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#!/usr/bin/python
from __future__ import division
from __future__ import print_function
import sys
import os
import re
import datetime
import zipfile
import tempfile
import argparse
import math
import warnings
import json
import csv
import numpy as np
import scipy.stats as scp
from lxml import etree as et
def get_rdml_lib_version():
"""Return the version string of the RDML library.
Returns:
The version string of the RDML library.
"""
return "1.0.0"
class NpEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.bool_):
return bool(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(NpEncoder, self).default(obj)
class RdmlError(Exception):
"""Basic exception for errors raised by the RDML-Python library"""
def __init__(self, message):
Exception.__init__(self, message)
pass
class secondError(RdmlError):
"""Just to have, not used yet"""
def __init__(self, message):
RdmlError.__init__(self, message)
pass
def _get_first_child(base, tag):
"""Get a child element of the base node with a given tag.
Args:
base: The base node element. (lxml node)
tag: Child elements group tag used to select the elements. (string)
Returns:
The first child lxml node element found or None.
"""
for node in base:
if node.tag.replace("{http://www.rdml.org}", "") == tag:
return node
return None
def _get_first_child_text(base, tag):
"""Get a child element of the base node with a given tag.
Args:
base: The base node element. (lxml node)
tag: Child elements group tag used to select the elements. (string)
Returns:
The text of first child node element found or an empty string.
"""
for node in base:
if node.tag.replace("{http://www.rdml.org}", "") == tag:
return node.text
return ""
def _get_first_child_bool(base, tag, triple=True):
"""Get a child element of the base node with a given tag.
Args:
base: The base node element. (lxml node)
tag: Child elements group tag used to select the elements. (string)
triple: If True, None is returned if not found, if False, False
Returns:
The a bool value of tag or if triple is True None.
"""
for node in base:
if node.tag.replace("{http://www.rdml.org}", "") == tag:
return _string_to_bool(node.text, triple)
if triple is False:
return False
else:
return None
def _get_step_sort_nr(elem):
"""Get the number of the step eg. for sorting.
Args:
elem: The node element. (lxml node)
Returns:
The a int value of the step node nr.
"""
if elem is None:
raise RdmlError('A step element must be provided for sorting.')
ret = _get_first_child_text(elem, "nr")
if ret == "":
raise RdmlError('A step element must have a \"nr\" element for sorting.')
return int(ret)
def _sort_list_int(elem):
"""Get the first element of the array as int. for sorting.
Args:
elem: The 2d list
Returns:
The a int value of the first list element.
"""
return int(elem[0])
def _sort_list_float(elem):
"""Get the first element of the array as float. for sorting.
Args:
elem: The 2d list
Returns:
The a float value of the first list element.
"""
return float(elem[0])
def _sort_list_digital_PCR(elem):
"""Get the first column of the list as int. for sorting.
Args:
elem: The list
Returns:
The a int value of the first list element.
"""
arr = elem.split("\t")
return int(arr[0]), arr[4]
def _string_to_bool(value, triple=True):
"""Translates a string into bool value or None.
Args:
value: The string value to evaluate. (string)
triple: If True, None is returned if not found, if False, False
Returns:
The a bool value of tag or if triple is True None.
"""
if value is None or value == "":
if triple is True:
return None
else:
return False
if type(value) is bool:
return value
if type(value) is int:
if value != 0:
return True
else:
return False
if type(value) is str:
if value.lower() in ['false', '0', 'f', '-', 'n', 'no']:
return False
else:
return True
return
def _value_to_booldic(value):
"""Translates a string, list or dic to a dictionary with true/false.
Args:
value: The string value to evaluate. (string)
Returns:
The a bool value of tag or if triple is True None.
"""
ret = {}
if type(value) is str:
ret[value] = True
if type(value) is list:
for ele in value:
ret[ele] = True
if type(value) is dict:
for key, val in value.items():
ret[key] = _string_to_bool(val, triple=False)
return ret
def _get_first_child_by_pos_or_id(base, tag, by_id, by_pos):
"""Get a child element of the base node with a given tag and position or id.
Args:
base: The base node element. (lxml node)
tag: Child elements group tag used to select the elements. (string)
by_id: The unique id to search for. (string)
by_pos: The position of the element in the list (int)
Returns:
The child node element found or raise error.
"""
if by_id is None and by_pos is None:
raise RdmlError('Either an ' + tag + ' id or a position must be provided.')
if by_id is not None and by_pos is not None:
raise RdmlError('Only an ' + tag + ' id or a position can be provided.')
allChildren = _get_all_children(base, tag)
if by_id is not None:
for node in allChildren:
if node.get('id') == by_id:
return node
raise RdmlError('The ' + tag + ' id: ' + by_id + ' was not found in RDML file.')
if by_pos is not None:
if by_pos < 0 or by_pos > len(allChildren) - 1:
raise RdmlError('The ' + tag + ' position ' + by_pos + ' is out of range.')
return allChildren[by_pos]
def _add_first_child_to_dic(base, dic, opt, tag):
"""Adds the first child element with a given tag to a dictionary.
Args:
base: The base node element. (lxml node)
dic: The dictionary to add the element to (dictionary)
opt: If false and id is not found in base, the element is added with an empty string (Bool)
tag: Child elements group tag used to select the elements. (string)
Returns:
The dictionary with the added element.
"""
for node in base:
if node.tag.replace("{http://www.rdml.org}", "") == tag:
dic[tag] = node.text
return dic
if not opt:
dic[tag] = ""
return dic
def _get_all_children(base, tag):
"""Get a list of all child elements with a given tag.
Args:
base: The base node element. (lxml node)
tag: Child elements group tag used to select the elements. (string)
Returns:
A list with all child node elements found or an empty list.
"""
ret = []
for node in base:
if node.tag.replace("{http://www.rdml.org}", "") == tag:
ret.append(node)
return ret
def _get_all_children_id(base, tag):
"""Get a list of ids of all child elements with a given tag.
Args:
base: The base node element. (lxml node)
tag: Child elements group tag used to select the elements. (string)
Returns:
A list with all child id strings found or an empty list.
"""
ret = []
for node in base:
if node.tag.replace("{http://www.rdml.org}", "") == tag:
ret.append(node.get('id'))
return ret
def _get_number_of_children(base, tag):
"""Count all child elements with a given tag.
Args:
base: The base node element. (lxml node)
tag: Child elements group tag used to select the elements. (string)
Returns:
A int number of the found child elements with the id.
"""
counter = 0
for node in base:
if node.tag.replace("{http://www.rdml.org}", "") == tag:
counter += 1
return counter
def _check_unique_id(base, tag, id):
"""Find all child elements with a given group and check if the id is already used.
Args:
base: The base node element. (lxml node)
tag: Child elements group tag used to select the elements. (string)
id: The unique id to search for. (string)
Returns:
False if the id is already used, True if not.
"""
for node in base:
if node.tag.replace("{http://www.rdml.org}", "") == tag:
if node.get('id') == id:
return False
return True
def _create_new_element(base, tag, id):
"""Create a new element with a given tag and id.
Args:
base: The base node element. (lxml node)
tag: Child elements group tag. (string)
id: The unique id of the new element. (string)
Returns:
False if the id is already used, True if not.
"""
if id is None or id == "":
raise RdmlError('An ' + tag + ' id must be provided.')
if not _check_unique_id(base, tag, id):
raise RdmlError('The ' + tag + ' id "' + id + '" must be unique.')
return et.Element(tag, id=id)
def _add_new_subelement(base, basetag, tag, text, opt):
"""Create a new element with a given tag and id.
Args:
base: The base node element. (lxml node)
basetag: Child elements group tag. (string)
tag: Child elements own tag, to be created. (string)
text: The text content of the new element. (string)
opt: If true, the element is optional (Bool)
Returns:
Nothing, the base lxml element is modified.
"""
if opt is False:
if text is None or text == "":
raise RdmlError('An ' + basetag + ' ' + tag + ' must be provided.')
et.SubElement(base, tag).text = text
else:
if text is not None and text != "":
et.SubElement(base, tag).text = text
def _change_subelement(base, tag, xmlkeys, value, opt, vtype, id_as_element=False):
"""Change the value of the element with a given tag.
Args:
base: The base node element. (lxml node)
tag: Child elements own tag, to be created. (string)
xmlkeys: The list of possible keys in the right order for xml (list strings)
value: The text content of the new element.
opt: If true, the element is optional (Bool)
vtype: If true, the element is optional ("string", "int", "float")
id_as_element: If true, handle tag "id" as element, else as attribute
Returns:
Nothing, the base lxml element is modified.
"""
# Todo validate values with vtype
goodVal = value
if vtype == "bool":
ev = _string_to_bool(value, triple=True)
if ev is None or ev == "":
goodVal = ""
else:
if ev:
goodVal = "true"
else:
goodVal = "false"
if opt is False:
if goodVal is None or goodVal == "":
raise RdmlError('A value for ' + tag + ' must be provided.')
if tag == "id" and id_as_element is False:
if base.get('id') != goodVal:
par = base.getparent()
groupTag = base.tag.replace("{http://www.rdml.org}", "")
if not _check_unique_id(par, groupTag, goodVal):
raise RdmlError('The ' + groupTag + ' id "' + goodVal + '" is not unique.')
base.attrib['id'] = goodVal
return
# Check if the tag already excists
elem = _get_first_child(base, tag)
if elem is not None:
if goodVal is None or goodVal == "":
base.remove(elem)
else:
elem.text = goodVal
else:
if goodVal is not None and goodVal != "":
new_node = et.Element(tag)
new_node.text = goodVal
place = _get_tag_pos(base, tag, xmlkeys, 0)
base.insert(place, new_node)
def _get_or_create_subelement(base, tag, xmlkeys):
"""Get element with a given tag, if not present, create it.
Args:
base: The base node element. (lxml node)
tag: Child elements own tag, to be created. (string)
xmlkeys: The list of possible keys in the right order for xml (list strings)
Returns:
The node element with the tag.
"""
# Check if the tag already excists
if _get_first_child(base, tag) is None:
new_node = et.Element(tag)
place = _get_tag_pos(base, tag, xmlkeys, 0)
base.insert(place, new_node)
return _get_first_child(base, tag)
def _remove_irrelevant_subelement(base, tag):
"""If element with a given tag has no children, remove it.
Args:
base: The base node element. (lxml node)
tag: Child elements own tag, to be created. (string)
Returns:
The node element with the tag.
"""
# Check if the tag already excists
elem = _get_first_child(base, tag)
if elem is None:
return
if len(elem) == 0:
base.remove(elem)
def _move_subelement(base, tag, id, xmlkeys, position):
"""Change the value of the element with a given tag.
Args:
base: The base node element. (lxml node)
tag: The id to search for. (string)
id: The unique id of the new element. (string)
xmlkeys: The list of possible keys in the right order for xml (list strings)
position: the new position of the element (int)
Returns:
Nothing, the base lxml element is modified.
"""
pos = _get_tag_pos(base, tag, xmlkeys, position)
ele = _get_first_child_by_pos_or_id(base, tag, id, None)
base.insert(pos, ele)
def _move_subelement_pos(base, tag, oldpos, xmlkeys, position):
"""Change the value of the element with a given tag.
Args:
base: The base node element. (lxml node)
tag: The id to search for. (string)
oldpos: The unique id of the new element. (string)
xmlkeys: The list of possible keys in the right order for xml (list strings)
position: the new position of the element (int)
Returns:
Nothing, the base lxml element is modified.
"""
pos = _get_tag_pos(base, tag, xmlkeys, position)
ele = _get_first_child_by_pos_or_id(base, tag, None, oldpos)
base.insert(pos, ele)
def _get_tag_pos(base, tag, xmlkeys, pos):
"""Returns a position were to add a subelement with the given tag inc. pos offset.
Args:
base: The base node element. (lxml node)
tag: The id to search for. (string)
xmlkeys: The list of possible keys in the right order for xml (list strings)
pos: The position relative to the tag elements (int)
Returns:
The int number of were to add the element with the tag.
"""
count = _get_number_of_children(base, tag)
offset = pos
if pos is None or pos < 0:
offset = 0
pos = 0
if pos > count:
offset = count
return _get_first_tag_pos(base, tag, xmlkeys) + offset
def _get_first_tag_pos(base, tag, xmlkeys):
"""Returns a position were to add a subelement with the given tag.
Args:
base: The base node element. (lxml node)
tag: The id to search for. (string)
xmlkeys: The list of possible keys in the right order for xml (list strings)
Returns:
The int number of were to add the element with the tag.
"""
listrest = xmlkeys[xmlkeys.index(tag):]
counter = 0
for node in base:
if node.tag.replace("{http://www.rdml.org}", "") in listrest:
return counter
counter += 1
return counter
def _writeFileInRDML(rdmlName, fileName, data):
"""Writes a file in the RDML zip, even if it existed before.
Args:
rdmlName: The name of the RDML zip file
fileName: The name of the file to write into the zip
data: The data string of the file
Returns:
Nothing, modifies the RDML file.
"""
needRewrite = False
if os.path.isfile(rdmlName):
with zipfile.ZipFile(rdmlName, 'r') as RDMLin:
for item in RDMLin.infolist():
if item.filename == fileName:
needRewrite = True
if needRewrite:
tempFolder, tempName = tempfile.mkstemp(dir=os.path.dirname(rdmlName))
os.close(tempFolder)
# copy everything except the filename
with zipfile.ZipFile(rdmlName, 'r') as RDMLin:
with zipfile.ZipFile(tempName, mode='w', compression=zipfile.ZIP_DEFLATED) as RDMLout:
RDMLout.comment = RDMLin.comment
for item in RDMLin.infolist():
if item.filename != fileName:
RDMLout.writestr(item, RDMLin.read(item.filename))
if data != "":
RDMLout.writestr(fileName, data)
os.remove(rdmlName)
os.rename(tempName, rdmlName)
else:
with zipfile.ZipFile(rdmlName, mode='a', compression=zipfile.ZIP_DEFLATED) as RDMLout:
RDMLout.writestr(fileName, data)
def _lrp_linReg(xIn, yUse):
"""A function which calculates the slope or the intercept by linear regression.
Args:
xIn: The numpy array of the cycles
yUse: The numpy array that contains the fluorescence
Returns:
An array with the slope and intercept.
"""
counts = np.ones(yUse.shape)
xUse = xIn.copy()
xUse[np.isnan(yUse)] = 0
counts[np.isnan(yUse)] = 0
cycSqared = xUse * xUse
cycFluor = xUse * yUse
sumCyc = np.nansum(xUse, axis=1)
sumFluor = np.nansum(yUse, axis=1)
sumCycSquared = np.nansum(cycSqared, axis=1)
sumCycFluor = np.nansum(cycFluor, axis=1)
n = np.nansum(counts, axis=1)
ssx = sumCycSquared - (sumCyc * sumCyc) / n
sxy = sumCycFluor - (sumCyc * sumFluor) / n
slope = sxy / ssx
intercept = (sumFluor / n) - slope * (sumCyc / n)
return [slope, intercept]
def _lrp_findStopCyc(fluor, aRow):
"""Find the stop cycle of the log lin phase in fluor.
Args:
fluor: The array with the fluorescence values
aRow: The row to work on
Returns:
An int with the stop cycle.
"""
# Take care of nan values
validTwoLessCyc = 3 # Cycles so +1 to array
while (validTwoLessCyc <= fluor.shape[1] and
(np.isnan(fluor[aRow, validTwoLessCyc - 1]) or
np.isnan(fluor[aRow, validTwoLessCyc - 2]) or
np.isnan(fluor[aRow, validTwoLessCyc - 3]))):
validTwoLessCyc += 1
# First and Second Derivative values calculation
fluorShift = np.roll(fluor[aRow], 1, axis=0) # Shift to right - real position is -0.5
fluorShift[0] = np.nan
firstDerivative = fluor[aRow] - fluorShift
if np.isfinite(firstDerivative).any():
FDMaxCyc = np.nanargmax(firstDerivative, axis=0) + 1 # Cycles so +1 to array
else:
return fluor.shape[1]
firstDerivativeShift = np.roll(firstDerivative, -1, axis=0) # Shift to left
firstDerivativeShift[-1] = np.nan
secondDerivative = firstDerivativeShift - firstDerivative
if FDMaxCyc + 2 <= fluor.shape[1]:
# Only add two cycles if there is an increase without nan
if (not np.isnan(fluor[aRow, FDMaxCyc - 1]) and
not np.isnan(fluor[aRow, FDMaxCyc]) and
not np.isnan(fluor[aRow, FDMaxCyc + 1]) and
fluor[aRow, FDMaxCyc + 1] > fluor[aRow, FDMaxCyc] > fluor[aRow, FDMaxCyc - 1]):
FDMaxCyc += 2
else:
FDMaxCyc = fluor.shape[1]
maxMeanSD = 0.0
stopCyc = fluor.shape[1]
for cycInRange in range(validTwoLessCyc, FDMaxCyc):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
tempMeanSD = np.mean(secondDerivative[cycInRange - 2: cycInRange + 1], axis=0)
# The > 0.000000000001 is to avoid float differences to the pascal version
if not np.isnan(tempMeanSD) and (tempMeanSD - maxMeanSD) > 0.000000000001:
maxMeanSD = tempMeanSD
stopCyc = cycInRange
if stopCyc + 2 >= fluor.shape[1]:
stopCyc = fluor.shape[1]
return stopCyc
def _lrp_findStartCyc(fluor, aRow, stopCyc):
"""A function which finds the start cycle of the log lin phase in fluor.
Args:
fluor: The array with the fluorescence values
aRow: The row to work on
stopCyc: The stop cycle
Returns:
An array [int, int] with the start cycle and the fixed start cycle.
"""
startCyc = stopCyc - 1
# startCyc might be NaN, so shift it to the first value
firstNotNaN = 1 # Cycles so +1 to array
while np.isnan(fluor[aRow, firstNotNaN - 1]) and firstNotNaN < startCyc:
firstNotNaN += 1
while startCyc > firstNotNaN and np.isnan(fluor[aRow, startCyc - 1]):
startCyc -= 1
# As long as there are no NaN and new values are increasing
while (startCyc > firstNotNaN and
not np.isnan(fluor[aRow, startCyc - 2]) and
fluor[aRow, startCyc - 2] <= fluor[aRow, startCyc - 1]):
startCyc -= 1
startCycFix = startCyc
if (not np.isnan(fluor[aRow, startCyc]) and
not np.isnan(fluor[aRow, startCyc - 1]) and
not np.isnan(fluor[aRow, stopCyc - 1]) and
not np.isnan(fluor[aRow, stopCyc - 2])):
startStep = np.log10(fluor[aRow, startCyc]) - np.log10(fluor[aRow, startCyc - 1])
stopStep = np.log10(fluor[aRow, stopCyc - 1]) - np.log10(fluor[aRow, stopCyc - 2])
if startStep > 1.1 * stopStep:
startCycFix += 1
return [startCyc, startCycFix]
def _lrp_testSlopes(fluor, aRow, stopCyc, startCycFix):
"""Splits the values and calculates a slope for the upper and the lower half.
Args:
fluor: The array with the fluorescence values
aRow: The row to work on
stopCyc: The stop cycle
startCycFix: The start cycle
Returns:
An array with [slopelow, slopehigh].
"""
# Both start with full range
loopStart = [startCycFix[aRow], stopCyc[aRow]]
loopStop = [startCycFix[aRow], stopCyc[aRow]]
# Now find the center ignoring nan
while True:
loopStart[1] -= 1
loopStop[0] += 1
while (loopStart[1] - loopStop[0]) > 1 and np.isnan(fluor[aRow, loopStart[1] - 1]):
loopStart[1] -= 1
while (loopStart[1] - loopStop[0]) > 1 and np.isnan(fluor[aRow, loopStop[1] - 1]):
loopStop[0] += 1
if (loopStart[1] - loopStop[0]) <= 1:
break
# basic regression per group
ssx = [0, 0]
sxy = [0, 0]
slope = [0, 0]
for j in range(0, 2):
sumx = 0.0
sumy = 0.0
sumx2 = 0.0
sumxy = 0.0
nincl = 0.0
for i in range(loopStart[j], loopStop[j] + 1):
if not np.isnan(fluor[aRow, i - 1]):
sumx += i
sumy += np.log10(fluor[aRow, i - 1])
sumx2 += i * i
sumxy += i * np.log10(fluor[aRow, i - 1])
nincl += 1
ssx[j] = sumx2 - sumx * sumx / nincl
sxy[j] = sumxy - sumx * sumy / nincl
slope[j] = sxy[j] / ssx[j]
return [slope[0], slope[1]]
def _lrp_lastCycMeanMax(fluor, vecSkipSample, vecNoPlateau):
"""A function which calculates the mean of the max fluor in the last ten cycles.
Args:
fluor: The array with the fluorescence values
vecSkipSample: Skip the sample
vecNoPlateau: Sample has no plateau
Returns:
An float with the max mean.
"""
maxFlour = np.nanmax(fluor[:, -11:], axis=1)
maxFlour[vecSkipSample] = np.nan
maxFlour[vecNoPlateau] = np.nan
# Ignore all nan slices, to fix them below
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
maxMean = np.nanmean(maxFlour)
if np.isnan(maxMean):
maxMean = np.nanmax(maxFlour)
return maxMean
def _lrp_meanPcrEff(tarGroup, vecTarget, pcrEff, vecSkipSample, vecNoPlateau, vecShortLogLin):
"""A function which calculates the mean efficiency of the selected target group excluding bad ones.
Args:
tarGroup: The target number
vecTarget: The vector with the targets numbers
pcrEff: The array with the PCR efficiencies
vecSkipSample: Skip the sample
vecNoPlateau: True if there is no plateau
vecShortLogLin: True indicates a short log lin phase
Returns:
An array with [meanPcrEff, pcrEffVar].
"""
cnt = 0
sumEff = 0.0
sumEff2 = 0.0
for j in range(0, len(pcrEff)):
if tarGroup is None or tarGroup == vecTarget[j]:
if (not (vecSkipSample[j] or vecNoPlateau[j] or vecShortLogLin[j])) and pcrEff[j] > 1.0:
cnt += 1
sumEff += pcrEff[j]
sumEff2 += pcrEff[j] * pcrEff[j]
if cnt > 1:
meanPcrEff = sumEff / cnt
pcrEffVar = (sumEff2 - (sumEff * sumEff) / cnt) / (cnt - 1)
else:
meanPcrEff = 1.0
pcrEffVar = 100
return [meanPcrEff, pcrEffVar]
def _lrp_startStopInWindow(fluor, aRow, upWin, lowWin):
"""Find the start and the stop of the part of the curve which is inside the window.
Args:
fluor: The array with the fluorescence values
aRow: The row to work on
upWin: The upper limit of the window
lowWin: The lower limit of the window
Returns:
The int startWinCyc, stopWinCyc and the bool notInWindow.
"""
startWinCyc = 0
stopWinCyc = 0
# Find the stopCyc and the startCyc cycle of the log lin phase
stopCyc = _lrp_findStopCyc(fluor, aRow)
[startCyc, startCycFix] = _lrp_findStartCyc(fluor, aRow, stopCyc)
if np.isfinite(fluor[aRow, startCycFix - 1:]).any():
stopMaxCyc = np.nanargmax(fluor[aRow, startCycFix - 1:]) + startCycFix
else:
return startCyc, startCyc, True
# If is true if outside the window
if fluor[aRow, startCyc - 1] > upWin or fluor[aRow, stopMaxCyc - 1] < lowWin:
notInWindow = True
if fluor[aRow, startCyc - 1] > upWin:
startWinCyc = startCyc
stopWinCyc = startCyc
if fluor[aRow, stopMaxCyc - 1] < lowWin:
startWinCyc = stopMaxCyc
stopWinCyc = stopMaxCyc
else:
notInWindow = False
# look for stopWinCyc
if fluor[aRow, stopMaxCyc - 1] < upWin:
stopWinCyc = stopMaxCyc
else:
for i in range(stopMaxCyc, startCyc, -1):
if fluor[aRow, i - 1] > upWin > fluor[aRow, i - 2]:
stopWinCyc = i - 1
# look for startWinCyc
if fluor[aRow, startCycFix - 1] > lowWin:
startWinCyc = startCycFix
else:
for i in range(stopMaxCyc, startCyc, -1):
if fluor[aRow, i - 1] > lowWin > fluor[aRow, i - 2]:
startWinCyc = i
return startWinCyc, stopWinCyc, notInWindow
def _lrp_paramInWindow(fluor, aRow, upWin, lowWin):
"""Calculates slope, nNull, PCR efficiency and mean x/y for the curve part in the window.
