name (relative to tacos package). """ f = pkgutil.get_data('mnms', resource).decode() config = yaml.safe_load(f) return config def get_default_data_model(): """Returns a soapack.interfaces.DataModel instance depending on the name of the data model specified in the users's soapack config 'mnms' block, under key 'default_data_model'. Returns ------- soapack.interfaces.DataModel instance An object used for loading raw data from disk. """ config = sints.dconfig['mnms'] dm_name = config['default_data_model'] # capitalize all chars except v3 dm_name = ''.join( [dm_name[i].upper() if dm_name[i] != 'v' else 'v' for i in range(len(dm_name))] ) return getattr(sints, dm_name)() def get_default_mask_version(): """Returns the mask version (string) depending on what is specified in the user's soapack config. If the 'mnms' block has a key 'default_mask_version', return the value of that key. If not, return the 'default_mask_version' from the user's default data model block. Returns ------- str A default mask version string to help find an analysis mask. With no arguments, a NoiseModel constructor will use this mask version to seek the analysis mask in directory mask_path/mask_version. """ config = sints.dconfig['mnms'] try: mask_version = config['default_mask_version'] except KeyError: dm_name = config['default_data_model'].lower() config = sints.dconfig[dm_name] mask_version = config['default_mask_version'] return mask_version def get_nsplits_by_qid(qid, data_model): """Get the number of splits in the raw data corresponding to this array 'qid'""" return int(data_model.adf[data_model.adf['#qid']==qid]['nsplits']) def slice_geometry_by_pixbox(ishape, iwcs, pixbox): pb = np.asarray(pixbox) return enmap.slice_geometry( ishape[-2:], iwcs, (slice(*pb[:, -2]), slice(*pb[:, -1])), nowrap=True ) def slice_geometry_by_geometry(ishape, iwcs, oshape, owcs): pb = enmap.pixbox_of(iwcs, oshape, owcs) return enmap.slice_geometry( ishape[-2:], iwcs, (slice(*pb[:, -2]), slice(*pb[:, -1])), nowrap=True ) # users must be careful with indices shape. it gets promoted to 2D array by # prepending a dimension if necessary. afterward, indices[i] (ie, indexing along # axis=0 of 2D indices array) corresponds to axis[i] def get_take_indexing_obj(arr, indices, axis=None): if axis is not None: shape = arr.shape slicelist = [slice(dim) for dim in shape] axis = np.atleast_1d(axis) indices = atleast_nd(indices, 2) for i in range(len(axis)): slicelist[axis[i]] = tuple(indices[i]) return tuple(slicelist) else: return np.array(indices) # this is like enmap.partial_flatten except you give the axes you *want* to flatten def flatten_axis(imap, axis=None, pos=0): if axis is None: return imap.reshape(-1) else: imap = np.moveaxis(imap, axis, range(len(axis))) # put the axes to be flattened in front imap = imap.reshape((-1,) + imap.shape[len(axis):]) # flatten the front dimensions imap = np.moveaxis(imap, 0, pos) # put front dimension in pos return imap # this is like enmap.partial_expand except you give the axes you *want* to restore # shape is the shape of the restored axes or the full original array with restored axes def unflatten_axis(imap, shape, axis=None, pos=0): if axis is None: return imap else: imap = np.moveaxis(imap, pos, 0) # put pos in front dimension shape = np.atleast_1d(shape) axis = np.atleast_1d(axis) if len(shape) == len(axis): shape = tuple(shape) elif len(shape) == len(axis) + len(imap.shape[1:]): shape = tuple(shape[axis]) else: raise ValueError('Shape arg must either be the same length as axis or the restored array shape') imap = imap.reshape(shape + imap.shape[1:]) # expand the front dimension into restored shape imap = np.moveaxis(imap, range(len(axis)), axis) # put the front restored axes into correct positions return imap def atleast_nd(arr, n, axis=None): arr = np.asanyarray(arr) if (axis is None) or (arr.ndim >= n): oaxis=tuple(range(n - arr.ndim)) # prepend the dims or do nothing in n < arr.ndim else: axis = np.atleast_1d(axis) assert (n - arr.ndim) >= len(axis), 'More axes than dimensions to add' oaxis = tuple(range(n - arr.ndim - len(axis))) + tuple(axis) # prepend the extra dims return np.expand_dims(arr, oaxis) def triu_indices(N): """Gives the upper triangular indices of an NxN matrix in diagonal-major order (compatible with healpy spectra ordering with new=True) Parameters ---------- N : int Side-length of matrix Returns ------- tuple of ndarray A row array and column array of ints, such that if arr is NxN, then arr[triu_indices(N)] gives the 1D elements of the upper triangle of arr in diagonal-major order. """ num_idxs = N*(N+1)//2 rows = [] cols = [] rowoffset = 0 coloffset = 0 for i in range(num_idxs): rownum = i - rowoffset colnum = rownum + coloffset rows.append(rownum) cols.append(colnum) if colnum >= N-1: # N-1 is last column rowoffset = i+1 # restart rows next iteration coloffset += 1 # cols start one higher return np.array(rows), np.array(cols) def triu_indices_1d(N): idxs = np.arange(N**2).reshape(N, N) return idxs[triu_indices(N)] def is_triangular(i): return np.roots((1, 1, -2*i))[1].is_integer() def triangular(N): return N*(N+1)//2 def triangular_idx(i): assert is_triangular(i), 'Arg must be a triangular number' return np.roots((1, 1, -2*i)).astype(int)[1] def triu_pos(i, N): return np.stack(triu_indices(N))[:, i] def triu_to_symm(arr, copy=False, axis1=0, axis2=1): assert arr.shape[axis1] == arr.shape[axis2], 'Axes of matrix must at axis1, axis2 dimension must have same size' if copy: arr = arr.copy() ncomp = arr.shape[axis1] for i in range(ncomp): for j in range(i+1, ncomp): # i+1 to only do upper triangle, not diagonal getslice = get_take_indexing_obj(arr, [[i], [j]], axis=(axis1, axis2)) setslice = get_take_indexing_obj(arr, [[j], [i]], axis=(axis1, axis2)) arr[setslice] = arr[getslice] return arr # we can collapse a symmetric 2D matrix whose first axis is passed as the arg # into a 1D list of the upper triangular components. # this will clobber if axis1 or axis2 is in map axes, see __array_wrap__ def to_flat_triu(arr, axis1=0, axis2=None, flat_triu_axis=None): # force axes to be positive axis1 = axis1%arr.ndim if axis2 is None: axis2 = axis1+1 axis2 = axis2%arr.ndim assert axis1 < axis2 assert arr.shape[axis1] == arr.shape[axis2], 'Axes of matrix at axis1, axis2 dimension must have same size' ncomp = arr.shape[axis1] # put flattened dims at flat_triu_axis position if flat_triu_axis is None: flat_triu_axis = axis1 arr = flatten_axis(arr, axis=(axis1, axis2), pos=flat_triu_axis) # get triangular indices for axis to be expanded slice_obj = get_take_indexing_obj(arr, triu_indices_1d(ncomp), axis=flat_triu_axis) return arr[slice_obj] # this clobbers ndarray subclasses because of call to append! # axis2 is position in expanded array, while flat_triu_axis is position in passed array def from_flat_triu(arr, axis1=0, axis2=None, flat_triu_axis=None): if flat_triu_axis is None: flat_triu_axis = axis1 # force axes to be positive axis1 = axis1%arr.ndim if axis2 is None: axis2 = axis1+1 axis2 = axis2%(arr.ndim+1) assert axis1 < axis2 assert is_triangular(arr.shape[flat_triu_axis]), 'flat_triu_axis length must be a triangular number' # place in new array with ncomp**2 instead of n_triu at flat_triu_axis ncomp = triangular_idx(arr.shape[flat_triu_axis]) ishape = list(arr.shape) ishape[flat_triu_axis] = ncomp**2 - triangular(ncomp) # this will be concatenated to arr arr = np.append(arr, np.zeros(ishape), axis=flat_triu_axis).astype(arr.dtype) # get triangular indices for axis to be expanded getslice = get_take_indexing_obj(arr, np.arange(triangular(ncomp)), axis=flat_triu_axis) setslice = get_take_indexing_obj(arr, triu_indices_1d(ncomp), axis=flat_triu_axis) arr[setslice] = arr[getslice] # reshape into correct shape arr = unflatten_axis(arr, (ncomp, ncomp), axis=(axis1, axis2), pos=flat_triu_axis) return triu_to_symm(arr, axis1=axis1, axis2=axis2) # get a logical mask that can be used to index an array, eg to build a mask # out of conditions. # if not keep_prepend_dims, perform op over all dims up to map dims; else # perform over specified axes def get_logical_mask(cond, op=np.logical_or, keep_prepend_dims=False, axis=0): if not keep_prepend_dims: axis = tuple(range(cond.ndim - 2)) return op.reduce(cond, axis=axis) def get_coadd_map(imap, ivar): """Return sum(imap[i]*ivar[i]) / sum(ivar[i]), where each sum is over splits. Parameters ---------- imap : (..., nsplit, 3, ny, nx) ndmap Data maps for N splits. ivar : (..., nsplit, 1, ny, nx) ndmap Inverse variance maps for N splits. Returns ------- coadd : (..., 1, 3, ny, nx) ndmap Inverse variance weighted coadd over splits, with singleton dimension in split axis. """ if hasattr(imap, 'wcs'): is_enmap = True wcs = imap.wcs else: is_enmap = False imap = atleast_nd(imap, 4) # make 4d by prepending ivar = atleast_nd(ivar, 4) # due to floating point precision, the coadd is not exactly the same # as a split where that split is the only non-zero ivar in that pixel num = np.sum(imap * ivar, axis=-4, keepdims=True) den = np.sum(ivar, axis=-4, keepdims=True) mask = den != 0 coadd = np.zeros_like(num) np.divide(num, den, where=mask, out=coadd) # find pixels where exactly one split has a nonzero ivar single_nonzero_ivar_mask = np.sum(ivar!=0, axis=-4, keepdims=True) == 1 # set the coadd in those pixels to be equal to the imap value of that split (ie, avoid floating # point errors in naive coadd calculation) single_nonzero_fill = np.sum(imap * (ivar!=0), axis=-4, where=single_nonzero_ivar_mask, keepdims=True) coadd = np.where(single_nonzero_ivar_mask, single_nonzero_fill, coadd) if is_enmap: coadd = enmap.ndmap(coadd, wcs) return coadd def get_ivar_eff(ivar, use_inf=False): """ Return ivar_eff = 1 / (1 / ivar - 1 / sum_ivar), where sum_ivar is the sum over splits. Parameters ---------- ivar : (..., nsplit, 1, ny, nx) ndmap Inverse variance maps for N splits. use_inf : bool, optional If set, use np.inf for values that approach infinity, instead of large numerical values. Returns ------- ivar_eff : (..., nsplit, 1, ny, nx) enmap Ivar_eff for each split. """ # Make 4d by prepending splits along -4 axis. ivar = atleast_nd(ivar, 4) # We want to calculate 1 / (1/ivar - 1/sum(ivar). It easier to do # ivar * sum(ivar) / (sum(ivar) - ivar) to avoid (some) divisions by zero. sum_ivar = np.sum(ivar, axis=-4, keepdims=True) num = sum_ivar * ivar # Numerator. den = sum_ivar - ivar # Denominator. # In pixels were ivar == sum_ivar we get inf. mask = den != 0 out = np.divide(num, den, where=mask, out=num) if use_inf: out[~mask] = np.inf else: # Fill with largest value allowed by dtype to mimic np.nan_to_num. out[~mask] = np.finfo(out.dtype).max return out def get_corr_fact(ivar): ''' Get correction factor sqrt(ivar_eff / ivar) that converts a draw from split difference d_i to a draw from split noise n_i. Parameters ---------- ivar : (..., nsplit, 1, ny, nx) enmap Inverse variance maps for N splits. Returns ------- corr_fact : (..., nsplit, 1, ny, nx) enmap Correction factor for each split. ''' corr_fact = get_ivar_eff(ivar, use_inf=True) corr_fact[~np.isfinite(corr_fact)] = 0 np.divide(corr_fact, ivar, out=corr_fact, where=ivar!=0) corr_fact[ivar==0] = 0 corr_fact **= 0.5 return corr_fact def get_noise_map(imap, ivar): return imap - get_coadd_map(imap, ivar) def get_whitened_noise_map(imap, ivar): return get_noise_map(imap, ivar)*np.sqrt(get_ivar_eff(ivar)) def rolling_average(x, N): cumsum = np.cumsum(np.insert(x, 0, 0)) return (cumsum[N:] - cumsum[:-N]) / float(N) def linear_crossfade(cNy,cNx,npix_y,npix_x=None, dtype=np.float32): if npix_x is None: npix_x = npix_y fys =

np.ones(cNy, dtype=dtype)

numpy.ones

import numpy as np a = np.array([1, 3, 6, 9, -3, -6]) b = np.array([1, 2, 3, 40, -9, 5]) # 1. print(np.add(a, b)) # 2. print(np.subtract(a, b)) # 3. print(np.multiply(a, b)) # 4. print(np.divide(a, b)) # 5. print(np.mod(a, b)) # 6. print(np.remainder(a, b)) # 7. print(np.divmod(a, b)) # 8. print(np.absolute(a)) print(np.absolute(b)) # 9. a =

np.array([1, 3, 6, 9, 3, 6])

numpy.array

import numpy as np import pytest from snc.environments.job_generators.discrete_review_job_generator \ import DeterministicDiscreteReviewJobGenerator as drjg from snc.environments.job_generators.discrete_review_job_generator \ import PoissonDiscreteReviewJobGenerator as prjg from snc.environments.controlled_random_walk import ControlledRandomWalk import snc.utils.snc_tools as snc import snc.environments.state_initialiser as stinit import snc.environments.examples as examples import \ snc.environments.examples_distribution_with_rebalancing as examples_distribution_with_rebalancing from snc.environments.job_generators.scaled_bernoulli_services_poisson_arrivals_generator import \ ScaledBernoulliServicesPoissonArrivalsGenerator from snc.environments.state_initialiser import DeterministicCRWStateInitialiser def test_is_binary(): c = np.ones((1, 1)) assert (snc.is_binary(c)) c = np.zeros((1, 1)) assert (snc.is_binary(c)) c = np.ones((5, 4)) assert (snc.is_binary(c)) c = np.zeros((5, 4)) assert (snc.is_binary(c)) c = np.random.randint(0, 1, (3, 6)) assert (snc.is_binary(c)) c = [] assert (not snc.is_binary(c)) c = np.random.random_sample((3, 5)) c[0] = 1 assert (not snc.is_binary(c)) def test_index_phys_resources_with_negative_values(): index_phys_resources = (-1, 0) # Other needed parameters cost_per_buffer = np.zeros((2, 1)) demand_rate = np.zeros((2, 1)) initial_state = np.zeros((2, 1)) capacity = np.zeros((2, 1)) constituency_mat = np.eye(2) buffer_processing_mat = np.eye(2) job_generator = drjg(demand_rate, buffer_processing_mat, sim_time_interval=1) s0 = stinit.DeterministicCRWStateInitialiser(initial_state) with pytest.raises(AssertionError): ControlledRandomWalk(cost_per_buffer, capacity, constituency_mat, job_generator, s0, index_phys_resources=index_phys_resources) def test_index_phys_resources_with_index_higher_than_num_resources(): index_phys_resources = (0, 2) # Other needed parameters cost_per_buffer = np.zeros((2, 1)) demand_rate = np.zeros((2, 1)) initial_state = np.zeros((2, 1)) capacity = np.zeros((2, 1)) constituency_mat = np.eye(2) buffer_processing_mat = np.eye(2) job_generator = drjg(demand_rate, buffer_processing_mat, sim_time_interval=1) s0 = stinit.DeterministicCRWStateInitialiser(initial_state) with pytest.raises(AssertionError): ControlledRandomWalk(cost_per_buffer, capacity, constituency_mat, job_generator, s0, index_phys_resources=index_phys_resources) def test_index_phys_resources_with_repeated_indexes(): index_phys_resources = (0, 0) # Other needed parameters cost_per_buffer = np.zeros((2, 1)) demand_rate = np.zeros((2, 1)) initial_state = np.zeros((2, 1)) capacity = np.zeros((2, 1)) constituency_mat = np.eye(2) buffer_processing_mat = np.eye(2) job_generator = drjg(demand_rate, buffer_processing_mat, sim_time_interval=1) s0 = stinit.DeterministicCRWStateInitialiser(initial_state) with pytest.raises(AssertionError): ControlledRandomWalk(cost_per_buffer, capacity, constituency_mat, job_generator, s0, index_phys_resources=index_phys_resources) def test_valid_index_phys_resources_1(): index_phys_resources = (0,) # Other needed parameters cost_per_buffer = np.zeros((2, 1)) demand_rate = np.zeros((2, 1)) initial_state = np.zeros((2, 1)) capacity = np.zeros((2, 1)) constituency_mat = np.eye(2) buffer_processing_mat = np.eye(2) job_generator = drjg(demand_rate, buffer_processing_mat, sim_time_interval=1) s0 = stinit.DeterministicCRWStateInitialiser(initial_state) env = ControlledRandomWalk(cost_per_buffer, capacity, constituency_mat, job_generator, s0, index_phys_resources=index_phys_resources) assert env.index_phys_resources == index_phys_resources def test_valid_index_phys_resources_1_2(): index_phys_resources = (0, 1) # Other needed parameters cost_per_buffer = np.zeros((2, 1)) demand_rate = np.zeros((2, 1)) initial_state = np.zeros((2, 1)) capacity = np.zeros((2, 1)) constituency_mat = np.eye(2) buffer_processing_mat = np.eye(2) job_generator = drjg(demand_rate, buffer_processing_mat, sim_time_interval=1) s0 = stinit.DeterministicCRWStateInitialiser(initial_state) env = ControlledRandomWalk(cost_per_buffer, capacity, constituency_mat, job_generator, s0, index_phys_resources=index_phys_resources) assert env.index_phys_resources == index_phys_resources def test_state_initialiser_uniform(): num_buffers = 5 capacity = 10 s0 = stinit.UniformRandomCRWStateInitialiser(num_buffers, capacity) init_state = np.zeros((num_buffers, 1)) num_samples = 100000 for i in range(num_samples): init_state += s0.get_initial_state() init_state /= num_samples np.all(np.isclose(init_state, ((capacity - 1) / 2) * np.ones((num_buffers, 1)))) def test_state_initialiser_deterministic(): initial_state = np.array([2, 3, 4, 5])[:, None] s0 = stinit.DeterministicCRWStateInitialiser(initial_state) assert np.all(s0.get_initial_state() == initial_state) def test_unfeasible_action(): """Check that the step method doesn't allow actions that violate the one action per resource constraint: C u <= 1.""" np.random.seed(42) env = examples.double_reentrant_line_only_shared_resources_model(alpha=0) env.reset_with_random_state(42) action =

np.array([[1], [0], [0], [1]])

numpy.array

import cv2 import matplotlib.pyplot as plt import numpy as np kernel =

np.ones((5, 5), np.uint8)

numpy.ones

import numpy as np from typing import * from numpy.typing import ArrayLike from scipy.spatial import Delaunay from tallem.utility import ask_package_install, package_exists def flywing(): ''' Fly wings example (Klingenberg, 2015 | https://en.wikipedia.org/wiki/Procrustes_analysis) ''' arr1 = np.array([[588.0, 443.0], [178.0, 443.0], [56.0, 436.0], [50.0, 376.0], [129.0, 360.0], [15.0, 342.0], [92.0, 293.0], [79.0, 269.0], [276.0, 295.0], [281.0, 331.0], [785.0, 260.0], [754.0, 174.0], [405.0, 233.0], [386.0, 167.0], [466.0, 59.0]]) arr2 = np.array([[477.0, 557.0], [130.129, 374.307], [52.0, 334.0], [67.662, 306.953], [111.916, 323.0], [55.119, 275.854], [107.935, 277.723], [101.899, 259.73], [175.0, 329.0], [171.0, 345.0], [589.0, 527.0], [591.0, 468.0], [299.0, 363.0], [306.0, 317.0], [406.0, 288.0]]) return([arr1, arr2]) def gaussian_blob(n_pixels: int, r: float): ''' Generates a closure which, given a 2D location *mu=(x,y)*, generates a white blob with [normalized] radius 0 < r <= 1 in a (n_pixels x n_pixels) image. If *mu* is in [0,1] x [0,1], the center of the white blob should be visible If *mu* has as both of its coordinates outside of [0,1]x[0,1], the blob may be partially visible If *mu* has both of its coordinates outside of [-r, 1+r]x[-r, 1+r], then image should be essentially black The returned closure completely autograd's numpy wrapper to do the image generation. Thus, the resulting function can be differentiated (w.r.t *mu*) using the reverse-mode differentiation process that *autograd* provides. This function also returns the global normalizing constant needed normalize the pixel intensities in [0,1], for plotting or other purposes. Return: (blob, c) where - blob := differentiable closure which, given a vector (x,y), generates the blob image a flat vector. - c := maximum value of the intensity of any given pixel for any choice of *mu*. ''' import autograd.numpy as auto_np sd = r/3.090232 sigma = sd**2 sigma_inv = 1.0/sigma denom = np.sqrt(((2*auto_np.pi)**2) * (sigma**2)) def blob(mu): # mu can be anywhere; center of image is [0.5, 0.5] loc = auto_np.linspace(0, 1, n_pixels, False) + 1/(2*n_pixels) x,y = auto_np.meshgrid(loc, loc) grid = auto_np.exp(-0.5*(sigma_inv * ((x-mu[0])**2 + (y-mu[1])**2)))/denom return(auto_np.ravel(grid).flatten()) return(blob, auto_np.exp(0)/denom) def plot_image(P, figsize=(8,8), max_val = "default"): if max_val == "default": max_val = np.max(P) import matplotlib.pyplot as plt fig = plt.figure(figsize=figsize) plt.imshow(P, cmap='gray', vmin=0, vmax=max_val) fig.gca().axes.get_xaxis().set_visible(False) fig.gca().axes.get_yaxis().set_visible(False) def plot_images(P, shape, max_val = "default", figsize=(8,8), layout = None): ''' P := numpy array where each row is a grayscale image shape := the shape to reshape each row of P prior to plotting ''' import matplotlib.pyplot as plt if max_val == "default": max_val = np.max(P) if P.ndim == 1: fig = plt.figure(figsize=figsize) plt.imshow(P.reshape(shape), cmap='gray', vmin=0, vmax=max_val) fig.gca().axes.get_xaxis().set_visible(False) fig.gca().axes.get_yaxis().set_visible(False) return(fig, ax) else: assert layout is not None, "missing layout" fig, axs = plt.subplots(*layout, figsize=figsize) axs = axs.flatten() for i, (img, ax) in enumerate(zip(P, axs)): #fig.add_subplot(layout[0], layout[1], i+1) plt.axis("off") ax.imshow(P[i,:].reshape(shape), cmap='gray', vmin=0, vmax=max_val, aspect='auto') ax.axes.xaxis.set_visible(False) ax.axes.yaxis.set_visible(False) plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.1, hspace=0.1) return(fig, axs) def scatter2D(P, layout = None, figsize=(8,8), **kwargs): import matplotlib.pyplot as plt if isinstance(P, np.ndarray): if "fig" in kwargs.keys() and "ax" in kwargs.keys(): fig, ax = kwargs["fig"], kwargs["ax"] kwargs.pop('fig', None) kwargs.pop('ax', None) else: fig = plt.figure(figsize=figsize) ax = fig.add_subplot() ax.scatter(*P.T, **kwargs) return(fig, ax) elif isinstance(P, Iterable): assert layout is not None, "missing layout" assert len(P) == np.prod(layout) if "fig" in kwargs.keys() and "ax" in kwargs.keys(): fig, ax = kwargs["fig"], kwargs["ax"] kwargs.pop('fig', None) else: fig = plt.figure(figsize=figsize) for i, p in enumerate(P): ax = fig.add_subplot(layout[0], layout[1], i+1) ax.scatter(*p.T, **kwargs) return(fig, ax) def scatter3D(P, angles = None, layout = None, figsize=(8,8), **kwargs): import matplotlib.pyplot as plt if isinstance(P, np.ndarray): import numbers if angles is not None: if isinstance(angles, numbers.Integral): angles = np.linspace(0, 360, angles, endpoint=False) assert len(angles) == np.prod(layout) if "fig" in kwargs.keys() and "ax" in kwargs.keys(): fig, ax = kwargs["fig"], kwargs["ax"] kwargs.pop('fig', None) kwargs.pop('ax', None) else: fig, ax = plt.subplots(*layout, figsize=figsize) for i, theta in enumerate(angles): ax = fig.add_subplot(layout[0], layout[1], i+1, projection='3d') ax.scatter3D(*P.T, **kwargs) ax.view_init(30, theta) else: if "fig" in kwargs.keys() and "ax" in kwargs.keys(): fig, ax = kwargs["fig"], kwargs["ax"] kwargs.pop('fig', None) kwargs.pop('ax', None) else: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(projection='3d') ax.scatter3D(*P.T, **kwargs) elif isinstance(P, Iterable): import numbers assert layout is not None, "missing layout" if angles is None: angles = np.repeat(60, len(P)) elif isinstance(angles, numbers.Integral): angles = np.linspace(0, 2*np.pi, len(P), endpoint=False) assert len(angles) == np.prod(layout) if "fig" in kwargs.keys() and "ax" in kwargs.keys(): fig, ax = kwargs["fig"], kwargs["ax"] kwargs.pop('fig', None) kwargs.pop('ax', None) else: fig, ax = plt.subplots(*layout, figsize=figsize) for i, p in enumerate(P): ax = fig.add_subplot(layout[0], layout[1], i+1, projection='3d') ax.scatter3D(*p.T, **kwargs) ax.view_init(30, angles[i]) plt.setp(plt.gcf().get_axes(), xticks=[], yticks=[]); return(fig, ax) def rotating_disk(n_pixels: int, r: float, sigma: float = 1.0): from scipy.ndimage import gaussian_filter import numpy as np I = np.zeros(shape=(n_pixels, n_pixels)) p = np.linspace(0, 1, n_pixels, False) + 1/(2*n_pixels) # center locations of pixels, in normalized space z = np.array([r, 0.0]).reshape((2,1)) d = np.array([0.5, 0.5]).reshape((2,1)) x,y = np.meshgrid(p, p) def disk_image(theta: float): R = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) c = (R @ z) + d # center of disk in [0,1]^2 D = np.flipud(np.sqrt((x - c[0])**2 + (y - c[1])**2)) D[D <= r] = -1.0 D[D > r] = 0.0 D[D == -1.0] = 1.0 return(np.ravel(gaussian_filter(D, sigma=1.0)).flatten()) return(disk_image, 1.0) # D = np.zeros(np.prod(x.shape)) # for i, (xi,yi) in enumerate(zip(x.flatten(),y.flatten())): # p = np.array([xi,yi]) # D[i] = np.dot(p-b, u)# np.linalg.norm(z-v) def white_bars(n_pixels: int, r: float, sigma: float = 1.0): ''' Returns a parameterization that yields a white vertical bar at various orientations in an image. Fixed parameters: n_pixels := number of pixels to make square image r := constant between [0,1] indicating how wide to make the bar sigma := kernel parameter for gaussian blur Returns: bar := closure w/ parameters y_offset in [0, 1] and theta in [0, pi] c := normalizing constant for plotting ''' from scipy.ndimage import gaussian_filter import numpy as np w = r p = np.linspace(0, 1, n_pixels, False) + 1/(2*n_pixels) # center locations of pixels, in normalized space x,y = np.meshgrid(p,p) c = np.array([0.5, 0.5]) # center of image def bar(theta: float, d: float): assert np.all(np.bitwise_and(d >= -1.0, d <= 1.0)), "d must be in the range [-1, 1]" assert np.all(np.bitwise_and(theta >= 0.0, theta <= np.pi)), "theta must be in the range [0, pi]" u = np.array([ 1.0, np.tan(theta) ]) u = u / np.linalg.norm(u) c = np.array([0.5, 0.5]) # center of image d = d * (np.sqrt(2) / 2) # scale where to place center of bar if theta > np.pi/2: d = -d b = c + d*u # center of bar D = np.zeros(np.prod(x.shape)) for i, (xi,yi) in enumerate(zip(x.flatten(),y.flatten())): p = np.array([xi,yi]) D[i] = np.dot(p-b, u)# np.linalg.norm(z-v) I = abs(D.reshape((n_pixels, n_pixels))).T I[I > w] = 1 I = 1.0 - I return(gaussian_filter(I, sigma=sigma)) c = np.max(bar(0.0, 0.0)) return(bar, c) # u = np.array([ 1.0, np.tan(theta) ]) # u = u / np.linalg.norm(u) # d = np.array([-di if ti <= np.pi/2 else di for di,ti in zip(d, theta)])*(np.sqrt(2) / 2) # U = np.c_[np.repeat(1.0, len(theta)), theta] # U = U / np.linalg.norm(U, axis = 1, keepdims = True) # B = c + d.reshape((len(d), 1)) * U # center of bars # D = [abs((x - b[0])*u[0] + (y - b[1])*u[1]).T for (u, b) in zip(U, B)] # # b = c + d*u # center of bar # # D = (x - b[0])*u[0] + (y - b[1])*u[1] # # I = abs(D.reshape((n_pixels, n_pixels))).T # images = np.zeros((B.shape[0], n_pixels**2)) # for i, img in enumerate(D): # img[img > w] = 1 # img = 1.0 - img # images[i,:] = np.ravel(gaussian_filter(img, sigma=sigma).flatten()) # return(images) # from scipy.ndimage import gaussian_filter # import numpy as np # w = r*np.sqrt(2) # p = np.linspace(0, 1, n_pixels, False) + 1/(2*n_pixels) # center locations of pixels, in normalized space # x,y = np.meshgrid(p,p) # def bar(y_offset: float, theta: float): # assert y_offset >= 0.0 and y_offset <= 1.0 # assert theta >= 0.0 and theta <= np.pi # # z = np.array([0.5, y_offset]) # intercept # # dist_to_line = np.cos(theta)*(z[1] - y) - np.sin(theta)*(z[0]-x) # # dist_to_line = ((y - y_offset)/np.tan(theta))*np.sin(theta) # # Z = np.array([np.array([xi,yi]) for xi,yi in zip(x.flatten(),y.flatten())]) # # fig,ax = scatter2D(Z, c="blue") # # fig,ax = scatter2D(np.array(P), c="red", fig=fig, ax=ax) # # fig,ax = scatter2D(np.array([0.5, 0.5]), c="green", fig=fig, ax=ax) # # fig,ax = scatter2D(np.c_[x.flatten(), x.flatten()*m + b], c="purple", fig=fig, ax=ax) # m, b = np.tan(theta), y_offset # #pt = np.array([1.0, m + b]) # z1 = np.array([0.50, b]) # z2 = np.array([1.0, 1.0*m + b]) # pt = z2 - z1 # d = [] # P = [] # for xi,yi in zip(x.flatten(),y.flatten()): # u = pt / np.linalg.norm(pt) # v = np.array([xi,yi]) # z = u*np.dot(v-np.array([0.5, b]), u)+np.array([0.5, b]) # d.append(np.linalg.norm(z-v)) # P.append(z) # dist_to_line = np.array(d) # # fig, ax = scatter2D(np.array(P)) # I = abs(dist_to_line.reshape((n_pixels, n_pixels))) # I = np.flipud(I) # make origin lower-left, not top-left # # I = (np.sqrt(2)/2)*(I/np.max(I)) # I[I > w] = np.sqrt(2) # I = np.sqrt(2) - I ## invert intensity # # I[I < (np.sqrt(2) - w)] = 0.0 # # B = I.copy() # # I[I <= w] = -1.0 # # I[I > w] = 0.0 # # I[I == -1.0] = np.max(B[B <= w]) - B[B <= w] # 1.0 # return(gaussian_filter(I, sigma=sigma)) # c = np.max(bar(0.0, 0.0)) # return(bar, c) # def _gaussian_pixel(d, n_pixels): # from scipy.stats import norm # sigma = d/3.0 # Sigma = auto_np.diag([sigma, sigma]) # sigma_inv = auto_np.linalg.inv(Sigma)[0,0] # denom = np.sqrt(((2*np.pi)**2) * auto_np.linalg.det(Sigma)) # normal_constant = norm.pdf(0, loc=0, scale=sigma) # def blob(mu): # generates blob at location mu # # mu = mu.reshape((2, 1)) # # np.exp(-0.5 * ((x - mu).T @ SigmaI @ (x - mu))).flatten() # #x, y = auto_np.meshgrid(auto_np.arange(n_pixels), auto_np.arange(n_pixels)) # loc = auto_np.linspace(0, 1, n_pixels, False) + (1/(2*n_pixels)) # x,y = auto_np.meshgrid(loc, loc) # grid = auto_np.exp(-0.5*(sigma_inv * ((x-mu[0])**2 + (y-mu[1])**2)))/denom # #grid = auto_np.exp(-0.5*((x - mu[0])**2 + (y - mu[1])**2))/denom # #return(auto_np.ravel(grid).flatten()) # return(grid/normal_constant) # return(blob) # plot_image(gaussian_pixel2(1/32, 11)([-0.5, 0.5])) def white_dot(n_pixels: int, r: float, n: Optional[int], method: Optional[str] = "grid", mu: Optional[ArrayLike] = None): ''' Generates a grayscale image data set where white blobs are placed on a (n_pixels x n_pixels) grid using a multivariate normal density whose standard deviation sigma (in both directions) is sigma=d/3. If 'n' is specified, then 'n' samples are generated from a larger space s([-d, 1+d]^2) where s(*) denotes the scaling of the interval [-d,1+d] by 'n_pixels'. Parameters: n_pixels := number of pixels wide/tall to make the resulting images r := relative radius of dot (in (0, 1]) n := (optional) number of samples desired method := (optional) how to generate samples in the parameter space. Can be either "grid" or "random". mu := (optional) locations of dot centers to generate the dots at Returns: samples := generated image samples params := (x,y,i) parameters associated with each sample, f := closure for generating more samples. See gaussian_blob() for more details. c := normalizing constant. See gaussian_blob() for more details. ''' assert r > 0 and r <= 1.0, "r must be in the range 0 < r <= 1.0" assert isinstance(n, int) or isinstance(n, tuple), "n must be integer of tuple of integers" ask_package_install("autograd") import numpy as np import autograd.numpy as auto_np ## First generate the closure to make the images blob, c = gaussian_blob(n_pixels, r) if not(mu is None): samples = np.vstack([blob(auto_np.array([x,y])) for x,y in mu]) params = mu elif method == "random": ## Generate uniformly random locations (in domain) assert n is not None, "'n' must be supplied if 'mu' is not." n1, n2 = (n, n) if isinstance(n, int) else (n[0], n[1]) samples, params = [], [] X, Y = np.random.uniform(size=n1,low=-r,high=1+r), np.random.uniform(size=n1,low=-r,high=1+r) for x,y in zip(X, Y): samples.append(blob(auto_np.array([x,y]))) params.append([x, y, 1.0]) NP = blob(auto_np.array([0.5, 0.5])) for t in np.random.uniform(size=n2, low=0.0, high=1.0): samples.append(t*NP) params.append([0.5, 0.5, 1-t]) ## Vertically stack samples, params = np.vstack(samples), np.vstack(params) elif method == "grid": assert n is not None, "'n' must be supplied if 'mu' is not." if isinstance(n, int): n1, n2 = (n, n) else: n1, n2 = (n[0], n[1]) ng = int(np.floor(np.sqrt(n1))) samples, params = [], [] for x in np.linspace(0.0-r,1.0+r,ng): for y in np.linspace(0.0-r,1.0+r,ng): samples.append(blob(auto_np.array([x, y]))) params.append([x, y, 1.0]) ## Generate the pole NP = blob(auto_np.array([0.5, 0.5])) for t in np.linspace(0, 1, n2): samples.append(t*NP) params.append([0.5, 0.5, 1-t]) ## Vertically stack samples, params = np.vstack(samples), np.vstack(params) ## Return the data return(samples, params, blob, c) def mobius_band(n_polar=66, n_wide=9, scale_band=0.25): ''' Generates stratified samples on a Mobius band embedded in R^3 To get uniform samples, N = (n_polar*n_wide) uniformly spaced coordinates are generated initially from the intrinsic space of M. These points are converted to their extrinsic (3D) coordinates and are then triangulated using a Delaunay triangulation. Finally, using the Delaunay triangles as stratum, a stratified sampling scheme is employed by sampling randomly from each triangle using its barycentric coordinates. This stratification ensures the samples are both sufficiently random but sufficiently "uniformly spaced" around the band. Returns: - M := (n x 3) matrix of embedding coordinates - B := (n x 2) matrix of intrinsic coordinates In the intrinsic coordinates, B[:,0] is the width parameter and B[:,1] is the angular coordinate ''' ## Generate random (deterministic) polar coordinates around Mobius Band np.random.seed(0) s = np.linspace(-scale_band, scale_band, 2*n_wide) # width/radius t = np.linspace(0, 2*np.pi, n_polar) # circular coordinate s, t = np.meshgrid(s, t) ## Triangulate to allow stratification M = np.c_[np.ravel(s), np.ravel(t)] V = M[Delaunay(M).simplices] ## Sample within each strata via random barycentric coordinates normalize = lambda x: x / np.sum(x) Y = np.array([np.sum(v * normalize(np.random.uniform(size=(3,1))), axis = 0) for v in V]) ## Convert to 3d S, T = Y[:,0], Y[:,1] phi = 0.5 * T r = 1 + S * np.cos(phi) MB = np.c_[r * np.cos(T), r * np.sin(T), S * np.sin(phi)] ## Return both 3D embedding + original parameters return(MB, Y) def embed(a: ArrayLike, D: int, method="givens"): ''' Embeds a point cloud into D dimensions using random orthogonal rotations ''' def givens(i,j,theta,n=2): G = np.eye(n) G[i,i] = np.cos(theta) G[j,j] = np.cos(theta) G[i,j] = -np.sin(theta) G[j,i] =

np.sin(theta)

numpy.sin

#!/usr/bin/env python # -*- coding: utf-8 -*- # # @Author: <NAME> (<EMAIL>) # @Filename: tiledb.py # @License: BSD 3-clause (http://www.opensource.org/licenses/BSD-3-Clause) import itertools import astropy import cycler import matplotlib.pyplot as plt import numpy from matplotlib import animation import lvmsurveysim.target from lvmsurveysim.schedule.tiledb import TileDB from lvmsurveysim.schedule.scheduler import Scheduler from lvmsurveysim.schedule.plan import ObservingPlan from lvmsurveysim import IFU, config from lvmsurveysim.schedule.plan import ObservingPlan from lvmsurveysim.utils.plot import __MOLLWEIDE_ORIGIN__, get_axes, transform_patch_mollweide, convert_to_mollweide numpy.seterr(invalid='raise') __all__ = ['Simulator'] class Simulator(object): """Simulates an observing schedule for a list of targets (tile database) following and observing plan. Parameters ---------- tiledb : ~lvmsurveysim.schedule.tiledb.TileDB The `~lvmsurveysim.schedule.tiledb.TileDB` instance with the table of tiles to schedule. observing_plan : l`.ObservingPlan` or None The `.ObservingPlan` to use (one for each observatory). If `None`, it will be created from the ``observing_plan`` section in the configuration file. Contains dates and sun/moon data for the duration of the survey as well as Observatory data. ifu : ~lvmsurveysim.ifu.IFU The `~lvmsurveysim.ifu.IFU` to use. Defaults to the one from the configuration file. Used only for plotting the survey footprint. Attributes ---------- tiledb : ~lvmsurveysim.schedule.tiledb.TileDB Instance of the tile database to observe. schedule : ~astropy.table.Table An astropy table with the results of the scheduling. Includes information about the JD of each observation, the target observed, the index of the pointing in the target tiling, coordinates, etc. """ def __init__(self, tiledb, observing_plan=None, ifu=None): assert isinstance(tiledb, lvmsurveysim.schedule.tiledb.TileDB), \ 'tiledb must be a lvmsurveysim.schedule.tiledb.TileDB instances.' # get rid of the special tiles, we do not need them for the simulator tdb = tiledb.tile_table tiledb.tile_table = tdb[numpy.where(tdb['TileID'] >= tiledb.tileid_start)[0]] if observing_plan is None: observing_plan = self._create_observing_plan() assert isinstance(observing_plan, ObservingPlan), 'observing_plan is not an instance of ObservingPlan.' self.zenith_avoidance = config['scheduler']['zenith_avoidance'] self.time_step = config['scheduler']['timestep'] self.observing_plan = observing_plan self.tiledb = tiledb self.targets = tiledb.targets self.ifu = ifu or IFU.from_config() self.schedule = None def __repr__(self): return (f'<Simulator (observing_plan={self.observing_plan.observatory.name}, ' f'tiles={len(self.tiledb.tile_table)})>') def save(self, path, overwrite=False): """ Saves the results of the scheduling simulation to a FITS file. """ assert isinstance(self.schedule, astropy.table.Table), \ 'cannot save empty schedule. Execute Scheduler.run() first.' targfile = str(self.targets.filename) if self.targets.filename != None else 'NA' self.schedule.meta['targfile'] = targfile self.schedule.write(path+'.fits', format='fits', overwrite=overwrite) @classmethod def load(cls, path, tiledb=None, observing_plan=None): """Creates a new instance from a schedule file. Parameters ---------- path : str or ~pathlib.Path The path to the schedule file and the basename, no extension. The routine expects to find path.fits and path.npy tiledb : ~lvmsurveysim.schedule.tiledb.TileDB or path-like Instance of the tile database to observe. observing_plan : `.ObservingPlan` or None The `.ObservingPlan` to use (one for each observatory). """ schedule = astropy.table.Table.read(path+'.fits') if not isinstance(tiledb, lvmsurveysim.schedule.tiledb.TileDB): assert tiledb != None and tiledb != 'NA', \ 'invalid or unavailable tiledb file path.' tiledb = TileDB.load(tiledb) observing_plan = observing_plan or [] sim = cls(tiledb, observing_plan=observing_plan) sim.schedule = schedule return sim def run(self, progress_bar=True): """Schedules the pointings for the whole survey defined in the observing plan. Parameters ---------- progress_bar : bool If `True`, shows a progress bar. """ # Make self.schedule a list so that we can add rows. Later we'll make # this an Astropy Table. self.schedule = [] plan = self.observing_plan # Instance of the Scheduler scheduler = Scheduler(plan) # observed exposure time for each pointing observed = numpy.zeros(len(self.tiledb.tile_table), dtype=numpy.float) # range of dates for the survey min_date = numpy.min(plan['JD']) max_date = numpy.max(plan['JD']) dates = range(min_date, max_date + 1) if progress_bar: generator = astropy.utils.console.ProgressBar(dates) else: generator = dates for jd in generator: if progress_bar is False: print(f'scheduling JD={jd}.') # Skips JDs not found in the plan or those that don't have good weather. if jd not in plan['JD'] or plan[plan['JD'] == jd]['is_clear'][0] == 0: continue observed += self.schedule_one_night(jd, scheduler, observed) # Convert schedule to Astropy Table. self.schedule = astropy.table.Table( rows=self.schedule, names=['JD', 'observatory', 'target', 'group', 'tileid', 'index', 'ra', 'dec', 'pa', 'airmass', 'lunation', 'shadow_height', "moon_dist", 'lst', 'exptime', 'totaltime'], dtype=[float, 'S10', 'S20', 'S20', int, int, float, float, float, float, float, float, float, float, float, float]) def schedule_one_night(self, jd, scheduler, observed): """Schedules a single night at a single observatory. This method is not intended to be called directly. Instead, use `.run`. Parameters ---------- jd : int The Julian Date to schedule. Must be included in ``plan``. scheduler : .Scheduler The Scheduler instance that will determine the observing sequence. observed : ~numpy.array An array of the length of the tiledb that records the observing time accumulated on each tile thus far Returns ------- exposure_times : `~numpy.ndarray` Array with the exposure times in seconds added to each tile during this night. """ # initialize the scheduler for the night scheduler.prepare_for_night(jd, self.observing_plan, self.tiledb) # shortcut tdb = self.tiledb.tile_table # begin at twilight current_jd = scheduler.evening_twi # While the current time is before morning twilight ... while current_jd < scheduler.morning_twi: # obtain the next tile to observe observed_idx, current_lst, hz, alt, lunation = scheduler.get_optimal_tile(current_jd, observed) if observed_idx == -1: # nothing available self._record_observation(current_jd, self.observing_plan.observatory, lst=current_lst, exptime=self.time_step, totaltime=self.time_step) current_jd += (self.time_step) / 86400.0 continue # observe it, give it one quantum of exposure exptime = tdb['VisitExptime'].data[observed_idx] observed[observed_idx] += exptime # collect observation data to put in table tileid_observed = tdb['TileID'].data[observed_idx] target_index = tdb['TargetIndex'].data[observed_idx] target_name = self.targets[target_index].name groups = self.targets[target_index].groups target_group = groups[0] if groups else 'None' target_overhead = self.targets[target_index].overhead # Get the index of the first value in index_to_target that matches # the index of the target. target_index_first = numpy.nonzero(tdb['TargetIndex'].data == target_index)[0][0] # Get the index of the pointing within its target. pointing_index = observed_idx - target_index_first # Record angular distance to moon dist_to_moon = scheduler.moon_to_pointings[observed_idx] # Update the table with the schedule. airmass = 1.0 / numpy.cos(numpy.radians(90.0 - alt)) self._record_observation(current_jd, self.observing_plan.observatory, target_name=target_name, target_group=target_group, tileid = tileid_observed, pointing_index=pointing_index, ra=tdb['RA'].data[observed_idx], dec=tdb['DEC'].data[observed_idx], pa=tdb['PA'].data[observed_idx], airmass=airmass, lunation=lunation, shadow_height= hz, #hz[valid_priority_idx[obs_tile_idx]], dist_to_moon=dist_to_moon, lst=current_lst, exptime=exptime, totaltime=exptime * target_overhead) current_jd += exptime * target_overhead / 86400.0 return observed def animate_survey(self, filename='lvm_survey.mp4', step=100, observatory=None, projection='mollweide'): """Create an animation of the survey progress and save as an mp4 file. Parameters ---------- filename : str Name of the mp4 file, defaults to ``'lvm_survey.mp4'``. step : int Number of observations per frame of movie. observatory : str Either ``'LCO'`` or ``'APO'`` or `None` (plots both). projection : str Which projection of the sphere to use. Defaults to Mollweide. """ data = self.schedule[self.schedule['target'] != '-'] if observatory: data = data[data['observatory'] == observatory] ll = int(len(data) / step) x,y = convert_to_mollweide(data['ra'], data['dec']) tt = [target.name for target in self.targets] g = numpy.array([tt.index(i) for i in data['target']], dtype=float) t = data['JD'] fig, ax = get_axes(projection=projection) # scat = ax.scatter(x[:1], y[:1], c=g[:1], s=1, edgecolor=None, edgecolors=None) scat = ax.scatter(x, y, c=g % 19, s=0.05, edgecolor=None, edgecolors=None, cmap='tab20') # fig.show() # return def animate(ii): if ii % 10 == 0: print('%.1f %% done\r' % (ii / ll * 100)) scat.set_offsets(numpy.stack((x[:ii * step], y[:ii * step]), axis=0).T) scat.set_array(g[:ii * step]) ax.set_title(str(t[ii])) return scat, anim = animation.FuncAnimation(fig, animate, frames=range(1, ll), interval=1, blit=True, repeat=False) anim.save(filename, fps=24, extra_args=['-vcodec', 'libx264']) def plot(self, observatory=None, projection='mollweide', tname=None, fast=False, annotate=False, edge=False): """Plots the observed pointings. Parameters ---------- observatory : str Plot only the points for that observatory. Otherwise, plots all the pointings. projection : str The projection to use, either ``'mollweide'`` or ``'rectangular'``. tname : str Select only a particular target name to plot fast : bool Plot IFU sized and shaped patches if `False`. This is the default. Allows accurate zooming and viewing. If `True`, plot scatter-plot dots instead of IFUs, for speed sacrificing accuracy. This is MUCH faster. annotate : bool Write the targets' names next to the target coordinates. Implies ``fast=True``. edge : bool Draw tile edges and make tiles partly transparent to better judge overlap. Makes zoomed-out view look odd, so use default False. Returns ------- figure : `matplotlib.figure.Figure` The figure with the plot. """ if annotate is True: fast = True color_cycler = cycler.cycler(bgcolor=['b', 'r', 'g', 'y', 'm', 'c', 'k']) fig, ax = get_axes(projection=projection) if tname != None: data = self.schedule[self.schedule['target'] == tname] else: data = self.schedule[self.schedule['target'] != '-'] if observatory: data = data[data['observatory'] == observatory] if fast is True: if projection == 'mollweide': x,y = convert_to_mollweide(data['ra'], data['dec']) else: x,y = data['ra'], data['dec'] tt = [target.name for target in self.targets] g = numpy.array([tt.index(i) for i in data['target']], dtype=float) ax.scatter(x, y, c=g % 19, s=0.05, edgecolor=None, edgecolors=None, cmap='tab20') if annotate is True: _, text_indices = numpy.unique(g, return_index=True) for i in range(len(tt)): plt.text(x[text_indices[i]], y[text_indices[i]], tt[i], fontsize=9) else: for ii, sty in zip(range(len(self.targets)), itertools.cycle(color_cycler)): target = self.targets[ii] name = target.name target_data = data[data['target'] == name] if edge: patches = [self.ifu.get_patch(scale=target.telescope.plate_scale, centre=[p['ra'], p['dec']], pa=p['pa'], edgecolor='k', linewidth=1, alpha=0.5, facecolor=sty['bgcolor'])[0] for p in target_data] else: patches = [self.ifu.get_patch(scale=target.telescope.plate_scale, centre=[p['ra'], p['dec']], pa=p['pa'], edgecolor='None', linewidth=0.0, facecolor=sty['bgcolor'])[0] for p in target_data] if projection == 'mollweide': patches = [transform_patch_mollweide(patch) for patch in patches] for patch in patches: ax.add_patch(patch) if observatory != None: ax.set_title(f'Observatory: {observatory}') return fig def _create_observing_plan(self): """Returns an `.ObservingPlan` from the configuration file.""" observatory = config['observing_plan'] obs_data = config['observing_plan'][observatory] start_date = obs_data['start_date'] end_date = obs_data['end_date'] return ObservingPlan(start_date, end_date, observatory=observatory) def _record_observation(self, jd, observatory, target_name='-', target_group='-', tileid=-1, pointing_index=-1, ra=-999., dec=-999., pa=-999., airmass=-999., lunation=-999., shadow_height=-999., dist_to_moon=-999., lst=-999., exptime=0., totaltime=0.): """Adds a row to the schedule.""" self.schedule.append((jd, observatory, target_name, target_group, tileid, pointing_index, ra, dec, pa, airmass, lunation, shadow_height, dist_to_moon, lst, exptime, totaltime)) def get_target_time(self, tname, group=False, observatory=None, lunation=None, return_lst=False): """Returns the JDs or LSTs for a target at an observatory. Parameters ---------- tname : str The name of the target or group. Use ``'-'`` for unused time. group : bool If not true, ``tname`` will be the name of a group not a single target. observatory : str The observatory to filter for. lunation : list Restrict the data to a range in lunation. Defaults to returning all lunations. Set to ``[lmin, lmax]`` to return values of ``lmin < lunation <= lmax``. return_lst : bool If `True`, returns an array with the LSTs of all the unobserved times. Returns ------- table : `~numpy.ndarray` An array containing the times the target is observed at an observatory, as JDs. If ``return_lst=True`` returns an array of the corresponding LSTs. """ column = 'group' if group is True else 'target' t = self.schedule[self.schedule[column] == tname] if observatory: t = t[t['observatory'] == observatory] if lunation != None: t = t[(t['lunation'] > lunation[0]) * (t['lunation'] <= lunation[1])] if return_lst: return t['lst'].data else: return t['JD'].data def print_statistics(self, out_file=None, out_format="ascii", overwrite_out=True, observatory=None, targets=None, return_table=False): """Prints a summary of observations at a given observatory. Parameters ---------- observatory : str The observatory to filter for. targets : `~lvmsurveysim.target.TargetList` The targets to summarize. If `None`, use ``self.targets``. return_table : bool If `True`, return a `~astropy.table.Table` with the results. out_file : str Outfile to write statistics. out_format : str Outfile format consistent with astropy.table dumps """ if targets is None: targets = self.targets names = [t.name for t in targets] time_on_target = {} # time spent exposing target exptime_on_target = {} # total time (exp + overhead) on target tile_area = {} # area of a single tile target_ntiles = {} # number of tiles in a target tiling target_ntiles_observed = {} # number of observed tiles target_nvisits = {} # number of visits for each tile surveytime = 0.0 # total time of survey names.append('-') # deals with unused time for tname, i in zip(names, range(len(names))): if (tname != '-'): target = self.targets[i] tile_area[tname] = target.get_pixarea(ifu=self.ifu) target_ntiles[tname] = len(numpy.where(self.tiledb.tile_table['TargetIndex'] == i)[0]) target_nvisits[tname] = float(target.n_exposures / target.min_exposures) else: tile_area[tname] = -999 target_ntiles[tname] = -999 target_nvisits[tname] = 1 tdata = self.schedule[self.schedule['target'] == tname] if observatory: tdata = tdata[tdata['observatory'] == observatory] exptime_on_target[tname] = numpy.sum(tdata['exptime'].data) target_ntiles_observed[tname] = len(tdata) / target_nvisits[tname] target_total_time = numpy.sum(tdata['totaltime'].data) time_on_target[tname] = target_total_time surveytime += target_total_time # targets that completely overlap with others have no tiles for t in self.targets: if target_ntiles[t.name] == 0: print(t.name + ' has no tiles') target_ntiles[t.name] = 1 rows = [ (t if t != '-' else 'unused', numpy.float(target_ntiles[t]), numpy.around(target_ntiles_observed[t], decimals=2), numpy.around(time_on_target[t] / 3600.0, decimals=2), numpy.around(exptime_on_target[t] / 3600.0, decimals=2), numpy.around(time_on_target[t] / surveytime, decimals=2), numpy.around(target_ntiles_observed[t] * tile_area[t], decimals=2) if t != '-' else -999, numpy.around(float(target_ntiles_observed[t]) / float(target_ntiles[t]), decimals=2) if t != '-' else -999) for t in names] stats = astropy.table.Table(rows=rows, names=['Target', 'tiles', 'tiles_obs', 'tottime/h', 'exptime/h', 'timefrac', 'area', 'areafrac'], dtype=('S8', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4')) print('%s :' % (observatory if observatory != None else 'APO+LCO')) stats.pprint(max_lines=-1, max_width=-1) if out_file != None: stats.write(out_file, format=out_format, overwrite=overwrite_out) if return_table: return stats def plot_survey(self, observatory=None, bin_size=30., targets=None, groups=None, use_groups=False, use_primary_group=True, show_ungrouped=True, cumulative=False, lst=False, lunation=None, show_unused=True, skip_fast=False, show_mpld3=False): """Plot the hours spent on target. Parameters ---------- observatory : str The observatory to plot. If `None`, all observatories. bin_size : int The number of days in each bin of the plot. targets : list A list with the names of the targets to plot. If empty, plots all targets. groups : list A list with the names of the groups to plot. If empty, plots all groups. use_groups : bool If set, the targets are grouped together using the ``Target.groups`` list. use_primary_group : bool If `True`, a target will only be added to its primary group (the first one in the group list). Only used when ``use_groups=True``. show_ungrouped : bool If `True`, targets that don't belong to any group are plotted individually. Only used when ``use_groups=True``. cumulative : bool or str If `True`, plots the cumulative sum of hours spent on each target. If ``'group'``, it plots the cumulative on-target hours normalised by the total hours needed to observe the target group. If ``'survey'``, plots the cumulative hours normalised by the total survey hours. When ``cumulative`` is not `False`, ``bin_size`` is set to 1. lst : bool Whether to bin the used time by LST instead of JD. show_unused : bool Display the unused time. lunation : list Range of lunations to include in statistics. Defaults to all lunations. Set to ``[lmin, lmax]`` to return values of ``lmin < lunation <= lmax``. Can be used to restrict lst usage plots to only bright, grey, or dark time. skip_fast : bool If set, do not plot targets that complete in the first 20% of the survey. Return ------ fig : `~matplotlib.figure.Figure` The Matplotlib figure of the plot. """ assert self.schedule != None, 'you still have not run a simulation.' if not targets: targets = [target.name for target in self.targets] ncols = 2 if len(targets) > 15 else 1 if lst: bin_size = 1. if bin_size == 30. else bin_size assert cumulative is False, 'cumulative cannot be used with lst=True.' if cumulative != False: bin_size = 1 fig, ax = plt.subplots(figsize=(12, 8)) # Leaves a margin on the right to put the legend fig.subplots_adjust(right=0.65 if ncols == 2 else 0.8) ax.set_prop_cycle(color=['r', 'g', 'b', 'c', 'm', 'y', 'g', 'b', 'c', 'm', 'y', 'r', 'b', 'c', 'm', 'y', 'r', 'g', 'c', 'm', 'y', 'r', 'g', 'b', ], linestyle=['-', '--', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':', '-', '--', '-.', ':']) min_b = (numpy.min(self.schedule['JD']) - 2451545.0) if not lst else 0.0 max_b = (numpy.max(self.schedule['JD']) - 2451545.0) if not lst else 24.0 b = numpy.arange(min_b, max_b + bin_size, bin_size) # Creates a list of groups to plot. If use_groups=False, # this is just the list of targets. if not use_groups: groups = [target.name for target in self.targets] else: groups = groups or self.targets.get_groups() # Adds the ungrouped targets. if show_ungrouped: for target in self.targets: if len(target.groups) == 0: groups.append(target.name) for group in groups: # Cumulated group heights group_heights = numpy.zeros(len(b) - 1, dtype=numpy.float) group_target_tot_time = 0.0 # If we are not using groups or the "group" # name is that of an ungrouped target. if not use_groups or group in self.targets._names: targets = [group] else: targets = self.targets.get_group_targets(group, primary=use_primary_group) for tname in targets: t = self.targets.get_target(tname) tindex = [target.name for target in self.targets].index(tname) # plot each target tt = self.get_target_time(tname, observatory=observatory, lunation=lunation, return_lst=lst) if len(tt) == 0: continue if not lst: tt -= 2451545.0 heights, bins = numpy.histogram(tt, bins=b) heights = numpy.array(heights, dtype=float) heights *= t.exptime * t.min_exposures / 3600.0 ntiles = len(numpy.where(self.tiledb.tile_table['TargetIndex'].data == tindex)[0]) target_tot_time = ntiles * t.exptime * t.n_exposures / 3600. if skip_fast: completion = heights.cumsum() / target_tot_time if numpy.quantile(completion, 0.2) >= 1: continue group_heights += heights group_target_tot_time += target_tot_time # Only plot the heights if they are not zero. This prevents # targets that are not observed at an observatory to be displayed. if numpy.sum(group_heights) > 0: if cumulative is False: ax.plot(bins[:-1] + numpy.diff(bins) / 2, group_heights, label=group) else: ax.plot(bins[:-1] +

numpy.diff(bins)

numpy.diff

import numpy as np import json network_snrs = [] ln_bfs = [] for i in range(2000): try: with open(f'injections/{i}_memory/IMR_mem_inj_non_mem_rec_result.json') as f: res = json.load(f) except Exception as e: print(e) with open(f'injections/{i}_memory/pp_result.json') as f: pp_res = json.load(f) snrs_squared = [res['meta_data']['likelihood']['interferometers'][ifo]['optimal_SNR']**2 for ifo in ['H1', 'L1', 'V1']] network_snrs.append(np.sqrt(np.sum(snrs_squared))) ln_bfs.append(pp_res['memory_log_bf']) print(i) print() ln_bfs = np.array(ln_bfs) network_snrs = np.array(network_snrs) low_snr_indices = np.where(network_snrs < 15) high_snr_indices =

np.where(network_snrs > 15)

numpy.where

import cftime import numpy as np import pandas as pd import xarray as xr from xclim.indices import generic class TestSelectResampleOp: def test_month(self, q_series): q = q_series(np.arange(1000)) o = generic.select_resample_op(q, "count", freq="YS", month=3) np.testing.assert_array_equal(o, 31) def test_season_default(self, q_series): # Will use freq='YS', so count J, F and D of each year. q = q_series(np.arange(1000)) o = generic.select_resample_op(q, "min", season="DJF") assert o[0] == 0 assert o[1] == 366 def test_season(self, q_series): q = q_series(np.arange(1000)) o = generic.select_resample_op(q, "count", freq="AS-DEC", season="DJF") assert o[0] == 31 + 29 class TestThresholdCount: def test_simple(self, tas_series): ts = tas_series(np.arange(365)) out = generic.threshold_count(ts, "<", 50, "Y") np.testing.assert_array_equal(out, [50, 0]) class TestDomainCount: def test_simple(self, tas_series): ts = tas_series(np.arange(365)) out = generic.domain_count(ts, low=10, high=20, freq="Y") np.testing.assert_array_equal(out, [10, 0]) class TestDailyDownsampler: def test_std_calendar(self): # standard calendar # generate test DataArray time_std = pd.date_range("2000-01-01", "2000-12-31", freq="D") da_std = xr.DataArray(

np.arange(time_std.size)

numpy.arange

# # Copyright 2020 Logical Clocks AB # # 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 time from copy import deepcopy from abc import abstractmethod import numpy as np from maggy.optimizer.abstractoptimizer import AbstractOptimizer class BaseAsyncBO(AbstractOptimizer): """Base class for asynchronous bayesian optimization Do not initialize this class! only it's subclasses **async bo** Bayesian Optimization consists of a surrogat model to approximate the black-box function and an acquisition function to select the next hyperparameter configuration. In an asynchronous setting, we need to encourage diversity when maximizing the acquisition function to prevent redundant sampling. **optimization direction** all optimizations are converted to minimizations problems via the `get_metrics_dict()` and `get_metrics_array()` methods, where the metrics are negated in case of a maximization. In the `final_store` the original metric values are saved. **pruner** Pruners can be run as a subroutine of an optimizer. It's interface `pruner.pruning_routine()` is called at the beginning of the `get_suggestion()` method and returns the hparam config and budget for the trial. The `trial_id` of the newly created Trial object is reported back to the pruner via `pruner.report_trial()`. **budget** If a pruner was specified ( and hence a multi-fidelity optimization is conducted ), the budget is added as an additional hyperparameter to the hparam dict so it is passed to the map_fun. Note, that when fitting the surrogate model the "budget" key is obviously omitted. **models** The surrogate models for different budgets are saved in the `models` dict with the budgets as key. In case of a single fidelity optimization without a pruner or pruner with interim_results, The only model has the key `0`. If we fit multiple models, sample new hparam configs always from the largest model available (i.e. biggest budget) **imputing busy trials** (poss. shift to simple.py) the imputed metrics are calculated on the fly in `get_metrics_array(include_busy_locations=True, budget=`budget`)` for currently evaluating trials that were sampled from model with `budget` **interim results** In literature most surrogate models of BO algorithms are fit with the hyperparameter config and the final metric of a trial. Using additionally interim metrics of trials, i.e. use metrics that were trained for varying budgets makes it possible to use early stopping in BO. (Also it makes it possible to have only one model in multi fidelity bo instead of one model per fidelity) For each interim metric a hyperparameter config augumented with the budget the metric was generated with is added to the training data of the surrogate model .. math:: z_t = [x_t, n_t] ; y_t = y_{t,nt} When maximizing the acquisition function to sample the next hyperparameter config to evaluate, always augument with the max budget N .. math:: xt ← argmax acq([x, N]) """ def __init__( self, num_warmup_trials=15, random_fraction=0.33, interim_results=False, interim_results_interval=10, **kwargs ): """ Attributes ---------- models (dict): The surrogate models for different budgets are saved in the `models` dict with the budgets as key. In case of a single fidelity optimization without a pruner. The only model has the key `0`. warm_up_configs(list[dict]): list of hparam configs used for warming up sampling_time_start (float): helper variable to calculate time needed for calculating next suggestion, i.e when sampling from model (`sample_type`=="model"). Calulating the time happens in `create_trial()` normalize_categorical (bool): If True, the encoded categorical hparam is also max-min normalized between 0 and 1 in searchspace.transform() :param num_warmup_trials: number of random trials at the beginning of experiment :type num_warmup_trials: int :param random_fraction: fraction of random samples, between [0,1] :type random_fraction: float :param interim_results: If True, use interim metrics from trials for fitting surrogate model. Else use final metrics only :type interim_results: bool :param interim_results_interval: Specifies which interim metrics are used (if interim_results==True) e.g. interval=10: the metric of every 10th epoch is used for fitting surrogate :type interim_results_interval: int """ super().__init__(**kwargs) # configure warmup routine self.num_warmup_trials = num_warmup_trials self.warmup_sampling = "random" self.warmup_configs = [] # keeps track of warmup warmup configs allowed_warmup_sampling_methods = ["random"] if self.warmup_sampling not in allowed_warmup_sampling_methods: raise ValueError( "expected warmup_sampling to be in {}, got {}".format( allowed_warmup_sampling_methods, self.warmup_sampling ) ) # surrogate model related aruments self.models = {} # fitted model of the estimator self.random_fraction = random_fraction self.interim_results = interim_results self.interim_results_interval = interim_results_interval # helper variable to calculate time needed for calculating next suggestion self.sampling_time_start = 0.0 # If True, the encoded categorical hparam is also max-min normalized between 0 and 1 in searchspace.transform() self.normalize_categorical = True if self.name() == "TPE": self.normalize_categorical = False def initialize(self): # validate hparam types # at least one hparam needs to be continuous & no DISCRETE hparams cont = False for hparam in self.searchspace.items(): if hparam["type"] == self.searchspace.DISCRETE: raise ValueError( "This version of Bayesian Optimization does not support DISCRETE Hyperparameters yet, please encode {} as INTEGER".format( hparam["name"] ) ) if hparam["type"] in [self.searchspace.DOUBLE, self.searchspace.INTEGER]: cont = True if not cont: raise ValueError( "In this version of Bayesian Optimization at least one hparam has to be continuous (DOUBLE or INTEGER)" ) self.warmup_routine() self.init_model() def get_suggestion(self, trial=None): self._log("### start get_suggestion ###") self.sampling_time_start = time.time() if trial: self._log( "last finished trial: {} with params {}".format( trial.trial_id, trial.params ) ) else: self._log("no previous finished trial") # check if experiment has finished if self._experiment_finished(): return None # pruning routine if self.pruner: next_trial_info = self.pruner.pruning_routine() if next_trial_info == "IDLE": self._log( "Worker is IDLE and has to wait until a new trial can be scheduled" ) return "IDLE" elif next_trial_info is None: # experiment is finished self._log("Experiment has finished") return None elif next_trial_info["trial_id"]: # copy hparams of given promoted trial and start new trial with it parent_trial_id = next_trial_info["trial_id"] parent_trial_hparams = deepcopy( self.get_hparams_dict(trial_ids=parent_trial_id)[parent_trial_id] ) # update trial info dict and create new trial object next_trial = self.create_trial( hparams=parent_trial_hparams, sample_type="promoted", run_budget=next_trial_info["budget"], ) # report new trial id to pruner self.pruner.report_trial( original_trial_id=parent_trial_id, new_trial_id=next_trial.trial_id, ) self._log( "use hparams from promoted trial {}. \n start trial {}: {} \n".format( parent_trial_id, next_trial.trial_id, next_trial.params ) ) return next_trial else: # start sampling procedure with given budget run_budget = next_trial_info["budget"] if self.interim_results: model_budget = 0 else: model_budget = run_budget else: run_budget = 0 model_budget = 0 # check if there are still trials in the warmup buffer if self.warmup_configs: self._log("take sample from warmup buffer") next_trial_params = self.warmup_configs.pop() next_trial = self.create_trial( hparams=next_trial_params, sample_type="random", run_budget=run_budget, ) elif np.random.rand() < self.random_fraction: # random fraction applies, sample randomly hparams = self.searchspace.get_random_parameter_values(1)[0] next_trial = self.create_trial( hparams=hparams, sample_type="random", run_budget=run_budget ) self._log("sampled randomly: {}".format(hparams)) else: # update model if self.pruner and not self.interim_results: # skip model building if we already have a bigger model if max(list(self.models.keys()) + [-np.inf]) <= model_budget: self.update_model(model_budget) else: self.update_model(model_budget) if not self.models: # in case there is no model yet, sample randomly hparams = self.searchspace.get_random_parameter_values(1)[0] next_trial = self.create_trial( hparams=hparams, sample_type="random", run_budget=run_budget ) self._log("sampled randomly: {}".format(hparams)) else: if self.pruner and not self.interim_results: # sample from largest model available model_budget = max(self.models.keys()) # sample from model with model budget self._log( "start sampling routine from model with budget {}".format( model_budget ) ) hparams = self.sampling_routine(model_budget) next_trial = self.create_trial( hparams=hparams, sample_type="model", run_budget=run_budget, model_budget=model_budget, ) self._log( "sampled from model with budget {}: {}".format( model_budget, hparams ) ) # check if Trial with same hparams has already been created i = 0 while self.hparams_exist(trial=next_trial): self._log("sample randomly to encourage exploration") hparams = self.searchspace.get_random_parameter_values(1)[0] next_trial = self.create_trial( hparams=hparams, sample_type="random_forced", run_budget=run_budget ) i += 1 if i > 3: self._log( "not possible to sample new config. Stop Experiment (most/all configs have already been used)" ) return None # report new trial id to pruner if self.pruner: self.pruner.report_trial( original_trial_id=None, new_trial_id=next_trial.trial_id ) self._log( "start trial {}: {}, {} \n".format( next_trial.trial_id, next_trial.params, next_trial.info_dict ) ) return next_trial def finalize_experiment(self, trials): return @abstractmethod def init_model(self): """initializes the surrogate model of the gaussian process the model gets created with the right parameters, but is not fit with any data yet. the `base_model` will be cloned in `update_model` and fit with observation data """ raise NotImplementedError @abstractmethod def update_model(self, budget=0): """update surrogate model with new observations Use observations of finished trials + liars from busy trials to build model. Only build model when there are at least as many observations as hyperparameters :param budget: the budget for which model should be updated. Default is 0 If budget > 0 : multifidelity optimization. Only use observations that were run with `budget` for updateing model for that `budget`. One model exists per budget. If == 0: single fidelity optimization. Only one model exists that is fitted with all observations :type budget: int """ raise NotImplementedError @abstractmethod def sampling_routine(self, budget=0): """Samples new config from model This methods holds logic for: - maximizing acquisition function based on current model and observations - async logic: i.e. imputing busy_locations with a liar to encourage diversity in sampling :param budget: the budget from which model should be sampled. Default is 0 If budget > 0 : multifidelity optimization. Only use observations that were run with `budget` for updateing model for that `budget`. One model exists per budget. If == 0: single fidelity optimization. Only one model exists that is fitted with all observations :type budget: int :return: hyperparameter config that minimizes the acquisition function :rtype: dict """ raise NotImplementedError def warmup_routine(self): """implements logic for warming up bayesian optimization through random sampling by adding hparam configs to `warmup_config` list """ # generate warmup hparam configs if self.warmup_sampling == "random": self.warmup_configs = self.searchspace.get_random_parameter_values( self.num_warmup_trials ) else: raise NotImplementedError( "warmup sampling {} doesnt exist, use random".format( self.warmup_sampling ) ) def _experiment_finished(self): """checks if experiment is finished In normal BO, experiment has finished when specified amount of trials have run, in BOHB/ASHA when all iterations have been finished :return: True if experiment has finished, False else :rtype: bool """ if self.pruner: if self.pruner.finished(): return True elif len(self.final_store) >= self.num_trials: self._log( "Finished experiment, ran {}/{} trials".format( len(self.final_store), self.num_trials ) ) return True else: return False def get_busy_locations(self, budget=0): """returns hparams of currently evaluating trials Considers only trials that were sampled from model with specified budget This is a helper functions used when async strategy is `impute` """ if not self.include_busy_locations(): raise ValueError( "Only Optimizer GP with async_strategy == `impute` can include busy locations. Got Optimizer {} {}".format( self.name(), "async_strategy: " + self.async_strategy if self.name() == "GP" else "", ) ) hparams_busy = np.array( [ self.searchspace.dict_to_list(trial.params) for trial_id, trial in self.trial_store.items() if trial.info_dict["sample_type"] == "model" and trial.info_dict["model_budget"] == budget ] ) return hparams_busy def get_imputed_metrics(self, budget=0): """returns imputed metrics for currently evaluating trials Considers only trials that were sampled from model with specified budget This is a helper function, only used when async strategy is `impute` """ if not self.include_busy_locations(): raise ValueError( "Only Optimizer GP with async_strategy == `impute` can include busy locations. Got Optimizer {} {}".format( self.name(), "async_strategy: " + self.async_strategy if self.name() == "GP" else "", ) ) metrics_busy = np.empty(0, dtype=float) for trial_id, trial in self.trial_store.items(): if ( trial.info_dict["sample_type"] == "model" and trial.info_dict["model_budget"] == budget ): imputed_metric = self.impute_metric(trial.params, budget) metrics_busy = np.append(metrics_busy, imputed_metric) # add info about imputed metric to trial info dict if "imputed_metrics" in trial.info_dict.keys(): trial.info_dict["imputed_metrics"].append(imputed_metric) else: trial.info_dict["imputed_metrics"] = [imputed_metric] return metrics_busy def get_XY(self, budget=0, interim_results=False, interim_results_interval=10): """get transformed hparams and metrics for fitting surrogate :param budget: budget for which model should be build. Default is 0 If budget > 0 : One model exists per budget. Only use observations that were run with `budget` for updateing model for that `budget`. If == 0: Only one model exists that is fitted with all observations :type budget: int :param interim_results: If True interim results from metric history are used and hparams are augumented with budget. i.e. for every interim result have one observation with .. math:: z_t = [x_t, n_t] ; y_t = y_{t,nt} Else use final metrics only :type interim_results: bool :param interim_results_interval: Specifies the interval of the interim results being used, If interim_results==True e.g. if 10, use every metric of every 10th epoch :type interim_results_interval: int :return: Tuple of 2 arrays, the first containing hparams and metrics. There are four scenarios: * Hparams and Metrics of finalized trials shapes: (n_finalized_trials, n_hparams), (n_finalized_trials,) * Hparams and Metrics of finalized trials + hparams and imputed metrics of busy_locations (if async_strategy=="asnyc") shapes: (n_finalized_trials+n_busy_locations, n_hparams), (n_finalized_trials+n_busy_locations,) * Hparams (augumented with budget) and interim metrics of finalized trials shapes: (n_interim_results, n_hparams + 1), (n_interim_results,) Note that first and final metric of each trial are always used and that trials may be trained with different budgets * Hparams (augumented with budget) and interim metrics of finalized trials + hparams and imputed final metric for evaluating trials (if async_strategy=="asnyc") shapes: (n_interim_results+n_busy_locations, n_hparams + 1), (n_interim_results+n_busy_locations,) :rtype: (np.ndarray, np.ndarray) """ if not interim_results: # return final metrics only # get hparams and final metrics of finalized trials hparams = self.get_hparams_array(budget=budget) metrics = self.get_metrics_array(budget=budget, interim_metrics=False) # if async strategy is `impute` if self.include_busy_locations(): # get hparams and imputed metrics of evaluating trials hparams_busy = self.get_busy_locations(budget=budget) imputed_metrics = self.get_imputed_metrics(budget=budget) assert hparams_busy.shape[0] == imputed_metrics.shape[0], ( "Number of evaluating trials and imputed " "metrics needs to be equal, " "got n_busy_locations: {}, " "n_imputed_metrics: {}".format( hparams_busy.shape[0], imputed_metrics.shape[0] ) ) # append to hparams and metrics if len(hparams_busy) > 0: hparams = np.concatenate((hparams, hparams_busy)) metrics = np.concatenate((metrics, imputed_metrics)) # transform hparams # note that through transform, budget param gets ommited from hparams if it was existent (pruner) hparams_transform = np.apply_along_axis( self.searchspace.transform, 1, hparams, normalize_categorical=self.normalize_categorical, ) X = hparams_transform y = metrics assert X.shape[1] == len( self.searchspace.keys() ), "shape[1] of X needs to be equal to number of hparams" elif interim_results: # so far only supported for GP and no pruner # return interim results and hparams augumented with budget # return every nth interim result according to interim_results_interval. always return first and last result # get and transform hparams of all finalized trials hparams = self.get_hparams_array(budget=budget) hparams_transform = np.apply_along_axis( self.searchspace.transform, 1, hparams, normalize_categorical=self.normalize_categorical, ) # get full metric history for all finalized trials metrics = self.get_metrics_array( interim_metrics=True, budget=budget ) # array of metric history arrays # get indices of hparams/metrics to be used for each trial interim_result_indices = np.array( [ self.get_interim_result_idx( metric_history, interim_results_interval ) for metric_history in metrics ] ) # araay of list. each list represents the indices of the metrics to be used of that trial # only use every nth interim result of metric history. specified with interim_result_indices metrics_filtered = np.array( [ metrics[trial_idx][interim_result_indices[trial_idx]] for trial_idx in range(metrics.shape[0]) ] ) # flatten results so they can be used for fitting posterior # if arrays in yi_filtered do not have same length, e.g. some trials were early stopped or pruner was activated, # yi_filtered is ragged (yi_filtered.shape = (n_trials,)) and flatten does not work, use np.hstack(). # if all trials have been trained on max budget (yi_filtered.shape = (n_trials, n_filtered_results)) use flatten() since it is better for performance. if len(metrics_filtered.shape) == 2: metrics_flat = metrics_filtered.flatten() else: metrics_flat = np.hstack(metrics_filtered) # augument hparams with budget, i.e. z_t = [x_t, n_t] for every interim result max_budget = self.get_max_budget() n_hparams = len(self.searchspace.keys()) hparams_augumented = np.empty((0, n_hparams + 1)) # create one hparam config augumented with the corresponding normalized budget for every interim result # loop through trials for indices, trial_hparams in zip( interim_result_indices, hparams_transform ): # loop through interim results for idx in indices: # idx is budget normalized_budget = self.searchspace._normalize_integer( [0, max_budget - 1], idx ) augumented_trial_hparams = np.append( deepcopy(trial_hparams), normalized_budget ) hparams_augumented = np.vstack( (hparams_augumented, augumented_trial_hparams) ) # add evaluating trials if impute strategy, i.e. z = [x, max_budget] y = imputed_metric if self.include_busy_locations(): # get params and imputed metrics of evaluating trials hparams_busy = self.get_busy_locations(budget=budget) imputed_metrics = self.get_imputed_metrics(budget=budget) assert ( hparams_busy.shape[0] == imputed_metrics.shape[0] ), "Number of evaluating trials and imputed metrics needs to be equal, got n_busy_locations: {}, n_imputed_metrics: {}".format( hparams_busy.shape[0], imputed_metrics.shape[0] ) if len(hparams_busy) > 0: # transform hparams hp_trans = np.apply_along_axis( self.searchspace.transform, 1, hparams_busy, normalize_categorical=self.normalize_categorical, ) # augument with max budget (i.e. always 1 in normalized form) hp_aug = np.append( hp_trans, np.ones(hp_trans.shape[0]).reshape(-1, 1), 1 ) # append to hparams and metrics hparams_augumented = np.concatenate((hparams_augumented, hp_aug)) metrics_flat =

np.concatenate((metrics_flat, imputed_metrics))

numpy.concatenate

import numpy as np import numpy.linalg as la def barrier_move_not_acceptable_for_qp(P, q, x, residual, f_val, v, f_prime, d_residual, s, t, alpha): moved = x + s * v moved_val = t * (.5 * np.matmul(np.matmul(moved.T, P), moved) + np.matmul(q.T, moved)) \ - np.sum(np.log(residual + s * d_residual)) return moved_val >= f_val + alpha * s * f_prime def log_barrier_for_qp(P, q, A, b, alpha, beta, mu, iterations, tol=1e-3, eps=1e-6): t = 1 gap = np.inf m = A.shape[0] n = A.shape[1] x = np.zeros((n, 1)) gap_results = [] for iteration in range(iterations): residual = (b - np.matmul(A, x)).astype('f') f_val = t * (.5 * np.matmul(x.T, np.matmul(P, x)) + np.matmul(q.T, x)) - np.sum(np.log(residual)) f_grad = t * (np.matmul(P, x) + q) + np.matmul(A.T, np.reciprocal(residual)) f_hess = t * P + np.matmul(A.T, np.matmul(np.diag(np.reciprocal(np.square(residual))[:, 0]), A)) v = -1 * la.lstsq(f_hess, f_grad, rcond=None)[0] f_prime = np.matmul(f_grad.T, v) d_residual = -np.matmul(A, v) s = 1 while np.min(residual + s * d_residual) <= 0: s *= beta while barrier_move_not_acceptable_for_qp(P, q, x, residual, f_val, v, f_prime, d_residual, s, t, alpha): s *= beta if s == 0: break x += s * v if -f_prime < eps: gap = m / t if gap < tol: print('Iteration: %d - final gap: %f' % (iteration, gap)) gap_results.append(gap) break t = mu * t gap_results.append(gap) return gap_results def calculate_new_r(P, q, A, t_inv, step, z, dz, x, dx, s, ds): newz = z + step * dz newx = x + step * dx news = s + step * ds return np.concatenate((np.matmul(P, newx) + q + np.matmul(A.T, newz), newz * news - t_inv), axis=0) def interior_point_for_qp(P, q, A, b, alpha, beta, mu, iterations, tol=1e-3, eps=1e-6): m = A.shape[0] n = A.shape[1] x = np.zeros((n, 1)) s = (b - np.matmul(A, x)).astype('f') z = np.reciprocal(s) surrogates = [] dual_residual_norms = [] for iteration in range(iterations): gap = np.asscalar(np.matmul(s.T, z)) res =

np.matmul(P, x)

numpy.matmul

import numpy as np """ 备注：本示例中所有的matrix都使用的是array来体现实际上python中存在专门的matrix # 主要注意matrix和array的区别 # matrix 使用更加接近matlab的使用方式简单示例如下： B = np.matrix('1,2;3,4') print(B*B) # 注意：若使用matrix，则*直接表示矩阵乘法 # 而元素对应乘积则使用 multiply() 函数 """ def vector_dot(): a = np.array([1, 3, 5, 7, 9]) b = np.array([1, 2, 3, 4, 5]) # 对应元素相乘 print(a * b) # 对应元素2次幂 print(a ** 2) # dot product print(a.dot(b)) print(type(a)) print(a.dtype) print(a.shape) def array_matrix_sum(): d = np.arange(10) # 求和：针对数组 print(d.sum()) # 求最值 print(np.min(d)) # 两种方式结果一致 print(d.max()) # 累加 print(d.cumsum()) x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # 求和：针对矩阵 print(x.sum()) print(np.sum(x, axis=0)) # 列sum print(x.sum(axis=1)) # 行sum def array_matrix_slice(): # start,stop,num d = np.linspace(0, 90, 10) print(d) # start,stop,num,endpoint=False d = np.linspace(0, 100, 10, endpoint=False) print(d) # 切片 indices = [1, 3, -1] print(d[indices]) print(d[[1, 3, -1]]) # 外[]表示d的下标，内[]表示下标列表 print(d[:5]) # 按照条件切片 print(d[d >= 50]) # 按照条件查找元素下标 index = np.where(d >= 50) print(index[0]) print(d[index[0]]) e = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) print(e) # 矩阵切片方式 print(e[1:3, 1:3]) # 选取矩阵中某点 print(e[3, 2]) print(e[(3, 2)]) # 花式索引 --- array[[x],[y]] # 坐标对应(1,1),(1,2),(2,1),(2,2) # 最终将回去的所有点排成一个一维数组 print(e[[1, 1, 2, 2], [1, 2, 1, 2]]) # print(e[np.arange(1, 3), 1:3]) # 返回布尔值，（针对对应元素） # 该布尔值也可以用于索引切片 print(e % 2 == 0) print(e[e % 2 == 0]) def array_stack(): a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) b = np.array([0, 1, 0, -1]) c = np.array([0, 1, 0, -1]).reshape(2, 2) print('-------------堆叠--------------') # 垂直堆叠 r = np.vstack([a, b]) print(r) # 水平堆叠 r2 =

np.hstack([a, c])

numpy.hstack

"""Dark matter - electron scattering """ import numericalunits as nu import numpy as np from scipy.interpolate import RegularGridInterpolator, interp1d from scipy.integrate import quad, dblquad from scipy.stats import binom import wimprates as wr export, __all__ = wr.exporter() __all__ += ['dme_shells', 'l_to_letter', 'l_to_number'] # Load form factor and construct interpolators shell_data = wr.load_pickle('dme/dme_ionization_ff.pkl') for _shell, _sd in shell_data.items(): _sd['log10ffsquared_itp'] = RegularGridInterpolator( (_sd['lnks'], _sd['lnqs']), np.log10(_sd['ffsquared']), bounds_error=False, fill_value=-float('inf'),) dme_shells = [(5, 1), (5, 0), (4, 2), (4, 1), (4, 0)] l_to_number = dict(s=0, p=1, d=2, f=3) l_to_letter = {v: k for k, v in l_to_number.items()} @export def shell_str(n, l): if isinstance(l, str): return str(n) + l return str(n) + l_to_letter[l] @export def dme_ionization_ff(shell, e_er, q): """Return dark matter electron scattering ionization form factor Outside the parametrized range, the form factor is assumed 0 to give conservative results. :param shell: Name of atomic shell, e.g. '4p' Note not all shells are included in the data. :param e_er: Electronic recoil energy :param q: Momentun transfer """ if isinstance(shell, tuple): shell = shell_str(*shell) lnq =

np.log(q / (nu.me * nu.c0 * nu.alphaFS))