Args:
fluor: The array with the fluorescence values
aRow: The row to work on
upWin: The upper limit of the window
lowWin: The lower limit of the window
Returns:
The calculated values: indMeanX, indMeanY, pcrEff, nnulls, ninclu, correl.
"""
startWinCyc, stopWinCyc, notInWindow = _lrp_startStopInWindow(fluor, aRow, upWin, lowWin)
sumx = 0.0
sumy = 0.0
sumx2 = 0.0
sumy2 = 0.0
sumxy = 0.0
nincl = 0.0
ssx = 0.0
ssy = 0.0
sxy = 0.0
for i in range(startWinCyc, stopWinCyc + 1):
fluorSamp = fluor[aRow, i - 1]
if not np.isnan(fluorSamp):
logFluorSamp = np.log10(fluorSamp)
sumx += i
sumy += logFluorSamp
sumx2 += i * i
sumy2 += logFluorSamp * logFluorSamp
sumxy += i * logFluorSamp
nincl += 1
if nincl > 1:
ssx = sumx2 - sumx * sumx / nincl
ssy = sumy2 - sumy * sumy / nincl
sxy = sumxy - sumx * sumy / nincl
if ssx > 0.0 and ssy > 0.0 and nincl > 0.0:
cslope = sxy / ssx
cinterc = sumy / nincl - cslope * sumx / nincl
correl = sxy / np.sqrt(ssx * ssy)
indMeanX = sumx / nincl
indMeanY = sumy / nincl
pcrEff = np.power(10, cslope)
nnulls = np.power(10, cinterc)
else:
correl = np.nan
indMeanX = np.nan
indMeanY = np.nan
pcrEff = np.nan
nnulls = np.nan
if notInWindow:
ninclu = 0
else:
ninclu = stopWinCyc - startWinCyc + 1
return indMeanX, indMeanY, pcrEff, nnulls, ninclu, correl
def _lrp_allParamInWindow(fluor, tarGroup, vecTarget, indMeanX, indMeanY, pcrEff, nnulls, ninclu, correl, upWin, lowWin, vecNoAmplification, vecBaselineError):
"""A function which calculates the mean of the max fluor in the last ten cycles.
Args:
fluor: The array with the fluorescence values
tarGroup: The target number
vecTarget: The vector with the targets numbers
indMeanX: The vector with the x mean position
indMeanY: The vector with the y mean position
pcrEff: The array with the PCR efficiencies
nnulls: The array with the calculated nnulls
ninclu: The array with the calculated ninclu
correl: The array with the calculated correl
upWin: The upper limit of the window
lowWin: The lower limit of the window
vecNoAmplification: True if there is a amplification error
vecBaselineError: True if there is a baseline error
Returns:
An array with [indMeanX, indMeanY, pcrEff, nnulls, ninclu, correl].
"""
for row in range(0, fluor.shape[0]):
if tarGroup is None or tarGroup == vecTarget[row]:
if not (vecNoAmplification[row] or vecBaselineError[row]):
if tarGroup is None:
indMeanX[row], indMeanY[row], pcrEff[row], nnulls[row], ninclu[row], correl[row] = _lrp_paramInWindow(fluor, row, upWin[0], lowWin[0])
else:
indMeanX[row], indMeanY[row], pcrEff[row], nnulls[row], ninclu[row], correl[row] = _lrp_paramInWindow(fluor, row, upWin[tarGroup], lowWin[tarGroup])
else:
correl[row] = np.nan
indMeanX[row] = np.nan
indMeanY[row] = np.nan
pcrEff[row] = np.nan
nnulls[row] = np.nan
ninclu[row] = 0
return indMeanX, indMeanY, pcrEff, nnulls, ninclu, correl
def _lrp_meanStopFluor(fluor, tarGroup, vecTarget, stopCyc, vecSkipSample, vecNoPlateau):
"""Return the mean of the stop fluor or the max fluor if all rows have no plateau.
Args:
fluor: The array with the fluorescence values
tarGroup: The target number
vecTarget: The vector with the targets numbers
stopCyc: The vector with the stop cycle of the log lin phase
vecSkipSample: Skip the sample
vecNoPlateau: True if there is no plateau
Returns:
The meanMax fluorescence.
"""
meanMax = 0.0
maxFluor = 0.0000001
cnt = 0
if tarGroup is None:
for aRow in range(0, fluor.shape[0]):
if not vecSkipSample[aRow]:
if not vecNoPlateau[aRow]:
cnt += 1
meanMax += fluor[aRow, stopCyc[aRow] - 1]
else:
for i in range(0, fluor.shape[1]):
if fluor[aRow, i] > maxFluor:
maxFluor = fluor[aRow, i]
else:
for aRow in range(0, fluor.shape[0]):
if tarGroup == vecTarget[aRow] and not vecSkipSample[aRow]:
if not vecNoPlateau[aRow]:
cnt += 1
meanMax += fluor[aRow, stopCyc[aRow] - 1]
else:
for i in range(0, fluor.shape[1]):
if fluor[aRow, i] > maxFluor:
maxFluor = fluor[aRow, i]
if cnt > 0:
meanMax = meanMax / cnt
else:
meanMax = maxFluor
return meanMax
def _lrp_maxStartFluor(fluor, tarGroup, vecTarget, startCyc, vecSkipSample):
"""Return the maximum of the start fluorescence
Args:
fluor: The array with the fluorescence values
tarGroup: The target number
vecTarget: The vector with the targets numbers
startCyc: The vector with the start cycle of the log lin phase
vecSkipSample: Skip the sample
Returns:
The maxStart fluorescence.
"""
maxStart = -10.0
if tarGroup is None:
for aRow in range(0, fluor.shape[0]):
if not vecSkipSample[aRow]:
if fluor[aRow, startCyc[aRow] - 1] > maxStart:
maxStart = fluor[aRow, startCyc[aRow] - 1]
else:
for aRow in range(0, fluor.shape[0]):
if tarGroup == vecTarget[aRow] and not vecSkipSample[aRow]:
if fluor[aRow, startCyc[aRow] - 1] > maxStart:
maxStart = fluor[aRow, startCyc[aRow] - 1]
return 0.999 * maxStart
def _lrp_setLogWin(tarGroup, newUpWin, foldWidth, upWin, lowWin, maxFluorTotal, minFluorTotal):
"""Sets a new window and ensures its within the total fluorescence values.
Args:
tarGroup: The target number
newUpWin: The new upper window
foldWidth: The foldWith to the lower window
upWin: The upper window fluorescence
lowWin: The lower window fluorescence
maxFluorTotal: The maximum fluorescence over all rows
minFluorTotal: The minimum fluorescence over all rows
Returns:
An array with [indMeanX, indMeanY, pcrEff, nnulls, ninclu, correl].
"""
# No rounding needed, only present for exact identical output with Pascal version
tempUpWin = np.power(10, | np.round(1000 * newUpWin) | numpy.round |
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 30 20:59:01 2019
@author: Administrator
"""
import numpy as np
import random
import cv2
import os
import h5py
class DataLoaderSceneFlow(object):
def __init__(self, img_path, disp_path, batch_size, patch_size=(256, 512), max_disp=129):
self.img_path = img_path
self.disp_path = disp_path
self.batch_size = batch_size
self.patch_size = patch_size
self.max_disp = max_disp
img_m = h5py.File(self.img_path)
disp_m = h5py.File(self.disp_path)
left_img = img_m["left_img"][:]
right_img = img_m["right_img"][:]
disp_img = disp_m["disp_img"][:]
left_img=left_img.transpose() #(N,H,W,3) array number=8864
right_img=right_img.transpose()#(N,H,W,3) array
disp_img=disp_img.transpose()#(N,H,W) array
print("load data success!!!")
self.num, self.heigh, self.weight = disp_img.shape
# =============================================================================
# state = np.random.get_state()
# np.random.shuffle(left_img)
# np.random.set_state(state)
# np.random.shuffle(right_img)
# np.random.set_state(state)
# np.random.shuffle(disp_img)
# =============================================================================
self.val_left = left_img[:1108]
self.val_right = right_img[:1108]
self.val_labels = disp_img[:1108]
self.shuffled_left_data = left_img[1108:]
self.shuffled_right_data = right_img[1108:]
self.shuffled_labels = disp_img[1108:]
def generator(self, is_training=True):
if is_training:
state = np.random.get_state()
np.random.shuffle(self.shuffled_left_data)
np.random.set_state(state)
np.random.shuffle(self.shuffled_right_data)
np.random.set_state(state)
np.random.shuffle(self.shuffled_labels)
print("start making data!!!")
if is_training:
for j in range((self.num-1108) // self.batch_size):
left, right, label = self.load_batch(self.shuffled_left_data[j * self.batch_size: (j + 1) * self.batch_size],
self.shuffled_right_data[
j * self.batch_size: (j + 1) * self.batch_size],
self.shuffled_labels[j * self.batch_size: (j + 1) * self.batch_size],
is_training)
left = np.array(left)
right = | np.array(right) | numpy.array |
"""Generate a single discrete time SIR model.
"""
from . import data_model
import numpy as np
from scipy import stats
import xarray as xr
# Generate Betas
# Beta, or the growth rate of the infection, depends on the covariates.
# Here we implement three different functional forms for the dependency.
SPLIT_TIME = 100
def generate_betas_from_single_random_covariate(num_locations):
"""Beta depend on a single covariate that is randomly generated.
Args:
num_locations: an int representing the number of locations to simulate
Returns:
beta: an xr.DataArray consisting of the growth rate
for each epidemic
v: an xr.DataArray consisting of the randomly generated covariate for each
location
alpha: an xr.DataArray consisting of the weights for each covariate
"""
v = xr.DataArray(
np.random.uniform(0.0, 1.0, (num_locations, 1)),
dims=['location', 'static_covariate'])
alpha = xr.DataArray(np.ones(1), dims=['static_covariate'])
beta = 0.4 * np.exp(alpha @ v)
return beta, v, alpha
def generate_betas_effect_mod(num_locations):
"""Betas depend on 2 discrete, randomly generated effects.
Args:
num_locations: an int representing the number of locations to simulate
Returns:
beta: an xr.DataArray consisting of the growth rate
for each epidemic
v: an xr.DataArray consisting of the randomly generated covariate for each
location
alpha: an xr.DataArray consisting of the weights for each covariate
"""
v = xr.DataArray(np.random.binomial(1, 0.5, size=(num_locations, 2)),
dims={'location': num_locations, 'static_covariate': 2})
hd = v.values[:, 0]
ws = v.values[:, 1]
beta_np = np.exp( | np.log(1.5) | numpy.log |
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df1=pd.read_csv('../input/forest-cover-type-prediction/train.csv')
df_test1=pd.read_csv('../input/forest-cover-type-prediction/test.csv')
df_test2=pd.read_csv('../input/forest-cover-type-prediction/test3.csv')
df=df1.copy()
df_test=df_test1.copy()
df
pd.set_option('display.max_columns',None)
df.drop(columns=['Id','Cover_Type'],inplace=True)
df_test.drop(columns=['Id'],inplace=True)
df_test
X_train=df
Y_train=df1.iloc[:,-1]
X_train
df_test
from collections import Counter
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve
from lightgbm import LGBMClassifier
sns.set(style='white', context='notebook', palette='deep')
kfold = StratifiedKFold(n_splits=10)
random_state = 2
classifiers = []
classifiers.append(SVC(random_state=random_state))
classifiers.append(DecisionTreeClassifier(random_state=random_state))
classifiers.append(AdaBoostClassifier(DecisionTreeClassifier(random_state=random_state),random_state=random_state,learning_rate=0.1))
classifiers.append(RandomForestClassifier(random_state=random_state))
classifiers.append(ExtraTreesClassifier(random_state=random_state))
classifiers.append(GradientBoostingClassifier(random_state=random_state))
classifiers.append(MLPClassifier(random_state=random_state))
classifiers.append(KNeighborsClassifier())
classifiers.append(LogisticRegression(random_state = random_state))
classifiers.append(LinearDiscriminantAnalysis())
classifiers.append(XGBClassifier(random_state = random_state))
classifiers.append(LGBMClassifier(random_state = random_state))
cv_results = []
for classifier in classifiers :
score=cross_val_score(classifier, X_train, y = Y_train, scoring = "accuracy", cv = kfold, n_jobs=-1)
cv_results.append(score)
print('{} crossvalidation score:{}\n'.format(classifier,score.mean()))
cv_means = []
cv_std = []
for cv_result in cv_results:
cv_means.append(cv_result.mean())
cv_std.append(cv_result.std())
cv_res = pd.DataFrame({"CrossValMeans":cv_means,"CrossValerrors": cv_std,"Algorithm":["SVC","DecisionTree","AdaBoost",
"RandomForest","ExtraTrees","GradientBoosting","MultipleLayerPerceptron","KNeighboors","LogisticRegression","LinearDiscriminantAnalysis",'XGboost','LGboost']})
g = sns.barplot("CrossValMeans","Algorithm",data = cv_res, palette="Set3",orient = "h",**{'xerr':cv_std})
g.set_xlabel("Mean Accuracy")
g = g.set_title("Cross validation scores")
from sklearn.model_selection import train_test_split
xtrain,xtest,ytrain,ytest=train_test_split(X_train.values,Y_train.values,test_size=0.2)
from sklearn.metrics import accuracy_score
RFC = RandomForestClassifier(random_state=random_state)
RFC.fit(xtrain,ytrain)
ypred=RFC.predict(xtest)
score=cross_val_score(RFC,X_train,Y_train,scoring='accuracy',cv=kfold,n_jobs=-1)
# Best score
print('Crossval score for random forest: {}'.format(score.mean()))
print('Accuracy score for random forest: {}'.format(accuracy_score(ytest,ypred)))
RFC.get_params()
from sklearn.metrics import accuracy_score
RFC2 = RandomForestClassifier(random_state=random_state,
n_estimators=500,
max_depth=32,
min_samples_leaf=1,
criterion='entropy')
RFC2.fit(xtrain,ytrain)
ypred=RFC2.predict(xtest)
score=cross_val_score(RFC2,X_train,Y_train,scoring='accuracy',cv=kfold,n_jobs=-1)
# Best score
print('Crossval score for random forest: {}'.format(score.mean()))
print('Accuracy score for random forest: {}'.format(accuracy_score(ytest,ypred)))
et=ExtraTreesClassifier(random_state=random_state)
et.fit(xtrain,ytrain)
ypred=et.predict(xtest)
score=cross_val_score(et,X_train,Y_train,scoring='accuracy',cv=kfold,n_jobs=-1)
# Best score
print('Crossval score for extra trees classifier: {}'.format(score.mean()))
print('Accuracy score for extra trees classifier: {}'.format(accuracy_score(ytest,ypred)))
et2=ExtraTreesClassifier()
et2.get_params()
et2=ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,
criterion='entropy', max_depth=38, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=500,
n_jobs=None, oob_score=False, random_state=0, verbose=0,
warm_start=False)
et2.fit(xtrain,ytrain)
ypred=et2.predict(xtest)
score=cross_val_score(et2,X_train,Y_train,scoring='accuracy',cv=kfold,n_jobs=-1)
# Best score
print('Crossval score for extra trees classifier: {}'.format(score.mean()))
print('Accuracy score for extra trees classifier: {}'.format(accuracy_score(ytest,ypred)))
lgb2=LGBMClassifier(random_state=random_state)
lgb2.fit(xtrain,ytrain)
ypred=lgb2.predict(xtest)
score=cross_val_score(lgb2,X_train,Y_train,scoring='accuracy',cv=kfold,n_jobs=-1)
# Best score
print('Crossval score for Lightgb classifier: {}'.format(score.mean()))
print('Accuracy score for Lightgb classifier: {}'.format(accuracy_score(ytest,ypred)))
lgb=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
importance_type='split', learning_rate=0.2, max_depth=-1,
min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
n_estimators=200, n_jobs=4, num_leaves=63, objective=None,
random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,
subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
lgb.fit(xtrain,ytrain)
ypred=lgb.predict(xtest)
score=cross_val_score(lgb,X_train,Y_train,scoring='accuracy',cv=kfold,n_jobs=-1)
# Best score
print('Crossval score for Lightgb classifier: {}'.format(score.mean()))
print('Accuracy score for Lightgb classifier: {}'.format(accuracy_score(ytest,ypred)))
vc= VotingClassifier(estimators=[('rfc', RFC2), ('extc', et2),
('lgb',lgb)], voting='soft', n_jobs=-1)
vc.fit(xtrain,ytrain)
ypred=vc.predict(xtest)
score=cross_val_score(vc,X_train,Y_train,scoring='accuracy',cv=kfold,n_jobs=-1)
# Best score
print('Crossval score for Lightgb classifier: {}'.format(score.mean()))
print('Accuracy score for Lightgb classifier: {}'.format(accuracy_score(ytest,ypred)))
"""
from sklearn.ensemble import StackingClassifier
estimators = [ ('rf', RFC2),
('et', et2)]
sc= StackingClassifier(estimators=estimators, final_estimator=lgb)
sc.fit(xtrain,ytrain)
ypred=sc.predict(xtest)
score=cross_val_score(sc,X_train,Y_train,scoring='accuracy',cv=kfold,n_jobs=-1)
# Best score
print('Crossval score for Lightgb classifier: {}'.format(score.mean()))
print('Accuracy score for Lightgb classifier: {}'.format(accuracy_score(ytest,ypred)))"""
vc.fit(X_train,Y_train)
ypred=vc.predict(df_test.values)
id=df_test1['Id']
dict={'ID':id,'Cover_Type':ypred}
dfsub=pd.DataFrame(dict)
dfsub.to_csv('./submission_ensemblevoting.csv', index=False)
"""
#ExtraTrees
et2= ExtraTreesClassifier()
## Search grid for optimal parameters
ex_param_grid = {
'criterion': ['gini','entropy'],
'max_depth':[5,10,25],
'max_features':[1,3,7],
'max_samples': [0.2],
'min_samples_leaf': [1,2,5],
'min_samples_split': [2,5,7],
'n_estimators': [100,200,300],
}
gset = GridSearchCV(et2,param_grid = ex_param_grid, cv=kfold, scoring="accuracy", n_jobs=-1, verbose = 1)
gset.fit(X_train,Y_train)
gset_best = gset.best_estimator_
# Best score
print(gset.best_score_)
print(gset.best_estimator_)"""
"""
# RFC Parameters tunning
RFC = RandomForestClassifier()
## Search grid for optimal parameters
rf_param_grid = {"max_depth": [None],
"max_features": [1, 3, 10],
"min_samples_split": [2, 3, 10],
"min_samples_leaf": [1, 3, 10],
"bootstrap": [False],
"n_estimators" :[100,300],
"criterion": ["gini"]}
rf_param_grid = {
'bootstrap': [True],
'max_depth': [80, 90, 100, 110],
'max_features': [2, 3],
'min_samples_leaf': [3, 4, 5],
'min_samples_split': [8, 10, 12],
'n_estimators': [100, 200, 300, 1000]
}
gsRFC = GridSearchCV(RFC,param_grid = rf_param_grid, cv=kfold, scoring="accuracy", n_jobs= 4, verbose = 1)
gsRFC.fit(X_train,Y_train)
RFC_best = gsRFC.best_estimator_
# Best score
gsRFC.best_score_"""
"""
RFC2 = RandomForestClassifier()
rf_param_grid = {
'bootstrap': [True],
'max_depth': [32],
'max_features': [2],
'min_samples_leaf': [1],
'min_samples_split': [6],
'n_estimators': [300]
}
gsRFC2 = GridSearchCV(RFC,param_grid = rf_param_grid, cv=kfold, scoring="accuracy", n_jobs= 4, verbose = 1)
gsRFC2.fit(X_train,Y_train)
gsRFC2.best_score_"""
pd.DataFrame(RFC.feature_importances_,
index=X_train.columns, columns=['Importance']).sort_values(
by='Importance', ascending=False)[:10]
pd.DataFrame(et.feature_importances_,
index=X_train.columns, columns=['Importance']).sort_values(
by='Importance', ascending=False)[:10]
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):
"""Generate a simple plot of the test and training learning curve"""
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
label="Cross-validation score")
plt.legend(loc="best")
return plt
g = plot_learning_curve(RFC,"Random Forest learning curves",X_train,Y_train,cv=kfold)
g = plot_learning_curve(et,"Extra trees learning curves",X_train,Y_train,cv=kfold)
g = plot_learning_curve(gsRFC2,"Random Forest tuned learning curves",X_train,Y_train,cv=kfold)
#g = plot_learning_curve(gsExtC.best_estimator_,"ExtraTrees learning curves",X_train,Y_train,cv=kfold)
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):
"""Generate a simple plot of the test and training learning curve"""
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = | np.std(test_scores, axis=1) | numpy.std |
#!/usr/bin/env python
from scipy import stats
from scipy.signal import find_peaks
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.neighbors import KernelDensity
from matplotlib import markers
from operator import itemgetter
from peak_cleanup import PeakCleanup
import argparse
import json
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import scipy.optimize as optimization
import seaborn as sns
import statistics
import uproot
def find_gain(infpn, df, feb_id, ch, print_peak_adcs, prominence=300, left_threshold=0.7, right_threshold=1.4):
# make the plot of a channel
chvar = 'chg[{}]'.format(ch)
# select data of the specified board
df_1b = df[df['feb_num'] == feb_id]
# make histogram and find peaks
bins = np.linspace(0, 4100, 821)
plt.figure(figsize=(12,6))
ax1 = plt.subplot2grid((2, 3), (0, 0), colspan=2)
histy, bin_edges, _ = ax1.hist(df_1b[chvar], bins=bins, histtype='step')
peaks, _ = find_peaks(histy, prominence=prominence)
ax1.scatter(np.array(bin_edges)[peaks], np.array(histy)[peaks],
marker=markers.CARETDOWN, color='r', s=20)
ax1.set_xlabel('ADC value')
# load the found peaks into a list
peak_adcs = list(np.array(bin_edges)[peaks])
if print_peak_adcs: print(peak_adcs)
# make the ADC difference vs PE id plot
if len(peak_adcs) >= 2:
peak_diff = [peak_adcs[1] - peak_adcs[0]] + [peak_adcs[i+1]-peak_adcs[i] for i in range(len(peak_adcs)-1)]
else:
return 0
bins_adc_diff = np.linspace(0, len(peak_adcs)-1, len(peak_adcs)).astype(int)
ax_adc_diff = plt.subplot2grid((2, 3), (0, 2))
ax_adc_diff.step(bins_adc_diff, peak_diff, ls='-')
ax_adc_diff.set_xticks(bins_adc_diff)
ax_adc_diff.set_xlabel('PE id')
ax_adc_diff.set_ylabel('adjacent ADC difference')
# plot mean and standard deviation of all the differences
y_mean = statistics.mean(peak_diff)
y_std = statistics.stdev(peak_diff)
n_std = 3
y_shifts = [y_mean + y_std*i for i in range(-n_std, n_std+1)]
color_std = ['y', 'magenta', 'g', 'r', 'g', 'magenta', 'y']
for i in range(2*n_std+1):
ax_adc_diff.axhline(y_shifts[i], ls='--', c=color_std[i], alpha=.25)
# # make kernel density plots
# ref: https://stackoverflow.com/questions/9814429/gaussian-kernel-density-estimation-kde-of-large-numbers-in-python
# # each bin is 5 ADC, so the bandwidth is multiple of 5
# if y_std > 0:
# x_kde = np.linspace(min(peak_diff)*.8, max(peak_diff)*1.2, 101)
# density = stats.gaussian_kde(peak_diff, bw_method=5/y_std)
# y_kde = density(x_kde)
# ax_kde = plt.subplot2grid((2, 3), (1, 0))
# ax_kde.plot(x_kde, y_kde)
# ax_kde.set_xlabel('adjacent ADC difference')
# ax_kde.set_title('kernel density estimation')
ax_kde = plt.subplot2grid((2, 3), (1, 0))
pc = PeakCleanup(peak_adcs)
pc.plot_to_axis(ax_kde, np.array(pc.peak_diffs), thresh=5)
ax_kde.set_title('before outlier removal')
# remove outliers
peak_adcs_orig = peak_adcs.copy()
peak_cleaner = PeakCleanup(peak_adcs)
# peak_cleaner.remove_outlier_twice()
peak_cleaner.remove_outlier_by_relative_interval(left_th=left_threshold, right_th=right_threshold)
peak_adcs = peak_cleaner.peak_adcs
# peak_diff2 = [peak_adcs[i+1]-peak_adcs[i] for i in range(len(peak_adcs)-1)]
# make kernel density plots after outlier removal
ax_kde2 = plt.subplot2grid((2, 3), (1, 1))
peak_cleaner.plot_to_axis(ax_kde2, | np.array(peak_cleaner.peak_diffs) | numpy.array |
#!/usr/bin/env python
# -*- coding: utf8 -*-
from __future__ import division
import os
from itertools import islice
import numpy as np
from .synthetic import broadening, _read_raw_moog
apogee_kurucz = {
'teff': (
3500,
3750,
4000,
4250,
4500,
4750,
5000,
5250,
5500,
5750,
6000,
6250,
6500,
6750,
7000,
7250,
7500,
7750,
8000,
8250,
8500,
8750,
9000,
9250,
9500,
9750,
10000,
10250,
10500,
10750,
11000,
11250,
11500,
11750,
12000,
12250,
12500,
12750,
13000,
14000,
15000,
16000,
17000,
18000,
19000,
20000,
21000,
22000,
23000,
24000,
25000,
26000,
27000,
28000,
29000,
30000,
),
'feh': (
-5.0,
-4.5,
-4.0,
-3.5,
-3.0,
-2.75,
-2.5,
-2.25,
-2.0,
-1.75,
-1.5,
-1.25,
-1.0,
-0.75,
-0.5,
-0.25,
0.0,
0.25,
0.5,
0.75,
1.0,
1.5,
),
'logg': (0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0),
}
marcs = {
'teff': (
2500,
2600,
2700,
2800,
2900,
3000,
3100,
3200,
3300,
3400,
3500,
3600,
3700,
3800,
3900,
4000,
4250,
4500,
4750,
5000,
5250,
5500,
5750,
6000,
6250,
6500,
6750,
7000,
7250,
7500,
7750,
8000,
),
'feh': (
-5.0,
-4.0,
-3.0,
-2.5,
-2.0,
-1.5,
-1.0,
-0.75,
-0.5,
-0.25,
0.0,
0.25,
0.5,
0.75,
1.0,
),
'logg': (0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0),
}
class GetModels:
'''
Find the names of the closest grid points for a given effective
temperature, surface gravity, and iron abundance (proxy for metallicity).
Inputs
------
teff : int
The effective temperature (K) for the model atmosphere
logg : float
The surface gravity (logarithmic in cgs) for the model atmosphere
feh : float
The metallicity for the model atmosphere
atmtype : str
The type of atmosphere models to use. Currently only Kurucz from '95.
'''
def __init__(self, teff, logg, feh, atmtype):
self.teff = teff
self.logg = logg
self.feh = feh
self.atmtype = atmtype
atmmodels = {
'apogee_kurucz': [apogee_kurucz, 'apogee_kurucz'],
'marcs': [marcs, 'marcs'],
}
if atmtype in atmmodels.keys():
self.grid = atmmodels[atmtype][0]
else:
raise NotImplementedError(
'You request for atmospheric models: %s is not available' % atmtype
)
self.grid['teff'] = np.asarray(self.grid['teff'])
self.grid['logg'] = np.asarray(self.grid['logg'])
self.grid['feh'] = np.asarray(self.grid['feh'])
# Checking for bounds in Teff, logg, and [Fe/H]
if (self.teff < self.grid['teff'][0]) or (self.teff > self.grid['teff'][-1]):
raise ValueError('Teff out of bounds: %s' % self.teff)
if (self.logg < self.grid['logg'][0]) or (self.logg > self.grid['logg'][-1]):
raise ValueError('logg out of bounds: %s' % self.logg)
if (self.feh < self.grid['feh'][0]) or (self.feh > self.grid['feh'][-1]):
raise ValueError('[Fe/H] out of bounds: %s' % self.feh)
def _model_path(self, teff_model, logg_model, feh_model):
'''Create the path for atmosphere models given Teff, logg, and [Fe/H]
Inputs
------
teff_model : int
The Teff from the model grid
logg_model : float
The logg from the model grid
feh_model : float
The [Fe/H] from the model grid
Output
------
name : str
The path to the atmosphere model
'''
cwd = os.path.dirname(os.path.abspath(__file__))
name = cwd + '/models/%s/' % self.atmtype
if feh_model < 0:
name += 'm%s/' % str(abs(feh_model)).replace('.', '')
else:
name += 'p%s/' % str(abs(feh_model)).replace('.', '')
name += '%ig%s.' % (teff_model, str(logg_model).replace('.', ''))
if feh_model < 0:
name += 'm%s.gz' % str(abs(feh_model)).replace('.', '')
else:
name += 'p%s.gz' % str(abs(feh_model)).replace('.', '')
return name
def _model_exists(self, teff_model, logg_model, feh_model, upper=True):
'''Check if a model exists. If not lower/raise Teff
Inputs
------
teff_model : int
The Teff from the model grid
logg_model : float
The logg from the model grid
feh_model : float
The [Fe/H] from the model grid
upper : bool
If True, then search for Teff higher than previous. False otherwise. (Default: True)
Outputs
-------
fname : str
Path for the model
teff_model : int
The new Teff. Same Teff is returned if the model exists at the right place
'''
fname = self._model_path(teff_model, logg_model, feh_model)
if os.path.isfile(fname):
return fname, teff_model, logg_model
else:
print('Models do not exist.')
return False
# Change the Teff (up or down) to compensate for the gap
teff_model0 = teff_model
idx = | np.where(teff_model == self.grid['teff']) | numpy.where |
import os
from os.path import join
import gzip
import shutil
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from scipy.linalg import block_diag
def get_decoder(manifold, x_dim, z_dim, rng_data_gen):
if manifold == "nn":
# NOTE: injectivity requires z_dim <= h_dim <= x_dim
h_dim = x_dim
neg_slope = 0.2
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# sampling NN weight matrices
W1 = rng_data_gen.normal(size=(z_dim, h_dim))
W1 = np.linalg.qr(W1.T)[0].T
# print("distance to identity:", np.max(np.abs(np.matmul(W1, W1.T) - np.eye(self.z_dim))))
W1 *= np.sqrt(2 / (1 + neg_slope ** 2)) * np.sqrt(2. / (z_dim + h_dim))
W1 = torch.Tensor(W1).to(device)
W1.requires_grad = False
W2 = rng_data_gen.normal(size=(h_dim, h_dim))
W2 = np.linalg.qr(W2.T)[0].T
# print("distance to identity:", np.max(np.abs(np.matmul(W2, W2.T) - np.eye(h_dim))))
W2 *= np.sqrt(2 / (1 + neg_slope ** 2)) * np.sqrt(2. / (2 * h_dim))
W2 = torch.Tensor(W2).to(device)
W2.requires_grad = False
W3 = rng_data_gen.normal(size=(h_dim, h_dim))
W3 = np.linalg.qr(W3.T)[0].T
# print("distance to identity:", np.max(np.abs(np.matmul(W3, W3.T) - np.eye(h_dim))))
W3 *= np.sqrt(2 / (1 + neg_slope ** 2)) * np.sqrt(2. / (2 * h_dim))
W3 = torch.Tensor(W3).to(device)
W3.requires_grad = False
W4 = rng_data_gen.normal(size=(h_dim, x_dim))
W4 = np.linalg.qr(W4.T)[0].T
# print("distance to identity:", np.max(np.abs(np.matmul(W4, W4.T) - np.eye(h_dim))))
W4 *= np.sqrt(2 / (1 + neg_slope ** 2)) * | np.sqrt(2. / (x_dim + h_dim)) | numpy.sqrt |
import imageio
import torch
import numpy as np
import time
import matplotlib.pyplot as plt
import copy
import random
import math
class Predict2D:
def __init__(self, config, model, device):
self.config = config
self.model = model
self.device = device
def find_heat_map_maxima(self, heatmaps, sigma=None, method="simple"):
""" heatmaps: (#LM, hm_size,hm_size) """
out_dim = heatmaps.shape[0] # number of landmarks
hm_size = heatmaps.shape[1]
# coordinates = np.zeros((out_dim, 2), dtype=np.float32)
coordinates = np.zeros((out_dim, 3), dtype=np.float32)
# TODO Need to figure out why x and y are switched here...probably something with row, col
# simple: Use only maximum pixel value in HM
if method == "simple":
for k in range(out_dim):
hm = copy.copy(heatmaps[k, :, :])
highest_idx = np.unravel_index(np.argmax(hm), (hm_size, hm_size))
px = highest_idx[0]
py = highest_idx[1]
value = hm[px, py] # TODO check if values is equal to np.max(hm)
coordinates[k, :] = (px - 1, py - 0.5, value) # TODO find out why it works with the subtractions
if method == "moment":
for k in range(out_dim):
hm = heatmaps[k, :, :]
highest_idx = np.unravel_index(np.argmax(hm), (hm_size, hm_size))
px = highest_idx[0]
py = highest_idx[1]
value = np.max(hm)
# Size of window around max (15 on each side gives an array of 2 * 5 + 1 values)
sz = 15
a_len = 2 * sz + 1
if px > sz and hm_size-px > sz and py > sz and hm_size-py > sz:
slc = hm[px-sz:px+sz+1, py-sz:py+sz+1]
ar = np.arange(a_len)
sum_x = np.sum(slc, axis=1)
s = np.sum(np.multiply(ar, sum_x))
ss = np.sum(sum_x)
pos = s / ss - sz
px = px + pos
sum_y = np.sum(slc, axis=0)
s = np.sum(np.multiply(ar, sum_y))
ss = np.sum(sum_y)
pos = s / ss - sz
py = py + pos
coordinates[k, :] = (px-1, py-0.5, value) # TODO find out why it works with the subtractions
return coordinates
def find_maxima_in_batch_of_heatmaps(self, heatmaps, cur_id, heatmap_maxima):
write_heatmaps = False
heatmaps = heatmaps.numpy()
batch_size = heatmaps.shape[0]
f = None
for idx in range(batch_size):
if write_heatmaps:
name_hm_maxima = self.config.temp_dir / ('hm_maxima' + str(cur_id + idx) + '.txt')
f = open(name_hm_maxima, 'w')
coordinates = self.find_heat_map_maxima(heatmaps[idx, :, :, :], method='moment')
for lm_no in range(coordinates.shape[0]):
px = coordinates[lm_no][0]
py = coordinates[lm_no][1]
value = coordinates[lm_no][2]
if value > 1.2: # TODO debug - really bad hack due to weird max in heatmaps
print("Found heatmap with value > 1.2 LM {} value {} pos {} {} ".format(lm_no, value, px, py))
value = 0
# if lm_no == 0:
# print('LM value and pos', lm_no, value, px, py)
# name_hm_maxima = self.config.temp_dir /
# ('hm_maxima' + str(cur_id + idx) + '_LM_' + str(lm_no) + '.png')
# imageio.imwrite(name_hm_maxima, heatmaps[idx, lm_no, :, :])
heatmap_maxima[lm_no, cur_id + idx, :] = (px, py, value)
if write_heatmaps:
out_str = str(px) + ' ' + str(py) + ' ' + str(value) + '\n'
f.write(out_str)
if write_heatmaps:
f.close()
def generate_image_with_heatmap_maxima(self, image, heat_map):
im_size = image.shape[0]
hm_size = heat_map.shape[2]
i = image.copy()
coordinates = self.find_heat_map_maxima(heat_map, method='moment')
# the predicted heat map is sometimes smaller than the input image
factor = im_size / hm_size
for c in range(coordinates.shape[0]):
px = coordinates[c][0]
py = coordinates[c][1]
if not np.isnan(px) and not | np.isnan(py) | numpy.isnan |
#!/usr/bin/python
DESC = '''Synthetic multipathway graph model
By: NP
Pending (AA):
visualize: https://stackoverflow.com/questions/35109590/how-to-graph-nodes-on-a-grid-in-networkx
'''
import argparse
import csv
import logging
import math
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
from numpy import linalg as LA
import scipy #Scipy 1.7? I think is incomplatible with networkx need to downgrade to scipy 1.4.1? to get it to work properly
import os
import pandas as pd
from pdb import set_trace
from scipy.sparse.linalg import eigs
from scipy.sparse import csgraph
#from scipy import stats
# from pandas import *: AA: avoid such 'import *' statements
import sqlite3
from sqlite3 import Error
import random
#import seaborn as sns #can't use problem with the stats from scipy so I can't use
FORMAT = "%(levelname)s:%(filename)s:%(funcName)s:%(message)s"
def self_mediated_dispersal(dispersalRange, side):
H = nx.Graph()
for i in range(0, side ** 2 - 1):
col = i % side # initializing which column its on
row = i // side # initializing rows
# AA: check whether np.floor is correct
for x in range(0, int(np.floor(dispersalRange)) + 1): # is connecting the x direction columns
for y in range(0, int(np.floor(dispersalRange)) + 1): # is connecting the y direction rows
rangeSquared = dispersalRange ** 2
distanceSquared = x ** 2 + y ** 2
if x == 0 and y == 0:
continue
if col + x < side and row + y < side and distanceSquared <= rangeSquared:
H.add_edge((row, col), (row + y, col + x))
return H
# AA: function names should be all lowercase (with underscores if required)
def locality_clique(gridSideLength, regionSideLength, localitySideLength, localityNumber): # clique template
# side is total number of localitySideLength
# regionSideLength is number of localitySideLength in a regionSideLength
# row is how many localitySideLength are in meta nodes 0 is the first meta node
# regionSideLength number is which meta node it is/which regionSideLength going from left to right up to down
x = (
regionSideLength - localitySideLength) // 2 # of meta node localitySideLength - number of white row nodes divided by 2. Gives the number of non white nodes surrounding white nodes in meta node
# numberOfSquares = side**2//regionSideLength//regionSideLength
initialRow = (localityNumber // (gridSideLength // regionSideLength)) * regionSideLength+x #Error where I forgot to include the x
initialCol = (localityNumber % (gridSideLength // regionSideLength)) * regionSideLength+x
G = nx.Graph()
for i in range(0, localitySideLength):
for j in range(0, localitySideLength):
for k in range(0, localitySideLength):
for l in range(0, localitySideLength):
if not (i == k and j == l):
G.add_edge((initialRow + i, initialCol + j), (initialRow + k,
initialCol + l)) # iterates through every combonation of nodes and pairs them all togehter
G.add_edge((initialRow + k, initialCol + l), (initialRow + i,
initialCol + j)) # probably don't need this line as in networkx edges are undirect
return G # returns a graph of all the white nodes in a metanode connected to every other white node
def locality_star(gridSideLength, regionSideLength, localitySideLength, localityNumber):
midRow = localitySideLength // 2 # picks either the middle node or the bottom right of the smallest regionSideLength node for white localitySideLength
midCol = localitySideLength // 2 # if even picks bottom right of 2x2 regionSideLength in the middle
z = (
regionSideLength - localitySideLength) // 2 # of meta node localitySideLength - number of white row nodes divided by 2. Gives the number of non white nodes surrounding white nodes in meta node
# numberOfSquares = gridSideLength*gridSideLength//regionSideLength//regionSideLength
initialRow = (localityNumber // (gridSideLength // regionSideLength)) * regionSideLength + z
initialCol = (localityNumber % (gridSideLength // regionSideLength)) * regionSideLength + z
G = nx.Graph()
for x in range(0, localitySideLength):
for y in range(0, localitySideLength):
if not (x == midRow and y == midCol):
G.add_edge((initialRow + x, initialCol + y), (
initialRow + midRow, initialCol + midCol)) # connects the head node to every other node except itself
return G
def complete_bipartite(graph1,
graph2): # function takes two graphs and returns a complete bipartate graph of one node set to another
nodes1 = list(graph1.nodes)
nodes2 = list(graph2.nodes)
newGraph = nx.Graph()
for x in range(0, len(nodes1)):
for y in range(0, len(nodes2)):
newGraph.add_edge(nodes1[x], nodes2[y])
return newGraph
def heat_map(G,betweenness,filename,title):
plt.clf()
numNodes = len(list(G.nodes()))
numRows = math.sqrt(numNodes)
numRows = int(numRows)
arr = [[0 for i in range(0,numRows)] for j in range(0,numRows)]
for x in range(0,numRows):
for y in range(0,numRows):
arr[x][y]=betweenness[x,y]
_ = plt.imshow(arr, cmap='autumn_r', interpolation='nearest')
plt.title(title)
plt.colorbar()
plt.axis('off')
#ax = sns.heatmap(arr, linewidth=0.5, cmap='coolwarm')
plt.savefig(filename, bbox_inches=0)
def creating_files(G,GS,GL,GLD,localityNum,localityNodes,longDistanceEdges):
#Need to check if simulation automatically assume bi-directionallity or not I don't think it does
THIS_FOLDER = os.path.dirname(os.path.abspath(__file__))
my_file = os.path.join(THIS_FOLDER, '0.nodes')
current_name = os.path.join(THIS_FOLDER, 'nodes0.csv')
# absolute path
database = my_file
nodesFile = '0.nodes'
if os.path.exists(nodesFile):
os.remove('0.nodes')
# creating maps
nodes = G.nodes()
nodes = sorted(nodes, key=lambda tup: (tup[0], tup[1]))
numRows = int(np.sqrt(len(nodes)))
with open('0.nodes', 'w') as storage_file:
storage_writer = csv.writer(storage_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL, lineterminator = '\n')
data = ['node', 'node_x','node_y',1,2,3,4,5,6,7,8,9,10,11,12]
storage_writer.writerow(data)
for node in nodes:
data = [node[0]*numRows+node[1],node[0],node[1],1,1,1,1,1,1,1,1,1,1,1,1]
storage_writer.writerow(data)
storage_file.close()
#os.rename(current_name,my_file)
edgeFile = '0.edges'
if os.path.exists(edgeFile):
os.remove('0.edges')
with open('0.edges', 'w') as storage_file:
storage_writer = csv.writer(storage_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL, lineterminator = '\n')
data = ['source', 'target','moore','source_x','source_y', 'target_x','target_y']
storage_writer.writerow(data)
#print(G.edges())
edges = list(G.edges())
for edge in edges:
node00 = edge[0][0]
node01 = edge[0][1]
node10 = edge[1][0]
node11 = edge[1][1]
nodeNumber0 = node00*numRows+node01
nodeNumber1 = node10*numRows+node11
distanceX = (node10-node00)**2
distanceY = (node11-node01)**2
distanceTotal = math.sqrt(distanceX+distanceY)
data = [nodeNumber0,nodeNumber1,distanceTotal,edge[0][0],edge[0][1],edge[1][0],edge[1][1]]
storage_writer.writerow(data)
data = [nodeNumber1,nodeNumber0,distanceTotal,edge[1][0],edge[1][1],edge[0][0],edge[0][1]]
storage_writer.writerow(data)
storage_file.close()
numberOfNodes = len(list(G.nodes()))
nodesFile = '1.nodes'
if os.path.exists(nodesFile):
os.remove('1.nodes')
with open('1.nodes', 'w') as storage_file:
storage_writer = csv.writer(storage_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL,
lineterminator='\n')
data = ['node', 'locality_number']
storage_writer.writerow(data)
nodes = G.nodes()
nodes = sorted(nodes, key=lambda tup: (tup[0], tup[1]))
x = 0
for node in range(0,localityNum):
localityNumber = node+numberOfNodes+1000
data = [localityNumber, node]
storage_writer.writerow(data)
storage_file.close()
nodesFile='1.edges'
if os.path.exists(nodesFile):
os.remove('1.edges')
with open('1.edges', 'w') as storage_file:
storage_writer = csv.writer(storage_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
lineterminator='\n')
data = ["source", "target", "weight", "month", "cell_id_x", "cell_id_y", "source_region", "target_region"]
storage_writer.writerow(data)
nodes = G.nodes()
nodes = sorted(nodes, key=lambda tup: (tup[0], tup[1]))
for edge in longDistanceEdges.edges():
edge0 = edge[0]+numberOfNodes+1000
edge1 = edge[1]+numberOfNodes+1000
for y in range(1,13): #makes sure that it goes through every single month and that its constant throughout
data = [edge0,edge1,1,y,0,0,edge[0],edge[1]]
storage_writer.writerow(data)
data = [edge1, edge0, 1, y, 0, 0, edge[1], edge[0]]
storage_writer.writerow(data)
storage_file.close()
nodesFile = 'hierarchy.tree'
if os.path.exists(nodesFile):
os.remove('hierarchy.tree')
seed_loc = pd.DataFrame(columns = ['node', 'probability', 'locality'])
with open('hierarchy.tree', 'w') as storage_file:
storage_writer = csv.writer(storage_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
lineterminator='\n')
data = ['parent', 'child', 'parent_node', 'child_node_x', 'child_node_y']
storage_writer.writerow(data)
nodes = G.nodes()
nodes = sorted(nodes, key=lambda tup: (tup[0], tup[1]))
x = 0
for node in range(0, localityNum):
localityNumber = node + numberOfNodes + 1000
data = [-1, localityNumber, -1, localityNum]
storage_writer.writerow(data)
for locality in range(0, localityNum):
localityNumber = locality + numberOfNodes + 1000
for node in localityNodes[locality]:
nodeNumber = node[0] * numRows + node[1]
data = [localityNumber, nodeNumber, locality, node[0], node[1]]
seed_loc = seed_loc.append({
'node': nodeNumber,
'locality': locality}, ignore_index = True)
storage_writer.writerow(data)
onlyShortDistance = [node for node in G.nodes() if node not in GL.nodes()]
#print(onlyShortDistance)
for node in onlyShortDistance:
nodeNumber = node[0] * numRows + node[1]
data = [-1,nodeNumber,-1,nodes[0],node[1]]
seed_loc = seed_loc.append({
'node': nodeNumber,
'locality': -1}, ignore_index = True)
storage_writer.writerow(data)
storage_file.close()
# seeding
percentageSeedNodes = 5
df = pd.DataFrame({'node': np.arange(G.number_of_nodes()),
'probability': np.full(G.number_of_nodes(), percentageSeedNodes/100)})
df.to_csv('seed_all.csv', index = False)
numNodesInLocality = (seed_loc.locality != -1).sum()
localityProb = min(1, G.number_of_nodes()/numNodesInLocality \
* percentageSeedNodes / 100)
nonLocalityProb = max(0, (G.number_of_nodes() * percentageSeedNodes / 100 - \
numNodesInLocality * localityProb) / G.number_of_nodes())
seed_loc.probability = nonLocalityProb
seed_loc.loc[seed_loc.locality != -1, 'probability'] = localityProb
seed_loc = seed_loc.astype({'node': int})
seed_loc.to_csv('seed_loc.csv', index = False)
## df = pd.DataFrame({'node': nodesInLocality,
## 'probability': np.full(numNodesInLocality, localityProb}))
## df = df.append(pd.DataFrame({'node': nodesInLocal
def main():
parser = argparse.ArgumentParser(description=DESC,
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("--number_of_nodes", required=True,
help="Number of nodes in the square grid. Must be a square.",
type=int)
parser.add_argument("--number_of_regions", required=True,
help="Number of square regions in the grid. Must be a square and be a factor of number_of_nodes.",
type=int)
parser.add_argument("--range", required=True,
help="Distance parameter", type=float)
parser.add_argument("--locality_size", required=True,
help="Number of nodes in a locality, which forms a square grid within a region. Must be a square and have the same parity as number_of_nodes/number_of_regions.",
type=int)
parser.add_argument("--locality_graph", required=True,
help="Type of locality graph (star/clique)")
parser.add_argument("--long_distance_type", required=True,
help="Type of graph for long distance pathway (ER/CL/SF)")
parser.add_argument("--ld_param", nargs="+",
help="Long Distance parameters",type=float)
parser.add_argument("--seed", required=True,
help="Random seed", type=int)
parser.add_argument("--directed", action="store_true",
help="says that the graph is a directional graph")
parser.add_argument("-m", "--multi", action="store_true",
help="says that the graph is a multi-edged graph")
parser.add_argument("--suppress_properties", action="store_true",
default = False,
help="Mode to just create graphs.")