numpy.log

"""Module to provide functionality to import structures.""" # pylint: disable=no-self-use import io import datetime from collections import OrderedDict import numpy as np import ipywidgets as ipw from traitlets import Instance, Int, List, Unicode, Union, dlink, link, default, observe from sklearn.decomposition import PCA # ASE imports import ase from ase import Atom, Atoms from ase.data import chemical_symbols, covalent_radii # AiiDA imports from aiida.engine import calcfunction from aiida.orm import CalcFunctionNode, CalcJobNode, Data, QueryBuilder, Node, WorkChainNode from aiida.plugins import DataFactory # Local imports from .utils import get_ase_from_file from .viewers import StructureDataViewer from .data import LigandSelectorWidget CifData = DataFactory('cif') # pylint: disable=invalid-name StructureData = DataFactory('structure') # pylint: disable=invalid-name SYMBOL_RADIUS = {key: covalent_radii[i] for i, key in enumerate(chemical_symbols)} class StructureManagerWidget(ipw.VBox): '''Upload a structure and store it in AiiDA database. Attributes: structure(Atoms): trait that contains the selected structure. 'None' if no structure is selected. structure_node(StructureData, CifData): trait that contains AiiDA structure object node_class(str): trait that contains structure_node type (as string). ''' input_structure = Union([Instance(Atoms), Instance(Data)], allow_none=True) structure = Union([Instance(Atoms), Instance(Data)], allow_none=True) structure_node = Instance(Data, allow_none=True, read_only=True) node_class = Unicode() SUPPORTED_DATA_FORMATS = {'CifData': 'cif', 'StructureData': 'structure'} def __init__(self, importers, viewer=None, editors=None, storable=True, node_class=None, **kwargs): """ Arguments: importers(list): list of tuples each containing the displayed name of importer and the importer object. Each object should contain 'structure' trait pointing to the imported structure. The trait will be linked to 'structure' trait of this class. storable(bool): Whether to provide Store button (together with Store format) node_class(str): AiiDA node class for storing the structure. Possible values: 'StructureData', 'CifData' or None (let the user decide). Note: If your workflows require a specific node class, better fix it here. """ # History of modifications self.history = [] # Undo functionality. btn_undo = ipw.Button(description='Undo', button_style='success') btn_undo.on_click(self.undo) self.structure_set_by_undo = False # To keep track of last inserted structure object self._inserted_structure = None # Structure viewer. if viewer: self.viewer = viewer else: self.viewer = StructureDataViewer(downloadable=False) dlink((self, 'structure'), (self.viewer, 'structure')) # Store button. self.btn_store = ipw.Button(description='Store in AiiDA', disabled=True) self.btn_store.on_click(self.store_structure) # Label and description that are stored along with the new structure. self.structure_label = ipw.Text(description='Label') self.structure_description = ipw.Text(description='Description') # Store format selector. data_format = ipw.RadioButtons(options=self.SUPPORTED_DATA_FORMATS, description='Data type:') link((data_format, 'label'), (self, 'node_class')) # Store button, store class selector, description. store_and_description = [self.btn_store] if storable else [] if node_class is None: store_and_description.append(data_format) elif node_class in self.SUPPORTED_DATA_FORMATS: self.node_class = node_class else: raise ValueError("Unknown data format '{}'. Options: {}".format(node_class, list(self.SUPPORTED_DATA_FORMATS.keys()))) self.output = ipw.HTML('') children = [ self._structure_importers(importers), self.viewer, ipw.HBox(store_and_description + [self.structure_label, self.structure_description]) ] structure_editors = self._structure_editors(editors) if structure_editors: structure_editors = ipw.VBox([btn_undo, structure_editors]) accordion = ipw.Accordion([structure_editors]) accordion.selected_index = None accordion.set_title(0, 'Edit Structure') children += [accordion] super().__init__(children=children + [self.output], **kwargs) def _structure_importers(self, importers): """Preparing structure importers.""" if not importers: raise ValueError("The parameter importers should contain a list (or tuple) of " "importers, got a falsy object.") # If there is only one importer - no need to make tabs. if len(importers) == 1: # Assigning a function which will be called when importer provides a structure. dlink((importers[0], 'structure'), (self, 'input_structure')) return importers[0] # Otherwise making one tab per importer. importers_tab = ipw.Tab() importers_tab.children = [i for i in importers] # One importer per tab. for i, importer in enumerate(importers): # Labeling tabs. importers_tab.set_title(i, importer.title) dlink((importer, 'structure'), (self, 'input_structure')) return importers_tab def _structure_editors(self, editors): """Preparing structure editors.""" if editors and len(editors) == 1: link((editors[0], 'structure'), (self, 'structure')) if editors[0].has_trait('selection'): link((editors[0], 'selection'), (self.viewer, 'selection')) if editors[0].has_trait('camera_orientation'): dlink((self.viewer._viewer, '_camera_orientation'), (editors[0], 'camera_orientation')) # pylint: disable=protected-access return editors[0] # If more than one editor was defined. if editors: editors_tab = ipw.Tab() editors_tab.children = tuple(editors) for i, editor in enumerate(editors): editors_tab.set_title(i, editor.title) link((editor, 'structure'), (self, 'structure')) if editor.has_trait('selection'): link((editor, 'selection'), (self.viewer, 'selection')) if editor.has_trait('camera_orientation'): dlink((self.viewer._viewer, '_camera_orientation'), (editor, 'camera_orientation')) # pylint: disable=protected-access return editors_tab return None def store_structure(self, _=None): """Stores the structure in AiiDA database.""" if self.structure_node is None: return if self.structure_node.is_stored: self.output.value = "Already stored in AiiDA [{}], skipping...".format(self.structure_node) return self.btn_store.disabled = True self.structure_node.label = self.structure_label.value self.structure_label.disabled = True self.structure_node.description = self.structure_description.value self.structure_description.disabled = True if isinstance(self.input_structure, Data) and self.input_structure.is_stored: # Make a link between self.input_structure and self.structure_node @calcfunction def user_modifications(source_structure): # pylint: disable=unused-argument return self.structure_node structure_node = user_modifications(self.input_structure) else: structure_node = self.structure_node.store() self.output.value = "Stored in AiiDA [{}]".format(structure_node) def undo(self, _): """Undo modifications.""" self.structure_set_by_undo = True if self.history: self.history = self.history[:-1] if self.history: self.structure = self.history[-1] else: self.input_structure = None self.structure_set_by_undo = False @staticmethod @default('node_class') def _default_node_class(): return 'StructureData' @observe('node_class') def _change_structure_node(self, _=None): with self.hold_trait_notifications(): self._sync_structure_node() def _sync_structure_node(self): """Synchronize the structure_node trait using the currently provided info.""" if len(self.history) > 1: # There are some modifications, so converting from ASE. structure_node = self._convert_to_structure_node(self.structure) else: structure_node = self._convert_to_structure_node(self.input_structure) self.set_trait('structure_node', structure_node) def _convert_to_structure_node(self, structure): """Convert structure of any type to the StructureNode object.""" if structure is None: return None StructureNode = DataFactory(self.SUPPORTED_DATA_FORMATS[self.node_class]) # pylint: disable=invalid-name # If the input_structure trait is set to Atoms object, structure node must be created from it. if isinstance(structure, Atoms): return StructureNode(ase=structure) # If the input_structure trait is set to AiiDA node, check what type if isinstance(structure, Data): # Transform the structure to the StructureNode if needed. if isinstance(structure, StructureNode): return structure # Using self.structure, as it was already converted to the ASE Atoms object. return StructureNode(ase=self.structure) @observe('structure_node') def _observe_structure_node(self, change): """Modify structure label and description when a new structure is provided.""" struct = change['new'] if struct is None: self.btn_store.disabled = True self.structure_label.value = '' self.structure_label.disabled = True self.structure_description.value = '' self.structure_description.disabled = True return if struct.is_stored: self.btn_store.disabled = True self.structure_label.value = struct.label self.structure_label.disabled = True self.structure_description.value = struct.description self.structure_description.disabled = True else: self.btn_store.disabled = False self.structure_label.value = self.structure.get_chemical_formula() self.structure_label.disabled = False self.structure_description.value = '' self.structure_description.disabled = False @observe('input_structure') def _observe_input_structure(self, change): """Returns ASE atoms object and sets structure_node trait.""" # If the `input_structure` trait is set to Atoms object, then the `structure` trait should be set to it as well. self.history = [] if isinstance(change['new'], Atoms): self.structure = change['new'] # If the `input_structure` trait is set to AiiDA node, then the `structure` trait should # be converted to an ASE Atoms object. elif isinstance(change['new'], CifData): # Special treatement of the CifData object str_io = io.StringIO(change['new'].get_content()) self.structure = ase.io.read(str_io, format='cif', reader='ase', store_tags=True) elif isinstance(change['new'], StructureData): self.structure = change['new'].get_ase() else: self.structure = None @observe('structure') def _structure_changed(self, change=None): """Perform some operations that depend on the value of `structure` trait. This function enables/disables `btn_store` widget if structure is provided/set to None. Also, the function sets `structure_node` trait to the selected node type. """ if not self.structure_set_by_undo: self.history.append(change['new']) # If structure trait was set to None, structure_node should become None as well. if self.structure is None: self.set_trait('structure_node', None) self.btn_store.disabled = True return self.btn_store.disabled = False with self.hold_trait_notifications(): self._sync_structure_node() class StructureUploadWidget(ipw.VBox): """Class that allows to upload structures from user's computer.""" structure = Union([Instance(Atoms), Instance(Data)], allow_none=True) def __init__(self, title='', description="Upload Structure"): self.title = title self.file_upload = ipw.FileUpload(description=description, multiple=False, layout={'width': 'initial'}) supported_formats = ipw.HTML( """<a href="https://wiki.fysik.dtu.dk/ase/_modules/ase/io/formats.html" target="_blank"> Supported structure formats </a>""") self.file_upload.observe(self._on_file_upload, names='value') super().__init__(children=[self.file_upload, supported_formats]) def _validate_and_fix_ase_cell(self, ase_structure, vacuum_ang=10.0): """ Checks if the ase Atoms object has a cell set, otherwise sets it to bounding box plus specified "vacuum" space """ cell = ase_structure.cell if (np.linalg.norm(cell[0]) < 0.1 or np.linalg.norm(cell[1]) < 0.1 or np.linalg.norm(cell[2]) < 0.1): # if any of the cell vectors is too short, consider it faulty # set cell as bounding box + vacuum_ang bbox =

np.ptp(ase_structure.positions, axis=0)

numpy.ptp

# -*- coding: utf-8 -*- """ Created on Thu May 7 09:12:56 2015 @author: anderson """ # importing modules from __future__ import division import numpy as np import matplotlib.pyplot as plt import scipy.signal as sig from . import adjust_spines def plot_single_hfo(hfo, envelope = False, xlim =[-1,1], cutoff = None, v = True, axes = None, figure_size = (15,10),dpi=600,saveplot = None): """ Function to plot single Spike Parameters ---------- hfo: HFOObj HFO object to plot envelope: boolean True (default) - plot envelope of filtered signal figure_size: tuple (5,5) (default) - Size of figure, tuple of integers with width, height in inches dpi: int 600 - DPI resolution """ if axes == None: # Creating the figure fig = plt.figure(figsize=figure_size,dpi=dpi) ax1 = fig.add_subplot(311) ax2 = fig.add_subplot(312) ax3 = fig.add_subplot(313) else: ax1 = axes[0] ax2 = axes[1] ax3 = axes[2] # number of points npoints = hfo.waveform.shape[0] time_v = np.linspace(-1,1,npoints,endpoint=True) # creating the axes ax1.plot(time_v,hfo.waveform[:,0],'b') ax1.plot(time_v[hfo.start_idx:hfo.end_idx],hfo.waveform[hfo.start_idx:hfo.end_idx,0],'k') adjust_spines(ax1, ['left']) ax1.set_xlim(xlim) filt = hfo.waveform[:,1] ax2.plot(time_v,filt) ax2.plot(time_v[hfo.start_idx:hfo.end_idx],filt[hfo.start_idx:hfo.end_idx],'k') if envelope: env = hfo.waveform[:,2] ax4 = ax2.twinx() ax4.plot(time_v,env,'g') adjust_spines(ax2, ['left', 'bottom']) ax2.set_xlim(xlim) hfo.spectrum.plot(cutoff = cutoff, v = v, ax = ax3) ax3.set_title('peak freq = ' + str(hfo.spectrum.peak_freq)) adjust_spines(ax3, ['left', 'bottom']) if saveplot != None: if type(saveplot) == str: plt.savefig(saveplot, bbox_inches='tight') else: raise Exception('saveplot should be a string') plt.draw() def plot_mean_hfo(evlist,color='blue', xlim =[-1,1], figure_size=(10,10),dpi=600,saveplot = None): """ Function to plot cluster of HFOs Parameters ---------- evlist: EventList EventList object to plot color: str Color of plot figure_size: tuple (5,5) (default) - Size of figure, tuple of integers with width, height in inches dpi: int 600 - DPI resolution """ f = plt.figure(figsize=figure_size,dpi=dpi) raw = np.array([]) # creating a empty array filt = np.array([]) # creating a empty array pxx = np.array([]) # creating a empty array nwave, a = evlist[0].waveform.shape time_v = np.linspace(-1,1,nwave,endpoint=True) npw, = evlist[0].spectrum.nPxx.shape F = evlist[0].spectrum.F for hfo in evlist: raw = np.append(raw, hfo.waveform[:,0]) #ax1.plot(time_v,hfo.waveform[:,0],lw=.5) filt = np.append(filt, hfo.waveform[:,1]) #ax2.plot(time_v,hfo.waveform[:,1],lw=.5) pxx =

np.append(pxx, hfo.spectrum.nPxx)

numpy.append

import numpy as np import tensorflow as tf from tensorflow.contrib import rnn def _print_success_message(): print('Tests Passed') def test_create_lookup_tables(create_lookup_tables): with tf.Graph().as_default(): test_text = ''' Moe_Szyslak Moe's Tavern Where the elite meet to drink Bart_Simpson Eh yeah hello is Mike there Last name Rotch Moe_Szyslak Hold on I'll check <NAME>ch Mike Rotch Hey has anybody seen Mike Rotch lately Moe_Szyslak Listen you little puke One of these days I'm gonna catch you and I'm gonna carve my name on your back with an ice pick Moe_Szyslak Whats the matter Homer You're not your normal effervescent self Homer_Simpson I got my problems Moe Give me another one Moe_Szyslak Homer hey you should not drink to forget your problems Barney_Gumble Yeah you should only drink to enhance your social skills''' test_text = test_text.lower() test_text = test_text.split() vocab_to_int, int_to_vocab = create_lookup_tables(test_text) # Check types assert isinstance(vocab_to_int, dict),\ 'vocab_to_int is not a dictionary.' assert isinstance(int_to_vocab, dict),\ 'int_to_vocab is not a dictionary.' # Compare lengths of dicts assert len(vocab_to_int) == len(int_to_vocab),\ 'Length of vocab_to_int and int_to_vocab don\'t match. ' \ 'vocab_to_int is length {}. int_to_vocab is length {}'.format(len(vocab_to_int), len(int_to_vocab)) # Make sure the dicts have the same words vocab_to_int_word_set = set(vocab_to_int.keys()) int_to_vocab_word_set = set(int_to_vocab.values()) assert not (vocab_to_int_word_set - int_to_vocab_word_set),\ 'vocab_to_int and int_to_vocab don\'t have the same words.' \ '{} found in vocab_to_int, but not in int_to_vocab'.format(vocab_to_int_word_set - int_to_vocab_word_set) assert not (int_to_vocab_word_set - vocab_to_int_word_set),\ 'vocab_to_int and int_to_vocab don\'t have the same words.' \ '{} found in int_to_vocab, but not in vocab_to_int'.format(int_to_vocab_word_set - vocab_to_int_word_set) # Make sure the dicts have the same word ids vocab_to_int_word_id_set = set(vocab_to_int.values()) int_to_vocab_word_id_set = set(int_to_vocab.keys()) assert not (vocab_to_int_word_id_set - int_to_vocab_word_id_set),\ 'vocab_to_int and int_to_vocab don\'t contain the same word ids.' \ '{} found in vocab_to_int, but not in int_to_vocab'.format(vocab_to_int_word_id_set - int_to_vocab_word_id_set) assert not (int_to_vocab_word_id_set - vocab_to_int_word_id_set),\ 'vocab_to_int and int_to_vocab don\'t contain the same word ids.' \ '{} found in int_to_vocab, but not in vocab_to_int'.format(int_to_vocab_word_id_set - vocab_to_int_word_id_set) # Make sure the dicts make the same lookup missmatches = [(word, id, id, int_to_vocab[id]) for word, id in vocab_to_int.items() if int_to_vocab[id] != word] assert not missmatches,\ 'Found {} missmatche(s). First missmatch: vocab_to_int[{}] = {} and int_to_vocab[{}] = {}'.format( len(missmatches), *missmatches[0]) assert len(vocab_to_int) > len(set(test_text))/2,\ 'The length of vocab seems too small. Found a length of {}'.format(len(vocab_to_int)) _print_success_message() def test_get_batches(get_batches): with tf.Graph().as_default(): test_batch_size = 128 test_seq_length = 5 test_int_text = list(range(1000*test_seq_length)) batches = get_batches(test_int_text, test_batch_size, test_seq_length) # Check type assert isinstance(batches, np.ndarray),\ 'Batches is not a Numpy array' # Check shape assert batches.shape == (7, 2, 128, 5),\ 'Batches returned wrong shape. Found {}'.format(batches.shape) for x in range(batches.shape[2]): assert np.array_equal(batches[0,0,x], np.array(range(x * 35, x * 35 + batches.shape[3]))),\ 'Batches returned wrong contents. For example, input sequence {} in the first batch was {}'.format(x, batches[0,0,x]) assert np.array_equal(batches[0,1,x], np.array(range(x * 35 + 1, x * 35 + 1 + batches.shape[3]))),\ 'Batches returned wrong contents. For example, target sequence {} in the first batch was {}'.format(x, batches[0,1,x]) last_seq_target = (test_batch_size-1) * 35 + 31 last_seq = np.array(range(last_seq_target, last_seq_target+ batches.shape[3])) last_seq[-1] = batches[0,0,0,0] assert

np.array_equal(batches[-1,1,-1], last_seq)

numpy.array_equal

import numpy as np from .utils import log_nowarn, squared_distance_matrix from .checks import _check_size, _check_labels def hgda_train(X, Y, priors=None): """Train a heteroscedastic GDA classifier. Parameters ---------- X : ndarray, shape (m, n) training features. Y : ndarray, shape (m,) training labels with values in {0, ..., k - 1}. priors : ndarray, shape (k,) Prior probabilities for the classes (if None they get estimated from Y). Returns ------- means : ndarray, shape (k, n) class mean vectors. invcovs : ndarray, shape (k, n, n) inverse of the class covariance matrices. priors : ndarray, shape (k,) class prior probabilities. """ _check_size("mn, m, k?", X, Y, priors) Y = _check_labels(Y) k = Y.max() + 1 m, n = X.shape means = np.empty((k, n)) invcovs = np.empty((k, n, n)) if priors is None: priors = np.bincount(Y) / m for c in range(k): means[c, :] = X[Y == c, :].mean(0) cov = np.cov(X[Y == c, :].T) invcovs[c, :, :] = np.linalg.inv(cov) return means, invcovs, priors def hgda_inference(X, means, invcovs, priors): """Heteroscedastic GDA inference. Parameters ---------- X : ndarray, shape (m, n) input features (one row per feature vector). means : ndarray, shape (k, n) class mean vectors. invcovs : ndarray, shape (k, n, n) inverse of the class covariance matrices. priors : ndarray, shape (k,) class prior probabilities. Returns ------- ndarray, shape (m,) predicted labels (one per feature vector). ndarray, shape (m, k) scores assigned to each class. """ _check_size("mn, kn, knn, k", X, means, invcovs, priors) m, n = X.shape k = means.shape[0] scores = np.empty((m, k)) for c in range(k): det = np.linalg.det(invcovs[c, :, :]) diff = X - means[c, :] q = ((diff @ invcovs[c, :, :]) * diff).sum(1) scores[:, c] = 0.5 * q - 0.5 * np.log(det) - log_nowarn(priors[c]) labels = np.argmin(scores, 1) return labels, -scores def ogda_train(X, Y, priors=None): """Train a omoscedastic GDA classifier. Parameters ---------- X : ndarray, shape (m, n) training features. Y : ndarray, shape (m,) training labels with values in {0, ..., k - 1}. priors : ndarray, shape (k,) Prior probabilities for the classes (if None they get estimated from Y). Returns ------- W : ndarray, shape (n, k) weight vectors, each row representing a different class. b : ndarray, shape (k,) vector of biases. """ _check_size("mn, m, k?", X, Y, priors) Y = _check_labels(Y) k = Y.max() + 1 m, n = X.shape means = np.empty((k, n)) cov = np.zeros((n, n)) if priors is None: priors =

np.bincount(Y)

numpy.bincount

import numpy as np from numpy.linalg import inv # Kalman Filter Class class KalmanFilter: """ Simple Kalman filter """ def __init__(self, XY, B=np.array([0]), M=np.array([0])): stateMatrix = np.zeros((4, 1), np.float32) # [x, y, delta_x, delta_y] if XY != 0: stateMatrix = np.array([[XY[0]], [XY[1]], [0], [0]], np.float32) # np.array([XY[0], XY[1],0,0],np.float32) estimateCovariance = np.eye(stateMatrix.shape[0]) transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) processNoiseCov = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) * 0.001 measurementStateMatrix = np.zeros((2, 1), np.float32) observationMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32) measurementNoiseCov = np.array([[1, 0], [0, 1]], np.float32) * 13 X=stateMatrix P=estimateCovariance F=transitionMatrix Q=processNoiseCov Z=measurementStateMatrix H=observationMatrix R=measurementNoiseCov """ Initialise the filter Args: X: State estimate P: Estimate covariance F: State transition model B: Control matrix M: Control vector Q: Process noise covariance Z: Measurement of the state X H: Observation model R: Observation noise covariance """ self.X = X self.P = P self.F = F self.B = B self.M = M self.Q = Q self.Z = Z self.H = H self.R = R def predict(self): """ Predict the future state Args: self.X: State estimate self.P: Estimate covariance self.B: Control matrix self.M: Control vector Returns: updated self.X """ # Project the state ahead self.X = self.F @ self.X + self.B @ self.M self.P = self.F @ self.P @ self.F.T + self.Q return self.X def correct(self, Z): """ Update the Kalman Filter from a measurement Args: self.X: State estimate self.P: Estimate covariance Z: State measurement Returns: updated X """ K = self.P @ self.H.T @ inv(self.H @ self.P @ self.H.T + self.R) self.X += K @ (Z - self.H @ self.X) self.P = self.P - K @ self.H @ self.P return self.X """ needed variables to instantly initialize kalman with just parsing X,Y variables (x,y) """ def init_kalman(XY): kalman = None stateMatrix = np.zeros((4, 1), np.float32) # [x, y, delta_x, delta_y] if XY != 0: stateMatrix = np.array([[XY[0]], [XY[1]],[0],[0]],np.float32) # np.array([XY[0], XY[1],0,0],np.float32) estimateCovariance = np.eye(stateMatrix.shape[0]) transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) processNoiseCov = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) * 0.001 measurementStateMatrix = np.zeros((2, 1), np.float32) observationMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32) measurementNoiseCov =

np.array([[1, 0], [0, 1]], np.float32)

numpy.array

# Copyright (c) 2016 by <NAME> and the other collaborators on GitHub at # https://github.com/rmjarvis/Piff All rights reserved. # # Piff is free software: Redistribution and use in source and binary forms # with or without modification, are permitted provided that the following # conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the disclaimer given in the accompanying LICENSE # file. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the disclaimer given in the documentation # and/or other materials provided with the distribution. """ .. module:: interp """ from __future__ import print_function import numpy as np import scipy.linalg import galsim import warnings from .interp import Interp from .star import Star, StarFit class BasisInterp(Interp): r"""An Interp class that works whenever the interpolating functions are linear sums of basis functions. Does things the "slow way" to be stable to degenerate fits to individual stars, instead of fitting to parameter sets produced by single stars. First time coding this we will assume that each element of the PSF parameter vector p is a linear combination of the same set of basis functions across the focal plane, .. math:: p_i = \sum_{j} q_{ij} K_j(u,v,other stellar params). The property degenerate_points is set to True to indicate that this interpolator uses the alpha/beta quadratic form of chisq for each sample, rather than assuming that a best-fit parameter vector is available at every sample. Internally we'll store the interpolation coefficients in a 2d array of dimensions (nparams, nbases) Note: This is an abstract base class. The concrete class you probably want to use is BasisPolynomial. """ def __init__(self): self.degenerate_points = True # This Interpolator uses chisq quadratic forms self.use_qr = False # The default. May be overridden by subclasses. self.q = None def initialize(self, stars, logger=None): """Initialize both the interpolator to some state prefatory to any solve iterations and initialize the stars for use with this interpolator. This class will initialize everything to have constant PSF parameter vector taken from the first Star in the list. :param stars: A list of Star instances to use to initialize. :param logger: A logger object for logging debug info. [default: None] :returns: A new list of Stars which have their parameters initialized. """ c = np.mean([s.fit.params for s in stars], axis=0) self.q = c[:,np.newaxis] * self.constant(1.)[np.newaxis,:] stars = self.interpolateList(stars) return stars def basis(self, star): """Return 1d array of polynomial basis values for this star :param star: A Star instance :returns: 1d numpy array with values of u^i v^j for 0<i+j<=order """ raise NotImplementedError("Cannot call `basis` for abstract base class BasisInterp. " "You probably want to use BasisPolynomial.") def constant(self, value=1.): """Return 1d array of coefficients that represent a polynomial with constant value. :param value: The value to use as the constant term. [default: 1.] :returns: 1d numpy array with values of u^i v^j for 0<i+j<=order """ raise NotImplementedError("Cannot call `constant` for abstract base class BasisInterp. " "You probably want to use BasisPolynomial.") def solve(self, stars, logger=None): """Solve for the interpolation coefficients given some data. The StarFit element of each Star in the list is assumed to hold valid alpha and beta members specifying depending of chisq on differential changes to its parameter vector. :param stars: A list of Star instances to interpolate between :param logger: A logger object for logging debug info. [default: None] """ logger = galsim.config.LoggerWrapper(logger) if self.q is None: raise RuntimeError("Attempt to solve() before initialize() of BasisInterp") # The inputs to this function are for each star i the design equation is # # A_i p = b_i # # The parameters at each stars location are modeled as # # p_i = q . K_i(u_i,v_i,...) # # where K is the basis vector for the particular location of this star and q is # our 2d array of fitting parameters. There is a row in q for each element of p, # and a column in q for each element in K. # # Each star then gives us nparam equations, which become rows of our full design matrix. # Consider the first equation for one star: # # A[0,:] p = b[0] # A[0,:] q K = b[0] # # Each element in the A[0,:]T KT outer product is the coefficient of a single element q_mn. # So we can rewrite this as # # (A[0,:,np.newaxis] * K[np.newaxis,:]).flatten() * q.flatten() = b[0] # # Thus, the equation we want for our big design matrix has rows with # # (A_i[j,:,np.newaxis] * K_i[np.newaxis,:]).flatten() # # and the b vector has elements # # b_i[j] # # Now, the typical usage of this class is such that the size of A is # # (nstars * npixels x nparam * nbasis) # # Typical numbers are: # # nstars ~ 100 # npixels ~ 500 # nparam ~ 400 # nbasis ~ 6 # # So the size of A is ~ 50,000 x 2,400, which for double precision is about 1 GB. # Considering that a particular CCD in a dense field might have up to 10 times this many # stars, this is a very steep memory demand for this function. # # Therefore, we make the default behavior be to construct AT A and solve AT A dq = AT b. # But we have an option to use QR decomposition of A instead, which may have stability # advantages, since the condition of AT A is the square of the condition of A, so the # QR method often has fewer numerical problems than the direct method for large matrices. # It just requires a lot of memory. logger = galsim.config.LoggerWrapper(logger) if self.use_qr: self._solve_qr(stars, logger) else: self._solve_direct(stars, logger) def _solve_qr(self, stars, logger): """The implementation of solve() when use_qr = True. """ # First, build up one chunk for each star. We'll stack them later. A_chunks = [] b_chunks = [] for s in stars: # Get the basis function values at this star K = self.basis(s) b_chunks.append(s.fit.b) A_chunks.append((s.fit.A[:,:,np.newaxis] * K[np.newaxis,:]).reshape( s.fit.A.shape[0], s.fit.A.shape[1] * len(K))) # Now stack the chunks into a single A and b. A = np.vstack(A_chunks) b = np.concatenate(b_chunks) if A.shape[0] < A.shape[1]: raise RuntimeError("Too few constraints for solution. (Probably too few stars)") # Note: The following snippet is the straightforward way to do this using the # scipy qr function. However, it generates the full Q matrix, which is slow. # Using the lapack functions directly is slightly more obfuscated, but faster. #Q,R = scipy.linalg.qr(A, mode='economic', overwrite_a=True) #dq = Q.T.dot(b) #dq = scipy.linalg.solve_triangular(R, dq, overwrite_b=True, check_finite=False) # This computes A -> Q R, where QR are stored in a single matrix along with an # ancillary tau matrix that helps define the Householder matrices used to build # the real Q. QR, tau, work, info = scipy.linalg.lapack.dgeqrf(A) m = A.shape[1] # Check the diagonal values of the R matrix. The following calculation isn't a # real condition number, since the regular QR decomposition isn't actually rank # revealing. However, we only get problems with the normal QR decomposition if # this number is very small, in which case we should switch to a QRP decomposition. abs_Rdiag = np.abs(np.diag(QR)) cond = np.min(abs_Rdiag) / np.max(abs_Rdiag) if cond < 1.e-12: # Note: this calculation is much slower, but it is safe to use even for # singular inputs, so it will always produce a valid answer. logger.info('Nominal condition is %s (min, max = %s, %s)', cond, np.min(abs_Rdiag), np.max(abs_Rdiag)) logger.info('Switching to QRP solution') QR, P, tau, work, info = scipy.linalg.lapack.dgeqp3(A, overwrite_a=True) P[:] -= 1 # Switch to python 0-based indexing. abs_Rdiag = np.abs(np.diag(QR)) cond = np.min(abs_Rdiag) / np.max(abs_Rdiag) logger.info('Condition for QRP is %s (min, max = %s, %s)', cond, np.min(abs_Rdiag), np.max(abs_Rdiag)) # Skip any rows of R that have essentially 0 on the diagonal. k = np.sum(abs_Rdiag > 1.e-15 * np.max(abs_Rdiag)) logger.debug('k = %d, m = %d',k,m) else: P = None k = m # The next steps are the same regardless of whether we pivoted or not. # This computes y = Q.T b dq, work, info = scipy.linalg.lapack.dormqr('L', 'T', QR, tau, b, work) # Cut dq down to the first m elements, since it is still the size of b here. dq = dq[:m] # Solve R dq = y (in place) scipy.linalg.lapack.dtrtrs(QR[:k,:k], dq[:k], overwrite_b=True) if P is not None: # Apply the permuation if we have one. dq1 = dq dq1[k:m] = 0. dq =

np.empty(m)

numpy.empty

from __future__ import division # Python 2 users only from __future__ import print_function __doc__ = """ numpy utility functions used for tSNE modelling""" import numpy as np def Hbeta_vec(distances, betas): """ Function that computes the Gaussian kernel values given a vector of squared Euclidean distances, and the precision of the Gaussian kernel. The function also computes the perplexity of the distribution. From Parametric t-SNE for matlab at https://lvdmaaten.github.io/tsne/ Parameters ---------- distances: 2-d array_like, (N,N) Square matrix of distances between data points betas: 1-d array_like, (N,) Vector of precisions of the Gaussian kernel. beta = (2 sigma**2)^-1 Returns ------- H: 1-d array_like, (N,) Entropy of each point p_matr: 2-d array_like, (N,N) array of probability values The scalar formula for p_matr is: p_matr = np.exp(-D * beta) / sum(np.exp(-D * beta)) This funcion is vectorized and calculates the full P matrix """ beta_matr = (betas[:,np.newaxis] * np.ones_like(distances)) p_matr = np.exp(-distances * beta_matr) sumP = np.sum(p_matr, axis=1) H = np.log(sumP) + (betas * np.sum(distances * p_matr, axis=1)) / sumP p_matr = p_matr / (sumP[:,np.newaxis]*np.ones_like(p_matr)) return H, p_matr def Hbeta_scalar(distances, beta): """ Function that computes the Gaussian kernel values given a vector of squared Euclidean distances, and the precision of the Gaussian kernel. The function also computes the perplexity of the distribution. From Parametric t-SNE for matlab at https://lvdmaaten.github.io/tsne/ Parameters ---------- distances: 1-d array_like, (N,) Distance between the current data point and all others beta: float Precision of the Gaussian kernel. beta = (2 sigma**2)^-1 Returns ------- H: float Entropy p_matr: 1-d array_like, (N,) array of probability values p_matr = np.exp(-D * beta) / sum(np.exp(-D * beta)) """ p_matr = np.exp(-distances * beta) sumP = np.sum(p_matr) H =

np.log(sumP)

numpy.log

import numpy as np from sklearn.model_selection import train_test_split import catboost class CatBoostClassifier: def __init__(self, **params): """ Catboost Classifier wrapper """ default = {'verbose': 0, 'n_estimators': 1000, 'allow_writing_files': False} for k, v in default.items(): if k not in params.keys(): params[k] = v self.default = default self.params = params self.model = catboost.CatBoostClassifier(**params) self.hasPredictProba = True self.classes_ = None self.trained = False self.callbacks = None self.verbose = 0 self.early_stopping_rounds = 100 if 'early_stopping_rounds' in params.keys(): self.early_stopping_rounds = params.pop('early_stopping_rounds') if 'verbose' in params.keys(): self.verbose = params.pop('verbose') self.set_params(**params) self._estimator_type = 'classifier' def set_params(self, **params): for k, v in self.default.items(): if k not in params.keys(): params[k] = v self.model.set_params(**params) return self def get_params(self, **args): return self.model.get_params(**args) def fit(self, X, y): # Split data train_x, test_x, train_y, test_y = train_test_split(X, y, stratify=y, test_size=0.1) # Set Attributes self.classes_ =

np.unique(y)

numpy.unique

""" Parallax fitting and computation of distances """ import os import warnings import collections from bisect import bisect_left import h5py import numpy as np import scipy.stats from scipy.interpolate import interp1d from astropy.coordinates import SkyCoord from healpy import ang2pix from dustmaps.sfd import SFDQuery from dustmaps.bayestar import BayestarWebQuery from astropy.utils.exceptions import AstropyWarning import basta.utils_distances as udist import basta.constants as cnsts import basta.stats as stats import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt # Don't print Astropy warnings (catch error caused by mock'ing astropy in Sphinx) try: warnings.filterwarnings("ignore", category=AstropyWarning) except AssertionError: pass try: from basta._dustpath import __dustpath__ except ModuleNotFoundError: print("\nCannot find path to dustmaps. Did you run 'setup.py'?\n") raise def LOS_reddening(distanceparams): """ Returns color excess E(B-V) for a line of sight using a pre-downloaded 3D extinction map provided by Green et al. 2015/2018 - see http://argonaut.skymaps.info/. The extinction map is only computed for distance modulus between :math:`4 < m-M < 19` in units of magnitude. Parameters ---------- distanceparams : dictionary Dictionary with distance parameters Returns ------- EBV : function excess color function """ if "EBV" in distanceparams: return lambda x: np.asarray( np.random.normal( distanceparams["EBV"][1], distanceparams["EBV"][2] - distanceparams["EBV"][1], size=[ len(i) if isinstance(i, collections.Iterable) else 1 for i in [x] ][0], ) ) frame = distanceparams["dustframe"] # Convert to galactic coordinates if frame == "icrs": ra = distanceparams["RA"] dec = distanceparams["DEC"] c = SkyCoord(ra=ra, dec=dec, frame="icrs", unit="deg") elif frame == "galactic": lon = distanceparams["lon"] lat = distanceparams["lat"] c = SkyCoord(l=lon, b=lat, frame="galactic", unit="deg") else: raise ValueError("Unknown dust map frame for computing reddening!") # Load extinction data cube pathmap = os.path.join(__dustpath__, "bayestar/bayestar2019.h5") dcube = h5py.File(pathmap, "r") # Distance modulus bins bin_edges = dcube["/pixel_info"].attrs["DM_bin_edges"] dmbin = bin_edges + (bin_edges[1] - bin_edges[0]) / 2.0 # If webquery fails use local copy of dustmap try: bayestar = BayestarWebQuery(version="bayestar2019") Egr_samples = bayestar(c, mode="samples") except Exception: # contains positional info pinfo = dcube["/pixel_info"][:] nsides = np.unique(dcube["/pixel_info"][:]["nside"]) # Convert coordinates to galactic frame lon = c.galactic.l.deg lat = c.galactic.b.deg # Convert l,b[deg] to theta,phi[rad] theta = np.pi / 2.0 - lat * np.pi / 180.0 phi = lon * np.pi / 180.0 # To check if we are within the maps coordinates Egr_samples = np.array([np.nan]) # Run through nsides for ncont in reversed(nsides): healpixNside = ang2pix(ncont, theta, phi, nest=True) indNside = np.where(np.asarray([x[0] for x in pinfo]) == ncont)[0] dcubepixNside = [x[1] for x in pinfo[indNside]] kNside = int(bisect_left(dcubepixNside, healpixNside)) + indNside[0] if healpixNside == dcubepixNside[kNside - indNside[0]]: index = kNside Egr_samples = dcube["/samples"][index] break # If coordinates outside dust map, use Schegel if np.isnan(Egr_samples).any(): print("WARNING: Coordinates outside dust map boundaries!") print("Default to Schegel 1998 dust map") sfd = SFDQuery() EBV_fun = lambda x: np.full_like(x, sfd(c)) return EBV_fun Egr_med, Egr_err = [], [] for i in range(len(dmbin)): Egr_med.append(np.nanmedian(Egr_samples[:, i])) Egr_err.append(np.nanstd(Egr_samples[:, i])) Egr_med_fun = interp1d( dmbin, Egr_med, bounds_error=False, fill_value=(0, np.max(Egr_med)) ) Egr_err_fun = interp1d( dmbin, Egr_err, bounds_error=False, fill_value=np.max(Egr_err) ) dcube.close() def EBV_fun(dm): Egr = np.asarray(np.random.normal(Egr_med_fun(dm), Egr_err_fun(dm))) EBV = cnsts.extinction.Conv_Bayestar * Egr return EBV return EBV_fun def get_EBV(dist, LOS_EBV, debug=False, outfilename=""): """ Estimate E(B-V) by drawing distances from a normal parallax distribution with EDSD prior. Parameters ----- dist : array The drawn distances LOS_EBV : func EBV function. debug : bool, optional Debug flag. If True, this function outputs two plots, one of distance modulus vs. E(B-V) and a histogram of the E(B-V). outfilename : str, optional Name of directory of where to put plots outputted if debug is True. Returns ------- EBVs : array E(B-V) at distances """ dmod = 5 * np.log10(dist / 10) EBVs = LOS_EBV(dmod) if debug: plt.figure() plt.plot(dmod, EBVs, ".") plt.xlabel("dmod") plt.ylabel("E(B-V)") plt.savefig(outfilename + "_DEBUG_dmod_EBVs.png") plt.close() return EBVs def get_absorption(EBV, fitparams, filt): """ Compute extinction coefficient Rzeta for band zeta. Using parameterized law from Casagrande & VandenBerg 2014. Valid for: logg = 4.1 Teff = 5250 - 7000K Fe/H = -2.0 - 0.25 a/Fe = -0.4 - 0.4 Assume nominal reddening law with RV=3.1. In a band zeta, Azeta = Rzeta*E(B-V). Parameters ---------- EBV : array E(B-V) values fitparams : dict The fitting params in inputparams. filt : str Name of the given filter Returns ------- R*EBV : array Extinction coefficient times E(B-V) """ N = len(EBV) table = cnsts.extinction.R i_filter = table["Filter"] == filt if not any(i_filter) or table["RZ_mean"][i_filter] == 0: print("WARNING: Unknown extinction coefficient for filter: " + filt) print(" Using reddening law coefficient R = 0.") return np.zeros(N) metal = "MeH" if "MeH" in fitparams else "FeH" if "Teff" not in fitparams or metal not in fitparams: R = np.ones_like(EBV) * table["RZ_mean"][i_filter].item() else: Teff_val, Teff_err = fitparams["Teff"] metal_val, metal_err = fitparams[metal] Teff = np.random.normal(Teff_val, Teff_err, size=N) FeH = np.random.normal(metal_val, metal_err, size=N) a0 = table["a0"][i_filter].item() a1 = table["a1"][i_filter].item() a2 = table["a2"][i_filter].item() a3 = table["a3"][i_filter].item() T4 = 1e-4 * Teff R = a0 + T4 * (a1 + a2 * T4) + a3 * FeH return R * EBV def add_absolute_magnitudes( inputparams, n=1000, k=1000, outfilename="", debug=False, use_gaussian_priors=False ): """ Convert apparent magnitudes to absolute magnitudes using the distance Extinction E(B-V) is estimated based on Green et al. (2015) dust map. Extinction is converted to reddening using Casagrande & VandenBerg 2014. The converted colors and magnitudes are added to fitsparams. Parameters ---------- inputparams : dict Inputparams used in BASTA run. n : int Number of samples from parallax range k : int Number of samples from apparent magnitude range. outfilename : str, optional Name of directory of where to put plots outputted if debug is True. debug : bool, optional Debug flag. If True, debugging plots will be outputted. use_gaussian_priors : bool, optional If True, gaussian priors will be used for apparent magnitude in the distance computation. Returns ------- inputparams : dict Modified version of inputparams including absolute magnitudes. """ if "parallax" not in inputparams["fitparams"]: return inputparams print("\nPreparing distance/parallax/magnitude input ...", flush=True) qs = [0.158655, 0.5, 0.841345] fitparams = inputparams["fitparams"] distanceparams = inputparams["distanceparams"] if use_gaussian_priors: inputparams["fitparams"][filt] = [val, std] return inputparams # Get apparent magnitudes from input data mobs = distanceparams["m"] mobs_err = distanceparams["m_err"] if len(mobs.keys()) == 0: raise ValueError("No filters were given") # Convert the inputted parallax in mas to as plxobs = fitparams["parallax"][0] * 1e-3 plxobs_err = fitparams["parallax"][1] * 1e-3 L = udist.EDSD(None, None) * 1e3 fitparams.pop("parallax") # Sample distances more densely around the mode of the distance distribution # See Bailer-Jones 2015, Eq 19 coeffs = [1 / L, -2, plxobs / (plxobs_err**2), -1 / (plxobs_err**2)] roots = np.roots(coeffs) if np.sum((np.isreal(roots))) == 1: (mode,) = np.real(roots[np.isreal(roots)]) else: assert np.sum((np.isreal(roots))) == 3 if plxobs >= 0: mode = np.amin(np.real(roots[np.isreal(roots)])) else: (mode,) = np.real(roots[roots > 0]) # By sampling linearly in quantiles, the probablity mass is equal for the samples bla = scipy.stats.norm.cdf(0, loc=mode, scale=1000) + 0.01 dist = scipy.stats.norm.ppf( np.linspace(bla, 0.96, n - n // 2), loc=mode, scale=1000 ) # We also want to sample across the entire range. lindist = 10 ** np.linspace(-0.4, 4.4, n // 2) assert np.all(np.isfinite(dist)) assert np.all(dist > 0) dist = np.concatenate([dist, lindist]) dist = np.sort(dist) lldist = udist.compute_distlikelihoods( dist, plxobs, plxobs_err, L, outfilename=outfilename, debug=debug ) dists = np.repeat(dist, k) lldists = np.repeat(lldist, k) # Get EBV values LOS_EBV = LOS_reddening(distanceparams) EBV = get_EBV(dist, LOS_EBV, debug=debug, outfilename=outfilename) EBVs = np.repeat(EBV, k) distanceparams["As"] = {} llabsms_joined = np.zeros(n * k) for filt in mobs.keys(): # Sample apparent magnitudes over the entire parameter range if filt in cnsts.distanceranges.filters: m = np.linspace( cnsts.distanceranges.filters[filt]["min"], cnsts.distanceranges.filters[filt]["max"], k - k // 2, ) else: m = np.linspace(-10, 25, k - k // 2) m = np.concatenate( [ m, scipy.stats.norm.ppf( np.linspace(0.04, 0.96, k // 2), loc=mobs[filt], scale=mobs_err[filt], ), ] ) m = np.sort(m) llm = udist.compute_mslikelihoods(m, mobs[filt], mobs_err[filt]) ms = np.tile(m, n) llms = np.tile(llm, n) assert len(dists) == len(ms) == n * k A = get_absorption(EBV, fitparams, filt) As = np.repeat(A, k) absms = udist.compute_absmag(ms, dists, As) # Construct likelihood distribution llabsms = llms + lldists llabsms_joined += llabsms llabsms -=

np.amax(llabsms)

numpy.amax

#Differential Photometry script written in April 2019 by SKB, MP, KWD for WIYN 0.9m HDI data #This script calculates photometry and differential photometry for all stars in an image and takes target positions to pull out differential photometry of target stars. Auto calculates comparison stars based on lowest percentile of variability of stars in the image. # Script is run through a shell jupyter notebook script. #Initially created by <NAME> as a juypter notebook 2018 #Turned into modular form by <NAME> April 2019 #Modified by <NAME>, <NAME>, <NAME> April 2019 # python 2/3 compatibility from __future__ import print_function # numerical python import numpy as np # file management tools import glob import os # good module for timing tests import time # plotting stuff import matplotlib.pyplot as plt import matplotlib.cm as cm # ability to read/write fits files from astropy.io import fits # fancy image combination technique from astropy.stats import sigma_clip # median absolute deviation: for photometry from astropy.stats import mad_std # photometric utilities from photutils import DAOStarFinder,aperture_photometry, CircularAperture, CircularAnnulus, Background2D, MedianBackground # periodograms from astropy.stats import LombScargle from regions import read_ds9, write_ds9 from astropy.wcs import WCS import warnings import pandas as pd from astropy.coordinates import SkyCoord from astropy import units as u from astropy.visualization import ZScaleInterval import numpy.ma as ma warnings.filterwarnings("ignore") np.set_printoptions(suppress=True) def construct_astrometry(hdr_wcs): ''' construct_astrometry make the pixel to RA/Dec conversion (and back) from the header of an astrometry.net return inputs ------------------------------ hdr_wcs : header with astrometry information, typically from astrometry.net returns ------------------------------ w : the WCS instance ''' # initialize the World Coordinate System w = WCS(naxis=2) # specify the pixel to RA/Dec conversion w.wcs.ctype = ["RA---TAN", "DEC--TAN"] w.wcs.cd = np.array([[hdr_wcs['CD1_1'],hdr_wcs['CD1_2']],[hdr_wcs['CD2_1'],hdr_wcs['CD2_2']]]) w.wcs.crpix = [hdr_wcs['CRPIX1'], hdr_wcs['CRPIX2']] w.wcs.crval = [hdr_wcs['CRVAL1'],hdr_wcs['CRVAL2']] w.wcs.cunit = [hdr_wcs['CUNIT1'],hdr_wcs['CUNIT2']] w.wcs.latpole = hdr_wcs['LATPOLE'] #w.wcs.lonpole = hdr_wcs['LONPOLE'] w.wcs.theta0 = hdr_wcs['LONPOLE'] w.wcs.equinox = hdr_wcs['EQUINOX'] # calculate the RA/Dec to pixel conversion w.wcs.fix() w.wcs.cdfix() w.wcs.set() # return the instance return w def StarFind(imname, FWHM, nsigma): ''' StarFind find all stars in a .fits image inputs ---------- imname: name of .fits image to open. FWHM: fwhm of stars in field nsigma: number of sigma above background above which to select sources. (~3 to 4 is a good estimate) outputs -------- xpos: x positions of sources ypos: y positions of sources nstars: number of stars found in image ''' #open image im,hdr=fits.getdata(imname, header=True) im = np.array(im).astype('float') #determine background bkg_sigma = mad_std(im) print('begin: DAOStarFinder') daofind = DAOStarFinder(fwhm=FWHM, threshold=nsigma*bkg_sigma, exclude_border=True) sources = daofind(im) #x and y positions xpos = sources['xcentroid'] ypos = sources['ycentroid'] #number of stars found nstars = len(xpos) print('found ' + str(nstars) + ' stars') return xpos, ypos, nstars def makeApertures(xpos, ypos, aprad,skybuff, skywidth): ''' makeApertures makes a master list of apertures and the annuli inputs --------- xpos: list - x positions of stars in image ypos: list - y positions of stars in image aprad: float - aperture radius skybuff: float - sky annulus inner radius skywidth: float - sky annulus outer radius outputs -------- apertures: list - list of aperture positions and radius annulus_apertures: list - list of annuli positions and radius see: https://photutils.readthedocs.io/en/stable/api/photutils.CircularAperture.html#photutils.CircularAperture for more details ''' # make the master list of apertures apertures = CircularAperture((xpos, ypos), r=aprad) annulus_apertures = CircularAnnulus((xpos, ypos), r_in=aprad+skybuff, r_out=aprad+skybuff+skywidth) apers = [apertures, annulus_apertures] return apertures, annulus_apertures def apertureArea(apertures): ''' returns the area of the aperture''' return apertures.area() ### should be apertures def backgroundArea(back_aperture): '''returns the area of the annuli''' return back_aperture.area() ### should be annulus_apertures def doPhotometry(imglist, xpos, ypos, aprad, skybuff, skywidth,timekey='MJD-OBS',verbose=1): ''' doPhotomoetry* determine the flux for each star from aperture photometry inputs ------- imglist: list - list of .fits images xpos, ypos: lists - lists of x and y positions of stars aprad, skybuff, skywidth: floats - aperture, sky annuli inner, sky annuli outer radii outputs ------- Times: list - time stamps of each observation from the .fits header Photometry: list - aperture photometry flux values found at each xpos, ypos position ''' #number of images nimages = len(imglist) nstars = len(xpos) print('Found {} images'.format(nimages)) #create lists for timestamps and flux values Times = np.zeros(nimages) Photometry = np.zeros((nimages,nstars)) print('making apertures') #make the apertures around each star apertures, annulus_apertures = makeApertures(xpos, ypos, aprad, skybuff, skywidth) #plot apertures plt.figure(figsize=(12,12)) interval = ZScaleInterval() vmin, vmax = interval.get_limits(fits.getdata(imglist[0])) plt.imshow(fits.getdata(imglist[0]), vmin=vmin,vmax=vmax, origin='lower') apertures.plot(color='white', lw=2) #annulus_apertures.plot(color='red', lw=2) plt.title('apertures') plt.show() #determine area of apertures area_of_ap = apertureArea(apertures) #determine area of annuli area_of_background = backgroundArea(annulus_apertures) checknum = np.linspace(0,nimages,10).astype(int) #go through each image and run aperture photometry for ind in np.arange(nimages): if ((ind in checknum) & (verbose==1)): print('running aperture photometry on image: ', ind ) if (verbose>1): print('running aperture photometry on image: ', ind ) #open image data_image, hdr = fits.getdata(imglist[ind], header=True) #find time stamp and append to list Times[ind] = hdr[timekey] #do photometry phot_table = aperture_photometry(data_image, (apertures,annulus_apertures)) #determine flux: (aperture flux) - [(area of aperture * annuli flux)/area of background ] flux0 = np.array(phot_table['aperture_sum_0']) - (area_of_ap/area_of_background)*np.array(phot_table['aperture_sum_1']) #append to list Photometry[ind,:] = flux0 return Times,Photometry def doPhotometryError(imglist,xpos, ypos,aprad, skybuff, skywidth, flux0, GAIN=1.3, manual = False, **kwargs): ''' doPhotometryError determine error in photometry from background noise two options: - use sigma clipping and use whole background - manually input background box positions as kwargs inputs -------- imglist: list - list of .fits images xpos, ypos: lists - lists of x and y positions of stars aprad, skybuff, skywidth: floats - aperture, sky annuli inner, sky annuli outer radii flux0: list - aperture photometry found from doPhotometry() function GAIN: float - average gain manual: boolean - switch between manually inputting box (True) or using sigma clipping (False) if True -- must have kwargs manual = False is default **kwargs kwargs[xboxcorner]: float - x edge of box in pixel coords kwargs[yboxcorner]: float - y edge of box in pixel coords kwargs[boxsize]: float - size of box in pixel coords ''' # find number of images in list nimages = len(imglist) nstars = len(xpos) print('Found {} images'.format(nimages)) #make apertures apertures, annulus_apertures = makeApertures(xpos, ypos, aprad, skybuff, skywidth) #find areas of apertures and annuli area_of_ap = apertureArea(apertures) area_of_background = backgroundArea(annulus_apertures) checknum = np.linspace(0,nimages,10).astype(int) #find error in photometry ePhotometry = np.zeros((nimages,nstars)) for ind in np.arange(nimages): #open images im = fits.getdata(imglist[ind]) if ind in checknum: print('running error analysis on image ', ind) #determine variance in background if manual == True: #manual method -- choose back size skyvar = np.std(im[kwargs['xboxcorner']:kwargs['xboxcorner']+kwargs['boxsize'],kwargs['yboxcorner']:kwargs['yboxcorner']+kwargs['boxsize']])**2. err1 = skyvar*(area_of_ap)**2./(kwargs['boxsize']*kwargs['boxsize']) # uncertainty in mean sky brightness if manual == False: #automatic method -- use sigma clipping filtered_data = sigma_clip(im, sigma=3) skyvar = np.std(filtered_data)**2. err1 = skyvar*(area_of_ap)**2./(

np.shape(im[0])

numpy.shape

import numpy as np import os import sample_loader import pathlib import glob import re import sys sys.path.append("../base/CropSample") sys.path.append("../base/IntegrateColumnIntoGrid") import CropSample import IntegrateColumnIntoGrid from scipy import ndimage import JSONHelper def crop_from_volume(s0, x, y, z): sdf = np.array([], dtype=np.float32) pdf =

np.array([], dtype=np.float32)

numpy.array

from luminaire.model.base_model import BaseModel, BaseModelObject, BaseModelHyperParams from luminaire.exploration.data_exploration import DataExploration from luminaire.model.model_utils import LADHolidays from typing import Dict, Tuple import pandas as pd import warnings warnings.filterwarnings('ignore') class LADStructuralHyperParams(BaseModelHyperParams): """ Exception class for Luminaire structural anomaly detection model. :param bool include_holidays_exog: whether to include holidays as exogenous variables in the regression. Holidays are defined in :class:`~model.model_utils.LADHolidays` :param int p: Order for the AR component of the model. :param int q: Order for the MA component of the model. :param bool is_log_transformed: A flag to specify whether to take a log transform of the input data. If the data contain negatives, is_log_transformed is ignored even though it is set to True. :param int max_ft_freq: The maximum frequency order for the Fourier transformation. """ def __init__(self, include_holidays_exog=True, p=2, q=2, is_log_transformed=True, max_ft_freq=3): super(LADStructuralHyperParams, self).__init__( model_name="LADStructuralModel", include_holidays_exog=include_holidays_exog, p=p, q=q, is_log_transformed=is_log_transformed, max_ft_freq=max_ft_freq, ) class LADStructuralError(Exception): """ Exception class for Luminaire structural anomaly detection model. """ def __init__(self, message): message = f'LAD structural failed! Error: {message}' super(LADStructuralError, self).__init__(message) class LADStructuralModel(BaseModel): """ A LAD structural time series model. :param dict hyper_params: Hyper parameters for Luminaire structural modeling. See :class:`luminaire.model.lad_structural.LADStructuralHyperParams` for detailed information. :param str freq: The frequency of the time-series. A `Pandas offset`_ such as 'D', 'H', or 'M'. Luminaire currently supports the following pandas frequency types: 'H', 'D', 'W', 'W-SUN', 'W-MON', 'W-TUE', 'W-WED', 'W-THU', 'W-FRI', 'W-SAT'. :param int min_ts_length: The minimum required length of the time series for training. :param int max_ts_length: The maximum required length of the time series for training. The input time series will be truncated if the length is greater than this value. :param float min_ts_mean: Minimum average values in the most recent window of the time series. This optional parameter can be used to avoid over-alerting from noisy low volume time series. :param int min_ts_mean_window: Size of the most recent window to calculate min_ts_mean. .. Note :: This class should be used to manually configure the structural model. Exact configuration parameters can be found in `luminaire.model.lad_structural.LADStructuralHyperParams`. Optimal configuration can be obtained by using Luminaire hyperparameter optimization. .. _statsmodels docs: http://www.statsmodels.org/stable/generated/statsmodels.tsa.arima_model.ARIMA.html .. _Pandas offset: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects >>> hyper = {"include_holidays_exog": 0, "is_log_transformed": 1, "max_ft_freq": 2, "p": 5, "q": 1} lad_struct_model = LADStructuralModel(hyper_params=hyper, freq='D') >>> lad_struct_model <luminaire.model.lad_structural.LADStructuralModel object at 0x103efe320> """ __version__ = "2.0" _target_metric = 'raw' _imputed_metric = 'interpolated' _sig_level = 0.10 _sig_level_extreme = 0.001 def __init__(self, hyper_params: LADStructuralHyperParams().params or None, freq, min_ts_length=None, max_ts_length=None, min_ts_mean=None, min_ts_mean_window=None, **kwargs): self.hyper_params = hyper_params max_scoring_length_dict = { 'H': 48, 'D': 10, 'W': 8, 'W-SUN': 8, 'W-MON': 8, 'W-TUE': 8, 'W-WED': 8, 'W-THU': 8, 'W-FRI': 8, 'W-SAT': 8, 'M': 24, 'MS': 24, } self.max_scoring_length = max_scoring_length_dict.get(freq) fit_diagnostic_lag_dict = { 'H': 144 * 2, 'D': 7 * 4, 'W': 12, 'W-SUN': 12, 'W-MON': 12, 'W-TUE': 12, 'W-WED': 12, 'W-THU': 12, 'W-FRI': 12, 'W-SAT': 12, 'M': 24, 'MS': 24, } self._fit_diagnostic_lag = fit_diagnostic_lag_dict.get(freq) super(LADStructuralModel, self).__init__(freq=freq, min_ts_mean=min_ts_mean, min_ts_mean_window=min_ts_mean_window, min_ts_length=min_ts_length, max_ts_length=max_ts_length, **hyper_params, **kwargs) @classmethod def _signals(cls, idx, m, n): """ This function computes the sinusoids given the significant frequencies :param list idx: A list containing the significant frequency indices obtained from the spectral density plot in fourier_extp() :param int m: Specifying the current frequency :param int n: Specifying the length of the time series :return: A numpy array containing the sinusoids corresponding to the significant frequencies """ import numpy as np signal = [] # Generating all the frequencies from a time series of length n fs =

np.fft.fftfreq(n)

numpy.fft.fftfreq

import numpy as np import pytest import torch from probflow.distributions import Bernoulli from probflow.models import Model from probflow.parameters import CenteredParameter def is_close(a, b, tol=1e-5): return np.abs(a - b) < tol def test_CenteredParameter_all_1d(): """Tests probflow.parameters.CenteredParameter w/ center_by=all + 1D""" # Create the parameter param = CenteredParameter(5) # posterior_mean should return mean sample1 = param.posterior_mean() sample2 = param.posterior_mean() assert sample1.ndim == 2 assert sample2.ndim == 2 assert sample1.shape[0] == 5 assert sample2.shape[0] == 5 assert sample1.shape[1] == 1 assert sample2.shape[1] == 1 assert np.all(sample1 == sample2) # mean should be 0! (that's the point of a centered parameter!) assert is_close(np.mean(sample1), 0) # posterior_sample should return samples sample1 = param.posterior_sample() sample2 = param.posterior_sample() assert sample1.ndim == 2 assert sample2.ndim == 2 assert sample1.shape[0] == 5 assert sample1.shape[1] == 1 assert sample2.shape[0] == 5 assert sample2.shape[1] == 1 assert np.all(sample1 != sample2) # mean should be 0! (that's the point of a centered parameter!) assert is_close(np.mean(sample1), 0) # posterior_sample should be able to return multiple samples sample1 = param.posterior_sample(10) sample2 = param.posterior_sample(10) assert sample1.ndim == 3 assert sample2.ndim == 3 assert sample1.shape[0] == 10 assert sample1.shape[1] == 5 assert sample1.shape[2] == 1 assert sample2.shape[0] == 10 assert sample2.shape[1] == 5 assert sample2.shape[2] == 1 assert np.all(sample1 != sample2) # mean should be 0! assert np.all(np.abs(np.mean(sample1, axis=1)) < 1e-5) def test_CenteredParameter_all_2d(): """Tests probflow.parameters.CenteredParameter w/ center_by=all + 2D""" # Shouldn't allow >2 dims with pytest.raises(ValueError): param = CenteredParameter([5, 6, 7]) # Create the parameter param = CenteredParameter([5, 6]) # posterior_mean should return mean sample1 = param.posterior_mean() sample2 = param.posterior_mean() assert sample1.ndim == 2 assert sample2.ndim == 2 assert sample1.shape[0] == 5 assert sample2.shape[0] == 5 assert sample1.shape[1] == 6 assert sample2.shape[1] == 6 assert np.all(sample1 == sample2) # mean should be 0! (that's the point of a centered parameter!) assert is_close(np.mean(sample1), 0) # posterior_sample should return samples sample1 = param.posterior_sample() sample2 = param.posterior_sample() assert sample1.ndim == 2 assert sample2.ndim == 2 assert sample1.shape[0] == 5 assert sample1.shape[1] == 6 assert sample2.shape[0] == 5 assert sample2.shape[1] == 6 assert np.all(sample1 != sample2) # mean should be 0! assert is_close(np.mean(sample1), 0) # posterior_sample should be able to return multiple samples sample1 = param.posterior_sample(10) sample2 = param.posterior_sample(10) assert sample1.ndim == 3 assert sample2.ndim == 3 assert sample1.shape[0] == 10 assert sample1.shape[1] == 5 assert sample1.shape[2] == 6 assert sample2.shape[0] == 10 assert sample2.shape[1] == 5 assert sample2.shape[2] == 6 assert np.all(sample1 != sample2) # mean should be 0! assert np.all(np.abs(np.mean(sample1.reshape((10, -1)), axis=1)) < 1e-5) def test_CenteredParameter_column(): """Tests probflow.parameters.CenteredParameter w/ center_by=column + 2D""" # Create the parameter param = CenteredParameter([5, 6], center_by="column") # posterior_mean should return mean sample1 = param.posterior_mean() sample2 = param.posterior_mean() assert sample1.ndim == 2 assert sample2.ndim == 2 assert sample1.shape[0] == 5 assert sample2.shape[0] == 5 assert sample1.shape[1] == 6 assert sample2.shape[1] == 6 assert

np.all(sample1 == sample2)

numpy.all

""" Base classes and functions for experiments. """ import numpy as np import torch import torch.nn.functional as F import torch.utils.data import matplotlib.pyplot as plt from IPython.display import display, clear_output from ipywidgets import Output from collections import defaultdict from tqdm import tqdm import logging import warnings import time from etn import coordinates, networks, transformers class Model(object): def __init__(self, tfs=[], coords=coordinates.identity_grid, net=None, equivariant=True, downsample=1, tf_opts={}, net_opts={}, seed=None, load_path=None, loglevel='INFO'): """ Model base class. """ # configure logging numeric_level = getattr(logging, loglevel.upper(), None) if not isinstance(numeric_level, int): raise ValueError('Invalid log level: %s' % loglevel) logging.basicConfig(level=numeric_level) logging.info(str(self)) if load_path is not None: logging.info('Loading model from file: %s -- using saved model configuration' % load_path) spec = torch.load(load_path) tfs = spec['tfs'] coords = spec['coords'] net = spec['net'] equivariant = spec['equivariant'] downsample = spec['downsample'] tf_opts = spec['tf_opts'] net_opts = spec['net_opts'] seed = spec['seed'] if net is None: raise ValueError('net parameter must be specified') if seed is not None: torch.manual_seed(seed) torch.cuda.manual_seed(seed) np.random.seed(seed) # build transformer sequence if len(tfs) > 0: pose_module = networks.EquivariantPosePredictor if equivariant else networks.DirectPosePredictor tfs = [getattr(transformers, tf) if type(tf) is str else tf for tf in tfs] seq = transformers.TransformerSequence(*[tf(pose_module, **tf_opts) for tf in tfs]) #seq = transformers.TransformerParallel(*[tf(pose_module, **tf_opts) for tf in tfs]) logging.info('Transformers: %s' % ' -> '.join([tf.__name__ for tf in tfs])) logging.info('Pose module: %s' % pose_module.__name__) else: seq = None # get coordinate function if given as a string if type(coords) is str: if hasattr(coordinates, coords): coords = getattr(coordinates, coords) elif hasattr(coordinates, coords + '_grid'): coords = getattr(coordinates, coords + '_grid') else: raise ValueError('Invalid coordinate system: ' + coords) logging.info('Coordinate transformation before classification: %s' % coords.__name__) # define network if type(net) is str: net = getattr(networks, net) network = net(**net_opts) logging.info('Classifier architecture: %s' % net.__name__) self.tfs = tfs self.coords = coords self.downsample = downsample self.net = net self.equivariant = equivariant self.tf_opts = tf_opts self.net_opts = net_opts self.seed = seed self.model = self._build_model(net=network, transformer=seq, coords=coords, downsample=downsample) logging.info('Net opts: %s' % str(net_opts)) logging.info('Transformer opts: %s' % str(tf_opts)) if load_path is not None: self.model.load_state_dict(spec['state_dict']) def _build_model(self, net, transformer, coords, downsample): return networks.TransformerCNN( net=net, transformer=transformer, coords=coords, downsample=downsample) def _save(self, path, **kwargs): spec = { 'tfs': [tf.__name__ for tf in self.tfs], 'coords': self.coords.__name__, 'net': self.net.__name__, 'equivariant': self.equivariant, 'downsample': self.downsample, 'tf_opts': self.tf_opts, 'net_opts': self.net_opts, 'seed': self.seed, 'state_dict': self.model.state_dict(), } spec.update(kwargs) torch.save(spec, path) def _load_dataset(self, path, num_examples=None): # override in subclasses to handle custom preprocessing / different data formats return Dataset(path=path, num_examples=num_examples) def train(self, num_epochs=300, num_examples=None, batch_size=128, valid_batch_size=100, train_path=None, valid_path=None, train_dataset_opts={}, valid_dataset_opts={}, optimizer='Adam', optimizer_opts={'amsgrad': True, 'lr': 2e-3, 'weight_decay': 0.}, lr_schedule={'step_size': 1, 'gamma': 0.99}, save_path=None, show_plot=False, device='cuda:0'): """Train the model.""" if save_path is not None: logging.info('Saving model with lowest validation error to %s' % save_path) else: warnings.warn('save_path not specified: model will not be saved') # load training and validation data if train_path is None: raise ValueError('train_path must be specified') if valid_path is None: raise ValueError('valid_path must be specified') logging.info('Loading training data from %s' % train_path) train_loader = torch.utils.data.DataLoader( self._load_dataset( path=train_path, num_examples=num_examples, **train_dataset_opts), shuffle=True, batch_size=batch_size, drop_last=True) logging.info('Loading validation data from %s' % valid_path) valid_loader = torch.utils.data.DataLoader( self._load_dataset( valid_path, **valid_dataset_opts), shuffle=False, batch_size=valid_batch_size, drop_last=False) self.model.to(device) optim = getattr(torch.optim, optimizer)(self.model.parameters(), **optimizer_opts) scheduler = torch.optim.lr_scheduler.StepLR(optim, **lr_schedule) if show_plot: plotter = Plotter(show_plot=True) plotter.show() best_err = float('inf') start_time = time.time() for i in range(num_epochs): # train for one epoch logging.info('Training epoch %d' % (i+1)) train_losses = self._train(optim, scheduler, train_loader, device) # evaluate on validation set logging.info('Evaluating model on validation set') valid_loss, valid_err = self._test(valid_loader, device) logging.info('Validation loss = %.2e, validation error = %.4f' % (valid_loss, valid_err)) # save model with lowest validation error seen so far if (save_path is not None) and (valid_err < best_err): logging.info('Saving model with better validation error: %.2e (previously %.2e)' % (valid_err, best_err)) best_err = valid_err self._save(save_path, epoch=i+1, valid_err=valid_err) # update plot if show_plot: plotter.update(train_loss=train_losses, valid_loss=valid_loss, valid_err=valid_err) logging.info('Finished training in %.1f s' % (time.time() - start_time)) return self def test(self, batch_size=100, test_path=None, test_dataset_opts={}, device='cuda:0'): """Test the model.""" if test_path is None: raise ValueError('test_path must be specified') logging.info('loading test data from %s' % test_path) loader = torch.utils.data.DataLoader( self._load_dataset( test_path, **test_dataset_opts), shuffle=False, batch_size=batch_size, drop_last=False) self.model.to(device) loss, err_rate = self._test(loader, device) logging.info('Test loss = %.2e' % loss) logging.info('Test error = %.4f' % err_rate) return loss, err_rate def predict(self, input, device='cuda:0', tf_output=False): """Predict a distribution over labels for a single example.""" self.model.eval() self.model.to(device) x = input.to(device) if x.dim() == 3: x = x.unsqueeze(0) with torch.no_grad(): out = self.model(x, tf_output=tf_output) logits = out[0] if tf_output else out probs = F.softmax(logits.squeeze(0), dim=-1) if tf_output: return probs, out[1] else: return probs def _train(self, optim, scheduler, loader, device): self.model.train() losses = [] for x, y in tqdm(loader): x, y = x.to(device), y.to(device) logits = self.model(x) loss = F.cross_entropy(logits, y) optim.zero_grad() loss.backward() optim.step() losses.append(loss.item()) scheduler.step() return losses def _test(self, loader, device): self.model.eval() total_loss = 0 total_err = 0 count = 0 for x, y in tqdm(loader): x, y = x.to(device), y.to(device) count += x.shape[0] with torch.no_grad(): logits = self.model(x) yhat = torch.argmax(logits, dim=-1) total_err += (y != yhat).sum().item() total_loss += F.cross_entropy(logits, y, reduction='sum').item() loss = total_loss / count err_rate = total_err / count return loss, err_rate class Dataset(torch.utils.data.Dataset): def __init__(self, path, num_examples=None, normalization=None): self.path = path self.normalization = normalization self.data, self.targets = torch.load(self.path) if self.data.dim() == 3: self.data = self.data.unsqueeze(1) # singleton channel dimension if num_examples is not None: self.data = self.data[:num_examples] self.targets = self.targets[:num_examples].type(torch.long) if normalization is not None: mean, std = normalization mean = torch.Tensor(mean).view(1, -1, 1, 1) std = torch.Tensor(std).view(1, -1, 1, 1) self.data.add_(-mean).div_(std) def __getitem__(self, index): return self.data[index], self.targets[index] def __len__(self): return len(self.data) class Plotter(object): def __init__(self, id_string='', width=12, height=2.5, show_plot=True): """A dynamic plotting widget for tracking training progress in notebooks.""" self.id_string = id_string self.width = width self.height = height self.output = Output() self.metrics = defaultdict(list) self.show_plot = show_plot def update(self, **metrics): for k, v in metrics.items(): if type(v) is list: self.metrics[k] += v else: self.metrics[k].append(v) self.output.clear_output(wait=True) with self.output: if self.show_plot: self.plot() plt.show() maxlen = max(map(len, self.metrics.keys())) print(self.id_string) for k, v in self.metrics.items(): print(('%' + str(maxlen) + 's') % k, '| current = %.2e' % v[-1], '| max = %.2e (iter %4d)' % (np.max(v), np.argmax(v)), '| min = %.2e (iter %4d)' % (np.min(v), np.argmin(v))) def show(self): display(self.output) def progress_string(self): s = self.id_string + '\n' maxlen = max(map(len, self.metrics.keys())) for k, v in self.metrics.items(): s += ''.join([('%' + str(maxlen) + 's') % k, '| current = %.2e' % v[-1], '| max = %.2e (iter %4d)' % (np.max(v),

np.argmax(v)