parser.add_argument("-d", "--debug", action="store_true")
parser.add_argument("-q", "--quiet", action="store_true")
args = parser.parse_args()
# set logger
if args.debug:
logging.basicConfig(level=logging.DEBUG, format=FORMAT)
elif args.quiet:
logging.basicConfig(level=logging.WARNING, format=FORMAT)
else:
logging.basicConfig(level=logging.INFO, format=FORMAT)
# checking if constraints are satisfied
if not np.sqrt(args.number_of_nodes).is_integer():
raise ValueError('number of nodes must be a square')
if not np.sqrt(args.number_of_regions).is_integer():
raise ValueError('number of regions must be a square')
if args.number_of_regions > args.number_of_nodes:
raise ValueError('number of regions > number_of_nodes')
if not | np.sqrt(args.locality_size) | numpy.sqrt |
import os.path as osp
import numpy as np
import math
from tqdm import tqdm
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.utils.data
from torchvision import transforms, datasets
from ofa.utils import AverageMeter, accuracy
from ofa.model_zoo import ofa_specialized
from ofa.imagenet_classification.elastic_nn.utils import set_running_statistics
import copy
import random
def evaluate_ofa_resnet_subnet(ofa_net, path, net_config, data_loader, batch_size, device='cuda:0'):
assert 'w' in net_config and 'd' in net_config and 'e' in net_config
assert len(net_config['w']) == 6 and len(net_config['e']) == 18 and len(net_config['d']) == 5
ofa_net.set_active_subnet(w=net_config['w'], d=net_config['d'], e=net_config['e'])
subnet = ofa_net.get_active_subnet().to(device)
calib_bn(subnet, path, 224, batch_size)
top1 = validate(subnet, path, 224, data_loader, batch_size, device)
return top1
def evaluate_ofa_resnet_ensemble_subnet(ofa_net, path, net_config1, net_config2, data_loader, batch_size, device='cuda:0'):
assert 'w' in net_config1 and 'd' in net_config1 and 'e' in net_config1
assert len(net_config1['w']) == 6 and len(net_config1['e']) == 18 and len(net_config1['d']) == 5
ofa_net.set_active_subnet(w=net_config1['w'], d=net_config1['d'], e=net_config1['e'])
subnet1 = ofa_net.get_active_subnet().to(device)
calib_bn(subnet1, path, 224, batch_size)
ofa_net.set_active_subnet(w=net_config2['w'], d=net_config2['d'], e=net_config2['e'])
subnet2 = ofa_net.get_active_subnet().to(device)
calib_bn(subnet2, path, 224, batch_size)
# assert net_config2['r'][0]==net_config1['r'][0]
subnets = []
subnets.append(subnet2)
subnets.append(subnet1)
top1 = ensemble_validate(subnets, path, 224, data_loader, batch_size, device)
return top1
def evaluate_ofa_subnet(ofa_net, path, net_config, data_loader, batch_size, device='cuda:0'):
assert 'ks' in net_config and 'd' in net_config and 'e' in net_config
assert len(net_config['ks']) == 20 and len(net_config['e']) == 20 and len(net_config['d']) == 5
ofa_net.set_active_subnet(ks=net_config['ks'], d=net_config['d'], e=net_config['e'])
subnet = ofa_net.get_active_subnet().to(device)
calib_bn(subnet, path, net_config['r'][0], batch_size)
top1 = validate(subnet, path, net_config['r'][0], data_loader, batch_size, device)
return top1
def evaluate_ofa_ensemble_subnet(ofa_net, path, net_config1, net_config2, data_loader, batch_size, device='cuda:0'):
assert 'ks' in net_config1 and 'd' in net_config1 and 'e' in net_config1
assert len(net_config1['ks']) == 20 and len(net_config1['e']) == 20 and len(net_config1['d']) == 5
ofa_net.set_active_subnet(ks=net_config1['ks'], d=net_config1['d'], e=net_config1['e'])
subnet1 = ofa_net.get_active_subnet().to(device)
calib_bn(subnet1, path, net_config1['r'][0], batch_size)
ofa_net.set_active_subnet(ks=net_config2['ks'], d=net_config2['d'], e=net_config2['e'])
subnet2 = ofa_net.get_active_subnet().to(device)
calib_bn(subnet2, path, net_config2['r'][0], batch_size)
assert net_config2['r'][0]==net_config1['r'][0]
subnets = []
subnets.append(subnet2)
subnets.append(subnet1)
top1 = ensemble_validate(subnets, path, net_config2['r'][0], data_loader, batch_size, device)
return top1
def calib_bn(net, path, image_size, batch_size, num_images=2000):
# print('Creating dataloader for resetting BN running statistics...')
dataset = datasets.ImageFolder(
osp.join(
path,
'train'),
transforms.Compose([
transforms.RandomResizedCrop(image_size),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=32. / 255., saturation=0.5),
transforms.ToTensor(),
transforms.Normalize(
mean=[
0.485,
0.456,
0.406],
std=[
0.229,
0.224,
0.225]
),
])
)
chosen_indexes = np.random.choice(list(range(len(dataset))), num_images)
sub_sampler = torch.utils.data.sampler.SubsetRandomSampler(chosen_indexes)
data_loader = torch.utils.data.DataLoader(
dataset,
sampler=sub_sampler,
batch_size=batch_size,
num_workers=16,
pin_memory=True,
drop_last=False,
)
# print('Resetting BN running statistics (this may take 10-20 seconds)...')
set_running_statistics(net, data_loader)
def ensemble_validate(nets, path, image_size, data_loader, batch_size=100, device='cuda:0'):
if 'cuda' in device:
print('use cuda')
for net in nets:
net = torch.nn.DataParallel(net).to(device)
else:
for net in nets:
net = net.to(device)
data_loader.dataset.transform = transforms.Compose([
transforms.Resize(int(math.ceil(image_size / 0.875))),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
),
])
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss().to(device)
for net in nets:
net.eval()
net = net.to(device)
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
with torch.no_grad():
with tqdm(total=len(data_loader), desc='Validate') as t:
for i, (images, labels) in enumerate(data_loader):
images, labels = images.to(device), labels.to(device)
# compute output
n = len(nets)
output = 0
for i, net in enumerate(nets):
if i == 0:
output =net(images)
else:
output+=net(images)
output = output/n
loss = criterion(output, labels)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, labels, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0].item(), images.size(0))
top5.update(acc5[0].item(), images.size(0))
t.set_postfix({
'loss': losses.avg,
'top1': top1.avg,
'top5': top5.avg,
'img_size': images.size(2),
})
t.update(1)
print('Results: loss=%.5f,\t top1=%.3f,\t top5=%.1f' % (losses.avg, top1.avg, top5.avg))
return top1.avg
def validate(net, path, image_size, data_loader, batch_size=100, device='cuda:0'):
if 'cuda' in device:
net = torch.nn.DataParallel(net).to(device)
else:
net = net.to(device)
data_loader.dataset.transform = transforms.Compose([
transforms.Resize(int(math.ceil(image_size / 0.875))),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
),
])
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss().to(device)
net.eval()
net = net.to(device)
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
with torch.no_grad():
with tqdm(total=len(data_loader), desc='Validate') as t:
for i, (images, labels) in enumerate(data_loader):
images, labels = images.to(device), labels.to(device)
# compute output
output = net(images)
loss = criterion(output, labels)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, labels, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0].item(), images.size(0))
top5.update(acc5[0].item(), images.size(0))
t.set_postfix({
'loss': losses.avg,
'top1': top1.avg,
'top5': top5.avg,
'img_size': images.size(2),
})
t.update(1)
print('Results: loss=%.5f,\t top1=%.1f,\t top5=%.1f' % (losses.avg, top1.avg, top5.avg))
return top1.avg
def evaluate_ofa_specialized(path, data_loader, batch_size=100, device='cuda:0', ensemble=False):
def select_platform_name():
valid_platform_name = [
'pixel1', 'pixel2', 'note10', 'note8', 's7edge', 'lg-g8', '1080ti', 'v100', 'tx2', 'cpu', 'flops'
]
print("Please select a hardware platform from ('pixel1', 'pixel2', 'note10', 'note8', 's7edge', 'lg-g8', '1080ti', 'v100', 'tx2', 'cpu', 'flops')!\n")
while True:
platform_name = input()
platform_name = platform_name.lower()
if platform_name in valid_platform_name:
return platform_name
print("Platform name is invalid! Please select in ('pixel1', 'pixel2', 'note10', 'note8', 's7edge', 'lg-g8', '1080ti', 'v100', 'tx2', 'cpu', 'flops')!\n")
def select_netid(platform_name):
platform_efficiency_map = {
'pixel1': {
143: 'pixel1_lat@[email protected]_finetune@75',
132: 'pixel1_lat@[email protected]_finetune@75',
79: 'pixel1_lat@[email protected]_finetune@75',
58: 'pixel1_lat@[email protected]_finetune@75',
40: 'pixel1_lat@[email protected]_finetune@25',
28: 'pixel1_lat@[email protected]_finetune@25',
20: 'pixel1_lat@[email protected]_finetune@25',
},
'pixel2': {
62: 'pixel2_lat@[email protected]_finetune@25',
50: 'pixel2_lat@[email protected]_finetune@25',
35: 'pixel2_lat@[email protected]_finetune@25',
25: 'pixel2_lat@[email protected]_finetune@25',
},
'note10': {
64: 'note10_lat@[email protected]_finetune@75',
50: 'note10_lat@[email protected]_finetune@75',
41: 'note10_lat@[email protected]_finetune@75',
30: 'note10_lat@[email protected]_finetune@75',
22: 'note10_lat@[email protected]_finetune@25',
16: 'note10_lat@[email protected]_finetune@25',
11: 'note10_lat@[email protected]_finetune@25',
8: 'note10_lat@[email protected]_finetune@25',
},
'note8': {
65: 'note8_lat@[email protected]_finetune@25',
49: 'note8_lat@[email protected]_finetune@25',
31: 'note8_lat@[email protected]_finetune@25',
22: 'note8_lat@[email protected]_finetune@25',
},
's7edge': {
88: 's7edge_lat@[email protected]_finetune@25',
58: 's7edge_lat@[email protected]_finetune@25',
41: 's7edge_lat@[email protected]_finetune@25',
29: 's7edge_lat@[email protected]_finetune@25',
},
'lg-g8': {
24: 'LG-G8_lat@[email protected]_finetune@25',
16: 'LG-G8_lat@[email protected]_finetune@25',
11: 'LG-G8_lat@[email protected]_finetune@25',
8: 'LG-G8_lat@[email protected]_finetune@25',
},
'1080ti': {
27: '1080ti_gpu64@[email protected]_finetune@25',
22: '1080ti_gpu64@[email protected]_finetune@25',
15: '1080ti_gpu64@[email protected]_finetune@25',
12: '1080ti_gpu64@[email protected]_finetune@25',
},
'v100': {
11: 'v100_gpu64@[email protected]_finetune@25',
9: 'v100_gpu64@[email protected]_finetune@25',
6: 'v100_gpu64@[email protected]_finetune@25',
5: 'v100_gpu64@[email protected]_finetune@25',
},
'tx2': {
96: 'tx2_gpu16@[email protected]_finetune@25',
80: 'tx2_gpu16@[email protected]_finetune@25',
47: 'tx2_gpu16@[email protected]_finetune@25',
35: 'tx2_gpu16@[email protected]_finetune@25',
},
'cpu': {
17: 'cpu_lat@[email protected]_finetune@25',
15: 'cpu_lat@[email protected]_finetune@25',
11: 'cpu_lat@[email protected]_finetune@25',
10: 'cpu_lat@[email protected]_finetune@25',
},
'flops': {
595: 'flops@[email protected]_finetune@75',
482: 'flops@[email protected]_finetune@75',
389: 'flops@[email protected]_finetune@75',
}
}
sub_efficiency_map = platform_efficiency_map[platform_name]
if not platform_name == 'flops':
print("Now, please specify a latency constraint for model specialization among", sorted(list(sub_efficiency_map.keys())), 'ms. (Please just input the number.) \n')
else:
print("Now, please specify a FLOPs constraint for model specialization among", sorted(list(sub_efficiency_map.keys())), 'MFLOPs. (Please just input the number.) \n')
while True:
efficiency_constraint = input()
if not efficiency_constraint.isdigit():
print('Sorry, please input an integer! \n')
continue
efficiency_constraint = int(efficiency_constraint)
if not efficiency_constraint in sub_efficiency_map.keys():
print('Sorry, please choose a value from: ', sorted(list(sub_efficiency_map.keys())), '.\n')
continue
return sub_efficiency_map[efficiency_constraint]
if not ensemble:
platform_name = select_platform_name()
net_id = select_netid(platform_name)
net, image_size = ofa_specialized(net_id=net_id, pretrained=True)
validate(net, path, image_size, data_loader, batch_size, device)
else:
nets = []
for i in range(2):
print('{}model'.format(i))
platform_name = select_platform_name()
net_id = select_netid(platform_name)
net, image_size = ofa_specialized(net_id=net_id, pretrained=True)
nets.append(net)
ensemble_validate(nets, path, image_size, data_loader, batch_size, device)
return net_id
net_id = ['pixel1_lat@[email protected]_finetune@75', 'pixel1_lat@[email protected]_finetune@75',
'pixel1_lat@[email protected]_finetune@75', 'pixel1_lat@[email protected]_finetune@75',
'pixel1_lat@[email protected]_finetune@25', 'pixel1_lat@[email protected]_finetune@25',
'pixel1_lat@[email protected]_finetune@25', 'pixel2_lat@[email protected]_finetune@25',
'pixel2_lat@[email protected]_finetune@25', 'pixel2_lat@[email protected]_finetune@25',
'pixel2_lat@[email protected]_finetune@25', 'note10_lat@[email protected]_finetune@75',
'note10_lat@[email protected]_finetune@75', 'note10_lat@[email protected]_finetune@75',
'note10_lat@[email protected]_finetune@25', 'note10_lat@[email protected]_finetune@25',
'note10_lat@[email protected]_finetune@25', 'note8_lat@[email protected]_finetune@25',
'note8_lat@[email protected]_finetune@25', 'note8_lat@[email protected]_finetune@25',
'note8_lat@[email protected]_finetune@25', 's7edge_lat@[email protected]_finetune@25',
's7edge_lat@[email protected]_finetune@25', 's7edge_lat@[email protected]_finetune@25',
's7edge_lat@[email protected]_finetune@25', 'LG-G8_lat@[email protected]_finetune@25',
'LG-G8_lat@[email protected]_finetune@25', 'LG-G8_lat@[email protected]_finetune@25',
'LG-G8_lat@[email protected]_finetune@25', '1080ti_gpu64@[email protected]_finetune@25',
'1080ti_gpu64@[email protected]_finetune@25', '1080ti_gpu64@[email protected]_finetune@25',
'1080ti_gpu64@[email protected]_finetune@25', 'v100_gpu64@[email protected]_finetune@25',
'v100_gpu64@[email protected]_finetune@25', 'v100_gpu64@[email protected]_finetune@25',
'v100_gpu64@[email protected]_finetune@25', 'tx2_gpu16@[email protected]_finetune@25',
'tx2_gpu16@[email protected]_finetune@25', 'tx2_gpu16@[email protected]_finetune@25',
'tx2_gpu16@[email protected]_finetune@25', 'cpu_lat@[email protected]_finetune@25',
'cpu_lat@[email protected]_finetune@25', 'cpu_lat@[email protected]_finetune@25',
'cpu_lat@[email protected]_finetune@25', 'flops@[email protected]_finetune@75',
'flops@[email protected]_finetune@75', 'flops@[email protected]_finetune@75', ]
def evaluate_ofa_space(path, data_loader, batch_size=100, device='cuda:0', ensemble=False):
net_acc=[]
for i, id in enumerate(net_id):
acc=""
for j in range(2, len(id)):
if id[j]=='.':
acc=id[j-2]+id[j-1]+id[j]+id[j+1]
net_acc.append(acc)
id =np.argsort(np.array(net_acc))
new_net_id = copy.deepcopy(net_id)
for i, sortid in enumerate(id):
new_net_id[i] = net_id[sortid]
print('new_net_id', new_net_id)
n = len(net_id)
best_acc = 0
space = []
best_team =[]
for i in range(1, n):
for j in range(i):
nets = []
team = []
team.append(j)
team.append(i)
net, image_size = ofa_specialized(net_id=new_net_id[j], pretrained=True)
nets.append(net)
net, image_size = ofa_specialized(net_id=new_net_id[i], pretrained=True)
nets.append(net)
acc = ensemble_validate(nets, path, image_size, data_loader, batch_size, device)
if acc>best_acc:
best_acc=acc
best_team = team
print('space {} best_acc{}'.format(i+1, best_acc))
space.append(best_acc)
print('space:{}'.format(space))
return net_id[best_team[0]], net_id[best_team[1]]
def evaluate_ofa_best_acc_team(path, data_loader, batch_size=100, device='cuda:0', ensemble=False):
net_acc=[]
for i, id in enumerate(net_id):
acc=""
for j in range(2, len(id)):
if id[j]=='.':
acc=id[j-2]+id[j-1]+id[j]+id[j+1]
net_acc.append(acc)
id =np.argsort(np.array(net_acc))
new_net_id = copy.deepcopy(net_id)
for i, sortid in enumerate(id):
new_net_id[i] = net_id[sortid]
print('new_net_id', new_net_id)
n = len(net_id)
best_acc = 0
space = []
best_team =[]
i = n-1
for j in range(18, n):
nets = []
team = []
team.append(j)
team.append(i)
net, image_size = ofa_specialized(net_id=new_net_id[j], pretrained=True)
nets.append(net)
net, image_size = ofa_specialized(net_id=new_net_id[i], pretrained=True)
nets.append(net)
acc = ensemble_validate(nets, path, image_size, data_loader, batch_size, device)
print('net i:{} netj:{} acc:{}'.format(new_net_id[i], new_net_id[j], acc))
if acc>best_acc:
best_acc=acc
best_team = team
print('space {} best_acc{}'.format(i+1, best_acc))
space.append(best_acc)
print('space:{}'.format(space))
return new_net_id[best_team[0]], new_net_id[best_team[1]]
def evaluate_ofa_random_sample(path, data_loader, batch_size=100, device='cuda:0', ensemble=False):
net_acc=[]
for i, id in enumerate(net_id):
acc=""
for j in range(2, len(id)):
if id[j]=='.':
acc=id[j-2]+id[j-1]+id[j]+id[j+1]
net_acc.append(acc)
id =np.argsort(np.array(net_acc))
new_net_id = copy.deepcopy(net_id)
for i, sortid in enumerate(id):
new_net_id[i] = net_id[sortid]
print('new_net_id', new_net_id)
n = len(net_id)
best_acc = 0
acc_list = []
space = []
best_team =[]
for k in range(20):
nets = []
team = []
i = random.randint(0, n-1)
j = (i + random.randint(1, n-1)) % n
print('i:{} j:{}'.format(i, j))
team.append(j)
team.append(i)
net, image_size = ofa_specialized(net_id=new_net_id[j], pretrained=True)
nets.append(net)
net, image_size = ofa_specialized(net_id=new_net_id[i], pretrained=True)
nets.append(net)
acc = ensemble_validate(nets, path, image_size, data_loader, batch_size, device)
print('net i:{} netj:{} acc:{}'.format(new_net_id[i], new_net_id[j], acc))
acc_list.append(acc)
if acc>best_acc:
best_acc=acc
best_team = team
avg_acc = np.mean(acc_list)
std_acc = np.std(acc_list, ddof=1)
var_acc = | np.var(acc_list) | numpy.var |
import gc, macpath, pickle, pickletools, errno, traceback, gzip, io, copy, random, bisect, os, time
from autograd.numpy.numpy_extra import ArrayNode
import numpy as np
from functools import reduce
def safe_mkdir(path):
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
return path
def expanduser(p):
if p is None:
return p
else:
return os.path.expanduser(macpath.expanduser(p))
def dump(x, filename, opener=open, optimize=False):
gc.collect()
filename = expanduser(filename)
safe_mkdir(os.path.dirname(filename))
with opener(filename, 'wb') as fp:
if optimize:
s = pickle.dumps(x, pickle.HIGHEST_PROTOCOL)
s = pickletools.optimize(s)
fp.write(s)
else:
pickle.dump(x, fp, pickle.HIGHEST_PROTOCOL)
return filename
def load(filename, exts = ['', '.gz']):
t0 = time.time()
filename = expanduser(filename)
tbs = list()
for ext in exts:
for opener in [open,gzip.open]:
try:
with opener(filename+ext, 'rb') as fp:
if opener == gzip.open:
with io.BufferedReader(fp) as fpb:
rval = pickle.load(fpb)
else:
rval = pickle.load(fp)
gc.collect()
t_load = time.time() - t0
if t_load > 10:
print('loaded in %i seconds' % int(t_load))
return rval
except:
tbs.append(traceback.format_exc())
raise Exception('\n-------------------------------------------------\n'.join(['']+tbs+['']))
def memodict(f):
class memodict(dict):
def __missing__(self, key):
ret = self[key] = f(key)
return ret
return memodict().__getitem__
class RaisingDotDict(dict):
_raiser = object()
def __getattr__(self, attr):
rval = self.get(attr,RaisingDotDict._raiser)
if id(rval) == id(RaisingDotDict._raiser):
raise Exception(attr)
return rval
def copy(self):
return RaisingDotDict(dict(self).copy())
__setattr__= dict.__setitem__
__delattr__= dict.__delitem__
def sqdist(x, y=None):
# x.shape = (d, nx)
if y is None:
xx = np.sum(x ** 2, axis=0).reshape(-1, 1)
rval = -2 * np.dot(x.T, x)
rval += xx
rval += xx.T
return rval
else:
xx = np.sum(x ** 2, axis=0).reshape(-1, 1)
yy = np.sum(y ** 2, axis=0).reshape(1, -1)
rval = -2 * np.dot(x.T, y)
rval += xx
rval += yy
return rval
def nuggetcov(x, y, sigma):
return sigma ** 2 * (sqdist(x, y) == 0.0)
def covexp(x, y, sigma):
return np.exp((-0.5 / sigma ** 2) * sqdist(x, y))
def covlap(x, y, sigma):
return np.exp((-1.0 / sigma) * np.sqrt(sqdist(x, y)))
def assert_close(x,y,z=None,verbose=False,**kw):
if isinstance(x, ArrayNode):
x = x.value
if isinstance(y, ArrayNode):
y = y.value
lvl = np.seterr(all='warn')
s = lambda: '\nx=\n%s\ny=\n%s\nx-y=\n%s\nx/y=%s\nz=\n%s' % (str(x), str(y), str(x-y), str(x/y), str(z))
| np.seterr(**lvl) | numpy.seterr |
import argparse
import json
import torch
import numpy as np
from PIL import Image
from utils import build_model, determine_device
def load_checkpoint(filepath):
checkpoint = torch.load(filepath)
output = len(checkpoint['class_to_idx'])
model = build_model(checkpoint['arch'], checkpoint['hidden_units'], output, checkpoint['dropout'])
model.load_state_dict(checkpoint['state_dict'])
model.class_to_idx = checkpoint['class_to_idx']
return model
def resize_image(image):
width, height = image.size
aspect_ratio = width / height
if width < height:
new_width = 256
new_height = int(new_width / aspect_ratio)
elif height < width:
new_height = 256
new_width = int(width * aspect_ratio)
else: # when both sides are equal
new_width = 256
new_height = 256
return image.resize((new_width, new_height))
def crop_image(image):
''' Crop the center of the image
'''
width, height = image.size
new_width, new_height = (224, 224)
left = (width - new_width)/2
top = (height - new_height)/2
right = (width + new_width)/2
bottom = (height + new_height)/2
return image.crop((left, top, right, bottom))
def process_image(image):
''' Scales, crops, and normalizes a PIL image for a PyTorch model,
returns an Numpy array
'''
# TODO: Process a PIL image for use in a PyTorch model
image = resize_image(image)
image = crop_image(image)
np_image = | np.array(image) | 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([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCI/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'dofst_k_oxygen_l2'
var_list[2].name = 'dofst_k_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/kg'
var_list[2].units = 'Hz'
var_list[3].units = 'dbar'
elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMCI/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'dofst_k_oxygen_l2'
var_list[2].name = 'dofst_k_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/kg'
var_list[2].units = 'Hz'
var_list[3].units = 'dbar'
elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCI/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'vel3d_k_eastward_velocity'
var_list[2].name = 'vel3d_k_northward_velocity'
var_list[3].name = 'vel3d_k_upward_velocity'
var_list[4].name = 'vel3d_k_heading'
var_list[5].name = 'vel3d_k_pitch'
var_list[6].name = 'vel3d_k_roll'
var_list[7].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm/s'
var_list[2].units = 'm/s'
var_list[3].units = 'm/s'
var_list[4].units = 'ddegrees'
var_list[5].units = 'ddegrees'
var_list[6].units = 'ddegrees'
var_list[7].units = 'dbar'
elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMCI/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'vel3d_k_eastward_velocity'
var_list[2].name = 'vel3d_k_northward_velocity'
var_list[3].name = 'vel3d_k_upward_velocity'
var_list[4].name = 'vel3d_k_heading'
var_list[5].name = 'vel3d_k_pitch'
var_list[6].name = 'vel3d_k_roll'
var_list[7].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm/s'
var_list[2].units = 'm/s'
var_list[3].units = 'm/s'
var_list[4].units = 'ddegrees'
var_list[5].units = 'ddegrees'
var_list[6].units = 'ddegrees'
var_list[7].units = 'dbar'
elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCI/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'ctdpf_ckl_seawater_temperature'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'density'
var_list[4].name = 'ctdpf_ckl_seawater_pressure'
var_list[5].name = 'ctdpf_ckl_seawater_conductivity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMCI/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'ctdpf_ckl_seawater_temperature'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'density'
var_list[4].name = 'ctdpf_ckl_seawater_pressure'
var_list[5].name = 'ctdpf_ckl_seawater_conductivity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCI/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_k_par'
var_list[2].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
elif platform_name == 'CP02PMCI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMCI/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_k_par'
var_list[2].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
elif platform_name == 'CP02PMCO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCO/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel'
var_list[0].name = 'time'
var_list[0].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
elif platform_name == 'CP02PMCO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost':
uframe_dataset_name = 'CP02PMCO/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered'
var_list[0].name = 'time'
var_list[0].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCO/WFP01/04-FLORTK000/telemetered/flort_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'fluorometric_chlorophyll_a'
var_list[3].name = 'fluorometric_cdom'
var_list[4].name = 'total_volume_scattering_coefficient'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMCO/WFP01/04-FLORTK000/recovered_wfp/flort_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'fluorometric_chlorophyll_a'
var_list[3].name = 'fluorometric_cdom'
var_list[4].name = 'total_volume_scattering_coefficient'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCO/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'dofst_k_oxygen_l2'
var_list[2].name = 'dofst_k_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/kg'
var_list[2].units = 'Hz'
var_list[3].units = 'dbar'
elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMCO/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'dofst_k_oxygen_l2'
var_list[2].name = 'dofst_k_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/kg'
var_list[2].units = 'Hz'
var_list[3].units = 'dbar'
elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCO/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'vel3d_k_eastward_velocity'
var_list[2].name = 'vel3d_k_northward_velocity'
var_list[3].name = 'vel3d_k_upward_velocity'
var_list[4].name = 'vel3d_k_heading'
var_list[5].name = 'vel3d_k_pitch'
var_list[6].name = 'vel3d_k_roll'
var_list[7].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm/s'
var_list[2].units = 'm/s'
var_list[3].units = 'm/s'
var_list[4].units = 'ddegrees'
var_list[5].units = 'ddegrees'
var_list[6].units = 'ddegrees'
var_list[7].units = 'dbar'
elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMCO/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'vel3d_k_eastward_velocity'
var_list[2].name = 'vel3d_k_northward_velocity'
var_list[3].name = 'vel3d_k_upward_velocity'
var_list[4].name = 'vel3d_k_heading'
var_list[5].name = 'vel3d_k_pitch'
var_list[6].name = 'vel3d_k_roll'
var_list[7].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm/s'
var_list[2].units = 'm/s'
var_list[3].units = 'm/s'
var_list[4].units = 'ddegrees'
var_list[5].units = 'ddegrees'
var_list[6].units = 'ddegrees'
var_list[7].units = 'dbar'
elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCO/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'ctdpf_ckl_seawater_temperature'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'density'
var_list[4].name = 'ctdpf_ckl_seawater_pressure'
var_list[5].name = 'ctdpf_ckl_seawater_conductivity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMCO/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'ctdpf_ckl_seawater_temperature'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'density'
var_list[4].name = 'ctdpf_ckl_seawater_pressure'
var_list[5].name = 'ctdpf_ckl_seawater_conductivity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCO/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_k_par'
var_list[2].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
elif platform_name == 'CP02PMCO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMCO/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_k_par'
var_list[2].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
elif platform_name == 'CP02PMUI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUI/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel'
var_list[0].name = 'time'
var_list[0].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
elif platform_name == 'CP02PMUI' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost':
uframe_dataset_name = 'CP02PMUI/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered'
var_list[0].name = 'time'
var_list[0].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUI/WFP01/04-FLORTK000/telemetered/flort_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'fluorometric_chlorophyll_a'
var_list[3].name = 'fluorometric_cdom'
var_list[4].name = 'total_volume_scattering_coefficient'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMUI/WFP01/04-FLORTK000/recovered_wfp/flort_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'fluorometric_chlorophyll_a'
var_list[3].name = 'fluorometric_cdom'
var_list[4].name = 'total_volume_scattering_coefficient'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUI/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'dofst_k_oxygen_l2'
var_list[2].name = 'dofst_k_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/kg'
var_list[2].units = 'Hz'
var_list[3].units = 'dbar'
elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMUI/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'dofst_k_oxygen_l2'
var_list[2].name = 'dofst_k_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/kg'
var_list[2].units = 'Hz'
var_list[3].units = 'dbar'
elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUI/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'vel3d_k_eastward_velocity'
var_list[2].name = 'vel3d_k_northward_velocity'
var_list[3].name = 'vel3d_k_upward_velocity'
var_list[4].name = 'vel3d_k_heading'
var_list[5].name = 'vel3d_k_pitch'
var_list[6].name = 'vel3d_k_roll'
var_list[7].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm/s'
var_list[2].units = 'm/s'
var_list[3].units = 'm/s'
var_list[4].units = 'ddegrees'
var_list[5].units = 'ddegrees'
var_list[6].units = 'ddegrees'
var_list[7].units = 'dbar'
elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMUI/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'vel3d_k_eastward_velocity'
var_list[2].name = 'vel3d_k_northward_velocity'
var_list[3].name = 'vel3d_k_upward_velocity'
var_list[4].name = 'vel3d_k_heading'
var_list[5].name = 'vel3d_k_pitch'
var_list[6].name = 'vel3d_k_roll'
var_list[7].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm/s'
var_list[2].units = 'm/s'
var_list[3].units = 'm/s'
var_list[4].units = 'ddegrees'
var_list[5].units = 'ddegrees'
var_list[6].units = 'ddegrees'
var_list[7].units = 'dbar'
elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUI/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'ctdpf_ckl_seawater_temperature'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'density'
var_list[4].name = 'ctdpf_ckl_seawater_pressure'
var_list[5].name = 'ctdpf_ckl_seawater_conductivity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMUI/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'ctdpf_ckl_seawater_temperature'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'density'
var_list[4].name = 'ctdpf_ckl_seawater_pressure'
var_list[5].name = 'ctdpf_ckl_seawater_conductivity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUI/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_k_par'
var_list[2].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
elif platform_name == 'CP02PMUI' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMUI/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_k_par'
var_list[2].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
elif platform_name == 'CP02PMUO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUO/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel'
var_list[0].name = 'time'
var_list[0].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
elif platform_name == 'CP02PMUO' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost':
uframe_dataset_name = 'CP02PMUO/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered'
var_list[0].name = 'time'
var_list[0].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUO/WFP01/04-FLORTK000/telemetered/flort_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'fluorometric_chlorophyll_a'
var_list[3].name = 'fluorometric_cdom'
var_list[4].name = 'total_volume_scattering_coefficient'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMUO/WFP01/04-FLORTK000/recovered_wfp/flort_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'fluorometric_chlorophyll_a'
var_list[3].name = 'fluorometric_cdom'
var_list[4].name = 'total_volume_scattering_coefficient'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUO/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'dofst_k_oxygen_l2'
var_list[2].name = 'dofst_k_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/kg'
var_list[2].units = 'Hz'
var_list[3].units = 'dbar'
elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMUO/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'dofst_k_oxygen_l2'
var_list[2].name = 'dofst_k_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/kg'
var_list[2].units = 'Hz'
var_list[3].units = 'dbar'
elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUO/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'vel3d_k_eastward_velocity'
var_list[2].name = 'vel3d_k_northward_velocity'
var_list[3].name = 'vel3d_k_upward_velocity'
var_list[4].name = 'vel3d_k_heading'
var_list[5].name = 'vel3d_k_pitch'
var_list[6].name = 'vel3d_k_roll'
var_list[7].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm/s'
var_list[2].units = 'm/s'
var_list[3].units = 'm/s'
var_list[4].units = 'ddegrees'
var_list[5].units = 'ddegrees'
var_list[6].units = 'ddegrees'
var_list[7].units = 'dbar'
elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMUO/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'vel3d_k_eastward_velocity'
var_list[2].name = 'vel3d_k_northward_velocity'
var_list[3].name = 'vel3d_k_upward_velocity'
var_list[4].name = 'vel3d_k_heading'
var_list[5].name = 'vel3d_k_pitch'
var_list[6].name = 'vel3d_k_roll'
var_list[7].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm/s'
var_list[2].units = 'm/s'
var_list[3].units = 'm/s'
var_list[4].units = 'ddegrees'
var_list[5].units = 'ddegrees'
var_list[6].units = 'ddegrees'
var_list[7].units = 'dbar'
elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUO/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'ctdpf_ckl_seawater_temperature'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'density'
var_list[4].name = 'ctdpf_ckl_seawater_pressure'
var_list[5].name = 'ctdpf_ckl_seawater_conductivity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMUO/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'ctdpf_ckl_seawater_temperature'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'density'
var_list[4].name = 'ctdpf_ckl_seawater_pressure'
var_list[5].name = 'ctdpf_ckl_seawater_conductivity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUO/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_k_par'
var_list[2].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
elif platform_name == 'CP02PMUO' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP02PMUO/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_k_par'
var_list[2].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
elif platform_name == 'CP03ISPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'Telemetered':
uframe_dataset_name = 'CP03ISPM/SBS01/01-MOPAK0000/telemetered/mopak_o_dcl_accel'
var_list[0].name = 'time'
var_list[0].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
elif platform_name == 'CP03ISPM' and node == 'BUOY' and instrument_class == 'MOPAK' and method == 'RecoveredHost':
uframe_dataset_name = 'CP03ISPM/SBS01/01-MOPAK0000/recovered_host/mopak_o_dcl_accel_recovered'
var_list[0].name = 'time'
var_list[0].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP03ISPM/WFP01/04-FLORTK000/telemetered/flort_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'fluorometric_chlorophyll_a'
var_list[3].name = 'fluorometric_cdom'
var_list[4].name = 'total_volume_scattering_coefficient'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'FLORT' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP03ISPM/WFP01/04-FLORTK000/recovered_wfp/flort_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'fluorometric_chlorophyll_a'
var_list[3].name = 'fluorometric_cdom'
var_list[4].name = 'total_volume_scattering_coefficient'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP03ISPM/WFP01/02-DOFSTK000/telemetered/dofst_k_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'dofst_k_oxygen_l2'
var_list[2].name = 'dofst_k_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/kg'
var_list[2].units = 'Hz'
var_list[3].units = 'dbar'
elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'DOSTA' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP03ISPM/WFP01/02-DOFSTK000/recovered_wfp/dofst_k_wfp_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'dofst_k_oxygen_l2'
var_list[2].name = 'dofst_k_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/kg'
var_list[2].units = 'Hz'
var_list[3].units = 'dbar'
elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'Telemetered':
uframe_dataset_name = 'CP03ISPM/WFP01/01-VEL3DK000/telemetered/vel3d_k_wfp_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'vel3d_k_eastward_velocity'
var_list[2].name = 'vel3d_k_northward_velocity'
var_list[3].name = 'vel3d_k_upward_velocity'
var_list[4].name = 'vel3d_k_heading'
var_list[5].name = 'vel3d_k_pitch'
var_list[6].name = 'vel3d_k_roll'
var_list[7].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm/s'
var_list[2].units = 'm/s'
var_list[3].units = 'm/s'
var_list[4].units = 'ddegrees'
var_list[5].units = 'ddegrees'
var_list[6].units = 'ddegrees'
var_list[7].units = 'dbar'
elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'VEL3D' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP03ISPM/WFP01/01-VEL3DK000/recovered_wfp/vel3d_k_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'vel3d_k_eastward_velocity'
var_list[2].name = 'vel3d_k_northward_velocity'
var_list[3].name = 'vel3d_k_upward_velocity'
var_list[4].name = 'vel3d_k_heading'
var_list[5].name = 'vel3d_k_pitch'
var_list[6].name = 'vel3d_k_roll'
var_list[7].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm/s'
var_list[2].units = 'm/s'
var_list[3].units = 'm/s'
var_list[4].units = 'ddegrees'
var_list[5].units = 'ddegrees'
var_list[6].units = 'ddegrees'
var_list[7].units = 'dbar'
elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP03ISPM/WFP01/03-CTDPFK000/telemetered/ctdpf_ckl_wfp_instrument'
var_list[0].name = 'time'
var_list[1].name = 'ctdpf_ckl_seawater_temperature'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'density'
var_list[4].name = 'ctdpf_ckl_seawater_pressure'
var_list[5].name = 'ctdpf_ckl_seawater_conductivity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'CTD' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP03ISPM/WFP01/03-CTDPFK000/recovered_wfp/ctdpf_ckl_wfp_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'ctdpf_ckl_seawater_temperature'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'density'
var_list[4].name = 'ctdpf_ckl_seawater_pressure'
var_list[5].name = 'ctdpf_ckl_seawater_conductivity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP03ISPM/WFP01/05-PARADK000/telemetered/parad_k__stc_imodem_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_k_par'
var_list[2].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
elif platform_name == 'CP03ISPM' and node == 'PROFILER' and instrument_class == 'PARAD' and method == 'RecoveredWFP':
uframe_dataset_name = 'CP03ISPM/WFP01/05-PARADK000/recovered_wfp/parad_k__stc_imodem_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_k_par'
var_list[2].name = 'int_ctd_pressure'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
elif platform_name == 'CP04OSPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst':
uframe_dataset_name = 'CP04OSPM/RII01/02-ADCPSL010/recovered_inst/adcp_velocity_earth'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP04OSPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP04OSPM/RII01/02-ADCPSL010/recovered_host/adcps_jln_stc_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP04OSPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered':
uframe_dataset_name = 'CP04OSPM/RII01/02-ADCPSL010/telemetered/adcps_jln_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP01CNPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst':
uframe_dataset_name = 'CP01CNPM/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP01CNPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP01CNPM/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP01CNPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered':
uframe_dataset_name = 'CP01CNPM/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMCI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst':
uframe_dataset_name = 'CP02PMCI/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMCI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP02PMCI/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMCI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCI/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMCO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst':
uframe_dataset_name = 'CP02PMCO/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMCO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP02PMCO/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMCO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMCO/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMUI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst':
uframe_dataset_name = 'CP02PMUI/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMUI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP02PMUI/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMUI' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUI/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMUO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst':
uframe_dataset_name = 'CP02PMUO/RII01/02-ADCPSL010/recovered_inst/adcp_velocity_earth'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMUO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP02PMUO/RII01/02-ADCPSL010/recovered_host/adcps_jln_stc_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP02PMUO' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered':
uframe_dataset_name = 'CP02PMUO/RII01/02-ADCPSL010/telemetered/adcps_jln_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP03ISPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredInst':
uframe_dataset_name = 'CP03ISPM/RII01/02-ADCPTG010/recovered_inst/adcp_velocity_earth'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP03ISPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP03ISPM/RII01/02-ADCPTG010/recovered_host/adcps_jln_stc_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CP03ISPM' and node == 'RISER' and instrument_class == 'ADCP' and method == 'Telemetered':
uframe_dataset_name = 'CP03ISPM/RII01/02-ADCPTG010/telemetered/adcps_jln_stc_instrument'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'adcps_jln_heading'
var_list[3].name = 'adcps_jln_pitch'
var_list[4].name = 'adcps_jln_roll'
var_list[5].name = 'adcps_jln_eastward_seawater_velocity2'
var_list[6].name = 'adcps_jln_northward_seawater_velocity2'
var_list[7].name = 'adcps_jln_upward_seawater_velocity2'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'cdegree'
var_list[3].units = 'cdegree'
var_list[4].units = 'cdegree'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL336/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL336/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL336/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL336/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL336/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL336/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL336/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL336/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL336' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL336/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL388/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL388/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL388/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL388/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL388/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL388/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL388/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL388/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL388' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL388/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL335/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL335/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL335/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL335/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL335/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL335/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL335/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL335/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL335' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL335/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL339/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL339/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL339/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL339/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL339/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL339/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL339/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL339/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL339' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL339/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL340/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL340/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL340/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL340/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL340/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL340/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL340/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL340/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL340' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL340/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL374/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL374/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL374/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL374/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL374/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL374/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL374/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL374/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL374' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL374/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL375/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL375/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL375/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL375/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL375/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL375/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL375/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL375/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL375' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL375/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL376/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL376/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL376/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL376/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL376/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL376/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL376/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL376/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL376' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL376/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL379/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL379/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL379/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL379/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL379/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL379/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL379/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL379/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL379' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL379/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL380/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL380/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL380/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL380/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL380/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL380/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL380/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL380/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL380' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL380/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL387/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL387/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL387/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL387/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL387/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL387/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL387/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL387/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL387' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL387/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL389/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL389/03-CTDGVM000/recovered_host/ctdgv_m_glider_instrument_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'degC'
var_list[2].units = 'unitless'
var_list[3].units = 'kg/m3'
var_list[4].units = 'dbar'
var_list[5].units = 'S/m'
var_list[6].units = 'degree_north'
var_list[7].units = 'degree_east'
elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL389/04-DOSTAM000/telemetered/dosta_abcdjm_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'DOSTA' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL389/04-DOSTAM000/recovered_host/dosta_abcdjm_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'sci_oxy4_oxygen'
var_list[2].name = 'sci_abs_oxygen'
var_list[3].name = 'int_ctd_pressure'
var_list[4].name = 'lat'
var_list[5].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol/L'
var_list[2].units = 'umol/kg'
var_list[3].units = 'dbar'
var_list[4].units = 'degree_north'
var_list[5].units = 'degree_east'
elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL389/02-FLORTM000/telemetered/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'FLORT' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL389/02-FLORTM000/recovered_host/flort_m_sample'
var_list[0].name = 'time'
var_list[1].name = 'seawater_scattering_coefficient'
var_list[2].name = 'sci_flbbcd_chlor_units'
var_list[3].name = 'sci_flbbcd_cdom_units'
var_list[4].name = 'sci_flbbcd_bb_units'
var_list[5].name = 'optical_backscatter'
var_list[6].name = 'int_ctd_pressure'
var_list[7].name = 'lat'
var_list[8].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'm-1'
var_list[2].units = 'ug/L'
var_list[3].units = 'ppb'
var_list[4].units = 'm-1 sr-1'
var_list[5].units = 'm-1'
var_list[6].units = 'dbar'
var_list[7].units = 'degree_north'
var_list[8].units = 'degree_east'
elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL389/05-PARADM000/telemetered/parad_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'PARAD' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL389/05-PARADM000/recovered_host/parad_m_glider_recovered'
var_list[0].name = 'time'
var_list[1].name = 'parad_m_par'
var_list[2].name = 'int_ctd_pressure'
var_list[3].name = 'lat'
var_list[4].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'umol photons m-2 s-1'
var_list[2].units = 'dbar'
var_list[3].units = 'degree_north'
var_list[4].units = 'degree_east'
elif platform_name == 'CPGL389' and node == 'GLIDER' and instrument_class == 'ADCP' and method == 'RecoveredHost':
uframe_dataset_name = 'CP05MOAS/GL389/01-ADCPAM000/recovered_host/adcp_velocity_glider'
var_list[0].name = 'time'
var_list[1].name = 'bin_depths'
var_list[2].name = 'heading'
var_list[3].name = 'pitch'
var_list[4].name = 'roll'
var_list[5].name = 'eastward_seawater_velocity'
var_list[6].name = 'northward_seawater_velocity'
var_list[7].name = 'upward_seawater_velocity'
var_list[8].name = 'int_ctd_pressure'
var_list[9].name = 'lat'
var_list[10].name = 'lon'
var_list[0].data = np.array([])
var_list[1].data = np.array([])
var_list[2].data = np.array([])
var_list[3].data = np.array([])
var_list[4].data = np.array([])
var_list[5].data = np.array([])
var_list[6].data = np.array([])
var_list[7].data = np.array([])
var_list[8].data = np.array([])
var_list[9].data = np.array([])
var_list[10].data = np.array([])
var_list[0].units = 'seconds since 1900-01-01'
var_list[1].units = 'meters'
var_list[2].units = 'deci-degrees'
var_list[3].units = 'deci-degrees'
var_list[4].units = 'deci-degrees'
var_list[5].units = 'm/s'
var_list[6].units = 'm/s'
var_list[7].units = 'm/s'
var_list[8].units = 'dbar'
var_list[9].units = 'degree_north'
var_list[10].units = 'degree_east'
elif platform_name == 'CPGL514' and node == 'GLIDER' and instrument_class == 'CTD' and method == 'Telemetered':
uframe_dataset_name = 'CP05MOAS/GL514/03-CTDGVM000/telemetered/ctdgv_m_glider_instrument'
var_list[0].name = 'time'
var_list[1].name = 'sci_water_temp'
var_list[2].name = 'practical_salinity'
var_list[3].name = 'sci_seawater_density'
var_list[4].name = 'sci_water_pressure_dbar'
var_list[5].name = 'sci_water_cond'
var_list[6].name = 'lat'
var_list[7].name = 'lon'
var_list[0].data = | np.array([]) | numpy.array |
from numpy import log, pi, arange, exp
from scipy.optimize import brentq
import matplotlib.pyplot as plot
from matplotlib import rc
import equation
def diagram_sum(x, d):
return 4.*pi/log(d**2 *2.*x)
def diagram_sum_3body(x, d):
point=equation.equation(3.*x,'2D',20.,0.1,d)
point.solve()
g3=point.g3
del point
return 4.*pi/log(d**2 *2.*x) + g3
drange=arange(0.6,5.,0.05)
xx=[d for d in drange]
ee=[1/d**2 for d in drange]
yy=[brentq(lambda mu:mu - diagram_sum(mu,d),(0.5+0.01)/(d**2),0.5/d**2 *exp(8 * pi * d**2), xtol=1e-3) for d in drange]
drange=arange(0.6,5.6,1.0)
zx=[d for d in drange]
ze=[1/d**2 for d in drange]
zz=[brentq(lambda mu:mu - diagram_sum_3body(mu,d),(1+0.01)/(2.*d**2),0.5/d**2 *exp(8 * pi * d**2), xtol=1e-2) for d in drange]
drange=arange(0.7,1.5,0.1)
wx=[d for d in drange]
we=[1/d**2 for d in drange]
wz=[brentq(lambda mu:mu - diagram_sum_3body(mu,d),(1+0.01)/(2.*d**2),0.5/d**2 *exp(8 * pi * d**2), xtol=1e-2) for d in drange]
drange=arange(0.6,0.7,0.025)
fx=[d for d in drange]
fe=[1/d**2 for d in drange]
fz=[brentq(lambda mu:mu - diagram_sum_3body(mu,d),(1+0.01)/(2.*d**2),0.5/d**2 * | exp(8 * pi * d**2) | numpy.exp |
"""
A pytest module to test the functions relating to primes.