numpy.argmax

from hazel.chromosphere import Hazel_atmosphere from hazel.photosphere import SIR_atmosphere from hazel.parametric import Parametric_atmosphere from hazel.stray import Straylight_atmosphere from hazel.configuration import Configuration from hazel.io import Generic_output_file from collections import OrderedDict from hazel.codes import hazel_code, sir_code from hazel.spectrum import Spectrum from hazel.transforms import transformed_to_physical, physical_to_transformed, jacobian_transformation import hazel.util import numpy as np import copy import os from pathlib import Path import scipy.stats import scipy.special import scipy.signal import scipy.linalg import scipy.optimize import warnings import logging import sys __all__ = ['Model'] class Model(object): def __init__(self, config=None, working_mode='synthesis', verbose=0, debug=False, rank=0, randomization=None, root=''): np.random.seed(123) if (rank != 0): return self.photospheres = [] self.chromospheres = [] self.chromospheres_order = [] self.atmospheres = {} self.order_atmospheres = [] self.straylight = [] self.parametric = [] self.spectrum = [] self.configuration = None self.n_cycles = 1 self.spectrum = {} self.topologies = [] self.straylights = [] self.working_mode = working_mode self.pixel = 0 self.debug = debug self.use_analytical_RF_if_possible = False self.nlte_available = False self.use_nlte = False self.root = root self.epsilon = 1e-2 self.svd_tolerance = 1e-8 self.step_limiter_inversion = 1.0 self.backtracking = 'brent' self.verbose = verbose self.logger = logging.getLogger("model") self.logger.setLevel(logging.DEBUG) self.logger.handlers = [] ch = logging.StreamHandler() # formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') formatter = logging.Formatter('%(asctime)s - %(message)s') ch.setFormatter(formatter) self.logger.addHandler(ch) # Set randomization if (randomization is None): self.n_randomization = 1 else: self.n_randomization = randomization if (self.verbose >= 1): self.logger.info('Hazel2 v1.0') if ('torch' in sys.modules and 'torch_geometric' in sys.modules): if (self.verbose >= 1): self.logger.info('PyTorch and PyTorch Geometric found. NLTE for Ca II is available') self.nlte_available = True if (config is not None): if (self.verbose >= 1): self.logger.info('Using configuration from file : {0}'.format(config)) self.configuration = Configuration(config) self.use_configuration(self.configuration.config_dict) # Initialize pyhazel hazel_code._init() def __getstate__(self): d = self.__dict__.copy() if 'logger' in d: d['logger'] = d['logger'].name return d def __setstate__(self, d): if 'logger' in d: d['logger'] = logging.getLogger(d['logger']) self.__dict__.update(d) def __str__(self): tmp = '' for l, par in self.__dict__.items(): if (l != 'LINES'): tmp += '{0}: {1}\n'.format(l, par) return tmp def use_configuration(self, config_dict): """ Use a configuration file Parameters ---------- config_dict : dict Dictionary containing all the options from the configuration file previously read Returns ------- None """ # Deal with the spectral regions tmp = config_dict['spectral regions'] # Output file self.output_file = config_dict['working mode']['output file'] # Backtracking mode if ('backtracking' in config_dict['working mode']): self.backtracking = config_dict['working mode']['backtracking'] else: self.backtracking = 'brent' if (self.verbose >= 1): self.logger.info('Backtracking mode : {0}'.format(self.backtracking)) # Working mode # self.working_mode = config_dict['working mode']['action'] # Add spectral regions for key, value in config_dict['spectral regions'].items(): self.add_spectral(value) # Set number of cycles if present if (self.working_mode == 'inversion'): if ('number of cycles' in config_dict['working mode']): if (config_dict['working mode']['number of cycles'] != 'None'): self.n_cycles = int(config_dict['working mode']['number of cycles']) if (self.verbose >= 1): self.logger.info('Using {0} cycles'.format(self.n_cycles)) # Use analytical RFs if possible if ('analytical rf if possible' in config_dict['working mode']): if (config_dict['working mode']['analytical rf if possible'] != 'None'): self.use_analytical_RF_if_possible = hazel.util.tobool(config_dict['working mode']['analytical rf if possible']) else: self.use_analytical_RF_if_possible = False else: self.use_analytical_RF_if_possible = False if (self.verbose >= 1): self.logger.info('Using analytical RFs if possible : {0}'.format(self.use_analytical_RF_if_possible)) # Set number of maximum iterations if ('maximum iterations' in config_dict['working mode']): if (config_dict['working mode']['number of cycles'] != 'None'): self.max_iterations = int(config_dict['working mode']['maximum iterations']) else: self.max_iterations = 10 else: self.max_iterations = 10 if (self.verbose >= 1): self.logger.info('Using {0} max. iterations'.format(self.max_iterations)) # Randomization if (self.verbose >= 1): if (self.n_randomization == 1): self.logger.info('Not using randomizations') else: self.logger.info('Using a maximum of {0} randomizations'.format(self.n_randomization)) # Set number of maximum iterations if ('relative error' in config_dict['working mode']): if (config_dict['working mode']['relative error'] != 'None'): self.relative_error = float(config_dict['working mode']['relative error']) if (self.verbose >= 1): self.logger.info('Stopping when relative error is below {0}'.format(self.relative_error)) else: self.relative_error = 1e-4 else: self.relative_error = 1e-4 # Save all cycles if ('save all cycles' not in config_dict['working mode']): self.save_all_cycles = False else: self.save_all_cycles = hazel.util.tobool(config_dict['working mode']['save all cycles']) if (self.verbose >= 1): self.logger.info('Saving all cycles : {0}'.format(self.save_all_cycles)) # Deal with the atmospheres tmp = config_dict['atmospheres'] self.atmospheres = {} if (self.verbose >= 1): self.logger.info('Adding atmospheres') for key, value in tmp.items(): if ('photosphere' in key): if (self.verbose >=1): self.logger.info(' - New available photosphere : {0}'.format(value['name'])) self.add_photosphere(value) if ('chromosphere' in key): if (self.verbose >= 1): self.logger.info(' - New available chromosphere : {0}'.format(value['name'])) self.add_chromosphere(value) if ('parametric' in key): if (self.verbose >= 1): self.logger.info(' - New available parametric : {0}'.format(value['name'])) self.add_parametric(value) if ('straylight' in key): if (self.verbose >= 1): self.logger.info(' - New available straylight : {0}'.format(value['name'])) self.add_straylight(value) self.setup() def setup(self): """ Setup the model for synthesis/inversion. This setup includes adding the topologies, removing unused atmospheres, reading the number of cycles for the inversion and some sanity checks Parameters ---------- None Returns ------- None """ # Adding topologies if (self.verbose >= 1): self.logger.info("Adding topologies") for value in self.topologies: self.add_topology(value) # Remove unused atmospheres defined in the configuration file and not in the topology if (self.verbose >= 1): self.logger.info("Removing unused atmospheres") self.remove_unused_atmosphere() # Calculate indices for atmospheres index_chromosphere = 1 index_photosphere = 1 self.n_photospheres = 0 self.n_chromospheres = 0 for k, v in self.atmospheres.items(): if (v.type == 'photosphere'): v.index = index_photosphere index_photosphere += 1 self.n_photospheres += 1 if (v.type == 'chromosphere'): v.index = index_chromosphere index_chromosphere += 1 self.n_chromospheres += 1 # Use analytical RFs if only photospheres are defined if (self.n_chromospheres == 0 and self.use_analytical_RF_if_possible): self.use_analytical_RF = True if (self.verbose >= 1): self.logger.info('Using analytical RFs : {0}'.format(self.use_analytical_RF)) else: self.use_analytical_RF = False # Check that number of pixels is the same for all atmospheric files if in synthesis mode if (self.working_mode == 'synthesis'): n_pixels = [v.n_pixel for k, v in self.atmospheres.items()] all_equal = all(x == n_pixels[0] for x in n_pixels) if (not all_equal): for k, v in self.atmospheres.items(): self.logger.info('{0} -> {1}'.format(k, v.n_pixel)) raise Exception("Files with model atmospheres do not contain the same number of pixels") else: if (self.verbose >= 1): self.logger.info('Number of pixels to read : {0}'.format(n_pixels[0])) self.n_pixels = n_pixels[0] if (self.working_mode == 'inversion'): n_pixels = [v.n_pixel for k, v in self.spectrum.items()] all_equal = all(x == n_pixels[0] for x in n_pixels) if (not all_equal): for k, v in self.spectrum.items(): self.logger.info('{0} -> {1}'.format(k, v.n_pixel)) raise Exception("Files with spectral regions do not contain the same number of pixels") else: if (self.verbose >= 1): self.logger.info('Number of pixels to invert : {0}'.format(n_pixels[0])) self.n_pixels = n_pixels[0] # Check that the number of pixels from all observations (in case of inversion) is the same # Check also if they are equal to those of the models # n_pixels = [v.n_pixel for k, v in self.atmospheres.items()] # all_equal = all(x == n_pixels[0] for x in n_pixels) # Check that the number of cycles is the same for all atmospheres (in case of inversion) if (self.working_mode == 'inversion'): cycles = [] for k, v in self.atmospheres.items(): for k2, v2 in v.cycles.items(): if (v2 is not None): cycles.append(len(v2)) all_equal = all(x == cycles[0] for x in cycles) if (not all_equal): raise Exception("Number of cycles in the nodes of active atmospheres is not always the same") else: if (self.n_cycles is None): self.n_cycles = cycles[0] # if (self.working_mode == 'inversion'): # cycles = [] # for tmp in ['I', 'Q', 'U', 'V']: # if ( cycles.append # for k, v in self.atmospheres.items(): # for k2, v2 in v.cycles.items(): # cycles.append(len(v2)) # all_equal = all(x == cycles[0] for x in cycles) # if (not all_equal): # raise Exception("Number of cycles in the nodes of active atmospheres is not always the same") # else: # if (self.n_cycles is None): # self.n_cycles = cycles[0] filename = os.path.join(os.path.dirname(__file__),'data/LINEAS') ff = open(filename, 'r') self.LINES = ff.readlines() ff.close() self.init_sir() for k, v in self.spectrum.items(): v.allocate_info_cycles(n_cycles=self.n_cycles) for k, v in self.atmospheres.items(): v.allocate_info_cycles(n_cycles=self.n_cycles) # Count total number of free parameters if (self.working_mode == 'inversion'): self.n_free_parameters = 0 for k, v in self.atmospheres.items(): for k2, v2 in v.cycles.items(): if (v2 is not None): self.n_free_parameters += max(hazel.util.onlyint(v2[0:self.n_cycles+1])) if (self.verbose >= 1): self.logger.info('Total number of free parameters in all cycles : {0}'.format(self.n_free_parameters)) def open_output(self): self.output_handler = Generic_output_file(self.output_file) self.output_handler.open(self) def close_output(self): self.output_handler.close() def write_output(self, randomization=0): if (self.working_mode == 'synthesis'): self.flatten_parameters_to_reference(cycle=0) self.output_handler.write(self, pixel=0, randomization=randomization) def add_spectral(self, spectral): """ Programmatically add a spectral region Parameters ---------- spectral : dict Dictionary containing the following data 'Name', 'Wavelength', 'Topology', 'Weights Stokes', 'Wavelength file', 'Wavelength weight file', 'Observations file', 'Mask file' Returns ------- None """ # Make sure that all keys of the input dictionary are in lower case # This is irrelevant if a configuration file is used because this has been # already done value = hazel.util.lower_dict_keys(spectral) if (self.verbose >= 1): self.logger.info('Adding spectral region {0}'.format(value['name'])) if ('wavelength file' not in value): value['wavelength file'] = None elif (value['wavelength file'] == 'None'): value['wavelength file'] = None if ('wavelength weight file' not in value): value['wavelength weight file'] = None elif (value['wavelength weight file'] == 'None'): value['wavelength weight file'] = None if ('observations file' not in value): value['observations file'] = None elif (value['observations file'] == 'None'): value['observations file'] = None if ('stokes weights' not in value): value['stokes weights'] = None elif (value['stokes weights'] == 'None'): value['stokes weights'] = None if ('mask file' not in value): value['mask file'] = None elif (value['mask file'] == 'None'): value['mask file'] = None if ('los' not in value): value['los'] = None elif (value['los'] == 'None'): value['los'] = None for tmp in ['i', 'q', 'u', 'v']: if ('weights stokes {0}'.format(tmp) not in value): value['weights stokes {0}'.format(tmp)] = [None]*10 elif (value['weights stokes {0}'.format(tmp)] == 'None'): value['weights stokes {0}'.format(tmp)] = [None]*10 if ('boundary condition' not in value): value['boundary condition'] = None elif (value['boundary condition'] == 'None'): value['boundary condition'] = None if ('instrumental profile' not in value): value['instrumental profile'] = None elif (value['instrumental profile'] == 'None'): value['instrumental profile'] = None # Wavelength file is not present if (value['wavelength file'] is None): # If the wavelength is defined if ('wavelength' in value): axis = value['wavelength'] wvl = np.linspace(float(axis[0]), float(axis[1]), int(axis[2])) wvl_lr = None if (self.verbose >= 1): self.logger.info(' - Using wavelength axis from {0} to {1} with {2} steps'.format(float(axis[0]), float(axis[1]), int(axis[2]))) else: raise Exception('Wavelength range is not defined. Please, use "Wavelength" or "Wavelength file"') else: # If both observed and synthetic wavelength points are given if ('wavelength' in value): axis = value['wavelength'] if (len(axis) != 3): raise Exception("Wavelength range is not given in the format: lower, upper, steps") wvl = np.linspace(float(axis[0]), float(axis[1]), int(axis[2])) if (self.verbose >= 1): self.logger.info(' - Using wavelength axis from {0} to {1} with {2} steps'.format(float(axis[0]), float(axis[1]), int(axis[2]))) self.logger.info(' - Reading wavelength axis from {0}'.format(value['wavelength file'])) wvl_lr = np.loadtxt(self.root + value['wavelength file']) else: if (self.verbose >= 1): self.logger.info(' - Reading wavelength axis from {0}'.format(value['wavelength file'])) wvl = np.loadtxt(self.root + value['wavelength file']) wvl_lr = None if (value['wavelength weight file'] is None): if (self.verbose >= 1 and self.working_mode == 'inversion'): self.logger.info(' - Setting all wavelength weights to 1') weights = np.ones((4,len(wvl))) else: if (self.verbose >= 1): self.logger.info(' - Reading wavelength weights from {0}'.format(value['wavelength weight file'])) weights = np.loadtxt(self.root + value['wavelength weight file'], skiprows=1).T # Observations file not present if (value['observations file'] is None): if (self.working_mode == 'inversion'): raise Exception("Inversion mode without observations is not allowed.") obs_file = None else: if (self.verbose >= 1): self.logger.info(' - Using observations from {0}'.format(value['observations file'])) obs_file = value['observations file'] if (value['mask file'] is None): mask_file = None if (self.verbose >= 1): self.logger.info(' - No mask for pixels') else: if (self.verbose >= 1): self.logger.info(' - Using mask from {0}'.format(value['mask file'])) mask_file = value['mask file'] if (value['instrumental profile'] is None): if (self.verbose >= 1): self.logger.info(' - No instrumental profile') else: if (self.verbose >= 1): self.logger.info(' - Instrumental profile : {0}'.format(value['instrumental profile'])) # if (value['straylight file'] is None): # if (self.verbose >= 1): # self.logger.info(' - Not using straylight') # stray_file = None # else: # if (self.verbose >= 1): # self.logger.info(' - Using straylight from {0}'.format(value['straylight file'])) # stray_file = value['straylight file'] if (value['los'] is None): if (self.working_mode == 'synthesis'): raise Exception("You need to provide the LOS for spectral region {0}".format(value['name'])) los = None else: los = np.array(value['los']).astype('float64') if (self.verbose >= 1): self.logger.info(' - Using LOS {0}'.format(value['los'])) if (value['boundary condition'] is None): if (self.verbose >= 1): self.logger.info(' - Using default boundary conditions [1,0,0,0] in spectral region {0} or read from file. Check carefully!'.format(value['name'])) boundary = np.array([1.0,0.0,0.0,0.0]) self.normalization = 'on-disk' else: boundary = np.array(value['boundary condition']).astype('float64') if (boundary[0] == 0.0): if (self.verbose >= 1): self.logger.info(' - Using off-limb normalization (peak intensity)') if (self.verbose >= 1): self.logger.info(' - Using boundary condition {0}'.format(value['boundary condition'])) stokes_weights = [] for st in ['i', 'q', 'u', 'v']: tmp = hazel.util.tofloat(value['weights stokes {0}'.format(st)]) tmp = [i if i is not None else 1.0 for i in tmp] stokes_weights.append(tmp) stokes_weights = np.array(stokes_weights) self.spectrum[value['name']] = Spectrum(wvl=wvl, weights=weights, observed_file=obs_file, name=value['name'], stokes_weights=stokes_weights, los=los, boundary=boundary, mask_file=mask_file, instrumental_profile=value['instrumental profile'], root=self.root, wvl_lr=wvl_lr) self.topologies.append(value['topology']) def add_photosphere(self, atmosphere): """ Programmatically add a photosphere Parameters ---------- atmosphere : dict Dictionary containing the following data 'Name', 'Spectral region', 'Height', 'Line', 'Wavelength', 'Reference atmospheric model', 'Ranges', 'Nodes' Returns ------- None """ # Make sure that all keys of the input dictionary are in lower case # This is irrelevant if a configuration file is used because this has been # already done atm = hazel.util.lower_dict_keys(atmosphere) self.atmospheres[atm['name']] = SIR_atmosphere(working_mode=self.working_mode, name=atm['name'], verbose=self.verbose) lines = [int(k) for k in list(atm['spectral lines'])] # If NLTE is available because PyTorch and PyTorch Geom are available # check whether the line is needed in NLTE or not if self.nlte_available: if ('nlte' not in atm): self.atmospheres[atm['name']].nlte = False else: self.atmospheres[atm['name']].nlte = hazel.util.tobool(atm['nlte']) if (self.verbose >= 1): self.logger.info(" * Line in NLTE if available") else: self.atmospheres[atm['name']].nlte = False if ('wavelength' not in atm): atm['wavelength'] = None elif (atm['wavelength'] == 'None'): atm['wavelength'] = None if (atm['wavelength'] is not None): wvl_range = [float(k) for k in atm['wavelength']] else: wvl_range = [np.min(self.spectrum[atm['spectral region']].wavelength_axis), np.max(self.spectrum[atm['spectral region']].wavelength_axis)] if ('reference frame' in atm): if ('line-of-sight' in atm['reference frame']): self.atmospheres[atm['name']].reference_frame = 'line-of-sight' if ('vertical' in atm['reference frame']): raise Exception('Magnetic fields in photospheres are always in the line-of-sight reference frame.') else: self.atmospheres[atm['name']].reference_frame = 'line-of-sight' if (self.verbose >= 1): self.logger.info(" * Adding line : {0}".format(lines)) self.logger.info(" * Magnetic field reference frame : {0}".format(self.atmospheres[atm['name']].reference_frame)) self.atmospheres[atm['name']].add_active_line(lines=lines, spectrum=self.spectrum[atm['spectral region']], wvl_range=np.array(wvl_range), verbose=self.verbose) if (self.atmospheres[atm['name']].graphnet_nlte is not None): self.set_nlte(True) if ('ranges' in atm): for k, v in atm['ranges'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].ranges[k2] = None else: self.atmospheres[atm['name']].ranges[k2] = hazel.util.tofloat(v) for k2, v2 in self.atmospheres[atm['name']].parameters.items(): self.atmospheres[atm['name']].regularization[k2] = None if ('regularization' in atm): for k, v in atm['regularization'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].regularization[k2] = None else: self.atmospheres[atm['name']].regularization[k2] = v if ('reference atmospheric model' in atm): my_file = Path(self.root + atm['reference atmospheric model']) if (not my_file.exists()): raise FileExistsError("Input file {0} for atmosphere {1} does not exist.".format(my_file, atm['name'])) self.atmospheres[atm['name']].load_reference_model(self.root + atm['reference atmospheric model'], self.verbose) if (self.atmospheres[atm['name']].model_type == '3d'): self.atmospheres[atm['name']].n_pixel = self.atmospheres[atm['name']].model_handler.get_npixel() if ('nodes' in atm): for k, v in atm['nodes'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): self.atmospheres[atm['name']].cycles[k2] = hazel.util.toint(v) if ('temperature change to recompute departure coefficients' in atm): self.atmospheres[atm['name']].t_change_departure = float(atm['temperature change to recompute departure coefficients']) else: self.atmospheres[atm['name']].t_change_departure = 0.0 def add_chromosphere(self, atmosphere): """ Programmatically add a chromosphere Parameters ---------- atmosphere : dict Dictionary containing the following data 'Name', 'Spectral region', 'Height', 'Line', 'Wavelength', 'Reference atmospheric model', 'Ranges', 'Nodes' Returns ------- None """ # Make sure that all keys of the input dictionary are in lower case # This is irrelevant if a configuration file is used because this has been # already done atm = hazel.util.lower_dict_keys(atmosphere) self.atmospheres[atm['name']] = Hazel_atmosphere(working_mode=self.working_mode, name=atm['name']) if ('wavelength' not in atm): atm['wavelength'] = None elif (atm['wavelength'] == 'None'): atm['wavelength'] = None if (atm['wavelength'] is not None): wvl_range = [float(k) for k in atm['wavelength']] else: wvl_range = [np.min(self.spectrum[atm['spectral region']].wavelength_axis), np.max(self.spectrum[atm['spectral region']].wavelength_axis)] self.atmospheres[atm['name']].add_active_line(line=atm['line'], spectrum=self.spectrum[atm['spectral region']], wvl_range=np.array(wvl_range)) if ('reference frame' in atm): if (atm['reference frame'] == 'line-of-sight'): self.atmospheres[atm['name']].reference_frame = 'line-of-sight' if (atm['reference frame'] == 'vertical'): self.atmospheres[atm['name']].reference_frame = 'vertical' else: self.atmospheres[atm['name']].reference_frame = 'vertical' if (self.verbose >= 1): self.logger.info(" * Adding line : {0}".format(atm['line'])) self.logger.info(" * Magnetic field reference frame : {0}".format(self.atmospheres[atm['name']].reference_frame)) if ('ranges' in atm): for k, v in atm['ranges'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].ranges[k2] = None else: self.atmospheres[atm['name']].ranges[k2] = hazel.util.tofloat(v) for k2, v2 in self.atmospheres[atm['name']].parameters.items(): self.atmospheres[atm['name']].regularization[k2] = None if ('regularization' in atm): for k, v in atm['regularization'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].regularization[k2] = None else: self.atmospheres[atm['name']].regularization[k2] = v if ('coordinates for magnetic field vector' in atm): if (atm['coordinates for magnetic field vector'] == 'cartesian'): self.atmospheres[atm['name']].coordinates_B = 'cartesian' if (atm['coordinates for magnetic field vector'] == 'spherical'): self.atmospheres[atm['name']].coordinates_B = 'spherical' else: self.atmospheres[atm['name']].coordinates_B = 'cartesian' self.atmospheres[atm['name']].select_coordinate_system() if (self.verbose >= 1): self.logger.info(" * Magnetic field coordinates system : {0}".format(self.atmospheres[atm['name']].coordinates_B)) if ('reference atmospheric model' in atm): my_file = Path(self.root + atm['reference atmospheric model']) if (not my_file.exists()): raise FileExistsError("Input file {0} for atmosphere {1} does not exist.".format(my_file, atm['name'])) self.atmospheres[atm['name']].load_reference_model(self.root + atm['reference atmospheric model'], self.verbose) if (self.atmospheres[atm['name']].model_type == '3d'): self.atmospheres[atm['name']].n_pixel = self.atmospheres[atm['name']].model_handler.get_npixel() # Set values of parameters self.atmospheres[atm['name']].height = float(atm['height']) if ('nodes' in atm): for k, v in atm['nodes'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): self.atmospheres[atm['name']].cycles[k2] = hazel.util.toint(v) def add_parametric(self, atmosphere): """ Programmatically add a parametric atmosphere Parameters ---------- atmosphere : dict Dictionary containing the following data 'Name', 'Spectral region', 'Wavelength', 'Reference atmospheric model', 'Type', 'Ranges', 'Nodes' Returns ------- None """ # Make sure that all keys of the input dictionary are in lower case # This is irrelevant if a configuration file is used because this has been # already done atm = hazel.util.lower_dict_keys(atmosphere) self.atmospheres[atm['name']] = Parametric_atmosphere(working_mode=self.working_mode) if ('wavelength' not in atm): atm['wavelength'] = None elif (atm['wavelength'] == 'None'): atm['wavelength'] = None if (atm['wavelength'] is not None): wvl_range = [float(k) for k in atm['wavelength']] else: wvl_range = [np.min(self.spectrum[atm['spectral region']].wavelength_axis), np.max(self.spectrum[atm['spectral region']].wavelength_axis)] self.atmospheres[atm['name']].add_active_line(spectrum=self.spectrum[atm['spectral region']], wvl_range=np.array(wvl_range)) if ('ranges' in atm): for k, v in atm['ranges'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].ranges[k2] = None else: self.atmospheres[atm['name']].ranges[k2] = hazel.util.tofloat(v) if ('reference atmospheric model' in atm): my_file = Path(self.root + atm['reference atmospheric model']) if (not my_file.exists()): raise FileExistsError("Input file {0} for atmosphere {1} does not exist.".format(my_file, atm['name'])) self.atmospheres[atm['name']].load_reference_model(self.root + atm['reference atmospheric model'], self.verbose) if (self.atmospheres[atm['name']].model_type == '3d'): self.atmospheres[atm['name']].n_pixel = self.atmospheres[atm['name']].model_handler.get_npixel() # Set values of parameters if ('nodes' in atm): for k, v in atm['nodes'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): self.atmospheres[atm['name']].cycles[k2] = hazel.util.toint(v) for k2, v2 in self.atmospheres[atm['name']].parameters.items(): self.atmospheres[atm['name']].regularization[k2] = None if ('regularization' in atm): for k, v in atm['regularization'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].regularization[k2] = None else: self.atmospheres[atm['name']].regularization[k2] = v def add_straylight(self, atmosphere): """ Programmatically add a straylight atmosphere Parameters ---------- atmosphere : dict Dictionary containing the following data 'Name', 'Spectral region', 'Reference atmospheric model', 'Ranges', 'Nodes' Returns ------- None """ # Make sure that all keys of the input dictionary are in lower case # This is irrelevant if a configuration file is used because this has been # already done atm = hazel.util.lower_dict_keys(atmosphere) self.atmospheres[atm['name']] = Straylight_atmosphere(working_mode=self.working_mode) if ('wavelength' not in atm): atm['wavelength'] = None elif (atm['wavelength'] == 'None'): atm['wavelength'] = None if (atm['wavelength'] is not None): wvl_range = [float(k) for k in atm['wavelength']] else: wvl_range = [np.min(self.spectrum[atm['spectral region']].wavelength_axis), np.max(self.spectrum[atm['spectral region']].wavelength_axis)] self.atmospheres[atm['name']].add_active_line(spectrum=self.spectrum[atm['spectral region']], wvl_range=np.array(wvl_range)) if ('ranges' in atm): for k, v in atm['ranges'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].ranges[k2] = None else: self.atmospheres[atm['name']].ranges[k2] = hazel.util.tofloat(v) my_file = Path(self.root + atm['reference atmospheric model']) if (not my_file.exists()): raise FileExistsError("Input file {0} for atmosphere {1} does not exist.".format(my_file, atm['name'])) if ('reference atmospheric model' in atm): self.atmospheres[atm['name']].load_reference_model(self.root + atm['reference atmospheric model'], self.verbose) if (self.atmospheres[atm['name']].model_type == '3d'): self.atmospheres[atm['name']].n_pixel = self.atmospheres[atm['name']].model_handler.get_npixel() # Set values of parameters if ('nodes' in atm): for k, v in atm['nodes'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): self.atmospheres[atm['name']].cycles[k2] = hazel.util.toint(v) for k2, v2 in self.atmospheres[atm['name']].parameters.items(): self.atmospheres[atm['name']].regularization[k2] = None if ('regularization' in atm): for k, v in atm['regularization'].items(): for k2, v2 in self.atmospheres[atm['name']].parameters.items(): if (k.lower() == k2.lower()): if (v == 'None'): self.atmospheres[atm['name']].regularization[k2] = None else: self.atmospheres[atm['name']].regularization[k2] = v def remove_unused_atmosphere(self): """ Remove unused atmospheres Parameters ---------- None Returns ------- None """ to_remove = [] for k, v in self.atmospheres.items(): if (not v.active): to_remove.append(k) if (self.verbose >= 1): self.logger.info(' - Atmosphere {0} deleted.'.format(k)) for k in to_remove: self.atmospheres.pop(k) def init_sir_external(self): """ Initialize SIR for this synthesis Parameters ---------- None Returns ------- None """ for k, v in self.atmospheres.items(): if (v.type == 'photosphere'): f = open('lte.grid', 'w') f.write("IMPORTANT: a) All items must be separated by commas. \n") f.write(" b) The first six characters of the last line \n") f.write(" in the header (if any) must contain the symbol --- \n") f.write("\n") f.write("Line and blends indices : Initial lambda Step Final lambda \n") f.write("(in this order) (mA) (mA) (mA) \n") f.write("-----------------------------------------------------------------------\n") ind_low = (np.abs(v.spectrum.wavelength_axis - v.wvl_range_lambda[0])).argmin() ind_top = (np.abs(v.spectrum.wavelength_axis - v.wvl_range_lambda[1])).argmin() low = v.spectrum.wavelength_axis[ind_low] top = v.spectrum.wavelength_axis[ind_top] # TODO delta = (v.spectrum.wavelength_axis[1] - v.spectrum.wavelength_axis[0]) filename = os.path.join(os.path.dirname(__file__),'data/LINEAS') ff = open(filename, 'r') flines = ff.readlines() ff.close() for i in range(len(v.lines)): for l in flines: tmp = l.split() index = int(tmp[0].split('=')[0]) if (index == v.lines[0]): wvl = float(tmp[2]) f.write("{0} : {1}, {2}, {3}\n".format(str(v.lines)[1:-1], 1e3*(low-wvl), 1e3*delta, 1e3*(top-wvl))) f.close() v.n_lambda = sir_code.init_externalfile(v.index, filename) def init_sir(self): """ Initialize SIR for this synthesis. This version does not make use of any external file, which might be not safe when running in MPI mode. Parameters ---------- None Returns ------- None """ lines = [] n_lines = 0 elements = {'H':1,'HE':2,'LI':3,'BE':4,'B':5,'C':6,'N':7,'O':8,'F':9,'NE':10, 'NA':11,'MG':12,'AL':13,'SI':14,'P':15,'S':16,'CL':17,'AR':18,'K':19,'CA':20,'SC':21,'TI':22,'V':23,'CR':24, 'MN':25,'FE':26,'CO':27,'NI':28,'CU':29,'ZN':30,'GA':31,'GE':32,'AS':33,'SE':34,'BR':35,'KR':36, 'RB':37,'SR':38,'Y':39,'ZR':40,'NB':41,'MO':42,'TC':43,'RU':44,'RH':45,'PD':46,'AG':47,'CD':48,'IN':49, 'SN':50,'SB':51,'TE':52,'I':53,'XE':54,'CS':55,'BA':56,'LA':57,'CE':58,'PR':59,'ND':60,'PM':61, 'SM':62,'EU':63,'GD':64,'TB':65,'DY':66,'HO':67,'ER':68,'TM':69,'YB':70,'LU':71,'HF':72,'TA':73,'W':74, 'RE':75,'OS':76,'IR':77,'PT':78,'AU':79,'HG':80,'TL':81,'PB':82,'BI':83,'PO':84,'AT':85,'RN':86, 'FR':87,'RA':88,'AC':89,'TH':90,'PA':91,'U':92} states = {'S': 0, 'P': 1, 'D': 2, 'F': 3, 'G': 4, 'H': 5, 'I': 6} for k, v in self.atmospheres.items(): if (v.type == 'photosphere'): n_lines += 1 ind_low = (np.abs(v.spectrum.wavelength_axis - v.wvl_range_lambda[0])).argmin() ind_top = (np.abs(v.spectrum.wavelength_axis - v.wvl_range_lambda[1])).argmin() low = v.spectrum.wavelength_axis[ind_low] top = v.spectrum.wavelength_axis[ind_top] # TODO delta = (v.spectrum.wavelength_axis[1] - v.spectrum.wavelength_axis[0]) nblend = len(v.lines) lines = np.zeros(len(v.lines), dtype=np.intc) atom = np.zeros(len(v.lines), dtype=np.intc) istage = np.zeros(len(v.lines), dtype=np.intc) wvl = np.zeros(len(v.lines)) zeff = np.zeros(len(v.lines)) energy = np.zeros(len(v.lines)) loggf = np.zeros(len(v.lines)) mult1 = np.zeros(len(v.lines), dtype=np.intc) mult2 = np.zeros(len(v.lines), dtype=np.intc) design1 = np.zeros(len(v.lines), dtype=np.intc) design2 = np.zeros(len(v.lines), dtype=np.intc) tam1 = np.zeros(len(v.lines)) tam2 = np.zeros(len(v.lines)) alfa = np.zeros(len(v.lines)) sigma = np.zeros(len(v.lines)) for i in range(len(v.lines)): lines[i] = v.lines[i] for l in self.LINES: tmp = l.split() index = int(tmp[0].split('=')[0]) if (index == v.lines[i]): atom[i] = elements[tmp[0].split('=')[1]] istage[i] = tmp[1] wvl[i] = float(tmp[2]) zeff[i] = float(tmp[3]) energy[i] = float(tmp[4]) loggf[i] = float(tmp[5]) mult1[i] = int(tmp[6][:-1]) mult2[i] = int(tmp[8][:-1]) design1[i] = states[tmp[6][-1]] design2[i] = states[tmp[8][-1]] tam1[i] = float(tmp[7].split('-')[0]) tam2[i] = float(tmp[9].split('-')[0]) if (len(tmp) == 12): alfa[i] = float(tmp[-2]) sigma[i] = float(tmp[-1]) else: alfa[i] = 0.0 sigma[i] = 0.0 lambda0 = 1e3*(low-wvl[0]) lambda1 = 1e3*(top-wvl[0]) n_steps = ind_top - ind_low + 1 v.n_lambda = n_steps sir_code.init(v.index, nblend, lines, atom, istage, wvl, zeff, energy, loggf, mult1, mult2, design1, design2, tam1, tam2, alfa, sigma, lambda0, lambda1, n_steps) def exit_hazel(self): for k, v in self.atmospheres.items(): if (v.type == 'chromosphere'): hazel_code.exit(v.index) def add_topology(self, atmosphere_order): """ Add a new topology Parameters ---------- topology : str Topology Returns ------- None """ # Transform the order to a list of lists if (self.verbose >= 1): self.logger.info(' - {0}'.format(atmosphere_order)) vertical_order = atmosphere_order.split('->') order = [] for k in vertical_order: name = k.strip().replace('(','').replace(')','').split('+') name = [k.strip() for k in name] tmp = [] for n in name: tmp.append(n) self.atmospheres[n].active = True order.append(tmp) order_flat = [item for sublist in order for item in sublist] # Check that straylight components, if any, are not at the last position for atm in order_flat[:-1]: if (self.atmospheres[atm].type == 'straylight'): raise Exception("Straylight components can only be at the last position of a topology.") self.order_atmospheres.append(order) # Check that there are no two photospheres linked with -> # because they do not make any sense n_photospheres_linked = [] for atmospheres in self.order_atmospheres: for order in atmospheres: for k, atm in enumerate(order): if (self.atmospheres[atm].type == 'photosphere'): n_photospheres_linked.append(k) if (len(n_photospheres_linked) != len(set(n_photospheres_linked))): raise Exception("There are several photospheres linked with ->. This is not allowed.") def normalize_ff(self): """ Normalize all filling factors so that they add to one to avoid later problems. We use a softmax function to make sure they all add to one and can be unconstrained ff_i = exp(x_i) / sum(exp(x_i)) Parameters ---------- None Returns ------- None """ for atmospheres in self.order_atmospheres: for order in atmospheres: total_ff = 0.0 for atm in order: if (self.atmospheres[atm].type != 'straylight'): if (self.working_mode == 'inversion'): self.atmospheres[atm].parameters['ff'], self.atmospheres[atm].ranges['ff'][0], self.atmospheres[atm].ranges['ff'][1] ff = transformed_to_physical(self.atmospheres[atm].parameters['ff'], self.atmospheres[atm].ranges['ff'][0], self.atmospheres[atm].ranges['ff'][1]) else: ff = transformed_to_physical(self.atmospheres[atm].parameters['ff'], -0.00001, 1.00001) total_ff += ff for atm in order: if (self.atmospheres[atm].type != 'straylight'): if (self.working_mode == 'inversion'): ff = transformed_to_physical(self.atmospheres[atm].parameters['ff'], self.atmospheres[atm].ranges['ff'][0], self.atmospheres[atm].ranges['ff'][1]) self.atmospheres[atm].parameters['ff'] = ff / total_ff self.atmospheres[atm].parameters['ff'] = physical_to_transformed(self.atmospheres[atm].parameters['ff'], self.atmospheres[atm].ranges['ff'][0], self.atmospheres[atm].ranges['ff'][1]) else: ff = transformed_to_physical(self.atmospheres[atm].parameters['ff'], -0.00001, 1.00001) self.atmospheres[atm].parameters['ff'] = ff / total_ff self.atmospheres[atm].parameters['ff'] = physical_to_transformed(self.atmospheres[atm].parameters['ff'], -0.00001, 1.00001) def synthesize_spectral_region(self, spectral_region, perturbation=False): """ Synthesize all atmospheres for a single spectral region and normalize to the continuum of the quiet Sun at disk center Parameters ---------- spectral_region : str Spectral region to synthesize perturbation : bool Set to True if you are synthesizing with a perturbation. In this case, the synthesis is saved in spectrum.stokes_perturbed instead of spectrum.stokes Returns ------- None """ stokes = None stokes_out = None # Loop over all atmospheres for i, atmospheres in enumerate(self.order_atmospheres): for n, order in enumerate(atmospheres): for k, atm in enumerate(order): if (self.atmospheres[atm].spectrum.name == spectral_region): # Update the boundary condition only for the first atmosphere if several are sharing ff if (n > 0 and k == 0): ind_low, ind_top = self.atmospheres[atm].wvl_range if (perturbation): stokes_out = self.atmospheres[atm].spectrum.stokes_perturbed[:, ind_low:ind_top] * hazel.util.i0_allen(self.atmospheres[atm].spectrum.wavelength_axis[ind_low:ind_top], 1.0)[None,:] else: stokes_out = self.atmospheres[atm].spectrum.stokes[:, ind_low:ind_top] * hazel.util.i0_allen(self.atmospheres[atm].spectrum.wavelength_axis[ind_low:ind_top], 1.0)[None,:] if (self.atmospheres[atm].type == 'straylight'): stokes, error = self.atmospheres[atm].synthesize(nlte=self.use_nlte) if (error == 1): raise stokes += (1.0 - self.atmospheres[atm].parameters['ff']) * stokes_out else: if (k == 0): if (self.use_analytical_RF): stokes, self.rf_analytical, error = self.atmospheres[atm].synthesize(stokes_out, returnRF=True, nlte=self.use_nlte) else: stokes, error = self.atmospheres[atm].synthesize(stokes_out, nlte=self.use_nlte) else: tmp, error = self.atmospheres[atm].synthesize(stokes_out, nlte=self.use_nlte) stokes += tmp ind_low, ind_top = self.atmospheres[atm].wvl_range mean_wvl = np.mean(self.atmospheres[atm].spectrum.wavelength_axis[ind_low:ind_top]) i0 = hazel.util.i0_allen(mean_wvl, 1.0) # Divide by i0 if (self.use_analytical_RF): for k, v in self.rf_analytical.items(): if (k != 'ff'): v /= i0 if (perturbation): self.atmospheres[atm].spectrum.stokes_perturbed[:, ind_low:ind_top] = stokes / i0#[None,:] else: self.atmospheres[atm].spectrum.stokes[:, ind_low:ind_top] = stokes / i0#[None,:] def set_nlte(self, option): """ Set calculation of Ca II 8542 A to NLTE Parameters ---------- option : bool Set to True to use NLTE, False to use LTE """ self.use_nlte = option if (self.verbose >= 1): self.logger.info('Setting NLTE for Ca II 8542 A to {0}'.format(self.use_nlte)) def synthesize(self, perturbation=False): """ Synthesize all atmospheres Parameters ---------- perturbation : bool Set to True if you are synthesizing with a perturbation. In this case, the synthesis is saved in spectrum.stokes_perturbed instead of spectrum.stokes Returns ------- None """ if (self.working_mode == 'inversion'): self.normalize_ff() for k, v in self.spectrum.items(): self.synthesize_spectral_region(k, perturbation=perturbation) if (v.normalization == 'off-limb'): if (perturbation): v.stokes_perturbed /= np.max(v.stokes_perturbed[0,:]) else: v.stokes /= np.max(v.stokes[0,:]) if (v.psf_spectral is not None): for i in range(4): if (perturbation): v.stokes_perturbed[i,:] = scipy.signal.convolve(v.stokes_perturbed[i,:], v.psf_spectral, mode='same', method='auto') else: v.stokes[i,:] = scipy.signal.convolve(v.stokes[i,:], v.psf_spectral, mode='same', method='auto') if (v.interpolate_to_lr): for i in range(4): if (perturbation): v.stokes_perturbed_lr[i,:] = np.interp(v.wavelength_axis_lr, v.wavelength_axis, v.stokes_perturbed[i,:]) else: v.stokes_lr[i,:] = np.interp(v.wavelength_axis_lr, v.wavelength_axis, v.stokes[i,:]) def find_active_parameters(self, cycle): """ Find all active parameters in all active atmospheres in the current cycle Parameters ---------- cycle : int Cycle to consider Returns ------- None """ pars = [] coupled = [] self.nodes = [] left = 0 right = 0 for atmospheres in self.order_atmospheres: for n, order in enumerate(atmospheres): for k, atm in enumerate(order): for l, par in self.atmospheres[atm].cycles.items(): if (par is not None): if (hazel.util.isint(par[cycle])): if (par[cycle] > 0): # [Atmosphere name, n_nodes, nodes, value, range] self.atmospheres[atm].nodes[l] = np.zeros(par[cycle]) self.atmospheres[atm].n_nodes[l] = par[cycle] right += par[cycle] n_lambda = len(self.atmospheres[atm].spectrum.wavelength_axis) tmp = {'atm': atm, 'n_nodes': par[cycle], 'parameter': l, 'ranges': self.atmospheres[atm].ranges[l], 'delta': self.atmospheres[atm].epsilon[l], 'left': left, 'right': right, 'regularization': self.atmospheres[atm].regularization[l], 'coupled': False} self.nodes.append(self.atmospheres[atm].nodes[l]) left = copy.copy(right) pars.append(tmp) else: self.atmospheres[atm].nodes[l] = 0.0 self.atmospheres[atm].n_nodes[l] = 0 else: n_lambda = len(self.atmospheres[atm].spectrum.wavelength_axis) tmp = {'atm': atm, 'n_nodes': par[cycle], 'parameter': l, 'coupled': True} coupled.append(tmp) self.active_meta = pars self.coupled_meta = coupled if (not self.nodes): raise Exception("No parameters to invert in cycle {0}. Please add them or reduce the number of cycles. ".format(cycle)) self.nodes =

np.concatenate(self.nodes)

numpy.concatenate

""" ======================================= The :mod:`mpi_array.locale_test` Module ======================================= Module defining :mod:`mpi_array.locale` unit-tests. Execute as:: python -m mpi_array.locale_test Classes ======= .. autosummary:: :toctree: generated/ :template: autosummary/inherits_TestCase_class.rst WinLndarrayTest - Tests for :obj:`mpi_array.locale.win_lndarray`. LndarrayTest - Tests for :obj:`mpi_array.locale.lndarray`. LndarrayProxyTest - Tests for :obj:`mpi_array.locale.LndarrayProxy`. """ from __future__ import absolute_import from array_split.split import shape_factors as _shape_factors import mpi4py.MPI as _mpi import numpy as _np # noqa: E402,F401 from . import locale as _locale from .license import license as _license, copyright as _copyright, version as _version from . import unittest as _unittest from . import logging as _logging # noqa: E402,F401 from .comms import create_distribution, LT_PROCESS, LT_NODE from .distribution import IndexingExtent, LocaleExtent from .distribution import GlobaleExtent __author__ = "<NAME>" __license__ = _license() __copyright__ = _copyright() __version__ = _version() class WinLndarrayTest(_unittest.TestCase): """ Tests for :obj:`mpi_array.locale.win_lndarray`. """ def test_construct_with_invalid_comm(self): """ Tests that ValueError is raised for invalid communicator argument passed to :obj:`mpi_array.locale.win_lndarray` constructor. """ comm = None self.assertRaises(ValueError, _locale.win_lndarray, shape=(100,), comm=comm) comm = _mpi.COMM_NULL self.assertRaises(ValueError, _locale.win_lndarray, shape=(100,), comm=comm) def test_construct(self): """ Tests for :obj:`mpi_array.locale.win_lndarray` construction. """ comm = _mpi.COMM_SELF ary = _locale.win_lndarray(shape=(10, 10, 10), dtype="int32", comm=comm) self.assertTrue(ary.comm is comm) self.assertTrue(ary.win is not None) comm = _mpi.COMM_WORLD if (_mpi.VERSION >= 3) and (comm.size > 1): comm = comm.Split_type(_mpi.COMM_TYPE_SHARED, key=comm.rank) if comm.size > 1: ary = _locale.win_lndarray(shape=(comm.size, 10, 10), dtype="int32", comm=comm) self.assertTrue(ary.comm is comm) self.assertTrue(ary.win is not None) ary[comm.rank] = comm.rank + 1 comm.barrier() for r in range(0, comm.size): self.assertTrue(_np.all(ary[r] == (r + 1))) class LndarrayTest(_unittest.TestCase): """ :obj:`unittest.TestCase` for :obj:`mpi_array.locale.lndarray`. """ def test_construct(self): """ Tests :meth:`mpi_array.locale.lndarray.__new__`. """ gshape = (11, 13, 51) with _locale.ones(shape=gshape, dtype="int16") as lary: slary = lary.lndarray # MPI windows own buffer data self.assertTrue(slary.flags.carray) self.assertFalse(slary.flags.owndata) self.assertFalse(slary.base.flags.owndata) self.assertTrue(slary.base.flags.carray) lshape = slary.shape bad_lshape = list(lshape) bad_lshape[-1] += 1 self.assertRaises( ValueError, _locale.lndarray, shape=lshape ) def test_view(self): """ Tests :meth:`mpi_array.locale.lndarray.__getitem__`. """ gshape = (11, 13, 51) with _locale.ones(shape=gshape, dtype="int16") as lary: slary = lary.lndarray # MPI windows own buffer data self.assertTrue(slary.flags.carray) self.assertFalse(slary.flags.owndata) self.assertFalse(slary.base.flags.owndata) self.assertTrue(slary.base.flags.carray) v = lary[0:slary.shape[0] // 2, 0:slary.shape[1] // 2, 0:slary.shape[2] // 2] self.assertTrue(isinstance(v, _locale.lndarray)) self.assertFalse(v.flags.owndata) self.assertFalse( ((v.size > 0) and (v.shape[0] > 1) and (v.shape[1] > 1)) and v.flags.carray, "v.size=%s, v.shape=%s, v.flags.carray=%s" % (v.size, v.shape, v.flags.carray) ) self.assertTrue(isinstance(v.base, _locale.lndarray)) self.assertFalse(v.base.flags.owndata) self.assertTrue(v.base.flags.carray) self.assertFalse(isinstance(v.base.base, _locale.lndarray)) self.assertTrue(isinstance(v.base.base, _np.ndarray)) self.assertFalse(v.base.base.flags.owndata) self.assertTrue(v.base.base.flags.carray) def test_numpy_sum(self): """ Test :func:`numpy.sum` reduction using a :obj:`mpi_array.locale.lndarray` as argument. """ gshape = (50, 50, 50) comms_and_distrib = create_distribution(gshape) with \ _locale.ones(shape=gshape, comms_and_distrib=comms_and_distrib, dtype="int32") \ as lary: slary = lary.lndarray l_sum = _np.sum(lary.rank_view_n) self.assertFalse(l_sum.flags.owndata) self.assertTrue(l_sum.base.flags.owndata) rank_logger = _logging.get_rank_logger(__name__ + self.id()) rank_logger.info("type(slary)=%s", type(slary)) rank_logger.info("type(slary.base)=%s", type(slary.base)) rank_logger.info("type(l_sum)=%s", type(l_sum)) rank_logger.info("type(l_sum.base)=%s", type(l_sum.base)) g_sum = comms_and_distrib.locale_comms.peer_comm.allreduce(l_sum, op=_mpi.SUM) self.assertEqual(_np.product(gshape), g_sum) class LndarrayProxyTest(_unittest.TestCase): """ :obj:`unittest.TestCase` for :obj:`mpi_array.locale.LndarrayProxy`. """ def test_construct_arg_checking(self): """ Test for :meth:`mpi_array.locale.LndarrayProxy.__new__`. """ self.assertRaises( ValueError, _locale.LndarrayProxy, shape=None, locale_extent=None, dtype="int64" ) gshape = (10, 11, 12, 13, 5) bad_lshape = gshape bad_lshape = (bad_lshape[0] - 1,) + bad_lshape[1:] self.assertRaises( ValueError, _locale.LndarrayProxy, shape=bad_lshape, locale_extent=LocaleExtent( peer_rank=_mpi.COMM_WORLD.rank, inter_locale_rank=_mpi.COMM_WORLD.rank, globale_extent=GlobaleExtent(stop=gshape), start=(0, 0, 0, 0, 0), stop=gshape ) ) def test_fill(self): """ Test for :meth:`mpi_array.locale.LndarrayProxy.fill`. """ comms_and_distrib = create_distribution(shape=(24, 35, 14, 7)) with \ _locale.empty( shape=(24, 35, 14, 7), comms_and_distrib=comms_and_distrib, dtype="int64" ) as lary: rank_val = comms_and_distrib.locale_comms.peer_comm.rank + 1 lary.fill(rank_val) self.assertTrue(_np.all(lary[lary.rank_view_slice_n] == rank_val)) def test_get_and_set_item(self): """ """ comms_and_distrib = create_distribution(shape=(24, 35, 14, 7)) with \ _locale.empty( shape=(24, 35, 14, 7), comms_and_distrib=comms_and_distrib, dtype="int64" ) as lary: rank_val = comms_and_distrib.locale_comms.peer_comm.rank + 1 lary[lary.rank_view_slice_n] = rank_val self.assertSequenceEqual( list(IndexingExtent(lary.rank_view_slice_n).shape), list(lary[lary.rank_view_slice_n].shape) ) self.assertTrue(_np.all(lary[lary.rank_view_slice_n] == rank_val)) self.assertSequenceEqual( list(IndexingExtent(lary.rank_view_slice_h).shape), list(lary[lary.rank_view_slice_h].shape) ) def test_empty_shared_1d(self): """ Test for :func:`_locale.empty` and :func:`_locale.empty_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape) with _locale.empty(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) self.assertSequenceEqual( list(lshape), list(IndexingExtent(lary.intra_partition.rank_view_slice_n).shape) ) with _locale.empty_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) self.assertSequenceEqual( list(lshape), list(IndexingExtent(lary1.intra_partition.rank_view_slice_n).shape) ) ary = _locale.empty_like(_np.zeros(lshape, dtype="int64")) self.assertEqual(_np.dtype("int64"), ary.dtype) self.assertSequenceEqual( list(lshape), list(ary.shape) ) def test_empty_non_shared_1d(self): """ Test for :func:`_locale.empty` and :func:`_locale.empty_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape, locale_type=LT_PROCESS) with _locale.empty(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) self.assertSequenceEqual(list(lshape), list(lary.shape)) self.assertSequenceEqual( list(lshape), list(IndexingExtent(lary.intra_partition.rank_view_slice_n).shape) ) with _locale.empty_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) self.assertSequenceEqual(list(lshape), list(lary1.shape)) self.assertSequenceEqual( list(lshape), list(IndexingExtent(lary1.intra_partition.rank_view_slice_n).shape) ) def test_zeros_shared_1d(self): """ Test for :func:`_locale.zeros` and :func:`_locale.zeros_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape) with _locale.zeros(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary == 0)) with _locale.zeros_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == 0)) def test_zeros_non_shared_1d(self): """ Test for :func:`_locale.zeros` and :func:`_locale.zeros_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape, locale_type=LT_PROCESS) with _locale.zeros(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary == 0)) with _locale.zeros_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == 0)) def test_ones_shared_1d(self): """ Test for :func:`_locale.ones` and :func:`_locale.ones_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape) with _locale.ones(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary == 1)) with _locale.ones_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == 1)) def test_ones_non_shared_1d(self): """ Test for :func:`_locale.ones` and :func:`_locale.ones_like`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape, locale_type=LT_PROCESS) with _locale.ones(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary == 1)) with _locale.ones_like(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == 1)) def test_copy_shared_1d(self): """ Test for :func:`_locale.copy`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape) with _locale.ones(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) lary.rank_view_n[...] = cand.locale_comms.peer_comm.rank with _locale.copy(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == lary)) def test_copy_non_shared_1d(self): """ Test for :func:`_locale.copy`. """ lshape = (10,) gshape = (_mpi.COMM_WORLD.size * lshape[0],) cand = create_distribution(shape=gshape, locale_type=LT_PROCESS) with _locale.ones(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) lary.rank_view_n[...] = cand.locale_comms.peer_comm.rank with _locale.copy(lary) as lary1: self.assertEqual(_np.dtype("int64"), lary1.dtype) cand.locale_comms.peer_comm.barrier() self.assertTrue(_np.all(lary1 == lary)) def do_test_views_2d(self, halo=0): """ Test for :meth:`mpi_array.locale.LndarrayProxy.rank_view_n` and :meth:`mpi_array.locale.LndarrayProxy.rank_view_h`. """ lshape = _np.array((4, 3), dtype="int64") gshape = lshape * _shape_factors(_mpi.COMM_WORLD.size, lshape.size)[::-1] cands = \ [ create_distribution(shape=gshape, locale_type=LT_NODE, halo=halo), create_distribution(shape=gshape, locale_type=LT_PROCESS, halo=halo) ] for cand in cands: with _locale.ones(comms_and_distrib=cand, dtype="int64") as lary: self.assertEqual(_np.dtype("int64"), lary.dtype) rank_logger = _logging.get_rank_logger(self.id(), comm=cand.locale_comms.peer_comm) rank_logger.info( ( "\n========================================================\n" + "locale_extent = %s\n" + "rank_view_slice_n = %s\n" + "rank_view_slice_h = %s\n" + "rank_view_relative_slice_n = %s\n" + "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^" ) % ( lary.locale_extent, lary.intra_partition.rank_view_slice_n, lary.intra_partition.rank_view_slice_h, lary.intra_partition.rank_view_relative_slice_n, ) ) if cand.locale_comms.intra_locale_comm.rank == 0: lary.view_h[...] = -1 cand.locale_comms.intra_locale_comm.barrier() lary.rank_view_n[...] = cand.locale_comms.peer_comm.rank cand.locale_comms.intra_locale_comm.barrier() if

_np.any(lary.halo > 0)

numpy.any

# Copyright 2020 BULL SAS All rights reserved """This module provides the different tests for the resampling policies. """ import unittest import numpy as np from bbo.noise_reduction.resampling_policies import ( ResamplingPolicy, SimpleResampling, DynamicResampling, DynamicResamplingParametric, DynamicResamplingNonParametric, DynamicResamplingNonParametric2, ) def resampling_schedule(nbr_it): return 1 / np.log(1 + nbr_it) class TestResampling(unittest.TestCase): """Tests the proper implementation of the parent resampling class. """ def test_parent_class(self): """Test the initialization of the Resampling parent class. """ resampling_policy = ResamplingPolicy() def test_not_implemented(self): """Test that a not implemented error is raised when calling the resample method""" resampling_policy = ResamplingPolicy() with self.assertRaises(NotImplementedError): resampling_policy.resample(history={}) class TestSimpleResampling(unittest.TestCase): """Tests the SimpleResampling class. """ def test_simple_resampling_resample(self): """Tests that the parameter are properly resampled when the last parameter should be resampled. """ history = { "fitness": np.array([10, 5, 4, 2, 15, 20]), "parameters": np.array([[1, 2], [2, 3], [1, 3], [4, 3], [2, 1], [2, 1]]), } resampler = SimpleResampling(nbr_resamples=3) self.assertTrue(resampler.resample(history)) def test_simple_resampling_no_resample(self): """Tests that the parameter are properly resampled when the last parameter should not be resampled. """ history = { "fitness": np.array([10, 5, 4, 2, 15, 20]), "parameters": np.array([[1, 2], [2, 3], [1, 3], [2, 1], [2, 1], [2, 1]]), } resampler = SimpleResampling(nbr_resamples=3) self.assertFalse(resampler.resample(history)) def test_simple_resampling_no_sequential(self): """Tests that when the samples are not sequentially ordered, the resampling still happens properly. """ history = { "fitness": np.array([10, 5, 4, 2, 15, 20]), "parameters": np.array([[1, 2], [2, 3], [1, 3], [4, 3], [2, 1], [2, 1]]), } resampler = SimpleResampling(nbr_resamples=2) self.assertFalse(resampler.resample(history)) class TestDynamicResampling(unittest.TestCase): """Tests the abstract class of dynamic resampling. """ def test_initialization(self): """Tests the proper initialization of the class. """ dynamic_resampling = DynamicResampling(percentage=0.5) def test_resampling_schedule_None(self): """Tests the good definition of the resampling schedule when set to None. """ dynamic_resampling = DynamicResampling(percentage=0.5) self.assertEqual(0.5, dynamic_resampling.resampling_schedule(1)) def test_resampling_schedule(self): """Tests the good definition of the resampling schedule. """ dynamic_resampling = DynamicResampling( percentage=0.5, resampling_schedule=resampling_schedule ) self.assertEqual(0.5 / np.log(2), dynamic_resampling.resampling_schedule(1)) def test_allow_resampling_schedule_None(self): """Tests that the allow resampling schedule behaves as expected when set to None. """ dynamic_resampling = DynamicResampling(percentage=0.5) self.assertEqual(1, dynamic_resampling.allow_resampling_schedule(1)) def test_allow_resampling_schedule(self): """Tests that the allow resampling schedule behaves as expected when set to a value. """ dynamic_resampling = DynamicResampling( percentage=0.5, allow_resampling_start=5, allow_resampling_schedule=resampling_schedule, ) self.assertEqual(5 / np.log(2), dynamic_resampling.allow_resampling_schedule(1)) class TestDynamicResamplingParametric(unittest.TestCase): """Tests that the DynamicResamplingParametric class behaves as expected. """ def test_error_percentage(self): """Tests that dynamic resampling raises an error when the percentage is below 0. """ with self.assertRaises(ValueError): DynamicResamplingParametric(percentage=-0.1) def test_dynamic_resampling_formula(self): """ Tests that the computation performs what is expected. """ test_dynamic_resampling = DynamicResamplingParametric(0.2) history = { "fitness": np.array([10, 5, 4, 14, 15, 16]), "parameters":

np.array([[1, 2], [2, 3], [1, 3], [2, 1], [2, 1], [2, 1]])

numpy.array

import argparse import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import pdb import os from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau from tensorflow.keras.layers import Input, Dense, Conv2D, BatchNormalization, MaxPool1D, Flatten from tensorflow.keras.models import Model, Sequential from tensorflow.keras import regularizers from tensorflow.keras import optimizers from tensorflow.keras.utils import to_categorical from tensorflow.keras.models import load_model def get_argparser(): parser = argparse.ArgumentParser() ### CNNs parser.add_argument("--units", default=16, type=int, help="Number of units in hidden layers of the DNN") parser.add_argument("--layers", default=2, type=int, help="Number of layers in the DNN") parser.add_argument("--reg", default=0.1, type=float, help="Regularization weight of the DNN") parser.add_argument("--data_type", default='rgb', type=str, help="Which data to evaluate on: (ir, rgb, cover)") return parser if __name__ == '__main__': parser = get_argparser() args = parser.parse_args() data_type = args.data_type ######################## # Load and process data ######################## layers_arr = [1, 2, 3, 4] units_arr = [4, 8, 16, 32, 64] reg_arr = [0.001, 0.005, 0.01, 0.05, 0.1] plot_obj_train = np.zeros((len(layers_arr), len(units_arr), len(reg_arr))) plot_obj_val = np.zeros((len(layers_arr), len(units_arr), len(reg_arr))) for layers_idx in range(len(layers_arr)): for units_idx in range(len(units_arr)): for reg_idx in range(len(reg_arr)): layers = layers_arr[layers_idx] units = units_arr[units_idx] regularizer = reg_arr[reg_idx] kernel = 5 model_name = f'CNN10-layers{layers}-units{units}-kernel{kernel}-reg{regularizer}-{data_type}' loss_obj = np.loadtxt(f'output-data/train-history-{model_name}.csv', delimiter=',') print(f'For model {model_name} Best train accuracy is {np.max(loss_obj[2])}') print(f'For model {model_name} Best val accuracy is {np.max(loss_obj[3])}') plot_obj_train[layers_idx, units_idx, reg_idx] = np.max(loss_obj[2]) plot_obj_val[layers_idx, units_idx, reg_idx] = np.max(loss_obj[3]) # Pick one regularization for ri in range(len(reg_arr)): reg_amt = reg_arr[ri] cut_plot_obj = plot_obj_train[:, :, ri] fig, ax = plt.subplots(1, 1, figsize=(10, 6)) width = 0.15 n_col = len(layers_arr) ax.set_title(f'Hyperparameter Search for {data_type.upper()} models', fontsize=24) ax.set_ylim([0, 1.05]) ax.bar(np.arange(n_col) - 2 * width, cut_plot_obj[:, 0], width, label='4 filters') ax.bar(np.arange(n_col) - 1 * width, cut_plot_obj[:, 1], width, label='8 filters') ax.bar(np.arange(n_col) + 0 * width, cut_plot_obj[:, 2], width, label='16 filters') ax.bar(np.arange(n_col) + 1 * width, cut_plot_obj[:, 3], width, label='32 filters') ax.bar(np.arange(n_col) + 2 * width, cut_plot_obj[:, 4], width, label='64 filters') ax.set_axisbelow(True) ax.grid(True, which='major', axis='y', linestyle = '--') ax.legend(loc='best', ncol=3, fontsize=20) ax.set_xticks(np.arange(n_col)) ax.set_xticklabels(layers_arr, fontsize=16) ax.set_xlabel('Layers', fontsize=24) ax.set_ylabel('Training Accuracy', fontsize=24) fig.tight_layout(rect=[0, 0, 1, 1]) plt.savefig(f'plot-hyperparam-{data_type}-train-{reg_amt}.pdf') #################################################### cut_plot_obj = plot_obj_val[:, :, 4] fig, ax = plt.subplots(1, 1, figsize=(10, 6)) width = 0.15 n_col = len(layers_arr) ax.set_title(f'Hyperparameter Search for {data_type.upper()} models', fontsize=24) ax.set_ylim([0, 1.05]) ax.bar(

np.arange(n_col)

numpy.arange

#! /usr/bin/env python # coding=utf-8 # Copyright (c) 2019 Uber Technologies, Inc. # # 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 numpy as np from scipy.signal import lfilter from scipy.signal.windows import get_window def _pre_emphasize_data(data, emphasize_value=0.97): filter_window = np.asarray([1, -emphasize_value]) pre_emphasized_data = lfilter(filter_window, 1, data) return pre_emphasized_data def get_length_in_samp(sampling_rate_in_hz, length_in_s): return int(sampling_rate_in_hz * length_in_s) def get_group_delay(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type): X_stft_transform = _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type=window_type) Y_stft_transform = _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type=window_type, data_transformation='group_delay') X_stft_transform_real = np.real(X_stft_transform) X_stft_transform_imag = np.imag(X_stft_transform) Y_stft_transform_real = np.real(Y_stft_transform) Y_stft_transform_imag = np.imag(Y_stft_transform) nominator = np.multiply(X_stft_transform_real, Y_stft_transform_real) + np.multiply( X_stft_transform_imag, Y_stft_transform_imag) denominator = np.square(np.abs(X_stft_transform)) group_delay = np.divide(nominator, denominator + 1e-10) assert not np.isnan( group_delay).any(), 'There are NaN values in group delay' return np.transpose(group_delay) def get_phase_stft_magnitude(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type): stft = _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type=window_type) abs_stft = np.abs(stft) phase = np.angle(stft) stft_phase = np.concatenate((phase, abs_stft), axis=1) return np.transpose(stft_phase) def get_stft_magnitude(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type): stft = _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type=window_type) stft_magnitude = np.abs(stft) return np.transpose(stft_magnitude) ################################################################################ # The following code for FBank is adapted from jameslyons/python_speech_features # MIT licensed implementation # https://github.com/jameslyons/python_speech_features/blob/40c590269b57c64a8c1f1ddaaff2162008d1850c/python_speech_features/base.py#L84################################################################################ ################################################################################ def get_fbank(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type, num_filter_bands): stft = _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type=window_type, zero_mean_offset=True) stft_power = np.abs(stft) ** 2 upper_limit_freq = int(sampling_rate_in_hz / 2) upper_limit_mel = _convert_hz_to_mel(upper_limit_freq) lower_limit_mel = 0 list_mel_points = np.linspace(lower_limit_mel, upper_limit_mel, num_filter_bands + 2) mel_fbank_matrix = _get_mel_fbank_matrix(list_mel_points, num_filter_bands, num_fft_points, sampling_rate_in_hz) mel_fbank_feature = np.dot(stft_power, np.transpose(mel_fbank_matrix)) log_mel_fbank_feature = np.log(mel_fbank_feature + 1.0e-10) return np.transpose(log_mel_fbank_feature) def _get_mel_fbank_matrix(list_mel_points, num_filter_bands, num_fft_points, sampling_rate_in_hz): num_ess_fft_points = get_non_symmetric_length(num_fft_points) freq_scale = (num_fft_points + 1) / sampling_rate_in_hz freq_bins_on_mel_scale = np.floor( freq_scale * _convert_mel_to_hz(list_mel_points)) mel_scaled_fbank = np.zeros((num_filter_bands, num_ess_fft_points), dtype=np.float32) for filt_idx in range(num_filter_bands): start_bin_freq = freq_bins_on_mel_scale[filt_idx] middle_bin_freq = freq_bins_on_mel_scale[filt_idx + 1] end_bin_freq = freq_bins_on_mel_scale[filt_idx + 2] mel_scaled_fbank[filt_idx] = _create_triangular_filter(start_bin_freq, middle_bin_freq, end_bin_freq, num_ess_fft_points) return mel_scaled_fbank def _create_triangular_filter(start_bin_freq, middle_bin_freq, end_bin_freq, num_ess_fft_points): filter_window = np.zeros(num_ess_fft_points, dtype=np.float32) filt_support_begin = middle_bin_freq - start_bin_freq filt_support_end = end_bin_freq - middle_bin_freq for freq in range(int(start_bin_freq), int(middle_bin_freq)): filter_window[freq] = (freq - start_bin_freq) / filt_support_begin for freq in range(int(middle_bin_freq), int(end_bin_freq)): filter_window[freq] = (end_bin_freq - freq) / filt_support_end return filter_window def _convert_hz_to_mel(hz): return 2595.0 * np.log10(1 + hz / 700.0) def _convert_mel_to_hz(mel): return 700.0 * (10 ** (mel / 2595.0) - 1) def _get_stft(raw_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type, data_transformation=None, zero_mean_offset=False): pre_emphasized_data = _pre_emphasize_data(raw_data) stft = _short_time_fourier_transform(pre_emphasized_data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type, data_transformation, zero_mean_offset) non_symmetric_stft = get_non_symmetric_data(stft) return non_symmetric_stft def _short_time_fourier_transform(data, sampling_rate_in_hz, window_length_in_s, window_shift_in_s, num_fft_points, window_type, data_transformation=None, zero_mean_offset=False): window_length_in_samp = get_length_in_samp(window_length_in_s, sampling_rate_in_hz) window_shift_in_samp = get_length_in_samp(window_shift_in_s, sampling_rate_in_hz) preprocessed_data_matrix = _preprocess_to_padded_matrix(data, window_length_in_samp, window_shift_in_samp, zero_mean_offset=zero_mean_offset) weighted_data_matrix = _weight_data_matrix( preprocessed_data_matrix, window_type, data_transformation=data_transformation ) fft = np.fft.fft(weighted_data_matrix, n=num_fft_points) return fft def _preprocess_to_padded_matrix(data, window_length_in_samp, window_shift_in_samp, zero_mean_offset=False): num_input = data.shape[0] num_output = get_num_output_padded_to_fit_input(num_input, window_length_in_samp, window_shift_in_samp) zero_padded_matrix = np.zeros((num_output, window_length_in_samp), dtype=np.float) for num_output_idx in range(num_output): start_idx = window_shift_in_samp * num_output_idx is_last_output = num_output_idx == num_output - 1 end_idx = start_idx + window_length_in_samp if not is_last_output else num_input end_padded_idx = window_length_in_samp if not is_last_output else end_idx - start_idx window_data = data[start_idx:end_idx] if zero_mean_offset: window_data = window_data -

np.mean(window_data)

numpy.mean

import numpy as np from nsgt.fft import rfftp, irfftp, fftp, ifftp import unittest class TestFFT(unittest.TestCase): def __init__(self, methodName, n=10000): super(TestFFT, self).__init__(methodName) self.n = n def test_rfft(self): seq = np.random.random(self.n) ft = rfftp() a = ft(seq) b = np.fft.rfft(seq) self.assertTrue(np.allclose(a, b)) def test_irfft(self): seq = np.random.random(self.n)+np.random.random(self.n)*1.j outn = (self.n-1)*2 + np.random.randint(0,2) # even or odd output size ft = irfftp() a = ft(seq, outn=outn) b = np.fft.irfft(seq, n=outn) self.assertTrue(np.allclose(a, b)) def test_fft(self): seq = np.random.random(self.n) ft = fftp() a = ft(seq) b = np.fft.fft(seq) self.assertTrue(np.allclose(a, b)) def test_ifft(self): seq = np.random.random(self.n)+np.random.random(self.n)*1.j ft = ifftp() a = ft(seq) b =

np.fft.ifft(seq)

numpy.fft.ifft

"""Krylov-subspaces model order reduction techniques """ import numpy as np import scipy.linalg as sclalg import sharpy.linear.src.libss as libss import time import sharpy.utils.settings as settings import sharpy.utils.cout_utils as cout import sharpy.utils.rom_interface as rom_interface import sharpy.utils.h5utils as h5 import sharpy.rom.utils.krylovutils as krylovutils import warnings as warn @rom_interface.rom class Krylov(rom_interface.BaseRom): """ Model Order Reduction Methods for Single Input Single Output (SISO) and MIMO Linear Time-Invariant (LTI) Systems using moment matching (Krylov Methods). Examples: General calling sequences for different systems SISO single point interpolation: >>> algorithm = 'one_sided_arnoldi' >>> interpolation_point = np.array([0.0]) >>> krylov_r = 4 >>> >>> rom = Krylov() >>> rom.initialise(sharpy_data, FullOrderModelSS) >>> rom.run(algorithm, krylov_r, interpolation_point) 2 by 2 MIMO with tangential, multipoint interpolation: >>> algorithm = 'dual_rational_arnoldi' >>> interpolation_point = np.array([0.0, 1.0j]) >>> krylov_r = 4 >>> right_vector = np.block([[1, 0], [0, 1]]) >>> left_vector = right_vector >>> >>> rom = Krylov() >>> rom.initialise(sharpy_data, FullOrderModelSS) >>> rom.run(algorithm, krylov_r, interpolation_point, right_vector, left_vector) 2 by 2 MIMO multipoint interpolation: >>> algorithm = 'mimo_rational_arnoldi' >>> interpolation_point = np.array([0.0]) >>> krylov_r = 4 >>> >>> rom = Krylov() >>> rom.initialise(sharpy_data, FullOrderModelSS) >>> rom.run(algorithm, krylov_r, interpolation_point) """ rom_id = 'Krylov' settings_types = dict() settings_default = dict() settings_description = dict() settings_options = dict() settings_types['print_info'] = 'bool' settings_default['print_info'] = True settings_description['print_info'] = 'Write ROM information to screen and log' settings_types['frequency'] = 'list(complex)' settings_default['frequency'] = [0] settings_description['frequency'] = 'Interpolation points in the continuous time complex plane [rad/s]' settings_types['algorithm'] = 'str' settings_default['algorithm'] = '' settings_description['algorithm'] = 'Krylov reduction method algorithm' settings_types['r'] = 'int' settings_default['r'] = 1 settings_description['r'] = 'Moments to match at the interpolation points' settings_types['single_side'] = 'str' settings_default['single_side'] = '' settings_description['single_side'] = 'Construct the rom using a single side. Leave blank (or empty string) for both.' settings_options['single_side'] = ['controllability', 'observability'] settings_types['tangent_input_file'] = 'str' settings_default['tangent_input_file'] = '' settings_description['tangent_input_file'] = 'Filepath to .h5 file containing tangent interpolation vectors' settings_types['restart_arnoldi'] = 'bool' settings_default['restart_arnoldi'] = False settings_description['restart_arnoldi'] = 'Restart Arnoldi iteration with r-=1 if ROM is unstable' settings_table = settings.SettingsTable() __doc__ += settings_table.generate(settings_types, settings_default, settings_description, settings_options) supported_methods = ('one_sided_arnoldi', 'two_sided_arnoldi', 'dual_rational_arnoldi', 'mimo_rational_arnoldi', 'mimo_block_arnoldi') def __init__(self): self.settings = dict() self.frequency = None self.algorithm = None self.ss = None self.r = 1 self.V = None self.H = None self.W = None self.ssrom = None self.sstype = None self.nfreq = None self.restart_arnoldi = None self.stable = None self.cpu_summary = dict() self.eigenvalue_table = None def initialise(self, in_settings=None): if in_settings is not None: self.settings = in_settings settings.to_custom_types(self.settings, self.settings_types, self.settings_default, self.settings_options) try: if self.settings['print_info']: cout.cout_wrap('Initialising Krylov Model Order Reduction') except ValueError: pass self.algorithm = self.settings['algorithm'] if self.algorithm not in self.supported_methods: raise NotImplementedError('Algorithm %s not recognised, check for spelling or it' 'could be that is not yet implemented' % self.algorithm) self.frequency = np.array(self.settings['frequency']) self.r = self.settings['r'].value self.restart_arnoldi = self.settings['restart_arnoldi'].value try: self.nfreq = self.frequency.shape[0] except AttributeError: self.nfreq = 1 def run(self, ss): """ Performs Model Order Reduction employing Krylov space projection methods. Supported methods include: ========================= ==================== ========================================================== Algorithm Interpolation Points Systems ========================= ==================== ========================================================== ``one_sided_arnoldi`` 1 SISO Systems ``two_sided_arnoldi`` 1 SISO Systems ``dual_rational_arnoldi`` K SISO systems and Tangential interpolation for MIMO systems ``mimo_rational_arnoldi`` K MIMO systems. Uses vector-wise construction (more robust) ``mimo_block_arnoldi`` K MIMO systems. Uses block Arnoldi methods (more efficient) ========================= ==================== ========================================================== Args: ss (sharpy.linear.src.libss.ss): State space to reduce Returns: (libss.ss): Reduced state space system """ self.ss = ss if self.settings['print_info']: cout.cout_wrap('Model Order Reduction in progress...') self.print_header() if self.ss.dt is None: self.sstype = 'ct' else: self.sstype = 'dt' self.frequency = np.exp(self.frequency * ss.dt) t0 = time.time() Ar, Br, Cr = self.__getattribute__(self.algorithm)(self.frequency, self.r) self.ssrom = libss.ss(Ar, Br, Cr, self.ss.D, self.ss.dt) self.stable = self.check_stability(restart_arnoldi=self.restart_arnoldi) if not self.stable: pass warn.warn('Reduced Order Model Unstable') # Under development # TL, TR = self.restart() # Wtr, Vr = self.restart() # TL, TR = self.stable_realisation() # self.ssrom = libss.ss(TL.T.dot(Ar.dot(TR)), TL.T.dot(Br), Cr.dot(TR), self.ss.D, self.ss.dt) # self.ssrom = libss.ss(Wtr.dot(self.ssrom.A.dot(Vr)), Wtr.dot(self.ssrom.B), self.ssrom.C.dot(Vr), self.ss.D, self.ss.dt) # self.stable = self.check_stability(restart_arnoldi=self.restart_arnoldi) t_rom = time.time() - t0 self.cpu_summary['run'] = t_rom if self.settings['print_info']: cout.cout_wrap('System reduced from order %d to ' % self.ss.states) cout.cout_wrap('\tn = %d states' % self.ssrom.states, 1) cout.cout_wrap('...Completed Model Order Reduction in %.2f s' % t_rom) return self.ssrom def print_header(self): cout.cout_wrap('Moment Matching Krylov Model Reduction') cout.cout_wrap('\tConstruction Algorithm:') cout.cout_wrap('\t\t%s' % self.algorithm, 1) cout.cout_wrap('\tInterpolation points:') if self.frequency.dtype == complex: cout.cout_wrap(self.nfreq * '\t\tsigma = %4f + %4fj [rad/s]\n' %tuple(self.frequency.view(float)), 1) else: cout.cout_wrap(self.nfreq * '\t\tsigma = %4f [rad/s]\n' % self.frequency, 1) cout.cout_wrap('\tKrylov order:') cout.cout_wrap('\t\tr = %d' % self.r, 1) def one_sided_arnoldi(self, frequency, r): r""" One-sided Arnoldi method expansion about a single interpolation point, :math:`\sigma`. The projection matrix :math:`\mathbf{V}` is constructed using an order :math:`r` Krylov space. The space for a single finite interpolation point known as a Pade approximation is described by: .. math:: \text{range}(\textbf{V}) = \mathcal{K}_r((\sigma\mathbf{I}_n - \mathbf{A})^{-1}, (\sigma\mathbf{I}_n - \mathbf{A})^{-1}\mathbf{b}) In the case of an interpolation about infinity, the problem is known as partial realisation and the Krylov space is .. math:: \text{range}(\textbf{V}) = \mathcal{K}_r(\mathbf{A}, \mathbf{b}) The resulting orthogonal projection leads to the following reduced order system: .. math:: \hat{\Sigma} : \left(\begin{array}{c|c} \hat{A} & \hat{B} \\ \hline \hat{C} & {D}\end{array}\right) \text{with } \begin{cases}\hat{A}=V^TAV\in\mathbb{R}^{k\times k},\,\\ \hat{B}=V^TB\in\mathbb{R}^{k\times m},\,\\ \hat{C}=CV\in\mathbb{R}^{p\times k},\,\\ \hat{D}=D\in\mathbb{R}^{p\times m}\end{cases} Args: frequency (complex): Interpolation point :math:`\sigma \in \mathbb{C}` r (int): Number of moments to match. Equivalent to Krylov space order and order of the ROM. Returns: tuple: The reduced order model matrices: :math:`\mathbf{A}_r`, :math:`\mathbf{B}_r` and :math:`\mathbf{C}_r` """ A = self.ss.A B = self.ss.B C = self.ss.C nx = A.shape[0] if frequency != np.inf and frequency is not None: lu_A = krylovutils.lu_factor(frequency, A) V = krylovutils.construct_krylov(r, lu_A, B, 'Pade', 'b') else: V = krylovutils.construct_krylov(r, A, B, 'partial_realisation', 'b') # Reduced state space model Ar = V.T.dot(A.dot(V)) Br = V.T.dot(B) Cr = C.dot(V) self.V = V self.W = V return Ar, Br, Cr def two_sided_arnoldi(self, frequency, r): r""" Two-sided projection with a single interpolation point following the Arnoldi procedure. Very similar to the one-sided method available, but it adds the projection :math:`\mathbf{W}` built using the Krylov space for the :math:`\mathbf{c}` vector: .. math:: \mathcal{K}_r((\sigma\mathbf{I}_n - \mathbf{A})^{-T}, (\sigma\mathbf{I}_n - \mathbf{A})^{-T}\mathbf{c}^T)\subseteq\mathcal{W}=\text{range}(\mathbf{W}) The oblique projection :math:`\mathbf{VW}^T` matches twice as many moments as the single sided projection. The resulting system takes the form: .. math:: \hat{\Sigma} : \left(\begin{array}{c|c} \hat{A} & \hat{B} \\ \hline \hat{C} & {D}\end{array}\right) \text{with } \begin{cases}\hat{A}=W^TAV\in\mathbb{R}^{k\times k},\,\\ \hat{B}=W^TB\in\mathbb{R}^{k\times m},\,\\ \hat{C}=CV\in\mathbb{R}^{p\times k},\,\\ \hat{D}=D\in\mathbb{R}^{p\times m}\end{cases} Args: frequency (complex): Interpolation point :math:`\sigma \in \mathbb{C}` r (int): Number of moments to match on each side. The resulting ROM will be of order :math:`2r`. Returns: tuple: The reduced order model matrices: :math:`\mathbf{A}_r`, :math:`\mathbf{B}_r` and :math:`\mathbf{C}_r`. """ A = self.ss.A B = self.ss.B C = self.ss.C nx = A.shape[0] if frequency != np.inf and frequency is not None: lu_A = krylovutils.lu_factor(frequency, A) V = krylovutils.construct_krylov(r, lu_A, B, 'Pade', 'b') W = krylovutils.construct_krylov(r, lu_A, C.T, 'Pade', 'c') else: V = krylovutils.construct_krylov(r, A, B, 'partial_realisation', 'b') W = krylovutils.construct_krylov(r, A, C.T, 'partial_realisation', 'c') T = W.T.dot(V) Tinv = sclalg.inv(T) reduction_checks(T, Tinv) self.W = W self.V = V # Reduced state space model Ar = W.T.dot(self.ss.A.dot(V.dot(Tinv))) Br = W.T.dot(self.ss.B) Cr = self.ss.C.dot(V.dot(Tinv)) return Ar, Br, Cr def real_rational_arnoldi(self, frequency, r): """ When employing complex frequencies, the projection matrix can be normalised to be real Following Algorithm 1b in Lee(2006) Args: frequency: r: Returns: """ raise NotImplementedError('Real valued rational Arnoldi Method Work in progress - use mimo_rational_arnoldi') ### Not working, having trouble with the last column of H. need to investigate the background behind the creation of H and see hwat can be done A = self.ss.A B = self.ss.B C = self.ss.C nx = A.shape[0] nfreq = frequency.shape[0] # Columns of matrix v v_ncols = 2 * np.sum(r) # Output projection matrices V = np.zeros((nx, v_ncols), dtype=float) H = np.zeros((v_ncols, v_ncols), dtype=float) res = np.zeros((nx,v_ncols+2), dtype=float) # lu_A = krylovutils.lu_factor(frequency[0] * np.eye(nx) - A) v_res = sclalg.lu_solve(lu_A, B) H[0, 0] = np.linalg.norm(v_res) V[:, 0] = v_res.real / H[0, 0] k = 0 for i in range(nfreq): for j in range(r[i]): # k = 2*(i*r[i] + j) print("i = %g\t j = %g\t k = %g" % (i, j, k)) # res[:, k] = np.imag(v_res) # if k > 0: # res[:, k-1] = np.real(v_res) # # # Working on the last finished column i.e. k-1 only when k>0 # if k > 0: # for t in range(k): # H[t, k-1] = V[:, t].T.dot(res[:, k-1]) # res[:, k-1] -= res[:, k-1] - H[t, k-1] * V[:, t] # # H[k, k-1] = np.linalg.norm(res[:, k-1]) # V[:, k] = res[:, k-1] / H[k, k-1] # # # Normalise working column k # for t in range(k+1): # H[t, k] = V[:, t].T.dot(res[:, k]) # res[:, k] -= H[t, k] * V[:, t] # # # Subdiagonal term # H[k+1, k] = np.linalg.norm(res[:, k]) # V[:, k + 1] = res[:, k] / np.linalg.norm(res[:, k]) # # if j == r[i] - 1 and i < nfreq - 1: # lu_A = krylovutils.lu_factor(frequency[i+1] * np.eye(nx) - A) # v_res = sclalg.lu_solve(lu_A, B) # else: # v_res = - sclalg.lu_solve(lu_A, V[:, k+1]) if k == 0: V[:, 0] = v_res.real / np.linalg.norm(v_res.real) else: res[:, k] = np.imag(v_res) res[:, k-1] = np.real(v_res) for t in range(k): H[t, k-1] = np.linalg.norm(res[:, k-1]) res[:, k-1] -= H[t, k-1]*V[:, t] H[k, k-1] = np.linalg.norm(res[:, k-1]) V[:, k] = res[:, k-1] / H[k, k-1] if k == 0: H[0, 0] = V[:, 0].T.dot(v_res.imag) res[:, 0] -= H[0, 0] * V[:, 0] else: for t in range(k+1): H[t, k] = V[:, t].T.dot(res[:, k]) res[:, k] -= H[t, k] * V[:, t] H[k+1, k] = np.linalg.norm(res[:, k]) V[:, k+1] = res[:, k] / H[k+1, k] if j == r[i] - 1 and i < nfreq - 1: lu_A = krylovutils.lu_factor(frequency[i+1], A) v_res = sclalg.lu_solve(lu_A, B) else: v_res = - sclalg.lu_solve(lu_A, V[:, k+1]) k += 2 # Add last column of H print(k) res[:, k-1] = - sclalg.lu_solve(lu_A, V[:, k-1]) for t in range(k-1): H[t, k-1] = V[:, t].T.dot(res[:, k-1]) res[:, k-1] -= H[t, k-1]*V[:, t] self.V = V self.H = H Ar = V.T.dot(A.dot(V)) Br = V.T.dot(B) Cr = C.dot(V) return Ar, Br, Cr def dual_rational_arnoldi(self, frequency, r): r""" Dual Rational Arnoli Interpolation for SISO sytems [1] and MIMO systems through tangential interpolation [2]. Effectively the same as the two_sided_arnoldi and the resulting V matrices for each interpolation point are concatenated .. math:: \bigcup\limits_{k = 1}^K\mathcal{K}_{b_k}((\sigma_i\mathbf{I}_n - \mathbf{A})^{-1}, (\sigma_i\mathbf{I}_n - \mathbf{A})^{-1}\mathbf{b})\subseteq\mathcal{V}&=\text{range}(\mathbf{V}) \\ \bigcup\limits_{k = 1}^K\mathcal{K}_{c_k}((\sigma_i\mathbf{I}_n - \mathbf{A})^{-T}, (\sigma_i\mathbf{I}_n - \mathbf{A})^{-T}\mathbf{c}^T)\subseteq\mathcal{Z}&=\text{range}(\mathbf{Z}) For MIMO systems, tangential interpolation is used through the right and left tangential direction vectors :math:`\mathbf{r}_i` and :math:`\mathbf{l}_i`. .. math:: \bigcup\limits_{k = 1}^K\mathcal{K}_{b_k}((\sigma_i\mathbf{I}_n - \mathbf{A})^{-1}, (\sigma_i\mathbf{I}_n - \mathbf{A})^{-1}\mathbf{Br}_i)\subseteq\mathcal{V}&=\text{range}(\mathbf{V}) \\ \bigcup\limits_{k = 1}^K\mathcal{K}_{c_k}((\sigma_i\mathbf{I}_n - \mathbf{A})^{-T}, (\sigma_i\mathbf{I}_n - \mathbf{A})^{-T}\mathbf{C}^T\mathbf{l}_i)\subseteq\mathcal{Z}&=\text{range}(\mathbf{Z}) Args: frequency (np.ndarray): Array containing the interpolation points :math:`\sigma = \{\sigma_1, \dots, \sigma_K\}\in\mathbb{C}` r (int): Krylov space order :math:`b_k` and :math:`c_k`. At the moment, different orders for the controllability and observability constructions are not supported. right_tangent (np.ndarray): Matrix containing the right tangential direction interpolation vector for each interpolation point in column form, i.e. :math:`\mathbf{r}\in\mathbb{R}^{m \times K}`. left_tangent (np.ndarray): Matrix containing the left tangential direction interpolation vector for each interpolation point in column form, i.e. :math:`\mathbf{l}\in\mathbb{R}^{p \times K}`. Returns: tuple: The reduced order model matrices: :math:`\mathbf{A}_r`, :math:`\mathbf{B}_r` and :math:`\mathbf{C}_r`. References: [1] Grimme [2] Gallivan """ A = self.ss.A B = self.ss.B C = self.ss.C nx = self.ss.states nu = self.ss.inputs ny = self.ss.outputs B.shape = (nx, nu) if nu != 1: left_tangent, right_tangent, rc, ro, fc, fo = self.load_tangent_vectors() assert right_tangent is not None and left_tangent is not None, 'Missing interpolation vectors for MIMO case' else: fc =

np.array(frequency)

numpy.array

""" Testing for the approximate neighbor search using Locality Sensitive Hashing Forest module (sklearn.neighbors.LSHForest). """ # Author: <NAME>, <NAME> import numpy as np import scipy.sparse as sp from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_array_less from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_not_equal from sklearn.utils.testing import assert_warns_message from sklearn.utils.testing import ignore_warnings from sklearn.metrics.pairwise import pairwise_distances from sklearn.neighbors import LSHForest from sklearn.neighbors import NearestNeighbors def test_neighbors_accuracy_with_n_candidates(): # Checks whether accuracy increases as `n_candidates` increases. n_candidates_values = np.array([.1, 50, 500]) n_samples = 100 n_features = 10 n_iter = 10 n_points = 5 rng = np.random.RandomState(42) accuracies = np.zeros(n_candidates_values.shape[0], dtype=float) X = rng.rand(n_samples, n_features) for i, n_candidates in enumerate(n_candidates_values): lshf = LSHForest(n_candidates=n_candidates) ignore_warnings(lshf.fit)(X) for j in range(n_iter): query = X[rng.randint(0, n_samples)].reshape(1, -1) neighbors = lshf.kneighbors(query, n_neighbors=n_points, return_distance=False) distances = pairwise_distances(query, X, metric='cosine') ranks = np.argsort(distances)[0, :n_points] intersection = np.intersect1d(ranks, neighbors).shape[0] ratio = intersection / float(n_points) accuracies[i] = accuracies[i] + ratio accuracies[i] = accuracies[i] / float(n_iter) # Sorted accuracies should be equal to original accuracies assert_true(np.all(np.diff(accuracies) >= 0), msg="Accuracies are not non-decreasing.") # Highest accuracy should be strictly greater than the lowest assert_true(np.ptp(accuracies) > 0, msg="Highest accuracy is not strictly greater than lowest.") def test_neighbors_accuracy_with_n_estimators(): # Checks whether accuracy increases as `n_estimators` increases. n_estimators = np.array([1, 10, 100]) n_samples = 100 n_features = 10 n_iter = 10 n_points = 5 rng = np.random.RandomState(42) accuracies = np.zeros(n_estimators.shape[0], dtype=float) X = rng.rand(n_samples, n_features) for i, t in enumerate(n_estimators): lshf = LSHForest(n_candidates=500, n_estimators=t) ignore_warnings(lshf.fit)(X) for j in range(n_iter): query = X[rng.randint(0, n_samples)].reshape(1, -1) neighbors = lshf.kneighbors(query, n_neighbors=n_points, return_distance=False) distances = pairwise_distances(query, X, metric='cosine') ranks = np.argsort(distances)[0, :n_points] intersection = np.intersect1d(ranks, neighbors).shape[0] ratio = intersection / float(n_points) accuracies[i] = accuracies[i] + ratio accuracies[i] = accuracies[i] / float(n_iter) # Sorted accuracies should be equal to original accuracies assert_true(np.all(np.diff(accuracies) >= 0), msg="Accuracies are not non-decreasing.") # Highest accuracy should be strictly greater than the lowest assert_true(np.ptp(accuracies) > 0, msg="Highest accuracy is not strictly greater than lowest.") @ignore_warnings def test_kneighbors(): # Checks whether desired number of neighbors are returned. # It is guaranteed to return the requested number of neighbors # if `min_hash_match` is set to 0. Returned distances should be # in ascending order. n_samples = 12 n_features = 2 n_iter = 10 rng = np.random.RandomState(42) X = rng.rand(n_samples, n_features) lshf = LSHForest(min_hash_match=0) # Test unfitted estimator assert_raises(ValueError, lshf.kneighbors, X[0]) ignore_warnings(lshf.fit)(X) for i in range(n_iter): n_neighbors = rng.randint(0, n_samples) query = X[rng.randint(0, n_samples)].reshape(1, -1) neighbors = lshf.kneighbors(query, n_neighbors=n_neighbors, return_distance=False) # Desired number of neighbors should be returned. assert_equal(neighbors.shape[1], n_neighbors) # Multiple points n_queries = 5 queries = X[rng.randint(0, n_samples, n_queries)] distances, neighbors = lshf.kneighbors(queries, n_neighbors=1, return_distance=True) assert_equal(neighbors.shape[0], n_queries) assert_equal(distances.shape[0], n_queries) # Test only neighbors neighbors = lshf.kneighbors(queries, n_neighbors=1, return_distance=False) assert_equal(neighbors.shape[0], n_queries) # Test random point(not in the data set) query = rng.randn(n_features).reshape(1, -1) lshf.kneighbors(query, n_neighbors=1, return_distance=False) # Test n_neighbors at initialization neighbors = lshf.kneighbors(query, return_distance=False) assert_equal(neighbors.shape[1], 5) # Test `neighbors` has an integer dtype assert_true(neighbors.dtype.kind == 'i', msg="neighbors are not in integer dtype.") def test_radius_neighbors(): # Checks whether Returned distances are less than `radius` # At least one point should be returned when the `radius` is set # to mean distance from the considering point to other points in # the database. # Moreover, this test compares the radius neighbors of LSHForest # with the `sklearn.neighbors.NearestNeighbors`. n_samples = 12 n_features = 2 n_iter = 10 rng = np.random.RandomState(42) X = rng.rand(n_samples, n_features) lshf = LSHForest() # Test unfitted estimator assert_raises(ValueError, lshf.radius_neighbors, X[0]) ignore_warnings(lshf.fit)(X) for i in range(n_iter): # Select a random point in the dataset as the query query = X[rng.randint(0, n_samples)].reshape(1, -1) # At least one neighbor should be returned when the radius is the # mean distance from the query to the points of the dataset. mean_dist = np.mean(pairwise_distances(query, X, metric='cosine')) neighbors = lshf.radius_neighbors(query, radius=mean_dist, return_distance=False) assert_equal(neighbors.shape, (1,)) assert_equal(neighbors.dtype, object) assert_greater(neighbors[0].shape[0], 0) # All distances to points in the results of the radius query should # be less than mean_dist distances, neighbors = lshf.radius_neighbors(query, radius=mean_dist, return_distance=True) assert_array_less(distances[0], mean_dist) # Multiple points n_queries = 5 queries = X[rng.randint(0, n_samples, n_queries)] distances, neighbors = lshf.radius_neighbors(queries, return_distance=True) # dists and inds should not be 1D arrays or arrays of variable lengths # hence the use of the object dtype. assert_equal(distances.shape, (n_queries,)) assert_equal(distances.dtype, object) assert_equal(neighbors.shape, (n_queries,)) assert_equal(neighbors.dtype, object) # Compare with exact neighbor search query = X[rng.randint(0, n_samples)].reshape(1, -1) mean_dist = np.mean(pairwise_distances(query, X, metric='cosine')) nbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X) distances_exact, _ = nbrs.radius_neighbors(query, radius=mean_dist) distances_approx, _ = lshf.radius_neighbors(query, radius=mean_dist) # Radius-based queries do not sort the result points and the order # depends on the method, the random_state and the dataset order. Therefore # we need to sort the results ourselves before performing any comparison. sorted_dists_exact = np.sort(distances_exact[0]) sorted_dists_approx = np.sort(distances_approx[0]) # Distances to exact neighbors are less than or equal to approximate # counterparts as the approximate radius query might have missed some # closer neighbors. assert_true(np.all(np.less_equal(sorted_dists_exact, sorted_dists_approx))) @ignore_warnings def test_radius_neighbors_boundary_handling(): X = [[0.999, 0.001], [0.5, 0.5], [0, 1.], [-1., 0.001]] n_points = len(X) # Build an exact nearest neighbors model as reference model to ensure # consistency between exact and approximate methods nnbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X) # Build a LSHForest model with hyperparameter values that always guarantee # exact results on this toy dataset. lsfh = LSHForest(min_hash_match=0, n_candidates=n_points).fit(X) # define a query aligned with the first axis query = [[1., 0.]] # Compute the exact cosine distances of the query to the four points of # the dataset dists = pairwise_distances(query, X, metric='cosine').ravel() # The first point is almost aligned with the query (very small angle), # the cosine distance should therefore be almost null: assert_almost_equal(dists[0], 0, decimal=5) # The second point form an angle of 45 degrees to the query vector assert_almost_equal(dists[1], 1 - np.cos(np.pi / 4)) # The third point is orthogonal from the query vector hence at a distance # exactly one: assert_almost_equal(dists[2], 1) # The last point is almost colinear but with opposite sign to the query # therefore it has a cosine 'distance' very close to the maximum possible # value of 2. assert_almost_equal(dists[3], 2, decimal=5) # If we query with a radius of one, all the samples except the last sample # should be included in the results. This means that the third sample # is lying on the boundary of the radius query: exact_dists, exact_idx = nnbrs.radius_neighbors(query, radius=1) approx_dists, approx_idx = lsfh.radius_neighbors(query, radius=1) assert_array_equal(np.sort(exact_idx[0]), [0, 1, 2]) assert_array_equal(np.sort(approx_idx[0]), [0, 1, 2]) assert_array_almost_equal(

np.sort(exact_dists[0])

numpy.sort

import numpy as np from utils import getMI class Node(): def __init__(self, nType, idx): ###################################################################################### ## description: ## ## the base class of the basic nodes (in/out-put) in LogicNet ## ## parameters: ## ## nType: node type (in/out/lut), also serves as the prefix of the node name ## ## idx: index of the node in the net (2-tuple) ## ## members: ## ## val: a numpy array of 0/1 values of the node after inference ## ## name: the node name ## ## idx: index of the node in the net (2-tuple) ## ## fis: a list of fanin nodes ## ###################################################################################### self.val = None self.name = nType + str(idx[0]) + '_' + str(idx[1]) self.idx = idx self.fis = [] # clears all fanins def disconnectAll(self): self.fis.clear() # adds a node to the fanin list def connect(self, fanin): self.fis.append(fanin) # sets the values of the 'input' node in a net def setVal(self, data): self.val = data # returns the values of the node def getVal(self): return self.val # retrieves and sets the values of the an 'output' node from its single fanin node def eval(self): assert len(self.fis) == 1 self.val = self.fis[0].getVal() # returns the node name def getName(self): return self.name # returns the mutual information between the correct labels and the node's values def getMI(self, labels): assert self.val is not None return getMI(self.val, labels) class LUT(Node): def __init__(self, idx, k=6): ###################################################################################### ## description: ## ## the class of the look-up-tables in LogicNet, inheritance of the "Node" ## ## parameters: ## ## idx: index of the node in the net (2-tuple) ## ## k: number of inputs of the LUT ## ## members: ## ## k: number of inputs of the LUT ## ## arr: the numpy array the stores the function of the LUT ## ###################################################################################### super().__init__('lut', idx) self.k = k shp = (2,) * k self.arr = (np.random.rand(*shp) < 0.5).astype(np.int8) # the magic method __getitem__ for LUT # for a lut object n, and a binary k-tuple X=(x_1, ..., x_k), the method returns n(X) # usage: (2 types of indexing supported) # 1. u[X] (with tuple) 2. u[x_1*2^(k-1) + ... + x_k] (with int) def __getitem__(self, key): if type(key) == int: return self.arr.flat[key] elif type(key) == tuple: return self.arr[key] else: print('Illegal indexing!!') assert False # the magic method __setitem__ for LUT # the method set n(X) to the given binary value v # usage: (2 types of indexing supported) # 1. u[X] = v 2. u[x_1*2^(k-1) + ... + x_k] = v def __setitem__(self, key, value): if type(key) == int: self.arr.flat[key] = value elif type(key) == tuple: self.arr[key] = value else: print('Illegal indexing!!') assert False def __prepInVal__(self): inVals = [nd.getVal() for nd in self.fis] inVals = np.array(inVals, dtype=np.int8).transpose() return inVals def __setRandOut__(self, cnt, randId): for i in randId: x = 0 for j in range(self.k): mask = 1 << j n = i ^ mask if cnt.flat[n] > 0: x += 1 elif cnt.flat[n] < 0: x -= 1 if x > 0: self[i] = 1 elif x < 0: self[i] = 0 # trains the LUT to fit the labels def train(self, labels): cnt = np.zeros(self.arr.shape, dtype=np.int32) inVals = self.__prepInVal__() assert len(inVals) == len(labels) for inVal, lab in zip(inVals, labels): inVal = tuple(inVal) cnt[inVal] += lab * 2 - 1 #if lab == 1: cnt[inVal] += 1 #elif lab == 0: cnt[inVal] -= 1 #else: assert False randId = [] for i in range(2**self.k): if cnt.flat[i] > 0: self[i] = 1 elif cnt.flat[i] < 0: self[i] = 0 else: randId.append(i) self.__setRandOut__(cnt, randId) self.eval() # evalutes the values of the LUT during inference def eval(self): inVals = self.__prepInVal__() self.val = np.array([self[tuple(inVal)] for inVal in inVals], dtype=np.int8) # converts the information of the LUT to strings def toStrings(self): names = [fi.getName() for fi in self.fis] + [self.name] pats = [] fmt = '0{}b'.format(str(self.k)) for i in range(2**self.k): if self[i] == 1: pats.append(format(i, fmt) + ' 1') if len(pats) == 2**self.k: # const 1 names, pats = [self.name], ['1'] elif len(pats) == 0: # const 0 names = [self.name] return names, pats # trains the LUT to mimic a neuron according to the input weights def trainFromNN(self, W, B): assert len(W) == self.k sigmoid = lambda x: 1 / (1 + np.exp(-x)) toBin = lambda x: np.array(list(

np.binary_repr(x, self.k)

numpy.binary_repr

from pathlib import Path from timeit import default_timer as timer import h5py import numpy as np import torch from methods.utils.data_utilities import (_segment_index, load_dcase_format, to_metrics2020_format) from torch.utils.data import Dataset, Sampler from tqdm import tqdm from utils.common import int16_samples_to_float32 class UserDataset(Dataset): """ User defined datset """ def __init__(self, args, cfg, dataset, dataset_type='train', overlap=''): """ Args: args: input args cfg: configurations dataset: dataset used dataset_type: 'train' | 'valid' | 'dev_test' | 'eval_test' overlap: '1' | '2' """ super().__init__() self.dataset_type = dataset_type self.read_into_mem = args.read_into_mem self.sample_rate = cfg['data']['sample_rate'] self.clip_length = dataset.clip_length self.label_resolution = dataset.label_resolution self.frame_length = int(self.clip_length / self.label_resolution) self.label_interp_ratio = int(self.label_resolution * self.sample_rate / cfg['data']['hop_length']) # Chunklen and hoplen and segmentation. Since all of the clips are 60s long, it only segments once here data = np.zeros((1, self.clip_length * self.sample_rate)) if 'train' in self.dataset_type: chunklen = int(cfg['data']['train_chunklen_sec'] * self.sample_rate) hoplen = int(cfg['data']['train_hoplen_sec'] * self.sample_rate) self.segmented_indexes, self.segmented_pad_width = _segment_index(data, chunklen, hoplen) elif self.dataset_type in ['valid', 'dev_test', 'eval_test']: chunklen = int(cfg['data']['test_chunklen_sec'] * self.sample_rate) hoplen = int(cfg['data']['test_hoplen_sec'] * self.sample_rate) self.segmented_indexes, self.segmented_pad_width = _segment_index(data, chunklen, hoplen, last_frame_always_paddding=True) self.num_segments = len(self.segmented_indexes) # Data and meta path fold_str_idx = dataset.fold_str_index ov_str_idx = dataset.ov_str_index data_sr_folder_name = '{}fs'.format(self.sample_rate) main_data_dir = Path(cfg['hdf5_dir']).joinpath(cfg['dataset']).joinpath('data').joinpath(data_sr_folder_name) dev_data_dir = main_data_dir.joinpath('dev').joinpath(cfg['data']['type']) eval_data_dir = main_data_dir.joinpath('eval').joinpath(cfg['data']['type']) main_meta_dir = Path(cfg['hdf5_dir']).joinpath(cfg['dataset']).joinpath('meta') dev_meta_dir = main_meta_dir.joinpath('dev') eval_meta_dir = main_meta_dir.joinpath('eval') if self.dataset_type == 'train': data_dirs = [dev_data_dir] self.meta_dir = dev_meta_dir train_fold = [int(fold.strip()) for fold in str(cfg['training']['train_fold']).split(',')] ov_set = str(cfg['training']['overlap']) if not overlap else overlap self.paths_list = [path for data_dir in data_dirs for path in sorted(data_dir.glob('*.h5')) \ if int(path.stem[fold_str_idx]) in train_fold and path.stem[ov_str_idx] in ov_set \ and not path.name.startswith('.')] elif self.dataset_type == 'valid': if cfg['training']['valid_fold'] != 'eval': data_dirs = [dev_data_dir] self.meta_dir = dev_meta_dir valid_fold = [int(fold.strip()) for fold in str(cfg['training']['valid_fold']).split(',')] ov_set = str(cfg['training']['overlap']) if not overlap else overlap self.paths_list = [path for data_dir in data_dirs for path in sorted(data_dir.glob('*.h5')) \ if int(path.stem[fold_str_idx]) in valid_fold and path.stem[ov_str_idx] in ov_set \ and not path.name.startswith('.')] ori_meta_dir = Path(cfg['dataset_dir']).joinpath('metadata_dev') else: data_dirs = [eval_data_dir] self.meta_dir = eval_meta_dir ov_set = str(cfg['training']['overlap']) if not overlap else overlap self.paths_list = [path for data_dir in data_dirs for path in sorted(data_dir.glob('*.h5')) \ if not path.name.startswith('.')] ori_meta_dir = Path(cfg['dataset_dir']).joinpath('metadata_eval') frame_begin_index = 0 self.valid_gt_sed_metrics2019 = [] self.valid_gt_doa_metrics2019 = [] self.valid_gt_dcaseformat = {} for path in self.paths_list: ori_meta_path = ori_meta_dir.joinpath(path.stem + '.csv') output_dict, sed_metrics2019, doa_metrics2019 = \ load_dcase_format(ori_meta_path, frame_begin_index=frame_begin_index, frame_length=self.frame_length, num_classes=len(dataset.label_set)) self.valid_gt_dcaseformat.update(output_dict) self.valid_gt_sed_metrics2019.append(sed_metrics2019) self.valid_gt_doa_metrics2019.append(doa_metrics2019) frame_begin_index += self.frame_length self.valid_gt_sed_metrics2019 = np.concatenate(self.valid_gt_sed_metrics2019, axis=0) self.valid_gt_doa_metrics2019 = np.concatenate(self.valid_gt_doa_metrics2019, axis=0) self.gt_metrics2020_dict = to_metrics2020_format(self.valid_gt_dcaseformat, self.valid_gt_sed_metrics2019.shape[0], label_resolution=self.label_resolution) elif self.dataset_type == 'dev_test': data_dirs = [dev_data_dir] self.meta_dir = dev_meta_dir dev_test_fold = [int(fold.strip()) for fold in str(cfg['inference']['test_fold']).split(',')] ov_set = str(cfg['inference']['overlap']) if not overlap else overlap self.paths_list = [path for data_dir in data_dirs for path in sorted(data_dir.glob('*.h5')) \ if int(path.stem[fold_str_idx]) in dev_test_fold and path.stem[ov_str_idx] in ov_set \ and not path.name.startswith('.')] elif self.dataset_type == 'eval_test': data_dirs = [eval_data_dir] self.meta_dir = eval_meta_dir self.paths_list = [path for data_dir in data_dirs for path in sorted(data_dir.glob('*.h5')) \ if not path.name.startswith('.')] self.paths_list = [Path(str(path) + '%' + str(n)) for path in self.paths_list for n in range(self.num_segments)] # Read into memory if self.read_into_mem: load_begin_time = timer() print('Start to load dataset: {}, ov={}......\n'.format(self.dataset_type + ' set', ov_set)) iterator = tqdm(self.paths_list, total=len(self.paths_list), unit='clips') self.dataset_list = [] for path in iterator: fn, n_segment = path.stem, int(path.name.split('%')[1]) data_path = Path(str(path).split('%')[0]) index_begin = self.segmented_indexes[n_segment][0] index_end = self.segmented_indexes[n_segment][1] pad_width_before = self.segmented_pad_width[n_segment][0] pad_width_after = self.segmented_pad_width[n_segment][1] with h5py.File(data_path, 'r') as hf: x = int16_samples_to_float32(hf['waveform'][:, index_begin: index_end]) pad_width = ((0, 0), (pad_width_before, pad_width_after)) x = np.pad(x, pad_width, mode='constant') if 'test' not in self.dataset_type: ov = fn[-1] index_begin_label = int(index_begin / (self.sample_rate * self.label_resolution)) index_end_label = int(index_end / (self.sample_rate * self.label_resolution)) # pad_width_before_label = int(pad_width_before / (self.sample_rate * self.label_resolution)) pad_width_after_label = int(pad_width_after / (self.sample_rate * self.label_resolution)) meta_path = self.meta_dir.joinpath(fn + '.h5') with h5py.File(meta_path, 'r') as hf: sed_label = hf['sed_label'][index_begin_label: index_end_label, ...] doa_label = hf['doa_label'][index_begin_label: index_end_label, ...] # NOTE: this is Catesian coordinates if pad_width_after_label != 0: sed_label_new = np.zeros((pad_width_after_label, 2, 14)) doa_label_new = np.zeros((pad_width_after_label, 2, 3)) sed_label = np.concatenate((sed_label, sed_label_new), axis=0) doa_label = np.concatenate((doa_label, doa_label_new), axis=0) self.dataset_list.append({ 'filename': fn, 'n_segment': n_segment, 'ov': ov, 'waveform': x, 'sed_label': sed_label, 'doa_label': doa_label }) else: self.dataset_list.append({ 'filename': fn, 'n_segment': n_segment, 'waveform': x }) iterator.close() print('Loading dataset time: {:.3f}\n'.format(timer()-load_begin_time)) def __len__(self): """Get length of the dataset """ return len(self.paths_list) def __getitem__(self, idx): """ Read features from the dataset """ if self.read_into_mem: data_dict = self.dataset_list[idx] fn = data_dict['filename'] n_segment = data_dict['n_segment'] x = data_dict['waveform'] if 'test' not in self.dataset_type: ov = data_dict['ov'] sed_label = data_dict['sed_label'] doa_label = data_dict['doa_label'] else: path = self.paths_list[idx] fn, n_segment = path.stem, int(path.name.split('%')[1]) data_path = Path(str(path).split('%')[0]) index_begin = self.segmented_indexes[n_segment][0] index_end = self.segmented_indexes[n_segment][1] pad_width_before = self.segmented_pad_width[n_segment][0] pad_width_after = self.segmented_pad_width[n_segment][1] with h5py.File(data_path, 'r') as hf: x = int16_samples_to_float32(hf['waveform'][:, index_begin: index_end]) pad_width = ((0, 0), (pad_width_before, pad_width_after)) x = np.pad(x, pad_width, mode='constant') if 'test' not in self.dataset_type: ov = fn[-1] index_begin_label = int(index_begin / (self.sample_rate * self.label_resolution)) index_end_label = int(index_end / (self.sample_rate * self.label_resolution)) # pad_width_before_label = int(pad_width_before / (self.sample_rate * self.label_resolution)) pad_width_after_label = int(pad_width_after / (self.sample_rate * self.label_resolution)) meta_path = self.meta_dir.joinpath(fn + '.h5') with h5py.File(meta_path, 'r') as hf: sed_label = hf['sed_label'][index_begin_label: index_end_label, ...] doa_label = hf['doa_label'][index_begin_label: index_end_label, ...] # NOTE: this is Catesian coordinates if pad_width_after_label != 0: sed_label_new = np.zeros((pad_width_after_label, 2, 14)) doa_label_new = np.zeros((pad_width_after_label, 2, 3)) sed_label = np.concatenate((sed_label, sed_label_new), axis=0) doa_label = np.concatenate((doa_label, doa_label_new), axis=0) if 'test' not in self.dataset_type: sample = { 'filename': fn, 'n_segment': n_segment, 'ov': ov, 'waveform': x, 'sed_label': sed_label, 'doa_label': doa_label } else: sample = { 'filename': fn, 'n_segment': n_segment, 'waveform': x } return sample class UserBatchSampler(Sampler): """User defined batch sampler. Only for train set. """ def __init__(self, clip_num, batch_size, seed=2020): self.clip_num = clip_num self.batch_size = batch_size self.random_state = np.random.RandomState(seed) self.indexes =

np.arange(self.clip_num)

numpy.arange

from rpn.generation import compute_deltas import numpy as np def make_corners(regions): """Transforms boxes from center format to corners""" x0, y0, x1, y1 = np.transpose(regions) ws = x1 - x0 // 2 hs = y1 - y0 // 2 return np.transpose([x0 + ws, y0 + hs, 2 * ws, 2 * hs]) def compute_iou(regions, gt_boxes): """IOU metric computation. Differs from rpn version as there is no place for precomputation Also returns indices instead of respective gt boxes as classes are chosen with their help""" regions_areas = regions[:, 2] * regions[:, 3] x0, y0, x1, y1 = make_corners(regions) ious = [] for gt_box in gt_boxes: gt_x0, gt_y0, gt_x1, gt_y1 = gt_box gt_area = (gt_x1 - gt_x0) * (gt_y1 - gt_y0) int_x0 = np.maximum(gt_x0, x0) int_y0 = np.maximum(gt_y0, y0) int_x1 =

np.minimum(gt_x1, x1)

numpy.minimum

# -*- coding: utf-8 -*- """ Copyright (c) 2019 <NAME> pySME is a Python script to run R SME package (https://cran.r-project.org/web/packages/sme/index.html). SME package generates smoothing-splines mixed-effects models from metabolomics data. This script follows methodology given by Berk et al. (2011) and utilizes bootstrapping to approximate p-values. Running this script requires R with SME package installed. """ import os import numpy as np from scipy import interpolate import pandas as pd import matplotlib.pyplot as plt from matplotlib import cm from rpy2.robjects.packages import importr from rpy2.robjects import pandas2ri import statsmodels.stats.multitest as smm import time import copy import smeutils smePack = importr('sme', lib_loc="C:/Users/user/Documents/R/win-library/3.6") statsPack = importr('stats') # Settings ==================================================================== # Input files info = pd.read_csv('./sme_info.csv') data = pd.read_csv('./sme_data.csv') info_arr = np.array(info) data_fid = np.array(data.columns) data_arr = np.array(data) selIdx = np.arange(len(data_fid)) # Parameters RUN = True N = 12 # Number of subjects t_n = 4 # Number of time points iplN = 100 # Number of interpolated time points n_bootstrap = 500 # Number of bootstrap sampling selIdx = selIdx[:] # List of metabolites to analyze relative = False # Scale data to initial values correctOutlier = False SAVE = False USEMEAN = True # SME Parameters ctra = "AICc" # Criteria init_l_mc = 1e-8 # Initial lambda_mu init_l_vc = 1e-8 # Initial lambda_v init_l_mt = 5e-8 # Initial lambda_mu init_l_vt = 5e-8 # Initial lambda_v maxIter = 100000 # Maximum iteration deltaEM = 1e-3 # Threshold for expetation maximization deltaNM = 1e-3 # Threshold for nelder mead normalizeTime = True seed = 1234 # RNG seed showFig = False # Flag to plot figures figSize = (20,16) # Size of figures plotLegend = False # Flag to plot legend colorMap = 'viridis' # kwarg for colormap plotSMEMeanOnly = False # Only plot SME mean trace mergePlot = True # Merge multiple plots plotHeatmap = False # Plot heatmap comparing two data groups t = np.array([1,3,5,7]) iplT = np.linspace(1, 7, iplN) iplTIdx = np.empty(t_n) for i in range(t_n): iplTIdx[i] = np.where(iplT == t[i])[0] iplTIdx = iplTIdx.astype(int) sel = np.array([data_fid[selIdx]]).flatten() #============================================================================== np.random.seed(seed) # Set seed #============================================================================== if relative: data = smeutils.normalizeData(data, N, t_n, data_fid) #============================================================================== t0 = time.time() fulldataRaw = pd.concat([info,data], axis=1) fulldataRaw = fulldataRaw.astype('float64') fulldata = copy.deepcopy(fulldataRaw) fulldata = fulldata.drop(fulldata.index[16]) # ind 5 has an outlier if correctOutlier: fulldata = smeutils.correctOutlier(fulldata, sel, t, t_n) # Initialize ================================================================== grp0_f = fulldata[(fulldata.grp == 0)]['ind'] grp1_f = fulldata[(fulldata.grp == 1)]['ind'] grp0 = np.unique(fulldata[(fulldata.grp == 0)]['ind']) grp1 = np.unique(fulldata[(fulldata.grp == 1)]['ind']) pandas2ri.activate() fd_ri = pandas2ri.py2ri(fulldata) fd_rigrp0 = fd_ri.rx(fd_ri.rx2("grp").ro == 0, True) fd_rigrp1 = fd_ri.rx(fd_ri.rx2("grp").ro == 1, True) fd_rigrp0tme = fd_rigrp0.rx2("tme") fd_rigrp0ind = fd_rigrp0.rx2("ind") fd_rigrp1tme = fd_rigrp1.rx2("tme") fd_rigrp1ind = fd_rigrp1.rx2("ind") ys0mu = np.empty((len(sel), iplN)) ys1mu = np.empty((len(sel), iplN)) ys0vHat = np.empty((len(sel), len(grp0), iplN)) ys1vHat = np.empty((len(sel), len(grp1), iplN)) l2 = np.empty(len(sel)) se = np.empty(len(sel)) se0 = np.empty((len(sel), len(grp0))) se1 = np.empty((len(sel), len(grp1))) sem = np.empty(len(sel)) tval = np.empty(len(sel)) ys0v = np.empty((len(sel), len(grp0), t_n)) ys1v = np.empty((len(sel), len(grp1), t_n)) ys0eta = np.empty((len(sel), len(grp0), t_n)) ys1eta = np.empty((len(sel), len(grp1), t_n)) ys0mubs = np.empty((n_bootstrap, len(sel), iplN)) ys1mubs = np.empty((n_bootstrap, len(sel), iplN)) ys0vHatbs = np.empty((n_bootstrap, len(sel), len(grp0), iplN)) ys1vHatbs = np.empty((n_bootstrap, len(sel), len(grp1), iplN)) l2bs = np.empty((n_bootstrap, len(sel))) sebs = np.empty((n_bootstrap, len(sel))) se0bs = np.empty((n_bootstrap, len(sel), len(grp0))) se1bs = np.empty((n_bootstrap, len(sel), len(grp1))) sembs = np.empty((n_bootstrap, len(sel))) tvalbs = np.empty((n_bootstrap, len(sel))) pval = np.empty(len(sel)) t1 = time.time() print(t1 - t0) # SME ========================================================================= if RUN: for m_i in range(len(sel)): fd_rigrp0obj = fd_rigrp0.rx2(sel[m_i]) fd_rigrp1obj = fd_rigrp1.rx2(sel[m_i]) fit0 = smePack.sme(fd_rigrp0obj, fd_rigrp0tme, fd_rigrp0ind, criteria=ctra, maxIter=maxIter, deltaEM=deltaEM, deltaNM=deltaNM, initial_lambda_mu=init_l_mc, initial_lambda_v=init_l_mc, normalizeTime=normalizeTime) fit1 = smePack.sme(fd_rigrp1obj, fd_rigrp1tme, fd_rigrp1ind, criteria=ctra, maxIter=maxIter, deltaEM=deltaEM, deltaNM=deltaNM, initial_lambda_mu=init_l_mt, initial_lambda_v=init_l_vt, normalizeTime=normalizeTime) fit0coef = np.array(fit0.rx2('coefficients')) fit1coef = np.array(fit1.rx2('coefficients')) spl0mu = interpolate.CubicSpline(t, fit0coef[0], bc_type='natural') ys0mu[m_i] = spl0mu(iplT) spl1mu = interpolate.CubicSpline(t, fit1coef[0], bc_type='natural') ys1mu[m_i] = spl1mu(iplT) l2[m_i] = np.sqrt(np.trapz(np.square(ys0mu[m_i] - ys1mu[m_i]), x=iplT)) for g0 in range(len(grp0)): spl0 = interpolate.CubicSpline(t, fit0coef[g0 + 1] + fit0coef[0], bc_type='natural') ys0vHat[m_i][g0] = spl0(iplT) ys0v[m_i][g0] = ys0mu[m_i][iplTIdx] - ys0vHat[m_i][g0][iplTIdx] ys0eta[m_i][g0] = fulldataRaw.loc[fulldataRaw.ind == grp0[g0], sel[m_i]] - ys0vHat[m_i][g0][iplTIdx] se0[m_i][g0] = np.trapz(np.square(ys0mu[m_i] - ys0vHat[m_i][g0]), x=iplT) for g1 in range(len(grp1)): spl1 = interpolate.CubicSpline(t, fit1coef[g1 + 1] + fit1coef[0], bc_type='natural') ys1vHat[m_i][g1] = spl1(iplT) ys1v[m_i][g1] = ys1mu[m_i][iplTIdx] - ys1vHat[m_i][g1][iplTIdx] ys1eta[m_i][g1] = fulldataRaw.loc[fulldataRaw.ind == grp1[g1], sel[m_i]] - ys1vHat[m_i][g1][iplTIdx] se1[m_i][g1] = np.trapz(np.square(ys1mu[m_i] - ys1vHat[m_i][g1]), x=iplT) se[m_i] = np.sqrt(np.mean(se0[m_i])/len(grp0) + np.mean(se1[m_i])/len(grp1)) sem = 0. tval = np.divide(l2, se + sem) ys0vFlat = ys0v.reshape((ys0v.shape[0], -1)) ys0etaFlat = ys0eta.reshape((ys0eta.shape[0], -1)) ys0etaFlat = np.delete(ys0etaFlat, 13, 1) # ind 5 has an outlier ys1vFlat = ys1v.reshape((ys1v.shape[0], -1)) ys1etaFlat = ys1eta.reshape((ys1eta.shape[0], -1)) t2 = time.time() print(t2 - t1) # Bootstrapping =============================================================== fulldataS = [] for bcount in range(n_bootstrap): print("Bootstrap run: " + str(bcount)) fulldataC = copy.deepcopy(fulldataRaw) for m_i in range(len(sel)): if USEMEAN: for Di in range(N): ysmuMean = (ys0mu[m_i][iplTIdx] + ys1mu[m_i][iplTIdx])/2 if Di in grp0: fulldataC[sel[m_i]][np.arange(0,t_n*N,N)+Di] = (ysmuMean + np.random.choice(ys0vFlat[m_i], size=t_n) + np.random.choice(ys0etaFlat[m_i], size=t_n)) else: fulldataC[sel[m_i]][np.arange(0,t_n*N,N)+Di] = (ysmuMean + np.random.choice(ys1vFlat[m_i], size=t_n) + np.random.choice(ys1etaFlat[m_i], size=t_n)) else: ct_rand = np.random.rand() for Di in range(N): if ct_rand < 0.5: if Di in grp0: fulldataC[sel[m_i]][np.arange(0,t_n*N,N)+Di] = (ys0mu[m_i][iplTIdx] + np.random.choice(ys0vFlat[m_i], size=t_n) + np.random.choice(ys0etaFlat[m_i], size=t_n)) else: fulldataC[sel[m_i]][np.arange(0,t_n*N,N)+Di] = (ys0mu[m_i][iplTIdx] +

np.random.choice(ys1vFlat[m_i], size=t_n)

numpy.random.choice

# 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]) 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(160, 'R 3 m :H', transformations) space_groups[160] = sg space_groups['R 3 m :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([0,0,1]) 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)) 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([1,2,7]) trans_den = N.array([3,3,6]) 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([1,2,7]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,7]) trans_den = N.array([3,3,6]) transformations.append((rot, 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([2,1,5]) trans_den = N.array([3,3,6]) 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([2,1,5]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([2,1,5]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(161, 'R 3 c :H', transformations) space_groups[161] = sg space_groups['R 3 c :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)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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(162, 'P -3 1 m', transformations) space_groups[162] = 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([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)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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(163, 'P -3 1 c', transformations) space_groups[163] = 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([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(164, 'P -3 m 1', transformations) space_groups[164] = 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([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)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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(165, 'P -3 c 1', transformations) space_groups[165] = 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([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([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([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)) 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(166, 'R -3 m :H', transformations) space_groups[166] = sg space_groups['R -3 m :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([0,0,1]) 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)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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)) 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([1,2,7]) trans_den = N.array([3,3,6]) 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([1,2,7]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([1,2,7]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_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([1,2,1]) trans_den = N.array([3,3,6]) 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([1,2,1]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,2,1]) trans_den = N.array([3,3,6]) transformations.append((rot, 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([2,1,5]) trans_den = N.array([3,3,6]) 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([2,1,5]) trans_den = N.array([3,3,6]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([2,1,5]) trans_den = N.array([3,3,6]) transformations.append((rot, 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([3,3,6]) 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([3,3,6]) transformations.append((rot, 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([3,3,6]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(167, 'R -3 c :H', transformations) space_groups[167] = sg space_groups['R -3 c :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([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,-1,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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)) sg = SpaceGroup(168, 'P 6', transformations) space_groups[168] = sg space_groups['P 6'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,1]) trans_den = N.array([1,1,6]) 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([0,0,5]) trans_den = N.array([1,1,6]) 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([-1,0,0,0,-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(169, 'P 61', transformations) space_groups[169] = sg space_groups['P 61'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,5]) trans_den = N.array([1,1,6]) 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([1,1,6]) 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([-1,0,0,0,-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(170, 'P 65', transformations) space_groups[170] = sg space_groups['P 65'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,1]) trans_den = N.array([1,1,3]) 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([0,0,2]) trans_den = N.array([1,1,3]) 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([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([-1,0,0,0,-1,0,0,0,1]) rot.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(171, 'P 62', transformations) space_groups[171] = sg space_groups['P 62'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,2]) trans_den = N.array([1,1,3]) 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([0,0,1]) trans_den = N.array([1,1,3]) 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([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([-1,0,0,0,-1,0,0,0,1]) rot.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(172, 'P 64', transformations) space_groups[172] = sg space_groups['P 64'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([0,0,1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(173, 'P 63', transformations) space_groups[173] = sg space_groups['P 63'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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)) sg = SpaceGroup(174, 'P -6', transformations) space_groups[174] = sg space_groups['P -6'] = sg transformations = [] rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_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,-1,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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)) sg = SpaceGroup(175, 'P 6/m', transformations) space_groups[175] = sg space_groups['P 6/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,-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,-1,1,0,0,0,1]) 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,-1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,1]) 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([-1,1,0,-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,-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,-1,1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(176, 'P 63/m', transformations) space_groups[176] = sg space_groups['P 63/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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([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([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_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(177, 'P 6 2 2', transformations) space_groups[177] = sg space_groups['P 6 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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,6]) 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([0,0,5]) trans_den = N.array([1,1,6]) 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([0,0,0]) 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([0,0,2]) trans_den = N.array([1,1,3]) 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([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([1,1,3]) 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([0,0,5]) trans_den = N.array([1,1,6]) 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([1,1,6]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(178, 'P 61 2 2', transformations) space_groups[178] = sg space_groups['P 61 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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,5]) trans_den = N.array([1,1,6]) 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([1,1,6]) 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([0,0,0]) 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([0,0,1]) trans_den = N.array([1,1,3]) 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([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([0,0,2]) 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([0,0,1]) trans_den = N.array([1,1,6]) 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([0,0,5]) trans_den = N.array([1,1,6]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(179, 'P 65 2 2', transformations) space_groups[179] = sg space_groups['P 65 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,-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,-1,1,0,0,0,1]) 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,-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([0,0,0]) 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([0,0,1]) trans_den = N.array([1,1,3]) 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([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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([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([0,0,0]) 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([0,0,1]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(180, 'P 62 2 2', transformations) space_groups[180] = sg space_groups['P 62 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,-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,-1,1,0,0,0,1]) 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,-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([0,0,0]) 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([0,0,2]) trans_den = N.array([1,1,3]) 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([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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([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([0,0,0]) 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([0,0,2]) trans_den = N.array([1,1,3]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(181, 'P 64 2 2', transformations) space_groups[181] = sg space_groups['P 64 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,-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,-1,1,0,0,0,1]) 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,-1,0,0,0,1]) rot.shape = (3, 3) trans_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([-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)) 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(182, 'P 63 2 2', transformations) space_groups[182] = sg space_groups['P 63 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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([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([-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([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_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(183, 'P 6 m m', transformations) space_groups[183] = sg space_groups['P 6 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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([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([-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)) 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(184, 'P 6 c c', transformations) space_groups[184] = sg space_groups['P 6 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([1,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([0,0,1]) 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)) 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([0,0,0]) 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(185, 'P 63 c m', transformations) space_groups[185] = sg space_groups['P 63 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,-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,-1,1,0,0,0,1]) 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,-1,0,0,0,1]) rot.shape = (3, 3) trans_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([0,0,1]) 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([0,0,0]) 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([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(186, 'P 63 m c', transformations) space_groups[186] = sg space_groups['P 63 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([-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,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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)) 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([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.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(187, 'P -6 m 2', transformations) space_groups[187] = sg space_groups['P -6 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([-1,1,0,-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,-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,-1,0,0,0,1]) rot.shape = (3, 3) trans_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)) 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([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)) sg = SpaceGroup(188, 'P -6 c 2', transformations) space_groups[188] = sg space_groups['P -6 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([-1,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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([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([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_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(189, 'P -6 2 m', transformations) space_groups[189] = sg space_groups['P -6 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([-1,1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([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,-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(190, 'P -6 2 c', transformations) space_groups[190] = sg space_groups['P -6 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([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,-1,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_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(191, 'P 6/m m m', transformations) space_groups[191] = sg space_groups['P 6/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,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([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([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([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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([-1,1,0,0,1,0,0,0,-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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([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([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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,-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(192, 'P 6/m c c', transformations) space_groups[192] = sg space_groups['P 6/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([1,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([-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([0,0,0]) 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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([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([0,0,0]) 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(193, 'P 63/m c m', transformations) space_groups[193] = sg space_groups['P 63/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,-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,-1,1,0,0,0,1]) 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,-1,0,0,0,1]) rot.shape = (3, 3) trans_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([-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)) 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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_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,-1]) trans_den = N.array([1,1,2]) 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([1,1,2]) 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([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)) 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(194, 'P 63/m m c', transformations) space_groups[194] = sg space_groups['P 63/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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.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(195, 'P 2 3', transformations) space_groups[195] = sg space_groups['P 2 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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(196, 'F 2 3', transformations) space_groups[196] = sg space_groups['F 2 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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(197, 'I 2 3', transformations) space_groups[197] = sg space_groups['I 2 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(198, 'P 21 3', transformations) space_groups[198] = sg space_groups['P 21 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([1,0,0]) 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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(199, 'I 21 3', transformations) space_groups[199] = sg space_groups['I 21 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.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(200, 'P m -3', transformations) space_groups[200] = sg space_groups['P m -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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) 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)) sg = SpaceGroup(201, 'P n -3 :2', transformations) space_groups[201] = sg space_groups['P n -3 :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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([0,0,-1,-1,0,0,0,-1,0]) 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,-1,0,0,0,-1,-1,0,0]) 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([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) 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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([0,0,-1,-1,0,0,0,-1,0]) 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,-1,0,0,0,-1,-1,0,0]) 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([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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(202, 'F m -3', transformations) space_groups[202] = sg space_groups['F m -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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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([0,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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([0,-1,0,0,0,1,1,0,0]) 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([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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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([0,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,0,-1,-1,0,0,0,-1,0]) 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,-1,0,0,0,-1,-1,0,0]) 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([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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([0,-1,0,0,0,1,1,0,0]) 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([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([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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([0,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,0,-1,-1,0,0,0,-1,0]) 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,-1,0,0,0,-1,-1,0,0]) 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([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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([0,-1,0,0,0,1,1,0,0]) 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([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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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([0,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) 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([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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([0,-1,0,0,0,1,1,0,0]) 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([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(203, 'F d -3 :2', transformations) space_groups[203] = sg space_groups['F d -3 :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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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(204, 'I m -3', transformations) space_groups[204] = sg space_groups['I m -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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([-1,-1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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([-1,0,-1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(205, 'P a -3', transformations) space_groups[205] = sg space_groups['P a -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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([1,0,0]) 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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,-1]) trans_den = N.array([1,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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([-1,0,0]) 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,0,-1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,-1,0,0]) 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([0,0,-1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,1,0,0]) 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([0,0,1,1,0,0,0,-1,0]) 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([0,0,1,-1,0,0,0,1,0]) 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,-1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) sg = SpaceGroup(206, 'I a -3', transformations) space_groups[206] = sg space_groups['I a -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([1,0,0,0,0,-1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,0,1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,-1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,-1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,-1,0,-1,0]) rot.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(207, 'P 4 3 2', transformations) space_groups[207] = sg space_groups['P 4 3 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([1,0,0,0,0,-1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,0,1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,1,0,1,0,0]) rot.shape = (3, 3) 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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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([0,0,1,0,-1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,-1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den = N.array([2,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,-1,0,-1,0]) rot.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(208, 'P 42 3 2', transformations) space_groups[208] = sg space_groups['P 42 3 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([1,0,0,0,0,-1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,0,1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,-1,0,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,-1,0,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,-1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,-1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,-1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_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([1,0,0,0,0,-1,0,1,0]) 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([1,0,0,0,0,1,0,-1,0]) 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([0,0,1,0,1,0,-1,0,0]) 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([0,0,-1,0,1,0,1,0,0]) 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([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([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([0,1,1]) trans_den = N.array([1,2,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,-1,0,1,0,0]) 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([0,0,-1,0,-1,0,-1,0,0]) 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([-1,0,0,0,0,1,0,1,0]) 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([-1,0,0,0,0,-1,0,-1,0]) 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([1,0,0,0,0,-1,0,1,0]) 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([1,0,0,0,0,1,0,-1,0]) 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([0,0,1,0,1,0,-1,0,0]) 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([0,0,-1,0,1,0,1,0,0]) 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([1,0,1]) 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([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,1,0,0,0,1,0]) 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([0,1,0,0,0,1,1,0,0]) 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,-1,0,0,0,-1,1,0,0]) 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([0,0,1,-1,0,0,0,-1,0]) 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,-1,0,0,0,1,-1,0,0]) 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([0,0,-1,-1,0,0,0,1,0]) 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([0,0,-1,1,0,0,0,-1,0]) 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([0,1,0,0,0,-1,-1,0,0]) 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([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([1,0,1]) trans_den = N.array([2,1,2]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,-1,0,1,0,0]) 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([0,0,-1,0,-1,0,-1,0,0]) 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([-1,0,0,0,0,1,0,1,0]) 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([-1,0,0,0,0,-1,0,-1,0]) 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([1,0,0,0,0,-1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,0,1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,0,1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,1,0,1,0,0]) rot.shape = (3, 3) 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)) rot = N.array([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) 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,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) 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,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) 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([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([0,0,1,0,-1,0,1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,0,-1,0,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,1,0,1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,0]) trans_den = N.array([2,2,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,0,-1,0,-1,0]) rot.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(209, 'F 4 3 2', transformations) space_groups[209] = sg space_groups['F 4 3 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([1,0,0,0,0,-1,0,1,0]) 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([1,0,0,0,0,1,0,-1,0]) 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([0,0,1,0,1,0,-1,0,0]) 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([0,0,-1,0,1,0,1,0,0]) 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([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([0,0,1,1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,-1,1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,1,-1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,-1,0,0,0,1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,-1,0,0,0,1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,0,-1,1,0,0,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([0,1,0,0,0,-1,-1,0,0]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([1,0,0,0,-1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,1,0,0,0,-1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, trans_den)) rot = N.array([-1,0,0,0,-1,0,0,0,1]) rot.shape = (3, 3) trans_num = N.array([0,0,0]) trans_den = N.array([1,1,1]) transformations.append((rot, trans_num, 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([0,0,1,0,-1,0,1,0,0]) 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([0,0,-1,0,-1,0,-1,0,0]) 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([-1,0,0,0,0,1,0,1,0]) 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([-1,0,0,0,0,-1,0,-1,0]) rot.shape = (3, 3) trans_num = N.array([1,1,1]) trans_den =

N.array([4,4,4])

numpy.array

from __future__ import print_function, absolute_import import numpy as np import vtk from collections import defaultdict from itertools import chain from six import itervalues from vtk.util.numpy_support import numpy_to_vtk def _basic_elem(elem, cell_type, ugrid, nid_dict): nids = [nid_dict[nid] for nid in elem.node_ids] ids = vtk.vtkIdList() ids.SetNumberOfIds(len(nids)) for i in range(len(nids)): ids.SetId(i, nids[i]) ugrid.InsertNextCell(cell_type, ids) return elem.eid def _grid(grid, cell_type, ugrid, nid_dict): nids = [nid_dict[nid] for nid in [grid.nid]] ids = vtk.vtkIdList() ids.SetNumberOfIds(len(nids)) for i in range(len(nids)): ids.SetId(i, nids[i]) ugrid.InsertNextCell(cell_type, ids) return grid.nid def _rbe2(elem, cell_type, ugrid, nid_dict): independent = elem.gn dependent = elem.Gmi nids = [] for i in range(len(dependent)): nids.append(nid_dict[independent]) nids.append(nid_dict[dependent[i]]) ids = vtk.vtkIdList() ids.SetNumberOfIds(len(nids)) for i in range(len(nids)): ids.SetId(i, nids[i]) ugrid.InsertNextCell(cell_type, ids) return elem.eid def _rbe3(elem, cell_type, ugrid, nid_dict): independent = elem.refgrid _dependent = elem.Gijs dependent = [] for dep in _dependent: if isinstance(dep, list): dependent.extend(dep) else: dependent.append(dep) nids = [] for i in range(len(dependent)): nids.append(nid_dict[independent]) nids.append(nid_dict[dependent[i]]) ids = vtk.vtkIdList() ids.SetNumberOfIds(len(nids)) for i in range(len(nids)): ids.SetId(i, nids[i]) ugrid.InsertNextCell(cell_type, ids) return elem.eid _category_list = ['grid', 'element', 'mpc', 'force', 'disp', 'cord'] _nastran_to_vtk = { 'GRID': (vtk.VTK_VERTEX, _grid, 'grid'), 'CQUAD4': (vtk.VTK_QUAD, _basic_elem, 'element'), 'CTRIA3': (vtk.VTK_TRIANGLE, _basic_elem, 'element'), 'CBEAM': (vtk.VTK_LINE, _basic_elem, 'element'), 'CBUSH': (vtk.VTK_LINE, _basic_elem, 'element'), 'CBAR': (vtk.VTK_LINE, _basic_elem, 'element'), 'RBE2': (vtk.VTK_POLY_LINE, _rbe2, 'mpc'), 'RBE3': (vtk.VTK_POLY_LINE, _rbe3, 'mpc') } _card_types = {} i = 0 for key in sorted(_nastran_to_vtk.keys()): _card_types[key] = i i += 1 class UGrid(vtk.vtkUnstructuredGrid): category_list = _category_list category_dict = {_category_list[i]: i for i in range(len(_category_list))} nastran_to_vtk = _nastran_to_vtk card_types = _card_types @classmethod def make_from_bdf(cls, bdf): # type: (BDF) -> UGrid nid_list = [] nid_dict = {} node_pos = np.empty((len(bdf.nodes), 3), dtype=np.float64) i = 0 for node in itervalues(bdf.nodes): node_pos[i] = node.get_position() nid = node.nid nid_list.append(nid) nid_dict[nid] = i i += 1 _points = vtk.vtkPoints() _points.SetData(numpy_to_vtk(node_pos)) points = vtk.vtkPoints() points.DeepCopy(_points) cells = [] cell_types = [] cell_count = 0 elem_types = [] eid_list = [] _nastran_to_vtk = cls.nastran_to_vtk bdf_data_to_plot = chain( itervalues(bdf.nodes), itervalues(bdf.elements), itervalues(bdf.rigid_elements) ) category_list = [] category_dict = cls.category_dict ugrid = vtk.vtkUnstructuredGrid() ugrid.SetPoints(points) for elem in bdf_data_to_plot: elem_type = elem.type cell_type, add_method, category = _nastran_to_vtk.get(elem_type, (None, None, None)) if cell_type is None: continue cell_types.append(cell_type) elem_types.append(elem_type) eid = add_method(elem, cell_type, ugrid, nid_dict) # returns element/grid id eid_list.append(eid) category_list.append(category_dict[category]) cell_count += 1 id_array = numpy_to_vtk(np.array(cells, dtype=np.int64), deep=1, array_type=vtk.VTK_ID_TYPE) vtk_cells = vtk.vtkCellArray() vtk_cells.SetCells(cell_count, id_array) cell_types = np.array(cell_types, 'B') vtk_cell_types = numpy_to_vtk(cell_types) cell_locations = np.array([i for i in range(cell_count)]) vtk_cell_locations = numpy_to_vtk(cell_locations, deep=1, array_type=vtk.VTK_ID_TYPE) vtk_cell_locations.SetName('index') ugrid.GetCellData().AddArray(vtk_cell_locations) _elem_types = vtk.vtkIdTypeArray() _elem_types.SetName('element_type') _elem_types.SetNumberOfValues(len(elem_types)) card_types = cls.card_types for i in range(len(elem_types)): _elem_types.SetValue(i, card_types[elem_types[i]]) ugrid.GetCellData().AddArray(_elem_types) _cat = numpy_to_vtk(np.array(category_list, dtype=np.int64), deep=1, array_type=vtk.VTK_ID_TYPE) _cat.SetName('category') ugrid.GetCellData().AddArray(_cat) id_array = numpy_to_vtk(

np.array(eid_list, dtype=np.int64)

numpy.array

""" Independent Model Ensemble of binary classifiers with resulting probabilities normalized across all classes. Assumes all classes are independent and are not related by any class hierarchy. """ import numpy as np from mainmodel.mainmodel import MainModel from classifiers.binary.optbinary import OptimalBinaryClassifier class OvAModel(MainModel): def __init__(self, **data_args): """ Initialize Independent Model - call init method of parent class """ self.name = "OVA Classifier" # Use output of Binary Classifier model for OvAModel - just need to # normalize those probabilities. if 'init_from_binary' in data_args.keys(): self.init_model = data_args['init_from_binary'] else: self.init_model = None super(OvAModel, self).__init__(**data_args) def run_cfv(self, start_time): """ Overwrite MainModel function to see if results from BinaryModel were passed in """ if self.init_model is not None: # Normalize probabilities per row (Except last column which is label) print("Using Binary Model results.") self.class_labels = self.init_model.class_labels self.class_counts = self.init_model.class_counts self.num_folds = self.init_model.num_folds self.X = self.init_model.X self.y = self.init_model.y self.results = self.init_model.results a = np.copy(self.init_model.results) new_results = [] for index in range(self.init_model.num_folds): trial_results = [] for row_index, row in enumerate(a[index]): num_classes = len(self.init_model.class_labels) prob_sum = 0 # compute normalizing sum for class_index in range(num_classes): prob_sum += row[class_index] new_probs = [] for class_index in range(num_classes): new_probs.append(row[class_index] / prob_sum) new_probs.append(row[num_classes]) # Add label trial_results.append(new_probs) new_results.append(

np.array(trial_results, dtype='object')

numpy.array

""" Created on April, 2019 @author: <NAME> Toolkit functions used for processing training data. Cite: <NAME>, et al. "Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation." Advances in Neural Information Processing Systems. 2018. """ import numpy as np from scipy.spatial import ConvexHull import re import cv2 import pickle import json from copy import deepcopy def sample_pnts_from_obj(data, n_pnts = 5000, mode = 'uniform'): # sample points on each object mesh. flags = data.keys() all_pnts = data['v'][:,:3] area_list = np.array(calculate_face_area(data)) distribution = area_list/np.sum(area_list) # sample points the probability depends on the face area new_pnts = [] if mode == 'random': random_face_ids = np.random.choice(len(data['f']), n_pnts, replace=True, p=distribution) random_face_ids, sample_counts = np.unique(random_face_ids, return_counts=True) for face_id, sample_count in zip(random_face_ids, sample_counts): face = data['f'][face_id] vid_in_face = [int(item.split('/')[0]) for item in face] weights = np.diff(np.sort(np.vstack( [np.zeros((1, sample_count)), np.random.uniform(0, 1, size=(len(vid_in_face) - 1, sample_count)),

np.ones((1, sample_count))

numpy.ones

####### ## 1. Dicom to PNG # In[] import SimpleITK as sitk import os from skimage import transform,exposure,io import numpy as np import cv2 os.environ["CUDA_VISIBLE_DEVICES"]="0" def windowing(img, center, width): low = center - width/2 high = center + width/2 img = (img - low)/width img[img<0.]=0. img[img>1.]=1. return img def dicom2png(src_Path, dst_Path, imshape): #print(src_Path) if(not os.path.isdir(src_Path)): return if(not os.path.isdir(dst_Path)): return for filename in os.listdir(src_Path): if(filename[-4:] != ".dcm"): continue filepath = src_Path + "/" + filename if(os.path.isdir(filepath)): continue print(filepath) img = sitk.ReadImage(filepath) img = sitk.GetArrayFromImage(img).astype("int16") shape = img.shape img = exposure.equalize_hist(img) img = img[0,:,:] if(imshape is not None): img = transform.resize(img, imshape) # #img = windowing(img, 8000, 3000) img = img*255 img = np.asarray(img, dtype = "int16") img = np.expand_dims(img, -1) cv2.imwrite(dst_Path +"/"+ filename[:-4]+"_" + str(shape[1]) + "_" + str(shape[2]) +".png", img) folder = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_0_Original" dst = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_1_BasicData" imshape = (2048,2048) for lower in os.listdir(folder) : if (not os.path.isdir(folder + "/" + lower)): continue if( not os.path.isdir(dst + "/" + lower)): os.mkdir(dst+ "/" + lower) dicom2png( folder + "/" + lower, dst+ "/" + lower, imshape) ################################################################################### ## 2. Lung Mask # In[] ################################################################################## ################################################################################# ## 3. Lung Mask Crop # In[] import cv2 import numpy as np import os from operator import eq import random import matplotlib.pyplot as plt from skimage import io import shutil import csv os.environ["CUDA_VISIBLE_DEVICES"]="0" folder = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_1_BasicData/Imgs" dst = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_2_ImgCropped" LungMaskPath = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_1_BasicData/Masks" csvfile = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_2_ImgCropped/metadata.csv" for lower in os.listdir(folder) : if (not os.path.isdir(folder + "/" + lower)): continue if( not os.path.isdir(dst + "/" + lower)): os.mkdir(dst+ "/" + lower) for file in os.listdir(folder + "/" + lower) : print(file) LungMask = cv2.imread(LungMaskPath + "/" + lower + "/" + file, 0) _, LungMask = cv2.threshold(LungMask, 127, 255, cv2.THRESH_BINARY) _, contours, _ = cv2.findContours(LungMask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) rects = [] for cnt in contours: rects.append(cv2.boundingRect(cnt)) tcomx = 10 tcomy = 10 bcomx = 10 bcomy = 10 top_x, top_y, bottom_x, bottom_y = 0, 0 ,0, 0 rects.sort() top_x = min([x for (x, y, w, h) in rects]) - tcomx #26 top_y = min([y for (x, y, w, h) in rects]) - tcomy #26 bottom_x = max([x+w for (x, y, w, h) in rects]) + bcomx #234 bottom_y = max([y+h for (x, y, w, h) in rects]) + bcomy #227 #print(top_x, top_y, bottom_x, bottom_y) if(top_x <=0 ) : top_x = tcomx if(top_y <=0 ) : top_y = tcomy if(bottom_x >= 1024 ) : bottom_x = 1024 - tcomx if(bottom_y >= 1024 ) : bottom_y = 1024 - tcomy Img = cv2.imread(folder + "/" + lower + "/" + file, 0) ImgCrop = Img[top_y*2:bottom_y*2, top_x*2:bottom_x*2] ImgCrop = cv2.resize(ImgCrop, (1024,1024)) cv2.imwrite(dst+ "/" + lower + "/" + file, ImgCrop) f = open(csvfile, 'a', encoding = "utf-8", newline='') f_writer = csv.writer(f) strline = [] strline.append(file) strline.append(str(top_y*2)) strline.append(str(bottom_y*2)) strline.append(str(top_x*2)) strline.append(str(bottom_x*2)) f_writer.writerow(strline) f.close() #################################################################################################################### ################################################################################### ## 4. Line Detection # In[] ################################################################################## ################################################################################### ## 5. Mask recur # In[] import os os.environ["CUDA_VISIBLE_DEVICES"]="0" import cv2 import csv import numpy as np from skimage.morphology import skeletonize Classlist = ["Aortic Knob", "Lt Lower CB", "Pulmonary Conus", "Rt Lower CB", "Rt Upper CB", "DAO" , "Carina" , "LAA"] filePath = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_3_LineMask" dstPath = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_4_LineMask_Recur" csvfile = "D:/[Data]/[Cardiomegaly]/4_ChestPA_ToLabel/[]_2_ImgCropped/metadata.csv" for clss in Classlist : clsfilepath = filePath + "/" + clss if(not os.path.isdir(dstPath+"/" + clss) ): os.mkdir(dstPath+"/" + clss) for file in os.listdir(clsfilepath): mask = cv2.imread(clsfilepath + "/" + file, 0) mask = np.asarray(mask) f = open(csvfile, 'r', encoding = "utf-8", newline='') f_reader = csv.reader(f) xtop = yleft = xbottom = yright = 0 for row in f_reader: if (row[0] == file): xtop = int(row[1]) yleft = int(row[3]) xbottom = int(row[2]) yright = int(row[4]) break f.close() if xtop == 0 or yleft == 0 or xbottom == 0 or yright == 0: continue #print(file) #print(xbottom, xtop, yright, yleft) mask_ = cv2.resize(mask, (yright- yleft, xbottom - xtop)) mask_ =

np.asarray(mask_)

numpy.asarray

"""Kernels for Gaussian process regression and classification. The kernels in this module allow kernel-engineering, i.e., they can be combined via the "+" and "*" operators or be exponentiated with a scalar via "**". These sum and product expressions can also contain scalar values, which are automatically converted to a constant kernel. All kernels allow (analytic) gradient-based hyperparameter optimization. The space of hyperparameters can be specified by giving lower und upper boundaries for the value of each hyperparameter (the search space is thus rectangular). Instead of specifying bounds, hyperparameters can also be declared to be "fixed", which causes these hyperparameters to be excluded from optimization. """ # Author: <NAME> <<EMAIL>> # License: BSD 3 clause # Note: this module is strongly inspired by the kernel module of the george # package. from abc import ABCMeta, abstractmethod from collections import namedtuple import math from inspect import signature import warnings import numpy as np from scipy.special import kv, gamma from scipy.spatial.distance import pdist, cdist, squareform from ..metrics.pairwise import pairwise_kernels from ..base import clone def _check_length_scale(X, length_scale): length_scale = np.squeeze(length_scale).astype(float) if np.ndim(length_scale) > 1: raise ValueError("length_scale cannot be of dimension greater than 1") if np.ndim(length_scale) == 1 and X.shape[1] != length_scale.shape[0]: raise ValueError("Anisotropic kernel must have the same number of " "dimensions as data (%d!=%d)" % (length_scale.shape[0], X.shape[1])) return length_scale class Hyperparameter(namedtuple('Hyperparameter', ('name', 'value_type', 'bounds', 'n_elements', 'fixed'))): """A kernel hyperparameter's specification in form of a namedtuple. .. versionadded:: 0.18 Attributes ---------- name : string The name of the hyperparameter. Note that a kernel using a hyperparameter with name "x" must have the attributes self.x and self.x_bounds value_type : string The type of the hyperparameter. Currently, only "numeric" hyperparameters are supported. bounds : pair of floats >= 0 or "fixed" The lower and upper bound on the parameter. If n_elements>1, a pair of 1d array with n_elements each may be given alternatively. If the string "fixed" is passed as bounds, the hyperparameter's value cannot be changed. n_elements : int, default=1 The number of elements of the hyperparameter value. Defaults to 1, which corresponds to a scalar hyperparameter. n_elements > 1 corresponds to a hyperparameter which is vector-valued, such as, e.g., anisotropic length-scales. fixed : bool, default: None Whether the value of this hyperparameter is fixed, i.e., cannot be changed during hyperparameter tuning. If None is passed, the "fixed" is derived based on the given bounds. """ # A raw namedtuple is very memory efficient as it packs the attributes # in a struct to get rid of the __dict__ of attributes in particular it # does not copy the string for the keys on each instance. # By deriving a namedtuple class just to introduce the __init__ method we # would also reintroduce the __dict__ on the instance. By telling the # Python interpreter that this subclass uses static __slots__ instead of # dynamic attributes. Furthermore we don't need any additional slot in the # subclass so we set __slots__ to the empty tuple. __slots__ = () def __new__(cls, name, value_type, bounds, n_elements=1, fixed=None): if not isinstance(bounds, str) or bounds != "fixed": bounds = np.atleast_2d(bounds) if n_elements > 1: # vector-valued parameter if bounds.shape[0] == 1: bounds = np.repeat(bounds, n_elements, 0) elif bounds.shape[0] != n_elements: raise ValueError("Bounds on %s should have either 1 or " "%d dimensions. Given are %d" % (name, n_elements, bounds.shape[0])) if fixed is None: fixed = isinstance(bounds, str) and bounds == "fixed" return super(Hyperparameter, cls).__new__( cls, name, value_type, bounds, n_elements, fixed) # This is mainly a testing utility to check that two hyperparameters # are equal. def __eq__(self, other): return (self.name == other.name and self.value_type == other.value_type and np.all(self.bounds == other.bounds) and self.n_elements == other.n_elements and self.fixed == other.fixed) class Kernel(metaclass=ABCMeta): """Base class for all kernels. .. versionadded:: 0.18 """ def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ params = dict() # introspect the constructor arguments to find the model parameters # to represent cls = self.__class__ init = getattr(cls.__init__, 'deprecated_original', cls.__init__) init_sign = signature(init) args, varargs = [], [] for parameter in init_sign.parameters.values(): if (parameter.kind != parameter.VAR_KEYWORD and parameter.name != 'self'): args.append(parameter.name) if parameter.kind == parameter.VAR_POSITIONAL: varargs.append(parameter.name) if len(varargs) != 0: raise RuntimeError("scikit-learn kernels should always " "specify their parameters in the signature" " of their __init__ (no varargs)." " %s doesn't follow this convention." % (cls, )) for arg in args: try: value = getattr(self, arg) except AttributeError: warnings.warn('From version 0.24, get_params will raise an ' 'AttributeError if a parameter cannot be ' 'retrieved as an instance attribute. Previously ' 'it would return None.', FutureWarning) value = None params[arg] = value return params def set_params(self, **params): """Set the parameters of this kernel. The method works on simple kernels as well as on nested kernels. The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object. Returns ------- self """ if not params: # Simple optimisation to gain speed (inspect is slow) return self valid_params = self.get_params(deep=True) for key, value in params.items(): split = key.split('__', 1) if len(split) > 1: # nested objects case name, sub_name = split if name not in valid_params: raise ValueError('Invalid parameter %s for kernel %s. ' 'Check the list of available parameters ' 'with `kernel.get_params().keys()`.' % (name, self)) sub_object = valid_params[name] sub_object.set_params(**{sub_name: value}) else: # simple objects case if key not in valid_params: raise ValueError('Invalid parameter %s for kernel %s. ' 'Check the list of available parameters ' 'with `kernel.get_params().keys()`.' % (key, self.__class__.__name__)) setattr(self, key, value) return self def clone_with_theta(self, theta): """Returns a clone of self with given hyperparameters theta. Parameters ---------- theta : array, shape (n_dims,) The hyperparameters """ cloned = clone(self) cloned.theta = theta return cloned @property def n_dims(self): """Returns the number of non-fixed hyperparameters of the kernel.""" return self.theta.shape[0] @property def hyperparameters(self): """Returns a list of all hyperparameter specifications.""" r = [getattr(self, attr) for attr in dir(self) if attr.startswith("hyperparameter_")] return r @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ theta = [] params = self.get_params() for hyperparameter in self.hyperparameters: if not hyperparameter.fixed: theta.append(params[hyperparameter.name]) if len(theta) > 0: return np.log(np.hstack(theta)) else: return np.array([]) @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ params = self.get_params() i = 0 for hyperparameter in self.hyperparameters: if hyperparameter.fixed: continue if hyperparameter.n_elements > 1: # vector-valued parameter params[hyperparameter.name] = np.exp( theta[i:i + hyperparameter.n_elements]) i += hyperparameter.n_elements else: params[hyperparameter.name] = np.exp(theta[i]) i += 1 if i != len(theta): raise ValueError("theta has not the correct number of entries." " Should be %d; given are %d" % (i, len(theta))) self.set_params(**params) @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : array, shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ bounds = [hyperparameter.bounds for hyperparameter in self.hyperparameters if not hyperparameter.fixed] if len(bounds) > 0: return np.log(np.vstack(bounds)) else: return np.array([]) def __add__(self, b): if not isinstance(b, Kernel): return Sum(self, ConstantKernel(b)) return Sum(self, b) def __radd__(self, b): if not isinstance(b, Kernel): return Sum(ConstantKernel(b), self) return Sum(b, self) def __mul__(self, b): if not isinstance(b, Kernel): return Product(self, ConstantKernel(b)) return Product(self, b) def __rmul__(self, b): if not isinstance(b, Kernel): return Product(ConstantKernel(b), self) return Product(b, self) def __pow__(self, b): return Exponentiation(self, b) def __eq__(self, b): if type(self) != type(b): return False params_a = self.get_params() params_b = b.get_params() for key in set(list(params_a.keys()) + list(params_b.keys())): if np.any(params_a.get(key, None) != params_b.get(key, None)): return False return True def __repr__(self): return "{0}({1})".format(self.__class__.__name__, ", ".join(map("{0:.3g}".format, self.theta))) @abstractmethod def __call__(self, X, Y=None, eval_gradient=False): """Evaluate the kernel.""" @abstractmethod def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ @abstractmethod def is_stationary(self): """Returns whether the kernel is stationary. """ class NormalizedKernelMixin: """Mixin for kernels which are normalized: k(X, X)=1. .. versionadded:: 0.18 """ def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return np.ones(X.shape[0]) class StationaryKernelMixin: """Mixin for kernels which are stationary: k(X, Y)= f(X-Y). .. versionadded:: 0.18 """ def is_stationary(self): """Returns whether the kernel is stationary. """ return True class CompoundKernel(Kernel): """Kernel which is composed of a set of other kernels. .. versionadded:: 0.18 Parameters ---------- kernels : list of Kernel objects The other kernels """ def __init__(self, kernels): self.kernels = kernels def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ return dict(kernels=self.kernels) @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ return np.hstack([kernel.theta for kernel in self.kernels]) @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ k_dims = self.k1.n_dims for i, kernel in enumerate(self.kernels): kernel.theta = theta[i * k_dims:(i + 1) * k_dims] @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : array, shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ return np.vstack([kernel.bounds for kernel in self.kernels]) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Note that this compound kernel returns the results of all simple kernel stacked along an additional axis. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Returns ------- K : array, shape (n_samples_X, n_samples_Y, n_kernels) Kernel k(X, Y) K_gradient : array, shape (n_samples_X, n_samples_X, n_dims, n_kernels) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if eval_gradient: K = [] K_grad = [] for kernel in self.kernels: K_single, K_grad_single = kernel(X, Y, eval_gradient) K.append(K_single) K_grad.append(K_grad_single[..., np.newaxis]) return np.dstack(K), np.concatenate(K_grad, 3) else: return np.dstack([kernel(X, Y, eval_gradient) for kernel in self.kernels]) def __eq__(self, b): if type(self) != type(b) or len(self.kernels) != len(b.kernels): return False return np.all([self.kernels[i] == b.kernels[i] for i in range(len(self.kernels))]) def is_stationary(self): """Returns whether the kernel is stationary. """ return np.all([kernel.is_stationary() for kernel in self.kernels]) def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X, n_kernels) Diagonal of kernel k(X, X) """ return np.vstack([kernel.diag(X) for kernel in self.kernels]).T class KernelOperator(Kernel): """Base class for all kernel operators. .. versionadded:: 0.18 """ def __init__(self, k1, k2): self.k1 = k1 self.k2 = k2 def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ params = dict(k1=self.k1, k2=self.k2) if deep: deep_items = self.k1.get_params().items() params.update(('k1__' + k, val) for k, val in deep_items) deep_items = self.k2.get_params().items() params.update(('k2__' + k, val) for k, val in deep_items) return params @property def hyperparameters(self): """Returns a list of all hyperparameter.""" r = [Hyperparameter("k1__" + hyperparameter.name, hyperparameter.value_type, hyperparameter.bounds, hyperparameter.n_elements) for hyperparameter in self.k1.hyperparameters] for hyperparameter in self.k2.hyperparameters: r.append(Hyperparameter("k2__" + hyperparameter.name, hyperparameter.value_type, hyperparameter.bounds, hyperparameter.n_elements)) return r @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ return np.append(self.k1.theta, self.k2.theta) @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ k1_dims = self.k1.n_dims self.k1.theta = theta[:k1_dims] self.k2.theta = theta[k1_dims:] @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : array, shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ if self.k1.bounds.size == 0: return self.k2.bounds if self.k2.bounds.size == 0: return self.k1.bounds return np.vstack((self.k1.bounds, self.k2.bounds)) def __eq__(self, b): if type(self) != type(b): return False return (self.k1 == b.k1 and self.k2 == b.k2) \ or (self.k1 == b.k2 and self.k2 == b.k1) def is_stationary(self): """Returns whether the kernel is stationary. """ return self.k1.is_stationary() and self.k2.is_stationary() class Sum(KernelOperator): """Sum-kernel k1 + k2 of two kernels k1 and k2. The resulting kernel is defined as k_sum(X, Y) = k1(X, Y) + k2(X, Y) .. versionadded:: 0.18 Parameters ---------- k1 : Kernel object The first base-kernel of the sum-kernel k2 : Kernel object The second base-kernel of the sum-kernel """ def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Returns ------- K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if eval_gradient: K1, K1_gradient = self.k1(X, Y, eval_gradient=True) K2, K2_gradient = self.k2(X, Y, eval_gradient=True) return K1 + K2, np.dstack((K1_gradient, K2_gradient)) else: return self.k1(X, Y) + self.k2(X, Y) def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return self.k1.diag(X) + self.k2.diag(X) def __repr__(self): return "{0} + {1}".format(self.k1, self.k2) class Product(KernelOperator): """Product-kernel k1 * k2 of two kernels k1 and k2. The resulting kernel is defined as k_prod(X, Y) = k1(X, Y) * k2(X, Y) .. versionadded:: 0.18 Parameters ---------- k1 : Kernel object The first base-kernel of the product-kernel k2 : Kernel object The second base-kernel of the product-kernel """ def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Returns ------- K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if eval_gradient: K1, K1_gradient = self.k1(X, Y, eval_gradient=True) K2, K2_gradient = self.k2(X, Y, eval_gradient=True) return K1 * K2, np.dstack((K1_gradient * K2[:, :, np.newaxis], K2_gradient * K1[:, :, np.newaxis])) else: return self.k1(X, Y) * self.k2(X, Y) def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return self.k1.diag(X) * self.k2.diag(X) def __repr__(self): return "{0} * {1}".format(self.k1, self.k2) class Exponentiation(Kernel): """Exponentiate kernel by given exponent. The resulting kernel is defined as k_exp(X, Y) = k(X, Y) ** exponent .. versionadded:: 0.18 Parameters ---------- kernel : Kernel object The base kernel exponent : float The exponent for the base kernel """ def __init__(self, kernel, exponent): self.kernel = kernel self.exponent = exponent def get_params(self, deep=True): """Get parameters of this kernel. Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ params = dict(kernel=self.kernel, exponent=self.exponent) if deep: deep_items = self.kernel.get_params().items() params.update(('kernel__' + k, val) for k, val in deep_items) return params @property def hyperparameters(self): """Returns a list of all hyperparameter.""" r = [] for hyperparameter in self.kernel.hyperparameters: r.append(Hyperparameter("kernel__" + hyperparameter.name, hyperparameter.value_type, hyperparameter.bounds, hyperparameter.n_elements)) return r @property def theta(self): """Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel's hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale. Returns ------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ return self.kernel.theta @theta.setter def theta(self, theta): """Sets the (flattened, log-transformed) non-fixed hyperparameters. Parameters ---------- theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel """ self.kernel.theta = theta @property def bounds(self): """Returns the log-transformed bounds on the theta. Returns ------- bounds : array, shape (n_dims, 2) The log-transformed bounds on the kernel's hyperparameters theta """ return self.kernel.bounds def __eq__(self, b): if type(self) != type(b): return False return (self.kernel == b.kernel and self.exponent == b.exponent) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Returns ------- K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if eval_gradient: K, K_gradient = self.kernel(X, Y, eval_gradient=True) K_gradient *= \ self.exponent * K[:, :, np.newaxis] ** (self.exponent - 1) return K ** self.exponent, K_gradient else: K = self.kernel(X, Y, eval_gradient=False) return K ** self.exponent def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return self.kernel.diag(X) ** self.exponent def __repr__(self): return "{0} ** {1}".format(self.kernel, self.exponent) def is_stationary(self): """Returns whether the kernel is stationary. """ return self.kernel.is_stationary() class ConstantKernel(StationaryKernelMixin, Kernel): """Constant kernel. Can be used as part of a product-kernel where it scales the magnitude of the other factor (kernel) or as part of a sum-kernel, where it modifies the mean of the Gaussian process. k(x_1, x_2) = constant_value for all x_1, x_2 .. versionadded:: 0.18 Parameters ---------- constant_value : float, default: 1.0 The constant value which defines the covariance: k(x_1, x_2) = constant_value constant_value_bounds : pair of floats >= 0, default: (1e-5, 1e5) The lower and upper bound on constant_value """ def __init__(self, constant_value=1.0, constant_value_bounds=(1e-5, 1e5)): self.constant_value = constant_value self.constant_value_bounds = constant_value_bounds @property def hyperparameter_constant_value(self): return Hyperparameter( "constant_value", "numeric", self.constant_value_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Only supported when Y is None. Returns ------- K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ X = np.atleast_2d(X) if Y is None: Y = X elif eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") K = np.full((X.shape[0], Y.shape[0]), self.constant_value, dtype=np.array(self.constant_value).dtype) if eval_gradient: if not self.hyperparameter_constant_value.fixed: return (K, np.full((X.shape[0], X.shape[0], 1), self.constant_value, dtype=np.array(self.constant_value).dtype)) else: return K, np.empty((X.shape[0], X.shape[0], 0)) else: return K def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return np.full(X.shape[0], self.constant_value, dtype=np.array(self.constant_value).dtype) def __repr__(self): return "{0:.3g}**2".format(np.sqrt(self.constant_value)) class WhiteKernel(StationaryKernelMixin, Kernel): """White kernel. The main use-case of this kernel is as part of a sum-kernel where it explains the noise of the signal as independently and identically normally-distributed. The parameter noise_level equals the variance of this noise. k(x_1, x_2) = noise_level if x_1 == x_2 else 0 .. versionadded:: 0.18 Parameters ---------- noise_level : float, default: 1.0 Parameter controlling the noise level (variance) noise_level_bounds : pair of floats >= 0, default: (1e-5, 1e5) The lower and upper bound on noise_level """ def __init__(self, noise_level=1.0, noise_level_bounds=(1e-5, 1e5)): self.noise_level = noise_level self.noise_level_bounds = noise_level_bounds @property def hyperparameter_noise_level(self): return Hyperparameter( "noise_level", "numeric", self.noise_level_bounds) def __call__(self, X, Y=None, eval_gradient=False): """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Only supported when Y is None. Returns ------- K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ X = np.atleast_2d(X) if Y is not None and eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") if Y is None: K = self.noise_level *

np.eye(X.shape[0])

numpy.eye

from abc import * import os import numpy as np import time import tensorflow as tf from keras.datasets import mnist import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, Activation, Flatten, Reshape from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D from keras.layers import LeakyReLU, Dropout, ReLU from keras.layers import BatchNormalization from keras.optimizers import Adam, RMSprop IMGDIR = './imgs' PLOTIMGROW=5 PLOTIMGCOL=5 class ElapsedTimer(object): def __init__(self): self.start = time.time() def elapsed(self, sec): if sec < 60: return "{} sec".format(sec) elif sec < 3600: return "{} min".format(sec / 60) else: return "{} hr".format(sec / 3600) def elapsed_time(self): print("Elapsed: {} ".format(self.elapsed(time.time() - self.start))) class GanBase(object): def __init__(self, row=28, col=28, chn=1): self.img_row = row self.img_col = col self.img_chn = chn self.latent_size = 100 self.hidden_size = 256 self.img_size = row * col * chn self.D = self.build_discriminator() # discriminator self.G = self.build_generator() # generator self.DM = self.build_discriminator_model() # discriminator loss self.AM = self.build_adversarial_model() # generator loss @abstractmethod def build_discriminator(self): pass @abstractmethod def build_generator(self): pass @abstractmethod def build_discriminator_model(self): pass @abstractmethod def build_adversarial_model(self): pass class DcGan(GanBase): def build_discriminator(self): seq = Sequential() depth = self.latent_size dropout = 0.4 # In: 28 x 28 x 1, depth = 1 # Out: 14 x 14 x 1, depth=64 input_shape = (self.img_row, self.img_col, self.img_chn) seq.add(Conv2D(depth*1, 5, strides=2, input_shape=input_shape, padding='same')) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dropout(dropout)) seq.add(Conv2D(depth*2, 5, strides=2, padding='same')) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dropout(dropout)) seq.add(Conv2D(depth*4, 5, strides=2, padding='same')) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dropout(dropout)) seq.add(Conv2D(depth*8, 5, strides=1, padding='same')) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dropout(dropout)) # Out: 1-dim probability seq.add(Flatten()) seq.add(Dense(1)) seq.add(Activation('sigmoid')) seq.summary() return seq def build_generator(self): seq = Sequential() dropout = 0.4 depth = self.latent_size * 4 dim = 7 # In: 100 # Out: dim x dim x depth seq.add(Dense(dim*dim*depth, input_dim=self.latent_size)) seq.add(BatchNormalization(momentum=0.9)) seq.add(Activation('relu')) seq.add(Reshape((dim, dim, depth))) seq.add(Dropout(dropout)) # In: dim x dim x depth # Out: 2*dim x 2*dim x depth/2 seq.add(UpSampling2D()) seq.add(Conv2DTranspose(int(depth/2), 5, padding='same')) seq.add(BatchNormalization(momentum=0.9)) seq.add(Activation('relu')) seq.add(UpSampling2D()) seq.add(Conv2DTranspose(int(depth/4), 5, padding='same')) seq.add(BatchNormalization(momentum=0.9)) seq.add(Activation('relu')) seq.add(Conv2DTranspose(int(depth/8), 5, padding='same')) seq.add(BatchNormalization(momentum=0.9)) seq.add(Activation('relu')) # Out: 28 x 28 x 1 grayscale image [0.0,1.0] per pix seq.add(Conv2DTranspose(1, 5, padding='same')) seq.add(Activation('sigmoid')) seq.summary() return seq def build_discriminator_model(self): optimizer = RMSprop(lr=0.0002, decay=6e-8) seq = Sequential() seq.add(self.D) seq.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) return seq def build_adversarial_model(self): optimizer = RMSprop(lr=0.0001, decay=3e-8) seq = Sequential() seq.add(self.G) seq.add(self.D) seq.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) return seq class BasicGan(GanBase): def build_discriminator(self): input_shape = (self.img_row, self.img_col, self.img_chn) seq = Sequential() seq.add(Flatten(input_shape=input_shape)) seq.add(Dense(self.hidden_size)) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dense(self.hidden_size)) seq.add(LeakyReLU(alpha=0.2)) seq.add(Dense(1)) seq.add(Activation('sigmoid')) #seq.summary() return seq def build_generator(self): output_shape = (self.img_row, self.img_col, self.img_chn) seq = Sequential() seq.add(Dense(self.hidden_size, input_dim=self.latent_size)) seq.add(ReLU()) seq.add(Dense(self.hidden_size)) seq.add(ReLU()) seq.add(Dense(self.img_size)) seq.add(Activation('tanh')) seq.add(Reshape(output_shape)) #seq.summary() return seq def build_discriminator_model(self): seq = Sequential() seq.add(self.D) seq.compile(optimizer=Adam(lr=0.0002), loss='binary_crossentropy', metrics=['accuracy']) seq.summary() return seq def build_adversarial_model(self): self.D.trainable = False seq = Sequential() seq.add(self.G) seq.add(self.D) seq.compile(optimizer=Adam(lr=0.0002), loss='binary_crossentropy', metrics=['accuracy']) seq.summary() return seq class MnistGan(object): def __init__(self): self.dcgan = BasicGan() #import pdb; pdb.set_trace() (self.x_train, _), (_, _) = mnist.load_data() self.x_train = self.x_train / 127.5 - 1. self.x_train = np.expand_dims(self.x_train, axis=3) def train(self, epoch_size=200, batch_size=128, save_interval=1): real_labels = np.ones([batch_size, 1]) fake_labels = np.zeros([batch_size, 1]) comp_labels = np.concatenate((real_labels, fake_labels)) for i in range(epoch_size): real_imgs = self.x_train[np.random.randint(0, self.x_train.shape[0], size=batch_size), :, :, :] fake_imgs = np.random.uniform(-1.0, 1.0, size=[batch_size, self.dcgan.latent_size]) fake_imgs = self.dcgan.G.predict(fake_imgs) comp_imgs = np.concatenate((real_imgs, fake_imgs)) # ============================================================ # # Train the discriminator # # ============================================================ # d_loss = self.dcgan.DM.train_on_batch(comp_imgs, comp_labels) # ============================================================ # # Train the generator # # ============================================================ # fake_imgs = np.random.uniform(-1.0, 1.0, size=[batch_size, self.dcgan.latent_size]) a_loss = self.dcgan.AM.train_on_batch(fake_imgs, real_labels) msg = "%04d: [D loss: %f, acc: %f]" % (i, d_loss[0], d_loss[1]) msg = "%s [A loss: %f, acc: %f]" % (msg, a_loss[0], a_loss[1]) print(msg) if i % save_interval == 0: fake_img =

np.random.uniform(-1, 1, size=[PLOTIMGROW*PLOTIMGCOL, self.dcgan.latent_size])

numpy.random.uniform

import numpy as np from dataloaders.datasets.blending_masks import blending_masks def test1(): ''' x x x x x x xyxyxy z z z z z z ''' masks = blending_masks( np.array([[ (0,0), (0,2) ]], dtype=np.int32), size=3) np.testing.assert_almost_equal( masks, np.array([[ [ [1., 1., 0.5], [1., 1., 0.5], [1., 1., 0.5], ], [ [0.5, 1., 1.], [0.5, 1., 1.], [0.5, 1., 1.], ] ]]) ) def test2(): ''' x x x x x x xyxyxy z z z z z z ''' masks = blending_masks(np.array([[(0,0)], [(2,0)]], dtype=np.int32), size=3) np.testing.assert_almost_equal( masks, np.array([ [[ [1., 1., 1.], [1., 1., 1.], [0.5, 0.5, 0.5], ]], [[ [0.5, 0.5, 0.5], [1., 1., 1.], [1., 1., 1.], ]] ]) ) def test3(): ''' x x x x x x xyxyxy z z z z z z ''' masks = blending_masks(np.array( [ [(0,0)], [(2,0)], [(4,0)] ], dtype=np.int32), size=3) np.testing.assert_almost_equal( masks, np.array([[ [ [1., 1., 0.5], [1., 1., 0.5], [1., 1., 0.5], ], [ [0.5, 1., 0.5], [0.5, 1., 0.5], [0.5, 1., 0.5], ], [ [0.5, 1., 1.], [0.5, 1., 1.], [0.5, 1., 1.], ] ]]) ) def test3(): offsets = np.array([ [(0, 0), (0, 75)], [(60, 0), (60, 75)], [(120, 0), (120,75)] ], dtype=np.int32) size = 100 masks = blending_masks(offsets, size=size) width = np.max(offsets[:,:,0]) + size height = np.max(offsets[:,:,1]) + size pic = np.zeros((width, height), dtype=np.float32) for x in range(offsets.shape[0]): for y in range(offsets.shape[1]): offx, offy = offsets[x,y] pic[offx:offx+size,offy:offy+size] += masks[x,y] np.testing.assert_almost_equal(np.min(pic), 1.) np.testing.assert_almost_equal(

np.max(pic)

numpy.max

from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import namedtuple from copy import copy, deepcopy from datetime import datetime, timedelta from textwrap import dedent import pytest from distutils.version import LooseVersion import numpy as np import pytz import pandas as pd from xarray import Variable, IndexVariable, Coordinate, Dataset from xarray.core import indexing from xarray.core.variable import as_variable, as_compatible_data from xarray.core.indexing import PandasIndexAdapter, LazilyIndexedArray from xarray.core.pycompat import PY3, OrderedDict from xarray.core.common import full_like, zeros_like, ones_like from . import TestCase, source_ndarray, requires_dask class VariableSubclassTestCases(object): def test_properties(self): data = 0.5 * np.arange(10) v = self.cls(['time'], data, {'foo': 'bar'}) self.assertEqual(v.dims, ('time',)) self.assertArrayEqual(v.values, data) self.assertEqual(v.dtype, float) self.assertEqual(v.shape, (10,)) self.assertEqual(v.size, 10) self.assertEqual(v.sizes, {'time': 10}) self.assertEqual(v.nbytes, 80) self.assertEqual(v.ndim, 1) self.assertEqual(len(v), 10) self.assertEqual(v.attrs, {'foo': u'bar'}) def test_attrs(self): v = self.cls(['time'], 0.5 * np.arange(10)) self.assertEqual(v.attrs, {}) attrs = {'foo': 'bar'} v.attrs = attrs self.assertEqual(v.attrs, attrs) self.assertIsInstance(v.attrs, OrderedDict) v.attrs['foo'] = 'baz' self.assertEqual(v.attrs['foo'], 'baz') def test_getitem_dict(self): v = self.cls(['x'], np.random.randn(5)) actual = v[{'x': 0}] expected = v[0] self.assertVariableIdentical(expected, actual) def _assertIndexedLikeNDArray(self, variable, expected_value0, expected_dtype=None): """Given a 1-dimensional variable, verify that the variable is indexed like a numpy.ndarray. """ self.assertEqual(variable[0].shape, ()) self.assertEqual(variable[0].ndim, 0) self.assertEqual(variable[0].size, 1) # test identity self.assertTrue(variable.equals(variable.copy())) self.assertTrue(variable.identical(variable.copy())) # check value is equal for both ndarray and Variable self.assertEqual(variable.values[0], expected_value0) self.assertEqual(variable[0].values, expected_value0) # check type or dtype is consistent for both ndarray and Variable if expected_dtype is None: # check output type instead of array dtype self.assertEqual(type(variable.values[0]), type(expected_value0)) self.assertEqual(type(variable[0].values), type(expected_value0)) elif expected_dtype is not False: self.assertEqual(variable.values[0].dtype, expected_dtype) self.assertEqual(variable[0].values.dtype, expected_dtype) def test_index_0d_int(self): for value, dtype in [(0, np.int_), (np.int32(0), np.int32)]: x = self.cls(['x'], [value]) self._assertIndexedLikeNDArray(x, value, dtype) def test_index_0d_float(self): for value, dtype in [(0.5, np.float_), (np.float32(0.5), np.float32)]: x = self.cls(['x'], [value]) self._assertIndexedLikeNDArray(x, value, dtype) def test_index_0d_string(self): for value, dtype in [('foo', np.dtype('U3' if PY3 else 'S3')), (u'foo', np.dtype('U3'))]: x = self.cls(['x'], [value]) self._assertIndexedLikeNDArray(x, value, dtype) def test_index_0d_datetime(self): d = datetime(2000, 1, 1) x = self.cls(['x'], [d]) self._assertIndexedLikeNDArray(x, np.datetime64(d)) x = self.cls(['x'], [np.datetime64(d)]) self._assertIndexedLikeNDArray(x, np.datetime64(d), 'datetime64[ns]') x = self.cls(['x'], pd.DatetimeIndex([d])) self._assertIndexedLikeNDArray(x, np.datetime64(d), 'datetime64[ns]') def test_index_0d_timedelta64(self): td = timedelta(hours=1) x = self.cls(['x'], [np.timedelta64(td)]) self._assertIndexedLikeNDArray(x, np.timedelta64(td), 'timedelta64[ns]') x = self.cls(['x'], pd.to_timedelta([td])) self._assertIndexedLikeNDArray(x, np.timedelta64(td), 'timedelta64[ns]') def test_index_0d_not_a_time(self): d = np.datetime64('NaT', 'ns') x = self.cls(['x'], [d]) self._assertIndexedLikeNDArray(x, d) def test_index_0d_object(self): class HashableItemWrapper(object): def __init__(self, item): self.item = item def __eq__(self, other): return self.item == other.item def __hash__(self): return hash(self.item) def __repr__(self): return '%s(item=%r)' % (type(self).__name__, self.item) item = HashableItemWrapper((1, 2, 3)) x = self.cls('x', [item]) self._assertIndexedLikeNDArray(x, item, expected_dtype=False) def test_0d_object_array_with_list(self): listarray = np.empty((1,), dtype=object) listarray[0] = [1, 2, 3] x = self.cls('x', listarray) assert x.data == listarray assert x[0].data == listarray.squeeze() assert x.squeeze().data == listarray.squeeze() def test_index_and_concat_datetime(self): # regression test for #125 date_range = pd.date_range('2011-09-01', periods=10) for dates in [date_range, date_range.values, date_range.to_pydatetime()]: expected = self.cls('t', dates) for times in [[expected[i] for i in range(10)], [expected[i:(i + 1)] for i in range(10)], [expected[[i]] for i in range(10)]]: actual = Variable.concat(times, 't') self.assertEqual(expected.dtype, actual.dtype) self.assertArrayEqual(expected, actual) def test_0d_time_data(self): # regression test for #105 x = self.cls('time', pd.date_range('2000-01-01', periods=5)) expected = np.datetime64('2000-01-01T00Z', 'ns') self.assertEqual(x[0].values, expected) def test_datetime64_conversion(self): times = pd.date_range('2000-01-01', periods=3) for values, preserve_source in [ (times, True), (times.values, True), (times.values.astype('datetime64[s]'), False), (times.to_pydatetime(), False), ]: v = self.cls(['t'], values) self.assertEqual(v.dtype, np.dtype('datetime64[ns]')) self.assertArrayEqual(v.values, times.values) self.assertEqual(v.values.dtype, np.dtype('datetime64[ns]')) same_source = source_ndarray(v.values) is source_ndarray(values) assert preserve_source == same_source def test_timedelta64_conversion(self): times = pd.timedelta_range(start=0, periods=3) for values, preserve_source in [ (times, True), (times.values, True), (times.values.astype('timedelta64[s]'), False), (times.to_pytimedelta(), False), ]: v = self.cls(['t'], values) self.assertEqual(v.dtype, np.dtype('timedelta64[ns]')) self.assertArrayEqual(v.values, times.values) self.assertEqual(v.values.dtype, np.dtype('timedelta64[ns]')) same_source = source_ndarray(v.values) is source_ndarray(values) assert preserve_source == same_source def test_object_conversion(self): data = np.arange(5).astype(str).astype(object) actual = self.cls('x', data) self.assertEqual(actual.dtype, data.dtype) def test_pandas_data(self): v = self.cls(['x'], pd.Series([0, 1, 2], index=[3, 2, 1])) self.assertVariableIdentical(v, v[[0, 1, 2]]) v = self.cls(['x'], pd.Index([0, 1, 2])) self.assertEqual(v[0].values, v.values[0]) def test_pandas_period_index(self): v = self.cls(['x'], pd.period_range(start='2000', periods=20, freq='B')) self.assertEqual(v[0], pd.Period('2000', freq='B')) assert "Period('2000-01-03', 'B')" in repr(v) def test_1d_math(self): x = 1.0 * np.arange(5) y = np.ones(5) # should we need `.to_base_variable()`? # probably a break that `+v` changes type? v = self.cls(['x'], x) base_v = v.to_base_variable() # unary ops self.assertVariableIdentical(base_v, +v) self.assertVariableIdentical(base_v, abs(v)) self.assertArrayEqual((-v).values, -x) # binary ops with numbers self.assertVariableIdentical(base_v, v + 0) self.assertVariableIdentical(base_v, 0 + v) self.assertVariableIdentical(base_v, v * 1) self.assertArrayEqual((v > 2).values, x > 2) self.assertArrayEqual((0 == v).values, 0 == x) self.assertArrayEqual((v - 1).values, x - 1) self.assertArrayEqual((1 - v).values, 1 - x) # binary ops with numpy arrays self.assertArrayEqual((v * x).values, x ** 2) self.assertArrayEqual((x * v).values, x ** 2) self.assertArrayEqual(v - y, v - 1) self.assertArrayEqual(y - v, 1 - v) # verify attributes are dropped v2 = self.cls(['x'], x, {'units': 'meters'}) self.assertVariableIdentical(base_v, +v2) # binary ops with all variables self.assertArrayEqual(v + v, 2 * v) w = self.cls(['x'], y, {'foo': 'bar'}) self.assertVariableIdentical(v + w, self.cls(['x'], x + y).to_base_variable()) self.assertArrayEqual((v * w).values, x * y) # something complicated self.assertArrayEqual((v ** 2 * w - 1 + x).values, x ** 2 * y - 1 + x) # make sure dtype is preserved (for Index objects) self.assertEqual(float, (+v).dtype) self.assertEqual(float, (+v).values.dtype) self.assertEqual(float, (0 + v).dtype) self.assertEqual(float, (0 + v).values.dtype) # check types of returned data self.assertIsInstance(+v, Variable) self.assertNotIsInstance(+v, IndexVariable) self.assertIsInstance(0 + v, Variable) self.assertNotIsInstance(0 + v, IndexVariable) def test_1d_reduce(self): x = np.arange(5) v = self.cls(['x'], x) actual = v.sum() expected = Variable((), 10) self.assertVariableIdentical(expected, actual) self.assertIs(type(actual), Variable) def test_array_interface(self): x = np.arange(5) v = self.cls(['x'], x) self.assertArrayEqual(np.asarray(v), x) # test patched in methods self.assertArrayEqual(v.astype(float), x.astype(float)) # think this is a break, that argsort changes the type self.assertVariableIdentical(v.argsort(), v.to_base_variable()) self.assertVariableIdentical(v.clip(2, 3), self.cls('x', x.clip(2, 3)).to_base_variable()) # test ufuncs self.assertVariableIdentical(np.sin(v), self.cls(['x'], np.sin(x)).to_base_variable()) self.assertIsInstance(np.sin(v), Variable) self.assertNotIsInstance(np.sin(v), IndexVariable) def example_1d_objects(self): for data in [range(3), 0.5 * np.arange(3), 0.5 * np.arange(3, dtype=np.float32), pd.date_range('2000-01-01', periods=3), np.array(['a', 'b', 'c'], dtype=object)]: yield (self.cls('x', data), data) def test___array__(self): for v, data in self.example_1d_objects(): self.assertArrayEqual(v.values, np.asarray(data)) self.assertArrayEqual(np.asarray(v), np.asarray(data)) self.assertEqual(v[0].values, np.asarray(data)[0]) self.assertEqual(np.asarray(v[0]), np.asarray(data)[0]) def test_equals_all_dtypes(self): for v, _ in self.example_1d_objects(): v2 = v.copy() self.assertTrue(v.equals(v2)) self.assertTrue(v.identical(v2)) self.assertTrue(v.no_conflicts(v2)) self.assertTrue(v[0].equals(v2[0])) self.assertTrue(v[0].identical(v2[0])) self.assertTrue(v[0].no_conflicts(v2[0])) self.assertTrue(v[:2].equals(v2[:2])) self.assertTrue(v[:2].identical(v2[:2])) self.assertTrue(v[:2].no_conflicts(v2[:2])) def test_eq_all_dtypes(self): # ensure that we don't choke on comparisons for which numpy returns # scalars expected = Variable('x', 3 * [False]) for v, _ in self.example_1d_objects(): actual = 'z' == v self.assertVariableIdentical(expected, actual) actual = ~('z' != v) self.assertVariableIdentical(expected, actual) def test_encoding_preserved(self): expected = self.cls('x', range(3), {'foo': 1}, {'bar': 2}) for actual in [expected.T, expected[...], expected.squeeze(), expected.isel(x=slice(None)), expected.set_dims({'x': 3}), expected.copy(deep=True), expected.copy(deep=False)]: self.assertVariableIdentical(expected.to_base_variable(), actual.to_base_variable()) self.assertEqual(expected.encoding, actual.encoding) def test_concat(self): x = np.arange(5) y = np.arange(5, 10) v = self.cls(['a'], x) w = self.cls(['a'], y) self.assertVariableIdentical(Variable(['b', 'a'], np.array([x, y])), Variable.concat([v, w], 'b')) self.assertVariableIdentical(Variable(['b', 'a'], np.array([x, y])), Variable.concat((v, w), 'b')) self.assertVariableIdentical(Variable(['b', 'a'], np.array([x, y])), Variable.concat((v, w), 'b')) with self.assertRaisesRegexp(ValueError, 'inconsistent dimensions'): Variable.concat([v, Variable(['c'], y)], 'b') # test indexers actual = Variable.concat( [v, w], positions=[np.arange(0, 10, 2), np.arange(1, 10, 2)], dim='a') expected = Variable('a', np.array([x, y]).ravel(order='F')) self.assertVariableIdentical(expected, actual) # test concatenating along a dimension v = Variable(['time', 'x'], np.random.random((10, 8))) self.assertVariableIdentical(v, Variable.concat([v[:5], v[5:]], 'time')) self.assertVariableIdentical(v, Variable.concat([v[:5], v[5:6], v[6:]], 'time')) self.assertVariableIdentical(v, Variable.concat([v[:1], v[1:]], 'time')) # test dimension order self.assertVariableIdentical(v, Variable.concat([v[:, :5], v[:, 5:]], 'x')) with self.assertRaisesRegexp(ValueError, 'all input arrays must have'): Variable.concat([v[:, 0], v[:, 1:]], 'x') def test_concat_attrs(self): # different or conflicting attributes should be removed v = self.cls('a', np.arange(5), {'foo': 'bar'}) w = self.cls('a', np.ones(5)) expected = self.cls('a', np.concatenate([np.arange(5), np.ones(5)])).to_base_variable() self.assertVariableIdentical(expected, Variable.concat([v, w], 'a')) w.attrs['foo'] = 2 self.assertVariableIdentical(expected, Variable.concat([v, w], 'a')) w.attrs['foo'] = 'bar' expected.attrs['foo'] = 'bar' self.assertVariableIdentical(expected, Variable.concat([v, w], 'a')) def test_concat_fixed_len_str(self): # regression test for #217 for kind in ['S', 'U']: x = self.cls('animal', np.array(['horse'], dtype=kind)) y = self.cls('animal', np.array(['aardvark'], dtype=kind)) actual = Variable.concat([x, y], 'animal') expected = Variable( 'animal', np.array(['horse', 'aardvark'], dtype=kind)) self.assertVariableEqual(expected, actual) def test_concat_number_strings(self): # regression test for #305 a = self.cls('x', ['0', '1', '2']) b = self.cls('x', ['3', '4']) actual = Variable.concat([a, b], dim='x') expected = Variable('x', np.arange(5).astype(str).astype(object)) self.assertVariableIdentical(expected, actual) self.assertEqual(expected.dtype, object) self.assertEqual(type(expected.values[0]), str) def test_copy(self): v = self.cls('x', 0.5 * np.arange(10), {'foo': 'bar'}) for deep in [True, False]: w = v.copy(deep=deep) self.assertIs(type(v), type(w)) self.assertVariableIdentical(v, w) self.assertEqual(v.dtype, w.dtype) if self.cls is Variable: if deep: self.assertIsNot(source_ndarray(v.values), source_ndarray(w.values)) else: self.assertIs(source_ndarray(v.values), source_ndarray(w.values)) self.assertVariableIdentical(v, copy(v)) def test_copy_index(self): midx = pd.MultiIndex.from_product([['a', 'b'], [1, 2], [-1, -2]], names=('one', 'two', 'three')) v = self.cls('x', midx) for deep in [True, False]: w = v.copy(deep=deep) self.assertIsInstance(w._data, PandasIndexAdapter) self.assertIsInstance(w.to_index(), pd.MultiIndex) self.assertArrayEqual(v._data.array, w._data.array) def test_real_and_imag(self): v = self.cls('x', np.arange(3) - 1j * np.arange(3), {'foo': 'bar'}) expected_re = self.cls('x', np.arange(3), {'foo': 'bar'}) self.assertVariableIdentical(v.real, expected_re) expected_im = self.cls('x', -np.arange(3), {'foo': 'bar'}) self.assertVariableIdentical(v.imag, expected_im) expected_abs = self.cls('x', np.sqrt(2 * np.arange(3) ** 2)).to_base_variable() self.assertVariableAllClose(abs(v), expected_abs) def test_aggregate_complex(self): # should skip NaNs v = self.cls('x', [1, 2j, np.nan]) expected = Variable((), 0.5 + 1j) self.assertVariableAllClose(v.mean(), expected) def test_pandas_cateogrical_dtype(self): data = pd.Categorical(np.arange(10, dtype='int64')) v = self.cls('x', data) print(v) # should not error assert v.dtype == 'int64' def test_pandas_datetime64_with_tz(self): data = pd.date_range(start='2000-01-01', tz=pytz.timezone('America/New_York'), periods=10, freq='1h') v = self.cls('x', data) print(v) # should not error if 'America/New_York' in str(data.dtype): # pandas is new enough that it has datetime64 with timezone dtype assert v.dtype == 'object' def test_multiindex(self): idx = pd.MultiIndex.from_product([list('abc'), [0, 1]]) v = self.cls('x', idx) self.assertVariableIdentical(Variable((), ('a', 0)), v[0]) self.assertVariableIdentical(v, v[:]) def test_load(self): array = self.cls('x', np.arange(5)) orig_data = array._data copied = array.copy(deep=True) array.load() assert type(array._data) is type(orig_data) assert type(copied._data) is type(orig_data) self.assertVariableIdentical(array, copied) class TestVariable(TestCase, VariableSubclassTestCases): cls = staticmethod(Variable) def setUp(self): self.d = np.random.random((10, 3)).astype(np.float64) def test_data_and_values(self): v = Variable(['time', 'x'], self.d) self.assertArrayEqual(v.data, self.d) self.assertArrayEqual(v.values, self.d) self.assertIs(source_ndarray(v.values), self.d) with self.assertRaises(ValueError): # wrong size v.values = np.random.random(5) d2 = np.random.random((10, 3)) v.values = d2 self.assertIs(source_ndarray(v.values), d2) d3 = np.random.random((10, 3)) v.data = d3 self.assertIs(source_ndarray(v.data), d3) def test_numpy_same_methods(self): v = Variable([], np.float32(0.0)) self.assertEqual(v.item(), 0) self.assertIs(type(v.item()), float) v = IndexVariable('x', np.arange(5)) self.assertEqual(2, v.searchsorted(2)) def test_datetime64_conversion_scalar(self): expected = np.datetime64('2000-01-01T00:00:00Z', 'ns') for values in [ np.datetime64('2000-01-01T00Z'), pd.Timestamp('2000-01-01T00'), datetime(2000, 1, 1), ]: v = Variable([], values) self.assertEqual(v.dtype, np.dtype('datetime64[ns]')) self.assertEqual(v.values, expected) self.assertEqual(v.values.dtype, np.dtype('datetime64[ns]')) def test_timedelta64_conversion_scalar(self): expected = np.timedelta64(24 * 60 * 60 * 10 ** 9, 'ns') for values in [ np.timedelta64(1, 'D'), pd.Timedelta('1 day'), timedelta(days=1), ]: v = Variable([], values) self.assertEqual(v.dtype, np.dtype('timedelta64[ns]')) self.assertEqual(v.values, expected) self.assertEqual(v.values.dtype, np.dtype('timedelta64[ns]')) def test_0d_str(self): v = Variable([], u'foo') self.assertEqual(v.dtype, np.dtype('U3')) self.assertEqual(v.values, 'foo') v = Variable([], np.string_('foo')) self.assertEqual(v.dtype, np.dtype('S3')) self.assertEqual(v.values, bytes('foo', 'ascii') if PY3 else 'foo') def test_0d_datetime(self): v = Variable([], pd.Timestamp('2000-01-01')) self.assertEqual(v.dtype, np.dtype('datetime64[ns]')) self.assertEqual(v.values, np.datetime64('2000-01-01T00Z', 'ns')) def test_0d_timedelta(self): for td in [pd.to_timedelta('1s'), np.timedelta64(1, 's')]: v = Variable([], td) self.assertEqual(v.dtype, np.dtype('timedelta64[ns]')) self.assertEqual(v.values, np.timedelta64(10 ** 9, 'ns')) def test_equals_and_identical(self): d = np.random.rand(10, 3) d[0, 0] = np.nan v1 = Variable(('dim1', 'dim2'), data=d, attrs={'att1': 3, 'att2': [1, 2, 3]}) v2 = Variable(('dim1', 'dim2'), data=d, attrs={'att1': 3, 'att2': [1, 2, 3]}) self.assertTrue(v1.equals(v2)) self.assertTrue(v1.identical(v2)) v3 = Variable(('dim1', 'dim3'), data=d) self.assertFalse(v1.equals(v3)) v4 = Variable(('dim1', 'dim2'), data=d) self.assertTrue(v1.equals(v4)) self.assertFalse(v1.identical(v4)) v5 = deepcopy(v1) v5.values[:] = np.random.rand(10, 3) self.assertFalse(v1.equals(v5)) self.assertFalse(v1.equals(None)) self.assertFalse(v1.equals(d)) self.assertFalse(v1.identical(None)) self.assertFalse(v1.identical(d)) def test_broadcast_equals(self): v1 = Variable((), np.nan) v2 = Variable(('x'), [np.nan, np.nan]) self.assertTrue(v1.broadcast_equals(v2)) self.assertFalse(v1.equals(v2)) self.assertFalse(v1.identical(v2)) v3 = Variable(('x'), [np.nan]) self.assertTrue(v1.broadcast_equals(v3)) self.assertFalse(v1.equals(v3)) self.assertFalse(v1.identical(v3)) self.assertFalse(v1.broadcast_equals(None)) v4 = Variable(('x'), [np.nan] * 3) self.assertFalse(v2.broadcast_equals(v4)) def test_no_conflicts(self): v1 = Variable(('x'), [1, 2, np.nan, np.nan]) v2 = Variable(('x'), [np.nan, 2, 3, np.nan]) self.assertTrue(v1.no_conflicts(v2)) self.assertFalse(v1.equals(v2)) self.assertFalse(v1.broadcast_equals(v2)) self.assertFalse(v1.identical(v2)) self.assertFalse(v1.no_conflicts(None)) v3 = Variable(('y'), [np.nan, 2, 3, np.nan]) self.assertFalse(v3.no_conflicts(v1)) d = np.array([1, 2, np.nan, np.nan]) self.assertFalse(v1.no_conflicts(d)) self.assertFalse(v2.no_conflicts(d)) v4 = Variable(('w', 'x'), [d]) self.assertTrue(v1.no_conflicts(v4)) def test_as_variable(self): data = np.arange(10) expected = Variable('x', data) self.assertVariableIdentical(expected, as_variable(expected)) ds = Dataset({'x': expected}) self.assertVariableIdentical(expected, as_variable(ds['x']).to_base_variable()) self.assertNotIsInstance(ds['x'], Variable) self.assertIsInstance(as_variable(ds['x']), Variable) FakeVariable = namedtuple('FakeVariable', 'values dims') fake_xarray = FakeVariable(expected.values, expected.dims) self.assertVariableIdentical(expected, as_variable(fake_xarray)) xarray_tuple = (expected.dims, expected.values) self.assertVariableIdentical(expected, as_variable(xarray_tuple)) with self.assertRaisesRegexp(TypeError, 'tuples to convert'): as_variable(tuple(data)) with self.assertRaisesRegexp( TypeError, 'without an explicit list of dimensions'): as_variable(data) actual = as_variable(data, name='x') self.assertVariableIdentical(expected.to_index_variable(), actual) actual = as_variable(0) expected = Variable([], 0) self.assertVariableIdentical(expected, actual) def test_repr(self): v = Variable(['time', 'x'], [[1, 2, 3], [4, 5, 6]], {'foo': 'bar'}) expected = dedent(""" <xarray.Variable (time: 2, x: 3)> array([[1, 2, 3], [4, 5, 6]]) Attributes: foo: bar """).strip() self.assertEqual(expected, repr(v)) def test_repr_lazy_data(self): v = Variable('x', LazilyIndexedArray(np.arange(2e5))) self.assertIn('200000 values with dtype', repr(v)) self.assertIsInstance(v._data, LazilyIndexedArray) def test_items(self): data = np.random.random((10, 11)) v = Variable(['x', 'y'], data) # test slicing self.assertVariableIdentical(v, v[:]) self.assertVariableIdentical(v, v[...]) self.assertVariableIdentical(Variable(['y'], data[0]), v[0]) self.assertVariableIdentical(Variable(['x'], data[:, 0]), v[:, 0]) self.assertVariableIdentical(Variable(['x', 'y'], data[:3, :2]), v[:3, :2]) # test array indexing x = Variable(['x'], np.arange(10)) y = Variable(['y'], np.arange(11)) self.assertVariableIdentical(v, v[x.values]) self.assertVariableIdentical(v, v[x]) self.assertVariableIdentical(v[:3], v[x < 3]) self.assertVariableIdentical(v[:, 3:], v[:, y >= 3]) self.assertVariableIdentical(v[:3, 3:], v[x < 3, y >= 3]) self.assertVariableIdentical(v[:3, :2], v[x[:3], y[:2]]) self.assertVariableIdentical(v[:3, :2], v[range(3), range(2)]) # test iteration for n, item in enumerate(v): self.assertVariableIdentical(Variable(['y'], data[n]), item) with self.assertRaisesRegexp(TypeError, 'iteration over a 0-d'): iter(Variable([], 0)) # test setting v.values[:] = 0 self.assertTrue(np.all(v.values == 0)) # test orthogonal setting v[range(10), range(11)] = 1 self.assertArrayEqual(v.values, np.ones((10, 11))) def test_isel(self): v = Variable(['time', 'x'], self.d) self.assertVariableIdentical(v.isel(time=slice(None)), v) self.assertVariableIdentical(v.isel(time=0), v[0]) self.assertVariableIdentical(v.isel(time=slice(0, 3)), v[:3]) self.assertVariableIdentical(v.isel(x=0), v[:, 0]) with self.assertRaisesRegexp(ValueError, 'do not exist'): v.isel(not_a_dim=0) def test_index_0d_numpy_string(self): # regression test to verify our work around for indexing 0d strings v = Variable([], np.string_('asdf')) self.assertVariableIdentical(v[()], v) v = Variable([], np.unicode_(u'asdf')) self.assertVariableIdentical(v[()], v) def test_indexing_0d_unicode(self): # regression test for GH568 actual = Variable(('x'), [u'tmax'])[0][()] expected = Variable((), u'tmax') self.assertVariableIdentical(actual, expected) def test_shift(self): v = Variable('x', [1, 2, 3, 4, 5]) self.assertVariableIdentical(v, v.shift(x=0)) self.assertIsNot(v, v.shift(x=0)) expected = Variable('x', [np.nan, 1, 2, 3, 4]) self.assertVariableIdentical(expected, v.shift(x=1)) expected = Variable('x', [np.nan, np.nan, 1, 2, 3]) self.assertVariableIdentical(expected, v.shift(x=2)) expected = Variable('x', [2, 3, 4, 5, np.nan]) self.assertVariableIdentical(expected, v.shift(x=-1)) expected = Variable('x', [np.nan] * 5) self.assertVariableIdentical(expected, v.shift(x=5)) self.assertVariableIdentical(expected, v.shift(x=6)) with self.assertRaisesRegexp(ValueError, 'dimension'): v.shift(z=0) v = Variable('x', [1, 2, 3, 4, 5], {'foo': 'bar'}) self.assertVariableIdentical(v, v.shift(x=0)) expected = Variable('x', [np.nan, 1, 2, 3, 4], {'foo': 'bar'}) self.assertVariableIdentical(expected, v.shift(x=1)) def test_shift2d(self): v = Variable(('x', 'y'), [[1, 2], [3, 4]]) expected = Variable(('x', 'y'), [[np.nan, np.nan], [np.nan, 1]]) self.assertVariableIdentical(expected, v.shift(x=1, y=1)) def test_roll(self): v = Variable('x', [1, 2, 3, 4, 5]) self.assertVariableIdentical(v, v.roll(x=0)) self.assertIsNot(v, v.roll(x=0)) expected = Variable('x', [5, 1, 2, 3, 4]) self.assertVariableIdentical(expected, v.roll(x=1)) self.assertVariableIdentical(expected, v.roll(x=-4)) self.assertVariableIdentical(expected, v.roll(x=6)) expected = Variable('x', [4, 5, 1, 2, 3]) self.assertVariableIdentical(expected, v.roll(x=2)) self.assertVariableIdentical(expected, v.roll(x=-3)) with self.assertRaisesRegexp(ValueError, 'dimension'): v.roll(z=0) def test_roll_consistency(self): v = Variable(('x', 'y'), np.random.randn(5, 6)) for axis, dim in [(0, 'x'), (1, 'y')]: for shift in [-3, 0, 1, 7, 11]: expected = np.roll(v.values, shift, axis=axis) actual = v.roll(**{dim: shift}).values self.assertArrayEqual(expected, actual) def test_transpose(self): v = Variable(['time', 'x'], self.d) v2 = Variable(['x', 'time'], self.d.T) self.assertVariableIdentical(v, v2.transpose()) self.assertVariableIdentical(v.transpose(), v.T) x = np.random.randn(2, 3, 4, 5) w = Variable(['a', 'b', 'c', 'd'], x) w2 = Variable(['d', 'b', 'c', 'a'], np.einsum('abcd->dbca', x)) self.assertEqual(w2.shape, (5, 3, 4, 2)) self.assertVariableIdentical(w2, w.transpose('d', 'b', 'c', 'a')) self.assertVariableIdentical(w, w2.transpose('a', 'b', 'c', 'd')) w3 = Variable(['b', 'c', 'd', 'a'], np.einsum('abcd->bcda', x)) self.assertVariableIdentical(w, w3.transpose('a', 'b', 'c', 'd')) def test_transpose_0d(self): for value in [ 3.5, ('a', 1), np.datetime64('2000-01-01'), np.timedelta64(1, 'h'), None, object(), ]: variable = Variable([], value) actual = variable.transpose() assert actual.identical(variable) def test_squeeze(self): v = Variable(['x', 'y'], [[1]]) self.assertVariableIdentical(Variable([], 1), v.squeeze()) self.assertVariableIdentical(Variable(['y'], [1]), v.squeeze('x')) self.assertVariableIdentical(Variable(['y'], [1]), v.squeeze(['x'])) self.assertVariableIdentical(Variable(['x'], [1]), v.squeeze('y')) self.assertVariableIdentical(Variable([], 1), v.squeeze(['x', 'y'])) v = Variable(['x', 'y'], [[1, 2]]) self.assertVariableIdentical(Variable(['y'], [1, 2]), v.squeeze()) self.assertVariableIdentical(Variable(['y'], [1, 2]), v.squeeze('x')) with self.assertRaisesRegexp(ValueError, 'cannot select a dimension'): v.squeeze('y') def test_get_axis_num(self): v = Variable(['x', 'y', 'z'], np.random.randn(2, 3, 4)) self.assertEqual(v.get_axis_num('x'), 0) self.assertEqual(v.get_axis_num(['x']), (0,)) self.assertEqual(v.get_axis_num(['x', 'y']), (0, 1)) self.assertEqual(v.get_axis_num(['z', 'y', 'x']), (2, 1, 0)) with self.assertRaisesRegexp(ValueError, 'not found in array dim'): v.get_axis_num('foobar') def test_set_dims(self): v = Variable(['x'], [0, 1]) actual = v.set_dims(['x', 'y']) expected = Variable(['x', 'y'], [[0], [1]]) self.assertVariableIdentical(actual, expected) actual = v.set_dims(['y', 'x']) self.assertVariableIdentical(actual, expected.T) actual = v.set_dims(OrderedDict([('x', 2), ('y', 2)])) expected = Variable(['x', 'y'], [[0, 0], [1, 1]]) self.assertVariableIdentical(actual, expected) v = Variable(['foo'], [0, 1]) actual = v.set_dims('foo') expected = v self.assertVariableIdentical(actual, expected) with self.assertRaisesRegexp(ValueError, 'must be a superset'): v.set_dims(['z']) def test_set_dims_object_dtype(self): v = Variable([], ('a', 1)) actual = v.set_dims(('x',), (3,)) exp_values = np.empty((3,), dtype=object) for i in range(3): exp_values[i] = ('a', 1) expected = Variable(['x'], exp_values) assert actual.identical(expected) def test_stack(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]], {'foo': 'bar'}) actual = v.stack(z=('x', 'y')) expected = Variable('z', [0, 1, 2, 3], v.attrs) self.assertVariableIdentical(actual, expected) actual = v.stack(z=('x',)) expected = Variable(('y', 'z'), v.data.T, v.attrs) self.assertVariableIdentical(actual, expected) actual = v.stack(z=(),) self.assertVariableIdentical(actual, v) actual = v.stack(X=('x',), Y=('y',)).transpose('X', 'Y') expected = Variable(('X', 'Y'), v.data, v.attrs) self.assertVariableIdentical(actual, expected) def test_stack_errors(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]], {'foo': 'bar'}) with self.assertRaisesRegexp(ValueError, 'invalid existing dim'): v.stack(z=('x1',)) with self.assertRaisesRegexp(ValueError, 'cannot create a new dim'): v.stack(x=('x',)) def test_unstack(self): v = Variable('z', [0, 1, 2, 3], {'foo': 'bar'}) actual = v.unstack(z=OrderedDict([('x', 2), ('y', 2)])) expected = Variable(('x', 'y'), [[0, 1], [2, 3]], v.attrs) self.assertVariableIdentical(actual, expected) actual = v.unstack(z=OrderedDict([('x', 4), ('y', 1)])) expected = Variable(('x', 'y'), [[0], [1], [2], [3]], v.attrs) self.assertVariableIdentical(actual, expected) actual = v.unstack(z=OrderedDict([('x', 4)])) expected = Variable('x', [0, 1, 2, 3], v.attrs) self.assertVariableIdentical(actual, expected) def test_unstack_errors(self): v = Variable('z', [0, 1, 2, 3]) with self.assertRaisesRegexp(ValueError, 'invalid existing dim'): v.unstack(foo={'x': 4}) with self.assertRaisesRegexp(ValueError, 'cannot create a new dim'): v.stack(z=('z',)) with self.assertRaisesRegexp(ValueError, 'the product of the new dim'): v.unstack(z={'x': 5}) def test_unstack_2d(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]]) actual = v.unstack(y={'z': 2}) expected = Variable(['x', 'z'], v.data) self.assertVariableIdentical(actual, expected) actual = v.unstack(x={'z': 2}) expected = Variable(['y', 'z'], v.data.T) self.assertVariableIdentical(actual, expected) def test_stack_unstack_consistency(self): v = Variable(['x', 'y'], [[0, 1], [2, 3]]) actual = (v.stack(z=('x', 'y')) .unstack(z=OrderedDict([('x', 2), ('y', 2)]))) self.assertVariableIdentical(actual, v) def test_broadcasting_math(self): x = np.random.randn(2, 3) v = Variable(['a', 'b'], x) # 1d to 2d broadcasting self.assertVariableIdentical( v * v, Variable(['a', 'b'], np.einsum('ab,ab->ab', x, x))) self.assertVariableIdentical( v * v[0], Variable(['a', 'b'], np.einsum('ab,b->ab', x, x[0]))) self.assertVariableIdentical( v[0] * v, Variable(['b', 'a'],