"""
import random
import numpy as np
import pytest
import galois
def test_primes():
assert galois.primes(19) == [2, 3, 5, 7, 11, 13, 17, 19]
assert galois.primes(20) == [2, 3, 5, 7, 11, 13, 17, 19]
with pytest.raises(TypeError):
galois.primes(20.0)
with pytest.raises(ValueError):
galois.primes(1)
def test_kth_prime():
assert galois.kth_prime(1) == 2
assert galois.kth_prime(2) == 3
assert galois.kth_prime(100) == 541
assert galois.kth_prime(1000) == 7919
with pytest.raises(TypeError):
galois.kth_prime(20.0)
with pytest.raises(ValueError):
galois.kth_prime(0)
with pytest.raises(ValueError):
galois.kth_prime(galois.prime.MAX_K + 1)
def test_prev_prime():
assert galois.prev_prime(8) == 7
assert galois.prev_prime(11) == 11
with pytest.raises(TypeError):
galois.prev_prime(20.0)
with pytest.raises(ValueError):
galois.prev_prime(1)
with pytest.raises(ValueError):
galois.prev_prime(galois.prime.MAX_PRIME + 1)
def test_next_prime():
assert galois.next_prime(8) == 11
assert galois.next_prime(11) == 13
with pytest.raises(TypeError):
galois.next_prime(20.0)
with pytest.raises(ValueError):
galois.next_prime(galois.prime.MAX_PRIME)
def test_mersenne_exponents():
# https://oeis.org/A000043
exponents = [2,3,5,7,13,17,19,31,61,89,107,127] # Up to 128 bits
assert galois.mersenne_exponents(128) == exponents
def test_mersenne_primes():
# https://oeis.org/A000668
primes = [3,7,31,127,8191,131071,524287,2147483647,2305843009213693951,618970019642690137449562111,162259276829213363391578010288127,170141183460469231731687303715884105727] # Up to 128 bits
assert galois.mersenne_primes(128) == primes
def test_prime_factorization_small():
x = 8
P = [2,]
K = [3,]
p, k = galois.prime_factors(x)
assert | np.array_equal(p, P) | numpy.array_equal |
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 12 2020
Class to read and manipulate CryoSat-2 waveform data
Reads CryoSat Level-1b data products from baselines A, B and C
Reads CryoSat Level-1b netCDF4 data products from baseline D
Supported CryoSat Modes: LRM, SAR, SARin, FDM, SID, GDR
INPUTS:
full_filename: full path of CryoSat .DBL or .nc file
PYTHON DEPENDENCIES:
numpy: Scientific Computing Tools For Python
http://www.numpy.org
http://www.scipy.org/NumPy_for_Matlab_Users
netCDF4: Python interface to the netCDF C library
https://unidata.github.io/netcdf4-python/netCDF4/index.html
UPDATE HISTORY:
Updated 08/2020: flake8 compatible binary regular expression strings
Forked 02/2020 from read_cryosat_L1b.py
Updated 11/2019: empty placeholder dictionary for baseline D DSD headers
Updated 09/2019: added netCDF4 read function for baseline D
Updated 04/2019: USO correction signed 32 bit int
Updated 10/2018: updated header read functions for python3
Updated 05/2016: using __future__ print and division functions
Written 03/2016
"""
from __future__ import print_function
from __future__ import division
import numpy as np
import pointCollection as pc
import netCDF4
import re
import os
class data(pc.data):
np.seterr(invalid='ignore')
def __default_field_dict__(self):
"""
Define the default fields that get read from the CryoSat-2 file
"""
field_dict = {}
field_dict['Location'] = ['days_J2k','Day','Second','Micsec','USO_Corr',
'Mode_ID','SSC','Inst_config','Rec_Count','Lat','Lon','Alt','Alt_rate',
'Sat_velocity','Real_beam','Baseline','ST_ID','Roll','Pitch','Yaw','MCD']
field_dict['Data'] = ['TD', 'H_0','COR2','LAI','FAI','AGC_CH1','AGC_CH2',
'TR_gain_CH1','TR_gain_CH2','TX_Power','Doppler_range','TR_inst_range',
'R_inst_range','TR_inst_gain','R_inst_gain','Internal_phase',
'External_phase','Noise_power','Phase_slope']
field_dict['Geometry'] = ['dryTrop','wetTrop','InvBar','DAC','Iono_GIM',
'Iono_model','ocTideElv','lpeTideElv','olTideElv','seTideElv','gpTideElv',
'Surf_type','Corr_status','Corr_error']
field_dict['Waveform_20Hz'] = ['Waveform','Linear_Wfm_Multiplier',
'Power2_Wfm_Multiplier','N_avg_echoes']
field_dict['METADATA'] = ['MPH','SPH']
return field_dict
def from_dbl(self, full_filename, field_dict=None, unpack=False, verbose=False):
"""
Read CryoSat Level-1b data from binary formats
"""
# file basename and file extension of input file
fileBasename,fileExtension=os.path.splitext(os.path.basename(full_filename))
# CryoSat file class
# OFFL (Off Line Processing/Systematic)
# NRT_ (Near Real Time)
# RPRO (ReProcessing)
# TEST (Testing)
# TIxx (Stand alone IPF1 testing)
# LTA_ (Long Term Archive)
regex_class = 'OFFL|NRT_|RPRO|TEST|TIxx|LTA_'
# CryoSat mission products
# SIR1SAR_FR: Level 1 FBR SAR Mode (Rx1 Channel)
# SIR2SAR_FR: Level 1 FBR SAR Mode (Rx2 Channel)
# SIR_SIN_FR: Level 1 FBR SARin Mode
# SIR_LRM_1B: Level-1 Product Low Rate Mode
# SIR_FDM_1B: Level-1 Product Fast Delivery Marine Mode
# SIR_SAR_1B: Level-1 SAR Mode
# SIR_SIN_1B: Level-1 SARin Mode
# SIR1LRC11B: Level-1 CAL1 Low Rate Mode (Rx1 Channel)
# SIR2LRC11B: Level-1 CAL1 Low Rate Mode (Rx2 Channel)
# SIR1SAC11B: Level-1 CAL1 SAR Mode (Rx1 Channel)
# SIR2SAC11B: Level-1 CAL1 SAR Mode (Rx2 Channel)
# SIR_SIC11B: Level-1 CAL1 SARin Mode
# SIR_SICC1B: Level-1 CAL1 SARIN Exotic Data
# SIR1SAC21B: Level-1 CAL2 SAR Mode (Rx1 Channel)
# SIR2SAC21B: Level-1 CAL2 SAR Mode (Rx2 Channel)
# SIR1SIC21B: Level-1 CAL2 SARin Mode (Rx1 Channel)
# SIR2SIC21B: Level-1 CAL2 SARin Mode (Rx1 Channel)
# SIR1LRM_0M: LRM and TRK Monitoring Data from Rx 1 Channel
# SIR2LRM_0M: LRM and TRK Monitoring Data from Rx 2 Channel
# SIR1SAR_0M: SAR Monitoring Data from Rx 1 Channel
# SIR2SAR_0M: SAR Monitoring Data from Rx 1 Channel
# SIR_SIN_0M: SARIN Monitoring Data
# SIR_SIC40M: CAL4 Monitoring Data
regex_products = ('SIR1SAR_FR|SIR2SAR_FR|SIR_SIN_FR|SIR_LRM_1B|SIR_FDM_1B|'
'SIR_SAR_1B|SIR_SIN_1B|SIR1LRC11B|SIR2LRC11B|SIR1SAC11B|SIR2SAC11B|'
'SIR_SIC11B|SIR_SICC1B|SIR1SAC21B|SIR2SAC21B|SIR1SIC21B|SIR2SIC21B|'
'SIR1LRM_0M|SIR2LRM_0M|SIR1SAR_0M|SIR2SAR_0M|SIR_SIN_0M|SIR_SIC40M')
# CRYOSAT LEVEL-1b PRODUCTS NAMING RULES
# Mission Identifier
# File Class
# File Product
# Validity Start Date and Time
# Validity Stop Date and Time
# Baseline Identifier
# Version Number
regex_pattern = r'(.*?)_({0})_({1})_(\d+T?\d+)_(\d+T?\d+)_(.*?)(\d+)'
rx = re.compile(regex_pattern.format(regex_class,regex_products),re.VERBOSE)
# extract file information from filename
MI,CLASS,PRODUCT,START,STOP,BASELINE,VERSION=rx.findall(fileBasename).pop()
# CryoSat-2 Mode record sizes
i_size_timestamp = 12
n_SARIN_BC_RW = 1024
n_SARIN_RW = 512
n_SAR_BC_RW = 256
n_SAR_RW = 125
n_LRM_RW = 128
n_blocks = 20
n_BeamBehaviourParams = 50
# check baseline from file to set i_record_size and allocation function
if (BASELINE == 'C'):
# calculate total record sizes of each dataset group
i_size_timegroup = i_size_timestamp + 4 + 2*2 + 6*4 + 3*3*4 + 3*2 + 4*4
i_size_measuregroup = 8 + 4*17 + 8
i_size_external_corr = 4*13 + 12
i_size_1Hz_LRM = i_size_timestamp + 3*4 + 8 + n_LRM_RW*2 + 2*4 + 2*2
i_size_1Hz_SAR = i_size_timestamp + 4*3 + 8 + n_SAR_RW*2 + 4 + 4 + 2 + 2
i_size_1Hz_SARIN = i_size_timestamp + 4*3 + 8 + n_SARIN_RW*2 + 4 + 4 + 2 + 2
i_size_LRM_waveform = n_LRM_RW*2 + 4 + 4 + 2 + 2
i_size_SAR_waveform = n_SAR_BC_RW*2 + 4 + 4 + 2 + 2 + n_BeamBehaviourParams*2
i_size_SARIN_waveform = n_SARIN_BC_RW*2 + 4 + 4 + 2 + 2 + n_SARIN_BC_RW*2 + \
n_SARIN_BC_RW*4 + n_BeamBehaviourParams*2
# Low-Resolution Mode Record Size
i_record_size_LRM_L1b = n_blocks * (i_size_timegroup + \
i_size_measuregroup + i_size_LRM_waveform) + i_size_external_corr + \
i_size_1Hz_LRM
# SAR Mode Record Size
i_record_size_SAR_L1b = n_blocks * (i_size_timegroup + \
i_size_measuregroup + i_size_SAR_waveform) + i_size_external_corr + \
i_size_1Hz_SAR
# SARIN Mode Record Size
i_record_size_SARIN_L1b = n_blocks * (i_size_timegroup + \
i_size_measuregroup + i_size_SARIN_waveform) + i_size_external_corr + \
i_size_1Hz_SARIN
# set read function for Baseline C
read_cryosat_variables = self.cryosat_baseline_C
else:
# calculate total record sizes of each dataset group
i_size_timegroup = i_size_timestamp + 4 + 2*2+ 6*4 + 3*3*4 + 4
i_size_measuregroup = 8 + 4*17 + 8
i_size_external_corr = 4*13 + 12
i_size_1Hz_LRM = i_size_timestamp + 3*4 + 8 + n_LRM_RW*2 + 2*4 + 2*2
i_size_1Hz_SAR = i_size_timestamp + 4*3 + 8 + n_SAR_RW*2 + 4 + 4 + 2 + 2
i_size_1Hz_SARIN = i_size_timestamp + 4*3 + 8 + n_SARIN_RW*2 + 4 + 4 + 2 + 2
i_size_LRM_waveform = n_LRM_RW*2 + 4 + 4 + 2 + 2
i_size_SAR_waveform = n_SAR_RW*2 + 4 + 4 + 2 + 2 + n_BeamBehaviourParams*2
i_size_SARIN_waveform = n_SARIN_RW*2 + 4 + 4 + 2 + 2 + n_SARIN_RW*2 + \
n_SARIN_RW*4 + n_BeamBehaviourParams*2
# Low-Resolution Mode Record Size
i_record_size_LRM_L1b = n_blocks * (i_size_timegroup + \
i_size_measuregroup + i_size_LRM_waveform) + i_size_external_corr + \
i_size_1Hz_LRM
# SAR Mode Record Size
i_record_size_SAR_L1b = n_blocks * (i_size_timegroup + \
i_size_measuregroup + i_size_SAR_waveform) + i_size_external_corr + \
i_size_1Hz_SAR
# SARIN Mode Record Size
i_record_size_SARIN_L1b = n_blocks * (i_size_timegroup + \
i_size_measuregroup + i_size_SARIN_waveform) + i_size_external_corr + \
i_size_1Hz_SARIN
# set read function for Baselines A and B
read_cryosat_variables = self.cryosat_baseline_AB
# get dataset MODE from PRODUCT portion of file name
# set record sizes and DS_TYPE for read_DSD function
self.MODE = re.findall('(LRM|SAR|SIN)', PRODUCT).pop()
if (self.MODE == 'LRM'):
i_record_size = i_record_size_LRM_L1b
DS_TYPE = 'CS_L1B'
elif (self.MODE == 'SAR'):
i_record_size = i_record_size_SAR_L1b
DS_TYPE = 'CS_L1B'
elif (self.MODE == 'SIN'):
i_record_size = i_record_size_SARIN_L1b
DS_TYPE = 'CS_L1B'
# read the input file to get file information
fid = os.open(os.path.expanduser(full_filename),os.O_RDONLY)
file_info = os.fstat(fid)
os.close(fid)
# num DSRs from SPH
j_num_DSR = np.int32(file_info.st_size//i_record_size)
# print file information
if verbose:
print(full_filename)
print('{0:d} {1:d} {2:d}'.format(j_num_DSR,file_info.st_size,i_record_size))
# Check if MPH/SPH/DSD headers
if (j_num_DSR*i_record_size == file_info.st_size):
print('No Header on file')
print('The number of DSRs is: {0:d}'.format(j_num_DSR))
else:
print('Header on file')
# Check if MPH/SPH/DSD headers
if (j_num_DSR*i_record_size != file_info.st_size):
# If there are MPH/SPH/DSD headers
s_MPH_fields = self.read_MPH(full_filename)
j_sph_size = np.int32(re.findall(r'[-+]?\d+',s_MPH_fields['SPH_SIZE']).pop())
s_SPH_fields = self.read_SPH(full_filename, j_sph_size)
# extract information from DSD fields
s_DSD_fields = self.read_DSD(full_filename, DS_TYPE=DS_TYPE)
# extract DS_OFFSET
j_DS_start = np.int32(re.findall(r'[-+]?\d+',s_DSD_fields['DS_OFFSET']).pop())
# extract number of DSR in the file
j_num_DSR = np.int32(re.findall(r'[-+]?\d+',s_DSD_fields['NUM_DSR']).pop())
# check the record size
j_DSR_size = np.int32(re.findall(r'[-+]?\d+',s_DSD_fields['DSR_SIZE']).pop())
# minimum size is start of the read plus number of records to read
j_check_size = j_DS_start + (j_DSR_size*j_num_DSR)
if verbose:
print('The offset of the DSD is: {0:d} bytes'.format(j_DS_start))
print('The number of DSRs is {0:d}'.format(j_num_DSR))
print('The size of the DSR is {0:d}'.format(j_DSR_size))
# check if invalid file size
if (j_check_size > file_info.st_size):
raise IOError('File size error')
# extract binary data from input CryoSat data file (skip headers)
fid = open(os.path.expanduser(full_filename), 'rb')
cryosat_header = fid.read(j_DS_start)
# iterate through CryoSat file and fill output variables
CS_L1b_mds = read_cryosat_variables(fid, j_num_DSR)
# add headers to output dictionary as METADATA
CS_L1b_mds['METADATA'] = {}
CS_L1b_mds['METADATA']['MPH'] = s_MPH_fields
CS_L1b_mds['METADATA']['SPH'] = s_SPH_fields
CS_L1b_mds['METADATA']['DSD'] = s_DSD_fields
# close the input CryoSat binary file
fid.close()
else:
# If there are not MPH/SPH/DSD headers
# extract binary data from input CryoSat data file
fid = open(os.path.expanduser(full_filename), 'rb')
# iterate through CryoSat file and fill output variables
CS_L1b_mds = read_cryosat_variables(fid, j_num_DSR)
# close the input CryoSat binary file
fid.close()
# if unpacking the units
if unpack:
CS_l1b_scale = self.cryosat_scaling_factors()
# for each dictionary key
for group in CS_l1b_scale.keys():
# for each variable
for key,val in CS_L1b_mds[group].items():
# check if val is the 20Hz waveform beam variables
if isinstance(val, dict):
# for each waveform beam variable
for k,v in val.items():
# scale variable
CS_L1b_mds[group][key][k] = CS_l1b_scale[group][key][k]*v.copy()
else:
# scale variable
CS_L1b_mds[group][key] = CS_l1b_scale[group][key]*val.copy()
# calculate GPS time of CryoSat data (seconds since Jan 6, 1980 00:00:00)
# from TAI time since Jan 1, 2000 00:00:00
GPS_Time = self.calc_GPS_time(CS_L1b_mds['Location']['Day'],
CS_L1b_mds['Location']['Second'], CS_L1b_mds['Location']['Micsec'])
# leap seconds for converting from GPS time to UTC time
leap_seconds = self.count_leap_seconds(GPS_Time)
# calculate dates as J2000 days (UTC)
CS_L1b_mds['Location']['days_J2k'] = (GPS_Time - leap_seconds)/86400.0 - 7300.0
# parameters to extract
if field_dict is None:
field_dict = self.__default_field_dict__()
# extract fields of interest using field dict keys
for group,variables in field_dict.items():
for field in variables:
if field not in self.fields:
self.fields.append(field)
setattr(self, field, CS_L1b_mds[group][field])
# update size and shape of input data
self.__update_size_and_shape__()
# return the data and header text
return self
def from_nc(self, full_filename, field_dict=None, unpack=False, verbose=False):
"""
Read CryoSat Level-1b data from netCDF4 format data
"""
# file basename and file extension of input file
fileBasename,fileExtension=os.path.splitext(os.path.basename(full_filename))
# CryoSat file class
# OFFL (Off Line Processing/Systematic)
# NRT_ (Near Real Time)
# RPRO (ReProcessing)
# TEST (Testing)
# TIxx (Stand alone IPF1 testing)
# LTA_ (Long Term Archive)
regex_class = 'OFFL|NRT_|RPRO|TEST|TIxx|LTA_'
# CryoSat mission products
# SIR1SAR_FR: Level 1 FBR SAR Mode (Rx1 Channel)
# SIR2SAR_FR: Level 1 FBR SAR Mode (Rx2 Channel)
# SIR_SIN_FR: Level 1 FBR SARin Mode
# SIR_LRM_1B: Level-1 Product Low Rate Mode
# SIR_FDM_1B: Level-1 Product Fast Delivery Marine Mode
# SIR_SAR_1B: Level-1 SAR Mode
# SIR_SIN_1B: Level-1 SARin Mode
# SIR1LRC11B: Level-1 CAL1 Low Rate Mode (Rx1 Channel)
# SIR2LRC11B: Level-1 CAL1 Low Rate Mode (Rx2 Channel)
# SIR1SAC11B: Level-1 CAL1 SAR Mode (Rx1 Channel)
# SIR2SAC11B: Level-1 CAL1 SAR Mode (Rx2 Channel)
# SIR_SIC11B: Level-1 CAL1 SARin Mode
# SIR_SICC1B: Level-1 CAL1 SARIN Exotic Data
# SIR1SAC21B: Level-1 CAL2 SAR Mode (Rx1 Channel)
# SIR2SAC21B: Level-1 CAL2 SAR Mode (Rx2 Channel)
# SIR1SIC21B: Level-1 CAL2 SARin Mode (Rx1 Channel)
# SIR2SIC21B: Level-1 CAL2 SARin Mode (Rx1 Channel)
# SIR1LRM_0M: LRM and TRK Monitoring Data from Rx 1 Channel
# SIR2LRM_0M: LRM and TRK Monitoring Data from Rx 2 Channel
# SIR1SAR_0M: SAR Monitoring Data from Rx 1 Channel
# SIR2SAR_0M: SAR Monitoring Data from Rx 1 Channel
# SIR_SIN_0M: SARIN Monitoring Data
# SIR_SIC40M: CAL4 Monitoring Data
regex_products = ('SIR1SAR_FR|SIR2SAR_FR|SIR_SIN_FR|SIR_LRM_1B|SIR_FDM_1B|'
'SIR_SAR_1B|SIR_SIN_1B|SIR1LRC11B|SIR2LRC11B|SIR1SAC11B|SIR2SAC11B|'
'SIR_SIC11B|SIR_SICC1B|SIR1SAC21B|SIR2SAC21B|SIR1SIC21B|SIR2SIC21B|'
'SIR1LRM_0M|SIR2LRM_0M|SIR1SAR_0M|SIR2SAR_0M|SIR_SIN_0M|SIR_SIC40M')
# CRYOSAT LEVEL-1b PRODUCTS NAMING RULES
# Mission Identifier
# File Class
# File Product
# Validity Start Date and Time
# Validity Stop Date and Time
# Baseline Identifier
# Version Number
regex_pattern = r'(.*?)_({0})_({1})_(\d+T?\d+)_(\d+T?\d+)_(.*?)(\d+)'
rx = re.compile(regex_pattern.format(regex_class,regex_products),re.VERBOSE)
# extract file information from filename
MI,CLASS,PRODUCT,START,STOP,BASELINE,VERSION=rx.findall(fileBasename).pop()
print(full_filename) if verbose else None
# get dataset MODE from PRODUCT portion of file name
self.MODE = re.findall(r'(LRM|FDM|SAR|SIN)', PRODUCT).pop()
# read level-2 CryoSat-2 data from netCDF4 file
CS_L1b_mds = self.cryosat_baseline_D(full_filename, unpack=unpack)
# calculate GPS time of CryoSat data (seconds since Jan 6, 1980 00:00:00)
# from TAI time since Jan 1, 2000 00:00:00
GPS_Time = self.calc_GPS_time(CS_L1b_mds['Location']['Day'],
CS_L1b_mds['Location']['Second'], CS_L1b_mds['Location']['Micsec'])
# leap seconds for converting from GPS time to UTC time
leap_seconds = self.count_leap_seconds(GPS_Time)
# calculate dates as J2000 days (UTC)
CS_L1b_mds['Location']['days_J2k'] = (GPS_Time - leap_seconds)/86400.0 - 7300.0
# parameters to extract
if field_dict is None:
field_dict = self.__default_field_dict__()
# extract fields of interest using field dict keys
for group,variables in field_dict.items():
for field in variables:
if field not in self.fields:
self.fields.append(field)
setattr(self, field, CS_L1b_mds[group][field])
# update size and shape of input data
self.__update_size_and_shape__()
# return the data and header text
return self
def calc_GPS_time(self, day, second, micsec):
"""
Calculate the GPS time (seconds since Jan 6, 1980 00:00:00)
"""
# TAI time is ahead of GPS by 19 seconds
return (day + 7300.0)*86400.0 + second.astype('f') + micsec/1e6 - 19
def count_leap_seconds(self, GPS_Time):
"""
Count number of leap seconds that have passed for given GPS times
"""
# GPS times for leap seconds
leaps = [46828800, 78364801, 109900802, 173059203, 252028804, 315187205,
346723206, 393984007, 425520008, 457056009, 504489610, 551750411,
599184012, 820108813, 914803214, 1025136015, 1119744016, 1167264017]
# number of leap seconds prior to GPS_Time
n_leaps = np.zeros_like(GPS_Time)
for i,leap in enumerate(leaps):
count = np.count_nonzero(GPS_Time >= leap)
if (count > 0):
i_records,i_blocks = np.nonzero(GPS_Time >= leap)
n_leaps[i_records,i_blocks] += 1.0
return n_leaps
def read_MPH(self, full_filename):
"""
Read ASCII Main Product Header (MPH) block from an ESA PDS file
"""
# read input data file
with open(os.path.expanduser(full_filename), 'rb') as fid:
file_contents = fid.read().splitlines()
# Define constant values associated with PDS file formats
# number of text lines in standard MPH
n_MPH_lines = 41
# check that first line of header matches PRODUCT
if not bool(re.match(br'PRODUCT\=\"(.*)(?=\")',file_contents[0])):
raise IOError('File does not start with a valid PDS MPH')
# read MPH header text
s_MPH_fields = {}
for i in range(n_MPH_lines):
# use regular expression operators to read headers
if bool(re.match(br'(.*?)\=\"(.*)(?=\")',file_contents[i])):
# data fields within quotes
field,value=re.findall(br'(.*?)\=\"(.*)(?=\")',file_contents[i]).pop()
s_MPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip()
elif bool(re.match(br'(.*?)\=(.*)',file_contents[i])):
# data fields without quotes
field,value=re.findall(br'(.*?)\=(.*)',file_contents[i]).pop()
s_MPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip()
# Return block name array to calling function
return s_MPH_fields
def read_SPH(self, full_filename, j_sph_size):
"""
Read ASCII Specific Product Header (SPH) block from a PDS file
"""
# read input data file
with open(os.path.expanduser(full_filename), 'rb') as fid:
file_contents = fid.read().splitlines()
# Define constant values associated with PDS file formats
# number of text lines in standard MPH
n_MPH_lines = 41
# compile regular expression operator for reading headers
rx = re.compile(br'(.*?)\=\"?(.*)',re.VERBOSE)
# check first line of header matches SPH_DESCRIPTOR
if not bool(re.match(br'SPH\_DESCRIPTOR\=',file_contents[n_MPH_lines+1])):
raise IOError('File does not have a valid PDS DSD')
# read SPH header text (no binary control characters)
s_SPH_lines = [li for li in file_contents[n_MPH_lines+1:] if rx.match(li)
and not re.search(br'[^\x20-\x7e]+',li)]
# extract SPH header text
s_SPH_fields = {}
c = 0
while (c < len(s_SPH_lines)):
# check if line is within DS_NAME portion of SPH header
if bool(re.match(br'DS_NAME',s_SPH_lines[c])):
# add dictionary for DS_NAME
field,value=re.findall(br'(.*?)\=\"(.*)(?=\")',s_SPH_lines[c]).pop()
key = value.decode('utf-8').rstrip()
s_SPH_fields[key] = {}
for line in s_SPH_lines[c+1:c+7]:
if bool(re.match(br'(.*?)\=\"(.*)(?=\")',line)):
# data fields within quotes
dsfield,dsvalue=re.findall(br'(.*?)\=\"(.*)(?=\")',line).pop()
s_SPH_fields[key][dsfield.decode('utf-8')] = dsvalue.decode('utf-8').rstrip()
elif bool(re.match(br'(.*?)\=(.*)',line)):
# data fields without quotes
dsfield,dsvalue=re.findall(br'(.*?)\=(.*)',line).pop()
s_SPH_fields[key][dsfield.decode('utf-8')] = dsvalue.decode('utf-8').rstrip()
# add 6 to counter to go to next entry
c += 6
# use regular expression operators to read headers
elif bool(re.match(br'(.*?)\=\"(.*)(?=\")',s_SPH_lines[c])):
# data fields within quotes
field,value=re.findall(br'(.*?)\=\"(.*)(?=\")',s_SPH_lines[c]).pop()
s_SPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip()
elif bool(re.match(br'(.*?)\=(.*)',s_SPH_lines[c])):
# data fields without quotes
field,value=re.findall(br'(.*?)\=(.*)',s_SPH_lines[c]).pop()
s_SPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip()
# add 1 to counter to go to next line
c += 1
# Return block name array to calling function
return s_SPH_fields
def read_DSD(self, full_filename, DS_TYPE=None):
"""
Read ASCII Data Set Descriptors (DSD) block from a PDS file
"""
# read input data file
with open(os.path.expanduser(full_filename), 'rb') as fid:
file_contents = fid.read().splitlines()
# Define constant values associated with PDS file formats
# number of text lines in standard MPH
n_MPH_lines = 41
# number of text lines in a DSD header
n_DSD_lines = 8
# Level-1b CryoSat DS_NAMES within files
regex_patterns = []
if (DS_TYPE == 'CS_L1B'):
regex_patterns.append(br'DS_NAME\="SIR_L1B_LRM[\s+]*"')
regex_patterns.append(br'DS_NAME\="SIR_L1B_SAR[\s+]*"')
regex_patterns.append(br'DS_NAME\="SIR_L1B_SARIN[\s+]*"')
elif (DS_TYPE == 'SIR_L1B_FDM'):
regex_patterns.append(br'DS_NAME\="SIR_L1B_FDM[\s+]*"')
# find the DSD starting line within the SPH header
c = 0
Flag = False
while ((Flag is False) and (c < len(regex_patterns))):
# find indice within
indice = [i for i,line in enumerate(file_contents[n_MPH_lines+1:]) if
re.search(regex_patterns[c],line)]
if indice:
Flag = True
else:
c+=1
# check that valid indice was found within header
if not indice:
raise IOError('Can not find correct DSD field')
# extract s_DSD_fields info
DSD_START = n_MPH_lines + indice[0] + 1
s_DSD_fields = {}
for i in range(DSD_START,DSD_START+n_DSD_lines):
# use regular expression operators to read headers
if bool(re.match(br'(.*?)\=\"(.*)(?=\")',file_contents[i])):
# data fields within quotes
field,value=re.findall(br'(.*?)\=\"(.*)(?=\")',file_contents[i]).pop()
s_DSD_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip()
elif bool(re.match(br'(.*?)\=(.*)',file_contents[i])):
# data fields without quotes
field,value=re.findall(br'(.*?)\=(.*)',file_contents[i]).pop()
s_DSD_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip()
# Return block name array to calling function
return s_DSD_fields
def cryosat_baseline_AB(self, fid, n_records):
"""
Read L1b MDS variables for CryoSat Baselines A and B
"""
n_SARIN_RW = 512
n_SAR_RW = 128
n_LRM_RW = 128
n_blocks = 20
n_BeamBehaviourParams = 50
# Bind all the variables of the l1b_mds together into a single dictionary
CS_l1b_mds = {}
# CryoSat-2 Time and Orbit Group
CS_l1b_mds['Location'] = {}
# Time: day part
CS_l1b_mds['Location']['Day'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32,fill_value=0)
# Time: second part
CS_l1b_mds['Location']['Second'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32)
# Time: microsecond part
CS_l1b_mds['Location']['Micsec'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32)
# USO correction factor
CS_l1b_mds['Location']['USO_Corr'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Mode ID
CS_l1b_mds['Location']['Mode_ID'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint16)
# Source sequence counter
CS_l1b_mds['Location']['SSC'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint16)
# Instrument configuration
CS_l1b_mds['Location']['Inst_config'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32)
# Record Counter
CS_l1b_mds['Location']['Rec_Count'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32)
# Lat: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Location']['Lat'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Lon: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Location']['Lon'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Alt: packed units (mm, 1e-3 m)
# Altitude of COG above reference ellipsoid (interpolated value)
CS_l1b_mds['Location']['Alt'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Instantaneous altitude rate derived from orbit: packed units (mm/s, 1e-3 m/s)
CS_l1b_mds['Location']['Alt_rate'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Satellite velocity vector. In ITRF: packed units (mm/s, 1e-3 m/s)
# ITRF= International Terrestrial Reference Frame
CS_l1b_mds['Location']['Sat_velocity'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32)