np.einsum('b,ab->ba', x[0], x)

numpy.einsum

import tensorflow as tf from tensorflow.python.framework import ops import sys import os import matplotlib.pyplot as plt import random from mpl_toolkits.mplot3d import Axes3D BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) devox_module = tf.load_op_library(os.path.join(BASE_DIR, 'tf_devoxelize.so')) sys.path.append('../voxelization') from tf_vox import group_voxel, voxelize, avg_voxel sys.path.append('../../utils') from tf_devox import trilinear_devoxelize import tf_util from plyfile import PlyData, PlyElement sys.path.append(os.path.join(BASE_DIR, '..', 'tf_ops/pc_distance')) import tf_nn_distance tf.enable_eager_execution() def get_cd_loss(pred, pc): """ pred: BxNx3, label: BxN, """ dists_forward,_,dists_backward,_ = tf_nn_distance.nn_distance(pred, pc) # loss = tf.reduce_mean(dists_forward+dists_backward) loss = (tf.reduce_mean(tf.sqrt(dists_forward)) + tf.reduce_mean(tf.sqrt(dists_backward)))/2 return loss def write_ply(tensor, name): np.savetxt(name, np.squeeze(tensor.numpy().transpose(0, 2, 1))) len = tensor.numpy().shape[2] file = os.path.join('.', name) f = open(file, "r+") lines = [line.lstrip().rstrip().replace(' ', ' ') for line in f] vertex_nums, face_nums, _ = lines[1].split() f = open(file, "w+") f.seek(0) head = "ply\nformat ascii 1.0\ncomment VCGLIB generated\nelement vertex " + '2048' + "\nproperty float x\nproperty float y\nproperty float z\nelement face " + str(0) + "\nproperty list uchar int vertex_indices\nend_header\n" f.write(head) for line in lines[:]: f.write(line + "\n") f.close() if __name__ == '__main__': import numpy as np import time for file in os.listdir('../../data'): file = os.path.join('../../data',file) plydata = PlyData.read(file) a_data = [] for i in range(plydata['vertex'].count): line = [plydata['vertex']['x'][i], plydata['vertex']['y'][i], plydata['vertex']['z'][i]] a_data.append(line) pc =

np.array(a_data)

numpy.array

import numpy as np import statistics import matplotlib.pyplot as plt import seaborn as sns np_normal_dis = np.random.normal(5, 0.5, 100) print(np_normal_dis.min()) print(np_normal_dis.max()) print(np_normal_dis.mean()) # print(np_normal_dis.median()) # not working print(np_normal_dis.std()) two_dimension_array = np.array([(1,2,3), [4,5,6]]) print(two_dimension_array) print('Max row:', np.amax(two_dimension_array, axis=0)) print('Max column:', np.amax(two_dimension_array, axis=1)) print('Min column:', np.amin(two_dimension_array, axis=0)) print('Min column:', np.amin(two_dimension_array, axis=1)) a = [1, 2, 3] print('Tile:', np.tile(a, 2)) # just repeat a list print('Repeat:', np.repeat(a, 2)) # sort repeat print(np.random.random()) # between 0 - 1 r = np.random.random(size=[2, 3]) # 2 row 3 column print(r) print(

np.random.choice(['a', 'e', 'i', 'o', 'u'], size=10)

numpy.random.choice

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sat Oct 14 21:31:56 2017 @author: Franz """ import scipy.signal import numpy as np import scipy.io as so import os.path import re import matplotlib.pylab as plt import h5py import matplotlib.patches as patches import numpy.random as rand import seaborn as sns import pandas as pd from functools import reduce import random import pdb class Mouse : def __init__(self, idf, list=None, typ='') : self.recordings = [] self.recordings.append(list) self.typ = typ self.idf = idf def add(self, rec) : self.recordings.append(rec) def __len__(self) : return len(self.recordings) def __repr__(self) : return ", ".join(self.recordings) ### PROCESSING OF RECORDING DATA ############################################## def load_stateidx(ppath, name, ann_name=''): """ load the sleep state file of recording (folder) $ppath/$name @Return: M,K sequence of sleep states, sequence of 0'1 and 1's indicating non- and annotated states """ ddir = os.path.join(ppath, name) ppath, name = os.path.split(ddir) if ann_name == '': ann_name = name sfile = os.path.join(ppath, name, 'remidx_' + ann_name + '.txt') f = open(sfile, 'r') lines = f.readlines() f.close() n = 0 for l in lines: if re.match('\d', l): n += 1 M = np.zeros(n, dtype='int') K = np.zeros(n, dtype='int') i = 0 for l in lines : if re.search('^\s+$', l) : continue if re.search('\s*#', l) : continue if re.match('\d+\s+-?\d+', l) : a = re.split('\s+', l) M[i] = int(a[0]) K[i] = int(a[1]) i += 1 return M,K def load_recordings(ppath, rec_file) : """ load_recordings(ppath, rec_file) load recording listing with syntax: [E|C] \s+ recording_name #COMMENT @RETURN: (list of controls, lis of experiments) """ exp_list = [] ctr_list = [] rfile = os.path.join(ppath, rec_file) f = open(rfile, newline=None) lines = f.readlines() f.close() for l in lines : if re.search('^\s+$', l) : continue if re.search('^\s*#', l) : continue a = re.split('\s+', l) if re.search('E', a[0]) : exp_list.append(a[1]) if re.search('C', a[0]) : ctr_list.append(a[1]) return ctr_list, exp_list def load_dose_recordings(ppath, rec_file): """ load recording list with following syntax: A line is either control or experiments; Control recordings look like: C \s recording_name Experimental recordings also come with an additional dose parameter (allowing for comparison of multiple doses with controls) E \s recording_name \s dose_1 E \s recording_name \s dose_2 """ rfile = os.path.join(ppath, rec_file) f = open(rfile, newline=None) lines = f.readlines() f.close() # first get all potential doses doses = {} ctr_list = [] for l in lines : if re.search('^\s+$', l): continue if re.search('^\s*#', l): continue a = re.split('\s+', l) if re.search('E', a[0]): if a[2] in doses: doses[a[2]].append(a[1]) else: doses[a[2]] = [a[1]] if re.search('C', a[0]): ctr_list.append(a[1]) return ctr_list, doses def get_snr(ppath, name): """ read and return sampling rate (SR) from file $ppath/$name/info.txt """ fid = open(os.path.join(ppath, name, 'info.txt'), newline=None) lines = fid.readlines() fid.close() values = [] for l in lines : a = re.search("^" + 'SR' + ":" + "\s+(.*)", l) if a : values.append(a.group(1)) return float(values[0]) def get_infoparam(ifile, field): """ NOTE: field is a single string and the function does not check for the type of the values for field. In fact, it just returns the string following field """ fid = open(ifile, newline=None) lines = fid.readlines() fid.close() values = [] for l in lines : a = re.search("^" + field + ":" + "\s+(.*)", l) if a : values.append(a.group(1)) return values def add_infoparam(ifile, field, vals): """ :param ifile: info file :param field: Parameters specifier, e.g. 'SR' :param vals: list with parameters """ fid = open(ifile, 'a') vals = [str(s) for s in vals] param = " ".join(vals) fid.write('%s:\t%s' % (field, param)) fid.write(os.linesep) fid.close() def laser_start_end(laser, SR=1525.88, intval=5): """laser_start_end(ppath, name) print start and end index of laser stimulation trains: For example, if you was stimulated for 2min every 20 min with 20 Hz, return the start and end index of the each 2min stimulation period (train) returns the tuple (istart, iend), both indices are inclusive, i.e. part of the sequence @Param: laser - laser, vector of 0s and 1s intval - minimum time separation [s] between two laser trains @Return: (istart, iend) - tuple of two np.arrays with laser start and end indices """ idx = np.where(laser > 0.5)[0] if len(idx) == 0 : return ([], []) idx2 = np.nonzero(np.diff(idx)*(1./SR) > intval)[0] istart = np.hstack([idx[0], idx[idx2+1]]) iend = np.hstack([idx[idx2], idx[-1]]) return (istart, iend) def load_laser(ppath, name): """ load laser from recording ppath/name @RETURN: @laser, vector of 0's and 1's """ # laser might be .mat or h5py file # perhaps we could find a better way of testing that file = os.path.join(ppath, name, 'laser_'+name+'.mat') try: laser = np.array(h5py.File(file,'r').get('laser')) except: laser = so.loadmat(file)['laser'] return np.squeeze(laser) def laser_protocol(ppath, name): """ What was the stimulation frequency and the inter-stimulation interval for recording $ppath/$name? @Return: iinter-stimulation intervals, avg. inter-stimulation interval, frequency """ laser = load_laser(ppath, name) SR = get_snr(ppath, name) # first get inter-stimulation interval (istart, iend) = laser_start_end(laser, SR) intv = np.diff(np.array(istart/float(SR))) d = intv/60.0 print("The laser was turned on in average every %.2f min," % (np.mean(d))) print("with a min. interval of %.2f min and max. interval of %.2f min." % (np.min(d), np.max(d))) print("Laser stimulation lasted for %f s." % (np.mean(np.array(iend/float(SR)-istart/float(SR)).mean()))) # print laser start times print("Start time of each laser trial:") j=1 for t in istart: print("trial %d: %.2f" % (j, (t / float(SR)) / 60)) j += 1 # for each laser stimulation interval, check laser stimulation frequency dt = 1/float(SR) freq = [] laser_up = [] laser_down = [] for (i,j) in zip(istart, iend): part = laser[i:j+1] (a,b) = laser_start_end(part, SR, 0.005) dur = (j-i+1)*dt freq.append(len(a) / dur) up_dur = (b-a+1)*dt*1000 down_dur = (a[1:]-b[0:-1]-1)*dt*1000 laser_up.append(np.mean(up_dur)) laser_down.append(np.mean(down_dur)) print(os.linesep + "Laser stimulation freq. was %.2f Hz," % np.mean(np.array(freq))) print("with laser up and down duration of %.2f and %.2f ms." % (np.mean(np.array(laser_up)), np.mean(np.array(laser_down)))) return d, np.mean(d), np.mean(np.array(freq)) def swap_eeg(ppath, rec, ch='EEG'): """ swap EEG and EEG2 or EMG with EMG2 if $ch='EMG' """ if ch == 'EEG': name = 'EEG' else: name = ch EEG = so.loadmat(os.path.join(ppath, rec, name+'.mat'))[name] EEG2 = so.loadmat(os.path.join(ppath, rec, name+'2.mat'))[name + '2'] tmp = EEG EEG = EEG2 EEG2 = tmp file_eeg1 = os.path.join(ppath, rec, '%s.mat' % name) file_eeg2 = os.path.join(ppath, rec, '%s2.mat' % name) so.savemat(file_eeg1, {name : EEG}) so.savemat(file_eeg2, {name+'2' : EEG2}) def eeg_conversion(ppath, rec, conv_factor=0.195): """ multiply all EEG and EMG channels with the given conversion factor and write the conversion factor as parameter (conversion:) into the info file. Only if there's no conversion factor in the info file specified, the conversion will be executed :param ppath: base filder :param rec: recording :param conv_factor: conversion factor :return: n/s """ ifile = os.path.join(ppath, rec, 'info.txt') conv = get_infoparam(ifile, 'conversion') if len(conv) > 0: print("found conversion: parameter in info file") print("returning: no conversion necessary!!!") return else: files = os.listdir(os.path.join(ppath, rec)) files = [f for f in files if re.match('^EEG', f)] for f in files: name = re.split('\.', f)[0] EEG = so.loadmat(os.path.join(ppath, rec, name+'.mat'), squeeze_me=True)[name] if EEG[0].dtype == 'int16': EEG = EEG * conv_factor file_eeg = os.path.join(ppath, rec, '%s.mat' % name) print(file_eeg) so.savemat(file_eeg, {name: EEG}) else: print('Wrong datatype! probably already converted; returning...') return files = os.listdir(os.path.join(ppath, rec)) files = [f for f in files if re.match('^EMG', f)] for f in files: name = re.split('\.', f)[0] EMG = so.loadmat(os.path.join(ppath, rec, name+'.mat'), squeeze_me=True)[name] if EMG[0].dtype == 'int16': EMG = EMG * conv_factor file_emg = os.path.join(ppath, rec, '%s.mat' % name) print(file_emg) so.savemat(file_emg, {name: EMG}) else: print('Wrong datatype! probably already converted; returning...') return add_infoparam(ifile, 'conversion', [conv_factor]) calculate_spectrum(ppath, rec) ### DEPRICATED ############################################ def video_pulse_detection(ppath, rec, SR=1000, iv = 0.01): """ return index of each video frame onset ppath/rec - recording @Optional SR - sampling rate of EEG(!) recording iv - minimum time inverval (in seconds) between two frames @Return index of each video frame onset """ V = np.squeeze(so.loadmat(os.path.join(ppath, rec, 'videotime_' + rec + '.mat'))['video']) TS = np.arange(0, len(V)) # indices where there's a jump in the signal t = TS[np.where(V<0.5)]; if len(t) == 0: idx = [] return idx # time points where the interval between jumps is longer than iv t2 = np.where(np.diff(t)*(1.0/SR)>=iv)[0] idx = np.concatenate(([t[0]],t[t2+1])) return idx # SIGNAL PROCESSING ########################################################### def my_lpfilter(x, w0, N=4): """ create a lowpass Butterworth filter with a cutoff of w0 * the Nyquist rate. The nice thing about this filter is that is has zero-phase distortion. A conventional lowpass filter would introduce a phase lag. w0 - filter cutoff; value between 0 and 1, where 1 corresponds to nyquist frequency. So if you want a filter with cutoff at x Hz, the corresponding w0 value is given by w0 = 2 * x / sampling_rate N - order of filter @Return: low-pass filtered signal See also my hp_filter, or my_bpfilter """ from scipy import signal b,a = signal.butter(N, w0) y = signal.filtfilt(b,a, x) return y def my_hpfilter(x, w0, N=4): """ create an N-th order highpass Butterworth filter with cutoff frequency w0 * sampling_rate/2 """ from scipy import signal # use scipy.signal.firwin to generate filter #taps = signal.firwin(numtaps, w0, pass_zero=False) #y = signal.lfilter(taps, 1.0, x) b,a = signal.butter(N, w0, 'high') y = signal.filtfilt(b,a, x, padlen = x.shape[0]-1) return y def my_bpfilter(x, w0, w1, N=4,bf=True): """ create N-th order bandpass Butterworth filter with corner frequencies w0*sampling_rate/2 and w1*sampling_rate/2 """ #from scipy import signal #taps = signal.firwin(numtaps, w0, pass_zero=False) #y = signal.lfilter(taps, 1.0, x) #return y from scipy import signal b,a = signal.butter(N, [w0, w1], 'bandpass') if bf: y = signal.filtfilt(b,a, x) else: y = signal.lfilter(b,a, x) return y def my_notchfilter(x, sr=1000, band=5, freq=60, ripple=10, order=3, filter_type='butter'): from scipy.signal import iirfilter,lfilter fs = sr nyq = fs/2.0 low = freq - band/2.0 high = freq + band/2.0 low = low/nyq high = high/nyq b, a = iirfilter(order, [low, high], rp=ripple, btype='bandstop', analog=False, ftype=filter_type) filtered_data = lfilter(b, a, x) return filtered_data def downsample_vec(x, nbin): """ y = downsample_vec(x, nbin) downsample the vector x by replacing nbin consecutive \ bin by their mean \ @RETURN: the downsampled vector """ n_down = int(np.floor(len(x) / nbin)) x = x[0:n_down*nbin] x_down = np.zeros((n_down,)) # 0 1 2 | 3 4 5 | 6 7 8 for i in range(nbin) : idx = list(range(i, int(n_down*nbin), int(nbin))) x_down += x[idx] return x_down / nbin def smooth_data(x, sig): """ y = smooth_data(x, sig) smooth data vector @x with gaussian kernel with standard deviation $sig """ sig = float(sig) if sig == 0.0: return x # gaussian: gauss = lambda x, sig : (1/(sig*np.sqrt(2.*np.pi)))*np.exp(-(x*x)/(2.*sig*sig)) bound = 1.0/10000 L = 10. p = gauss(L, sig) while (p > bound): L = L+10 p = gauss(L, sig) #F = map(lambda x: gauss((x, sig)), np.arange(-L, L+1.)) # py3: F = [gauss(x, sig) for x in np.arange(-L, L+1.)] F = F / np.sum(F) return scipy.signal.fftconvolve(x, F, 'same') def power_spectrum(data, length, dt): """ scipy's implementation of Welch's method using hanning window to estimate the power spectrum The function returns power density with units V**2/Hz see also https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.welch.html The label on the y-axis should say PSD [V**2/Hz] @Parameters data - time series; float vector! length - length of hanning window, even integer! @Return: power density, frequencies The function returns power density in units V^2 / Hz Note that np.var(data) ~ np.sum(power density) * (frequencies[1]-frequencies[0]) """ f, pxx = scipy.signal.welch(data, fs=1.0/dt, window='hanning', nperseg=int(length), noverlap=int(length/2)) return pxx, f def spectral_density(data, length, nfft, dt): """ calculate the spectrogram for the time series given by data with time resolution dt The powerspectrum for each window of length $length is computed using Welch's method. The windows for the powerspectrum calculation are half-overlapping. If length contains 5s of data, then the first windows goes from 0s to 5s, the second window from 2.5 to 7.5s, ... The last window ends at ceil(len(data)/length)*5s Another example, assume we have 13 s of data, with 5 s windows, the the powerdensity is calculated for the following time windows: 0 -- 5, 2.5 -- 7.5, 5 -- 10, 7.5 -- 12.5, 10 -- 15 In total there are thus 2*ceil(13/5)-1 = 5 windows The last window starts at 2*3-2 * (5/2) = 10 s Note: the returned time axis starts at time point goes from 0 to 10s in 2.5s steps @Parameters: data - time series length - window length of data used to calculate powerspectrum. Note that the time resolution of the spectrogram is length/2 nfft - size of the window used to calculate the powerspectrum. determines the frequency resolution. @Return: Powspectrum, frequencies, time axis """ n = len(data) k = int(np.ceil((1.0*n)/length)) data = np.concatenate((data, np.zeros((length*k-n,)))) fdt = length*dt/2 # time step for spectrogram t = np.arange(0, fdt*(2*k-2)+fdt/2.0, fdt) # frequency axis of spectrogram f = np.linspace(0, 1, int(np.ceil(nfft/2.0))+1) * (0.5/dt) # the power spectrum is calculated for 2*k-1 time points Pow = np.zeros((len(f), k*2-1)) j = 0 for i in range(0, k-2+1): w1=data[(length*i):(i+1)*length] w2=data[length*i+int(length/2):(i+1)*length+int(length/2)] Pow[:,j] = power_spectrum(w1, nfft, dt)[0] Pow[:,j+1] = power_spectrum(w2, nfft, dt)[0] j += 2 # last time point Pow[:,j],f = power_spectrum(data[length*(k-1):k*length], nfft, dt) return Pow, f, t def calculate_spectrum(ppath, name, fres=0.5): """ calculate EEG and EMG spectrogram used for sleep stage detection. Function assumes that data vectors EEG.mat and EMG.mat exist in recording folder ppath/name; these are used to calculate the powerspectrum fres - resolution of frequency axis all data saved in "true" mat files :return EEG Spectrogram, EMG Spectrogram, frequency axis, time axis """ SR = get_snr(ppath, name) swin = round(SR)*5 fft_win = round(swin/5) # approximate number of data points per second if (fres == 1.0) or (fres == 1): fft_win = int(fft_win) elif fres == 0.5: fft_win = 2*int(fft_win) else: print("Resolution %f not allowed; please use either 1 or 0.5" % fres) (peeg2, pemg2) = (False, False) # Calculate EEG spectrogram EEG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EEG.mat'))['EEG']) Pxx, f, t = spectral_density(EEG, int(swin), int(fft_win), 1/SR) if os.path.isfile(os.path.join(ppath, name, 'EEG2.mat')): peeg2 = True EEG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EEG2.mat'))['EEG2']) Pxx2, f, t = spectral_density(EEG, int(swin), int(fft_win), 1/SR) #save the stuff to a .mat file spfile = os.path.join(ppath, name, 'sp_' + name + '.mat') if peeg2 == True: so.savemat(spfile, {'SP':Pxx, 'SP2':Pxx2, 'freq':f, 'dt':t[1]-t[0],'t':t}) else: so.savemat(spfile, {'SP':Pxx, 'freq':f, 'dt':t[1]-t[0],'t':t}) # Calculate EMG spectrogram EMG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EMG.mat'))['EMG']) Qxx, f, t = spectral_density(EMG, int(swin), int(fft_win), 1/SR) if os.path.isfile(os.path.join(ppath, name, 'EMG2.mat')): pemg2 = True EMG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EMG2.mat'))['EMG2']) Qxx2, f, t = spectral_density(EMG, int(swin), int(fft_win), 1/SR) # save the stuff to .mat file spfile = os.path.join(ppath, name, 'msp_' + name + '.mat') if pemg2 == True: so.savemat(spfile, {'mSP':Qxx, 'mSP2':Qxx2, 'freq':f, 'dt':t[1]-t[0],'t':t}) else: so.savemat(spfile, {'mSP':Qxx, 'freq':f, 'dt':t[1]-t[0],'t':t}) return Pxx, Qxx, f, t def whiten_spectrogram(ppath, name, fmax=50): """ experimental :param ppath: :param name: :param fmax: :return: """ P = so.loadmat(os.path.join(ppath, name, 'sp_' + name + '.mat'), squeeze_me=True) SPE = P['SP'] freq = P['freq'] ifreq = np.where(freq <= fmax)[0] SPE = SPE[ifreq,:] nfilt = 5 filt = np.ones((nfilt, nfilt)) filt = np.divide(filt, filt.sum()) #SPE = scipy.signal.convolve2d(SPE, filt, boundary='symm', mode='same') m = np.mean(SPE,axis=1) SPE -= np.tile(m, (SPE.shape[1], 1)).T SPE = SPE.T C = np.dot(SPE.T, SPE) [evals, L] = np.linalg.eigh(C) idx = np.argsort(evals) D = np.diag(np.sqrt(evals[idx])) L = L[:,idx] W = np.dot(L, np.dot(np.linalg.inv(D),np.dot(L.T,SPE.T))) nfilt = 2 filt = np.ones((nfilt,nfilt)) filt = np.divide(filt, filt.sum()) W = scipy.signal.convolve2d(W, filt, boundary='symm', mode='same') return W, D, L def normalize_spectrogram(ppath, name, fmax=0, band=[], vm=5, pplot=True, sptype='', filt_dim=[]): """ Normalize EEG spectrogram by deviding each frequency band by its average value. :param ppath, name: base folder, recording name :param fmax: maximum frequency; frequency axis of spectrogram goes from 0 to fmax if fmax=0, use complete frequency axis :param band: list or tuple, define lower and upper range of a frequency band, if pplot=True, plot band, along with spectrogram; if band=[], disregard :param vm: color range for plotting spectrogram :pplot: if True, plot spectrogram along with power band :sptype: if sptype='fine' plot 'special' spectrogram, save under sp_fine_$name.mat; otherwise plot 'normal' spectrogram sp_$name.mat :filt_dim: list or tuple; the two values define the dimensions of box filter used to filter the normalized spectrogram; if filt_dim=[], then no filtering :return SPE, t, freq: normalized spectrogram (np.array), time axis, frequency axis """ if (len(sptype) == 0) or (sptype=='std'): P = so.loadmat(os.path.join(ppath, name, 'sp_' + name + '.mat'), squeeze_me=True) elif sptype == 'fine': P = so.loadmat(os.path.join(ppath, name, 'sp_fine_' + name + '.mat'), squeeze_me=True) SPE = P['SP'] freq = P['freq'] t = P['t'] if fmax > 0: ifreq = np.where(freq <= fmax)[0] else: ifreq = np.arange(0, len(freq)) freq = freq[ifreq] nfilt = 4 filt = np.ones((nfilt,nfilt)) filt = np.divide(filt, filt.sum()) SPE = SPE[ifreq,:] # before #SPE = SPE[ifreq] #W = scipy.signal.convolve2d(SPE, filt, boundary='symm', mode='same') #sp_mean = W.mean(axis=1) sp_mean = SPE.mean(axis=1) SPE = np.divide(SPE, np.tile(sp_mean, (SPE.shape[1], 1)).T) if len(filt_dim) > 0: filt = np.ones(filt_dim) filt = np.divide(filt, filt.sum()) SPE = scipy.signal.convolve2d(SPE, filt, boundary='symm', mode='same') # get high gamma peaks if len(band) > 0: iband = np.where((freq >= band[0]) & (freq <= band[-1]))[0] pow_band = SPE[iband,:].mean(axis=0) thr = pow_band.mean() + pow_band.std() idx = np.where(pow_band > thr)[0] # plot normalized spectrogram, along with band if pplot: plt.ion() plt.figure() if len(band) > 0: med = np.median(SPE.mean(axis=0)) ax1 = plt.subplot(211) plt.pcolormesh(t, freq, SPE, vmin=0, vmax=vm*med, cmap='jet') plt.subplot(212, sharex=ax1) plt.plot(t,SPE[iband,:].mean(axis=0)) plt.plot(t[idx], pow_band[idx], '.') plt.draw() return SPE, t, freq[ifreq] def recursive_spectrogram(ppath, name, sf=0.3, alpha=0.3, pplot=True): """ calculate EEG/EMG spectrogram in a way that can be implemented by a closed-loop system. The spectrogram is temporally filtered using a recursive implementation of a lowpass filter @Parameters: ppath/name - mouse EEG recording sf - smoothing factor along frequency axis alpha - temporal lowpass filter time constant pplot - if pplot==True, plot figure @Return: SE, SM - EEG, EMG spectrogram """ EEG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EEG.mat'))['EEG']) EMG = np.squeeze(so.loadmat(os.path.join(ppath, name, 'EMG.mat'))['EMG']) len_eeg = len(EEG) fdt = 2.5 SR = get_snr(ppath, name) # we calculate the powerspectrum for 5s windows swin = int(np.round(SR) * 5.0) # but we sample new data each 2.5 s swinh = int(swin/2.0) fft_win = int(swin / 5.0) # number of 2.5s long samples spoints = int(np.floor(len_eeg / swinh)) SE = np.zeros((int(fft_win/2+1), spoints)) SM = np.zeros((int(fft_win/2+1), spoints)) print("Starting calculating spectrogram for %s..." % name) for i in range(2, spoints): # we take the last two swinh windows (the new 2.5 s long sample and the one from # the last iteration) x = EEG[(i-2)*swinh:i*swinh] [p, f] = power_spectrum(x.astype('float'), fft_win, 1.0/SR) p = smooth_data(p, sf) # recursive low pass filtering of spectrogram: # the current state is an estimate of the current sample and the previous state SE[:,i] = alpha*p + (1-alpha) * SE[:,i-1] # and the same of EMG x = EMG[(i-2)*swinh:i*swinh] [p, f] = power_spectrum(x.astype('float'), fft_win, 1.0/SR) p = smooth_data(p, sf) SM[:,i] = alpha*p + (1-alpha) * SM[:,i-1] if pplot: # plot EEG spectrogram t = np.arange(0, SM.shape[1])*fdt plt.figure() ax1 = plt.subplot(211) im = np.where((f>=0) & (f<=30))[0] med = np.median(SE.max(axis=0)) ax1.imshow(np.flipud(SE[im,:]), vmin=0, vmax=med*2) plt.xticks(()) ix = list(range(0, 30, 10)) fi = f[im][::-1] plt.yticks(ix, list(map(int, fi[ix]))) box_off(ax1) plt.axis('tight') plt.ylabel('Freq (Hz)') # plot EMG amplitude ax2 = plt.subplot(212) im = np.where((f>=10) & (f<100))[0] df = np.mean(np.diff(f)) # amplitude is the square root of the integral ax2.plot(t, np.sqrt(SM[im,:].sum(axis=0)*df)/1000.0) plt.xlim((0, t[-1])) plt.ylabel('EMG Ampl (mV)') plt.xlabel('Time (s)') box_off(ax2) plt.show(block=False) return SE, SM, f def recursive_sleepstate_rem(ppath, recordings, sf=0.3, alpha=0.3, past_mu=0.2, std_thdelta = 1.5, past_len=120, sdt=2.5, psave=False, xemg=False): """ predict a REM period only based on EEG/EMG history; the same algorithm is also used for closed-loop REM sleep manipulation. The algorithm uses for REM sleep detection a threshold on delta power, EMG power, and theta/delta power. For theta/delta I use two thresholds: A hard (larger) threshold and a soft (lower) threshold. Initially, theta/delta has to cross the hard threshold to initiate a REM period. Then, as long as, theta/delta is above the soft threshold (and EMG power stays low) REM sleep continues. @Parameters: ppath base folder with recordings recordings list of recordings sf smoothing factor for each powerspectrum alpha smoothing factor along time dimension past_mu percentage (0 .. 1) of brain states that are allowed to have EMG power larger than threshold during the last $past_len seconds past_len window to calculate $past_mu std_thdelta the hard theta/delta threshold is given by, mean(theta/delta) + $std_thdelta * std(theta/delta) sdt time bin for brain sttate, typically 2.5s psave if True, save threshold parameters to file. """ idf = re.split('_', recordings[0])[0] # 02/05/2020 changed from int to float: past_len = float(np.round(past_len/sdt)) # calculate spectrogram (SE, SM) = ([],[]) for rec in recordings: A,B, freq = recursive_spectrogram(ppath, rec, sf=sf, alpha=alpha) SE.append(A) SM.append(B) # fuse lists SE and SM SE = np.squeeze(reduce(lambda x,y: np.concatenate((x,y)), SE)) if not xemg: SM = np.squeeze(reduce(lambda x,y: np.concatenate((x,y)), SM)) else: SM = SE # EEG, EMG bands ntbins = SE.shape[1] r_delta = [0.5, 4] r_theta = [5,12] # EMG band r_mu = [300, 500] i_delta = np.where((freq >= r_delta[0]) & (freq <= r_delta[1]))[0] i_theta = np.where((freq >= r_theta[0]) & (freq <= r_theta[1]))[0] i_mu = np.where((freq >= r_mu[0]) & (freq <= r_mu[1]))[0] pow_delta = np.sum(SE[i_delta,:], axis=0) pow_theta = np.sum(SE[i_theta,:], axis=0) pow_mu = np.sum(SM[i_mu,:], axis=0) # theta/delta th_delta = np.divide(pow_theta, pow_delta) thr_th_delta1 = np.nanmean(th_delta) + std_thdelta*np.nanstd(th_delta) thr_th_delta2 = np.nanmean(th_delta) + 0.0*np.nanstd(th_delta) thr_delta = pow_delta.mean() thr_mu = pow_mu.mean() + 0.5*

np.nanstd(pow_mu)

numpy.nanstd

''' Authors: <NAME>, <NAME>, <NAME> Email ID: <EMAIL>, <EMAIL>, <EMAIL> ''' import keras import tensorflow as tf from keras.models import Sequential from keras.models import Model #from tensorflow.keras import layers #from tensorflow.keras import optimizers from keras.layers import Dense from keras.layers import LSTM from keras.layers import Activation from sklearn.neighbors import KernelDensity from keras.layers import Masking from keras.layers import Input from keras.layers import Concatenate from keras import optimizers from scipy.stats import spearmanr from scipy import stats from statistics import mean import copy import mlflow import seaborn as sns import numpy as np from matplotlib import pyplot as plt from numpy import genfromtxt from sklearn.utils import shuffle import csv import random import math import sklearn import mlflow import mlflow.keras from sklearn.metrics import mean_squared_error from matplotlib import pyplot as plt import os import glob import multiprocessing as mp from keras.callbacks import EarlyStopping from keras.callbacks import ModelCheckpoint import matplotlib.cm import matplotlib import argparse from sklearn.neighbors import KNeighborsRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.tree import DecisionTreeRegressor from sklearn.svm import SVR from sklearn.kernel_ridge import KernelRidge from xgboost import XGBRegressor from sklearn.neighbors import RadiusNeighborsRegressor from xgboost import XGBRFRegressor os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' numLatency = 118 embeddingsFile = "onnxEmbeddings.csv" lat = [] maxVal = 0 matplotlib.use('Agg') def parse_latency(file): global lat data = np.genfromtxt(file, delimiter=',') latency = np.mean(data, axis=1) latency = latency[:numLatency] lat.append(latency) #latency = latency/np.amax(latency) return latency def parse_features(): Features = [] maxLayer = 0 maxFlops = 0 maxChannel = 0 maxDim = 224 maxKernel = 7 maxPadding = 3 with open(embeddingsFile, newline='') as f: reader = csv.reader(f) data = list(reader) for i in range(len(data)): temp = [data[i][j * 13:(j + 1) * 13] for j in range((len(data[i]) + 12) // 13 )] maxLayer = max(maxLayer, len(temp)) for j in range(len(temp)): maxFlops=max(maxFlops, float(temp[j][12])) maxChannel = max(maxChannel, int(temp[j][7])) maxChannel = max(maxChannel, int(temp[j][8])) Features.append(temp) numpyFeatures = np.ones((len(Features), maxLayer, 13)) numpyFeatures = numpyFeatures*-1 for i in range(len(Features)): temp = Features[i] for j in range(len(temp)): for k in range(len(temp[j])): numpyFeatures[i][j][k] = temp[j][k] if k == 5 or k == 6: numpyFeatures[i][j][k] = numpyFeatures[i][j][k]/maxDim elif k == 7 or k == 8: numpyFeatures[i][j][k] = numpyFeatures[i][j][k]/maxChannel elif k == 9: numpyFeatures[i][j][k] = numpyFeatures[i][j][k]/maxKernel elif k == 12: numpyFeatures[i][j][k] = numpyFeatures[i][j][k]/maxFlops return numpyFeatures, maxLayer def learn_xgb_model(hardware, maxLayer, lat_mean, features, featuresShape, splitPercentage=0.99, shuffleFeatures=True): numSample = len(lat_mean) features = features[:numSample] if shuffleFeatures == True: features, lat_mean = shuffle(features,lat_mean) trainf = features[:int(splitPercentage*len(features))] trainy = lat_mean[:int(splitPercentage*len(features))] testf = features[int(splitPercentage*len(features)):] testy = lat_mean[int(splitPercentage*len(features)):] print("================= Dataset Stage ==============") print(trainf.shape, trainy.shape, testf.shape, testy.shape) trainf = np.reshape(trainf, (trainf.shape[0], trainf.shape[1]*trainf.shape[2])) testf = np.reshape(testf, (testf.shape[0], testf.shape[1]*testf.shape[2])) model = XGBRegressor() model.fit(trainf, trainy) trainPredict = model.predict(trainf) testPredict = model.predict(testf) trainScore = math.sqrt(mean_squared_error(trainy, trainPredict)) testScore = math.sqrt(mean_squared_error(testy, testPredict)) ### Train Model characteristics r2_score = sklearn.metrics.r2_score(trainy, trainPredict) s_coefficient, pvalue = spearmanr(trainy, trainPredict) writeToFile('Train Score: %f RMSE' % (trainScore)) writeToFile("The R^2 Value for %s: %f"%(hardware, r2_score)) writeToFile("The Spearnman Coefficient and p-value for %s: %f and %f"%(hardware, s_coefficient, pvalue)) plt.figure() plt.xlabel("Actual Latency (in ms)") plt.ylabel("Predicted Latency (in ms)") sns.scatterplot(trainy, trainPredict) plt.savefig(args.name+'/plots/'+hardware+'_'+args.learning_type+'_'+str(splitPercentage)+'_train.png') r2_score = sklearn.metrics.r2_score(testy, testPredict) s_coefficient, pvalue = spearmanr(testy, testPredict) writeToFile('Test Score: %f RMSE' % (testScore)) writeToFile("The R^2 Value for %s: %f"%(hardware, r2_score)) writeToFile("The Spearnman Coefficient and p-value for %s: %f and %f"%(hardware, s_coefficient, pvalue)) plt.figure() plt.xlabel("Actual Latency (in ms)") plt.ylabel("Predicted Latency (in ms)") sns.scatterplot(testy, testPredict) plt.savefig(args.name+'/plots/'+hardware+"_"+args.learning_type+'_'+str(1-splitPercentage)+'_test.png') return model def learn_xgb_model_collab(hardware, maxLayer, lat_mean, features, featuresShape, splitPercentage=0.99, shuffleFeatures=True): print('Learning' + hardware) numSample = len(lat_mean) features = features[:numSample] if shuffleFeatures == True: features, lat_mean = shuffle(features,lat_mean) testf = features testy = lat_mean testf = np.reshape(testf, (testf.shape[0], testf.shape[1]*testf.shape[2])) results = [] index = [] for i in range(10, numSample): trainf = features[:i] trainy = lat_mean[:i] # print("================= Dataset Stage ==============") # print(trainf.shape, trainy.shape, testf.shape, testy.shape) trainf = np.reshape(trainf, (trainf.shape[0], trainf.shape[1]*trainf.shape[2])) model = XGBRegressor() model.fit(trainf, trainy) testPredict = model.predict(testf) testScore = math.sqrt(mean_squared_error(testy, testPredict)) r2_score = sklearn.metrics.r2_score(testy, testPredict) s_coefficient, pvalue = spearmanr(testy, testPredict) results.append(r2_score) index.append(i) matplotlib.rcParams['figure.dpi'] = 500 plt.figure() plt.xlabel("Number of Datapoints") plt.ylabel("Average R^2") sns.lineplot(index, results) plt.savefig(args.name+'/plots/'+hardware+'_indiLearn.png') f = open(args.name+'/meta/plotdata.txt', a) s1 = ','.join(map(str, index)) s2 = ','.join(map(str, results)) f.write(hardware+'\n'+s1+'\n'+s2+'\n') f.close() def learn_lstm_model(hardware, maxLayer, lat_mean, features, featuresShape): numSample = len(lat_mean) features = features[:numSample] features, lat_mean = shuffle(features,lat_mean) trainf = features[:int(0.99*len(features))] trainy = lat_mean[:int(0.99*len(features))] #testf = features[:int(1.0*len(features))] #testy = lat_mean[:int(1.0*len(features))] testf = features[int(0.99*len(features)):] testy = lat_mean[int(0.99*len(features)):] print("================= Dataset Stage ==============") print(trainf.shape, trainy.shape, testf.shape, testy.shape) #mlflow.keras.autolog() #Create an LSTM model model=Sequential() model.add(Masking(mask_value=-1,input_shape=(maxLayer, featuresShape))) model.add(LSTM(20, activation='relu')) model.add(Dense(1, name = 'fc')) opt = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False) #initial_learning_rate = 0.01 # lr_schedule = optimizers.schedules.ExponentialDecay(initial_learning_rate, #opt = optimizers.SGD(learning_rate = initial_learning_rate) model.compile(loss='mean_squared_error', optimizer=opt, metrics=[keras.metrics.MeanAbsolutePercentageError()]) model.summary() #filepath="checkpoint-{loss:.5f}-{val_loss:.5f}-{val_mean_absolute_percentage_error}.hdf5" filepath=args.name+'/models/model.hdf5' #checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')#montor can be val_loss or loss checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')#montor can be val_loss or loss es = EarlyStopping(monitor='loss', mode='min', verbose=1, patience=50) val = model.fit(trainf, trainy, epochs=250, batch_size=512, verbose=1, callbacks=[es, checkpoint]) #val = model.fit(trainf, trainy, epochs=250, batch_size=512, verbose=1, callbacks=[es, checkpoint], validation_data=(testf, testy)) model.load_weights(filepath) trainPredict = model.predict(trainf) testPredict = model.predict(testf) trainScore = math.sqrt(mean_squared_error(trainy, trainPredict)) writeToFile('Train Score: %f RMSE' % (trainScore)) testScore = math.sqrt(mean_squared_error(testy, testPredict)) ### Train Model characteristics r2_score = sklearn.metrics.r2_score(trainy, trainPredict) s_coefficient, pvalue = spearmanr(trainy, trainPredict) writeToFile('Train Score: %f RMSE' % (trainScore)) writeToFile("The R^2 Value for %s: %f"%(hardware, r2_score)) writeToFile("The Spearnman Coefficient and p-value for %s: %f and %f"%(hardware, s_coefficient, pvalue)) plt.figure() plt.xlabel("Actual Latency (in ms)") plt.ylabel("Predicted Latency (in ms)") sns.scatterplot(trainy, trainPredict[:,0]) #plt.title(hardware+' R2: '+str(r2_score)+' SpearVal: '+str(s_coefficient)) plt.savefig(args.name+'/plots/'+hardware+"_"+args.learning_type+'_train.png') r2_score = sklearn.metrics.r2_score(testy, testPredict) s_coefficient, pvalue = spearmanr(testy, testPredict) writeToFile('Test Score: %f RMSE' % (testScore)) writeToFile("The R^2 Value for %s: %f"%(hardware, r2_score)) writeToFile("The Spearnman Coefficient and p-value for %s: %f and %f"%(hardware, s_coefficient, pvalue)) plt.figure() plt.xlabel("Actual Latency (in ms)") plt.ylabel("Predicted Latency (in ms)") sns.scatterplot(testy, testPredict[:,0]) #plt.title(hardware+' R2: '+str(r2_score)+' SpearVal: '+str(s_coefficient)) plt.savefig(args.name+'/plots/'+hardware+"_"+args.learning_type+'_test.png') ### Adding Other Regressors extractor = Model(outputs=model.get_layer('fc').input, inputs=model.input) extractor.summary() knn = KNeighborsRegressor() trainPredict = extractor.predict(trainf) testPredict = extractor.predict(testf) randForest = RandomForestRegressor() decisionTree = DecisionTreeRegressor() svr = SVR() kernelrdidge = KernelRidge() xgb = XGBRegressor() xgbrf = XGBRFRegressor() modellist = [ ('knn', knn), ('randomForest', randForest), ('dTree', decisionTree), ('svr', svr), ('kerenlrdige', kernelrdidge), ('xgb', xgb), ('xgbrf', xgbrf) ] for name, model_lowB in modellist: model_lowB.fit(trainPredict, trainy) modeltestPred = model_lowB.predict(testPredict) testScore = math.sqrt(mean_squared_error(testy, modeltestPred)) r2_score = sklearn.metrics.r2_score(testy, modeltestPred) s_coefficient, pvalue = spearmanr(testy, modeltestPred) writeToFile('Test Score with %s : %f RMSE' % (name, testScore)) writeToFile("The R^2 Value with %s for %s: %f"%(hardware, name, r2_score)) writeToFile("The Spearnman Coefficient and p-value for %s with %s : %f and %f"%(hardware, name, s_coefficient, pvalue)) plt.figure() plt.xlabel("Actual Latency (in ms)") plt.ylabel("Predicted Latency (in ms)") sns.scatterplot(testy, modeltestPred) #plt.title(name + hardware+' R2: '+str(r2_score)+' SpearVal: '+str(s_coefficient)) plt.savefig(args.name+'/plots/'+hardware+args.learning_type+'_'+name+'.png') return (model, modellist, extractor) ''' This function takes in the dictionary of hardware_names to its maxLayer, latency and features map net_dict[key][2] - refers to the network features for a hardware and net_dict[key][1] - refers to the latency for that hardware 1. First determine the mean and std of the latencies for each hardware in the dictionary 2. Sample from the distribution - i.e. from Mu-8*sigma to Mu+2*sigma, at each parts of the distribution, find all indices that intersect in all the hardwares considered here. For ex., if network no. 2374 falls between mu-1*sigma and mu for all the hardware devices in the dictionary, then add 2374 to the representation set for all the hardware 3. Find maxSamples such networks that become the golden representation of the hardware 4. Return the list of lists of maxSamples network representation for all hardwares and also the indices of the representation networks 5. The indices will be used by any hardware not on the list to make and append it's representation TODO: Not using max samples for now - change ''' def sample_hwrepresentation(net_dict, maxSamples): mean_lat = [] sd_lat = [] final_indices = [] #Determining the Mean and Standard Deviation of Latencies for key in net_dict: net_dict[key][2] = net_dict[key][2][:numLatency,:,:] #Not required actually.. Simply doing net_dict[key][1] = net_dict[key][1][:numLatency] print(np.mean(net_dict[key][1]), np.std(net_dict[key][1])) mean_lat.append(np.mean(net_dict[key][1])) sd_lat.append(np.std(net_dict[key][1])) for i in range(-2,8): #This range might not be enough -- the range should be more generic when hardware increases index_where = [] index = 0 for key in net_dict: index_where.append(np.where(np.logical_and(net_dict[key][1] > mean_lat[index]+i*sd_lat[index], net_dict[key][1] <= mean_lat[index]+(i+1)*sd_lat[index]))) index += 1 for j in range(len(index_where)): index_where[0] = np.intersect1d(index_where[0], index_where[j]) final_intersection = index_where[0] if len(final_intersection) >= 4: loop_index = 4 else: loop_index = len(final_intersection) hw_features_cncat = [] for j in range(loop_index): final_indices.append(final_intersection[j]) print("The final indices size is %f"%(len(final_indices))) for key in net_dict: hw_features_per_device = [] for j in range(len(final_indices)): hw_features_per_device.append(net_dict[key][1][final_indices[j]]) net_dict[key][1] = np.delete(net_dict[key][1], final_indices, axis=0) net_dict[key][2] = np.delete(net_dict[key][2], final_indices, axis=0) hw_features_cncat.append(hw_features_per_device) print(len(final_indices), net_dict[key][2].shape) return final_indices, hw_features_cncat def random_indices(maxSamples): rand_indices = [] for i in range(maxSamples): rand_indices.append(random.randint(0,numLatency-1)) return rand_indices ''' Function which computes total MACs of each network and samples maxSamples indices from it based on FLOPS. ''' def flopsBasedIndices(maxSamples): with open('../DiverseRandNetworkGenerator/Embeddings.csv') as f: reader = csv.reader(f) data = list(reader) totalFLOPSList = np.zeros(len(data)) for i in range(len(data)): temp = [data[i][j * 13:(j + 1) * 13] for j in range((len(data[i]) + 12) // 13 )] for j in range(len(temp)): totalFLOPSList[i]+=int(temp[j][12]) mean = np.mean(totalFLOPSList) sd = np.std(totalFLOPSList) def random_sampling(net_dict, rand_indices, maxSamples): for key in net_dict: net_dict[key][2] = net_dict[key][2][:numLatency,:,:] net_dict[key][1] = net_dict[key][1][:numLatency] hw_features_cncat = [] #rand_indices = [] #final_indices = [] #for i in range(maxSamples): # rand_indices.append(random.randint(0,5000)) for key in net_dict: hw_features_per_device = [] for j in range(maxSamples): hw_features_per_device.append(net_dict[key][1][rand_indices[j]]) hw_features_cncat.append(hw_features_per_device) #If this is not done separately, the code will break for key in net_dict: net_dict[key][1] =

np.delete(net_dict[key][1], rand_indices, axis=0)

numpy.delete

import numpy as np class ProblemTest: ''' Parametes object. Effectively represents each problem that we want to submit to the solver ''' def __init__(self): self.N = 100 self.M = 1000 self.K_pol = 3 self.K_cus = 0 self.U = 200 self.T = 1 self.u_min, self.u_max = -10, 10 self.initial_condition = 0 self.sigma = 2 self.transition_function_deterministic = lambda n, x, u: x + u * self.dt self.transition_function_stochastic = lambda n, x, u: np.sqrt(self.dt) * self.sigma * np.random.randn(self.M) self.terminal_condition_fnc = lambda x: x ** 2 self.running_reward = lambda x, u: x ** 2 + u ** 2 self.transition_function = lambda n, x, u: self.transition_function_deterministic(n, x, u) + \ self.transition_function_stochastic(n, x, u) self.generate_training_points = lambda x: np.random.randn(self.M) self.dt = self.T / self.N self.K = self.K_pol + self.K_cus self.custom_basis =

np.array([])

numpy.array

import os import sys sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../') import argparse import matplotlib.pyplot as plt import numpy as np from datasetapi.voc_dataset.voc_data_processing import generate_wh_xyminmax_list from datasetapi.coco_dataset.coco_data_processing import get_coco_wh_xyminmax, generate_calibset import random random.seed(2021) # ids_or_names = ["abn1", # "abn2", # "abn3", # "abn4", # "abn5", # "abn6", # "abn7", # "abn8", # "abn9", # "abn10"] def plot_wh(wh_list, calib_wh_list, ids_or_names=None, plot_idx=None): """ :param wh_list: w & h list of training set :param calib_wh_list: w & h list of calibration set :param ids_or_names: category or id list :param plot_idx: which category or id will be plot :return: """ r = lambda: random.randint(0,255) colors=[] for i in range(len(ids_or_names) + 1): color = ('#%02X%02X%02X' % (r(),r(),r())) colors.append(color) cls_list = [] plot_w_list = [] plot_h_list = [] calib_cls_list = [] calib_plot_w_list = [] calib_plot_h_list = [] for i in range(len(ids_or_names)): cls_list.append([]) plot_w_list.append([]) plot_h_list.append([]) calib_cls_list.append([]) calib_plot_w_list.append([]) calib_plot_h_list.append([]) for wh in wh_list: cls_list[ids_or_names.index(wh[0])].append(wh[0]) # cls_name_or_id = wh[0] plot_w_list[ids_or_names.index(wh[0])].append(wh[1]) # w = wh[1] plot_h_list[ids_or_names.index(wh[0])].append(wh[2]) # h = wh[2] plot_list = [cls_list, plot_w_list, plot_h_list] for calib_wh in calib_wh_list: # calib_cls_name = calib_wh[0] calib_cls_list[ids_or_names.index(calib_wh[0])].append(calib_wh[0]) calib_plot_w_list[ids_or_names.index(calib_wh[0])].append(calib_wh[1]) # w calib_plot_h_list[ids_or_names.index(calib_wh[0])].append(calib_wh[2]) # h calib_plot_list = [calib_cls_list, calib_plot_w_list, calib_plot_h_list] area = np.pi * 2**2 if plot_idx is None: for i in range(len(plot_list[0])): cls = plot_list[0][i][0] x = plot_list[1][i] # w y = plot_list[2][i] # h plt.scatter(x, y, s=area, c=colors[ids_or_names.index(cls)], alpha=0.1, label=cls) calib_cls = calib_plot_list[0][i][0] calib_x = calib_plot_list[1][i] # w calib_y = calib_plot_list[2][i] # h plt.scatter(calib_x, calib_y, s=area, marker='x', c=colors[ids_or_names.index(calib_cls)], alpha=0.5) else: cls = plot_list[0][plot_idx][0] x = plot_list[1][plot_idx] # w y = plot_list[2][plot_idx] # h calib_cls = calib_plot_list[0][plot_idx][0] calib_x = calib_plot_list[1][plot_idx] # w calib_y = calib_plot_list[2][plot_idx] # h if type(cls) is str: plt.scatter(x, y, s=area, c=colors[ids_or_names.index(cls)], alpha=0.1, label='trainset_cls:'+cls) plt.scatter(calib_x, calib_y, marker='x', c=colors[ids_or_names.index(calib_cls) + 1], alpha=0.3, label='calib_cls:'+calib_cls) elif type(cls) is int: plt.scatter(x, y, s=area, c=colors[ids_or_names.index(cls)], alpha=0.1, label='trainset_cls:'+str(cls)) plt.scatter(calib_x, calib_y, s=area, marker='x', c=colors[ids_or_names.index(calib_cls) + 1], alpha=0.3, label='calib_cls:'+str(calib_cls)) line_color = colors[ids_or_names.index(calib_cls) + 1] plt.plot([np.min(calib_x),np.max(calib_x)], [np.min(calib_y),

np.min(calib_y)

numpy.min

import numpy as np from scipy import interpolate from sklearn.model_selection import KFold def evaluate(distances, labels, nrof_folds=10): thresholds = np.arange(0, 4, 0.01) tpr, fpr, accuracy, best_thresholds = calculate_roc(thresholds, distances, labels, nrof_folds=nrof_folds) thresholds = np.arange(0, 4, 0.001) val, val_std, far = calculate_val(thresholds, distances, labels, 1e-3, nrof_folds=nrof_folds) return tpr, fpr, accuracy, val, val_std, far, best_thresholds def calculate_roc(thresholds, distances, labels, nrof_folds=10): nrof_pairs = min(len(labels), len(distances)) nrof_thresholds = len(thresholds) k_fold = KFold(n_splits=nrof_folds, shuffle=False) tprs = np.zeros((nrof_folds,nrof_thresholds)) fprs = np.zeros((nrof_folds,nrof_thresholds)) accuracy = np.zeros((nrof_folds)) indices = np.arange(nrof_pairs) for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): # Find the best threshold for the fold acc_train = np.zeros((nrof_thresholds)) for threshold_idx, threshold in enumerate(thresholds): _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, distances[train_set], labels[train_set]) best_threshold_index = np.argmax(acc_train) for threshold_idx, threshold in enumerate(thresholds): tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, distances[test_set], labels[test_set]) _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], distances[test_set], labels[test_set]) tpr = np.mean(tprs,0) fpr = np.mean(fprs,0) return tpr, fpr, accuracy, thresholds[best_threshold_index] def calculate_accuracy(threshold, dist, actual_issame): predict_issame = np.less(dist, threshold) tp = np.sum(np.logical_and(predict_issame, actual_issame)) fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame))) fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame)) tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn) fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn) acc = float(tp+tn)/dist.size return tpr, fpr, acc def calculate_val(thresholds, distances, labels, far_target=1e-3, nrof_folds=10): nrof_pairs = min(len(labels), len(distances)) nrof_thresholds = len(thresholds) k_fold = KFold(n_splits=nrof_folds, shuffle=False) val = np.zeros(nrof_folds) far = np.zeros(nrof_folds) indices = np.arange(nrof_pairs) for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): far_train = np.zeros(nrof_thresholds) for threshold_idx, threshold in enumerate(thresholds): _, far_train[threshold_idx] = calculate_val_far(threshold, distances[train_set], labels[train_set]) if np.max(far_train)>=far_target: f = interpolate.interp1d(far_train, thresholds, kind='slinear') threshold = f(far_target) else: threshold = 0.0 val[fold_idx], far[fold_idx] = calculate_val_far(threshold, distances[test_set], labels[test_set]) val_mean =

np.mean(val)