# Real beam direction vector. In CRF: packed units (micro-m, 1e-6 m)
# CRF= CryoSat Reference Frame.
CS_l1b_mds['Location']['Real_beam'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32)
# Interferometric baseline vector. In CRF: packed units (micro-m, 1e-6 m)
CS_l1b_mds['Location']['Baseline'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32)
# Measurement Confidence Data Flags
# Generally the MCD flags indicate problems when set
# If MCD is 0 then no problems or non-nominal conditions were detected
# Serious errors are indicated by setting bit 31
CS_l1b_mds['Location']['MCD'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32)
# CryoSat-2 Measurement Group
# Derived from instrument measurement parameters
CS_l1b_mds['Data'] = {}
# Window Delay reference (two-way) corrected for instrument delays
CS_l1b_mds['Data']['TD'] = np.ma.zeros((n_records,n_blocks),dtype=np.int64)
# H0 Initial Height Word from telemetry
CS_l1b_mds['Data']['H_0'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# COR2 Height Rate: on-board tracker height rate over the radar cycle
CS_l1b_mds['Data']['COR2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Coarse Range Word (LAI) derived from telemetry
CS_l1b_mds['Data']['LAI'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Fine Range Word (FAI) derived from telemetry
CS_l1b_mds['Data']['FAI'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Automatic Gain Control Channel 1: AGC gain applied on Rx channel 1.
# Gain calibration corrections are applied (Sum of AGC stages 1 and 2
# plus the corresponding corrections) (dB/100)
CS_l1b_mds['Data']['AGC_CH1'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Automatic Gain Control Channel 2: AGC gain applied on Rx channel 2.
# Gain calibration corrections are applied (dB/100)
CS_l1b_mds['Data']['AGC_CH2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Total Fixed Gain On Channel 1: gain applied by the RF unit. (dB/100)
CS_l1b_mds['Data']['TR_gain_CH1'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Total Fixed Gain On Channel 2: gain applied by the RF unit. (dB/100)
CS_l1b_mds['Data']['TR_gain_CH2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Transmit Power in microWatts
CS_l1b_mds['Data']['TX_Power'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Doppler range correction: Radial component (mm)
# computed for the component of satellite velocity in the nadir direction
CS_l1b_mds['Data']['Doppler_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Instrument Range Correction: transmit-receive antenna (mm)
# Calibration correction to range on channel 1 computed from CAL1.
CS_l1b_mds['Data']['TR_inst_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Instrument Range Correction: receive-only antenna (mm)
# Calibration correction to range on channel 2 computed from CAL1.
CS_l1b_mds['Data']['R_inst_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Instrument Gain Correction: transmit-receive antenna (dB/100)
# Calibration correction to gain on channel 1 computed from CAL1
CS_l1b_mds['Data']['TR_inst_gain'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Instrument Gain Correction: receive-only (dB/100)
# Calibration correction to gain on channel 2 computed from CAL1
CS_l1b_mds['Data']['R_inst_gain'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Internal Phase Correction (microradians)
CS_l1b_mds['Data']['Internal_phase'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# External Phase Correction (microradians)
CS_l1b_mds['Data']['External_phase'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Noise Power measurement (dB/100): converted from telemetry units to be
# the noise floor of FBR measurement echoes.
# Set to -9999.99 when the telemetry contains zero.
CS_l1b_mds['Data']['Noise_power'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Phase slope correction (microradians)
# Computed from the CAL-4 packets during the azimuth impulse response
# amplitude (SARIN only). Set from the latest available CAL-4 packet.
CS_l1b_mds['Data']['Phase_slope'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
CS_l1b_mds['Data']['Spares1'] = np.ma.zeros((n_records,n_blocks,4),dtype=np.int8)
# CryoSat-2 External Corrections Group
CS_l1b_mds['Geometry'] = {}
# Dry Tropospheric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['dryTrop'] = np.ma.zeros((n_records),dtype=np.int32)
# Wet Tropospheric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['wetTrop'] = np.ma.zeros((n_records),dtype=np.int32)
# Inverse Barometric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['InvBar'] = np.ma.zeros((n_records),dtype=np.int32)
# Delta Inverse Barometric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['DAC'] = np.ma.zeros((n_records),dtype=np.int32)
# GIM Ionospheric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['Iono_GIM'] = np.ma.zeros((n_records),dtype=np.int32)
# Model Ionospheric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['Iono_model'] = np.ma.zeros((n_records),dtype=np.int32)
# Ocean tide Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['ocTideElv'] = np.ma.zeros((n_records),dtype=np.int32)
# Long period equilibrium ocean tide Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['lpeTideElv'] = np.ma.zeros((n_records),dtype=np.int32)
# Ocean loading tide Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['olTideElv'] = np.ma.zeros((n_records),dtype=np.int32)
# Solid Earth tide Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['seTideElv'] = np.ma.zeros((n_records),dtype=np.int32)
# Geocentric Polar tide Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['gpTideElv'] = np.ma.zeros((n_records),dtype=np.int32)
# Surface Type: enumerated key to classify surface at nadir
# 0 = Open Ocean
# 1 = Closed Sea
# 2 = Continental Ice
# 3 = Land
CS_l1b_mds['Geometry']['Surf_type'] = np.ma.zeros((n_records),dtype=np.uint32)
CS_l1b_mds['Geometry']['Spare1'] = np.ma.zeros((n_records,4),dtype=np.int8)
# Corrections Status Flag
CS_l1b_mds['Geometry']['Corr_status'] = np.ma.zeros((n_records),dtype=np.uint32)
# Correction Error Flag
CS_l1b_mds['Geometry']['Corr_error'] = np.ma.zeros((n_records),dtype=np.uint32)
CS_l1b_mds['Geometry']['Spare2'] = np.ma.zeros((n_records,4),dtype=np.int8)
# CryoSat-2 Average Waveforms Groups
CS_l1b_mds['Waveform_1Hz'] = {}
if (self.MODE == 'LRM'):
# Low-Resolution Mode
# Data Record Time (MDSR Time Stamp)
CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32)
CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32)
CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32)
# Lat: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32)
# Lon: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32)
# Alt: packed units (mm, 1e-3 m)
# Altitude of COG above reference ellipsoid (interpolated value)
CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32)
# Window Delay (two-way) corrected for instrument delays
CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64)
# 1 Hz Averaged Power Echo Waveform
CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_LRM_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Echo Scale Power (a power of 2)
CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16)
CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16)
elif (self.MODE == 'SAR'):
# SAR Mode
# Data Record Time (MDSR Time Stamp)
CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32)
CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32)
CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32)
# Lat: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32)
# Lon: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32)
# Alt: packed units (mm, 1e-3 m)
# Altitude of COG above reference ellipsoid (interpolated value)
CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32)
# Window Delay (two-way) corrected for instrument delays
CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64)
# 1 Hz Averaged Power Echo Waveform
CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_SAR_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Echo Scale Power (a power of 2)
CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16)
CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16)
elif (self.MODE == 'SIN'):
# SARIN Mode
# Same as the LRM/SAR groups but the waveform array is 512 bins instead of
# 128 and the number of echoes averaged is different.
# Data Record Time (MDSR Time Stamp)
CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32)
CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32)
CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32)
# Lat: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32)
# Lon: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32)
# Alt: packed units (mm, 1e-3 m)
# Altitude of COG above reference ellipsoid (interpolated value)
CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32)
# Window Delay (two-way) corrected for instrument delays
CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64)
# 1 Hz Averaged Power Echo Waveform
CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_SARIN_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Echo Scale Power (a power of 2)
CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16)
CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16)
# CryoSat-2 Waveforms Groups
# Beam Behavior Parameters
Beam_Behavior = {}
# Standard Deviation of Gaussian fit to range integrated stack power.
Beam_Behavior['SD'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# Stack Center: Mean of Gaussian fit to range integrated stack power.
Beam_Behavior['Center'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# Stack amplitude parameter scaled in dB/100.
Beam_Behavior['Amplitude'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# 3rd moment: providing the degree of asymmetry of the range integrated
# stack power distribution.
Beam_Behavior['Skewness'] = np.zeros((n_records,n_blocks),dtype=np.int16)
# 4th moment: Measure of peakiness of range integrated stack power distribution.
Beam_Behavior['Kurtosis'] = np.zeros((n_records,n_blocks),dtype=np.int16)
Beam_Behavior['Spare'] = np.zeros((n_records,n_blocks,n_BeamBehaviourParams-5),dtype=np.int16)
# CryoSat-2 mode specific waveforms
CS_l1b_mds['Waveform_20Hz'] = {}
if (self.MODE == 'LRM'):
# Low-Resolution Mode
# Averaged Power Echo Waveform [128]
CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_LRM_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Echo Scale Power (a power of 2 to scale echo to Watts)
CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
elif (self.MODE == 'SAR'):
# SAR Mode
# Averaged Power Echo Waveform [128]
CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_SAR_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Echo Scale Power (a power of 2 to scale echo to Watts)
CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# Beam behaviour parameters
CS_l1b_mds['Waveform_20Hz']['Beam'] = Beam_Behavior
elif (self.MODE == 'SIN'):
# SARIN Mode
# Averaged Power Echo Waveform [512]
CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_SARIN_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Echo Scale Power (a power of 2 to scale echo to Watts)
CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# Beam behaviour parameters
CS_l1b_mds['Waveform_20Hz']['Beam'] = Beam_Behavior
# Coherence [512]: packed units (1/1000)
CS_l1b_mds['Waveform_20Hz']['Coherence'] = np.zeros((n_records,n_blocks,n_SARIN_RW),dtype=np.int16)
# Phase Difference [512]: packed units (microradians)
CS_l1b_mds['Waveform_20Hz']['Phase_diff'] = np.zeros((n_records,n_blocks,n_SARIN_RW),dtype=np.int32)
# for each record in the CryoSat file
for r in range(n_records):
# CryoSat-2 Time and Orbit Group
for b in range(n_blocks):
CS_l1b_mds['Location']['Day'].data[r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Location']['Second'][r,b] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Location']['Micsec'][r,b] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Location']['USO_Corr'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Location']['Mode_ID'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Location']['SSC'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Location']['Inst_config'][r,b] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Location']['Rec_Count'][r,b] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Location']['Lat'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Location']['Lon'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Location']['Alt'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Location']['Alt_rate'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Location']['Sat_velocity'][r,b,:] = np.fromfile(fid,dtype='>i4',count=3)
CS_l1b_mds['Location']['Real_beam'][r,b,:] = np.fromfile(fid,dtype='>i4',count=3)
CS_l1b_mds['Location']['Baseline'][r,b,:] = np.fromfile(fid,dtype='>i4',count=3)
CS_l1b_mds['Location']['MCD'][r,b] = np.fromfile(fid,dtype='>u4',count=1)
# CryoSat-2 Measurement Group
# Derived from instrument measurement parameters
for b in range(n_blocks):
CS_l1b_mds['Data']['TD'][r,b] = np.fromfile(fid,dtype='>i8',count=1)
CS_l1b_mds['Data']['H_0'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['COR2'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['LAI'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['FAI'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['AGC_CH1'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['AGC_CH2'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['TR_gain_CH1'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['TR_gain_CH2'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['TX_Power'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['Doppler_range'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['TR_inst_range'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['R_inst_range'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['TR_inst_gain'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['R_inst_gain'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['Internal_phase'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['External_phase'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['Noise_power'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['Phase_slope'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Data']['Spares1'][r,b,:] = np.fromfile(fid,dtype='>i1',count=4)
# CryoSat-2 External Corrections Group
CS_l1b_mds['Geometry']['dryTrop'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['wetTrop'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['InvBar'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['DAC'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['Iono_GIM'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['Iono_model'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['ocTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['lpeTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['olTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['seTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['gpTideElv'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Geometry']['Surf_type'][r] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Geometry']['Spare1'][r,:] = np.fromfile(fid,dtype='>i1',count=4)
CS_l1b_mds['Geometry']['Corr_status'][r] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Geometry']['Corr_error'][r] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Geometry']['Spare2'][r,:] = np.fromfile(fid,dtype='>i1',count=4)
# CryoSat-2 Average Waveforms Groups
if (self.MODE == 'LRM'):
# Low-Resolution Mode
CS_l1b_mds['Waveform_1Hz']['Day'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Second'][r] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Waveform_1Hz']['Micsec'][r] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Waveform_1Hz']['Lat'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Lon'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Alt'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['TD'][r] = np.fromfile(fid,dtype='>i8',count=1)
CS_l1b_mds['Waveform_1Hz']['Waveform'][r,:] = np.fromfile(fid,dtype='>u2',count=n_LRM_RW)
CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'][r] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_1Hz']['Flags'][r] = np.fromfile(fid,dtype='>u2',count=1)
elif (self.MODE == 'SAR'):
# SAR Mode
CS_l1b_mds['Waveform_1Hz']['Day'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Second'][r] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Waveform_1Hz']['Micsec'][r] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Waveform_1Hz']['Lat'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Lon'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Alt'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['TD'][r] = np.fromfile(fid,dtype='>i8',count=1)
CS_l1b_mds['Waveform_1Hz']['Waveform'][r,:] = np.fromfile(fid,dtype='>u2',count=n_SAR_RW)
CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'][r] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_1Hz']['Flags'][r] = np.fromfile(fid,dtype='>u2',count=1)
elif (self.MODE == 'SIN'):
# SARIN Mode
CS_l1b_mds['Waveform_1Hz']['Day'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Second'][r] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Waveform_1Hz']['Micsec'][r] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Waveform_1Hz']['Lat'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Lon'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Alt'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['TD'][r] = np.fromfile(fid,dtype='>i8',count=1)
CS_l1b_mds['Waveform_1Hz']['Waveform'][r,:] = np.fromfile(fid,dtype='>u2',count=n_SARIN_RW)
CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'][r] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'][r] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_1Hz']['Flags'][r] = np.fromfile(fid,dtype='>u2',count=1)
# CryoSat-2 Waveforms Groups
if (self.MODE == 'LRM'):
# Low-Resolution Mode
for b in range(n_blocks):
CS_l1b_mds['Waveform_20Hz']['Waveform'][r,b,:] = np.fromfile(fid,dtype='>u2',count=n_LRM_RW)
CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_20Hz']['Flags'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
elif (self.MODE == 'SAR'):
# SAR Mode
for b in range(n_blocks):
CS_l1b_mds['Waveform_20Hz']['Waveform'][r,b,:] = np.fromfile(fid,dtype='>u2',count=n_SAR_RW)
CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_20Hz']['Flags'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['SD'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['Center'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['Amplitude'][r,b] = np.fromfile(fid,dtype='>i2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['Skewness'][r,b] = np.fromfile(fid,dtype='>i2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['Kurtosis'][r,b] = np.fromfile(fid,dtype='>i2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['Spare'][r,b,:] = np.fromfile(fid,dtype='>i2',count=(n_BeamBehaviourParams-5))
elif (self.MODE == 'SIN'):
# SARIN Mode
for b in range(n_blocks):
CS_l1b_mds['Waveform_20Hz']['Waveform'][r,b,:] = np.fromfile(fid,dtype='>u2',count=n_SARIN_RW)
CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_20Hz']['Flags'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['SD'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['Center'][r,b] = np.fromfile(fid,dtype='>u2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['Amplitude'][r,b] = np.fromfile(fid,dtype='>i2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['Skewness'][r,b] = np.fromfile(fid,dtype='>i2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['Kurtosis'][r,b] = np.fromfile(fid,dtype='>i2',count=1)
CS_l1b_mds['Waveform_20Hz']['Beam']['Spare'][r,b,:] = np.fromfile(fid,dtype='>i2',count=(n_BeamBehaviourParams-5))
CS_l1b_mds['Waveform_20Hz']['Coherence'][r,b,:] = np.fromfile(fid,dtype='>i2',count=n_SARIN_RW)
CS_l1b_mds['Waveform_20Hz']['Phase_diff'][r,b,:] = np.fromfile(fid,dtype='>i4',count=n_SARIN_RW)
# set the mask from day variables
mask_20Hz = CS_l1b_mds['Location']['Day'].data == CS_l1b_mds['Location']['Day'].fill_value
Location_keys = [key for key in CS_l1b_mds['Location'].keys() if not re.search(r'Spare',key)]
Data_keys = [key for key in CS_l1b_mds['Data'].keys() if not re.search(r'Spare',key)]
Geometry_keys = [key for key in CS_l1b_mds['Geometry'].keys() if not re.search(r'Spare',key)]
Wfm_1Hz_keys = [key for key in CS_l1b_mds['Waveform_1Hz'].keys() if not re.search(r'Spare',key)]
Wfm_20Hz_keys = [key for key in CS_l1b_mds['Waveform_20Hz'].keys() if not re.search(r'Spare',key)]
for key in Location_keys:
CS_l1b_mds['Location'][key].mask = mask_20Hz.copy()
for key in Data_keys:
CS_l1b_mds['Data'][key].mask = mask_20Hz.copy()
# return the output dictionary
return CS_l1b_mds
def cryosat_baseline_C(self, fid, n_records):
"""
Read L1b MDS variables for CryoSat Baseline C
"""
n_SARIN_BC_RW = 1024
n_SARIN_RW = 512
n_SAR_BC_RW = 256
n_SAR_RW = 128
n_LRM_RW = 128
n_blocks = 20
n_BeamBehaviourParams = 50
# Bind all the variables of the l1b_mds together into a single dictionary
CS_l1b_mds = {}
# CryoSat-2 Time and Orbit Group
CS_l1b_mds['Location'] = {}
# Time: day part
CS_l1b_mds['Location']['Day'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32,fill_value=0)
# Time: second part
CS_l1b_mds['Location']['Second'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32)
# Time: microsecond part
CS_l1b_mds['Location']['Micsec'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32)
# USO correction factor
CS_l1b_mds['Location']['USO_Corr'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Mode ID
CS_l1b_mds['Location']['Mode_ID'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint16)
# Source sequence counter
CS_l1b_mds['Location']['SSC'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint16)
# Instrument configuration
CS_l1b_mds['Location']['Inst_config'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32)
# Record Counter
CS_l1b_mds['Location']['Rec_Count'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32)
# Lat: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Location']['Lat'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Lon: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Location']['Lon'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Alt: packed units (mm, 1e-3 m)
# Altitude of COG above reference ellipsoid (interpolated value)
CS_l1b_mds['Location']['Alt'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Instantaneous altitude rate derived from orbit: packed units (mm/s, 1e-3 m/s)
CS_l1b_mds['Location']['Alt_rate'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Satellite velocity vector. In ITRF: packed units (mm/s, 1e-3 m/s)
# ITRF= International Terrestrial Reference Frame
CS_l1b_mds['Location']['Sat_velocity'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32)
# Real beam direction vector. In CRF: packed units (micro-m/s, 1e-6 m/s)
# CRF= CryoSat Reference Frame.
CS_l1b_mds['Location']['Real_beam'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32)
# Interferometric baseline vector. In CRF: packed units (micro-m/s, 1e-6 m/s)
CS_l1b_mds['Location']['Baseline'] = np.ma.zeros((n_records,n_blocks,3),dtype=np.int32)
# Star Tracker ID
CS_l1b_mds['Location']['ST_ID'] = np.ma.zeros((n_records,n_blocks),dtype=np.int16)
# Antenna Bench Roll Angle (Derived from star trackers)
# packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Location']['Roll'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Antenna Bench Pitch Angle (Derived from star trackers)
# packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Location']['Pitch'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Antenna Bench Yaw Angle (Derived from star trackers)
# packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Location']['Yaw'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Measurement Confidence Data Flags
# Generally the MCD flags indicate problems when set
# If MCD is 0 then no problems or non-nominal conditions were detected
# Serious errors are indicated by setting bit 31
CS_l1b_mds['Location']['MCD'] = np.ma.zeros((n_records,n_blocks),dtype=np.uint32)
CS_l1b_mds['Location']['Spares'] = np.ma.zeros((n_records,n_blocks,2),dtype=np.int16)
# CryoSat-2 Measurement Group
# Derived from instrument measurement parameters
CS_l1b_mds['Data'] = {}
# Window Delay reference (two-way) corrected for instrument delays
CS_l1b_mds['Data']['TD'] = np.ma.zeros((n_records,n_blocks),dtype=np.int64)
# H0 Initial Height Word from telemetry
CS_l1b_mds['Data']['H_0'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# COR2 Height Rate: on-board tracker height rate over the radar cycle
CS_l1b_mds['Data']['COR2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Coarse Range Word (LAI) derived from telemetry
CS_l1b_mds['Data']['LAI'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Fine Range Word (FAI) derived from telemetry
CS_l1b_mds['Data']['FAI'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Automatic Gain Control Channel 1: AGC gain applied on Rx channel 1.