numpy.mean

#!/usr/bin/env python # Copyright 2021 # author: <NAME> <<EMAIL>> # # 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 numpy as np from scipy.stats import ttest_ind import netCDF4 as nc import pickle import os from PIL import Image as PIL_Image import sys import shutil import glob import datetime import time import calendar from numpy import genfromtxt from scipy.optimize import curve_fit from scipy.cluster.vq import kmeans,vq from scipy.interpolate import interpn, interp1d from math import e as e_constant import math import matplotlib.dates as mdates from mpl_toolkits.mplot3d import Axes3D import matplotlib.cm as cm from matplotlib.collections import LineCollection from matplotlib.ticker import (MultipleLocator, NullFormatter, ScalarFormatter) from matplotlib.colors import ListedColormap, BoundaryNorm import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation import matplotlib import warnings warnings.filterwarnings("ignore") plt.style.use('classic') # font size # font_size = 14 # matplotlib.rc('font', **{'family': 'serif', 'serif': ['Arial'], 'size': font_size}) # matplotlib.rc('font', weight='bold') p_progress_writing = False SMALL_SIZE = 8 MEDIUM_SIZE = 10 BIGGER_SIZE = 12 plt.rc('font', size=SMALL_SIZE) # controls default text sizes plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title time_format = '%d-%m-%Y_%H:%M' time_format_khan = '%Y%m%d.0%H' time_format_mod = '%Y-%m-%d_%H:%M:%S' time_format_twolines = '%H:%M\n%d-%m-%Y' time_format_twolines_noYear_noMin_intMonth = '%H\n%d-%m' time_format_twolines_noYear = '%H:%M\n%d-%b' time_format_twolines_noYear_noMin = '%H\n%d-%b' time_format_date = '%Y-%m-%d' time_format_time = '%H:%M:%S' time_format_parsivel = '%Y%m%d%H%M' time_format_parsivel_seconds = '%Y%m%d%H%M%S' time_str_formats = [ time_format, time_format_mod, time_format_twolines, time_format_twolines_noYear, time_format_date, time_format_time, time_format_parsivel ] default_cm = cm.jet cm_vir = cm.viridis listed_cm_colors_list = ['silver', 'red', 'green', 'yellow', 'blue', 'black'] listed_cm = ListedColormap(listed_cm_colors_list, 'indexed') colorbar_tick_labels_list_cloud_phase = ['Clear', 'Water', 'SLW', 'Mixed', 'Ice', 'Unknown'] listed_cm_colors_list_cloud_phase = ['white', 'red', 'green', 'yellow', 'blue', 'purple'] listed_cm_cloud_phase = ListedColormap(listed_cm_colors_list_cloud_phase, 'indexed') avogadros_ = 6.022140857E+23 # molecules/mol gas_const = 83144.598 # cm3 mbar k-1 mol-1 gas_const_2 = 8.3144621 # J mol-1 K-1 gas_const_water = 461 # J kg-1 K-1 gas_const_dry = 287 # J kg-1 K-1 boltzmann_ = gas_const / avogadros_ # cm3 mbar / k molecules gravity_ = 9.80665 # m/s poisson_ = 2/7 # for dry air (k) latent_heat_v = 2.501E+6 # J/kg latent_heat_f = 3.337E+5 # J/kg latent_heat_s = 2.834E+6 # J/kg heat_capacity__Cp = 1005.7 # J kg-1 K-1 dry air heat_capacity__Cv = 719 # J kg-1 K-1 water vapor Rs_da = 287.05 # Specific gas const for dry air, J kg^{-1} K^{-1} Rs_v = 461.51 # Specific gas const for water vapour, J kg^{-1} K^{-1} Cp_da = 1004.6 # Specific heat at constant pressure for dry air Cv_da = 719. # Specific heat at constant volume for dry air Cp_v = 1870. # Specific heat at constant pressure for water vapour Cv_v = 1410. # Specific heat at constant volume for water vapour Cp_lw = 4218 # Specific heat at constant pressure for liquid water Epsilon = 0.622 # Epsilon=Rs_da/Rs_v; The ratio of the gas constants degCtoK = 273.15 # Temperature offset between K and C (deg C) rho_w = 1000. # Liquid Water density kg m^{-3} grav = 9.80665 # Gravity, m s^{-2} Lv = 2.5e6 # Latent Heat of vaporisation boltzmann = 5.67e-8 # Stefan-Boltzmann constant mv = 18.0153e-3 # Mean molar mass of water vapor(kg/mol) m_a = 28.9644e-3 # Mean molar mass of air(kg/mol) Rstar_a = 8.31432 # Universal gas constant for air (N m /(mol K)) path_output = '/g/data/k10/la6753/' # Misc class Object_create(object): pass def list_files_recursive(path_, filter_str=None): # create list of raw spectra files file_list = [] # r=root, d=directories, f = files if filter_str is None: for r, d, f in os.walk(path_): for file in f: file_list.append(os.path.join(r, file)) else: for r, d, f in os.walk(path_): for file in f: if filter_str in file: file_list.append(os.path.join(r, file)) return file_list def list_files(path_, filter_str='*'): file_list = sorted(glob.glob(str(path_ + filter_str))) return file_list def coincidence(arr_1,arr_2): # only coincidences check_ = arr_1 * arr_2 check_[check_ == check_] = 1 arr_1_checked = arr_1 * check_ arr_2_checked = arr_2 * check_ return arr_1_checked[~np.isnan(arr_1_checked)], arr_2_checked[~np.isnan(arr_2_checked)] def array_2d_fill_gaps_by_interpolation_linear(array_): rows_ = array_.shape[0] cols_ = array_.shape[1] output_array_X = np.zeros((rows_, cols_), dtype=float) output_array_Y = np.zeros((rows_, cols_), dtype=float) row_sum = np.sum(array_, axis=1) col_index = np.arange(array_.shape[1]) col_sum = np.sum(array_, axis=0) row_index = np.arange(array_.shape[0]) for r_ in range(array_.shape[0]): if row_sum[r_] != row_sum[r_]: # get X direction interpolation coin_out = coincidence(col_index, array_[r_, :]) output_array_X[r_, :][np.isnan(array_[r_, :])] = np.interp( col_index[np.isnan(array_[r_, :])], coin_out[0], coin_out[1]) for c_ in range(array_.shape[1]): if col_sum[c_] != col_sum[c_]: # get Y direction interpolation coin_out = coincidence(row_index, array_[:, c_]) output_array_Y[:, c_][np.isnan(array_[:, c_])] = np.interp( row_index[np.isnan(array_[:, c_])], coin_out[0], coin_out[1]) output_array = np.array(array_) output_array[np.isnan(array_)] = 0 return output_array + ((output_array_X + output_array_Y)/2) def array_2d_fill_gaps_by_interpolation_cubic(array_): rows_ = array_.shape[0] cols_ = array_.shape[1] output_array_X = np.zeros((rows_, cols_), dtype=float) output_array_Y = np.zeros((rows_, cols_), dtype=float) row_sum = np.sum(array_, axis=1) col_index = np.arange(array_.shape[1]) col_sum = np.sum(array_, axis=0) row_index = np.arange(array_.shape[0]) for r_ in range(array_.shape[0]): if row_sum[r_] != row_sum[r_]: # get X direction interpolation coin_out = coincidence(col_index, array_[r_, :]) interp_function = interp1d(coin_out[0], coin_out[1], kind='cubic') output_array_X[r_, :][np.isnan(array_[r_, :])] = interp_function(col_index[np.isnan(array_[r_, :])]) for c_ in range(array_.shape[1]): if col_sum[c_] != col_sum[c_]: # get Y direction interpolation coin_out = coincidence(row_index, array_[:, c_]) interp_function = interp1d(coin_out[0], coin_out[1], kind='cubic') output_array_Y[:, c_][np.isnan(array_[:, c_])] = interp_function(row_index[np.isnan(array_[:, c_])]) output_array = np.array(array_) output_array[np.isnan(array_)] = 0 return output_array + ((output_array_X + output_array_Y)/2) def combine_2_time_series(time_1_reference, data_1, time_2, data_2, forced_time_step=None, forced_start_time=None, forced_stop_time=None, cumulative_var_1=False, cumulative_var_2=False): """ takes two data sets with respective time series, and outputs the coincident stamps from both data sets It does this by using mean_discrete() for both sets with the same start stamp and averaging time, the averaging time is the forced_time_step :param time_1_reference: 1D array, same units as time_2, this series will define the returned time step reference :param data_1: can be 1D or 2D array, first dimention most be same as time_1 :param time_2: 1D array, same units as time_1 :param data_2: can be 1D or 2D array, first dimention most be same as time_2 :param window_: optional, if 0 (default) the values at time_1 and time_2 most match exactly, else, the match can be +- window_ :param forced_time_step: if not none, the median of the differential of the time_1_reference will be used :param forced_start_time: if not none, the returned series will start at this time stamp :param forced_stop_time: if not none, the returned series will stop at this time stamp :param cumulative_var_1: True is you want the variable to be accumulated instead of means, only of 1D data :param cumulative_var_2: True is you want the variable to be accumulated instead of means, only of 1D data :return: Index_averaged_1: 1D array, smallest coincident time, without time stamp gaps :return: Values_mean_1: same shape as data_1 both according to Index_averaged_1 times :return: Values_mean_2: same shape as data_2 both according to Index_averaged_1 times """ # define forced_time_step if forced_time_step is None: forced_time_step = np.median(np.diff(time_1_reference)) # find time period if forced_start_time is None: first_time_stamp = max(np.nanmin(time_1_reference), np.nanmin(time_2)) else: first_time_stamp = forced_start_time if forced_stop_time is None: last_time_stamp = min(np.nanmax(time_1_reference), np.nanmax(time_2)) else: last_time_stamp = forced_stop_time # do the averaging print('starting averaging of data 1') if cumulative_var_1: Index_averaged_1, Values_mean_1 = mean_discrete(time_1_reference, data_1, forced_time_step, first_time_stamp, last_index=last_time_stamp, cumulative_parameter_indx=0) else: Index_averaged_1, Values_mean_1 = mean_discrete(time_1_reference, data_1, forced_time_step, first_time_stamp, last_index=last_time_stamp) print('starting averaging of data 2') if cumulative_var_2: Index_averaged_2, Values_mean_2 = mean_discrete(time_2, data_2, forced_time_step, first_time_stamp, last_index=last_time_stamp, cumulative_parameter_indx=0) else: Index_averaged_2, Values_mean_2 = mean_discrete(time_2, data_2, forced_time_step, first_time_stamp, last_index=last_time_stamp) # check that averaged indexes are the same if np.nansum(np.abs(Index_averaged_1 - Index_averaged_2)) != 0: print('error during averaging of series, times do no match ????') return None, None, None # return the combined, trimmed data return Index_averaged_1, Values_mean_1, Values_mean_2 def split_str_chunks(s, n): """Produce `n`-character chunks from `s`.""" out_list = [] for start in range(0, len(s), n): out_list.append(s[start:start+n]) return out_list def coincidence_multi(array_list): # only coincidences parameters_list = array_list check_ = parameters_list[0] for param_ in parameters_list[1:]: check_ = check_ * param_ check_[check_ == check_] = 1 new_arr_list = [] for param_ in parameters_list: new_arr_list.append(param_ * check_) check_ = check_ * param_ # delete empty rows_ list_list = [] for param_ in parameters_list: t_list = [] for i in range(check_.shape[0]): if check_[i] == check_[i]: t_list.append(param_[i]) list_list.append(t_list) # concatenate ar_list = [] for ii in range(len(parameters_list)): ar_list.append(np.array(list_list[ii])) return ar_list def coincidence_zero(arr_1,arr_2): # only coincidences check_ = arr_1 * arr_2 # delete empty rows_ list_1 = [] list_2 = [] for i in range(check_.shape[0]): if check_[i] != 0: list_1.append(arr_1[i]) list_2.append(arr_2[i]) return np.array(list_1),np.array(list_2) def discriminate(X_, Y_, Z_, value_disc_list, discrmnt_invert_bin = False): if discrmnt_invert_bin: Z_mask = np.ones(Z_.shape[0]) Z_mask[Z_ > value_disc_list[0]] = np.nan Z_mask[Z_ >= value_disc_list[1]] = 1 Y_new = Y_ * Z_mask X_new = X_ * Z_mask else: Z_mask = np.ones(Z_.shape[0]) Z_mask[Z_ < value_disc_list[0]] = np.nan Z_mask[Z_ > value_disc_list[1]] = np.nan Y_new = Y_ * Z_mask X_new = X_ * Z_mask return X_new, Y_new def add_ratio_to_values(header_, values_, nominator_index, denominator_index, ratio_name, normalization_value=1.): nominator_data = values_[:,nominator_index] denominator_data = values_[:,denominator_index] ratio_ = normalization_value * nominator_data / denominator_data values_new = np.column_stack((values_,ratio_)) header_new = np.append(header_,ratio_name) return header_new, values_new def bin_data(x_val_org,y_val_org, start_bin_edge=0, bin_size=1, min_bin_population=1): # get coincidences only x_val,y_val = coincidence(x_val_org,y_val_org) # combine x and y in matrix M_ = np.column_stack((x_val,y_val)) # checking if always ascending to increase efficiency always_ascending = 1 for x in range(x_val.shape[0]-1): if x_val[x]==x_val[x] and x_val[x+1]==x_val[x+1]: if x_val[x+1] < x_val[x]: always_ascending = 0 if always_ascending == 0: M_sorted = M_[M_[:,0].argsort()] # sort by first column M_ = M_sorted # convert data to list of bins y_binned = [] x_binned = [] last_row = 0 last_row_temp = last_row while start_bin_edge <= np.nanmax(x_val): y_val_list = [] for row_ in range(last_row, M_.shape[0]): if start_bin_edge <= M_[row_, 0] < start_bin_edge + bin_size: if M_[row_, 1] == M_[row_, 1]: y_val_list.append(M_[row_, 1]) last_row_temp = row_ if M_[row_, 0] >= start_bin_edge + bin_size: last_row_temp = row_ break x_binned.append(start_bin_edge) if len(y_val_list) >= min_bin_population: y_binned.append(y_val_list) else: y_binned.append([]) start_bin_edge += bin_size last_row = last_row_temp # add series if bin_size >= 1: x_binned_int = np.array(x_binned, dtype=int) else: x_binned_int = x_binned return x_binned_int, y_binned def shiftedColorMap(cmap, midpoint=0.5, name='shiftedcmap'): cdict = { 'red': [], 'green': [], 'blue': [], 'alpha': [] } # regular index to compute the colors reg_index = np.linspace(0, 1, 257) # shifted index to match the data shift_index = np.hstack([ np.linspace(0.0, midpoint, 128, endpoint=False), np.linspace(midpoint, 1.0, 129, endpoint=True) ]) for ri, si in zip(reg_index, shift_index): r, g, b, a = cmap(ri) cdict['red'].append((si, r, r)) cdict['green'].append((si, g, g)) cdict['blue'].append((si, b, b)) cdict['alpha'].append((si, a, a)) newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict) plt.register_cmap(cmap=newcmap) return newcmap def student_t_test(arr_1, arr_2): return ttest_ind(arr_1, arr_2, nan_policy='omit') def k_means_clusters(array_, cluster_number, forced_centers=None): if forced_centers is None: centers_, x = kmeans(array_,cluster_number) data_id, x = vq(array_, centers_) return centers_, data_id else: data_id, x = vq(array_, forced_centers) return forced_centers, data_id def grid_(x, y, z, resX=100, resY=100): "Convert 3 column data to matplotlib grid" xi = np.linspace(min(x), max(x), resX) yi = np.linspace(min(y), max(y), resY) Z = matplotlib.mlab.griddata(x, y, z, xi, yi) X, Y = np.meshgrid(xi, yi) return X, Y, Z def find_max_index_2d_array(array_): return np.unravel_index(np.argmax(array_, axis=None), array_.shape) def find_min_index_2d_array(array_): return np.unravel_index(np.argmin(array_, axis=None), array_.shape) def find_max_index_1d_array(array_): return np.argmax(array_, axis=None) def find_min_index_1d_array(array_): return np.argmin(array_, axis=None) def time_series_interpolate_discrete(Index_, Values_, index_step, first_index, position_=0., last_index=None): """ this will average values from Values_ that are between Index_[n:n+avr_size) :param Index_: n by 1 numpy array to look for position, :param Values_: n by m numpy array, values to be averaged :param index_step: in same units as Index_ :param first_index: is the first discrete index on new arrays. :param position_: will determine where is the stamp located; 0 = beginning, .5 = mid, 1 = top (optional, default = 0) :param last_index: in case you want to force the returned series to some fixed period/length :return: Index_averaged, Values_averaged """ # checking if always ascending to increase efficiency always_ascending = 1 for x in range(Index_.shape[0]-1): if Index_[x]==Index_[x] and Index_[x+1]==Index_[x+1]: if Index_[x+1] < Index_[x]: always_ascending = 0 if always_ascending == 0: MM_ = np.column_stack((Index_,Values_)) MM_sorted = MM_[MM_[:,0].argsort()] # sort by first column Index_ = MM_sorted[:,0] Values_ = MM_sorted[:,1:] # error checking! if Index_.shape[0] != Values_.shape[0]: print('error during shape check! Index_.shape[0] != Values_.shape[0]') return None, None if Index_[-1] < first_index: print('error during shape check! Index_[-1] < first_index') return None, None # initialize output matrices if last_index is None: final_index = np.nanmax(Index_) else: final_index = last_index total_averaged_rows = int((final_index-first_index) / index_step) + 1 if len(Values_.shape) == 1: Values_mean = np.zeros(total_averaged_rows) Values_mean[:] = np.nan else: Values_mean = np.zeros((total_averaged_rows,Values_.shape[1])) Values_mean[:,:] = np.nan Index_interp = np.zeros(total_averaged_rows) for r_ in range(total_averaged_rows): Index_interp[r_] = first_index + (r_ * index_step) Index_interp -= (position_ * index_step) Values_interp = np.interp(Index_interp, Index_, Values_) Index_interp = Index_interp + (position_ * index_step) return Index_interp, Values_interp def array_2D_sort_ascending_by_column(array_, column_=0): array_sorted = array_[array_[:, column_].argsort()] return array_sorted def get_ax_range(ax): x_1 = ax.axis()[0] x_2 = ax.axis()[1] y_1 = ax.axis()[2] y_2 = ax.axis()[3] return x_1, x_2, y_1, y_2 def get_array_perimeter_only(array_): return np.concatenate([array_[0, :-1], array_[:-1, -1], array_[-1, ::-1], array_[-2:0:-1, 0]]) # WRF def wrf_var_search(wrf_nc_file, description_str): description_str_lower = description_str.lower() for var_ in sorted(wrf_nc_file.variables): try: if description_str_lower in wrf_nc_file.variables[var_].description.lower(): print(var_, '|', wrf_nc_file.variables[var_].description) except: pass def create_virtual_sonde_from_wrf(sonde_dict, filelist_wrf_output, wrf_filename_time_format = 'wrfout_d03_%Y-%m-%d_%H_%M_%S'): # create time array filelist_wrf_output_noPath = [] for filename_ in filelist_wrf_output: filelist_wrf_output_noPath.append(filename_.split('/')[-1]) wrf_time_file_list = np.array(time_str_to_seconds(filelist_wrf_output_noPath, wrf_filename_time_format)) # create lat and lon arrays wrf_domain_file = nc.Dataset(filelist_wrf_output[0]) # p(sorted(wrf_domain_file.variables)) # wrf_vars = sorted(wrf_domain_file.variables) # for i_ in range(len(wrf_vars)): # try: # print(wrf_vars[i_], '\t\t', wrf_domain_file.variables[wrf_vars[i_]].description) # except: # print(wrf_vars[i_]) wrf_lat = wrf_domain_file.variables['XLAT'][0, :, :].filled(np.nan) wrf_lon = wrf_domain_file.variables['XLONG'][0, :, :].filled(np.nan) wrf_lat_U = wrf_domain_file.variables['XLAT_U'][0, :, :].filled(np.nan) wrf_lon_U = wrf_domain_file.variables['XLONG_U'][0, :, :].filled(np.nan) wrf_lat_V = wrf_domain_file.variables['XLAT_V'][0, :, :].filled(np.nan) wrf_lon_V = wrf_domain_file.variables['XLONG_V'][0, :, :].filled(np.nan) wrf_domain_file.close() # load sonde's profile sonde_hght = sonde_dict['hght'] # m ASL sonde_pres = sonde_dict['pres'] # hPa sonde_time = sonde_dict['time'] # seconds since epoc sonde_lati = sonde_dict['lati'] # degrees sonde_long = sonde_dict['long'] # degrees # create output lists of virtual sonde list_p__ = [] list_hgh = [] list_th_ = [] list_th0 = [] list_qv_ = [] list_U__ = [] list_V__ = [] list_tim = [] list_lat = [] list_lon = [] wrf_point_abs_address_old = 0 # loop thru real sonde's points for t_ in range(sonde_hght.shape[0]): p_progress_bar(t_, sonde_hght.shape[0]) point_hght = sonde_hght[t_] point_pres = sonde_pres[t_] point_time = sonde_time[t_] point_lati = sonde_lati[t_] point_long = sonde_long[t_] # find closest cell via lat, lon index_tuple = find_index_from_lat_lon_2D_arrays(wrf_lat,wrf_lon, point_lati,point_long) index_tuple_U = find_index_from_lat_lon_2D_arrays(wrf_lat_U,wrf_lon_U, point_lati,point_long) index_tuple_V = find_index_from_lat_lon_2D_arrays(wrf_lat_V,wrf_lon_V, point_lati,point_long) # find closest file via time file_index = time_to_row_sec(wrf_time_file_list, point_time) # open wrf file wrf_domain_file = nc.Dataset(filelist_wrf_output[file_index]) # get pressure array from wrf wrf_press = (wrf_domain_file.variables['PB'][0, :, index_tuple[0], index_tuple[1]].data + wrf_domain_file.variables['P'][0, :, index_tuple[0], index_tuple[1]].data) / 100 # hPa # find closest model layer via pressure layer_index = find_min_index_1d_array(np.abs(wrf_press - point_pres)) # define point absolute address and check if it is a new point wrf_point_abs_address_new = (index_tuple[0], index_tuple[1], file_index, layer_index) if wrf_point_abs_address_new != wrf_point_abs_address_old: wrf_point_abs_address_old = wrf_point_abs_address_new # get wrf data index_tuple_full = (0, layer_index, index_tuple[0], index_tuple[1]) index_tuple_full_U = (0, layer_index, index_tuple_U[0], index_tuple_U[1]) index_tuple_full_V = (0, layer_index, index_tuple_V[0], index_tuple_V[1]) # save to arrays list_p__.append(float(wrf_press[layer_index])) list_hgh.append(float(point_hght)) list_th_.append(float(wrf_domain_file.variables['T'][index_tuple_full])) list_th0.append(float(wrf_domain_file.variables['T00'][0])) list_qv_.append(float(wrf_domain_file.variables['QVAPOR'][index_tuple_full])) list_U__.append(float(wrf_domain_file.variables['U'][index_tuple_full_U])) list_V__.append(float(wrf_domain_file.variables['V'][index_tuple_full_V])) list_tim.append(float(wrf_time_file_list[file_index])) list_lat.append(float(wrf_lat[index_tuple[0], index_tuple[1]])) list_lon.append(float(wrf_lon[index_tuple[0], index_tuple[1]])) wrf_domain_file.close() # convert lists to arrays array_p__ = np.array(list_p__) array_hgh = np.array(list_hgh) array_th_ = np.array(list_th_) array_th0 = np.array(list_th0) array_qv_ = np.array(list_qv_) array_U__ = np.array(list_U__) array_V__ = np.array(list_V__) array_tim = np.array(list_tim) array_lat = np.array(list_lat) array_lon = np.array(list_lon) # calculate derivative variables wrf_temp_K = calculate_temperature_from_potential_temperature(array_th_ + array_th0, array_p__) wrf_temp_C = kelvin_to_celsius(wrf_temp_K) wrf_e = MixR2VaporPress(array_qv_, array_p__*100) wrf_td_C = DewPoint(wrf_e) wrf_td_C[wrf_td_C > wrf_temp_C] = wrf_temp_C[wrf_td_C > wrf_temp_C] wrf_RH = calculate_RH_from_QV_T_P(array_qv_, wrf_temp_K, array_p__*100) wrf_WD, wrf_WS = cart_to_polar(array_V__, array_U__) wrf_WD_met = wrf_WD + 180 wrf_WD_met[wrf_WD_met >= 360] = wrf_WD_met[wrf_WD_met >= 360] - 360 wrf_WS_knots = ws_ms_to_knots(wrf_WS) # create virtual sonde dict wrf_sonde_dict = {} wrf_sonde_dict['hght'] = array_hgh wrf_sonde_dict['pres'] = array_p__ wrf_sonde_dict['temp'] = wrf_temp_C wrf_sonde_dict['dwpt'] = wrf_td_C wrf_sonde_dict['sknt'] = wrf_WS_knots wrf_sonde_dict['drct'] = wrf_WD_met wrf_sonde_dict['relh'] = wrf_RH wrf_sonde_dict['time'] = array_tim wrf_sonde_dict['lati'] = array_lat wrf_sonde_dict['long'] = array_lon return wrf_sonde_dict def wrf_get_temp_K(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_press = (wrf_domain_file.variables['PB'][0, :, :, :].data + wrf_domain_file.variables['P'][0, :, :, :].data) / 100 # hPa wrf_theta = (wrf_domain_file.variables['T'][0, :, :, :].data + wrf_domain_file.variables['T00'][0].data) # K wrf_temp_K = calculate_temperature_from_potential_temperature(wrf_theta, wrf_press) if original_arg_type_str: wrf_domain_file.close() return wrf_temp_K def wrf_get_press_hPa(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_press = (wrf_domain_file.variables['PB'][0, :, :, :].data + wrf_domain_file.variables['P'][0, :, :, :].data) / 100 # hPa if original_arg_type_str: wrf_domain_file.close() return wrf_press def wrf_get_height_m(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_height = (wrf_domain_file.variables['PH'][0,:-1,:,:].data + wrf_domain_file.variables['PHB'][0,:-1,:,:].data) / gravity_ if original_arg_type_str: wrf_domain_file.close() return wrf_height def wrf_get_terrain_height_m(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_height = (wrf_domain_file.variables['PH'][0,0,:,:].data + wrf_domain_file.variables['PHB'][0,0,:,:].data) / gravity_ if original_arg_type_str: wrf_domain_file.close() return wrf_height def wrf_get_water_vapor_mixing_ratio(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_QVAPOR = wrf_domain_file.variables['QVAPOR'][0,:,:,:].data if original_arg_type_str: wrf_domain_file.close() return wrf_QVAPOR def wrf_get_cloud_water_mixing_ratio(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_QCLOUD = wrf_domain_file.variables['QCLOUD'][0,:,:,:].data if original_arg_type_str: wrf_domain_file.close() return wrf_QCLOUD def wrf_get_ice_mixing_ratio(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_QICE = wrf_domain_file.variables['QICE'][0,:,:,:].data if original_arg_type_str: wrf_domain_file.close() return wrf_QICE def wrf_get_lat_lon(wrf_nc): original_arg_type_str = False if type(wrf_nc) == str: original_arg_type_str = True wrf_domain_file = nc.Dataset(wrf_nc) else: wrf_domain_file = wrf_nc # get pressure array from wrf wrf_lat = wrf_domain_file.variables['XLAT'][0, :, :].filled(np.nan) wrf_lon = wrf_domain_file.variables['XLONG'][0, :, :].filled(np.nan) if original_arg_type_str: wrf_domain_file.close() return wrf_lat, wrf_lon def wrf_rename_files_fix_time_format(filename_original_list, original_character=':', replacement_character='_'): for i_, filename_ in enumerate(filename_original_list): p_progress_bar(i_, len(filename_original_list)) new_filename = filename_.replace(original_character,replacement_character) os.rename(filename_, new_filename) # meteorology def calculate_saturation_vapor_pressure_wexler(T_array_K): # result in mb (hPa) G0 = -0.29912729E+4 G1 = -0.60170128E+4 G2 = 0.1887643854E+2 G3 = -0.28354721E-1 G4 = 0.17838301E-4 G5 = -0.84150417E-9 G6 = 0.44412543E-12 G7 = 0.2858487E+1 e_s = np.exp((G0 * (T_array_K ** -2)) + (G1 * (T_array_K ** -1)) + G2 + (G3 * T_array_K) + (G4 * (T_array_K ** 2)) + (G5 * (T_array_K ** 3)) + (G6 * (T_array_K ** 4)) + (G7 * np.log(T_array_K))) return e_s * 0.01 def calculate_saturation_mixing_ratio(P_array_mb, T_array_K): e_s = calculate_saturation_vapor_pressure_wexler(T_array_K) q_s = 621.97 * (e_s / (P_array_mb - e_s)) return q_s def calculate_potential_temperature(T_array_K, P_array_hPa): potential_temp = T_array_K * ((1000 / P_array_hPa) ** poisson_) return potential_temp def calculate_equivalent_potential_temperature(T_array_K, P_array_hPa, R_array_kg_over_kg): P_o = 1000 T_e = T_array_K + (latent_heat_v * R_array_kg_over_kg / heat_capacity__Cp) theta_e = T_e * ((P_o/P_array_hPa)**poisson_) return theta_e def calculate_temperature_from_potential_temperature(theta_array_K, P_array_hPa): temperature_ = theta_array_K * ( (P_array_hPa/1000) ** poisson_ ) return temperature_ def calculate_mountain_height_from_sonde(sonde_dict): """ calculates H_hat from given values of u_array, v_array, T_array, effective_height, rh_array, q_array, p_array """ # Set initial conditions height = 1000 # metres # define arrays WS_array = ws_knots_to_ms(sonde_dict['SKNT']) U_array, V_array = polar_to_cart(sonde_dict['DRCT'], WS_array) T_array = celsius_to_kelvin(sonde_dict['TEMP']) RH_array = sonde_dict['RELH'] P_array = sonde_dict['PRES'] Z_array = sonde_dict['HGHT'] Q_array = sonde_dict['MIXR']/1000 TH_array = sonde_dict['THTA'] # calculated arrays q_s = calculate_saturation_mixing_ratio(P_array, T_array) e_ = gas_const_dry / gas_const_water # gradients d_ln_TH = np.gradient(np.log(TH_array)) d_z = np.gradient(Z_array) d_q_s = np.gradient(q_s) # Dry Brunt - Vaisala N_dry = gravity_ * d_ln_TH / d_z N_dry[RH_array >= 90] = 0 # Moist Brunt - Vaisala term_1_1 = 1 + ( latent_heat_v * q_s / (gas_const_dry * T_array) ) term_1_2 = 1 + ( e_ * (latent_heat_v**2) * q_s / (heat_capacity__Cp * gas_const_dry * (T_array**2) ) ) term_2_1 = d_ln_TH / d_z term_2_2 = latent_heat_v / (heat_capacity__Cp * T_array) term_2_3 = d_q_s / d_z term_3 = d_q_s / d_z # should be d_q_w but sonde data has no cloud water data N_moist = gravity_ * ( (term_1_1 / term_1_2) * (term_2_1 + ( term_2_2 * term_2_3) ) - term_3 ) N_moist[RH_array < 90] = 0 # define output array N_2 = (N_dry + N_moist)**2 H_hat_2 = N_2 * (height**2) / (U_array**2) return H_hat_2 def calculate_mountain_height_from_era5(era5_pressures_filename, era5_surface_filename, point_lat, point_lon, return_arrays=False, u_wind_mode='u', range_line_degrees=None, time_start_str_YYYYmmDDHHMM='',time_stop_str_YYYYmmDDHHMM='', reference_height=1000, return_debug_arrays=False): """ calculates H_hat from given values of u_array, v_array, T_array, effective_height, rh_array, q_array, p_array u_wind_mode: can be u, wind_speed, normal_to_range. If normal_to_range, then range_line most not be none if range_line_degrees is not None, u_wind_mode will automatically be set to normal_to_range range_line_degrees: degress (decimals) from north, clockwise, of the mountain range line. """ # load files era5_sur = nc.Dataset(era5_surface_filename, 'r') era5_pre = nc.Dataset(era5_pressures_filename, 'r') # check if times are the same for both files dif_sum = np.sum(np.abs(era5_pre.variables['time'][:] - era5_sur.variables['time'][:])) if dif_sum > 0: print('Error, times in selected files are not the same') return # check if lat lon are the same for both files dif_sum = np.sum(np.abs(era5_pre.variables['latitude'][:] - era5_sur.variables['latitude'][:])) dif_sum = dif_sum + np.sum(np.abs(era5_pre.variables['longitude'][:] - era5_sur.variables['longitude'][:])) if dif_sum > 0: print('Error, latitude or longitude in selected files are not the same') return # find lat lon index lat_index, lon_index = find_index_from_lat_lon(era5_sur.variables['latitude'][:], era5_sur.variables['longitude'][:], [point_lat], [point_lon]) lat_index = lat_index[0] lon_index = lon_index[0] # copy arrays time_array = time_era5_to_seconds(np.array(era5_sur.variables['time'][:])) r_1 = 0 r_2 = -1 if time_start_str_YYYYmmDDHHMM != '': r_1 = time_to_row_str(time_array, time_start_str_YYYYmmDDHHMM) if time_stop_str_YYYYmmDDHHMM != '': r_2 = time_to_row_str(time_array, time_stop_str_YYYYmmDDHHMM) time_array = time_array[r_1:r_2] sp_array = np.array(era5_sur.variables['sp'][r_1:r_2, lat_index, lon_index]) / 100 # hPa P_array = np.array(era5_pre.variables['level'][:]) # hPa if range_line_degrees is not None: WD_, WS_ = cart_to_polar(np.array(era5_pre.variables['v'][r_1:r_2,:,lat_index,lon_index]).flatten(), np.array(era5_pre.variables['u'][r_1:r_2,:,lat_index,lon_index]).flatten()) WD_delta = WD_ - range_line_degrees range_normal_component = WS_ * np.sin(np.deg2rad(WD_delta)) U_array = range_normal_component.reshape((sp_array.shape[0], P_array.shape[0])) else: if u_wind_mode == 'u': U_array = np.array(era5_pre.variables['u'][r_1:r_2,:,lat_index,lon_index]) else: U_array = np.sqrt(np.array(era5_pre.variables['v'][r_1:r_2,:,lat_index,lon_index]) ** 2 + np.array(era5_pre.variables['u'][r_1:r_2,:,lat_index,lon_index]) ** 2) T_array = np.array(era5_pre.variables['t'][r_1:r_2, :, lat_index, lon_index]) Q_L_array = np.array(era5_pre.variables['crwc'][r_1:r_2, :, lat_index, lon_index]) RH_array = np.array(era5_pre.variables['r'][r_1:r_2, :, lat_index, lon_index]) Z_array = np.array(era5_pre.variables['z'][r_1:r_2, :, lat_index, lon_index]) / gravity_ # calculate arrays TH_array = np.zeros((time_array.shape[0], P_array.shape[0]), dtype=float) for t_ in range(time_array.shape[0]): TH_array[t_,:] = calculate_potential_temperature(T_array[t_,:], P_array[:]) # calculated arrays q_s = calculate_saturation_mixing_ratio(P_array, T_array) e_ = gas_const_dry / gas_const_water # create output dict H_hat_2 = {} # loop tru time stamps for t_ in range(time_array.shape[0]): p_progress_bar(t_,time_array.shape[0]) # find surface pressure at this time stamp surface_p = sp_array[t_] # find pressure at 1000 meters pressure_1000m = np.interp(reference_height, Z_array[t_, :], P_array) pressure_1000m_index = np.argmin(np.abs(P_array - pressure_1000m)) # find extrapolations ql_0 = np.interp(np.log(surface_p), np.log(P_array), Q_L_array[t_, :]) z__0 = np.interp(np.log(surface_p), np.log(P_array), Z_array[t_, :]) th_0 = np.interp(np.log(surface_p), np.log(P_array), TH_array[t_, :]) qs_0 = np.interp(np.log(surface_p), np.log(P_array), q_s[t_, :]) t__1000 = np.interp(reference_height, Z_array[t_, :], T_array[t_, :]) u__1000 = np.interp(reference_height, Z_array[t_, :], U_array[t_, :]) ql_1000 = np.interp(reference_height, Z_array[t_, :], Q_L_array[t_, :]) z__1000 = reference_height th_1000 = np.interp(reference_height, Z_array[t_, :], TH_array[t_, :]) qs_1000 = np.interp(reference_height, Z_array[t_, :], q_s[t_, :]) # gradients d_ln_TH = np.log(th_1000) - np.log(th_0) d_z = z__1000 - z__0 d_q_s = qs_1000 - qs_0 d_q_w = (d_q_s) + (ql_1000 - ql_0) # Brunt - Vaisala if np.max(RH_array[t_, pressure_1000m_index:])>= 90: # Moist term_1_1 = 1 + ( latent_heat_v * qs_1000 / (gas_const_dry * t__1000) ) term_1_2 = 1 + ( e_ * (latent_heat_v**2) * qs_1000 / (heat_capacity__Cp * gas_const_dry * (t__1000**2) ) ) term_2_1 = d_ln_TH / d_z term_2_2 = latent_heat_v / (heat_capacity__Cp * t__1000) term_2_3 = d_q_s / d_z term_3 = d_q_w / d_z N_2 = gravity_ * ( (term_1_1 / term_1_2) * (term_2_1 + ( term_2_2 * term_2_3) ) - term_3 ) else: # Dry N_2 = gravity_ * d_ln_TH / d_z # populate each time stamp H_hat_2[time_array[t_]] = N_2 * (reference_height ** 2) / (u__1000 ** 2) era5_sur.close() era5_pre.close() if return_arrays: H_hat_2_time = sorted(H_hat_2.keys()) H_hat_2_time = np.array(H_hat_2_time) H_hat_2_vals = np.zeros(H_hat_2_time.shape[0], dtype=float) for r_ in range(H_hat_2_time.shape[0]): H_hat_2_vals[r_] = H_hat_2[H_hat_2_time[r_]] if return_debug_arrays: return H_hat_2_time, H_hat_2_vals, N_2, u__1000 ** 2 else: return H_hat_2_time, H_hat_2_vals else: return H_hat_2 def calculate_mountain_height_from_WRF(filename_SP, filename_PR, filename_UU, filename_VV, filename_TH, filename_QR, filename_QV, filename_PH, return_arrays=False, u_wind_mode='u', range_line_degrees=None, reference_height=1000): """ calculates H_hat from WRF point output text files u_wind_mode: can be u, wind_speed, normal_to_range. If normal_to_range, then range_line most not be none if range_line_degrees is not None, u_wind_mode will automatically be set to normal_to_range range_line_degrees: degress (decimals) from north, clockwise, of the mountain range line. :param filename_SP: fullpath filename of surface pressure file :param filename_PR: fullpath filename of pressure file :param filename_UU: fullpath filename of u wind file :param filename_VV: fullpath filename of v wind file :param filename_TH: fullpath filename of potential temperature file :param filename_QR: fullpath filename of rain water mixing ratio file :param filename_QV: fullpath filename of Water vapor mixing ratio file :param filename_PH: fullpath filename of geopotential height file :param return_arrays: if true, will return also brunt vaisalla and wind component squared :param u_wind_mode: can be u, wind_speed, normal_to_range. If normal_to_range, then range_line most not be none :param range_line_degrees: if not None, u_wind_mode will automatically be set to normal_to_range :param reference_height: mean height of mountain range :return: H_hat_2 """ # load arrays from text SP_array = genfromtxt(filename_SP, dtype=float, skip_header=1)[:,9] / 100 # hPa PR_array = genfromtxt(filename_PR, dtype=float, skip_header=1)[:,1:] / 100 # hPa UU_array = genfromtxt(filename_UU, dtype=float, skip_header=1)[:,1:] VV_array = genfromtxt(filename_VV, dtype=float, skip_header=1)[:,1:] TH_array = genfromtxt(filename_TH, dtype=float, skip_header=1)[:,1:] QR_array = genfromtxt(filename_QR, dtype=float, skip_header=1)[:,1:] QV_array = genfromtxt(filename_QV, dtype=float, skip_header=1)[:,1:] Z_array = genfromtxt(filename_PH, dtype=float, skip_header=1)[:,1:] # already in meters # calculate arrays if range_line_degrees is not None: WD_, WS_ = cart_to_polar(UU_array.flatten(), VV_array.flatten()) WD_delta = WD_ - range_line_degrees range_normal_component = WS_ * np.sin(np.deg2rad(WD_delta)) U_array = range_normal_component.reshape((UU_array.shape[0], UU_array.shape[1])) else: if u_wind_mode == 'u': U_array = UU_array else: U_array = np.sqrt(UU_array ** 2 + VV_array ** 2) T_array = calculate_temperature_from_potential_temperature(TH_array, PR_array) RH_array = calculate_RH_from_QV_T_P(QV_array, T_array, PR_array*100) q_s = calculate_saturation_mixing_ratio(PR_array, T_array) e_ = gas_const_dry / gas_const_water # create output array H_hat_2 = np.zeros(PR_array.shape[0], dtype=float) # loop tru time stamps for r_ in range(PR_array.shape[0]): p_progress_bar(r_, PR_array.shape[0]) # find surface pressure at this time stamp surface_p = SP_array[r_] # find pressure at 1000 meters pressure_1000m = np.interp(reference_height, Z_array[r_, :], PR_array[r_, :]) pressure_1000m_index = np.argmin(np.abs(PR_array[r_, :] - pressure_1000m)) # find extrapolations ql_0 = np.interp(np.log(surface_p), np.log(PR_array[r_, :]), QR_array[r_, :]) z__0 = np.interp(np.log(surface_p), np.log(PR_array[r_, :]), Z_array[r_, :]) th_0 = np.interp(np.log(surface_p), np.log(PR_array[r_, :]), TH_array[r_, :]) qs_0 = np.interp(np.log(surface_p), np.log(PR_array[r_, :]), q_s[r_, :]) t__1000 = np.interp(reference_height, Z_array[r_, :], T_array[r_, :]) u__1000 = np.interp(reference_height, Z_array[r_, :], U_array[r_, :]) ql_1000 = np.interp(reference_height, Z_array[r_, :], QR_array[r_, :]) z__1000 = reference_height th_1000 = np.interp(reference_height, Z_array[r_, :], TH_array[r_, :]) qs_1000 = np.interp(reference_height, Z_array[r_, :], q_s[r_, :]) # gradients d_ln_TH = np.log(th_1000) - np.log(th_0) d_z = z__1000 - z__0 d_q_s = qs_1000 - qs_0 d_q_w = (d_q_s) + (ql_1000 - ql_0) # Brunt - Vaisala if np.max(RH_array[r_, pressure_1000m_index:])>= 90: # Moist term_1_1 = 1 + ( latent_heat_v * qs_1000 / (gas_const_dry * t__1000) ) term_1_2 = 1 + ( e_ * (latent_heat_v**2) * qs_1000 / (heat_capacity__Cp * gas_const_dry * (t__1000**2) ) ) term_2_1 = d_ln_TH / d_z term_2_2 = latent_heat_v / (heat_capacity__Cp * t__1000) term_2_3 = d_q_s / d_z term_3 = d_q_w / d_z N_2 = gravity_ * ( (term_1_1 / term_1_2) * (term_2_1 + ( term_2_2 * term_2_3) ) - term_3 ) else: # Dry N_2 = gravity_ * d_ln_TH / d_z # populate each time stamp H_hat_2[r_] = N_2 * (reference_height ** 2) / (u__1000 ** 2) if return_arrays: return H_hat_2, N_2, u__1000 ** 2 else: return H_hat_2 def calculate_dewpoint_from_T_RH(T_, RH_): """ from Magnus formula, using Bolton's constants :param T_: ambient temperature [Celsius] :param RH_: relative humidity :return: Td_ dew point temperature [celsius] """ a = 6.112 b = 17.67 c = 243.5 y_ = np.log(RH_/100) + ((b*T_)/(c+T_)) Td_ = (c * y_) / (b - y_) return Td_ def calculate_RH_from_QV_T_P(arr_qvapor, arr_temp_K, arr_press_Pa): tv_ = 6.11 * e_constant**((2500000/461) * ((1/273) - (1/arr_temp_K))) pv_ = arr_qvapor * (arr_press_Pa/100) / (arr_qvapor + 0.622) return np.array(100 * pv_ / tv_) def calculate_profile_input_for_cluster_analysis_from_ERA5(p_profile, t_profile, td_profile, q_profile, u_profile, v_profile, h_profile, surface_p): """ takes data from ERA5 for only one time stamp for all pressure levels from 250 to 1000 hPa :param p_profile: in hPa :param t_profile: in Celsius :param td_profile: in Celsius :param q_profile: in kg/kg :param u_profile: in m/s :param v_profile: in m/s :param h_profile: in m :param surface_p: in hPa :return: surface_p, qv_, qu_, tw_, sh_, tt_ """ # trim profiles from surface to top # find which levels should be included levels_total = 0 for i_ in range(p_profile.shape[0]): if p_profile[i_] > surface_p: break levels_total += 1 ####################################### find extrapolations surface_t = np.interp(np.log(surface_p), np.log(p_profile), t_profile) surface_td = np.interp(np.log(surface_p), np.log(p_profile), td_profile) surface_q = np.interp(np.log(surface_p), np.log(p_profile), q_profile) surface_u = np.interp(np.log(surface_p), np.log(p_profile), u_profile) surface_v = np.interp(np.log(surface_p), np.log(p_profile), v_profile) surface_h = np.interp(np.log(surface_p), np.log(p_profile), h_profile) # create temp arrays T_array = np.zeros(levels_total + 1, dtype=float) Td_array = np.zeros(levels_total + 1, dtype=float) Q_array = np.zeros(levels_total + 1, dtype=float) U_array = np.zeros(levels_total + 1, dtype=float) V_array = np.zeros(levels_total + 1, dtype=float) H_array = np.zeros(levels_total + 1, dtype=float) P_array = np.zeros(levels_total + 1, dtype=float) T_array[:levels_total] = t_profile[:levels_total] Td_array[:levels_total] = td_profile[:levels_total] Q_array[:levels_total] = q_profile[:levels_total] U_array[:levels_total] = u_profile[:levels_total] V_array[:levels_total] = v_profile[:levels_total] H_array[:levels_total] = h_profile[:levels_total] P_array[:levels_total] = p_profile[:levels_total] T_array[-1] = surface_t Td_array[-1] = surface_td Q_array[-1] = surface_q U_array[-1] = surface_u V_array[-1] = surface_v H_array[-1] = surface_h P_array[-1] = surface_p ###################################### r_850 = np.argmin(np.abs(P_array - 850)) r_500 = np.argmin(np.abs(P_array - 500)) dp_ = np.abs(np.gradient(P_array)) tt_ = (T_array[r_850] - (2 * T_array[r_500]) + Td_array[r_850]) qu_ = np.sum(Q_array * U_array * dp_) / gravity_ qv_ = np.sum(Q_array * V_array * dp_) / gravity_ tw_ = np.sum(Q_array * dp_) / gravity_ del_u = U_array[r_850] - U_array[r_500] del_v = V_array[r_850] - V_array[r_500] del_z = H_array[r_850] - H_array[r_500] sh_ = ((del_u / del_z) ** 2 + (del_v / del_z) ** 2) ** 0.5 return surface_p, qv_, qu_, tw_, sh_, tt_ def barometric_equation(presb_pa, tempb_k, deltah_m, Gamma=-0.0065): """The barometric equation models the change in pressure with height in the atmosphere. INPUTS: presb_k (pa): The base pressure tempb_k (K): The base temperature deltah_m (m): The height differential between the base height and the desired height Gamma [=-0.0065]: The atmospheric lapse rate OUTPUTS pres (pa): Pressure at the requested level REFERENCE: http://en.wikipedia.org/wiki/Barometric_formula """ return presb_pa * \ (tempb_k/(tempb_k+Gamma*deltah_m))**(grav*m_a/(Rstar_a*Gamma)) def barometric_equation_inv(heightb_m, tempb_k, presb_pa, prest_pa, Gamma=-0.0065): """The barometric equation models the change in pressure with height in the atmosphere. This function returns altitude given initial pressure and base altitude, and pressure change. INPUTS: heightb_m (m): presb_pa (pa): The base pressure tempb_k (K) : The base temperature deltap_pa (m): The pressure differential between the base height and the desired height Gamma [=-0.0065]: The atmospheric lapse rate OUTPUTS heightt_m REFERENCE: http://en.wikipedia.org/wiki/Barometric_formula """ return heightb_m + \ tempb_k * ((presb_pa/prest_pa)**(Rstar_a*Gamma/(grav*m_a))-1) / Gamma def Theta(tempk, pres, pref=100000.): """Potential Temperature INPUTS: tempk (K) pres (Pa) pref: Reference pressure (default 100000 Pa) OUTPUTS: Theta (K) Source: Wikipedia Prints a warning if a pressure value below 2000 Pa input, to ensure that the units were input correctly. """ try: minpres = min(pres) except TypeError: minpres = pres if minpres < 2000: print("WARNING: P<2000 Pa; did you input a value in hPa?") return tempk * (pref/pres)**(Rs_da/Cp_da) def TempK(theta, pres, pref=100000.): """Inverts Theta function.""" try: minpres = min(pres) except TypeError: minpres = pres if minpres < 2000: print("WARNING: P<2000 Pa; did you input a value in hPa?") return theta * (pres/pref)**(Rs_da/Cp_da) def ThetaE(tempk, pres, e): """Calculate Equivalent Potential Temperature for lowest model level (or surface) INPUTS: tempk: Temperature [K] pres: Pressure [Pa] e: Water vapour partial pressure [Pa] OUTPUTS: theta_e: equivalent potential temperature References: Eq. (9.40) from Holton (2004) Eq. (22) from Bolton (1980) <NAME> and <NAME> (2013), 'Land-Ocean Warming Contrast over a Wide Range of Climates: Convective Quasi-Equilibrium Theory and Idealized Simulations', J. Climate """ # tempc tempc = tempk - degCtoK # Calculate theta theta = Theta(tempk, pres) # T_lcl formula needs RH es = VaporPressure(tempc) RH = 100. * e / es # theta_e needs q (water vapour mixing ratio) qv = MixRatio(e, pres) # Calculate the temp at the Lifting Condensation Level T_lcl = ((tempk-55)*2840 / (2840-(np.log(RH/100)*(tempk-55)))) + 55 # print "T_lcl :%.3f"%T_lcl # DEBUG STUFF #### theta_l = tempk * \ (100000./(pres-e))**(Rs_da/Cp_da)*(tempk/T_lcl)**(0.28*qv) # print "theta_L: %.3f"%theta_l # Calculate ThetaE theta_e = theta_l * np.exp((Lv * qv) / (Cp_da * T_lcl)) return theta_e def ThetaE_Bolton(tempk, pres, e, pref=100000.): """Theta_E following Bolton (1980) INPUTS: tempk: Temperature [K] pres: Pressure [Pa] e: Water vapour partial pressure [Pa] See http://en.wikipedia.org/wiki/Equivalent_potential_temperature """ # Preliminary: T = tempk qv = MixRatio(e, pres) Td = DewPoint(e) + degCtoK kappa_d = Rs_da / Cp_da # Calculate TL (temp [K] at LCL): TL = 56 + ((Td-56.)**-1+(np.log(T/Td)/800.))**(-1) # print "TL: %.3f"%TL # Calculate Theta_L: thetaL = T * (pref/(pres-e))**kappa_d*(T/TL)**(0.28*qv) # print "theta_L: %.3f"%thetaL # put it all together to get ThetaE thetaE = thetaL * np.exp((3036./TL-0.78)*qv*(1+0.448*qv)) return thetaE def ThetaV(tempk, pres, e): """Virtual Potential Temperature INPUTS tempk (K) pres (Pa) e: Water vapour pressure (Pa) (Optional) OUTPUTS theta_v : Virtual potential temperature """ mixr = MixRatio(e, pres) theta = Theta(tempk, pres) return theta * (1+mixr/Epsilon) / (1+mixr) def GammaW(tempk, pres): """Function to calculate the moist adiabatic lapse rate (deg C/Pa) based on the environmental temperature and pressure. INPUTS: tempk (K) pres (Pa) RH (%) RETURNS: GammaW: The moist adiabatic lapse rate (Deg C/Pa) REFERENCE: http://glossary.ametsoc.org/wiki/Moist-adiabatic_lapse_rate (Note that I multiply by 1/(grav*rho) to give MALR in deg/Pa) """ tempc = tempk-degCtoK es = VaporPressure(tempc) ws = MixRatio(es, pres) # tempv=VirtualTempFromMixR(tempk,ws) tempv = VirtualTemp(tempk, pres, es) latent = Latentc(tempc) Rho = pres / (Rs_da*tempv) # This is the previous implementation: # A=1.0+latent*ws/(Rs_da*tempk) # B=1.0+Epsilon*latent*latent*ws/(Cp_da*Rs_da*tempk*tempk) # Gamma=(A/B)/(Cp_da*Rho) # This is algebraically identical but a little clearer: A = -1. * (1.0+latent*ws/(Rs_da*tempk)) B = Rho * (Cp_da+Epsilon*latent*latent*ws/(Rs_da*tempk*tempk)) Gamma = A / B return Gamma def DensHumid(tempk, pres, e): """Density of moist air. This is a bit more explicit and less confusing than the method below. INPUTS: tempk: Temperature (K) pres: static pressure (Pa) mixr: mixing ratio (kg/kg) OUTPUTS: rho_air (kg/m^3) SOURCE: http://en.wikipedia.org/wiki/Density_of_air """ pres_da = pres - e rho_da = pres_da / (Rs_da * tempk) rho_wv = e/(Rs_v * tempk) return rho_da + rho_wv def Density(tempk, pres, mixr): """Density of moist air INPUTS: tempk: Temperature (K) pres: static pressure (Pa) mixr: mixing ratio (kg/kg) OUTPUTS: rho_air (kg/m^3) """ virtualT = VirtualTempFromMixR(tempk, mixr) return pres / (Rs_da * virtualT) def VirtualTemp(tempk, pres, e): """Virtual Temperature INPUTS: tempk: Temperature (K) e: vapour pressure (Pa) p: static pressure (Pa) OUTPUTS: tempv: Virtual temperature (K) SOURCE: hmmmm (Wikipedia).""" tempvk = tempk / (1-(e/pres)*(1-Epsilon)) return tempvk def VirtualTempFromMixR(tempk, mixr): """Virtual Temperature INPUTS: tempk: Temperature (K) mixr: Mixing Ratio (kg/kg) OUTPUTS: tempv: Virtual temperature (K) SOURCE: hmmmm (Wikipedia). This is an approximation based on a m """ return tempk * (1.0+0.6*mixr) def Latentc(tempc): """Latent heat of condensation (vapourisation) INPUTS: tempc (C) OUTPUTS: L_w (J/kg) SOURCE: http://en.wikipedia.org/wiki/Latent_heat#Latent_heat_for_condensation_of_water """ return 1000 * (2500.8 - 2.36*tempc + 0.0016*tempc**2 - 0.00006*tempc**3) def VaporPressure(tempc, phase="liquid"): """Water vapor pressure over liquid water or ice. INPUTS: tempc: (C) OR dwpt (C), if SATURATION vapour pressure is desired. phase: ['liquid'],'ice'. If 'liquid', do simple dew point. If 'ice', return saturation vapour pressure as follows: Tc>=0: es = es_liquid Tc <0: es = es_ice RETURNS: e_sat (Pa) SOURCE: http://cires.colorado.edu/~voemel/vp.html (#2: CIMO guide (WMO 2008), modified to return values in Pa) This formulation is chosen because of its appealing simplicity, but it performs very well with respect to the reference forms at temperatures above -40 C. At some point I'll implement Goff-Gratch (from the same resource). """ over_liquid = 6.112 * np.exp(17.67*tempc/(tempc+243.12))*100. over_ice = 6.112 * np.exp(22.46*tempc/(tempc+272.62))*100. # return where(tempc<0,over_ice,over_liquid) if phase == "liquid": # return 6.112*exp(17.67*tempc/(tempc+243.12))*100. return over_liquid elif phase == "ice": # return 6.112*exp(22.46*tempc/(tempc+272.62))*100. return np.where(tempc < 0, over_ice, over_liquid) else: raise NotImplementedError def SatVap(dwpt, phase="liquid"): """This function is deprecated, return ouput from VaporPres""" print("WARNING: This function is deprecated, please use VaporPressure()" + " instead, with dwpt as argument") return VaporPressure(dwpt, phase) def MixRatio(e, p): """Mixing ratio of water vapour INPUTS e (Pa) Water vapor pressure p (Pa) Ambient pressure RETURNS qv (kg kg^-1) Water vapor mixing ratio` """ return Epsilon * e / (p - e) def MixR2VaporPress(qv, p): """Return Vapor Pressure given Mixing Ratio and Pressure INPUTS qv (kg kg^-1) Water vapor mixing ratio` p (Pa) Ambient pressure RETURNS e (Pa) Water vapor pressure """ return qv * p / (Epsilon + qv) def DewPoint(e): """ Use Bolton's (1980, MWR, p1047) formulae to find tdew. INPUTS: e (Pa) Water Vapor Pressure OUTPUTS: Td (C) """ ln_ratio = np.log(e/611.2) Td = ((17.67-ln_ratio)*degCtoK+243.5*ln_ratio)/(17.67-ln_ratio) return Td - degCtoK def WetBulb(tempc, RH): """Stull (2011): Wet-Bulb Temperature from Relative Humidity and Air Temperature. INPUTS: tempc (C) RH (%) OUTPUTS: tempwb (C) """ Tw = tempc * np.arctan(0.151977*(RH+8.313659)**0.5) + \ np.arctan(tempc+RH) - np.arctan(RH-1.676331) + \ 0.00391838*RH**1.5*np.arctan(0.023101*RH) - \ 4.686035 return Tw # unit conversions def convert_unit_and_save_data_ppb_ugm3(filename_, station_name): # https://uk-air.defra.gov.uk/assets/documents/reports/cat06/0502160851_Conversion_Factors_Between_ppb_and.pdf # http://www2.dmu.dk/AtmosphericEnvironment/Expost/database/docs/PPM_conversion.pdf parameters_unit_scaling = {'11' : 1.96, # O3 '10' : 1.25, # NO '9' : 1.88, # NO2 '16' : 2.62, # SO2 '8' : 1.15} # CO new_unit_name = '[$\mu$g/m$^3$]' parameter_name_mod = {'9' : 'NO$_2$', '11' : 'O$_3$', '12' : 'PM$_1$$_0$', '13' : 'PM$_2$$_.$$_5$', '7' : 'CO$_2$', '16' : 'SO$_2$', } # station_name = 'QF_01' data_array = open_csv_file(filename_) current_header = data_array[0,:] new_header = np.array(current_header) v_current = np.array(data_array[1:,:],dtype=float) v_new = np.array(v_current) for keys_ in parameters_unit_scaling.keys(): v_new[:, int(keys_)] = v_current[:, int(keys_)] * parameters_unit_scaling[str(keys_)] # add station name suffix for i_ in range(5,22): if str(i_) in parameter_name_mod.keys(): parameter_name = parameter_name_mod[str(i_)] else: parameter_name = current_header[i_].split('_')[0] if str(i_) in parameters_unit_scaling.keys(): parameter_unit = new_unit_name else: parameter_unit = current_header[i_].split('_')[1] new_header[i_] = station_name + '_' + parameter_name + '_' + parameter_unit data_array[1:,:] = v_new data_array[0,:] = new_header filename_new = filename_.split('\\')[-1].split('.')[0] + '_unit_converted.csv' current_filename_without_path = filename_.split('\\')[-1] current_filename_path = filename_[:-len(current_filename_without_path)] numpy_save_txt(current_filename_path + filename_new, data_array) print('done!') def save_data_with_unit_conversion_ppb_ugm3(file_list_path): file_list = sorted(glob.glob(str(file_list_path + '\\' + '*.csv'))) # https://uk-air.defra.gov.uk/assets/documents/reports/cat06/0502160851_Conversion_Factors_Between_ppb_and.pdf # http://www2.dmu.dk/AtmosphericEnvironment/Expost/database/docs/PPM_conversion.pdf parameters_unit_scaling = {'12' : 1.96, # O3 '13' : 1.25, # NO '14' : 1.88, # NO2 '15' : 2.62, # SO2 '16' : 1.15} # CO parameters_new_names = ['YYYY', # 0 'MM', # 1 'DD', # 2 'HH', # 3 'mm', # 4 'Day of the week', # 5 'WD degrees', # 6 'WS m/s', # 7 'Temp Celsius', # 8 'RH %', # 9 'SR W/m2', # 10 'ATP mbar', # 11 'O3 ug/m3', # 12 'NO ug/m3', # 13 'NO2 ug/m3', # 14 'SO2 ug/m3', # 15 'CO mg/m3', # 16 'CO2 ppm', # 17 'PM10 ug/m3', # 18 'PM2.5 ug/m3', # 19 'THC ppm', # 20 'Rain mm', # 21 'Ox ppb', # 22 'NOx ppb'] # 23 for month_ in range(1,13): print(month_) filename_old = file_list[month_ -1] data_array = open_csv_file(file_list[month_ -1]) v_ppb = np.array(data_array[1:,:],dtype=float) v_ug_m3 = np.array(v_ppb) for keys_ in parameters_unit_scaling.keys(): v_ug_m3[:, int(keys_)] = v_ppb[:, int(keys_)] * parameters_unit_scaling[str(keys_)] data_array[0, :] = parameters_new_names data_array[1:,:] = v_ug_m3 filename_new = filename_old.split('\\')[-1].split('.')[0] + '_ugm3.csv' numpy_save_txt(file_list_path + '\\' + filename_new, data_array) print('done!') def RH_to_abs_conc(arr_RH,arr_T): a_ = 1-(373.15/arr_T) c_1 = 13.3185 c_2 = -1.97 c_3 = -.6445 c_4 = -.1299 Po_H2O = 1013.25 * e_constant ** ((c_1 * (a_**1)) + (c_2 * (a_**2)) + (c_3 * (a_**3)) + (c_4 * (a_**4)) ) # mbar return (arr_RH * Po_H2O) / (100 * boltzmann_ * arr_T) def Mixing_Ratio_to_molecules_per_cm3(arr_MR, ATP_mbar, Temp_C): arr_temp = Temp_C + 273.15 # kelvin arr_Molec_per_cm3 = arr_MR * ( ATP_mbar / ( boltzmann_ * arr_temp ) ) # molecules / cm3 return arr_Molec_per_cm3 def molecules_per_cm3_to_Mixing_Ratio(arr_Molec_per_cm3, ATP_mbar, Temp_C): arr_temp = Temp_C + 273.15 # kelvin arr_MR = (arr_Molec_per_cm3 * boltzmann_ * arr_temp) / ATP_mbar return arr_MR def ws_knots_to_ms(arr_): return arr_ * .514444 def ws_ms_to_knots(arr_): return arr_ / .514444 def kelvin_to_celsius(arr_temp_k): return arr_temp_k - 273.15 def celsius_to_kelvin(arr_temp_c): return arr_temp_c + 273.15 # geo reference def find_index_from_lat_lon(series_lat, series_lon, point_lat_list, point_lon_list): lat_index_list = [] lon_index_list = [] # mask arrays lat_m = series_lat lon_m = series_lon if np.sum(lat_m) != np.sum(lat_m) or np.sum(lon_m) != np.sum(lon_m): lat_m = np.ma.masked_where(np.isnan(lat_m), lat_m) lat_m = np.ma.masked_where(np.isinf(lat_m), lat_m) lon_m = np.ma.masked_where(np.isnan(lon_m), lon_m) lon_m = np.ma.masked_where(np.isinf(lon_m), lon_m) if type(point_lat_list) == tuple or type(point_lat_list) == list: for lat_ in point_lat_list: lat_index_list.append(np.argmin(np.abs(lat_m - lat_))) for lon_ in point_lon_list: lon_index_list.append(np.argmin(np.abs(lon_m - lon_))) else: lat_index_list = np.argmin(np.abs(lat_m - point_lat_list)) lon_index_list = np.argmin(np.abs(lon_m - point_lon_list)) return lat_index_list, lon_index_list def find_index_from_lat_lon_2D_arrays(lat_arr, lon_arr, point_lat, point_lon): lat_del_arr = lat_arr - point_lat lon_del_arr = lon_arr - point_lon dist_arr = ( lat_del_arr**2 + lon_del_arr**2 )**0.5 return find_min_index_2d_array(dist_arr) def find_index_from_lat_lon_1D_arrays(lat_arr, lon_arr, point_lat, point_lon): lat_del_arr = lat_arr - point_lat lon_del_arr = lon_arr - point_lon dist_arr = ( lat_del_arr**2 + lon_del_arr**2 )**0.5 return find_min_index_1d_array(dist_arr) def distance_array_lat_lon_2D_arrays_degrees(lat_arr, lon_arr, point_lat, point_lon): lat_del_arr = lat_arr - point_lat lon_del_arr = lon_arr - point_lon return ( lat_del_arr**2 + lon_del_arr**2 )**0.5 def meter_per_degrees(lat_point): lat_mean_rad = np.deg2rad(np.abs(lat_point)) m_per_deg_lat = 111132.954 - 559.822 * np.cos(2 * lat_mean_rad) + 1.175 * np.cos(4 * lat_mean_rad) m_per_deg_lon = 111132.954 * np.cos(lat_mean_rad) return np.abs(m_per_deg_lat), np.abs(m_per_deg_lon) def degrees_per_meter(lat_point): m_per_deg_lat, m_per_deg_lon = meter_per_degrees(lat_point) return 1/m_per_deg_lat, 1/m_per_deg_lon def distance_array_lat_lon_2D_arrays_degress_to_meters(lat_arr, lon_arr, point_lat, point_lon): m_per_deg_lat, m_per_deg_lon = meter_per_degrees(

np.nanmean(lat_arr)

numpy.nanmean

import math import warnings import numpy as np from scipy import stats from statsmodels.tsa import stattools from scipy.interpolate import interp1d from scipy.stats import chi2 from .Base import Base from .features.irregular_autoregressive import IAR_phi, CIAR_phiR_beta from .features.structure_function import SF_ML_amplitude, SF_ML_gamma from .features.damped_random_walk import GP_DRW_sigma, GP_DRW_tau from .features.harmonics import Harmonics from .features.periods import Period_fit_v2, PeriodPowerRate, PeriodLS_v2 from .features.conditional_autoregressive import CAR_mean, CAR_sigma, CAR_tau class Amplitude(Base): """Half the difference between the maximum and the minimum magnitude""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] n = len(magnitude) sorted_mag = np.sort(magnitude) return (np.median(sorted_mag[int(-math.ceil(0.05 * n)):]) - np.median(sorted_mag[0:int(math.ceil(0.05 * n))])) / 2.0 class Rcs(Base): """Range of cumulative sum""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sigma = np.std(magnitude) N = len(magnitude) m = np.mean(magnitude) s = np.cumsum(magnitude - m) * 1.0 / (N * sigma) R = np.max(s) - np.min(s) return R class StetsonK(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'error'] def fit(self, data): magnitude = data[0] error = data[2] n = len(magnitude) mean_mag = (np.sum(magnitude/(error*error))/np.sum(1.0 / (error * error))) sigmap = (np.sqrt(n * 1.0 / (n - 1)) * (magnitude - mean_mag) / error) k = (1 / np.sqrt(n * 1.0) * np.sum(np.abs(sigmap)) / np.sqrt(np.sum(sigmap ** 2))) return k class Meanvariance(Base): """variability index""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] return np.std(magnitude) / np.mean(magnitude) class Autocor_length(Base): def __init__(self, shared_data, lags=100): super().__init__(shared_data) self.Data = ['magnitude'] self.nlags = lags def fit(self, data): magnitude = data[0] ac = stattools.acf(magnitude, nlags=self.nlags, fft=False) k = next((index for index, value in enumerate(ac) if value < np.exp(-1)), None) while k is None: if self.nlags > len(magnitude): warnings.warn('Setting autocorrelation length as light curve length') return len(magnitude) self.nlags = self.nlags + 100 ac = stattools.acf(magnitude, nlags=self.nlags, fft=False) k = next((index for index, value in enumerate(ac) if value < np.exp(-1)), None) return k class SlottedA_length(Base): def __init__(self, shared_data, T=-99): """ lc: MACHO lightcurve in a pandas DataFrame k: lag (default: 1) T: tau (slot size in days. default: 4) """ super().__init__(shared_data) self.Data = ['magnitude', 'time'] SlottedA_length.SAC = [] self.T = T def slotted_autocorrelation(self, data, time, T, K, second_round=False, K1=100): slots = np.zeros((K, 1)) i = 1 # make time start from 0 time = time - np.min(time) # subtract mean from mag values m = np.mean(data) data = data - m prod = np.zeros((K, 1)) pairs = np.subtract.outer(time, time) pairs[np.tril_indices_from(pairs)] = 10000000 ks = np.int64(np.floor(np.abs(pairs) / T + 0.5)) # We calculate the slotted autocorrelation for k=0 separately idx = np.where(ks == 0) prod[0] = ((sum(data ** 2) + sum(data[idx[0]] * data[idx[1]])) / (len(idx[0]) + len(data))) slots[0] = 0 # We calculate it for the rest of the ks if second_round is False: for k in np.arange(1, K): idx = np.where(ks == k) if len(idx[0]) != 0: prod[k] = sum(data[idx[0]] * data[idx[1]]) / (len(idx[0])) slots[i] = k i = i + 1 else: prod[k] = np.infty else: for k in np.arange(K1, K): idx = np.where(ks == k) if len(idx[0]) != 0: prod[k] = sum(data[idx[0]] * data[idx[1]]) / (len(idx[0])) slots[i - 1] = k i = i + 1 else: prod[k] = np.infty np.trim_zeros(prod, trim='b') slots = np.trim_zeros(slots, trim='b') return prod / prod[0], np.int64(slots).flatten() def fit(self, data): magnitude = data[0] time = data[1] N = len(time) if self.T == -99: deltaT = time[1:] - time[:-1] sorted_deltaT = np.sort(deltaT) self.T = sorted_deltaT[int(N * 0.05)+1] K = 100 [SAC, slots] = self.slotted_autocorrelation(magnitude, time, self.T, K) SAC2 = SAC[slots] SlottedA_length.autocor_vector = SAC2 k = next((index for index, value in enumerate(SAC2) if value < np.exp(-1)), None) while k is None: K = K+K if K > (np.max(time) - np.min(time)) / self.T: break else: [SAC, slots] = self.slotted_autocorrelation(magnitude, time, self.T, K, second_round=True, K1=K/2) SAC2 = SAC[slots] k = next((index for index, value in enumerate(SAC2) if value < np.exp(-1)), None) return slots[k] * self.T def getAtt(self): return SlottedA_length.autocor_vector class StetsonK_AC(SlottedA_length): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'error'] def fit(self, data): try: a = StetsonK_AC(self.shared_data) autocor_vector = a.getAtt() N_autocor = len(autocor_vector) sigmap = (np.sqrt(N_autocor * 1.0 / (N_autocor - 1)) * (autocor_vector - np.mean(autocor_vector)) / np.std(autocor_vector)) K = (1 / np.sqrt(N_autocor * 1.0) * np.sum(np.abs(sigmap)) / np.sqrt(np.sum(sigmap ** 2))) return K except: print("error: please run SlottedA_length first to generate values for StetsonK_AC ") class StetsonL(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'error', 'magnitude2', 'error2'] def fit(self, data): aligned_magnitude = data[4] aligned_magnitude2 = data[5] aligned_error = data[7] aligned_error2 = data[8] N = len(aligned_magnitude) mean_mag = (np.sum(aligned_magnitude/(aligned_error*aligned_error)) / np.sum(1.0 / (aligned_error * aligned_error))) mean_mag2 = (np.sum(aligned_magnitude2/(aligned_error2*aligned_error2)) / np.sum(1.0 / (aligned_error2 * aligned_error2))) sigmap = (np.sqrt(N * 1.0 / (N - 1)) * (aligned_magnitude[:N] - mean_mag) / aligned_error) sigmaq = (np.sqrt(N * 1.0 / (N - 1)) * (aligned_magnitude2[:N] - mean_mag2) / aligned_error2) sigma_i = sigmap * sigmaq J = (1.0 / len(sigma_i) * np.sum(np.sign(sigma_i) * np.sqrt(np.abs(sigma_i)))) K = (1 / np.sqrt(N * 1.0) * np.sum(np.abs(sigma_i)) / np.sqrt(np.sum(sigma_i ** 2))) return J * K / 0.798 class Con(Base): """Index introduced for selection of variable starts from OGLE database. To calculate Con, we counted the number of three consecutive measurements that are out of 2sigma range, and normalized by N-2 Pavlos not happy """ def __init__(self, shared_data, consecutiveStar=3): super().__init__(shared_data) self.Data = ['magnitude'] self.consecutiveStar = consecutiveStar def fit(self, data): magnitude = data[0] N = len(magnitude) if N < self.consecutiveStar: return 0 sigma = np.std(magnitude) m = np.mean(magnitude) count = 0 for i in range(N - self.consecutiveStar + 1): flag = 0 for j in range(self.consecutiveStar): if (magnitude[i + j] > m + 2 * sigma or magnitude[i + j] < m - 2 * sigma): flag = 1 else: flag = 0 break if flag: count = count + 1 return count * 1.0 / (N - self.consecutiveStar + 1) class Color(Base): """Average color for each MACHO lightcurve mean(B1) - mean(B2) """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'magnitude2'] def fit(self, data): magnitude = data[0] magnitude2 = data[3] return np.mean(magnitude) - np.mean(magnitude2) class Beyond1Std(Base): """Percentage of points beyond one st. dev. from the weighted (by photometric errors) mean """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'error'] def fit(self, data): magnitude = data[0] error = data[2] n = len(magnitude) weighted_mean = np.average(magnitude, weights=1 / error ** 2) # Standard deviation with respect to the weighted mean var = sum((magnitude - weighted_mean) ** 2) std = np.sqrt((1.0 / (n - 1)) * var) count = np.sum(np.logical_or(magnitude > weighted_mean + std, magnitude < weighted_mean - std)) return float(count) / n class SmallKurtosis(Base): """Small sample kurtosis of the magnitudes. See http://www.xycoon.com/peakedness_small_sample_test_1.htm """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] n = len(magnitude) mean = np.mean(magnitude) std = np.std(magnitude) S = sum(((magnitude - mean) / std) ** 4) c1 = float(n * (n + 1)) / ((n - 1) * (n - 2) * (n - 3)) c2 = float(3 * (n - 1) ** 2) / ((n - 2) * (n - 3)) return c1 * S - c2 class Std(Base): """Standard deviation of the magnitudes""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] return np.std(magnitude) class Skew(Base): """Skewness of the magnitudes""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] return stats.skew(magnitude) class StetsonJ(Base): """Stetson (1996) variability index, a robust standard deviation""" def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'error', 'magnitude2', 'error2'] #lc fields are [data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd] def fit(self, data): aligned_magnitude = data[4] aligned_magnitude2 = data[5] aligned_error = data[7] aligned_error2 = data[8] N = len(aligned_magnitude) mean_mag = (np.sum(aligned_magnitude/(aligned_error*aligned_error)) / np.sum(1.0 / (aligned_error * aligned_error))) mean_mag2 = (np.sum(aligned_magnitude2 / (aligned_error2*aligned_error2)) / np.sum(1.0 / (aligned_error2 * aligned_error2))) sigmap = (np.sqrt(N * 1.0 / (N - 1)) * (aligned_magnitude[:N] - mean_mag) / aligned_error) sigmaq = (np.sqrt(N * 1.0 / (N - 1)) * (aligned_magnitude2[:N] - mean_mag2) / aligned_error2) sigma_i = sigmap * sigmaq J = (1.0 / len(sigma_i) * np.sum(np.sign(sigma_i) * np.sqrt(np.abs(sigma_i)))) return J class MaxSlope(Base): """ Examining successive (time-sorted) magnitudes, the maximal first difference (value of delta magnitude over delta time) """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time'] def fit(self, data): magnitude = data[0] time = data[1] slope = np.abs(magnitude[1:] - magnitude[:-1]) / (time[1:] - time[:-1]) np.max(slope) return np.max(slope) class MedianAbsDev(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] median = np.median(magnitude) devs = (abs(magnitude - median)) return np.median(devs) class MedianBRP(Base): """Median buffer range percentage Fraction (<= 1) of photometric points within amplitude/10 of the median magnitude """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] median = np.median(magnitude) amplitude = (np.max(magnitude) - np.min(magnitude)) / 10 n = len(magnitude) count = np.sum(np.logical_and(magnitude < median + amplitude, magnitude > median - amplitude)) return float(count) / n class PairSlopeTrend(Base): """ Considering the last 30 (time-sorted) measurements of source magnitude, the fraction of increasing first differences minus the fraction of decreasing first differences. """ def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] data_last = magnitude[-30:] return (float(len(np.where(np.diff(data_last) > 0)[0]) - len(np.where(np.diff(data_last) <= 0)[0])) / 30) class FluxPercentileRatioMid20(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_60_index = math.ceil(0.60 * lc_length) F_40_index = math.ceil(0.40 * lc_length) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.ceil(0.95 * lc_length) F_40_60 = sorted_data[F_60_index] - sorted_data[F_40_index] F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] F_mid20 = F_40_60 / F_5_95 return F_mid20 class FluxPercentileRatioMid35(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_325_index = math.ceil(0.325 * lc_length) F_675_index = math.ceil(0.675 * lc_length) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.ceil(0.95 * lc_length) F_325_675 = sorted_data[F_675_index] - sorted_data[F_325_index] F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] F_mid35 = F_325_675 / F_5_95 return F_mid35 class FluxPercentileRatioMid50(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_25_index = math.ceil(0.25 * lc_length) F_75_index = math.ceil(0.75 * lc_length) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.ceil(0.95 * lc_length) F_25_75 = sorted_data[F_75_index] - sorted_data[F_25_index] F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] F_mid50 = F_25_75 / F_5_95 return F_mid50 class FluxPercentileRatioMid65(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_175_index = math.ceil(0.175 * lc_length) F_825_index = math.ceil(0.825 * lc_length) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.ceil(0.95 * lc_length) F_175_825 = sorted_data[F_825_index] - sorted_data[F_175_index] F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] F_mid65 = F_175_825 / F_5_95 return F_mid65 class FluxPercentileRatioMid80(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_10_index = math.ceil(0.10 * lc_length) F_90_index = math.ceil(0.90 * lc_length) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.floor(0.95 * lc_length) F_10_90 = sorted_data[F_90_index] - sorted_data[F_10_index] F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] F_mid80 = F_10_90 / F_5_95 return F_mid80 class PercentDifferenceFluxPercentile(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] median_data = np.median(magnitude) sorted_data = np.sort(magnitude) lc_length = len(sorted_data) F_5_index = math.ceil(0.05 * lc_length) F_95_index = math.ceil(0.95 * lc_length) F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index] percent_difference = F_5_95 / median_data return percent_difference class PercentAmplitude(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] median_data = np.median(magnitude) distance_median = np.abs(magnitude - median_data) max_distance = np.max(distance_median) percent_amplitude = max_distance / median_data return percent_amplitude class LinearTrend(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time'] def fit(self, data): magnitude = data[0] time = data[1] regression_slope = stats.linregress(time, magnitude)[0] return regression_slope class Eta_color(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'magnitude2'] def fit(self, data): aligned_magnitude = data[4] aligned_magnitude2 = data[5] aligned_time = data[6] B_Rdata = aligned_magnitude - aligned_magnitude2 w = 1.0 / np.power(aligned_time[1:] - aligned_time[:-1], 2) sigma2 = np.var(B_Rdata) S1 = sum(w * (B_Rdata[1:] - B_Rdata[:-1]) ** 2) S2 = sum(w) eta_B_R = (1 / sigma2) * (S1 / S2) return eta_B_R class Eta_e(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time'] def fit(self, data): magnitude = data[0] time = data[1] w = 1.0 / np.power(np.subtract(time[1:], time[:-1]), 2) sigma2 = np.var(magnitude) S1 = np.sum(w * np.power(np.subtract(magnitude[1:], magnitude[:-1]), 2)) S2 = np.sum(w) eta_e = (1 / sigma2) * (S1 / S2) return eta_e class Mean(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] B_mean = np.mean(magnitude) return B_mean class Q31(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] percentiles = np.percentile(magnitude, (25, 75)) return percentiles[1] - percentiles[0] class Q31_color(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time', 'magnitude2'] def fit(self, data): aligned_magnitude = data[4] aligned_magnitude2 = data[5] N = len(aligned_magnitude) b_r = aligned_magnitude[:N] - aligned_magnitude2[:N] percentiles = np.percentile(b_r, (25, 75)) return percentiles[1] - percentiles[0] class AndersonDarling(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude'] def fit(self, data): magnitude = data[0] ander = stats.anderson(magnitude)[0] return 1 / (1.0 + np.exp(-10 * (ander - 0.3))) class Psi_CS_v2(Base): def __init__(self, shared_data): super().__init__(shared_data) self.Data = ['magnitude', 'time'] def fit(self, data): try: magnitude = data[0] time = data[1] new_time_v2 = self.shared_data['new_time_v2'] folded_data = magnitude[np.argsort(new_time_v2)] sigma = np.std(folded_data) N = len(folded_data) m = np.mean(folded_data) s = np.cumsum(folded_data - m) * 1.0 / (N * sigma) R = np.max(s) -

np.min(s)

numpy.min

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Apr 17 14:45:00 2017 @author: shenda class order: ['A', 'N', 'O', '~'] """ import numpy as np from sklearn import ensemble from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from OptF import OptF from copy import deepcopy import xgboost as xgb import ReadData import random from collections import Counter class MyXGB(object): """ bottom basic classifier, a warpper for xgboost the proba order is ['N', 'A', 'O', '~'] """ def __init__(self, n_estimators=5000, max_depth=10, subsample=0.85, colsample_bytree=0.85, min_child_weight=4, num_round = 500): self.param = {'learning_rate':0.1, 'eta':0.1, 'silent':1, 'objective':'multi:softprob', 'num_class': 4} self.bst = None self.num_round = num_round self.pred = None my_seed = random.randint(0, 1000) self.param['n_estimators'] = n_estimators self.param['max_depth'] = max_depth self.param['subsample'] = subsample self.param['colsample_bytree'] = colsample_bytree self.param['min_child_weight'] = min_child_weight # self.param['random_state'] = my_seed self.param['seed'] = my_seed self.param['n_jobs'] = -1 print(self.param.items()) print(self.num_round) def fit(self, train_data, train_label): train_label = ReadData.Label2Index(train_label) dtrain = xgb.DMatrix(train_data, label=train_label) self.bst = xgb.train(self.param, dtrain, num_boost_round=self.num_round) def predict_prob(self, test_data): dtest = xgb.DMatrix(test_data) self.pred = self.bst.predict(dtest) return self.pred def predict(self, test_data): pred_prob = self.predict_prob(test_data) pred_num = np.argmax(pred_prob, axis=1) pred = ReadData.Index2Label(pred_num) return pred def get_importance(self): return self.bst.get_score(importance_type='gain') def plot_importance(self): xgb.plot_importance(self.bst) class MyLR(object): """ Top level classifier, a warpper for Logistic Regression """ def __init__(self): self.clf = LogisticRegression() def fit(self, train_qrs_data, train_qrs_label): train_data =np.array(train_qrs_data) train_label = train_qrs_label self.clf.fit(train_data, train_label) def predict(self, test_qrs_data): test_data = np.array(test_qrs_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_qrs_data): test_qrs_data = np.array(test_qrs_data) if test_qrs_data.ndim == 1: test_qrs_data = np.expand_dims(np.array(test_qrs_data), axis=0) test_data = np.array(test_qrs_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_qrs_data) return self.clf.predict_proba(test_data) class MyKNN(object): """ bottom basic support unequal length vector """ def __init__(self, n_neighbors=3): self.n_neighbors = n_neighbors self.train_data = None self.train_label = None self.labels = ['N', 'A', 'O', '~'] # self.thresh = [0.5, 0.3, 0.2, ] def fit(self, train_data, train_label): self.train_data = np.array(train_data) self.train_label = np.array(train_label) def dist(self, vec1, vec2): res = 0.0 if len(vec1) <= len(vec2): vec1 = np.r_[vec1, np.zeros(len(vec2)-len(vec1))] else: vec2 = np.r_[vec2, np.zeros(len(vec1)-len(vec2))] dist_num = np.linalg.norm(vec1 - vec2) return dist_num def predict_prob(self, test_data): test_data = np.array(test_data) pred = [] for i in test_data: tmp_dist_list = [] tmp_pred = [] for j in self.train_data: tmp_dist_list.append(self.dist(i, j)) pred_n_neighbors = self.train_label[np.argsort(tmp_dist_list)[:self.n_neighbors]] pred_counter = Counter(pred_n_neighbors) # print(pred_counter) for ii in self.labels: tmp_pred.append(pred_counter[ii]) pred.append(tmp_pred) return pred def predict(self, test_data): pred = self.predict_prob(test_data) pred_label = [] for i in pred: pred_label.append(self.labels[np.argsort(i)[-1]]) return pred_label class MyGBDT(object): """ bottom basic a warpper for GradientBoostingClassifier """ def __init__(self): self.clf = ensemble.GradientBoostingClassifier() def fit(self, train_data, train_label): train_data =np.array(train_data) self.clf.fit(train_data, train_label) def predict(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) test_data = np.array(test_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_data) return self.clf.predict_proba(test_data) class MyExtraTrees(object): """ bottom basic a warpper for ExtraTreesClassifier """ def __init__(self): self.clf = ensemble.ExtraTreesClassifier(n_estimators=100) def fit(self, train_data, train_label): train_data =np.array(train_data) self.clf.fit(train_data, train_label) def predict(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) test_data = np.array(test_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_data) return self.clf.predict_proba(test_data) class MyAdaBoost(object): """ bottom basic a warpper for AdaBoostClassifier """ def __init__(self): self.clf = ensemble.AdaBoostClassifier(n_estimators=100, learning_rate=0.1) def fit(self, train_data, train_label): train_data =np.array(train_data) self.clf.fit(train_data, train_label) def predict(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) test_data = np.array(test_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_data) return self.clf.predict_proba(test_data) class MyRF(object): """ bottom basic a warpper for Random Forest """ def __init__(self): self.clf = ensemble.RandomForestClassifier( n_estimators=1000, n_jobs=-1) def fit(self, train_data, train_label): train_data =np.array(train_data) self.clf.fit(train_data, train_label) def predict(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_data): test_data = np.array(test_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) test_data = np.array(test_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_data) return self.clf.predict_proba(test_data) class MyOptF(object): """ bottom basic classifier, a warpper for Opt F-score """ def __init__(self, alpha=0.5, epochs=10): self.clf = OptF(alpha, epochs) def fit(self, train_data, train_label): self.clf.fit(train_data, deepcopy(train_label)) def predict(self, test_data): return list(self.clf.predict(test_data)) def predict_prob(self, test_data): return self.clf.predict_prob(test_data) class RF(object): """ Top level classifier, a warpper for Random Forest use long_feature and qrs_feature seperatedly, thus no use any more deprecated """ def __init__(self): self.clf = ensemble.RandomForestClassifier() def fit(self, train_long_data, train_long_label, train_qrs_data, train_qrs_label): train_data = np.c_[np.array(train_long_data), np.array(train_qrs_data)] train_label = train_long_label self.clf.fit(train_data, train_label) def predict(self, test_long_data, test_qrs_data): test_data = np.c_[np.array(test_long_data), np.array(test_qrs_data)] if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_long_data, test_qrs_data): test_long_data = np.array(test_long_data) test_qrs_data = np.array(test_qrs_data) if test_long_data.ndim == 1 or test_qrs_data.ndim == 1: test_long_data = np.expand_dims(np.array(test_long_data), axis=0) test_qrs_data = np.expand_dims(np.array(test_qrs_data), axis=0) test_data = np.c_[np.array(test_long_data), np.array(test_qrs_data)] else: test_data = np.c_[np.array(test_long_data), np.array(test_qrs_data)] return list(list(self.clf.predict_proba(test_data))[0]) class RFSimp(object): """ Top level classifier, a warpper for Random Forest use long/qrs feature deprecated """ def __init__(self): self.clf = ensemble.RandomForestClassifier() def fit(self, train_qrs_data, train_qrs_label): train_data =np.array(train_qrs_data) train_label = train_qrs_label self.clf.fit(train_data, train_label) def predict(self, test_qrs_data): test_data = np.array(test_qrs_data) if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_qrs_data): test_qrs_data = np.array(test_qrs_data) if test_qrs_data.ndim == 1: test_qrs_data = np.expand_dims(np.array(test_qrs_data), axis=0) test_data = np.array(test_qrs_data) return list(list(self.clf.predict_proba(test_data))[0]) else: test_data = np.array(test_qrs_data) return self.clf.predict_proba(test_data) class LR(object): """ Top level classifier, a warpper for Logistic Regression deprecated """ def __init__(self): self.clf = LogisticRegression() def fit(self, train_long_data, train_long_label, train_qrs_data, train_qrs_label): train_data = np.c_[np.array(train_long_data), np.array(train_qrs_data)] train_label = train_long_label self.clf.fit(train_data, train_label) def predict(self, test_long_data, test_qrs_data): test_data = np.c_[np.array(test_long_data), np.array(test_qrs_data)] if test_data.ndim == 1: test_data = np.expand_dims(np.array(test_data), axis=0) return list(self.clf.predict(test_data)) def predict_prob(self, test_long_data, test_qrs_data): test_long_data = np.array(test_long_data) test_qrs_data = np.array(test_qrs_data) if test_long_data.ndim == 1 or test_qrs_data.ndim == 1: test_long_data = np.expand_dims(np.array(test_long_data), axis=0) test_qrs_data = np.expand_dims(np.array(test_qrs_data), axis=0) test_data = np.c_[np.array(test_long_data), np.array(test_qrs_data)] else: test_data = np.c_[np.array(test_long_data),

np.array(test_qrs_data)

numpy.array

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # 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 paddle from paddle import ParamAttr import paddle.nn as nn import paddle.nn.functional as F from paddle.nn.initializer import Normal, Constant from paddlex.ppdet.core.workspace import register from paddlex.ppdet.modeling import bbox_utils from paddlex.ppdet.modeling.proposal_generator.target_layer import RBoxAssigner import numpy as np class S2ANetAnchorGenerator(nn.Layer): """ AnchorGenerator by paddle """ def __init__(self, base_size, scales, ratios, scale_major=True, ctr=None): super(S2ANetAnchorGenerator, self).__init__() self.base_size = base_size self.scales = paddle.to_tensor(scales) self.ratios = paddle.to_tensor(ratios) self.scale_major = scale_major self.ctr = ctr self.base_anchors = self.gen_base_anchors() @property def num_base_anchors(self): return self.base_anchors.shape[0] def gen_base_anchors(self): w = self.base_size h = self.base_size if self.ctr is None: x_ctr = 0.5 * (w - 1) y_ctr = 0.5 * (h - 1) else: x_ctr, y_ctr = self.ctr h_ratios = paddle.sqrt(self.ratios) w_ratios = 1 / h_ratios if self.scale_major: ws = (w * w_ratios[:] * self.scales[:]).reshape([-1]) hs = (h * h_ratios[:] * self.scales[:]).reshape([-1]) else: ws = (w * self.scales[:] * w_ratios[:]).reshape([-1]) hs = (h * self.scales[:] * h_ratios[:]).reshape([-1]) base_anchors = paddle.stack( [ x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1), x_ctr + 0.5 * (ws - 1), y_ctr + 0.5 * (hs - 1) ], axis=-1) base_anchors = paddle.round(base_anchors) return base_anchors def _meshgrid(self, x, y, row_major=True): yy, xx = paddle.meshgrid(x, y) yy = yy.reshape([-1]) xx = xx.reshape([-1]) if row_major: return xx, yy else: return yy, xx def forward(self, featmap_size, stride=16): # featmap_size*stride project it to original area base_anchors = self.base_anchors feat_h = featmap_size[0] feat_w = featmap_size[1] shift_x = paddle.arange(0, feat_w, 1, 'int32') * stride shift_y = paddle.arange(0, feat_h, 1, 'int32') * stride shift_xx, shift_yy = self._meshgrid(shift_x, shift_y) shifts = paddle.stack([shift_xx, shift_yy, shift_xx, shift_yy], axis=-1) all_anchors = base_anchors[:, :] + shifts[:, :] all_anchors = all_anchors.reshape([feat_h * feat_w, 4]) return all_anchors def valid_flags(self, featmap_size, valid_size): feat_h, feat_w = featmap_size valid_h, valid_w = valid_size assert valid_h <= feat_h and valid_w <= feat_w valid_x = paddle.zeros([feat_w], dtype='uint8') valid_y = paddle.zeros([feat_h], dtype='uint8') valid_x[:valid_w] = 1 valid_y[:valid_h] = 1 valid_xx, valid_yy = self._meshgrid(valid_x, valid_y) valid = valid_xx & valid_yy valid = valid[:, None].expand( [valid.size(0), self.num_base_anchors]).reshape([-1]) return valid class AlignConv(nn.Layer): def __init__(self, in_channels, out_channels, kernel_size=3, groups=1): super(AlignConv, self).__init__() self.kernel_size = kernel_size self.align_conv = paddle.vision.ops.DeformConv2D( in_channels, out_channels, kernel_size=self.kernel_size, padding=(self.kernel_size - 1) // 2, groups=groups, weight_attr=ParamAttr(initializer=Normal(0, 0.01)), bias_attr=None) @paddle.no_grad() def get_offset(self, anchors, featmap_size, stride): """ Args: anchors: [M,5] xc,yc,w,h,angle featmap_size: (feat_h, feat_w) stride: 8 Returns: """ anchors = paddle.reshape(anchors, [-1, 5]) # (NA,5) dtype = anchors.dtype feat_h, feat_w = featmap_size pad = (self.kernel_size - 1) // 2 idx = paddle.arange(-pad, pad + 1, dtype=dtype) yy, xx = paddle.meshgrid(idx, idx) xx = paddle.reshape(xx, [-1]) yy = paddle.reshape(yy, [-1]) # get sampling locations of default conv xc = paddle.arange(0, feat_w, dtype=dtype) yc = paddle.arange(0, feat_h, dtype=dtype) yc, xc = paddle.meshgrid(yc, xc) xc = paddle.reshape(xc, [-1, 1]) yc = paddle.reshape(yc, [-1, 1]) x_conv = xc + xx y_conv = yc + yy # get sampling locations of anchors # x_ctr, y_ctr, w, h, a = np.unbind(anchors, dim=1) x_ctr = anchors[:, 0] y_ctr = anchors[:, 1] w = anchors[:, 2] h = anchors[:, 3] a = anchors[:, 4] x_ctr = paddle.reshape(x_ctr, [x_ctr.shape[0], 1]) y_ctr = paddle.reshape(y_ctr, [y_ctr.shape[0], 1]) w = paddle.reshape(w, [w.shape[0], 1]) h = paddle.reshape(h, [h.shape[0], 1]) a = paddle.reshape(a, [a.shape[0], 1]) x_ctr = x_ctr / stride y_ctr = y_ctr / stride w_s = w / stride h_s = h / stride cos, sin = paddle.cos(a), paddle.sin(a) dw, dh = w_s / self.kernel_size, h_s / self.kernel_size x, y = dw * xx, dh * yy xr = cos * x - sin * y yr = sin * x + cos * y x_anchor, y_anchor = xr + x_ctr, yr + y_ctr # get offset filed offset_x = x_anchor - x_conv offset_y = y_anchor - y_conv # x, y in anchors is opposite in image coordinates, # so we stack them with y, x other than x, y offset = paddle.stack([offset_y, offset_x], axis=-1) # NA,ks*ks*2 # [NA, ks, ks, 2] --> [NA, ks*ks*2] offset = paddle.reshape(offset, [offset.shape[0], -1]) # [NA, ks*ks*2] --> [ks*ks*2, NA] offset = paddle.transpose(offset, [1, 0]) # [NA, ks*ks*2] --> [1, ks*ks*2, H, W] offset = paddle.reshape(offset, [1, -1, feat_h, feat_w]) return offset def forward(self, x, refine_anchors, stride): featmap_size = (x.shape[2], x.shape[3]) offset = self.get_offset(refine_anchors, featmap_size, stride) x = F.relu(self.align_conv(x, offset)) return x @register class S2ANetHead(nn.Layer): """ S2Anet head Args: stacked_convs (int): number of stacked_convs feat_in (int): input channels of feat feat_out (int): output channels of feat num_classes (int): num_classes anchor_strides (list): stride of anchors anchor_scales (list): scale of anchors anchor_ratios (list): ratios of anchors target_means (list): target_means target_stds (list): target_stds align_conv_type (str): align_conv_type ['Conv', 'AlignConv'] align_conv_size (int): kernel size of align_conv use_sigmoid_cls (bool): use sigmoid_cls or not reg_loss_weight (list): loss weight for regression """ __shared__ = ['num_classes'] __inject__ = ['anchor_assign'] def __init__(self, stacked_convs=2, feat_in=256, feat_out=256, num_classes=15, anchor_strides=[8, 16, 32, 64, 128], anchor_scales=[4], anchor_ratios=[1.0], target_means=0.0, target_stds=1.0, align_conv_type='AlignConv', align_conv_size=3, use_sigmoid_cls=True, anchor_assign=RBoxAssigner().__dict__, reg_loss_weight=[1.0, 1.0, 1.0, 1.0, 1.0], cls_loss_weight=[1.0, 1.0]): super(S2ANetHead, self).__init__() self.stacked_convs = stacked_convs self.feat_in = feat_in self.feat_out = feat_out self.anchor_list = None self.anchor_scales = anchor_scales self.anchor_ratios = anchor_ratios self.anchor_strides = anchor_strides self.anchor_base_sizes = list(anchor_strides) self.target_means = target_means self.target_stds = target_stds assert align_conv_type in ['AlignConv', 'Conv', 'DCN'] self.align_conv_type = align_conv_type self.align_conv_size = align_conv_size self.use_sigmoid_cls = use_sigmoid_cls self.cls_out_channels = num_classes if self.use_sigmoid_cls else 1 self.sampling = False self.anchor_assign = anchor_assign self.reg_loss_weight = reg_loss_weight self.cls_loss_weight = cls_loss_weight self.s2anet_head_out = None # anchor self.anchor_generators = [] for anchor_base in self.anchor_base_sizes: self.anchor_generators.append( S2ANetAnchorGenerator(anchor_base, anchor_scales, anchor_ratios)) self.anchor_generators = paddle.nn.LayerList(self.anchor_generators) self.add_sublayer('s2anet_anchor_gen', self.anchor_generators) self.fam_cls_convs = nn.Sequential() self.fam_reg_convs = nn.Sequential() for i in range(self.stacked_convs): chan_in = self.feat_in if i == 0 else self.feat_out self.fam_cls_convs.add_sublayer( 'fam_cls_conv_{}'.format(i), nn.Conv2D( in_channels=chan_in, out_channels=self.feat_out, kernel_size=3, padding=1, weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)), bias_attr=ParamAttr(initializer=Constant(0)))) self.fam_cls_convs.add_sublayer('fam_cls_conv_{}_act'.format(i), nn.ReLU()) self.fam_reg_convs.add_sublayer( 'fam_reg_conv_{}'.format(i), nn.Conv2D( in_channels=chan_in, out_channels=self.feat_out, kernel_size=3, padding=1, weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)), bias_attr=ParamAttr(initializer=Constant(0)))) self.fam_reg_convs.add_sublayer('fam_reg_conv_{}_act'.format(i), nn.ReLU()) self.fam_reg = nn.Conv2D( self.feat_out, 5, 1, weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)), bias_attr=ParamAttr(initializer=Constant(0))) prior_prob = 0.01 bias_init = float(-

np.log((1 - prior_prob) / prior_prob)

numpy.log

#!/usr/bin/env pytest import numpy as np import pytest import siglib as sl @pytest.mark.parametrize( "x,frame_length,frame_step,pad,pad_value,expected", ( (np.arange(10), 5, 5, True, 0j, np.arange(10, dtype=np.complex).reshape(2, 5)), (np.arange(10), 5, 5, False, 0j, np.arange(10, dtype=np.complex).reshape(2, 5)), ), ) def test_frame(x, frame_length, frame_step, pad, pad_value, expected): result = sl.frame(x, frame_length, frame_step, pad=pad, pad_value=pad_value) np.testing.assert_equal(result, expected) @pytest.mark.parametrize( "x,ntaps,expected", ( (np.zeros(10), 5, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), (np.arange(10), 5, [4, 4, 4, 4, 4, 5, 6, 7, 8, 9]), ), ) def test_closing(x, ntaps, expected): result = sl.closing(x, ntaps) expected = np.array(expected, dtype=np.complex) np.testing.assert_equal(result, expected) @pytest.mark.parametrize( "x,ntaps,expected", ( (np.zeros(10), 5, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), (np.arange(10), 5, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), ), ) def test_opening(x, ntaps, expected): result = sl.opening(x, ntaps) expected = np.array(expected, dtype=np.complex) np.testing.assert_equal(result, expected) @pytest.mark.parametrize( "x,idx,ntaps,expected", ((np.arange(10 ** 2), np.array([45.567]), 5, [44.96565413]),), ) def test_resample(x, idx, ntaps, expected): result = sl.resample(x, idx, ntaps) np.testing.assert_allclose(result, expected, rtol=1e-9) @pytest.mark.parametrize( "x,delay,pad,pad_value,expected", ( ( [1 + 3j, 4 + 2j, 5 + 6j, 1 + 0j], 1, True, 1 + 0j, [10 - 10j, 32 + 14j, 5.0 - 6j, 1.0 + 0j], ), ( [1 + 3j, 4 + 2j, 5 + 6j, 1 + 0j], 1, False, 1 + 0j, [10 - 10j, 32 + 14j, 5.0 - 6j], ), ), ) def test_dcm(x, delay, pad, pad_value, expected): x =

np.array(x)

numpy.array

""" Simplified transforms.py (c) Delirium Digital Limited 2020 Written by <NAME> """ import cv2 import numpy as np import math # from scipy.spatial.transform import Rotation as R import tensorflow as tf import logging logger = logging.getLogger(__name__) def resize_image(data_numpy, x_scale, y_scale): return cv2.resize(data_numpy, None, fx=x_scale, fy=y_scale, interpolation=cv2.INTER_LINEAR) def rotate_image(image, angle): """ Rotate image around center by angle (degrees) """ (h, w) = image.shape[:2] center = (w / 2, h / 2) scale = 1.0 M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h)) return rotated def scale_keypoints(keypoints, x_scale=1, y_scale=1, z_scale=1): """ Scale keypoints based on x and y scales (useful for image crop and resize) """ scaled_keypoints = [] scale_matrix = np.array([x_scale, y_scale, z_scale]) for k in keypoints: scaled_keypoints.append(k * scale_matrix) return np.array(scaled_keypoints) def center_keypoints(keypoints, center_x, center_y): """ Center keypoints TODO: Is this needed? """ centered_keypoints = [] for keypoint in keypoints: kp = [center_x + keypoint[0], center_y - keypoint[1], keypoint[2]] centered_keypoints.append(kp) return np.array(centered_keypoints) def offset_keypoints(keypoints, x_offset=0, y_offset=0, z_offset=0): keypoints[:, 0] += x_offset keypoints[:, 1] += y_offset keypoints[:, 2] += z_offset return keypoints def flip_keypoints(keypoints): """ Flipped keypoints horizontally (x = -x) """ for i, k in enumerate(keypoints): keypoints[i, 0] = -keypoints[i, 0] return keypoints def rotate(x, y, xo, yo, theta): """rotate x,y around xo,yo by theta (rad)""" xr = math.cos(theta) * (x - xo) - math.sin(theta) * (y - yo) + xo yr = math.sin(theta) * (x - xo) + math.cos(theta) * (y - yo) + yo return [xr, yr] def rotate_keypoints(keypoints, origin, angle): """ Rotate keypoints Doesn't impact keypoints_vis as by this point the image is square (TODO: Validate this is true!) :param keypoints: :param angle: Angle in degrees (to align with rotate_image and opencv angles) :param origin: point to rotate around :return keypoints: """ rads = -np.deg2rad(angle) rotated_keypoints = [] for keypoint in keypoints: kp_r = rotate(keypoint[0], keypoint[1], origin[0], origin[1], rads) rotated_keypoints.append(kp_r) return rotated_keypoints def flip_back(output_flipped, matched_parts): """ output_flipped: numpy.ndarray(batch_size, num_keypoints, height, width) """ assert output_flipped.ndim == 4, \ 'output_flipped should be [batch_size, num_keypoints, height, width]' output_flipped = output_flipped[:, :, :, ::-1] for pair in matched_parts: tmp = output_flipped[:, pair[0], :, :].copy() output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] output_flipped[:, pair[1], :, :] = tmp return output_flipped # From https://github.com/nsantavas/Attention-A-Lightweight-2D-Hand-Pose-Estimation-Approach/blob/2136e93586f0d0eb40518f47d2b9f78c6220ea33/model_train.py#L62 def augment_image(image): """ Augment dataset with random brightness, saturation, contrast, and image quality. Then it is casted to bfloat16 and normalized """ image = tf.cast(image, tf.uint8) image = tf.image.random_jpeg_quality(image, min_jpeg_quality=70, max_jpeg_quality=100) image = tf.cast(image, tf.float32) image = tf.image.random_brightness(image, max_delta=25 / 255) image = tf.image.random_saturation(image, lower=0.3, upper=1.7) image = tf.image.random_contrast(image, lower=0.3, upper=1.7) image = tf.cast(image, tf.float32) return image # ------------------------------------------------------------------------------ # Code below from https://github.com/microsoft/human-pose-estimation.pytorch/blob/master/lib/utils/transforms.py # # Copyright (c) Microsoft # Licensed under the MIT License. # Written by <NAME> (<EMAIL>) # ------------------------------------------------------------------------------ def fliplr_joints(joints, joints_vis, width, matched_parts): """ flip coords """ # Flip horizontal joints[:, 0] = width - joints[:, 0] - 1 # Change left-right parts for pair in matched_parts: joints[pair[0], :], joints[pair[1], :] = \ joints[pair[1], :], joints[pair[0], :].copy() joints_vis[pair[0]], joints_vis[pair[1]] = \ joints_vis[pair[1]], joints_vis[pair[0]].copy() _joints_vis = np.transpose(np.array([joints_vis, joints_vis, joints_vis]), axes=[1, 0]) return joints*_joints_vis, joints_vis def get_affine_transform(center, scale, rot, output_size, shift=

np.array([0, 0], dtype=np.float32)

numpy.array

""" Project ------- """ from typing import Any from typing import Callable from typing import Dict from typing import List from typing import Optional from typing import Tuple from typing import Union import numpy as np import scipy.sparse as sp # type: ignore from anndata import AnnData # type: ignore import metacells.parameters as pr import metacells.utilities as ut __all__ = [ "renormalize_query_by_atlas", "project_query_onto_atlas", "find_systematic_genes", "project_atlas_to_query", "find_biased_genes", "compute_query_projection", ] @ut.logged() @ut.timed_call() def renormalize_query_by_atlas( # pylint: disable=too-many-statements,too-many-branches what: str = "__x__", *, adata: AnnData, qdata: AnnData, var_annotations: Dict[str, Any], layers: Dict[str, Any], varp_annotations: Dict[str, Any], ) -> Optional[AnnData]: """ Add an ``ATLASNORM`` pseudo-gene to query metacells data to compensate for the query having filtered out many genes. This renormalizes the gene fractions in the query to fit the atlas in case the query has aggressive filtered a significant amount of genes. **Input** Annotated query ``qdata`` and atlas ``adata``, where the observations are cells and the variables are genes, where ``X`` is a per-variable-per-observation matrix or the name of a per-variable-per-observation annotation containing such a matrix. **Returns** None if no normalization is needed (or possible). Otherwise, a copy of the query metacells data, with an additional variable (gene) called ``ATLASNORM`` to the query data, such that the total number of UMIs for each query metacells is as expected given the total number of UMIs of the genes common to the query and the atlas. This is skipped if the query and the atlas have exactly the same list of genes, or if if the query already contains a high number of genes missing from the atlas so that the total number of UMIs for the query metacells is already at least the expected based on the common genes. **Computation Parameters** 1. Computes how many UMIs should be added to each query metacell so that its (total UMIs / total common gene UMIs) would be the same as the (total atlas UMIs / total atlas common UMIs). If this is zero (or negative), stop. 2. Add an ``ATLASNORM`` pseudo-gene to the query with the above amount of UMIs. For each per-variable (gene) observation, add the value specified in ``var_annotations``, whose list of keys must cover the set of per-variable annotations in the query data. For each per-observation-per-variable layer, add the value specified in ``layers``, whose list of keys must cover the existing layers. For each per-variable-per-variable annotation, add the value specified in ``varp_annotations``. """ for name in qdata.var.keys(): if "|" not in name and name not in var_annotations.keys(): raise RuntimeError(f"missing default value for variable annotation {name}") for name in qdata.layers.keys(): if name not in layers.keys(): raise RuntimeError(f"missing default value for layer {name}") for name in qdata.varp.keys(): if name not in varp_annotations.keys(): raise RuntimeError(f"missing default value for variable-variable {name}") if list(qdata.var_names) == list(adata.var_names): return None query_genes_list = list(qdata.var_names) atlas_genes_list = list(adata.var_names) common_genes_list = list(sorted(set(qdata.var_names) & set(adata.var_names))) query_gene_indices = np.array([query_genes_list.index(gene) for gene in common_genes_list]) atlas_gene_indices = np.array([atlas_genes_list.index(gene) for gene in common_genes_list]) common_qdata = ut.slice(qdata, name=".common", vars=query_gene_indices) common_adata = ut.slice(adata, name=".common", vars=atlas_gene_indices) assert list(common_qdata.var_names) == list(common_adata.var_names) atlas_total_umis_per_metacell = ut.get_o_numpy(adata, what, sum=True) atlas_common_umis_per_metacell = ut.get_o_numpy(common_adata, what, sum=True) atlas_total_umis = np.sum(atlas_total_umis_per_metacell) atlas_common_umis = np.sum(atlas_common_umis_per_metacell) atlas_disjoint_umis_fraction = atlas_total_umis / atlas_common_umis - 1.0 ut.log_calc("atlas_total_umis", atlas_total_umis) ut.log_calc("atlas_common_umis", atlas_common_umis) ut.log_calc("atlas_disjoint_umis_fraction", atlas_disjoint_umis_fraction) query_total_umis_per_metacell = ut.get_o_numpy(qdata, what, sum=True) query_common_umis_per_metacell = ut.get_o_numpy(common_qdata, what, sum=True) query_total_umis = np.sum(query_total_umis_per_metacell) query_common_umis =

np.sum(query_common_umis_per_metacell)

numpy.sum

import os import sys import yaml import json import time import argparse import numpy as np import pickle import matplotlib.pyplot as plt from torch.utils.data import DataLoader import src.utils import src.dataset import src.evaluation if __name__ == "__main__": # config file parser = argparse.ArgumentParser(description="Test linear model.") parser.add_argument('--config', type=str, default="config_linear_test.yaml") args = parser.parse_args() ### END CONFIG ### ### PATHS & CONFIG project_root = os.getcwd() data_root = os.path.join(project_root, "datasets/maad") exp_root = os.path.join(project_root, "experiments") config_root = os.path.join(project_root, "config") # config config_path = os.path.join(config_root, args.config) with open(config_path, "r") as fin: config = yaml.load(fin, Loader=yaml.FullLoader) # data data_path_test = os.path.join(data_root, config["dataset"]["set"]) # experiment path method = config["model"]["type"] run_name = method date_time = src.utils.get_current_time() run_name = date_time + "_" + run_name exp_dir = os.path.join(exp_root, run_name) if not os.path.exists(exp_dir): os.makedirs(exp_dir) # create evaluation directory eval_dir = "eval_" + src.utils.get_current_time() eval_path = os.path.join(exp_dir, eval_dir) if not os.path.isdir(eval_path): os.makedirs(eval_path) ### DATA dset_test = src.dataset.MAADDataset(data_path_test, obs_len=config["model"]["obs_len"], adj_type="identity") loader_test = DataLoader(dset_test, batch_size=1, shuffle=False, num_workers=1) ### PREDICTION print("\nPredicting...") pred_start = time.time() prediction_data = {} step = 0 for cnt, batch in enumerate(loader_test): step += 1 # get data obs_traj, obs_traj_rel, frame_ids, seq_ids, labels, V_obs, A_obs = batch # prepare data obs_traj = obs_traj.numpy()[0] # its anyway batch size = 1 frame_ids = frame_ids.numpy()[0].tolist() seq_ids = seq_ids.numpy()[0] labels = labels.numpy()[0] # init linear trajectory linear_traj = np.zeros(obs_traj.shape) N = obs_traj.shape[0] # model each agent individually for i in range(N): # get agent trajectory agent_traj = obs_traj[i] # trajectory features start_pos = agent_traj[:, 0] end_pos = agent_traj[:, -1] n_ts = agent_traj.shape[1] if method == "cvm": # CVM velocity = agent_traj[:, 1] - agent_traj[:, 0] approx_agent_traj = np.zeros(agent_traj.shape) + velocity[:, np.newaxis] approx_agent_traj[:, 0] = start_pos approx_agent_traj = np.cumsum(approx_agent_traj, axis=1) elif method == "lti": # LTI x_interp = np.linspace(start_pos[0], end_pos[0], n_ts) y_interp = np.linspace(start_pos[1], end_pos[1], n_ts) approx_agent_traj = np.zeros(agent_traj.shape) approx_agent_traj[0] = x_interp approx_agent_traj[1] = y_interp else: sys.exit("Unknown model type {}. Abort!".format(method)) # add to matrix linear_traj[i] = approx_agent_traj if True: plt.plot(agent_traj[0], agent_traj[1]) plt.plot(approx_agent_traj[0], approx_agent_traj[1]) # prepare data for dict export obs_traj = np.transpose(obs_traj, (2, 0, 1)) linear_traj = np.transpose(linear_traj, (2, 0, 1)) frame_ids = np.transpose(frame_ids, (2, 0, 1)) labels =

np.transpose(labels, (2, 0, 1))

numpy.transpose

import numpy as np import xarray as xr import matplotlib.pyplot as plt import cartopy.crs as ccrs import cartopy.feature as cfeature from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER def format_lon_lat(ax, proj, lon_min, lon_max, lat_min, lat_max, title=''): from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter #from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER ax.set_extent([lon_min, lon_max, lat_min, lat_max], proj) # Add coastline ax.coastlines('50m') # Modify the title ax.set_title(title) # Set lon labels lon_labels =

np.arange(lon_min, lon_max + 1, 10)

numpy.arange

""" Copyright 2018 IBM Corporation 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. """ from gensim.utils import simple_preprocess from gensim.models import Word2Vec from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity import numpy as np class CustomParVec(): ''' Custom Paragraph Vector. Each paragraph(or sentence) is the sum of each of its word's Word2Vec vector representation scaled by that word's tf-idf. ''' def __init__(self, word_sentence_list, workers = 2, dimensions = 100, min_word_count = 2, context = 5, downsampling = 0, tfidf = True, pre_trained = None, supervised_docs = None): ''' Args: word_sentence_list (list[list[str]]) : List of lists of words that make up a paragraph(or sentence). workers (int) : Number of threads to run in parallel (Default = 2). dimensions (int) : Vector dimensionality (Default = 100). min_word_count (int) : Minimum word count (Default = 2). context (int) : Context window size (Default = 5). downsampling (int) : Downsample setting for frequent words (Default = 0). tfidf (bool) : Specify whether or not to use idf in scaling (Default = True). pre_trained (Word2Vec) : Use a pre-trained Word2Vec model (Default = None). supervised_docs (list[str]) : List of sentences from some "ground truth" (Default = None). ''' self.dimensions = dimensions # Set the number of dimension if not pre_trained: self.word2vec_model = Word2Vec(word_sentence_list, workers=workers, \ size=self.dimensions, min_count = min_word_count, \ window = context, sample = downsampling) self.word2vec_model.init_sims(replace=True) # used for memory efficiency else: self.word2vec_model = pre_trained self.sentences = [' '.join(words) for words in word_sentence_list] # Keep a list of the full sentences themselves. self.tf_idf_obj = TfidfVectorizer(use_idf = tfidf) # Create TfidfVectorizer object self.tf_idf_obj.fit(self.sentences) # Transform and fit tf-idf to all sentences(could be paragraphs) self.tf_idf = self.tf_idf_obj.transform(self.sentences) self.word_index = self.tf_idf_obj.get_feature_names() # Keep track of words by index for lookups if supervised_docs: self.extra_tf_idf_obj = TfidfVectorizer(use_idf = tfidf) # Create TfidfVectorizer object self.extra_tf_idf_obj.fit(supervised_docs) # Transform and fit tf-idf to all sentences(could be paragraphs) self.extra_tf_idf = self.extra_tf_idf_obj.transform(supervised_docs) self.extra_word_index = self.extra_tf_idf_obj.get_feature_names() # Keep track of words by index for lookups else: self.extra_tf_idf_obj = None def learnVectors(self): ''' Create a vector representation of every paragraph(or sentence) in the initial data provided. Yields: numpy.ndarray: Next numpy array representing the paragraph (or sentence). ''' rows, cols = self.tf_idf.nonzero() # Get the rows and column indices of non zero tf-idf values curr_line = 0 curr_vec = np.zeros(self.dimensions) for row, col in zip(rows, cols): if curr_line == row: # Check that the current word belongs to the same paragraph (or sentence). try: # Infer the vector of the current word by scaling the word's word2vec vector by its tf-idf value. # Add that inferred vector to the current vector representing the current paragraph. curr_vec += (self.word2vec_model[(self.word_index[col])] * self.tf_idf[row, col]) except: continue else: # If we are on the next paragraph, yield the current vector and reset it. yield(curr_vec) curr_line = row curr_vec = np.zeros(self.dimensions) try: curr_vec = self.word2vec_model[(self.word_index[col])] * self.tf_idf[row, col] except: continue def train(self): self.vectors = list(self.learnVectors()) def getMostSimilar(self, sentence, top_n = 10, threshold = 0.5, sentences = None, vectors = None): ''' Given a new sentence, find the closest top_n elements Args: sentence(string) : Text we want to find most similar to. top_n (int) : Total number of most similar tuples we want returned (Default value is 5). threshold (float) : Minimum Cosine Distance to be returned sentences (list[string]) : List of sentences to be compared to vectors[list[numpy nd array]] : Vector embedding of sentences Returns: list[(float, string)]: A list of (cosine similarity, sentence) tuples of size top_n closest to the input sentence. ''' inferred_vector = self.inferVector(sentence) if sentences and vectors: corpus = sentences vecs = vectors else: corpus = self.sentences vecs = self.vectors cos_similarities = np.ravel(cosine_similarity(inferred_vector.reshape(1,-1), vecs)) most_similar = np.argpartition(-cos_similarities, top_n)[:top_n] return [(cos_similarities[sentence_index], corpus[sentence_index]) for sentence_index in most_similar if cos_similarities[sentence_index] >= threshold] def inferVector(self, line): if self.extra_tf_idf_obj: return self.inferVector2(line) return self.inferVector1(line) def inferVector1(self, line): ''' Given a new line, infer a custom vector representation using the corpus tfidf. Args: line : new sentence to be inferred Returns: numpy.ndarray : vector representation of the line ''' line = ' '.join(simple_preprocess(line)) # pre-process the line line_tf_idf = self.tf_idf_obj.transform([line]) # infer the tf-idf values for the words in the line rows, cols = line_tf_idf.nonzero() new_vec = np.zeros(self.dimensions) # Apply the same sentence to vector conversion as above. for col in cols: try: new_vec += (self.word2vec_model[(self.word_index[col])] * line_tf_idf[0, col]) except: continue return

np.asarray(new_vec)

numpy.asarray

import os.path import numpy as np class MnistNShot: def __init__(self, root, batchsz, n_way, k_shot, k_query, imgsz): """ Different from mnistNShot, the :param root: :param batchsz: task num :param n_way: :param k_shot: :param k_qry: :param imgsz: """ self.resize = imgsz if not os.path.isfile(os.path.join(root, 'mnist.npy')): # TODO : May add formatting npy file here raise FileNotFoundError("Must have the npy file first") else: # if data.npy exists, just load it. self.x = np.load(os.path.join(root, 'mnist.npy')) print('load from mnist.npy.') # TODO: can not shuffle here, we must keep training and test set distinct! # mnist 10 classes self.x_train, self.x_test = self.x[:10], self.x[0:] # self.normalization() # normalization manually self.x_train /= 255. self.x_test /= 255. self.batchsz = batchsz self.n_cls = self.x.shape[0] # 1623 self.n_way = n_way # n way self.k_shot = k_shot # k shot self.k_query = k_query # k query # must less than number of images per class # assert (k_shot + k_query) <= 20 # save pointer of current read batch in total cache self.indexes = {"train": 0, "test": 0} self.datasets = {"train": self.x_train, "test": self.x_test} # original data cached print("DB: train", self.x_train.shape, "test", self.x_test.shape) self.datasets_cache = {"train": self.load_data_cache(self.datasets["train"]), # current epoch data cached "test": self.load_data_cache(self.datasets["test"])} def normalization(self): """ Normalizes our data, to have a mean of 0 and sdt of 1 """ # TODO: you can set mean and std here self.mean =

np.mean(self.x_train)

numpy.mean

import glob, os, time, sys import numpy as np import scipy.linalg as sl, scipy.stats, scipy.special class OutlierGibbs(object): """Gibbs-based pulsar-timing outlier analysis. Based on: Article by <NAME>: "Robust and Accurate Inference via a Mixture of Gaussian and Student's t Errors", https://doi.org/10.1080/10618600.2018.1537925 arXiv:1707.03057. Article by <NAME>: "Controlling Outlier Contamination In Multimessenger Time-domain Searches For Supermasssive Binary Black Holes", (In prep. 2021) Code from https://github.com/jellis18/gibbs_student_t Authors: <NAME>, <NAME>, <NAME> Example usage: > gibbs = OutlierGibbs(pta, model='mixture', vary_df=True, theta_prior='beta', vary_alpha=True) > params = np.array([p.sample() for p in gibbs.params]).flatten() > gibbs.sample(params, outdir='./outlier/', niter=10000, resume=False) > poutlier = np.mean(gibbs.poutchain, axis = 0) # Gives marginalized outlier probability of each TOA """ def __init__(self, pta, model='mixture', m=0.01, tdf=4, vary_df=True, theta_prior='beta', alpha=1e10, vary_alpha=True, pspin=None): """ Parameters ----------- pta : object instance of a pta object for a single pulsar model : str type of outlier model [default = mixture of Gaussian and Student's t] tdf : int degrees of freedom for Student's t outlier distribution [default = 4] m : float a-priori proportion of observations that are outliers [default = 0.01] vary_df : boolean vary the Student's t degrees of freedom [default = True] theta_prior : str prior outlier probability [default = beta distribution] alpha : float relative width of outlier to inlier distribution [default = 1e10] vary_alpha : boolean vary the relative outlier to inlier width [default = True] pspin : float pulsar spin period for vvh17 model, arXiv:1609.02144 [default = None] """ self.pta = pta if np.any(['basis_ecorr' in key for key in self.pta._signal_dict.keys()]): pass else: print('ERROR: Gibbs outlier analysis must use basis_ecorr, not kernel ecorr') # a-priori proportion of observations that are outliers self.mp = m # a-priori outlier probability distribution self.theta_prior = theta_prior # spin period self.pspin = pspin # vary t-distribution d.o.f self.vary_df = vary_df # vary alpha self.vary_alpha = vary_alpha # For now assume one pulsar self._residuals = self.pta.get_residuals()[0] # which likelihood model self._lmodel = model # auxiliary variable stuff xs = [p.sample() for p in pta.params] self._b = np.zeros(self.pta.get_basis(xs)[0].shape[1]) # for caching self.TNT = None self.d = None # outlier detection variables self._pout = np.zeros_like(self._residuals) self._z = np.zeros_like(self._residuals) if not vary_alpha: self._alpha = np.ones_like(self._residuals) * alpha else: self._alpha = np.ones_like(self._residuals) self._theta = self.mp self.tdf = tdf if model in ['t', 'mixture', 'vvh17']: self._z = np.ones_like(self._residuals) @property def params(self): ret = [] for param in self.pta.params: ret.append(param) return ret def map_params(self, xs): return {par.name: x for par, x in zip(self.params, xs)} def get_hyper_param_indices(self): ind = [] for ct, par in enumerate(self.params): if 'ecorr' in par.name or 'log10_A' in par.name or 'gamma' in par.name: ind.append(ct) return np.array(ind) def get_white_noise_indices(self): ind = [] for ct, par in enumerate(self.params): if 'efac' in par.name or 'equad' in par.name: ind.append(ct) return np.array(ind) def update_hyper_params(self, xs): # get hyper parameter indices hind = self.get_hyper_param_indices() # get initial log-likelihood and log-prior lnlike0, lnprior0 = self.get_lnlikelihood(xs), self.get_lnprior(xs) xnew = xs.copy() for ii in range(10): # standard gaussian jump (this allows for different step sizes) q = xnew.copy() sigmas = 0.05 * len(hind) probs = [0.1, 0.15, 0.5, 0.15, 0.1] sizes = [0.1, 0.5, 1.0, 3.0, 10.0] scale = np.random.choice(sizes, p=probs) par = np.random.choice(hind, size=1) # only one hyper param at a time q[par] += np.random.randn(len(q[par])) * sigmas * scale # get log-like and log prior at new position lnlike1, lnprior1 = self.get_lnlikelihood(q), self.get_lnprior(q) # metropolis step diff = (lnlike1 + lnprior1) - (lnlike0 + lnprior0) if diff > np.log(np.random.rand()): xnew = q lnlike0 = lnlike1 lnprior0 = lnprior1 else: xnew = xnew return xnew def update_white_params(self, xs): # get white noise parameter indices wind = self.get_white_noise_indices() xnew = xs.copy() lnlike0, lnprior0 = self.get_lnlikelihood_white(xnew), self.get_lnprior(xnew) for ii in range(20): # standard gaussian jump (this allows for different step sizes) q = xnew.copy() sigmas = 0.05 * len(wind) probs = [0.1, 0.15, 0.5, 0.15, 0.1] sizes = [0.1, 0.5, 1.0, 3.0, 10.0] scale = np.random.choice(sizes, p=probs) par = np.random.choice(wind, size=1) q[par] += np.random.randn(len(q[par])) * sigmas * scale # get log-like and log prior at new position lnlike1, lnprior1 = self.get_lnlikelihood_white(q), self.get_lnprior(q) # metropolis step diff = (lnlike1 + lnprior1) - (lnlike0 + lnprior0) if diff > np.log(np.random.rand()): xnew = q lnlike0 = lnlike1 lnprior0 = lnprior1 else: xnew = xnew return xnew def update_b(self, xs): # map parameter vector params = self.map_params(xs) # start likelihood calculations loglike = 0 # get auxiliaries Nvec = self._alpha**self._z * self.pta.get_ndiag(params)[0] phiinv = self.pta.get_phiinv(params, logdet=False)[0] residuals = self._residuals T = self.pta.get_basis(params)[0] if self.TNT is None and self.d is None: self.TNT = np.dot(T.T, T / Nvec[:,None]) self.d = np.dot(T.T, residuals/Nvec) #d = self.pta.get_TNr(params)[0] #TNT = self.pta.get_TNT(params)[0] # Red noise piece Sigma = self.TNT + np.diag(phiinv) try: u, s, _ = sl.svd(Sigma) mn = np.dot(u, np.dot(u.T, self.d)/s) Li = u * np.sqrt(1/s) except np.linalg.LinAlgError: Q, R = sl.qr(Sigma) Sigi = sl.solve(R, Q.T) mn = np.dot(Sigi, self.d) u, s, _ = sl.svd(Sigi) Li = u * np.sqrt(1/s) b = mn + np.dot(Li, np.random.randn(Li.shape[0])) return b def update_theta(self, xs): if self._lmodel in ['t', 'gaussian']: return self._theta elif self._lmodel in ['mixture', 'vvh17']: n = len(self._residuals) if self.theta_prior == 'beta': mk = n * self.mp k1mm = n * (1-self.mp) else: mk, k1mm = 1.0, 1.0 # from Tak, <NAME> (2018): k = sample size, m = 0.01 ret = scipy.stats.beta.rvs(np.sum(self._z) + mk, n - np.sum(self._z) + k1mm) return ret def update_z(self, xs): # map parameters params = self.map_params(xs) if self._lmodel in ['t', 'gaussian']: return self._z elif self._lmodel in ['mixture', 'vvh17']: Nvec0 = self.pta.get_ndiag(params)[0] Tmat = self.pta.get_basis(params)[0] Nvec = self._alpha * Nvec0 theta_mean = np.dot(Tmat, self._b) top = self._theta * scipy.stats.norm.pdf(self._residuals, loc=theta_mean, scale=np.sqrt(Nvec)) if self._lmodel == 'vvh17': top = self._theta / self.pspin bot = top + (1-self._theta) * scipy.stats.norm.pdf(self._residuals, loc=theta_mean, scale=np.sqrt(Nvec0)) q = top / bot q[np.isnan(q)] = 1 self._pout = q return scipy.stats.binom.rvs(1, list(map(lambda x: min(x, 1), q))) def update_alpha(self, xs): # map parameters params = self.map_params(xs) # equation 12 of Tak, <NAME> if np.sum(self._z) >= 1 and self.vary_alpha: Nvec0 = self.pta.get_ndiag(params)[0] Tmat = self.pta.get_basis(params)[0] theta_mean = np.dot(Tmat, self._b) top = ((self._residuals - theta_mean)**2 * self._z / Nvec0 + self.tdf) / 2 bot = scipy.stats.gamma.rvs((self._z + self.tdf) / 2) return top / bot else: return self._alpha def update_df(self, xs): if self.vary_df: # 1. evaluate the log conditional posterior of df for 1, 2, ..., 30. log_den_df = np.array(list(map(self.get_lnlikelihood_df, np.arange(1,31)))) # 2. normalize the probabilities den_df = np.exp(log_den_df - log_den_df.max()) den_df /= den_df.sum() # 3. sample one of values (1, 2, ..., 30) according to the probabilities df = np.random.choice(np.arange(1, 31), p=den_df) return df else: return self.tdf def get_lnlikelihood_white(self, xs): # map parameters params = self.map_params(xs) matrix = self.pta.get_ndiag(params)[0] # Nvec and Tmat Nvec = self._alpha**self._z * matrix Tmat = self.pta.get_basis(params)[0] # whitened residuals mn = np.dot(Tmat, self._b) yred = self._residuals - mn # log determinant of N logdet_N = np.sum(np.log(Nvec)) # triple product in likelihood function rNr = np.sum(yred**2/Nvec) # first component of likelihood function loglike = -0.5 * (logdet_N + rNr) return loglike def get_lnlikelihood(self, xs): # map parameter vector params = self.map_params(xs) # start likelihood calculations loglike = 0 # get auxiliaries Nvec = self._alpha**self._z * self.pta.get_ndiag(params)[0] phiinv, logdet_phi = self.pta.get_phiinv(params, logdet=True)[0] residuals = self._residuals T = self.pta.get_basis(params)[0] if self.TNT is None and self.d is None: self.TNT = np.dot(T.T, T / Nvec[:,None]) self.d = np.dot(T.T, residuals/Nvec) # log determinant of N logdet_N = np.sum(np.log(Nvec)) # triple product in likelihood function rNr = np.sum(residuals**2/Nvec) # first component of likelihood function loglike += -0.5 * (logdet_N + rNr) # Red noise piece Sigma = self.TNT + np.diag(phiinv) try: cf = sl.cho_factor(Sigma) expval = sl.cho_solve(cf, self.d) except np.linalg.LinAlgError: return -np.inf logdet_sigma = np.sum(2 * np.log(np.diag(cf[0]))) loglike += 0.5 * (np.dot(self.d, expval) - logdet_sigma - logdet_phi) return loglike def get_lnlikelihood_df(self, df): n = len(self._residuals) ll = -(df/2) * np.sum(np.log(self._alpha)+1/self._alpha) + \ n * (df/2) * np.log(df/2) - n*scipy.special.gammaln(df/2) return ll def get_lnprior(self, xs): return sum(p.get_logpdf(x) for p, x in zip(self.params, xs)) def sample(self, xs, outdir='./', niter=10000, resume=False): print(f'Creating chain directory: {outdir}') os.system(f'mkdir -p {outdir}') self.chain = np.zeros((niter, len(xs))) self.bchain = np.zeros((niter, len(self._b))) self.thetachain = np.zeros(niter) self.zchain = np.zeros((niter, len(self._residuals))) self.alphachain = np.zeros((niter, len(self._residuals))) self.poutchain = np.zeros((niter, len(self._residuals))) self.dfchain = np.zeros(niter) self.iter = 0 startLength = 0 xnew = xs if resume: print('Resuming from previous run...') # read in previous chains tmp_chains = [] tmp_chains.append(np.loadtxt(f'{outdir}/chain.txt')) tmp_chains.append(np.loadtxt(f'{outdir}/bchain.txt')) tmp_chains.append(np.loadtxt(f'{outdir}/thetachain.txt')) tmp_chains.append(np.loadtxt(f'{outdir}/zchain.txt')) tmp_chains.append(np.loadtxt(f'{outdir}/alphachain.txt')) tmp_chains.append(np.loadtxt(f'{outdir}/poutchain.txt')) tmp_chains.append(

np.loadtxt(f'{outdir}/dfchain.txt')

numpy.loadtxt

# -*- coding: utf-8 -*- """Clark_Whitehead_HW5_Neural_Network_final.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1c1Doh3Fy7Hv0paTgVngGdJeH6vg0vJX1 # Neural Network In this assignment you will build an artificial neural network _from scratch_, meaning without modern machine learning packages (e.g. scikit-learn, tensorflow, and pytorch). You should use numpy, and can use other standard python libraries if you like. ## Part 1: Neural Network with Stochastic Gradient Descent (4 points) Define a class NeuralNetwork that implements an artificial neural network with a single hidden layer. The hidden layer should have non-linear activation function (e.g. Rectificed Linear Units, ReLU), and the output should have a Softmax activation function. Use the template provided. The hard part of this is the **train** method, which requires computing lots of gradients. See the [notes](https://laulima.hawaii.edu/access/content/group/MAN.90549.202130/Notes_6__Introduction_to_Neural_Networks.pdf) on Laulima to see the equations for calculating these analytically. Translating these equations into code is non-trivial. The **backpropagation** algorithm is a dynamic programming algorithm that computes the gradients layer by layer, and can be written very elegantly in terms of matrix manipulations (a couple lines of numpy code). _Reminder: Do NOT copy and paste code from the internet. Write your own code._ ## Part 2: Apply Your Model to Fashion Dataset (3 points) We will test the model on the Fashion MNIST dataset. This is a 10-class classification task designed to be similar to the MNIST digit recognition dataset. The classes are different items of clothing (shoes, shirts, handbags, etc.) instead of digits. Here is an [introduction](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) and [github page](https://github.com/zalandoresearch/fashion-mnist). 1. Demonstrate overfitting in your network by plotting the training and test set losses vs. epoch. An *epoch* is one iteration through the training set; in SGD this means one weight update for each training example. You can use a smaller dataset so that you overfit faster, but clearly state how many examples are in your train and test sets. 2. Optimize the hyperparameters (learning rate and number of hidden neurons) of the neural network. Because the test dataset is fairly large (10k examples), you don't need to split off a separate validation set for this analysis. Report the best performance (test accuracy) and the best hyperparameters. 3. Visualize the 10 test examples with the largest loss. ## Part 3: Better and Faster: Mini-Batch SGD (3 points) Implement mini-batch gradient descent in your NeuralNetwork train method. This is much more efficient to update the weights on *batches* of training data, e.g. 100 examples at a time, which serves two purposes: (1) each update is a better, less-noisy estimate of the true gradient, and (2) the matrix multiplications can be parallelized for an almost-linear speedup with multiple cores or a GPU (by default, numpy should automatically use multiple CPUs for matrix multiplications). This requires implementing the forward and backpropagation computations efficiently, using matrix multiplications rather than for loops. """ import numpy as np from sklearn.metrics import accuracy_score # Download Fashion MNIST Dataset import gzip import os from urllib.request import urlretrieve import numpy as np import matplotlib.pyplot as plt def fashion_mnist(): """ Download compressed Fashion MNIST data to local directory, and unpack data into numpy arrays. Return (train_images, train_labels, test_images, test_labels). Args: None Returns: Tuple of (train_images, train_labels, test_images, test_labels), each a matrix. Rows are examples. Columns of images are pixel values. Columns of labels are a onehot encoding of the correct class. """ url = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/' files = ['train-images-idx3-ubyte.gz', 'train-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz', 't10k-labels-idx1-ubyte.gz'] path = './' # Download data to current directory. os.makedirs(path, exist_ok=True) # Create path if it doesn't exist. # Download any missing files for file in files: if file not in os.listdir(path): urlretrieve(url + file, os.path.join(path, file)) print("Downloaded %s to %s" % (file, path)) def _images(path): """Return images loaded locally.""" with gzip.open(path) as f: # First 16 bytes are magic_number, n_imgs, n_rows, n_cols pixels = np.frombuffer(f.read(), 'B', offset=16) return pixels.reshape(-1, 784).astype('float32') / 255 def _labels(path): """Return labels loaded locally.""" with gzip.open(path) as f: # First 8 bytes are magic_number, n_labels integer_labels = np.frombuffer(f.read(), 'B', offset=8) def _onehot(integer_labels): """Return matrix whose rows are onehot encodings of integers.""" n_rows = len(integer_labels) n_cols = integer_labels.max() + 1 onehot = np.zeros((n_rows, n_cols), dtype='uint8') onehot[np.arange(n_rows), integer_labels] = 1 return onehot return _onehot(integer_labels) train_images = _images(os.path.join(path, files[0])) train_labels = _labels(os.path.join(path, files[1])) test_images = _images(os.path.join(path, files[2])) test_labels = _labels(os.path.join(path, files[3])) return train_images, train_labels, test_images, test_labels train_images, train_labels, test_images, test_labels = fashion_mnist() # Plot examples from dataset. plt.figure(1, figsize=(14,6)) for i in range(10): plt.subplot(1,10, i+1) plt.imshow(train_images[i,:].reshape(28,28), cmap='bone') plt.title(f'Label: {train_labels[i].argmax()}') plt.xticks([]) plt.yticks([]) print(test_images.shape) train_images_1000 = train_images[0:1000, :] train_labels_1000 = train_labels[0:1000, :] train_images_100 = train_images[0:100, :] train_labels_100 = train_labels[0:100, :] test_images_1000 = test_images[0:1000, :] test_labels_1000 = test_labels[0:1000, :] test_images_100 = test_images[0:100, :] test_labels_100 = test_labels[0:100, :] train_images_10 = train_images[0:10, :] train_labels_10 = train_labels[0:10, :] train_images_1 = train_images[0:1, :] train_labels_1 = train_labels[0:1, :] print(train_images_10.shape) #x_train shape = 11x4 x_train = np.array([[1.2, 4.3, 2.1, 1.9], [6.2, 8.3, 5.1, 9.9], [2.3, 4.3, 3.1, 0.9], [4.1, 4.4, 1.1, 0.3], [6.1, 7.1, 8.1, 9.1], [1.0, 2.0, 1.0, 1.0], [5.1, 5.1, 5.1, 5.1], [1.8, 4.0, 3.9, 2.7], [4.4, 0.8, 1.9, 2.7], [6.9, 8.8, 5.7, 7.1]]) #y_train shape = 1x11 y_train = np.array([[1,0], [0,1], [1,0], [1,0], [0,1], [1,0], [0,1], [1,0], [1,0], [0,1]]) # Part 1: Defining the neural network. class NeuralNetwork(): def __init__(self, inputs, hidden, outputs): """ Initialize a simple neural network with a single hidden layer. This method randomly initializes the parameters of the model, saving them as private variables. Each layer is parameterized by a weight matrix and a bias vector; a useful trick is store the weights and biases for a layer together, as a single matrix. Args: inputs: int, input dimension hidden: int, number of hidden neurons outputs: int, number of output neurons Returns: None """ # Initialize the weights and biases of the neural network as private variables. # Store a weight matrix for each layer. self.hidden = hidden self.train_acc = [] self.test_acc = [] self.train_loss = [] self.test_loss = [] self.w1 = 2 * np.random.rand(inputs, hidden) - 1 self.w2 = 2 * np.random.rand(hidden, outputs) - 1 def loss(self, y_true, y_pred): """ Compute categorical cross-entropy loss function. Sum loss contributions over the outputs (axis=1), but average over the examples (axis=0) Args: y_true: NxD numpy array with N examples and D outputs (one-hot labels). y_pred: NxD numpy array with N examples and D outputs (probabilities). Returns: loss: array of length N representing loss for each example. """ # WRITE ME loss = np.sum(y_pred * y_true, axis=1) #multiply the one_hot from y_true by it's predicted value loss = -np.log(loss) #take neg log of the predicted value loss = np.mean(loss) #take mean of loss from whole batch return loss def softmax(self, input): normalization = input - np.max(input, axis=1, keepdims=True) #subtract max value from all #values in order to keep exp from exploding exp = np.exp(input) #take e to the x where x=inputs for all z before softmax output = exp / np.sum(exp, axis=1, keepdims=True) #divide all exp by sum so their combined total = 1 output = np.clip(output, 1e-7, 1 - 1e-7) #clip in case there is a 0 or a 1. Necessary for loss so we don't take log of 0 # output = np.sum(output * targets, axis=1, keepdims=1) return output def relu(self, input): output = np.maximum(0, input) return output def predict(self, X): """ Make predictions on inputs X. Args: X: NxM numpy array where n-th row is an input. Returns: y_pred: NxD array where n-th row is vector of probabilities. """ # WRITE ME self.h = np.dot(X, self.w1) self.h = self.relu(self.h) output = np.dot(self.h, self.w2) y_pred = self.softmax(output) return y_pred def evaluate(self, X, y): """ Make predictions and compute loss. Args: X: NxM numpy array where n-th row is an input. y: NxD numpy array with N examples and D outputs (one-hot labels). Returns: loss: array of length N representing loss for each example. """ # WRITE ME y_pred = self.predict(X) y_true = y loss = self.loss(y_pred, y_true) return loss def train(self, X, y, X_test, y_test, max_epochs, lr=0.0001): """ Train the neural network using stochastic gradient descent. Args: X: NxM numpy array where n-th row is an input. y: NxD numpy array with N examples and D outputs (one-hot labels). lr: scalar learning rate. Use small value for debugging. max_epochs: int, each epoch is one iteration through the training data. Returns: None """ # WRITE ME for i in range(max_epochs): y_pred = self.predict(X) self.train_loss.append(self.loss(y, y_pred)) acc_pred = np.where(y_pred > 0.5, 1, 0) self.train_acc.append(accuracy_score(acc_pred, y)) #derivatives dl_dz = y_pred - y dl_dw2 = np.dot(self.h.T, dl_dz) dh_dzh = self.h > 0 dl_dzh =

np.dot(dl_dz, self.w2.T)

numpy.dot

# -*- coding: utf-8 -*- # run in py3 !! import os os.environ["CUDA_VISIBLE_DEVICES"] = "1"; import tensorflow as tf config = tf.ConfigProto() # config.gpu_options.per_process_gpu_memory_fraction=0.5 config.gpu_options.allow_growth = True tf.Session(config=config) import numpy as np from sklearn import preprocessing import tensorflow as tf import time import matplotlib as mpl mpl.use('Agg') import matplotlib.pyplot as plt from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import pandas as pd from keras import backend as K import keras.layers.convolutional as conv from keras.layers import merge from keras.wrappers.scikit_learn import KerasRegressor from keras import utils from keras.layers.pooling import MaxPooling1D, MaxPooling2D from keras.layers import pooling from keras.models import Sequential, Model from keras.regularizers import l1, l2 from keras import layers from keras.layers import Dense, Dropout, Activation, Flatten, Input, Convolution1D, Convolution2D, LSTM from keras.optimizers import SGD, RMSprop from keras.layers.normalization import BatchNormalization from keras import initializers from keras.callbacks import EarlyStopping from keras import callbacks from keras import backend as K from keras.utils import to_categorical from keras.callbacks import EarlyStopping, ModelCheckpoint, Callback from keras.models import Model from keras import initializers, layers from keras.optimizers import SGD, Adadelta, Adam from keras.regularizers import l1, l2 from keras import regularizers import sys sys.path.append('.') from hist_figure import his_figures if len(sys.argv) > 1: prefix = sys.argv[1] else: prefix = time.time() DATAPATH = '5fold/' RESULT_PATH = './results/' feature_num = 25 batch_num = 2 # batch_size = 32 batch_size = 512 SEQ_LENGTH = 20 STATEFUL = False scaler = None # tmp, for fit_transform # id,usage,date,com_date,week,month,year # com_date,date,id,month,usage,week,year def get_data(path_to_dataset='df_dh.csv', sequence_length=20, stateful=False, issplit=True): fold_index = 1 ### dtypes = {'sub': 'float', 'super': 'float', 'error': 'float', 'com_date': 'int', 'week': 'str', 'month': 'str', 'year': 'str', 'numbers': 'int', 'log': 'float', 'id': 'str', 'usage': 'float'} parse_dates = ['date'] print(path_to_dataset) df = pd.read_csv(DATAPATH + path_to_dataset, header=0, dtype=dtypes, parse_dates=parse_dates, encoding="utf-8") # print(path_to_dataset) print(df.columns) df = df[df['error'] >= 0] # df_test = pd.read_csv(DATAPATH+"test"+str(fold_index)+".csv", header = 0, dtype=dtypes, parse_dates=parse_dates,encoding="utf-8") def helper(x): split = list(map(int, x.strip('[').strip(']').split(','))) d = {} for counter, value in enumerate(split): k = str(len(split)) + "-" + str(counter) d[k] = value return d # df_train_temp = df_train['week'].apply(helper).apply(pd.Series) df_week = df['week'].apply(helper).apply(pd.Series).as_matrix() # 7 df_month = df['month'].apply(helper).apply(pd.Series).as_matrix() # 12 df_year = df['year'].apply(helper).apply(pd.Series).as_matrix() # 3 df_empty = df[['super', 'com_date', 'error', 'numbers']].copy() # print(df_empty) df_super = df_empty.ix[:, [0]] df_com_date = df_empty.ix[:, [1]] df_error = df_empty.ix[:, [2]] df_numbers = df_empty.ix[:, [3]] X_train_ = np.column_stack((df_super, df_com_date, df_numbers, df_week, df_month)) Y_train_ = df_error.as_matrix() ss_x = preprocessing.MaxAbsScaler() ss_y = preprocessing.MaxAbsScaler() global scaler scaler = ss_y # ss_x = preprocessing.StandardScaler() array_new = ss_x.fit_transform(df_empty.ix[:, [0]]) df_super = pd.DataFrame(array_new) array_new = ss_x.fit_transform(df_empty.ix[:, [1]]) df_com_date = pd.DataFrame(array_new) array_new = ss_x.fit_transform(df_empty.ix[:, [3]]) df_numbers = pd.DataFrame(array_new) array_new = ss_y.fit_transform(df_empty.ix[:, [2]]) df_error = pd.DataFrame(array_new) df_week = ss_x.fit_transform(df_week) df_week = pd.DataFrame(df_week) df_month = ss_x.fit_transform(df_month) df_month = pd.DataFrame(df_month) X_train =

np.column_stack((df_super, df_com_date, df_numbers, df_week, df_month))

numpy.column_stack

import caffe from skimage import color, io import cv2 import numpy as np import glog as log import os import tifffile import time def load_network(prototxt='models/test.prototxt', caffemodel='models/trn_iter_15000.caffemodel', gpu_num=0): caffe.set_mode_gpu() caffe.set_device(gpu_num) log.info('DCNN Weights: %s' % caffemodel) log.info('DCNN Model Definition: %s' % prototxt) return caffe.Net(prototxt, caffemodel, caffe.TEST) # fac = 2 ^ (number of times image is downsampled in the network) def prepare_input(img_in, fac=8, extra_pad=0, pad_mode='reflect', read_img=True): if read_img: log.info('Reading %s' % img_in) img_in = cv2.imread(img_in) if img_in.ndim == 2: # force color conversion img_in = np.repeat(img_in[:,:,np.newaxis],3,axis=2) # Pad H,W to make them multiples of fac H=img_in.shape[0] W=img_in.shape[1] padH = (fac - (H % fac)) % fac padW = (fac - (W % fac)) % fac if padH>0 or padW>0 or extra_pad>0: img_in = np.pad(img_in,pad_width=((extra_pad,padH+extra_pad),(extra_pad,padW+extra_pad),(0,0)),mode=pad_mode) img_in=img_in.astype(np.float32) return (img_in, H, W) # Forward Pass def forward_once(net, img_in, H, W): t=time.time() # H*W*C to C*H*W img_in=

np.transpose(img_in,[2,0,1])

numpy.transpose

from torch.utils.data import DataLoader, Dataset, Sampler from pathlib import Path from collections import defaultdict import json import random from multiprocessing import Pool import h5py import pickle import math from tqdm import tqdm import torch import numpy as np import csv from copy import deepcopy from torch.utils.data.distributed import DistributedSampler from transformers import T5TokenizerFast, BartTokenizer from tokenization import VLT5TokenizerFast, VLT5Tokenizer project_dir = Path(__file__).resolve().parent.parent # VLT5 workspace_dir = project_dir.parent dataset_dir = workspace_dir.joinpath('datasets/').resolve() vcr_dir = dataset_dir.joinpath('VCR') vcr_img_dir = vcr_dir.joinpath('vcr1images') vcr_feature_dir = vcr_dir.joinpath('features') class VCRFineTuneDataset(Dataset): def __init__(self, split='train', raw_dataset=None, rank=-1, topk=-1, verbose=True, args=None, mode='train'): super().__init__() self.topk = topk self.verbose = verbose self.args = args self.mode = mode # Loading datasets to data self.split = split self.sources = split.split(',') if self.verbose: print('Data sources: ', self.sources) if 't5' in self.args.backbone: if self.args.use_vision: self.tokenizer = VLT5TokenizerFast.from_pretrained( args.backbone, max_length=self.args.max_text_length, do_lower_case=self.args.do_lower_case) else: self.tokenizer = T5TokenizerFast.from_pretrained( args.backbone, max_length=self.args.max_text_length, do_lower_case=self.args.do_lower_case) elif 'bart' in self.args.backbone: self.tokenizer = BartTokenizer.from_pretrained( args.backbone, # max_length=self.args.max_text_length, do_lower_case=self.args.do_lower_case) additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \ [f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)] special_tokens_dict = {'additional_special_tokens': additional_special_tokens} num_added_toks = self.tokenizer.add_special_tokens(special_tokens_dict) self.img_ids_to_source = {} data_info_dicts = [] for source in self.sources: data_info_path = dataset_dir.joinpath(f'VCR/{source}.jsonl') with open(data_info_path) as f: _data_info_dicts = [json.loads(s) for s in f] for _d in _data_info_dicts: self.img_ids_to_source[_d['img_id']] = source _d['source'] = source data_info_dicts.extend(_data_info_dicts) if self.verbose: print(f"Loaded {len(_data_info_dicts)} data from", source) data = data_info_dicts self.rank = rank if self.topk > 0: data = data[:self.topk] if self.verbose: print(f"Use only {self.topk} data") self.data = data if self.verbose: print("# all sentences:", len(self.data)) self.n_boxes = args.n_boxes self.source_to_h5 = { 'train': vcr_feature_dir.joinpath(f'train_boxes36.h5'), 'val': vcr_feature_dir.joinpath(f'val_boxes36.h5'), 'test': vcr_feature_dir.joinpath(f'test_boxes36.h5'), 'train_GT': vcr_feature_dir.joinpath(f'train_boxes_GT.h5'), 'val_GT': vcr_feature_dir.joinpath(f'val_boxes_GT.h5'), 'test_GT': vcr_feature_dir.joinpath(f'test_boxes_GT.h5'), } def __len__(self): return len(self.data) def __getitem__(self, idx): out_dict = {} out_dict['args'] = self.args datum = self.data[idx] uid = f"{datum['img_id']}_{datum['question_number']}" out_dict['uid'] = uid test = 'test' in datum['annot_id'] out_dict['is_test'] = test ###### Image ###### assert self.args.use_vision img_id = datum['img_id'] out_dict['img_id'] = img_id img_path = vcr_img_dir.joinpath(datum['img_fn']) # assert img_path.exists() out_dict['img_path'] = img_path source = self.img_ids_to_source[img_id] f = self.source_to_h5[source] f_GT = self.source_to_h5[f'{source}_GT'] if isinstance(f, Path): f = h5py.File(f, 'r') self.source_to_h5[source] = f if isinstance(f_GT, Path): f_GT = h5py.File(f_GT, 'r') self.source_to_h5[f'{source}_GT'] = f_GT img_h = f[f'{img_id}/img_h'][()] img_w = f[f'{img_id}/img_w'][()] gt_boxes = f_GT[f'{img_id}/boxes'][()] # (x1, y1, x2, y2) n_gt_boxes = min(len(gt_boxes), 36) gt_boxes = gt_boxes[:n_gt_boxes] n_pred_boxes = 36 - n_gt_boxes pred_boxes = f[f'{img_id}/boxes'][:n_pred_boxes] boxes = np.concatenate([gt_boxes, pred_boxes], axis=0) # Normalize the boxes (to 0 ~ 1) boxes[:, (0, 2)] /= img_w boxes[:, (1, 3)] /= img_h np.testing.assert_array_less(boxes, 1+1e-5)

np.testing.assert_array_less(-boxes, 0+1e-5)

numpy.testing.assert_array_less

import numpy as np import pandas as pd import pytest from scipy import stats from locan import LocData from locan.analysis import BlinkStatistics from locan.analysis.blinking import _blink_statistics, _DistributionFits def test__blink_statistics_0(): # frame with on and off periods up to three frames and starting with one-frame on-period. frames = np.array([0, 4, 6, 7, 8, 12, 13]) results = _blink_statistics(frames, memory=0, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert np.array_equal(results["on_periods"], [1, 1, 3, 2]) assert np.array_equal(results["off_periods"], [3, 1, 3]) assert np.array_equal(results["on_periods_frame"], [0, 4, 6, 12]) assert np.array_equal(results["off_periods_frame"], [1, 5, 9]) assert all( [ np.array_equal(one, two) for one, two in zip( results["on_periods_indices"], [[0], [1], [2, 3, 4], [5, 6]] ) ] ) results = _blink_statistics(frames, memory=1, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert np.array_equal(results["on_periods"], [1, 5, 2]) assert np.array_equal(results["off_periods"], [3, 3]) assert np.array_equal(results["on_periods_frame"], [0, 4, 12]) assert np.array_equal(results["off_periods_frame"], [1, 9]) assert all( [ np.array_equal(one, two) for one, two in zip( results["on_periods_indices"], [[0], [1, 2, 3, 4], [5, 6]] ) ] ) results = _blink_statistics(frames, memory=10, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert np.array_equal(results["on_periods"], [14]) assert np.array_equal(results["off_periods"], []) assert np.array_equal(results["on_periods_frame"], [0]) assert np.array_equal(results["off_periods_frame"], []) assert all( [ np.array_equal(one, two) for one, two in zip(results["on_periods_indices"], [[0, 1, 2, 3, 4, 5, 6]]) ] ) def test__blink_statistics_1(): # frame with on and off periods up to three frames and starting with two-frame on-period. frames = np.array([0, 1, 3, 6, 7, 8, 12, 13]) results = _blink_statistics(frames, memory=0, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert np.array_equal(results["on_periods"], [2, 1, 3, 2]) assert np.array_equal(results["off_periods"], [1, 2, 3]) assert np.array_equal(results["on_periods_frame"], [0, 3, 6, 12]) assert np.array_equal(results["off_periods_frame"], [2, 4, 9]) assert all( [ np.array_equal(one, two) for one, two in zip( results["on_periods_indices"], [[0, 1], [2], [3, 4, 5], [6, 7]] ) ] ) results = _blink_statistics(frames, memory=1, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert np.array_equal(results["on_periods"], [4, 3, 2]) assert np.array_equal(results["off_periods"], [2, 3]) assert np.array_equal(results["on_periods_frame"], [0, 6, 12]) assert np.array_equal(results["off_periods_frame"], [4, 9]) assert all( [ np.array_equal(one, two) for one, two in zip( results["on_periods_indices"], [[0, 1, 2], [3, 4, 5], [6, 7]] ) ] ) results = _blink_statistics(frames, memory=10, remove_heading_off_periods=False) assert len(results["on_periods"]) == len(results["on_periods_frame"]) assert len(results["off_periods"]) == len(results["off_periods_frame"]) assert

np.array_equal(results["on_periods"], [14])

numpy.array_equal

""" Experiment runner for the model with knowledge graph attached to interaction data """ from __future__ import division from __future__ import print_function import argparse import datetime import time import tensorflow as tf import numpy as np import scipy.sparse as sp import sys import json from preprocessing import create_trainvaltest_split, \ sparse_to_tuple, preprocess_user_item_features, globally_normalize_bipartite_adjacency, \ load_data_monti, load_official_trainvaltest_split, normalize_features from model import RecommenderGAE, RecommenderSideInfoGAE from utils import construct_feed_dict def run(user_features, movie_features, learning_rate=0.01, epochs=500, hidden=[500, 75], feat_hidden=64, accumulation='sum', dropout=0.7, num_basis_functions=2, features=False, symmetric=True, testing=True): """accumulation can be sum or stack""" # Set random seed # seed = 123 # use only for unit testing seed = int(time.time()) np.random.seed(seed) tf.set_random_seed(seed) tf.reset_default_graph() # Settings # ap = argparse.ArgumentParser() # # ap.add_argument("-d", "--dataset", type=str, default="ml_100k", # # choices=['ml_100k', 'ml_1m', 'ml_10m', 'douban', 'yahoo_music', 'flixster'], # # help="Dataset string.") # ap.add_argument("-lr", "--learning_rate", type=float, default=0.01, # help="Learning rate") # ap.add_argument("-e", "--epochs", type=int, default=2500, # help="Number training epochs") # ap.add_argument("-hi", "--hidden", type=int, nargs=2, default=[500, 75], # help="Number hidden units in 1st and 2nd layer") # ap.add_argument("-fhi", "--feat_hidden", type=int, default=64, # help="Number hidden units in the dense layer for features") # ap.add_argument("-ac", "--accumulation", type=str, default="sum", choices=['sum', 'stack'], # help="Accumulation function: sum or stack.") # ap.add_argument("-do", "--dropout", type=float, default=0.7, # help="Dropout fraction") # ap.add_argument("-nb", "--num_basis_functions", type=int, default=2, # help="Number of basis functions for Mixture Model GCN.") # ap.add_argument("-ds", "--data_seed", type=int, default=1234, # help="""Seed used to shuffle data in data_utils, taken from cf-nade (1234, 2341, 3412, 4123, 1324). # Only used for ml_1m and ml_10m datasets. """) # ap.add_argument("-sdir", "--summaries_dir", type=str, default='logs/' + str(datetime.datetime.now()).replace(' ', '_'), # help="Directory for saving tensorflow summaries.") # # Boolean flags # fp = ap.add_mutually_exclusive_group(required=False) # fp.add_argument('-nsym', '--norm_symmetric', dest='norm_symmetric', # help="Option to turn on symmetric global normalization", action='store_true') # fp.add_argument('-nleft', '--norm_left', dest='norm_symmetric', # help="Option to turn on left global normalization", action='store_false') # ap.set_defaults(norm_symmetric=True) # fp = ap.add_mutually_exclusive_group(required=False) # fp.add_argument('-f', '--features', dest='features', # help="Whether to use features (1) or not (0)", action='store_true') # fp.add_argument('-no_f', '--no_features', dest='features', # help="Whether to use features (1) or not (0)", action='store_false') # ap.set_defaults(features=False) # fp = ap.add_mutually_exclusive_group(required=False) # fp.add_argument('-ws', '--write_summary', dest='write_summary', # help="Option to turn on summary writing", action='store_true') # fp.add_argument('-no_ws', '--no_write_summary', dest='write_summary', # help="Option to turn off summary writing", action='store_false') # ap.set_defaults(write_summary=False) # fp = ap.add_mutually_exclusive_group(required=False) # fp.add_argument('-t', '--testing', dest='testing', # help="Option to turn on test set evaluation", action='store_true') # fp.add_argument('-v', '--validation', dest='testing', # help="Option to only use validation set evaluation", action='store_false') # ap.set_defaults(testing=False) # args = vars(ap.parse_args()) # print('Settings:') # print(args, '\n') # Define parameters DATASET = 'ml_100k' DATASEED = 1234 NB_EPOCH = epochs DO = dropout HIDDEN = hidden FEATHIDDEN = feat_hidden BASES = num_basis_functions LR = learning_rate WRITESUMMARY = False SUMMARIESDIR = 'logs/' + str(datetime.datetime.now()).replace(' ', '_') FEATURES = features SYM = symmetric TESTING = testing ACCUM = accumulation SELFCONNECTIONS = False SPLITFROMFILE = True VERBOSE = True NUMCLASSES = 5 # Splitting dataset in training, validation and test set print("Using official MovieLens dataset split u1.base/u1.test with 20% validation set size...") u_features = user_features v_features = movie_features _, _, adj_train, train_labels, train_u_indices, train_v_indices, \ val_labels, val_u_indices, val_v_indices, test_labels, \ test_u_indices, test_v_indices, class_values = load_official_trainvaltest_split('ml_100k', TESTING) num_users, num_items = adj_train.shape num_side_features = 0 # feature loading if not FEATURES: u_features = sp.identity(num_users, format='csr') v_features = sp.identity(num_items, format='csr') u_features, v_features = preprocess_user_item_features(u_features, v_features) elif FEATURES and u_features is not None and v_features is not None: # use features as side information and node_id's as node input features print("Normalizing feature vectors...") u_features_side = normalize_features(u_features) v_features_side = normalize_features(v_features) u_features_side, v_features_side = preprocess_user_item_features(u_features_side, v_features_side) u_features_side = np.array(u_features_side.todense(), dtype=np.float32) v_features_side = np.array(v_features_side.todense(), dtype=np.float32) num_side_features = u_features_side.shape[1] # node id's for node input features id_csr_v = sp.identity(num_items, format='csr') id_csr_u = sp.identity(num_users, format='csr') u_features, v_features = preprocess_user_item_features(id_csr_u, id_csr_v) else: raise ValueError('Features flag is set to true but no features are loaded from dataset ' + DATASET) # global normalization support = [] support_t = [] adj_train_int = sp.csr_matrix(adj_train, dtype=np.int32) for i in range(NUMCLASSES): # build individual binary rating matrices (supports) for each rating support_unnormalized = sp.csr_matrix(adj_train_int == i + 1, dtype=np.float32) if support_unnormalized.nnz == 0 and DATASET != 'yahoo_music': # yahoo music has dataset split with not all ratings types present in training set. # this produces empty adjacency matrices for these ratings. sys.exit('ERROR: normalized bipartite adjacency matrix has only zero entries!!!!!') support_unnormalized_transpose = support_unnormalized.T support.append(support_unnormalized) support_t.append(support_unnormalized_transpose) support = globally_normalize_bipartite_adjacency(support, symmetric=SYM) support_t = globally_normalize_bipartite_adjacency(support_t, symmetric=SYM) if SELFCONNECTIONS: support.append(sp.identity(u_features.shape[0], format='csr')) support_t.append(sp.identity(v_features.shape[0], format='csr')) num_support = len(support) support = sp.hstack(support, format='csr') support_t = sp.hstack(support_t, format='csr') if ACCUM == 'stack': div = HIDDEN[0] // num_support if HIDDEN[0] % num_support != 0: print("""\nWARNING: HIDDEN[0] (=%d) of stack layer is adjusted to %d such that it can be evenly split in %d splits.\n""" % (HIDDEN[0], num_support * div, num_support)) HIDDEN[0] = num_support * div # Collect all user and item nodes for test set test_u = list(set(test_u_indices)) test_v = list(set(test_v_indices)) test_u_dict = {n: i for i, n in enumerate(test_u)} test_v_dict = {n: i for i, n in enumerate(test_v)} test_u_indices = np.array([test_u_dict[o] for o in test_u_indices]) test_v_indices =

np.array([test_v_dict[o] for o in test_v_indices])

numpy.array

import os import sys import imp import numpy as np import warnings warnings.filterwarnings("ignore") # Test for Torch def torch(test_models, model_path, img_path): results_o, results_d, op_sets = dict(), dict(), dict() from PIL import Image import torch import torchvision.models as models from torchvision import transforms from torch.autograd import Variable # Torch to IR from ox.pytorch.pytorch_parser import PytorchParser for model in test_models: if 'inception' in model: image_size = 299 else: image_size = 224 image = Image.open(img_path) transformation = transforms.Compose([ transforms.Resize((image_size, image_size)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) image_tensor = transformation(image).float() image_tensor = image_tensor.unsqueeze_(0) x = Variable(image_tensor) inputshape = [3, image_size, image_size] arch_filename = os.path.join(model_path, 'PyTorch', model+'.pth') # test model if 'resnet50' in model: model_eval = models.resnet50() elif 'inception' in model: from models.torch import inception model_eval = inception.inceptionresnetv2(pretrained=False) elif 'shufflenet' in model: from models.torch import shufflenet model_eval = shufflenet.shufflenet() elif 'fcn' in model: from models.torch import fcn model_eval = fcn.FCNs() elif 'lstm' in model: from models.torch import lstm model_eval = lstm.Lstm() model_eval.eval() predict = model_eval(x).data.numpy() preds = np.squeeze(predict) print('\033[1;31;40m') print(' Result of', model, ': ', np.argmax(preds)) print('\033[0m') results_o[model] = preds torch.save(model_eval, arch_filename) # convert IR_filename = os.path.join(model_path, 'IR', model+'_torch') parser = PytorchParser(arch_filename, inputshape) ops = parser.run(IR_filename) op_sets[model] = ops del parser del PytorchParser # IR to Torch from ox.pytorch.pytorch_emitter import PytorchEmitter for model in test_models: if 'inception' in model: image_size = 299 else: image_size = 224 image = Image.open(img_path) transformation = transforms.Compose([ transforms.Resize((image_size, image_size)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) image_tensor = transformation(image).float() image_tensor = image_tensor.unsqueeze_(0) x = Variable(image_tensor) inputshape = [3, image_size, image_size] arch_filename = os.path.join(model_path, 'IR', model+'_torch.pb') weight_filename = os.path.join(model_path, 'IR', model+'_torch.npy') converted_file = os.path.join(model_path, 'PyTorch', model+'_ox') emitter = PytorchEmitter((arch_filename, weight_filename)) emitter.run(converted_file + '.py', converted_file + '.npy', 'test') model_converted = imp.load_source('PytorchModel', converted_file + '.py').KitModel(converted_file + '.npy') model_converted.eval() predict = model_converted(x).data.numpy() preds = np.squeeze(predict) print('\033[1;31;40m') print(' Result of ', model+'_ox : ', np.argmax(preds)) print('\033[0m') results_d[model] = np.mean(np.power(results_o[model] - preds, 2)) del emitter del PytorchEmitter return results_d, op_sets # Test for Tensorflow def tensorflow(test_models, model_path, img_path): results_o, results_d, op_sets = dict(), dict(), dict() import tensorflow as tf from PIL import Image image = Image.open(img_path) # Tensorflow to IR from ox.tensorflow.tensorflow_parser import TensorflowParser for model in test_models: arch_filename = os.path.join(model_path, 'tensorflow', model, model+'.ckpt.meta') weight_filename = os.path.join(model_path, 'tensorflow', model, model+'.ckpt') # test model if 'resnet50' in model: img = np.array(image.resize((299, 299), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import resnet50 preds = resnet50.test(x, model_path) elif 'inception' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import inception_v3 preds = inception_v3.test(x, model_path) elif 'shufflenet' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import shufflenet preds = shufflenet.test(x, model_path) elif 'fcn' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import fcn preds = fcn.test(x, model_path) elif 'lstm' in model: img = np.array(image.resize((224, 224), Image.ANTIALIAS)) x = np.expand_dims(img, axis=0) from models.tf import lstm preds = lstm.test(x, model_path) print('\033[1;31;40m') print(' Result of', model, ': ', np.argmax(preds)) print('\033[0m') preds = np.squeeze(preds) if 'fcn' in model: preds = np.array(preds).astype(np.int32) results_o[model] = preds import tensorflow.contrib.keras as keras keras.backend.clear_session() # parser IR_filename = os.path.join(model_path, 'IR', model+'_tf') parser = TensorflowParser(arch_filename, weight_filename, ["OX_output"]) ops = parser.run(IR_filename) op_sets[model] = ops del parser del TensorflowParser # IR to Tensorflow from ox.tensorflow.tensorflow_emitter import TensorflowEmitter for model in test_models: arch_filename = os.path.join(model_path, 'IR', model+'_tf.pb') weight_filename = os.path.join(model_path, 'IR', model+'_tf.npy') converted_file = os.path.join(model_path, 'tensorflow', model, model+'_ox') emitter = TensorflowEmitter((arch_filename, weight_filename)) emitter.run(converted_file + '.py', None, 'test') # test model if 'resnet' in model: img = image.resize((299, 299), Image.ANTIALIAS) else: img = image.resize((224, 224), Image.ANTIALIAS) img = np.array(img) x = np.expand_dims(img, axis=0) if 'lstm' in model: x = np.reshape(x, (-1, 224 * 224 * 3)) model_converted = imp.load_source('TFModel', converted_file + '.py').KitModel(weight_filename) input_tf, model_tf = model_converted with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) predict = sess.run(model_tf, feed_dict = {input_tf : x}) del model_converted del sys.modules['TFModel'] preds = np.squeeze(predict) if 'fcn' in model: preds =

np.array(preds)

numpy.array

import warnings import numpy as np import quaternionic import pytest # Array methods def test_new(array): q = array(1, 2, 3, 4) assert q.dtype == np.float assert q.shape == (4,) assert q.w == 1.0 and q.x == 2.0 and q.y == 3.0 and q.z == 4.0 q = array([1, 2, 3, 4]) assert q.dtype == np.float assert q.shape == (4,) assert q.w == 1.0 and q.x == 2.0 and q.y == 3.0 and q.z == 4.0 q = array([[1, 2, 3, 4]]) assert q.dtype == np.float assert q.shape == (1, 4) assert q.w == 1.0 and q.x == 2.0 and q.y == 3.0 and q.z == 4.0 with pytest.raises(ValueError): array(np.array(3.14)) with pytest.raises(ValueError): array(np.array([])) with pytest.raises(ValueError): array(np.random.rand(4, 3)) def test_getitem(array): q = array(np.random.normal(size=(17, 3, 4))) p = q[1:-1] assert isinstance(p, array) assert p.shape == (q.shape[0]-2,) + q.shape[1:] assert np.array_equal(p.ndarray, q.ndarray[1:-1]) with pytest.raises(ValueError): q[..., 1:3] def test_array_finalize(array): q = array([1, 2, 3, 4]) with pytest.raises(ValueError): q[1:3] def test_repr(array): q = array(np.random.normal(size=(17, 3, 4))) assert repr(q) == 'quaternionic.' + repr(q.ndarray) def test_str(array): q = array(np.random.normal(size=(17, 3, 4))) assert str(q) == str(q.ndarray) def test_ones_like(Qs): z = np.ones_like(Qs) assert np.all(z.ndarray[:, 1:] == 0) assert

np.all(z.ndarray[:, 0] == 1)

numpy.all

import pandas as pd import numpy as np import os from sim.Bus import Bus from sim.Route import Route from sim.Busstop import Bus_stop from sim.Passenger import Passenger import matplotlib.pyplot as plt pd.options.mode.chained_assignment = None def getBusRoute(data): my_path = os.path.abspath(os.path.dirname(__file__)) path = my_path + "/data/" + data + "/" _path_trips = path + 'trips.txt' _path_st = path + 'stop_times.txt' trips = pd.DataFrame(pd.read_csv(_path_trips)) stop_times = pd.DataFrame(pd.read_csv(_path_st)) stop_times.dropna(subset=['arrival_time'], inplace=True) bus_routes = {} trip_ids = set(stop_times['trip_id']) try: service_id = trips.iloc[np.random.randint(0, trips.shape[0])]['service_id'] trips = trips[trips['service_id'] == service_id] except: pass # each route_id may correspond to multiple trip_id for trip_id in trip_ids: # A completely same route indicates the same shape_id in trip file, but this field is not 100% provided by opendata try: if 'shape_id' in trips.columns: route_id = str(trips[trips['trip_id'] == trip_id].iloc[0]['shape_id']) block_id = '' dir = '' else: route_id = str(trips[trips['trip_id'] == trip_id].iloc[0]['route_id']) block_id = str(trips[trips['trip_id'] == trip_id].iloc[0]['block_id']) dir = str(trips[trips['trip_id'] == trip_id].iloc[0]['trip_headsign']) except: continue # Identifies a set of dates when service is available for one or more routes. trip = stop_times[stop_times['trip_id'] == trip_id] try: trip['arrival_time'] = pd.to_datetime(trip['arrival_time'], format='%H:%M:%S') except: trip['arrival_time'] = pd.to_datetime(trip['arrival_time'], format="%Y-%m-%d %H:%M:%S") trip = trip.sort_values(by='arrival_time') trip_dist = trip.iloc[:]['shape_dist_traveled'].to_list() if len(trip_dist) <= 0 or np.isnan(trip_dist[0]): continue schedule = ((trip.iloc[:]['arrival_time'].dt.hour * 60 + trip.iloc[:]['arrival_time'].dt.minute) * 60 + trip.iloc[:]['arrival_time'].dt.second).to_list() if len(schedule) <= 2 or np.isnan(schedule[0]): continue b = Bus(id=trip_id, route_id=route_id, stop_list=trip.iloc[:]['stop_id'].to_list(), dispatch_time=schedule[0], block_id=block_id, dir=dir) b.left_stop = [] b.speed = (trip_dist[1] - trip_dist[0]) / (schedule[1] - schedule[0]) b.c_speed = b.speed for i in range(len(trip_dist)): if str(b.stop_list[i]) in b.stop_dist: b.left_stop.append(str(b.stop_list[i]) + '_' + str(i)) b.stop_dist[str(b.stop_list[i]) + '_' + str(i)] = trip_dist[i] b.schedule[str(b.stop_list[i]) + '_' + str(i)] = schedule[i] else: b.left_stop.append(str(b.stop_list[i])) b.stop_dist[str(b.stop_list[i])] = trip_dist[i] b.schedule[str(b.stop_list[i])] = schedule[i] b.stop_list = b.left_stop[:] b.set() if route_id in bus_routes: bus_routes[route_id].append(b) else: bus_routes[route_id] = [b] # Do not consider the route with only 1 trip bus_routes_ = {} for k, v in bus_routes.items(): if len(v) > 1: bus_routes_[k] = v return bus_routes_ def getStopList(data, read=0): my_path = os.path.abspath(os.path.dirname(__file__)) path = my_path + "/data/" + data + "/" _path_stops = path + 'stops.txt' _path_st = path + 'stop_times.txt' _path_trips = path + 'trips.txt' stops = pd.DataFrame(pd.read_csv(_path_stops)) stop_times = pd.DataFrame(pd.read_csv(_path_st)) trips = pd.DataFrame(pd.read_csv(_path_trips)) stop_list = {} select_stops = pd.merge(stops, stop_times, on=['stop_id'], how='left') select_stops = select_stops.sort_values(by='shape_dist_traveled', ascending=False) select_stops = select_stops.drop_duplicates(subset='stop_id', keep="first").sort_values(by='shape_dist_traveled', ascending=True) for i in range(select_stops.shape[0]): stop = Bus_stop(id=str(select_stops.iloc[i]['stop_id']), lat=select_stops.iloc[i]['stop_lat'], lon=select_stops.iloc[i]['stop_lon']) stop.loc = select_stops.iloc[i]['shape_dist_traveled'] try: stop.next_stop = str(select_stops.iloc[i + 1]['stop_id']) except: stop.next_stop = None stop_list[str(select_stops.iloc[i]['stop_id'])] = stop _path_demand = path + 'demand.csv' pax_num = 0 try: demand = pd.DataFrame(pd.read_csv(_path_demand)) except: print('No available demand file') return stop_list, 0 try: demand['Ride_Start_Time'] = pd.to_datetime(demand['Ride_Start_Time'], format='%H:%M:%S') except: demand['Ride_Start_Time'] = pd.to_datetime(demand['Ride_Start_Time'], format="%Y-%m-%d %H:%M:%S") demand['Ride_Start_Time_sec'] = (demand.iloc[:]['Ride_Start_Time'].dt.hour * 60 + demand.iloc[:][ 'Ride_Start_Time'].dt.minute) * 60 + demand.iloc[:]['Ride_Start_Time'].dt.second demand.dropna(subset=['ALIGHTING_STOP_STN'], inplace=True) demand = demand[demand.ALIGHTING_STOP_STN != demand.BOARDING_STOP_STN] demand = demand.sort_values(by='Ride_Start_Time_sec') for stop_id, stop in stop_list.items(): demand_by_stop = demand[demand['BOARDING_STOP_STN'] == int(stop_id)] # macro demand setting if read == 0: t = 0 while t < 24: d = demand_by_stop[(demand_by_stop['Ride_Start_Time_sec'] >= t * 3600) & ( demand_by_stop['Ride_Start_Time_sec'] < (t + 1) * 3600)] stop.dyna_arr_rate.append(d.shape[0] / 3600.) for dest_id in stop_list.keys(): od = d[demand['ALIGHTING_STOP_STN'] == int(dest_id)] if od.empty: continue if dest_id not in stop.dest: stop.dest[dest_id] = [0 for _ in range(24)] stop.dest[dest_id][t] = od.shape[0] / 3600. t += 1 else: # micro demand setting for i in range(demand_by_stop.shape[0]): pax = Passenger(id=demand_by_stop.iloc[i]['TripID'], origin=stop_id, plan_board_time=float(demand_by_stop.iloc[i]['Ride_Start_Time_sec'])) pax.dest = str(int(demand_by_stop.iloc[i]['ALIGHTING_STOP_STN'])) pax.realcost = float(demand_by_stop.iloc[i]['Ride_Time']) * 60. pax.route = str(demand_by_stop.iloc[i]['Srvc_Number']) + '_' + str( int(demand_by_stop.iloc[i]['Direction'])) stop.pax[pax.id] = pax pax_num += 1 return stop_list, pax_num def demand_analysis(engine=None): if engine is not None: stop_list = list(engine.busstop_list.keys()) stop_hash = {} i = 0 for p in stop_list: stop_hash[p] = i i += 1 # output data for stack area graph demand = [] for t in range(24): d = np.zeros(len(stop_list)) for s in stop_list: for pid, pax in engine.busstop_list[s].pax.items(): if int((pax.plan_board_time - 0) / 3600) == t: d[stop_hash[s]] += 1 demand.append(d) df = pd.DataFrame(demand, columns=[str(i) for i in range(len(stop_list))]) df.to_csv('demand.csv') return def sim_validate(engine, data): actual_onboard = [] sim_onboard = [] sim_travel_cost = [] actual_travel_cost = [] for pid, pax in engine.pax_list.items(): actual_onboard.append(pax.plan_board_time) sim_onboard.append(pax.onboard_time) sim_travel_cost.append(abs(pax.onboard_time - pax.alight_time)) actual_travel_cost.append(pax.realcost) actual_onboard = np.array(actual_onboard) sim_onboard = np.array(sim_onboard) actual_travel_cost = np.array(actual_travel_cost) sim_travel_cost = np.array(sim_travel_cost) print('Boarding RMSE:%g' % (np.sqrt(np.mean((actual_onboard - sim_onboard) ** 2)))) print('Travel RMSE:%g' % (np.sqrt(np.mean((actual_travel_cost - sim_travel_cost) ** 2)))) sim_comp = pd.DataFrame() sim_comp['actual_onboard'] = actual_onboard sim_comp['sim_onboard'] = sim_onboard sim_comp['sim_travel_cost'] = sim_travel_cost sim_comp['actual_travel_cost'] = actual_travel_cost sim_comp.to_csv('G:\\mcgill\\MAS\\gtfs_testbed\\result\\sim_comp' + str(data) + '.csv') print('ok') def visualize_pax(engine): for pax_id, pax in engine.pax_list.items(): if pax.onboard_time < 999999999: plt.plot([int(pax_id), int(pax_id)], [pax.arr_time, pax.onboard_time]) plt.show() def train_result_track(eng, ep, qloss_log, ploss_log, log, name='', seed=0): reward_bus_wise = [] reward_bus_wisep1 = [] reward_bus_wisep2 = [] rs = [] wait_cost = log['wait_cost'] travel_cost = log['travel_cost'] delay = log['delay'] hold_cost = log['hold_cost'] headways_var = log['headways_var'] headways_mean = log['headways_mean'] AOD = log["AOD"] for bid, r in eng.reward_signal.items(): if len(r) > 0: # .bus_list[bid].forward_bus!=None and engine.bus_list[bid].backward_bus!=None : reward_bus_wise.append(np.mean(r)) rs += r reward_bus_wisep1.append(np.mean(eng.reward_signalp1[bid])) reward_bus_wisep2.append(np.mean(eng.reward_signalp2[bid])) if ep % 1 == 0: train_log = pd.DataFrame() train_log['bunching'] = [log['bunching']] train_log['ploss'] = [np.mean(ploss_log)] train_log['qloss'] = [np.mean(qloss_log)] train_log['reward'] = [np.mean(reward_bus_wise)] train_log['reward1'] = [np.mean(reward_bus_wisep1)] train_log['reward2'] = [np.mean(reward_bus_wisep2)] train_log['avg_hold'] = np.mean(hold_cost) train_log['action'] = np.mean(np.array(eng.action_record)) train_log['wait'] = [np.mean(wait_cost)] train_log['travel'] = [np.mean(travel_cost)] train_log['delay'] = [np.mean(delay)] train_log['AOD'] = AOD for k, v in headways_mean.items(): train_log['headway_mean' + str(k)] = [np.mean(v)] for k, v in headways_var.items(): train_log['headway_var' + str(k)] = [np.mean(v)] res = pd.DataFrame() res['stw'] = log['stw'] res['sto'] = log['sto'] res['sth'] = log['sth'] print( 'Episode: %g | reward: %g | reward_var: %g | reward1: %g | reward2: %g | ploss: %g | qloss: %g |\n wait ' 'cost: %g | travel cost: %g | max hold :%g| min hold :%g| avg hold :%g | var hold :%g' % ( ep - 1, np.mean(reward_bus_wise),

np.var(rs)

numpy.var

import numpy as np def braille(): return { 'a' : np.array([[1, 0], [0, 0], [0, 0]], dtype=bool), 'b' : np.array([[1, 0], [1, 0], [0, 0]], dtype=bool), 'c' : np.array([[1, 1], [0, 0], [0, 0]], dtype=bool), 'd' : np.array([[1, 1], [0, 1], [0, 0]], dtype=bool), 'e' : np.array([[1, 0], [0, 1], [0, 0]], dtype=bool), 'f' : np.array([[1, 1], [1, 0], [0, 0]], dtype=bool), 'g' : np.array([[1, 1], [1, 1], [0, 0]], dtype=bool), 'h' : np.array([[1, 0], [1, 1], [0, 0]], dtype=bool), 'i' : np.array([[0, 1], [1, 0], [0, 0]], dtype=bool), 'j' : np.array([[0, 1], [1, 1], [0, 0]], dtype=bool), 'k' : np.array([[1, 0], [0, 0], [1, 0]], dtype=bool), 'l' : np.array([[1, 0], [1, 0], [1, 0]], dtype=bool), 'm' : np.array([[1, 1], [0, 0], [1, 0]], dtype=bool), 'n' : np.array([[1, 1], [0, 1], [1, 0]], dtype=bool), 'o' : np.array([[1, 0], [0, 1], [1, 0]], dtype=bool), 'p' : np.array([[1, 1], [1, 0], [1, 0]], dtype=bool), 'q' : np.array([[1, 1], [1, 1], [1, 0]], dtype=bool), 'r' : np.array([[1, 0], [1, 1], [1, 0]], dtype=bool), 's' : np.array([[0, 1], [1, 0], [1, 0]], dtype=bool), 't' : np.array([[0, 1], [1, 1], [1, 0]], dtype=bool), 'u' : np.array([[1, 0], [0, 0], [1, 1]], dtype=bool), 'v' : np.array([[1, 0], [1, 0], [1, 1]], dtype=bool), 'w' : np.array([[0, 1], [1, 1], [0, 1]], dtype=bool), 'x' : np.array([[1, 1], [0, 0], [1, 1]], dtype=bool), 'y' : np.array([[1, 1], [0, 1], [1, 1]], dtype=bool), 'z' : np.array([[1, 0], [0, 1], [1, 1]], dtype=bool), '1' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 0], [0, 0], [0, 0]], dtype=bool)], '2' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 0], [1, 0], [0, 0]], dtype=bool)], '3' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 1], [0, 0], [0, 0]], dtype=bool)], '4' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 1], [1, 0], [0, 0]], dtype=bool)], '5' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 0], [0, 1], [0, 0]], dtype=bool)], '6' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 1], [1, 0], [0, 0]], dtype=bool)], '7' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 1], [1, 1], [0, 0]], dtype=bool)], '8' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[1, 0], [1, 1], [0, 0]], dtype=bool)], '9' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool), np.array([[0, 1], [1, 0], [0, 0]], dtype=bool)], '0' : [np.array([[0, 1], [0, 1], [1, 1]], dtype=bool),

np.array([[0, 1], [1, 1], [0, 0]], dtype=bool)

numpy.array

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([])

numpy.array

import numpy as np class SECS: """Spherical Elementary Current System (SECS). The algorithm is implemented directly in spherical coordinates from the equations of the 1999 Amm & Viljanen paper [1]_. Parameters ---------- sec_df_loc : ndarray (nsec x 3 [lat, lon, r]) The latitude, longiutde, and radius of the divergence free (df) SEC locations. sec_cf_loc : ndarray (nsec x 3 [lat, lon, r]) The latitude, longiutde, and radius of the curl free (cf) SEC locations. References ---------- .. [1] <NAME>., and <NAME>. "Ionospheric disturbance magnetic field continuation from the ground to the ionosphere using spherical elementary current systems." Earth, Planets and Space 51.6 (1999): 431-440. doi:10.1186/BF03352247 """ def __init__(self, sec_df_loc=None, sec_cf_loc=None): if sec_df_loc is None and sec_cf_loc is None: raise ValueError("Must initialize the object with SEC locations") self.sec_df_loc = sec_df_loc self.sec_cf_loc = sec_cf_loc if self.sec_df_loc is not None: self.sec_df_loc = np.asarray(sec_df_loc) if self.sec_df_loc.shape[-1] != 3: raise ValueError("SEC DF locations must have 3 columns (lat, lon, r)") if self.sec_df_loc.ndim == 1: # Add an empty dimension if only one SEC location is passed in self.sec_df_loc = self.sec_df_loc[np.newaxis, ...] if self.sec_cf_loc is not None: self.sec_cf_loc = np.asarray(sec_cf_loc) if self.sec_cf_loc.shape[-1] != 3: raise ValueError("SEC CF locations must have 3 columns (lat, lon, r)") if self.sec_cf_loc.ndim == 1: # Add an empty dimension if only one SEC location is passed in self.sec_cf_loc = self.sec_cf_loc[np.newaxis, ...] # Storage of the scaling factors self.sec_amps = None self.sec_amps_var = None @property def has_df(self): """Whether this system has any divergence free currents.""" return self.sec_df_loc is not None @property def has_cf(self): """Whether this system has any curl free currents.""" return self.sec_cf_loc is not None @property def nsec(self): """The number of elementary currents in this system.""" nsec = 0 if self.has_df: nsec += len(self.sec_df_loc) if self.has_cf: nsec += len(self.sec_cf_loc) return nsec def fit(self, obs_loc, obs_B, obs_std=None, epsilon=0.05): """Fits the SECS to the given observations. Given a number of observation locations and measurements, this function fits the SEC system to them. It uses singular value decomposition (SVD) to fit the SEC amplitudes with the `epsilon` parameter used to regularize the solution. Parameters ---------- obs_locs : ndarray (nobs x 3 [lat, lon, r]) Contains latitude, longitude, and radius of the observation locations (place where the measurements are made) obs_B: ndarray (ntimes x nobs x 3 [Bx, By, Bz]) An array containing the measured/observed B-fields. obs_std : ndarray (ntimes x nobs x 3 [varX, varY, varZ]), optional Standard error of vector components at each observation location. This can be used to weight different observations more/less heavily. An infinite value eliminates the observation from the fit. Default: ones(nobs x 3) equal weights epsilon : float Value used to regularize/smooth the SECS amplitudes. Multiplied by the largest singular value obtained from SVD. Default: 0.05 """ if obs_loc.shape[-1] != 3: raise ValueError("Observation locations must have 3 columns (lat, lon, r)") if obs_B.ndim == 2: # Just a single snapshot given, so expand the dimensionality obs_B = obs_B[np.newaxis, ...] # Assume unit standard error of all measurements if obs_std is None: obs_std = np.ones(obs_B.shape) ntimes = len(obs_B) # Calculate the transfer functions T_obs = self._calc_T(obs_loc) # Store the fit sec_amps in the object self.sec_amps = np.empty((ntimes, self.nsec)) self.sec_amps_var = np.empty((ntimes, self.nsec)) # Calculate the singular value decomposition (SVD) # NOTE: T_obs has shape (nobs, 3, nsec), we reshape it # to (nobs*3, nsec); obs_std has shape (ntimes, nobs, 3), # we reshape it to (ntimes, nobs*3), then loop over ntimes # to solve using (potentially) time-dependent observation # standard errors to weight the observations for i in range(ntimes): # Only (re-)calculate SVD when necessary if i == 0 or not np.all(obs_std[i] == obs_std[i-1]): # Weight T_obs with obs_std svd_in = (T_obs.reshape(-1, self.nsec) / obs_std[i].ravel()[:, np.newaxis]) # Find singular value decompostion U, S, Vh = np.linalg.svd(svd_in, full_matrices=False) # Eliminate singular values less than epsilon by setting their # reciprocal to zero (setting S to infinity firsts avoids # divide-by-zero warings) S[S < epsilon * S.max()] = np.inf W = 1./S # Update VWU if obs_std changed VWU = Vh.T @ (np.diag(W) @ U.T) # Solve for SEC amplitudes and error variances # shape: ntimes x nsec self.sec_amps[i, :] = (VWU @ (obs_B[i] / obs_std[i]).reshape(-1).T).T # Maybe we want the variance of the predictions sometime later...? # shape: ntimes x nsec valid = np.isfinite(obs_std[i].reshape(-1)) self.sec_amps_var[i, :] = np.sum( (VWU[:,valid] * obs_std[i].reshape(-1)[valid])**2, axis=1) return self def fit_unit_currents(self): """Sets all SECs to a unit current amplitude.""" self.sec_amps = np.ones((1, self.nsec)) return self def predict(self, pred_loc, J=False): """Calculate the predicted magnetic field or currents. After a set of observations has been fit to this system we can predict the magnetic fields or currents at any other location. This function uses those fit amplitudes to predict at the requested locations. Parameters ---------- pred_loc: ndarray (npred x 3 [lat, lon, r]) An array containing the locations where the predictions are desired. J: boolean Whether to predict currents (J=True) or magnetic fields (J=False) Default: False (magnetic field prediction) Returns ------- ndarray (ntimes x npred x 3 [lat, lon, r]) The predicted values calculated from the current amplitudes that were fit to this system. """ if pred_loc.shape[-1] != 3: raise ValueError("Prediction locations must have 3 columns (lat, lon, r)") if self.sec_amps is None: raise ValueError("There are no currents associated with the SECs," + "you need to call .fit() first to fit to some observations.") # T_pred shape=(npred x 3 x nsec) # sec_amps shape=(nsec x ntimes) if J: # Predicting currents T_pred = self._calc_J(pred_loc) else: # Predicting magnetic fields T_pred = self._calc_T(pred_loc) # NOTE: dot product is slow on multi-dimensional arrays (i.e. > 2 dimensions) # Therefore this is implemented as tensordot, and the arguments are # arranged to eliminate needs of transposing things later. # The dot product is done over the SEC locations, so the final output # is of shape: (ntimes x npred x 3) return np.squeeze(np.tensordot(self.sec_amps, T_pred, (1, 2))) def predict_B(self, pred_loc): """Calculate the predicted magnetic fields. After a set of observations has been fit to this system we can predict the magnetic fields or currents at any other location. This function uses those fit amplitudes to predict at the requested locations. Parameters ---------- pred_loc: ndarray (npred x 3 [lat, lon, r]) An array containing the locations where the predictions are desired. Returns ------- ndarray (ntimes x npred x 3 [lat, lon, r]) The predicted values calculated from the current amplitudes that were fit to this system. """ return self.predict(pred_loc) def predict_J(self, pred_loc): """Calculate the predicted currents. After a set of observations has been fit to this system we can predict the magnetic fields or currents at any other location. This function uses those fit amplitudes to predict at the requested locations. Parameters ---------- pred_loc: ndarray (npred x 3 [lat, lon, r]) An array containing the locations where the predictions are desired. Returns ------- ndarray (ntimes x npred x 3 [lat, lon, r]) The predicted values calculated from the current amplitudes that were fit to this system. """ return self.predict(pred_loc, J=True) def _calc_T(self, obs_loc): """Calculates the T transfer matrix. The magnetic field transfer matrix to go from SEC locations to observation locations. It assumes unit current amplitudes that will then be scaled with the proper amplitudes later. """ if self.has_df: T = T_df(obs_loc=obs_loc, sec_loc=self.sec_df_loc) if self.has_cf: T1 = T_cf(obs_loc=obs_loc, sec_loc=self.sec_cf_loc) # df is already present in T if self.has_df: T = np.concatenate([T, T1], axis=2) else: T = T1 return T def _calc_J(self, obs_loc): """Calculates the J transfer matrix. The current transfer matrix to go from SEC locations to observation locations. It assumes unit current amplitudes that will then be scaled with the proper amplitudes later. """ if self.has_df: J = J_df(obs_loc=obs_loc, sec_loc=self.sec_df_loc) if self.has_cf: J1 = J_cf(obs_loc=obs_loc, sec_loc=self.sec_cf_loc) # df is already present in T if self.has_df: J = np.concatenate([J, J1], axis=2) else: J = J1 return J def T_df(obs_loc, sec_loc): """Calculates the divergence free magnetic field transfer function. The transfer function goes from SEC location to observation location and assumes unit current SECs at the given locations. Parameters ---------- obs_loc : ndarray (nobs, 3 [lat, lon, r]) The locations of the observation points. sec_loc : ndarray (nsec, 3 [lat, lon, r]) The locations of the SEC points. Returns ------- ndarray (nobs, 3, nsec) The T transfer matrix. """ nobs = len(obs_loc) nsec = len(sec_loc) obs_r = obs_loc[:, 2][:, np.newaxis] sec_r = sec_loc[:, 2][np.newaxis, :] theta = calc_angular_distance(obs_loc[:, :2], sec_loc[:, :2]) alpha = calc_bearing(obs_loc[:, :2], sec_loc[:, :2]) # magnetic permeability mu0 = 4*np.pi*1e-7 # simplify calculations by storing this ratio x = obs_r/sec_r sin_theta = np.sin(theta) cos_theta = np.cos(theta) factor = 1./np.sqrt(1 - 2*x*cos_theta + x**2) # Amm & Viljanen: Equation 9 Br = mu0/(4*np.pi*obs_r) * (factor - 1) # Amm & Viljanen: Equation 10 (transformed to try and eliminate trig operations and # divide by zeros) Btheta = -mu0/(4*np.pi*obs_r) * (factor*(x - cos_theta) + cos_theta) # If sin(theta) == 0: Btheta = 0 # There is a possible 0/0 in the expansion when sec_loc == obs_loc Btheta = np.divide(Btheta, sin_theta, out=np.zeros_like(sin_theta), where=sin_theta != 0) # When observation points radii are outside of the sec locations under_locs = sec_r < obs_r # NOTE: If any SECs are below observations the math will be done on all points. # This could be updated to only work on the locations where this condition # occurs, but would make the code messier, with minimal performance gain # except for very large matrices. if np.any(under_locs): # Flipped from previous case x = sec_r/obs_r # Amm & Viljanen: Equation A.7 Br2 = mu0*x/(4*np.pi*obs_r) * (1./np.sqrt(1 - 2*x*cos_theta + x**2) - 1) # Amm & Viljanen: Equation A.8 Btheta2 = - mu0 / (4*np.pi*obs_r) * ((obs_r-sec_r*cos_theta) / np.sqrt(obs_r**2 - 2*obs_r*sec_r*cos_theta + sec_r**2) - 1) Btheta2 = np.divide(Btheta2, sin_theta, out=np.zeros_like(sin_theta), where=sin_theta != 0) # Update only the locations where secs are under observations Btheta[under_locs] = Btheta2[under_locs] Br[under_locs] = Br2[under_locs] # Transform back to Bx, By, Bz at each local point T =

np.empty((nobs, 3, nsec))

numpy.empty

""" Procedures needed for variable importance estimation. Created on Thu Dec 8 09:11:57 2020. @author: MLechner # -*- coding: utf-8 -*- """ from concurrent import futures import math import numpy as np import ray from mcf import mcf_forest_functions as mcf_forest from mcf import mcf_data_functions as mcf_data from mcf import general_purpose as gp from mcf import general_purpose_system_files as gp_sys from mcf import general_purpose_mcf as gp_mcf def variable_importance(indatei, forest, v_dict, v_x_type, v_x_values, c_dictin, x_name_mcf, regrf=False): """Compute variable importance measure. Parameters ---------- indatei: Str. forest : List of list. Estimated forest. v_dict : DICT. Variables. v_x_type : List of Int. v_x_values : List of Int. c_dictin : DICT. Parameters x_name_mcf : List. Variable names from MCF procedure regrf : Bool. Returns ------- vim : Dictionary. Variable importance measures and names of variable Procedure: a) Predict Total of OOB of estimated forest_est (should already be there) b) For each variable, randomize one or groups of covariates c) recompute OOB-MSE with the ys of these more noisy variables Use multiprocessing in new oob prediction in the same way as in forest Building Outer loop over variables and group of variables Inner loop: Loop over trees Initially take out the indices of leaf 0 [16]--> OOB data For every observation determine it terminal leaf - save index together with number of terminal leaf For all terminal leaves compute OOB prediction """ if c_dictin['with_output'] and c_dictin['verbose']: print('\nVariable importance measures (OOB data)') print('\nSingle variables') (x_name, _, _, c_dict, _, data_np, y_i, y_nn_i, x_i, _, _, d_i, w_i, _ ) = mcf_data.prepare_data_for_forest(indatei, v_dict, v_x_type, v_x_values, c_dictin, regrf=regrf) no_of_vars = len(x_name) partner_k = determine_partner_k(x_name) # Loop over all variables to get respective OOB values of MSE if x_name != x_name_mcf: raise Exception('Wrong order of variable names', x_name, x_name_mcf) number_of_oobs = 1 + no_of_vars oob_values = [None] * number_of_oobs if c_dict['no_parallel'] < 1.5: maxworkers = 1 else: if c_dict['mp_automatic']: maxworkers = gp_mcf.find_no_of_workers(c_dict['no_parallel'], c_dict['sys_share']) else: maxworkers = c_dict['no_parallel'] if c_dict['with_output'] and c_dict['verbose']: print('Number of parallel processes: ', maxworkers) if c_dict['mp_with_ray'] and maxworkers > 1: if c_dict['mem_object_store_2'] is None: ray.init(num_cpus=maxworkers, include_dashboard=False) else: ray.init(num_cpus=maxworkers, include_dashboard=False, object_store_memory=c_dict['mem_object_store_2']) if c_dict['with_output'] and c_dict['verbose']: print("Size of Ray Object Store: ", round(c_dict['mem_object_store_2']/(1024*1024)), " MB") data_np_ref = ray.put(data_np) forest_ref = ray.put(forest) if (c_dict['mp_type_vim'] == 2 and not c_dict['mp_with_ray']) or ( maxworkers == 1): for jdx in range(number_of_oobs): oob_values[jdx], _ = get_oob_mcf( data_np, y_i, y_nn_i, x_i, d_i, w_i, c_dict, jdx, True, [], forest, False, regrf, partner_k[jdx]) if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(jdx+1, number_of_oobs) else: # Fast but needs a lot of memory because it copied a lot maxworkers = min(maxworkers, number_of_oobs) if c_dict['mp_with_ray']: tasks = [ray_get_oob_mcf.remote( data_np_ref, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, True, [], forest_ref, True, regrf, partner_k[idx]) for idx in range(number_of_oobs)] still_running = list(tasks) jdx = 0 while len(still_running) > 0: finished, still_running = ray.wait(still_running) finished_res = ray.get(finished) for res in finished_res: iix = res[1] oob_values[iix] = res[0] if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(jdx+1, number_of_oobs) jdx += 1 else: with futures.ProcessPoolExecutor(max_workers=maxworkers) as fpp: ret_fut = {fpp.submit( get_oob_mcf, data_np, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, True, [], forest, True, regrf, partner_k[idx]): idx for idx in range(number_of_oobs)} for jdx, frv in enumerate(futures.as_completed(ret_fut)): results_fut = frv.result() del ret_fut[frv] del frv iix = results_fut[1] oob_values[iix] = results_fut[0] if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(jdx+1, number_of_oobs) oob_values = np.array(oob_values) if regrf: oob_values = oob_values.reshape(-1) mse_ref = oob_values[0] # reference value vim = vim_print(mse_ref, oob_values[1:], x_name, 0, c_dict['with_output'], True, partner_k) # Variables are grouped no_g, no_m_g = number_of_groups_vi(no_of_vars) partner_k = None if no_g > 0: if c_dict['with_output']: print('\nGroups of variables') ind_groups = vim_grouping(vim, no_g) n_g = len(ind_groups) oob_values = [None] * n_g if c_dict['mp_with_ray'] and maxworkers > 1: tasks = [ray_get_oob_mcf.remote( data_np_ref, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, False, ind_groups, forest_ref, True, regrf, partner_k) for idx in range(n_g)] still_running = list(tasks) idx = 0 while len(still_running) > 0: finished, still_running = ray.wait(still_running) finished_res = ray.get(finished) for res in finished_res: iix = res[1] oob_values[iix] = res[0] if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(idx+1, n_g) idx += 1 else: for idx in range(n_g): oob_values[idx], _ = get_oob_mcf( data_np, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, False, ind_groups, forest, False, regrf, partner_k) if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(idx+1, n_g) if regrf: oob_values = np.array(oob_values) oob_values = oob_values.reshape(-1) vim_g = vim_print(mse_ref, np.array(oob_values), x_name, ind_groups, c_dict['with_output'], False) else: vim_g = None # Groups are accumulated from worst to best if no_m_g > 0: if c_dict['with_output']: print('\nMerged groups of variables') ind_groups = vim_grouping(vim_g, no_m_g, True) n_g = len(ind_groups) oob_values = [None] * n_g if c_dict['mp_with_ray'] and maxworkers > 1: tasks = [ray_get_oob_mcf.remote( data_np_ref, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, False, ind_groups, forest_ref, True, regrf, partner_k) for idx in range(n_g)] still_running = list(tasks) idx = 0 while len(still_running) > 0: finished, still_running = ray.wait(still_running) finished_res = ray.get(finished) for res in finished_res: iix = res[1] oob_values[iix] = res[0] if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(idx+1, n_g) idx += 1 else: for idx in range(n_g): oob_values[idx], _ = get_oob_mcf( data_np, y_i, y_nn_i, x_i, d_i, w_i, c_dict, idx, False, ind_groups, forest, False, regrf, partner_k) if c_dict['with_output'] and c_dict['verbose']: gp.share_completed(idx+1, n_g) if regrf: oob_values = np.array(oob_values) oob_values = oob_values.reshape(-1) vim_mg = vim_print(mse_ref, np.array(oob_values), x_name, ind_groups, c_dict['with_output'], False) else: vim_mg = None if c_dict['mp_with_ray']: del finished, still_running, data_np_ref, forest_ref, tasks ray.shutdown() return vim, vim_g, vim_mg, x_name def number_of_groups_vi(no_x_names): """Determine no of groups for groupwise variable importance measure. Parameters ---------- no_x_names :INT. No of variables considered in analysis. Returns ------- groups : INT. merged_groups : INT. """ if no_x_names >= 100: groups = 20 merged_groups = 19 elif 20 <= no_x_names < 100: groups = 10 merged_groups = 9 elif 10 <= no_x_names < 20: groups = 5 merged_groups = 4 elif 4 <= no_x_names < 10: groups = 2 merged_groups = 0 else: groups = 0 merged_groups = 0 return groups, merged_groups def vim_grouping(vim, no_groups, accu=False): """Group variables according to their variable importance measure. Parameters ---------- vim : Tuple (Numpy array list of INT). Relative vim and index. no_g : INT. No of groups. accu : Bool. Build groups by accumulation. Default = False. Returns ------- ind_groups : List of list of INT. Grouping of indices. """ indices = vim[1] no_ind = len(indices) if not accu: group_size = no_ind / no_groups group_size_int = math.floor(group_size) one_more = 0 start_i = 0 ind_groups = [] for idx in range(no_groups): if accu: if idx < 2: ind_groups = [indices[0], indices[0] + indices[idx]] else: new_group = ind_groups[idx-1] + indices[idx] ind_groups.append(new_group) else: if idx == (no_groups - 1): end_i = no_ind - 1 else: end_i = start_i + group_size_int - 1 one_more += group_size - group_size_int if one_more >= 1: one_more -= 1 end_i += 1 ind_groups.append(indices[start_i:end_i+1]) start_i = end_i + 1 return ind_groups def vim_print(mse_ref, mse_values, x_name, ind_list=0, with_output=True, single=True, partner_k=None): """Print Variable importance measure and create sorted output. Parameters ---------- mse_ref : Numpy Float. Reference value of non-randomized x. mse_values : Numpy array. MSE's for randomly permuted x. x_name : List of strings. Variable names. ind_list : List of INT, optional. Variable positions. Default is 0. with_output : Boolean, optional. Default is True. single : Boolean, optional. The default is True. partner_k : List of None and Int or None. Index of variables that were jointly randomized. Default is None. Returns ------- vim: Tuple of Numpy array and list of lists. MSE sorted and sort index. """ if partner_k is not None: for idx, val in enumerate(partner_k): if val is not None: if (idx > (val-1)) and (idx > 0): mse_values[idx-1] = mse_values[val-1] mse = mse_values / np.array(mse_ref) * 100 var_indices = np.argsort(mse) var_indices = np.flip(var_indices) vim_sorted = mse[var_indices] if single: x_names_sorted =

np.array(x_name, copy=True)

numpy.array

#!/usr/bin.env/python # -*- coding: utf-8 -*- """ This module provides normalisation methods using landmark registration, first described with application to cytometry data by Hahne et al [1] with further expansion by Finak et al [2]. Landmark registration is implemented in the LandmarkReg class using Scikit-FDA. [1] <NAME>, <NAME>, <NAME>, <NAME>, Gascoyne RD, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>. Per-channel basis normalization methods for flow cytometry data. Cytometry A. 2010 Feb;77(2):121-31. doi: 10.1002/cyto.a.20823. PMID: 19899135; PMCID: PMC3648208. [2] <NAME>, <NAME>, <NAME>, et al. High-throughput flow cytometry data normalization for clinical trials. Cytometry A. 2014;85(3):277-286. doi:10.1002/cyto.a.22433 Copyright 2020 <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. """ from skfda.preprocessing.registration import landmark_registration_warping, landmark_shift_deltas from skfda.representation.grid import FDataGrid from sklearn.cluster import KMeans from detecta import detect_peaks from KDEpy import FFTKDE import matplotlib.pyplot as plt import pandas as pd import numpy as np __author__ = "<NAME>" __copyright__ = "Copyright 2020, cytopy" __credits__ = ["<NAME>", "<NAME>", "<NAME>", "<NAME>"] __license__ = "MIT" __version__ = "2.0.0" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Production" def peaks(y: np.ndarray, x: np.ndarray, **kwargs): """ Detect peaks of some function, y, in the grid space, x. Parameters ---------- y: numpy.ndarray x: numpy.ndarray kwargs: Additional keyword arguments passed to detecta.detect_peaks function Returns ------- List """ p = detect_peaks(y, **kwargs) return [x[i] for i in p] def filter_by_closest_centroid(x: np.ndarray, labels: np.ndarray, centroid: float): """ Filter peaks ('x') to keep only those closest to their nearest centroid (centroid of clustered peaks). Labels indicate where the peak originated from; either target sample (0) or reference (1). Parameters ---------- x: numpy.ndarray labels: numpy.ndarray centroid: float Returns ------- float, float Peaks closest to centroid in cluster 1, Peaks closest to centroid in cluster 2 """ x, labels = np.array(x), np.array(labels) y1 = x[np.where(labels == 0)] y2 = x[np.where(labels == 1)] if len(y1) > 1: y1 = y1[np.abs(y1 - centroid).argmin()] else: y1 = y1[0] if len(y2) > 1: y2 = y2[np.abs(y2 - centroid).argmin()] else: y2 = y2[0] return y1, y2 def cluster_landmarks(p: np.ndarray, plabels: np.ndarray): """ Cluster peaks (p). plabels indicate where the peak originated from; either target sample (0) or reference (1). The number of clusters, determined by KMeans clustering is equal to the number of peaks for the target sample. Parameters ---------- p: numpy.ndarray Peaks plabels: numpy.ndarray Peak labels Returns ------- numpy.ndarray, numpy.ndarray K Means labels for each peak, cluster centroids """ km = KMeans(n_clusters=len(np.where(np.array(plabels) == 0)[0]), random_state=42) km_labels = km.fit_predict(np.array(p).reshape(-1, 1)) centroids = km.cluster_centers_.reshape(-1) return km_labels, centroids def zero_entropy_clusters(km_labels: np.ndarray, plabels: np.ndarray, centroids: np.ndarray): """ Determine which clusters (if any) have zero entropy (only contains peaks from a single sample; either target or reference) Parameters ---------- km_labels: numpy.ndarray K means cluster labels plabels: numpy.ndarray Origin of the peak; either target (0) or reference (1) centroids: numpy.ndarray Cluster centroids Returns ------- List List of centroids for clusters with zero entropy """ zero_entropy = list() for i in

np.unique(km_labels)

numpy.unique

# Copyright 2016 The TensorFlow Authors All Rights Reserved. # # 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. # ============================================================================== """Various function to manipulate graphs for computing distances. """ import skimage.morphology import numpy as np import networkx as nx import itertools import logging import graph_tool as gt import graph_tool.topology import graph_tool.generation import src.utils as utils # Compute shortest path from all nodes to or from all source nodes def get_distance_node_list(gtG, source_nodes, direction, weights=None): gtG_ = gt.Graph(gtG) v = gtG_.add_vertex() if weights is not None: weights = gtG_.edge_properties[weights] for s in source_nodes: e = gtG_.add_edge(s, int(v)) if weights is not None: weights[e] = 0. if direction == 'to': dist = gt.topology.shortest_distance( gt.GraphView(gtG_, reversed=True), source=gtG_.vertex(int(v)), target=None, weights=weights) elif direction == 'from': dist = gt.topology.shortest_distance( gt.GraphView(gtG_, reversed=False), source=gtG_.vertex(int(v)), target=None, weights=weights) dist = np.array(dist.get_array()) dist = dist[:-1] if weights is None: dist = dist - 1 return dist # Functions for semantically labelling nodes in the traversal graph. def generate_lattice(sz_x, sz_y): """Generates a lattice with sz_x vertices along x and sz_y vertices along y direction Each of these vertices is step_size distance apart. Origin is at (0,0). """ g = gt.generation.lattice([sz_x, sz_y]) x, y = np.meshgrid(np.arange(sz_x), np.arange(sz_y)) x = np.reshape(x, [-1, 1]); y = np.reshape(y, [-1, 1]); nodes = np.concatenate((x, y), axis=1) return g, nodes def add_diagonal_edges(g, nodes, sz_x, sz_y, edge_len): offset = [sz_x + 1, sz_x - 1] for o in offset: s = np.arange(nodes.shape[0] - o - 1) t = s + o ind = np.all(np.abs(nodes[s, :] - nodes[t, :]) == np.array([[1, 1]]), axis=1) s = s[ind][:, np.newaxis] t = t[ind][:, np.newaxis] st = np.concatenate((s, t), axis=1) for i in range(st.shape[0]): e = g.add_edge(st[i, 0], st[i, 1], add_missing=False) g.ep['wts'][e] = edge_len def convert_traversible_to_graph(traversible, ff_cost=1., fo_cost=1., oo_cost=1., connectivity=4): assert (connectivity == 4 or connectivity == 8) sz_x = traversible.shape[1] sz_y = traversible.shape[0] g, nodes = generate_lattice(sz_x, sz_y) # Assign costs. edge_wts = g.new_edge_property('float') g.edge_properties['wts'] = edge_wts wts = np.ones(g.num_edges(), dtype=np.float32) edge_wts.get_array()[:] = wts if connectivity == 8: add_diagonal_edges(g, nodes, sz_x, sz_y, np.sqrt(2.)) se = np.array([[int(e.source()), int(e.target())] for e in g.edges()]) s_xy = nodes[se[:, 0]] t_xy = nodes[se[:, 1]] s_t = np.ravel_multi_index((s_xy[:, 1], s_xy[:, 0]), traversible.shape) t_t = np.ravel_multi_index((t_xy[:, 1], t_xy[:, 0]), traversible.shape) s_t = traversible.ravel()[s_t] t_t = traversible.ravel()[t_t] wts = np.zeros(g.num_edges(), dtype=np.float32) wts[np.logical_and(s_t == True, t_t == True)] = ff_cost wts[np.logical_and(s_t == False, t_t == False)] = oo_cost wts[np.logical_xor(s_t, t_t)] = fo_cost edge_wts = g.edge_properties['wts'] for i, e in enumerate(g.edges()): edge_wts[e] = edge_wts[e] * wts[i] # d = edge_wts.get_array()*1. # edge_wts.get_array()[:] = d*wts return g, nodes def label_nodes_with_class(nodes_xyt, class_maps, pix): """ Returns: class_maps__: one-hot class_map for each class. node_class_label: one-hot class_map for each class, nodes_xyt.shape[0] x n_classes """ # Assign each pixel to a node. selem = skimage.morphology.disk(pix) class_maps_ = class_maps * 1. for i in range(class_maps.shape[2]): class_maps_[:, :, i] = skimage.morphology.dilation(class_maps[:, :, i] * 1, selem) class_maps__ = np.argmax(class_maps_, axis=2) class_maps__[np.max(class_maps_, axis=2) == 0] = -1 # For each node pick out the label from this class map. x = np.round(nodes_xyt[:, [0]]).astype(np.int32) y = np.round(nodes_xyt[:, [1]]).astype(np.int32) ind = np.ravel_multi_index((y, x), class_maps__.shape) node_class_label = class_maps__.ravel()[ind][:, 0] # Convert to one hot versions. class_maps_one_hot = np.zeros(class_maps.shape, dtype=np.bool) node_class_label_one_hot = np.zeros((node_class_label.shape[0], class_maps.shape[2]), dtype=np.bool) for i in range(class_maps.shape[2]): class_maps_one_hot[:, :, i] = class_maps__ == i node_class_label_one_hot[:, i] = node_class_label == i return class_maps_one_hot, node_class_label_one_hot def label_nodes_with_class_geodesic(nodes_xyt, class_maps, pix, traversible, ff_cost=1., fo_cost=1., oo_cost=1., connectivity=4): """Labels nodes in nodes_xyt with class labels using geodesic distance as defined by traversible from class_maps. Inputs: nodes_xyt class_maps: counts for each class. pix: distance threshold to consider close enough to target. traversible: binary map of whether traversible or not. Output: labels: For each node in nodes_xyt returns a label of the class or -1 is unlabelled. """ g, nodes = convert_traversible_to_graph(traversible, ff_cost=ff_cost, fo_cost=fo_cost, oo_cost=oo_cost, connectivity=connectivity) class_dist = np.zeros_like(class_maps * 1.) n_classes = class_maps.shape[2] if False: # Assign each pixel to a class based on number of points. selem = skimage.morphology.disk(pix) class_maps_ = class_maps * 1. class_maps__ = np.argmax(class_maps_, axis=2) class_maps__[np.max(class_maps_, axis=2) == 0] = -1 # Label nodes with classes. for i in range(n_classes): # class_node_ids = np.where(class_maps__.ravel() == i)[0] class_node_ids = np.where(class_maps[:, :, i].ravel() > 0)[0] dist_i = get_distance_node_list(g, class_node_ids, 'to', weights='wts') class_dist[:, :, i] = np.reshape(dist_i, class_dist[:, :, i].shape) class_map_geodesic = (class_dist <= pix) class_map_geodesic = np.reshape(class_map_geodesic, [-1, n_classes]) # For each node pick out the label from this class map. x = np.round(nodes_xyt[:, [0]]).astype(np.int32) y = np.round(nodes_xyt[:, [1]]).astype(np.int32) ind = np.ravel_multi_index((y, x), class_dist[:, :, 0].shape) node_class_label = class_map_geodesic[ind[:, 0], :] class_map_geodesic = class_dist <= pix return class_map_geodesic, node_class_label def _get_next_nodes_undirected(n, sc, n_ori): nodes_to_add = [] nodes_to_validate = [] (p, q, r) = n nodes_to_add.append((n, (p, q, r), 0)) if n_ori == 4: for _ in [1, 2, 3, 4]: if _ == 1: v = (p - sc, q, r) elif _ == 2: v = (p + sc, q, r) elif _ == 3: v = (p, q - sc, r) elif _ == 4: v = (p, q + sc, r) nodes_to_validate.append((n, v, _)) return nodes_to_add, nodes_to_validate def _get_next_nodes(n, sc, n_ori): nodes_to_add = [] nodes_to_validate = [] (p, q, r) = n for r_, a_ in zip([-1, 0, 1], [1, 0, 2]): nodes_to_add.append((n, (p, q, np.mod(r + r_, n_ori)), a_)) if n_ori == 6: if r == 0: v = (p + sc, q, r) elif r == 1: v = (p + sc, q + sc, r) elif r == 2: v = (p, q + sc, r) elif r == 3: v = (p - sc, q, r) elif r == 4: v = (p - sc, q - sc, r) elif r == 5: v = (p, q - sc, r) elif n_ori == 4: if r == 0: v = (p + sc, q, r) elif r == 1: v = (p, q + sc, r) elif r == 2: v = (p - sc, q, r) elif r == 3: v = (p, q - sc, r) nodes_to_validate.append((n, v, 3)) return nodes_to_add, nodes_to_validate def generate_graph(valid_fn_vec=None, sc=1., n_ori=6, starting_location=(0, 0, 0), vis=False, directed=True): timer = utils.Timer() timer.tic() if directed: G = nx.DiGraph(directed=True) else: G = nx.Graph() G.add_node(starting_location) new_nodes = G.nodes() while len(new_nodes) != 0: nodes_to_add = [] nodes_to_validate = [] for n in new_nodes: if directed: na, nv = _get_next_nodes(n, sc, n_ori) else: na, nv = _get_next_nodes_undirected(n, sc, n_ori) nodes_to_add = nodes_to_add + na if valid_fn_vec is not None: nodes_to_validate = nodes_to_validate + nv else: node_to_add = nodes_to_add + nv # Validate nodes. vs = [_[1] for _ in nodes_to_validate] valids = valid_fn_vec(vs) for nva, valid in zip(nodes_to_validate, valids): if valid: nodes_to_add.append(nva) new_nodes = [] for n, v, a in nodes_to_add: if not G.has_node(v): new_nodes.append(v) G.add_edge(n, v, action=a) timer.toc(average=True, log_at=1, log_str='src.graph_utils.generate_graph') return (G) def vis_G(G, ax, vertex_color='r', edge_color='b', r=None): if edge_color is not None: for e in G.edges(): XYT = zip(*e) x = XYT[-3] y = XYT[-2] t = XYT[-1] if r is None or t[0] == r: ax.plot(x, y, edge_color) if vertex_color is not None: XYT = zip(*G.nodes()) x = XYT[-3] y = XYT[-2] t = XYT[-1] ax.plot(x, y, vertex_color + '.') def convert_to_graph_tool(G): timer = utils.Timer() timer.tic() gtG = gt.Graph(directed=G.is_directed()) gtG.ep['action'] = gtG.new_edge_property('int') nodes_list = G.nodes() nodes_array = np.array(nodes_list) nodes_id = np.zeros((nodes_array.shape[0],), dtype=np.int64) for i in range(nodes_array.shape[0]): v = gtG.add_vertex() nodes_id[i] = int(v) # d = {key: value for (key, value) in zip(nodes_list, nodes_id)} d = dict(itertools.izip(nodes_list, nodes_id)) for src, dst, data in G.edges_iter(data=True): e = gtG.add_edge(d[src], d[dst]) gtG.ep['action'][e] = data['action'] nodes_to_id = d timer.toc(average=True, log_at=1, log_str='src.graph_utils.convert_to_graph_tool') return gtG, nodes_array, nodes_to_id def _rejection_sampling(rng, sampling_d, target_d, bins, hardness, M): bin_ind = np.digitize(hardness, bins) - 1 i = 0 ratio = target_d[bin_ind] / (M * sampling_d[bin_ind]) while i < ratio.size and rng.rand() > ratio[i]: i = i + 1 return i def heuristic_fn_vec(n1, n2, n_ori, step_size): # n1 is a vector and n2 is a single point. dx = (n1[:, 0] - n2[0, 0]) / step_size dy = (n1[:, 1] - n2[0, 1]) / step_size dt = n1[:, 2] - n2[0, 2] dt = np.mod(dt, n_ori) dt = np.minimum(dt, n_ori - dt) if n_ori == 6: if dx * dy > 0: d = np.maximum(np.abs(dx), np.abs(dy)) else: d = np.abs(dy - dx) elif n_ori == 4: d = np.abs(dx) +

np.abs(dy)

numpy.abs

# -*- coding: utf-8 -*- """ Created on Wed Dec 22 15:40:32 2021 @author: <NAME> Script takes as input a dataset of the type "Spooled files_000x.tif", images from the Andor camera, assumes every odd number for x is a shot with the tweezer off (just MOT), while every even number is with tweezer on as well. Subtracts MOT from tweezer + MOT and averages over multiple attemps """ #%% Imports import numpy as np from PIL import Image import cv2 import matplotlib.pyplot as plt #%% Loading Data # Data location and define empty lists data_location = "U:/KAT1/Images/dipole/MOToffDetuningSweep/" file_name = "15ms_121mhz" file_prefix = "/Spooled files_" # File numbers, step is 2 because only odd/even iterations. Stop_number is the # amount of pixel fixels you made start_number = 4 stop_number = 40 step_number = 2 #%% Load even and odd files lists, corresponding to tweezer on or off def load_image_list(start, stop, step, data_location, file_prefix): iteration_list = [] # Load files for i in range(start, stop, step): im = Image.open(data_location + file_name + file_prefix + str(i).zfill(4) + ".tif") array =

np.array(im)

numpy.array

#!/usr/bin/python3 # -*- coding: utf-8 -*- ''' API ======================================================================== Connection point for all package utilities to be provided ''' import collections import numba import numpy as np import sys import time import types from bisect import bisect_left import matplotlib.pyplot as plt from . import GLOBALS from . import auxiliaries as aux from .auxiliaries import to_ndarray, wait from .errorfunctions import get_errorfunction, maxsumabs from . import models from . import reference as ref # Careful with this circular import # This global dictionary G is for passing some telemtery and debug arguments global G G = GLOBALS.dictionary _sqrtrange = (aux.sqrtrange_python, aux.sqrtrange_numba) fitsets = {'Poly10': models.Poly10} #%%═════════════════════════════════════════════════════════════════════ ## ROOT FINDING def interval(f, x1, y1, x2, y2, fit1): '''Returns the last x where f(x)<0''' is_debug = G['debug'] if is_debug: #─────────────────────────────────────────────────────┐ print(f'\t{x1=}\t{y1=}') print(f'\t{x2=}\t{y2=}') G['mid'], = G['ax_root'].plot(x1, y1,'.', color = 'blue') #──────────────────────────────────────────────────────────────────┘ # sqrtx1 = x1**0.5 # sqrtx2 = x2**0.5 while x2 - x1 > 2: # Arithmetic mean between linear estimate and half linest = x1 - y1 / (y2 - y1) * (x2 - x1) halfest = (x2 + x1) / 2 # sqrtest1 = sqrtx1 - y1 * (sqrtx2 - sqrtx1) / (y2 - y1) # sqrtest1 = sqrtest1*sqrtest1 # sqrtest2 = int(x1 + (x2 - x1) / (y2 / y1 - 1)**2) # x_mid = int((x2 + x1) / 2) x_mid = int((linest + halfest) / 2) if x_mid == x1: # To stop repetition in close cases x_mid += 1 elif x_mid == x2: x_mid -= 1 y_mid, fit2 = f(x_mid) if is_debug: #─────────────────────────────────────────────────┐ print(f'\t{x_mid=}\t{y_mid=}') G['mid'].set_xdata(x_mid) G['mid'].set_ydata(y_mid) #──────────────────────────────────────────────────────────────┘ if y_mid > 0: if is_debug: #─────────────────────────────────────────────┐ wait('\tError over tolerance\n') G['ax_root'].plot(x2, y2,'.', color = 'black') #──────────────────────────────────────────────────────────┘ x2, y2 = x_mid, y_mid # sqrtx2 = x_mid **0.5 if is_debug: #─────────────────────────────────────────────┐ G['xy2'].set_xdata(x2) G['xy2'].set_ydata(y2) #──────────────────────────────────────────────────────────┘ else: if is_debug: #─────────────────────────────────────────────┐ wait('\tError under tolerance\n') G['ax_root'].plot(x1, y1,'.', color = 'black') #──────────────────────────────────────────────────────────┘ x1, y1, fit1 = x_mid, y_mid, fit2 # sqrtx1 = x_mid ** 0.5 if is_debug: #─────────────────────────────────────────────┐ G['xy1'].set_xdata(x1) G['xy1'].set_ydata(y1) #──────────────────────────────────────────────────────────┘ if x2 - x1 == 2: # Points have only one point in between y_mid, fit2 = f(x1+1) # Testing that point return (x1+1, fit2) if (y_mid <0) else (x1, fit1) # If under, give that fit else: return x1, fit1 #─────────────────────────────────────────────────────────────────────── def droot(f, y1, x2, limit): '''Finds the upper limit to interval ''' is_debug = G['debug'] x1 = 0 y2, fit2 = f(x2) fit1 = None if is_debug: #─────────────────────────────────────────────────────┐ G['xy1'], = G['ax_root'].plot(x1, y1,'g.') G['xy2'], = G['ax_root'].plot(x2, y2,'b.') #──────────────────────────────────────────────────────────────────┘ while y2 < 0: if is_debug: #─────────────────────────────────────────────────┐ wait('Calculating new attempt in droot\n') G['ax_root'].plot(x1, y1,'k.') #──────────────────────────────────────────────────────────────┘ x1, y1, fit1 = x2, y2, fit2 x2 *= 2 x2 += 1 if is_debug: #─────────────────────────────────────────────────┐ print(f'{limit=}') print(f'{x1=}\t{y1=}') print(f'{x2=}\t{y2=}') G['xy1'].set_xdata(x1) G['xy1'].set_ydata(y1) G['xy2'].set_xdata(x2) #──────────────────────────────────────────────────────────────┘ if x2 >= limit: if is_debug: #─────────────────────────────────────────────┐ G['ax_root'].plot([limit, limit], [y1,0],'b.') #──────────────────────────────────────────────────────────┘ y2, fit2 = f(limit) if y2<0: if is_debug: #─────────────────────────────────────────┐ wait('End reached within tolerance\n') #──────────────────────────────────────────────────────┘ return limit, fit2 else: if is_debug: #─────────────────────────────────────────┐ wait('End reached outside tolerance\n') #──────────────────────────────────────────────────────┘ x2 = limit break y2, fit2 = f(x2) if is_debug: #─────────────────────────────────────────────────┐ G['ax_root'].plot(x1, y1,'k.') print(f'{x1=}\t{y1=}') print(f'{x2=}\t{y2=}') G['ax_root'].plot(x2, y2,'k.') G['xy2'].set_ydata(y2) #──────────────────────────────────────────────────────────────┘ if is_debug: #─────────────────────────────────────────────────────┐ G['xy2'].set_color('red') wait('Points for interval found\n') #──────────────────────────────────────────────────────────────────┘ return interval(f, x1, y1, x2, y2, fit1) #─────────────────────────────────────────────────────────────────────── # @numba.jit(nopython=True,cache=True) def n_lines(x: np.ndarray, y: np.ndarray, x0: float, y0: np.ndarray, tol: float ) -> float: '''Estimates number of lines required to fit within error tolerance''' if (length := len(x)) > 1: inds = _sqrtrange[0](length - 2) # indices so that x[-1] is not included res = (y[-1] - y0) / (x[-1] - x0)*(x[inds] - x0).reshape([-1,1]) - (y[inds] - y0) reference = maxsumabs(res, tol) if reference < 0: reference = 0 return 0.5 * reference ** 0.5 + 1 else: return 1. #─────────────────────────────────────────────────────────────────────── def _get_f2zero(x, y, x0, y0, sqrtrange, f_fit, errorfunction): def f2zero(i: int) -> tuple: '''Function such that i is optimal when f2zero(i) = 0''' inds = sqrtrange(i) residuals, fit = f_fit(x[inds], y[inds], x0, y0) return errorfunction(residuals), fit return f2zero #─────────────────────────────────────────────────────────────────────── def _get_f2zero_debug(x, y, x0, y0, sqrtrange, f_fit, errorfunction): def f2zero_debug(i: int) -> tuple: '''Function such that i is optimal when f2zero(i) = 0''' inds = sqrtrange(i) residuals, fit = f_fit(x[inds], y[inds], x0, y0) if len(residuals) == 1: print(f'\t\t{residuals=}') print(f'\t\tstart = {G["start"]} end = {i + G["start"]} points = {i + 1}') print(f'\t\tx0\t{x0}\n\t\tx[0]\t{x[inds][0]}\n\t\tx[-1]\t{x[inds][-1]}\n\t\txstart = {G["x"][G["start"]]}') indices_all = np.arange(-1, i + 1) + G['start'] G['x_plot'] = G['x'][indices_all] G['y_plot'] = G['fyc'](fit, G['x_plot']) G['line_fit'].set_xdata(G['x_plot']) G['line_fit'].set_ydata(G['y_plot']) # print(f'{G["y_plot"].shape=}') # print(f'{G["y"][indices_all].shape=}') res_all = G['y_plot'] - G['y'][indices_all].flatten() print(f'\t\t{residuals.shape=}\n\t\t{res_all.shape=}') G['ax_res'].clear() G['ax_res'].grid() G['ax_res'].axhline(color = 'red', linestyle = '--') G['ax_res'].set_ylabel('Residual relative to tolerance') G['ax_res'].plot(indices_all - G['start'],

np.abs(res_all)

numpy.abs

import dxfgrabber import numpy as np import sys, os.path, inspect import re import pcbnew import bulge import pcbpoint from sets import Set import pdb # how close together to points need to be to each other to be considered # connected? thresh = 0.01 # when breaking and arc into segments, what's the max seg length we want. arc_line_length = 5.0 # I mostly depend on pcbpoint to deal with scaling issues. For radius below, # still need to scale. # the internal coorinate space of pcbnew is 10E-6 mm. (a millionth of a mm) # the coordinate 121550000 corresponds to 121.550000 SCALE = 1000000.0 class graphic_actions: def __init__(self, print_unhandled=False): self.print_unhandled = print_unhandled def line_action(self, start, end): if (self.print_unhandled): print("line: {} {}".format(start, end)) def circle_action(self, center, radius): if (self.print_unhandled): print("circle center: {} radius: {}".format(center, radius)) def arc_action(self, center, radius, start_angle, end_angle): if (self.print_unhandled): print("arc center: {} radius {} angles: {} {}".format(center, radius, start_angle, end_angle)) def poly_action(self, points): if (self.print_unhandled): print("poly: {}".format(points)) # here is some helper stuff # dxf arcs are different from pcbnew arcs # dxf arcs have a center point, radius and start/stop angles # pcbnew arcs have a center pointer, radius, and a start point, # angle (counter clockwise) def dxfarc2pcbarc(self, center, radius, start_angle, end_angle): # need negative angles because pcbnew flips over x axis start_angle, end_angle = (min(start_angle, end_angle), max(start_angle, end_angle)) return (center, start_angle-end_angle, # location of start of arc center.polar(radius, start_angle)) class segment_actions(graphic_actions): def __init__(self, board, layer, print_unhandled=False): graphic_actions.__init__(self, print_unhandled) self.board = board self.layer = layer def make_basic_seg(self): seg = pcbnew.DRAWSEGMENT(self.board) seg.SetLayer(self.layer) seg.SetShape(pcbnew.S_SEGMENT) self.board.Add(seg) return seg def line_action(self, start, end): seg = self.make_basic_seg() seg.SetShape(pcbnew.S_SEGMENT) seg.SetStart(pcbpoint.pcbpoint(start).wxpoint()) seg.SetEnd(pcbpoint.pcbpoint(end).wxpoint()) def circle_action(self, center, radius): seg = self.make_basic_seg() seg.SetShape(pcbnew.S_CIRCLE) seg.SetCenter(pcbpoint.pcbpoint(center).wxpoint()) # kicad has a goofy way of specifying circles. instead # of a radius, you give a point on the circle. The radius # can be computed from there. seg.SetEnd((pcbpoint.pcbpoint(center)+ pcbpoint.pcbpoint(radius, 0)).wxpoint() ) def arc_action(self, center, radius, start_angle, end_angle): # dxf arcs are different from pcbnew arcs # dxf arcs have a center point, radius and start/stop angles # pcbnew arcs have a center pointer, radius, and a start point, # angle (counter clockwise) seg = self.make_basic_seg() center, angle, arcstart = self.dxfarc2pcbarc(pcbpoint.pcbpoint(center), radius, start_angle, end_angle) seg.SetShape(pcbnew.S_ARC) seg.SetCenter(pcbpoint.pcbpoint(center).wxpoint()) # negative angle since y goes the wrong way. seg.SetAngle(angle*10) seg.SetArcStart(pcbpoint.pcbpoint(arcstart).wxpoint()) def poly_action(self, points): seg = self.make_basic_seg() seg.SetShape(pcbnew.S_POLYGON) sps = seg.GetPolyShape() o = sps.NewOutline() for pt in points: ppt = pcbpoint.pcbpoint(pt).wxpoint() sps.Append(ppt.x, ppt.y) class zone_actions(graphic_actions): def __init__(self, board, net, layer, print_unhandled=False): graphic_actions.__init__(self, print_unhandled) self.board = board self.net = net self.layer = layer # poly is the only thing that really makes sense in # the zone context def poly_action(self, points): pcbpt = pcbpoint.pcbpoint(points[0]).wxpoint() zone_container = self.board.InsertArea(self.net.GetNet(), 0, self.layer, pcbpt.x, pcbpt.y, pcbnew.CPolyLine.DIAGONAL_EDGE) shape_poly_set = zone_container.Outline() shapeid = 0 for pt in points[1:]: pcbpt = pcbpoint.pcbpoint(pt).wxpoint() shape_poly_set.Append(pcbpt.x, pcbpt.y) zone_container.Hatch() class mounting_actions(graphic_actions): def __init__(self, board, footprint_mapping, flip=False, clearance=None, print_unhandled=False): graphic_actions.__init__(self, print_unhandled) self.footprint_mapping = footprint_mapping self.board = board self.flip = flip self.clearance = clearance def circle_action(self, center, radius): d = str(radius*2) if d not in self.footprint_mapping: print("diameter {} not found in footprint mapping".format(d)) return fp = self.footprint_mapping[d] mod = pcbnew.InstantiateFootprint(fp[0], fp[1]) mod.SetPosition(pcbpoint.pcbpoint(center).wxpoint()) if (self.flip): mod.Flip(pcbpoint.pcbpoint(center).wxpoint()) if (self.clearance != None): for pad in mod.Pads(): pad.SetLocalClearance(self.clearance) self.board.Add(mod) # http://www.ariel.com.au/a/python-point-int-poly.html # determine if a point is inside a given polygon or not # Polygon is a list of (x,y) pairs. def point_inside_polygon(x,y,poly): n = len(poly) inside =False p1x,p1y = poly[0] for i in range(n+1): p2x,p2y = poly[i % n] if y > min(p1y,p2y): if y <= max(p1y,p2y): if x <= max(p1x,p2x): if p1y != p2y: xinters = (y-p1y)*(p2x-p1x)/(p2y-p1y)+p1x if p1x == p2x or x <= xinters: inside = not inside p1x,p1y = p2x,p2y return inside def longest_angle_for_polygon(poly): prevpt = poly[-1] length = None retval = None for pt in poly: d = prevpt.distance(pt) if (length and (length>d)): prevpt = pt continue length = d retval = prevpt.angle(pt) prevpt = pt return retval def center_for_polygon(poly): # based on this: # https://en.wikipedia.org/wiki/Centroid#Centroid_of_a_polygon prev = poly[-1] x = 0.0 y = 0.0 area = 0.0 for cur in poly: x = x + (prev.x+cur.x)*(prev.x*cur.y - cur.x*prev.y) y = y + (prev.y+cur.y)*(prev.x*cur.y - cur.x*prev.y) area = area + prev.x*cur.y - cur.x*prev.y prev = cur area = area/2.0 x = x/6.0/area y = y/6.0/area return pcbpoint.pcbpoint(x,y,noscale=True) class orient_actions(graphic_actions): def __init__(self, board, modnames, print_unhandled=False): graphic_actions.__init__(self, print_unhandled) self.board = board self.modnames = Set(modnames) # I only care about poly because I want directionality (which a cirle doesn't have) # and I want to check for enclosing (which doesn't make sense for line, arc def poly_action(self, points): for mod in self.board.GetModules(): #modname = mod.GetFPID().GetLibItemName().c_str() #if (modname != "LED_5730"): # continue modname = mod.GetReference() if (modname not in self.modnames): continue pos = pcbpoint.pcbpoint(mod.GetPosition()) inside = point_inside_polygon(pos.x, pos.y, points) if (not inside): continue angle = longest_angle_for_polygon(points) if (angle>0): angle = angle - 180.0 mod.SetOrientation(angle*10) mod.SetPosition(center_for_polygon(points).wxpoint()) class myarc: def __init__(self, center, radius, start_angle, end_angle): self.center = center = pcbpoint.pcbpoint(center) self.radius = radius self.start_angle = start_angle self.end_angle = end_angle self.start_point = center.polar(radius, start_angle) self.end_point = center.polar(radius, end_angle) self.other = Set() def reverse(self): self.start_angle, self.end_angle = (self.end_angle, self.start_angle) self.start_point, self.end_point = (self.end_point, self.start_point) def __str__(self): return "arc c{} r{} {},{} {},{}".format(self.center, self.radius, self.start_angle, self.end_angle, self.start_point, self.end_point) class myline: def __init__(self, start_point, end_point): self.start_point = pcbpoint.pcbpoint(start_point) self.end_point = pcbpoint.pcbpoint(end_point) self.other = Set() def reverse(self): self.start_point, self.end_point = (self.end_point, self.start_point) def __str__(self): return "line {} {}".format(self.start_point, self.end_point) def mydist(o1, o2): return min(o1.start_point.distance(o2.start_point), o1.start_point.distance(o2.end_point), o1.end_point.distance(o2.start_point), o1.end_point.distance(o2.end_point)) # it is possible for two polygons to meet at a point. This implementation # will destroy those. worry about it later. def remove_non_duals(e): if (len(e.other) == 2): return # if I have three lines joining in one spot. this doesn't deal # with the properly. I try to mitigate that by beginning with # lines that connect at only one edge first. others = e.other e.other = Set() for other in others: other.other.remove(e) remove_non_duals(other) def merge_arcs_and_lines(elts): # yes, this is a O(n^2) algorithm. scanline would be better # this is quicker to implement for e1 in elts: for e2 in elts: if (e1==e2): continue if mydist(e1, e2)<thresh: e1.other.add(e2) e2.other.add(e1) # this needs some work. if I have a line that connects to a poly, # I want to first lose that line and only then break triple connections. for e in elts: if (len(e.other) == 1): remove_non_duals(e) for e in elts: remove_non_duals(e) merged = [] for e in elts: if (len(e.other) != 2): continue members = [e] # after this pop, the e.other set will have one member other = e.other.pop() other.other.remove(e) while (other): if (other == e): break nextelt = other.other.pop() nextelt.other.remove(other) members.append(other) other = nextelt if (len(members) < 2): raise ValueError("There should be at least two members in this merged poly") prev = members[-1] # here, I'm about to reorder the point order of the member lines/arcs. I # want to end_point to match the next member. if ((members[0].start_point.distance(prev.end_point) > thresh) and (members[0].end_point.distance(prev.end_point) > thresh)): prev.reverse() for m in members: if (m.start_point.distance(prev.end_point) > thresh): m.reverse() if (m.start_point.distance(prev.end_point) > thresh): raise ValueError("expecting the start and end to match here {} {}".format(prev, m)) prev = m merged.append(members) return merged def break_curve(center, radius, start_angle, end_angle): retpts = [] center = pcbpoint.pcbpoint(center) # in this file, I generally use degrees (kicad uses degrees), # in this function, radians more convenient. start_radians, end_radians = (

np.deg2rad(start_angle)

numpy.deg2rad

# -*- coding: utf-8 -*- """LDFA-H: Latent Dynamic Factor Analysis of High-dimensional time-series This module implements the fitting algorithm of LDFA-H and the accessory functions to facilitate the associate analyses or inferences. Todo ---- * Correct function ``fit_Phi``. .. _[1] Bong et al. (2020). Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings. Submitted to NeurIPS2020. """ import time, sys, traceback import numpy as np from scipy import linalg import ldfa.optimize as core def _generate_lambda_glasso(bin_num, lambda_glasso, offset, lambda_diag=None): """Generate sparsity penalty matrix Lambda for a submatrix in Pi.""" lambda_glasso_out = np.full((bin_num, bin_num), -1) + (1+lambda_glasso) * \ (np.abs(np.arange(bin_num) - np.arange(bin_num)[:,np.newaxis]) <= offset) if lambda_diag: lambda_glasso_out[np.arange(bin_num), np.arange(bin_num)] = lambda_diag return lambda_glasso_out def _switch_back(Sigma, Phi_S, Gamma_T, Phi_T, beta): """Make the initial Phi_S positive definite.""" w, v = np.linalg.eig(Sigma) sqrtS = (v*np.sqrt(w)[...,None,:])@v.transpose(0,2,1) alpha = np.min(np.linalg.eigvals( sqrtS @ linalg.block_diag(*Gamma_T) @ sqrtS),-1)/2 return (Sigma - alpha[:,None,None]*linalg.block_diag(*Phi_T), [P + (b*alpha)@b.T for P, b in zip(Phi_S, beta)]) def _make_PD(m, thres_eigen=1e-4): """Make a coariance PSD based on its eigen decomposition.""" s, v = np.linalg.eigh(m) if np.min(s)<=thres_eigen*np.max(s): delta = thres_eigen*np.max(s) - np.min(s) s = s + delta return ([email protected](s)@np.linalg.inv(v)) def _temporal_est(V_eps_T, ar_order): """Perform temporal estimate given V_T. .. _[1] <NAME>. and <NAME>. (2008). Regularized estimation of large covariance matrices. Ann. Statist., 36(1):199–227. """ num_time = V_eps_T.shape[0] resids = np.zeros(num_time) Amatrix = np.zeros([num_time, num_time]) resids[0] = V_eps_T[0,0] for i in np.arange(1, ar_order): Amatrix[i,:i] = np.linalg.pinv(V_eps_T[:i,:i]) @ V_eps_T[:i,i] resids[i] = V_eps_T[i,i] \ - V_eps_T[i,:i] @ np.linalg.pinv(V_eps_T[:i,:i]) @ V_eps_T[:i,i] for i in np.arange(ar_order, num_time): Amatrix[i,i-ar_order:i] = np.linalg.pinv(V_eps_T[i-ar_order:i,i-ar_order:i]) \ @ V_eps_T[i-ar_order:i,i] resids[i] = V_eps_T[i,i] \ - V_eps_T[i,i-ar_order:i] \ @ np.linalg.pinv(V_eps_T[i-ar_order:i,i-ar_order:i]) \ @ V_eps_T[i-ar_order:i,i] # invIA = np.linalg.pinv(np.eye(num_time) - Amatrix) # Psi_T_hat = invIA @ np.diag(resids) @ invIA.T # Gamma_T_hat = np.linalg.inv(Psi_T_hat) Gamma_T_hat = (np.eye(num_time)-Amatrix).T @ np.diag(1/resids) \ @ (np.eye(num_time)-Amatrix) Psi_T_hat = np.linalg.pinv(Gamma_T_hat) return Gamma_T_hat, Psi_T_hat def fit(data, num_f, lambda_cross, offset_cross, lambda_auto=None, offset_auto=None, lambda_aug=0, ths_ldfa=1e-2, max_ldfa=1000, ths_glasso=1e-8, max_glasso=1000, ths_lasso=1e-8, max_lasso=1000, params_init=dict(), make_PD=False, verbose=False): """The main function to perform multi-factor LDFA-H estimation. Parameters ---------- data: list of (N, p_k, T) ndarrays Observed data from K areas. Data from each area k consists of p_k-variate time-series over T time bins in N trials. num_f: int The number of factors. lambda_cross, lambda_auto: float The sparsity penalty parameter for the inverse cross-correlation and inverse auto-correlation matrix, respectively. The default value for lambda_auto is 0. offset_cross, offset_auto: int The bandwidth parameter for the inverse cross-correlation matrix and inverse auto-correlation matrix, respectively. The default value for offset_auto is the given value of offset_cross. ths_ldfa, ths_glasso, ths_lasso: float, optional The threshold values for deciding the convergence of the main iteration, the glasso iteration, and the lasso iteration, respectively. max_ldfa, max_glasso, max_lasso: int, optional The maximum number of iteration for the main iteration, the glasso iteration, and the lasso iteration, respectively. beta_init: list of (p_k, num_f) ndarrays, optional Custom initial values for beta. If not given, beta is initialized by CCA. make_PD: boolean, optional Switch for manual positive definitization. If data does not generate a positive definite estimate of the covariance matrix, ``make_PD = True`` helps with maintaining the matrix positive definite throughout the fitting algorithm. The default value is False for the sake of running time. verbose: boolean, optional Swith for vocal feedback throughout the fitting algorithm. The default value is False. Returns ------- Pi: (K*T, K*T) ndarray The estimated sparse inverse correlation matrix. Rho: (K*T, K*T) ndarray The estimated correlation matrix before sparsification. Note that Rho != Pi^{-1}. params: dict The dictionary of the estimated parameters. It provides with the estimation of Omega: (num_f, K*T, K*T) ndarray; Gamma_S: a list of (p_k, p_k) ndarrays for k = 1, ..., K; Gamma_T: a list of (T, T) ndarrays for k = 1, ..., K; beta: a list of (p_k, num_f) ndarrays for k = 1, ..., K; and mu: a list of (p_k, T) ndarrays for k = 1, ..., K. Examples -------- Pi, Rho, params =\ fit(data, num_f, lambda_cross, offset_cross, lambda_auto, offset_auto) .. _[1] Bong et al. (2020). Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings. Submitted to NeurIPS2020. """ dims = [dat.shape[1] for dat in data] num_time = data[0].shape[2] num_trial = data[0].shape[0] # get full_graph if lambda_auto is None: lambda_auto = lambda_cross if offset_auto is None: offset_auto = offset_cross lambda_glasso_auto = _generate_lambda_glasso(num_time, lambda_auto, offset_auto) lambda_glasso_cross = _generate_lambda_glasso(num_time, lambda_cross, offset_cross) lambda_glasso = np.array(np.block( [[lambda_glasso_auto if j==i else lambda_glasso_cross for j, _ in enumerate(data)] for i, _ in enumerate(data)])) # set mu mu= [np.mean(dat, 0) for dat in data] # initialization if all(key in params_init for key in ('Omega', 'mu', 'beta', 'Gamma_S', 'Gamma_T')): Omega = params_init['Omega']; mu = params_init['mu']; beta = params_init['beta'] Gamma_S = params_init['Gamma_S']; Gamma_T = params_init['Gamma_T'] Sigma = np.linalg.inv(Omega) sig = np.sqrt(np.diagonal(Sigma,0,1,2)) Rho = Sigma/sig[:,None,:]/sig[:,:,None] Pi = np.linalg.inv(Rho) else: if 'beta' in params_init: beta = [b.copy() for b in params['beta_init']] weight = [np.linalg.pinv(b) for b in beta] elif len(data)==2: # initialize beta by CCA S_xt = np.tensordot( np.concatenate([dat-m for dat, m in zip(data,mu)], 1), np.concatenate([dat-m for dat, m in zip(data,mu)], 1), axes=((0,2),(0,2)))/num_trial/num_time S_1 = S_xt[:dims[0],:dims[0]] S_12 = S_xt[:dims[0],dims[0]:] S_2 = S_xt[dims[0]:,dims[0]:] U_1 = linalg.inv(linalg.sqrtm(S_1)) U_2 = linalg.inv(linalg.sqrtm(S_2)) u, s, vh = np.linalg.svd(U_1 @ S_12 @ U_2) weight = [u[:,:num_f].T @ U_1, vh[:num_f] @ U_2] beta = [linalg.inv(U_1) @ u[:,:num_f], linalg.inv(U_2) @ vh[:num_f].T] else: print("Default initialization only supports 2 populations now.") raise weight = [w*np.sqrt(np.sum(b**2, 0))[:,None] for w, b in zip(weight,beta)] beta = [b/np.sqrt(np.sum(b**2, 0)) for b in beta] # initialization on other parameters m_z_x = np.concatenate([np.matmul(w, dat-m)[...,None,:] for m, w, dat in zip(mu, weight, data)], -2) V_z_x = np.zeros((num_f,len(dims),num_time)*2) m_zk_x = m_z_x.transpose((2,0,1,3)) V_zk_x = np.diagonal(V_z_x,0,1,4).transpose(4,0,1,2,3) # mu = [np.mean(dat - b @ m, 0) # for dat, m, b in zip(data, m_zk_x, beta)] m_eps = [dat - b @ m1 - m2 for dat, m1, b, m2 in zip(data, m_zk_x, beta, mu)] v_eps = [(np.sum(np.square(m))*(1+lambda_aug)/num_trial + np.trace(V.reshape(num_f*num_time,num_f*num_time))) # /(d-1) for m, V, d in zip(m_eps, V_zk_x, dims)] V_eps_S = [np.tensordot(m,m,axes=((0,2),(0,2)))*(1+lambda_aug)/num_trial/v + [email protected](np.diagonal(V,0,1,3),-1)@b.T/v for m, V, v, b, d in zip(m_eps, V_zk_x, v_eps, beta, dims)] V_eps_T = [np.tensordot(m,np.linalg.pinv(V2)@m, axes=((0,1),(0,1))) *(lambda_aug*np.eye(num_time)+1)/d/num_trial + np.tensordot(V1,[email protected](V2)@b,axes=([0,2],[0,1]))/d for m, V1, V2, b, d in zip(m_eps, V_zk_x, V_eps_S, beta, dims)] sd_eps_T = [np.sqrt(np.diag(V)) for V in V_eps_T] R_eps_T = [V/sd/sd[:,None] for V, sd in zip(V_eps_T, sd_eps_T)] Phi_T = [R*sd*sd[:,None] for sd, R in zip(sd_eps_T, R_eps_T)] Gamma_T = [np.linalg.inv(P) for P in Phi_T] V_zf = (np.diagonal(V_z_x,0,0,3).transpose((4,0,1,2,3)) + (m_z_x.reshape((-1,num_f,len(dims)*num_time)).transpose((1,2,0)) @ m_z_x.reshape((-1,num_f,len(dims)*num_time)).transpose((1,0,2)) * (lambda_aug*np.eye(2*num_time)+1)/ num_trial)\ .reshape((num_f,len(dims),num_time,len(dims),num_time))) Sigma, Phi_S = _switch_back( V_zf.reshape(num_f,len(dims)*num_time,len(dims)*num_time), V_eps_S, Gamma_T, Phi_T, beta) if make_PD: Phi_S = [_make_PD(P) for P in Phi_S] for f in np.arange(num_f): Sigma[f] = _make_PD(Sigma[f]) Gamma_S = [np.linalg.inv(P) for P in Phi_S] sig = np.sqrt(np.diagonal(Sigma,0,1,2)) Rho = Sigma/sig[:,None,:]/sig[:,:,None] Pi =

np.linalg.inv(Rho)

numpy.linalg.inv

import numpy as np from index_mesh import IndexMesh from combine_cells import combine_cells from util import find_local_numbering class EMIMesh(IndexMesh): def __init__(self, Nx, Ny, Nz, N): super().__init__(Nx, Ny, Nz, N) def set_up_mesh(G): "Set up vectors containing the indices of the different types of nodes" # Read geometry Nx = G.Nx Ny = G.Ny Nz = G.Nz N = G.N mesh = EMIMesh(Nx, Ny, Nz, N) # x-direction ranges from west (w) to east (e) # y-direction ranges from south (s) to north (n) # z-direction ranges from low (l) to high (h) # Set up indices for outer boundary # Set up indices for outer boundary (corners) mesh.e_lsw = np.array([mesh[ 0, 0, 0]]) mesh.e_lse = np.array([mesh[Nx-1, 0, 0]]) mesh.e_lnw = np.array([mesh[ 0, Ny-1, 0]]) mesh.e_lne = np.array([mesh[Nx-1, Ny-1, 0]]) mesh.e_hsw = np.array([mesh[ 0, 0, Nz-1]]) mesh.e_hse = np.array([mesh[Nx-1, 0, Nz-1]]) mesh.e_hnw = np.array([mesh[ 0, Ny-1, Nz-1]]) mesh.e_hne = np.array([mesh[Nx-1, Ny-1, Nz-1]]) # Set up indices for outer boundary (lines) # x direction mesh.e_ls = mesh[ 1:Nx-1, 0, 0] mesh.e_ln = mesh[ 1:Nx-1, Ny-1, 0] mesh.e_hs = mesh[ 1:Nx-1, 0, Nz-1] mesh.e_hn = mesh[ 1:Nx-1, Ny-1, Nz-1] # y direction mesh.e_lw = mesh[ 0, 1:Ny-1, 0] mesh.e_le = mesh[ Nx-1, 1:Ny-1, 0] mesh.e_hw = mesh[ 0, 1:Ny-1, Nz-1] mesh.e_he = mesh[ Nx-1, 1:Ny-1, Nz-1] # z direction mesh.e_sw = mesh[ 0, 0, 1:Nz-1] mesh.e_se = mesh[ Nx-1, 0, 1:Nz-1] mesh.e_nw = mesh[ 0, Ny-1, 1:Nz-1] mesh.e_ne = mesh[ Nx-1, Ny-1, 1:Nz-1] # Set up indices for outer boundary (sides) mesh.e_l = mesh[1:-1, 1:-1, 0] mesh.e_h = mesh[1:-1, 1:-1, Nz-1] mesh.e_s = mesh[1:-1, 0, 1:-1] mesh.e_n = mesh[1:-1, Ny-1, 1:-1] mesh.e_w = mesh[0, 1:-1, 1:-1] mesh.e_e = mesh[Nx-1, 1:-1, 1:-1] # Set up indices for the cell mesh, cells = combine_cells(G, mesh) print("Combined cells") # Set up indices for the remaining extracellular cells vec = np.ones(N) index_sets_to_exclude_from_e = ( mesh.e_lsw, mesh.e_lse, mesh.e_lnw, mesh.e_lne, mesh.e_hsw, mesh.e_hse, mesh.e_hnw, mesh.e_hne, mesh.e_hw, mesh.e_he, mesh.e_hs, mesh.e_hn, mesh.e_lw, mesh.e_le, mesh.e_ls, mesh.e_ln, mesh.e_ne, mesh.e_sw, mesh.e_se, mesh.e_nw, mesh.e_w, mesh.e_e, mesh.e_s, mesh.e_n, mesh.e_h, mesh.e_l, mesh.m_lsw, mesh.m_lse, mesh.m_lnw, mesh.m_lne, mesh.m_hsw, mesh.m_hse, mesh.m_hnw, mesh.m_hne, mesh.m_hw, mesh.m_he, mesh.m_hs, mesh.m_hn, mesh.m_lw, mesh.m_le, mesh.m_ls, mesh.m_ln, mesh.m_ne, mesh.m_sw, mesh.m_se, mesh.m_nw, mesh.m_w, mesh.m_e, mesh.m_s, mesh.m_n, mesh.m_h, mesh.m_l, mesh.i_all ) for index_set in index_sets_to_exclude_from_e: vec[index_set] = 0 mesh.e = np.flatnonzero(vec) # Set up indices for the membrane potential mesh.v = np.concatenate([ mesh.m_lsw, mesh.m_lse, mesh.m_lnw, mesh.m_lne, mesh.m_hsw, mesh.m_hse, mesh.m_hnw, mesh.m_hne, mesh.m_hw, mesh.m_he, mesh.m_hs, mesh.m_hn, mesh.m_lw, mesh.m_le, mesh.m_ls, mesh.m_ln, mesh.m_ne, mesh.m_sw, mesh.m_se, mesh.m_nw, mesh.m_w, mesh.m_e, mesh.m_s, mesh.m_n, mesh.m_h, mesh.m_l ]) mesh.v.sort() # Set up indices for the gap junction mesh.w = np.unique(np.concatenate([mesh.gx_e, mesh.gy_n])) mesh.w.sort() # Set up indices for the nodes not in the intracellular domain mesh.not_i = np.concatenate([ mesh.e_lsw, mesh.e_lse, mesh.e_lnw, mesh.e_lne, mesh.e_hsw, mesh.e_hse, mesh.e_hnw, mesh.e_hne, mesh.e_hw, mesh.e_he, mesh.e_hs, mesh.e_hn, mesh.e_lw, mesh.e_le, mesh.e_ls, mesh.e_ln, mesh.e_ne, mesh.e_sw, mesh.e_se, mesh.e_nw, mesh.e_w, mesh.e_e, mesh.e_s, mesh.e_n, mesh.e_h, mesh.e_l, mesh.e, mesh.gx_he, mesh.gx_le, mesh.gx_se, mesh.gx_ne, mesh.gx_lse, mesh.gx_hse, mesh.gx_lne, mesh.gx_hne, mesh.gy_ne, mesh.gy_nw, mesh.gy_ln, mesh.gy_hn, mesh.gy_lnw, mesh.gy_hnw, mesh.gy_lne, mesh.gy_hne ]) mesh.not_i.sort() # Set up indices for the extracellular domain mesh.e_all = np.concatenate([ mesh.e_lsw, mesh.e_lse, mesh.e_lnw, mesh.e_lne, mesh.e_hsw, mesh.e_hse, mesh.e_hnw, mesh.e_hne, mesh.e_hw, mesh.e_he, mesh.e_hs, mesh.e_hn, mesh.e_lw, mesh.e_le, mesh.e_ls, mesh.e_ln, mesh.e_ne, mesh.e_sw, mesh.e_se, mesh.e_nw, mesh.e_w, mesh.e_e, mesh.e_s, mesh.e_n, mesh.e_h, mesh.e_l, mesh.m_lsw, mesh.m_lse, mesh.m_lnw, mesh.m_lne, mesh.m_hsw, mesh.m_hse, mesh.m_hnw, mesh.m_hne, mesh.m_hw, mesh.m_he, mesh.m_hs, mesh.m_hn, mesh.m_lw, mesh.m_le, mesh.m_ls, mesh.m_ln, mesh.m_ne, mesh.m_sw, mesh.m_se, mesh.m_nw, mesh.m_w, mesh.m_e, mesh.m_s, mesh.m_n, mesh.m_h, mesh.m_l, mesh.gx_he, mesh.gx_le, mesh.gx_se, mesh.gx_ne, mesh.gy_hn, mesh.gy_ln, mesh.gy_nw, mesh.gy_ne, mesh.gx_lse, mesh.gx_lne, mesh.gx_hse, mesh.gx_hne, mesh.gy_lnw, mesh.gy_lne, mesh.gy_hnw, mesh.gy_hne, mesh.e ]) mesh.e_all.sort() mesh.i_all = np.unique(np.concatenate([ mesh.v, mesh.w, mesh.i, mesh.i_lsw, mesh.i_lse, mesh.i_lnw, mesh.i_lne, mesh.i_hsw, mesh.i_hse, mesh.i_hnw, mesh.i_hne, mesh.i_hw, mesh.i_he, mesh.i_hs, mesh.i_hn, mesh.i_lw, mesh.i_le, mesh.i_ls, mesh.i_ln, mesh.i_ne, mesh.i_sw, mesh.i_se, mesh.i_nw, mesh.i_w, mesh.i_e, mesh.i_s, mesh.i_n, mesh.i_h, mesh.i_l, mesh.gx_e, mesh.gy_n, mesh.gxi_w, mesh.gxi_hw, mesh.gxi_lw, mesh.gxi_sw, mesh.gxi_nw, mesh.gyi_hs, mesh.gyi_ls, mesh.gyi_sw, mesh.gyi_se, mesh.gyi_s, mesh.gxi_lsw, mesh.gxi_lnw, mesh.gxi_hsw, mesh.gxi_hnw, mesh.gyi_lsw, mesh.gyi_lse, mesh.gyi_hsw, mesh.gyi_hse ])) mesh.i_all.sort() mesh.i_inner_all = np.unique(np.concatenate([ mesh.i, mesh.i_lsw, mesh.i_lse, mesh.i_lnw, mesh.i_lne, mesh.i_hsw, mesh.i_hse, mesh.i_hnw, mesh.i_hne, mesh.i_hw, mesh.i_he, mesh.i_hs, mesh.i_hn, mesh.i_lw, mesh.i_le, mesh.i_ls, mesh.i_ln, mesh.i_ne, mesh.i_sw, mesh.i_se, mesh.i_nw, mesh.i_w, mesh.i_e, mesh.i_s, mesh.i_n, mesh.i_l, mesh.i_h ])) mesh.i_inner_all.sort() # local numbering in mesh.v # cells[n].v should not overlap with cells[k].v, where k != n cells_v_idx = find_local_numbering(N, mesh.v, [cell.v for cell in cells]) for i, cell in enumerate(cells): cell.v_idx = cells_v_idx[i] del cells_v_idx # local numbering in e_all cells_v_of_e = find_local_numbering(N, mesh.e_all, [cell.v for cell in cells]) for i, cell in enumerate(cells): cell.v_of_e = cells_v_of_e[i] del cells_v_of_e # local numbering in i_all cells_v_of_i = find_local_numbering(N, mesh.i_all, [cell.v for cell in cells]) cells_gxw_of_i = find_local_numbering(N, mesh.i_all, [cell.gx_w for cell in cells]) cells_gxe_of_i = find_local_numbering(N, mesh.i_all, [cell.gx_e for cell in cells]) cells_gys_of_i = find_local_numbering(N, mesh.i_all, [cell.gy_s for cell in cells]) cells_gyn_of_i = find_local_numbering(N, mesh.i_all, [cell.gy_n for cell in cells]) for i, cell in enumerate(cells): cell.v_of_i = cells_v_of_i[i] cell.gxw_of_i = cells_gxw_of_i[i] cell.gxe_of_i = cells_gxe_of_i[i] cell.gys_of_i = cells_gys_of_i[i] cell.gyn_of_i = cells_gyn_of_i[i] del cells_v_of_i del cells_gxw_of_i del cells_gxe_of_i del cells_gys_of_i del cells_gyn_of_i # cells[n].c_all can overlap with cells[k].c_all, where k != n # solution: treat cell grid as checkered board so that direct neighbours are split in different sets. # Swipe over the white first, then the black. #cells[n].c_of_i cell_indices_white = [] cell_indices_black = [] for j in range(G.num_cells_y): for i in range(G.num_cells_x): cell_index = j*G.num_cells_x + i if (i + j) & 1 == 1: cell_indices_black.append(cell_index) else: cell_indices_white.append(cell_index) cells_c_of_i_white = find_local_numbering(N, mesh.i_all, [cells[i].c_all for i in cell_indices_white]) cells_c_of_i_black = find_local_numbering(N, mesh.i_all, [cells[i].c_all for i in cell_indices_black]) for i, cell_index in enumerate(cell_indices_white): cells[cell_index].c_of_i = cells_c_of_i_white[i] for i, cell_index in enumerate(cell_indices_black): cells[cell_index].c_of_i = cells_c_of_i_black[i] del cell_indices_white del cell_indices_black del cells_c_of_i_white del cells_c_of_i_black # local numbering in w cells_gxw_of_w = find_local_numbering(N, mesh.w, [cell.gx_w for cell in cells]) cells_gxe_of_w = find_local_numbering(N, mesh.w, [cell.gx_e for cell in cells]) cells_gys_of_w = find_local_numbering(N, mesh.w, [cell.gy_s for cell in cells]) cells_gyn_of_w = find_local_numbering(N, mesh.w, [cell.gy_n for cell in cells]) for i, cell in enumerate(cells): cell.gxw_of_w = cells_gxw_of_w[i] cell.gxe_of_w = cells_gxe_of_w[i] cell.gys_of_w = cells_gys_of_w[i] cell.gyn_of_w = cells_gyn_of_w[i] del cells_gxw_of_w del cells_gxe_of_w del cells_gys_of_w del cells_gyn_of_w # Inner membrane points mesh.v_inner = np.concatenate([mesh.m_l, mesh.m_h, mesh.m_s, mesh.m_n, mesh.m_e, mesh.m_w]) mesh.v_inner.sort() vec = np.zeros(N, dtype=np.int64) vec[mesh.v_inner] = 1 vec = vec[mesh.v] mesh.v_inner_of_v = np.flatnonzero(vec) # Set up way to extract membrane points from the intracellular and extracellular domains vec = np.zeros(N, dtype=np.int64) vec[mesh.v] = 1 vec_of_i = vec[mesh.i_all] vec_of_e = vec[mesh.e_all] mesh.v_of_i = np.flatnonzero(vec_of_i) mesh.v_of_e = np.flatnonzero(vec_of_e) # v inner vec = np.zeros(N, dtype=np.int64) vec[mesh.v_inner] = 1 vec_of_i = vec[mesh.i_all] vec_of_e = vec[mesh.e_all] mesh.v_inner_of_i = np.flatnonzero(vec_of_i) mesh.v_inner_of_e = np.flatnonzero(vec_of_e) # Set up way to extract intercalated disc points from the intracellular domain vec = np.zeros(N, dtype=np.int64) vec[mesh.w] = 1 vec_of_i = vec[mesh.i_all] mesh.w_of_i =

np.flatnonzero(vec_of_i)

numpy.flatnonzero

# Copyright 2018, FBPIC contributors # Authors: <NAME>, <NAME> # License: 3-Clause-BSD-LBNL """ This file is part of the Fourier-Bessel Particle-In-Cell code (FB-PIC) It defines a class for continuous particle injection with a moving window. """ import warnings import numpy as np from scipy.constants import c import sys, inspect class ContinuousInjector( object ): """ Class that stores a number of attributes that are needed for continuous injection by a moving window. """ def __init__(self, Npz, zmin, zmax, dz_particles, Npr, rmin, rmax, Nptheta, n, dens_func, ux_m, uy_m, uz_m, ux_th, uy_th, uz_th ): """ Initialize continuous injection Parameters ---------- See the docstring of the `Particles` object """ # Register properties of the injected plasma self.Npr = Npr self.rmin = rmin self.rmax = rmax self.Nptheta = Nptheta self.n = n self.dens_func = dens_func self.ux_m = ux_m self.uy_m = uy_m self.uz_m = uz_m self.ux_th = ux_th self.uy_th = uy_th self.uz_th = uz_th # Register spacing between evenly-spaced particles in z if Npz != 0: self.dz_particles = (zmax - zmin)/Npz else: # Fall back to the user-provided `dz_particles`. # Note: this is an optional argument of `Particles` and so # it is not always available. self.dz_particles = dz_particles # Register variables that define the positions # where the plasma is injected. self.v_end_plasma = c * uz_m / np.sqrt(1 + ux_m**2 + uy_m**2 + uz_m**2) # These variables are set by `initialize_injection_positions` self.nz_inject = None self.z_inject = None self.z_end_plasma = None def initialize_injection_positions( self, comm, v_moving_window, species_z, dt ): """ Initialize the positions that keep track of the injection of particles. This is automatically called at the beginning of `step`. Parameters ---------- comm: a BoundaryCommunicator object Contains information about grid MPI decomposition v_moving_window: float (in m/s) The speed of the moving window species_z: 1darray of float (in m) (One element per macroparticle) Used in order to infer the position of the end of the plasma dt: float (in s) Timestep of the simulation """ # The injection position is only initialized for the last proc if comm.rank != comm.size-1: return # Initialize the injection position only if it has not be initialized if self.z_inject is not None: return # Initialize plasma *ahead* of the right *physical* # boundary of the box in the damping region (including the # injection area) so that after `exchange_period` iterations # (without adding new plasma), there will still be plasma # inside the physical domain and the damping region (without the # injection area). This ensures that there are never particles in the # rightmost guard region and that there are always particles inside # the damped region, where the field can be non-zero. New particles, # which are injected in the Injection region, do not see any fields. _, zmax_global_domain_with_damp = comm.get_zmin_zmax( local=False, with_damp=True, with_guard=False ) self.z_inject = zmax_global_domain_with_damp \ + (3-comm.n_inject)*comm.dz \ + comm.exchange_period*dt*(v_moving_window-self.v_end_plasma) self.nz_inject = 0 # Try to detect the position of the end of the plasma: # Find the maximal position of the continously-injected particles if len( species_z ) > 0: # Add half of the spacing between particles (the # injection function itself will add a half-spacing again) self.z_end_plasma = species_z.max() + 0.5*self.dz_particles else: # Default value for empty species _, zmax_global_physical_domain = comm.get_zmin_zmax( local=False, with_damp=False, with_guard=False ) self.z_end_plasma = zmax_global_physical_domain # Check that the particle spacing has been properly calculated if self.dz_particles is None: raise ValueError( 'The simulation uses continuous injection of particles, \n' 'but was unable to calculate the spacing between particles.\n' 'This may be because you used the `Particles` API directly.\n' 'In this case, please pass the argument `dz_particles` \n' 'initializing the `Particles` object.') def reset_injection_positions( self ): """ Reset the variables that keep track of continuous injection to `None` This is typically called when restarting a simulation from a checkpoint """ self.nz_inject = None self.z_inject = None self.z_end_plasma = None def increment_injection_positions( self, v_moving_window, duration ): """ Update the positions between which the new particles will be generated, the next time when `generate_particles` is called. This function is automatically called when the moving window moves. Parameters ---------- v_moving_window: float (in m/s) The speed of the moving window duration: float (in seconds) The duration since the last time that the moving window moved. """ # Move the injection position self.z_inject += v_moving_window * duration # Take into account the motion of the end of the plasma self.z_end_plasma += self.v_end_plasma * duration # Increment the number of particle to add along z nz_new = int( (self.z_inject - self.z_end_plasma)/self.dz_particles ) self.nz_inject += nz_new # Increment the virtual position of the end of the plasma # (When `generate_particles` is called, then the plasma # is injected between z_end_plasma - nz_inject*dz_particles # and z_end_plasma, and afterwards nz_inject is set to 0.) self.z_end_plasma += nz_new * self.dz_particles def generate_particles( self, time ): """ Generate new particles at the right end of the plasma (i.e. between z_end_plasma - nz_inject*dz and z_end_plasma) Parameters ---------- time: float (in second) The current physical time of the simulation """ # Create a temporary density function that takes into # account the fact that the plasma has moved if self.dens_func is not None: args = _check_dens_func_arguments( self.dens_func ) if args == ['z', 'r']: def dens_func(z, r): return self.dens_func( z - self.v_end_plasma*time, r ) elif args == ['x', 'y', 'z']: def dens_func(x, y, z): return self.dens_func( x, y, z - self.v_end_plasma*time ) else: dens_func = None # Create new particle cells # Determine the positions between which new particles will be created Npz = self.nz_inject zmax = self.z_end_plasma zmin = self.z_end_plasma - self.nz_inject*self.dz_particles # Create the particles Ntot, x, y, z, ux, uy, uz, inv_gamma, w = generate_evenly_spaced( Npz, zmin, zmax, self.Npr, self.rmin, self.rmax, self.Nptheta, self.n, dens_func, self.ux_m, self.uy_m, self.uz_m, self.ux_th, self.uy_th, self.uz_th ) # Reset the number of particle cells to be created self.nz_inject = 0 return( Ntot, x, y, z, ux, uy, uz, inv_gamma, w ) # Utility functions # ----------------- def generate_evenly_spaced( Npz, zmin, zmax, Npr, rmin, rmax, Nptheta, n, dens_func, ux_m, uy_m, uz_m, ux_th, uy_th, uz_th ): """ Generate evenly-spaced particles, according to the density function `dens_func`, and with the momenta given by the `ux/y/z` arguments. Parameters ---------- See the docstring of the `Particles` object """ # Generate the particles and eliminate the ones that have zero weight ; # infer the number of particles Ntot if Npz*Npr*Nptheta > 0: # Get the 1d arrays of evenly-spaced positions for the particles dz = (zmax-zmin)*1./Npz z_reg = zmin + dz*( np.arange(Npz) + 0.5 ) dr = (rmax-rmin)*1./Npr r_reg = rmin + dr*( np.arange(Npr) + 0.5 ) dtheta = 2*np.pi/Nptheta theta_reg = dtheta * np.arange(Nptheta) # Get the corresponding particles positions # (copy=True is important here, since it allows to # change the angles individually) zp, rp, thetap = np.meshgrid( z_reg, r_reg, theta_reg, copy=True, indexing='ij' ) # Prevent the particles from being aligned along any direction unalign_angles( thetap, Npz, Npr, method='random' ) # Flatten them (This performs a memory copy) r = rp.flatten() x = r * np.cos( thetap.flatten() ) y = r * np.sin( thetap.flatten() ) z = zp.flatten() # Get the weights (i.e. charge of each macroparticle), which # are equal to the density times the volume r d\theta dr dz w = n * r * dtheta*dr*dz # Modulate it by the density profile if dens_func is not None : args = _check_dens_func_arguments( dens_func ) if args == ['x', 'y', 'z']: w *= dens_func( x=x, y=y, z=z ) elif args == ['z', 'r']: w *= dens_func( z=z, r=r ) # Select the particles that have a non-zero weight selected = (w > 0) if np.any(w < 0): warnings.warn( 'The specified particle density returned negative densities.\n' 'No particles were generated in areas of negative density.\n' 'Please check the validity of the `dens_func`.') # Infer the number of particles and select them Ntot = int(selected.sum()) x = x[ selected ] y = y[ selected ] z = z[ selected ] w = w[ selected ] # Initialize the corresponding momenta uz = uz_m * np.ones(Ntot) + uz_th * np.random.normal(size=Ntot) ux = ux_m * np.ones(Ntot) + ux_th * np.random.normal(size=Ntot) uy = uy_m * np.ones(Ntot) + uy_th * np.random.normal(size=Ntot) inv_gamma = 1./np.sqrt( 1 + ux**2 + uy**2 + uz**2 ) # Return the particle arrays return( Ntot, x, y, z, ux, uy, uz, inv_gamma, w ) else: # No particles are initialized ; the arrays are still created Ntot = 0 return( Ntot, np.empty(0), np.empty(0), np.empty(0), np.empty(0), np.empty(0), np.empty(0), np.empty(0),

np.empty(0)

numpy.empty

# -*- coding: utf-8 -*- """ Created on Thu Nov 14 10:28:23 2019 @author: thele """ from . import measurement_functions from . import condition_functions import numpy as np class Investigation_stage(): def __init__(self,jump,measure,check,configs,timer,pygor=None): self.jump = jump self.measure = measure self.check = check self.configure_investigation_sequence(configs) self.inv_max = len(self.aquisition_functions) self.isdynamic = configs.get('cond_meas',[False]*self.inv_max) self.stage_results = [] self.timer = timer self.pygor = pygor self.cond=list(range(1,1+self.inv_max)) self.conditional_idx_list = [] def configure_investigation_sequence(self,configs): seq_keys = configs["measurement_seq"] self.aquisition_functions = [] self.function_configs = [] self.cond_functions = [] for seq_key in seq_keys: afunc_name = configs[seq_key].get('func','do_nothing') cfunc_name = configs[seq_key].get('condition','check_nothing') self.aquisition_functions += [getattr(measurement_functions,afunc_name)] self.cond_functions += [getattr(condition_functions,cfunc_name)] self.function_configs += [configs[seq_key]] def do_extra_measure(self,params,minc,maxc,**kwags): self.jump(params) self.timer.start() plunger_jump = lambda params:self.jump(params,True) kwags['pygor'] = self.pygor anchor_vals = self.check() results_full = {} results = [] all_resutls = [None]*self.inv_max for i in range(self.inv_max): data = self.aquisition_functions[i](plunger_jump,self.measure,anchor_vals,self.function_configs[i],**kwags) self.timer.logtime() check_result,continue_on,meta_info = self.cond_functions[i](data,minc,maxc,self.function_configs[i],**kwags) if len(self.stage_results)>0: np.array(self.stage_results,dtype=np.float) past_results = np.array(self.stage_results,dtype=np.float)[:,i] continue_on = bool_cond(check_result,past_results[~np.isnan(past_results)],**self.isdynamic[i]) if isinstance(self.isdynamic[i],dict) else continue_on all_resutls[i] = check_result if isinstance(meta_info,dict): new_kwags = meta_info.get('kwags',None) if new_kwags is not None: kwags['last_check'] = new_kwags results += [[check_result,continue_on,meta_info,data]] if not continue_on: break self.stage_results += [all_resutls] self.timer.stop() results_full['extra_measure'] = results results_full['conditional_idx'] = self.cond[i] results_full['times'] = self.timer.times_list[-1] return results_full def bool_cond(score,past,min_thresh=0.0001,min_data=10,quantile=0.85): th_score = np.maximum(min_thresh,

np.quantile(past, quantile)

numpy.quantile

import numpy as np from scipy.signal import savgol_filter from qube.postprocess.dataset import Axis def create_name(name, suffix=None, prefix=None): elements = [] if prefix: elements.append(str(prefix)) elements.append(str(name)) if suffix: elements.append(str(suffix)) name = '_'.join(elements) return name def duplicate_dataset(dataset, suffix=None, prefix=None, custom_name=None): new_ds = dataset.copy(shallow_copy=False) if custom_name: name = custom_name else: name = dataset.name new_ds.name = create_name(name, suffix, prefix) return new_ds def remove_dim_in_axes(axes, dim=None): new_axes = [] if dim is not None: for si in axes: ax = si.copy(shallow_copy=False) if si.dim != dim and si.dim > dim: ax.dim = si.dim - 1 new_axes.append(ax) elif si.dim < dim: new_axes.append(ax) return new_axes def histogram1d(dataset, bins=10, range=None, normed=None, weights=None, density=None): ds = dataset.copy() ds.name = f'{ds.name}_hist1d' hist, bins = np.histogram( ds.value, bins=bins, range=range, normed=normed, weights=weights, density=density, ) bins = bins[:-1] # remove 1 point for ax.plot axis = Axis( name=ds.name, value=bins, unit=ds.unit, dim=0, ) ds.value = hist ds.unit = 'Counts' ds.axes = {axis.name: axis} return ds def take(dataset, indices, axis=None): ds = dataset.copy() ds.name = f'{ds.name}_take' ds.value = np.take(ds.value, indices=indices, axis=axis) old_axes = ds.get_axes(counters=False) ds.clear_axes() if axis is not None: for si in old_axes: ax = si.copy(shallow_copy=False) if si.dim == axis: ax.value =

np.take(ax.value, indices=indices)

numpy.take

from abc import abstractmethod import numpy as np import torch from codes.c_models.base_model import RNNModel from codes.d_agents.a0_base_agent import BaseAgent from codes.e_utils import replay_buffer from codes.e_utils.common_utils import float32_preprocessor from codes.e_utils.names import RLAlgorithmName class OffPolicyAgent(BaseAgent): """ Abstract Agent interface """ def __init__(self, worker_id, action_shape, params, device): super(OffPolicyAgent, self).__init__(worker_id, action_shape, params, device) if hasattr(self.params, "PER_PROPORTIONAL") and self.params.PER_PROPORTIONAL: self.buffer = replay_buffer.PrioritizedReplayBuffer( experience_source=None, buffer_size=self.params.REPLAY_BUFFER_SIZE, n_step=self.params.N_STEP, beta_start=0.4, beta_frames=self.params.MAX_GLOBAL_STEP ) elif hasattr(self.params, "PER_RANK_BASED") and self.params.PER_RANK_BASED: self.buffer = replay_buffer.RankBasedPrioritizedReplayBuffer( experience_source=None, buffer_size=self.params.REPLAY_BUFFER_SIZE, params=self.params, alpha=0.7, beta_start=0.5, beta_frames=self.params.MAX_GLOBAL_STEP ) else: self.buffer = replay_buffer.ExperienceReplayBuffer( experience_source=None, buffer_size=self.params.REPLAY_BUFFER_SIZE ) def train_off_policy(self, step_idx, local_step_idx): train_results = self.on_train(step_idx, local_step_idx) self.training_steps += 1 return train_results @abstractmethod def on_train(self, step_idx, local_step_idx): raise NotImplementedError def unpack_batch(self, batch): state, actions, rewards, dones, last_states, agent_states = [], [], [], [], [], [] if isinstance(self.model, RNNModel): actor_hidden_states = [] if self.params.RL_ALGORITHM in [RLAlgorithmName.DDPG_V0]: critic_hidden_states = [] critic_1_hidden_states = None critic_2_hidden_states = None elif self.params.RL_ALGORITHM in [RLAlgorithmName.TD3_V0]: critic_hidden_states = None critic_1_hidden_states = [] critic_2_hidden_states = [] else: raise ValueError() else: actor_hidden_states = critic_hidden_states = critic_1_hidden_states = critic_2_hidden_states = None for exp in batch: state.append(np.array(exp.state, copy=False)) actions.append(exp.action) rewards.append(exp.reward) dones.append(exp.last_state is None) if exp.last_state is None: last_states.append(exp.state) # the result will be masked anyway else: last_states.append(

np.array(exp.last_state, copy=False)

numpy.array