# Gain calibration corrections are applied (Sum of AGC stages 1 and 2
# plus the corresponding corrections) (dB/100)
CS_l1b_mds['Data']['AGC_CH1'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Automatic Gain Control Channel 2: AGC gain applied on Rx channel 2.
# Gain calibration corrections are applied (dB/100)
CS_l1b_mds['Data']['AGC_CH2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Total Fixed Gain On Channel 1: gain applied by the RF unit. (dB/100)
CS_l1b_mds['Data']['TR_gain_CH1'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Total Fixed Gain On Channel 2: gain applied by the RF unit. (dB/100)
CS_l1b_mds['Data']['TR_gain_CH2'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Transmit Power in microWatts
CS_l1b_mds['Data']['TX_Power'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Doppler range correction: Radial component (mm)
# computed for the component of satellite velocity in the nadir direction
CS_l1b_mds['Data']['Doppler_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Instrument Range Correction: transmit-receive antenna (mm)
# Calibration correction to range on channel 1 computed from CAL1.
CS_l1b_mds['Data']['TR_inst_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Instrument Range Correction: receive-only antenna (mm)
# Calibration correction to range on channel 2 computed from CAL1.
CS_l1b_mds['Data']['R_inst_range'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Instrument Gain Correction: transmit-receive antenna (dB/100)
# Calibration correction to gain on channel 1 computed from CAL1
CS_l1b_mds['Data']['TR_inst_gain'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Instrument Gain Correction: receive-only (dB/100)
# Calibration correction to gain on channel 2 computed from CAL1
CS_l1b_mds['Data']['R_inst_gain'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Internal Phase Correction (microradians)
CS_l1b_mds['Data']['Internal_phase'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# External Phase Correction (microradians)
CS_l1b_mds['Data']['External_phase'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Noise Power measurement (dB/100)
CS_l1b_mds['Data']['Noise_power'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
# Phase slope correction (microradians)
# Computed from the CAL-4 packets during the azimuth impulse response
# amplitude (SARIN only). Set from the latest available CAL-4 packet.
CS_l1b_mds['Data']['Phase_slope'] = np.ma.zeros((n_records,n_blocks),dtype=np.int32)
CS_l1b_mds['Data']['Spares1'] = np.ma.zeros((n_records,n_blocks,4),dtype=np.int8)
# CryoSat-2 External Corrections Group
CS_l1b_mds['Geometry'] = {}
# Dry Tropospheric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['dryTrop'] = np.ma.zeros((n_records),dtype=np.int32)
# Wet Tropospheric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['wetTrop'] = np.ma.zeros((n_records),dtype=np.int32)
# Inverse Barometric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['InvBar'] = np.ma.zeros((n_records),dtype=np.int32)
# Delta Inverse Barometric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['DAC'] = np.ma.zeros((n_records),dtype=np.int32)
# GIM Ionospheric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['Iono_GIM'] = np.ma.zeros((n_records),dtype=np.int32)
# Model Ionospheric Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['Iono_model'] = np.ma.zeros((n_records),dtype=np.int32)
# Ocean tide Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['ocTideElv'] = np.ma.zeros((n_records),dtype=np.int32)
# Long period equilibrium ocean tide Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['lpeTideElv'] = np.ma.zeros((n_records),dtype=np.int32)
# Ocean loading tide Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['olTideElv'] = np.ma.zeros((n_records),dtype=np.int32)
# Solid Earth tide Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['seTideElv'] = np.ma.zeros((n_records),dtype=np.int32)
# Geocentric Polar tide Correction packed units (mm, 1e-3 m)
CS_l1b_mds['Geometry']['gpTideElv'] = np.ma.zeros((n_records),dtype=np.int32)
# Surface Type: enumerated key to classify surface at nadir
# 0 = Open Ocean
# 1 = Closed Sea
# 2 = Continental Ice
# 3 = Land
CS_l1b_mds['Geometry']['Surf_type'] = np.ma.zeros((n_records),dtype=np.uint32)
CS_l1b_mds['Geometry']['Spare1'] = np.ma.zeros((n_records,4),dtype=np.int8)
# Corrections Status Flag
CS_l1b_mds['Geometry']['Corr_status'] = np.ma.zeros((n_records),dtype=np.uint32)
# Correction Error Flag
CS_l1b_mds['Geometry']['Corr_error'] = np.ma.zeros((n_records),dtype=np.uint32)
CS_l1b_mds['Geometry']['Spare2'] = np.ma.zeros((n_records,4),dtype=np.int8)
# CryoSat-2 Average Waveforms Groups
CS_l1b_mds['Waveform_1Hz'] = {}
if (self.MODE == 'LRM'):
# Low-Resolution Mode
# Data Record Time (MDSR Time Stamp)
CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32)
CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32)
CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32)
# Lat: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32)
# Lon: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32)
# Alt: packed units (mm, 1e-3 m)
# Altitude of COG above reference ellipsoid (interpolated value)
CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32)
# Window Delay (two-way) corrected for instrument delays
CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64)
# 1 Hz Averaged Power Echo Waveform
CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_LRM_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Echo Scale Power (a power of 2 to scale echo to Watts)
CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16)
CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16)
elif (self.MODE == 'SAR'):
# SAR Mode
# Data Record Time (MDSR Time Stamp)
CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32)
CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32)
CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32)
# Lat: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32)
# Lon: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32)
# Alt: packed units (mm, 1e-3 m)
# Altitude of COG above reference ellipsoid (interpolated value)
CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32)
# Window Delay (two-way) corrected for instrument delays
CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64)
# 1 Hz Averaged Power Echo Waveform
CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_SAR_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Echo Scale Power (a power of 2 to scale echo to Watts)
CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16)
CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16)
elif (self.MODE == 'SIN'):
# SARIN Mode
# Same as the LRM/SAR groups but the waveform array is 512 bins instead of
# 128 and the number of echoes averaged is different.
# Data Record Time (MDSR Time Stamp)
CS_l1b_mds['Waveform_1Hz']['Day'] = np.zeros((n_records),dtype=np.int32)
CS_l1b_mds['Waveform_1Hz']['Second'] = np.zeros((n_records),dtype=np.uint32)
CS_l1b_mds['Waveform_1Hz']['Micsec'] = np.zeros((n_records),dtype=np.uint32)
# Lat: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lat'] = np.zeros((n_records),dtype=np.int32)
# Lon: packed units (0.1 micro-degree, 1e-7 degrees)
CS_l1b_mds['Waveform_1Hz']['Lon'] = np.zeros((n_records),dtype=np.int32)
# Alt: packed units (mm, 1e-3 m)
# Altitude of COG above reference ellipsoid (interpolated value)
CS_l1b_mds['Waveform_1Hz']['Alt'] = np.zeros((n_records),dtype=np.int32)
# Window Delay (two-way) corrected for instrument delays
CS_l1b_mds['Waveform_1Hz']['TD'] = np.zeros((n_records),dtype=np.int64)
# 1 Hz Averaged Power Echo Waveform
CS_l1b_mds['Waveform_1Hz']['Waveform'] = np.zeros((n_records,n_SARIN_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_1Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Echo Scale Power (a power of 2 to scale echo to Watts)
CS_l1b_mds['Waveform_1Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_1Hz']['N_avg_echoes'] = np.zeros((n_records),dtype=np.uint16)
CS_l1b_mds['Waveform_1Hz']['Flags'] = np.zeros((n_records),dtype=np.uint16)
# CryoSat-2 Waveforms Groups
# Beam Behavior Parameters
Beam_Behavior = {}
# Standard Deviation of Gaussian fit to range integrated stack power.
Beam_Behavior['SD'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# Stack Center: Mean of Gaussian fit to range integrated stack power.
Beam_Behavior['Center'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# Stack amplitude parameter scaled in dB/100.
Beam_Behavior['Amplitude'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# 3rd moment: providing the degree of asymmetry of the range integrated
# stack power distribution.
Beam_Behavior['Skewness'] = np.zeros((n_records,n_blocks),dtype=np.int16)
# 4th moment: Measure of peakiness of range integrated stack power distribution.
Beam_Behavior['Kurtosis'] = np.zeros((n_records,n_blocks),dtype=np.int16)
# Standard deviation as a function of boresight angle (microradians)
Beam_Behavior['SD_boresight_angle'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# Stack Center angle as a function of boresight angle (microradians)
Beam_Behavior['Center_boresight_angle'] = np.zeros((n_records,n_blocks),dtype=np.int16)
Beam_Behavior['Spare'] = np.zeros((n_records,n_blocks,n_BeamBehaviourParams-7),dtype=np.int16)
# CryoSat-2 mode specific waveform variables
CS_l1b_mds['Waveform_20Hz'] = {}
if (self.MODE == 'LRM'):
# Low-Resolution Mode
# Averaged Power Echo Waveform [128]
CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_LRM_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Echo Scale Power (a power of 2)
CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
elif (self.MODE == 'SAR'):
# SAR Mode
# Averaged Power Echo Waveform [256]
CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_SAR_BC_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Echo Scale Power (a power of 2)
CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# Beam behaviour parameters
CS_l1b_mds['Waveform_20Hz']['Beam'] = Beam_Behavior
elif (self.MODE == 'SIN'):
# SARIN Mode
# Averaged Power Echo Waveform [1024]
CS_l1b_mds['Waveform_20Hz']['Waveform'] = np.zeros((n_records,n_blocks,n_SARIN_BC_RW),dtype=np.uint16)
# Echo Scale Factor (to scale echo to watts)
CS_l1b_mds['Waveform_20Hz']['Linear_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Echo Scale Power (a power of 2)
CS_l1b_mds['Waveform_20Hz']['Power2_Wfm_Multiplier'] = np.zeros((n_records,n_blocks),dtype=np.int32)
# Number of echoes averaged
CS_l1b_mds['Waveform_20Hz']['N_avg_echoes'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
CS_l1b_mds['Waveform_20Hz']['Flags'] = np.zeros((n_records,n_blocks),dtype=np.uint16)
# Beam behaviour parameters
CS_l1b_mds['Waveform_20Hz']['Beam'] = Beam_Behavior
# Coherence [1024]: packed units (1/1000)
CS_l1b_mds['Waveform_20Hz']['Coherence'] = np.zeros((n_records,n_blocks,n_SARIN_BC_RW),dtype=np.int16)
# Phase Difference [1024]: packed units (microradians)
CS_l1b_mds['Waveform_20Hz']['Phase_diff'] = np.zeros((n_records,n_blocks,n_SARIN_BC_RW),dtype=np.int32)
# for each record in the CryoSat file
for r in range(n_records):
# CryoSat-2 Time and Orbit Group
for b in range(n_blocks):
CS_l1b_mds['Location']['Day'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Location']['Second'][r,b] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Location']['Micsec'][r,b] = np.fromfile(fid,dtype='>u4',count=1)
CS_l1b_mds['Location']['USO_Corr'][r,b] = np.fromfile(fid,dtype='>i4',count=1)
CS_l1b_mds['Location']['Mode_ID'][r,b] = | np.fromfile(fid,dtype='>u2',count=1) | numpy.fromfile |
import numpy as np
import cv2
from nn import ANN
def init_train_data():
X = []
for i in range(12, 22):
name = "images/shubham" + str(i) + ".jpg"
arr = cv2.imread(str(name)) # 640x480x3 array
arr = np.reshape(arr,(1,480*640*3))/255
X.append(arr[0])
for i in range(5):
name = "images/mahika" + str(i) + ".jpg"
arr = cv2.imread(name) # 640x480x3 array
arr = np.reshape(arr, (1, 480*640*3))/255
X.append(arr[0])
for i in range(1, 5):
name = "images/dad" + str(i)+".jpg"
arr = cv2.imread(name) # 640x480x3 array
arr = np.reshape(arr, (1,480*640*3))/255
X.append(arr[0])
for i in range(5):
name = "images/mum" + str(i) + ".jpg"
arr = cv2.imread(name) # 640x480x3 array
arr = np.reshape(arr, (1,480*640*3))/255
X.append(arr[0])
X = np.array(X)
X = X.T
Y = []
for i in range(X.shape[1]):
if i<10:
k = [1, 0, 0, 0]
Y.append(k)
elif i >= 10 and i < 15:
k = [0, 1, 0, 0]
Y.append(k)
elif i >= 15 and i < 19:
k = [0, 0, 1, 0]
Y.append(k)
else:
k = [0, 0, 0, 1]
Y.append(k)
Y = np.array(Y)
Y = Y.T
return X, Y
def create_input_data(video):
X = []
check, frame = video.read()
frame = | np.reshape(frame, (1,480*640*3)) | numpy.reshape |
import codecs
from collections import defaultdict
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import scipy
import scipy.spatial.distance
import sys
import utils
__author__ = "<NAME>"
__version__ = "CS224u, Stanford, Spring 2020"
def euclidean(u, v):
return scipy.spatial.distance.euclidean(u, v)
def vector_length(u):
return np.sqrt(u.dot(u))
def length_norm(u):
return u / vector_length(u)
def cosine(u, v):
return scipy.spatial.distance.cosine(u, v)
def matching(u, v):
return np.sum(np.minimum(u, v))
def jaccard(u, v):
return 1.0 - (matching(u, v) / np.sum(np.maximum(u, v)))
def neighbors(word, df, distfunc=cosine):
"""Tool for finding the nearest neighbors of `word` in `df` according
to `distfunc`. The comparisons are between row vectors.
Parameters
----------
word : str
The anchor word. Assumed to be in `rownames`.
df : pd.DataFrame
The vector-space model.
distfunc : function mapping vector pairs to floats (default: `cosine`)
The measure of distance between vectors. Can also be `euclidean`,
`matching`, `jaccard`, as well as any other distance measure
between 1d vectors.
Raises
------
ValueError
If word is not in `df.index`.
Returns
-------
pd.Series
Ordered by closeness to `word`.
"""
if word not in df.index:
raise ValueError('{} is not in this VSM'.format(word))
w = df.loc[word]
dists = df.apply(lambda x: distfunc(w, x), axis=1)
return dists.sort_values()
def observed_over_expected(df):
col_totals = df.sum(axis=0)
total = col_totals.sum()
row_totals = df.sum(axis=1)
expected = np.outer(row_totals, col_totals) / total
oe = df / expected
return oe
def pmi(df, positive=True):
df = observed_over_expected(df)
# Silence distracting warnings about log(0):
with np.errstate(divide='ignore'):
df = np.log(df)
df[np.isinf(df)] = 0.0 # log(0) = 0
if positive:
df[df < 0] = 0.0
return df
def tfidf(df):
# Inverse document frequencies:
doccount = float(df.shape[1])
freqs = df.astype(bool).sum(axis=1)
idfs = np.log(doccount / freqs)
idfs[np.isinf(idfs)] = 0.0 # log(0) = 0
# Term frequencies:
col_totals = df.sum(axis=0)
tfs = df / col_totals
return (tfs.T * idfs).T
def ngram_vsm(df, n=2):
"""Create a character-level VSM from `df`.
Parameters
----------
df : pd.DataFrame
n : int
The n-gram size.
Returns
-------
pd.DataFrame
This will have the same column dimensionality as `df`, but the
rows will be expanded with representations giving the sum of
all the original rows in `df` that contain that row's n-gram.
"""
unigram2vecs = defaultdict(list)
for w, x in df.iterrows():
for c in get_character_ngrams(w, n):
unigram2vecs[c].append(x)
unigram2vecs = {c: np.array(x).sum(axis=0)
for c, x in unigram2vecs.items()}
cf = pd.DataFrame(unigram2vecs).T
cf.columns = df.columns
return cf
def get_character_ngrams(w, n):
"""Map a word to its character-level n-grams, with boundary
symbols '<w>' and '</w>'.
Parameters
----------
w : str
n : int
The n-gram size.
Returns
-------
list of str
"""
if n > 1:
w = ["<w>"] + list(w) + ["</w>"]
else:
w = list(w)
return ["".join(w[i: i+n]) for i in range(len(w)-n+1)]
def character_level_rep(word, cf, n=4):
"""Get a representation for `word` as the sum of all the
representations of `n`grams that it contains, according to `cf`.
Parameters
----------
word : str
The word to represent.
cf : pd.DataFrame
The character-level VSM (e.g, the output of `ngram_vsm`).
n : int
The n-gram size.
Returns
-------
np.array
"""
ngrams = get_character_ngrams(word, n)
ngrams = [n for n in ngrams if n in cf.index]
reps = cf.loc[ngrams].values
return reps.sum(axis=0)
def tsne_viz(df, colors=None, output_filename=None, figsize=(40, 50), random_state=None):
"""2d plot of `df` using t-SNE, with the points labeled by `df.index`,
aligned with `colors` (defaults to all black).
Parameters
----------
df : pd.DataFrame
The matrix to visualize.
colors : list of colornames or None (default: None)
Optional list of colors for the vocab. The color names just
need to be interpretable by matplotlib. If they are supplied,
they need to have the same length as `df.index`. If `colors=None`,
then all the words are displayed in black.
output_filename : str (default: None)
If not None, then the output image is written to this location.
The filename suffix determines the image type. If `None`, then
`plt.plot()` is called, with the behavior determined by the
environment.
figsize : (int, int) (default: (40, 50))
Default size of the output in display units.
random_state : int or None
Optionally set the `random_seed` passed to `PCA` and `TSNE`.
"""
# Colors:
vocab = df.index
if not colors:
colors = ['black' for i in vocab]
# Recommended reduction via PCA or similar:
n_components = 50 if df.shape[1] >= 50 else df.shape[1]
dimreduce = PCA(n_components=n_components, random_state=random_state)
X = dimreduce.fit_transform(df)
# t-SNE:
tsne = TSNE(n_components=2, random_state=random_state)
tsnemat = tsne.fit_transform(X)
# Plot values:
xvals = tsnemat[: , 0]
yvals = tsnemat[: , 1]
# Plotting:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=figsize)
ax.plot(xvals, yvals, marker='', linestyle='')
# Text labels:
for word, x, y, color in zip(vocab, xvals, yvals, colors):
try:
ax.annotate(word, (x, y), fontsize=8, color=color)
except UnicodeDecodeError: ## Python 2 won't cooperate!
pass
# Output:
if output_filename:
plt.savefig(output_filename, bbox_inches='tight')
else:
plt.show()
def lsa(df, k=100):
"""Latent Semantic Analysis using pure scipy.
Parameters
----------
df : pd.DataFrame
The matrix to operate on.
k : int (default: 100)
Number of dimensions to truncate to.
Returns
-------
pd.DataFrame
The SVD-reduced version of `df` with dimension (m x k), where
m is the rowcount of mat and `k` is either the user-supplied
k or the column count of `mat`, whichever is smaller.
"""
rowmat, singvals, colmat = | np.linalg.svd(df, full_matrices=False) | numpy.linalg.svd |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Mean, weighted mean, median, and weighted median.
WeightedStats includes four functions (mean, weighted_mean, median,
weighted_median) which accept lists as arguments, and two functions
(numpy_weighted_mean, numpy weighted_median) which accept either lists
or numpy arrays.
Example:
import weightedstats as ws
my_data = [1, 2, 3, 4, 5]
my_weights = [10, 1, 1, 1, 9]
# Ordinary (unweighted) mean and median
ws.mean(my_data) # equivalent to ws.weighted_mean(my_data)
ws.median(my_data) # equivalent to ws.weighted_median(my_data)
# Weighted mean and median
ws.weighted_mean(my_data, weights=my_weights)
ws.weighted_median(my_data, weights=my_weights)
# Special weighted mean and median functions for use with numpy arrays
ws.numpy_weighted_mean(my_data, weights=my_weights)
ws.numpy_weighted_median(my_data, weights=my_weights)
"""
from __future__ import division
import sys
__title__ = "WeightedStats"
__version__ = "0.4.1"
__author__ = "<NAME>"
__email__ = "<EMAIL>"
__license__ = "MIT"
def mean(data):
"""Calculate the mean of a list."""
return sum(data) / float(len(data))
def weighted_mean(data, weights=None):
"""Calculate the weighted mean of a list."""
if weights is None:
return mean(data)
total_weight = float(sum(weights))
weights = [weight / total_weight for weight in weights]
w_mean = 0
for i, weight in enumerate(weights):
w_mean += weight * data[i]
return w_mean
def numpy_weighted_mean(data, weights=None):
"""Calculate the weighted mean of an array/list using numpy."""
import numpy as np
weights = np.array(weights).flatten() / float(sum(weights))
return np.dot(np.array(data), weights)
def median(data):
"""Calculate the median of a list."""
data.sort()
num_values = len(data)
half = num_values // 2
if num_values % 2:
return data[half]
return 0.5 * (data[half-1] + data[half])
def weighted_median(data, weights=None):
"""Calculate the weighted median of a list."""
if weights is None:
return median(data)
midpoint = 0.5 * sum(weights)
if any([j > midpoint for j in weights]):
return data[weights.index(max(weights))]
if any([j > 0 for j in weights]):
sorted_data, sorted_weights = zip(*sorted(zip(data, weights)))
cumulative_weight = 0
below_midpoint_index = 0
while cumulative_weight <= midpoint:
below_midpoint_index += 1
cumulative_weight += sorted_weights[below_midpoint_index-1]
cumulative_weight -= sorted_weights[below_midpoint_index-1]
if abs(cumulative_weight - midpoint) < sys.float_info.epsilon:
bounds = sorted_data[below_midpoint_index-2:below_midpoint_index]
return sum(bounds) / float(len(bounds))
return sorted_data[below_midpoint_index-1]
def numpy_weighted_median(data, weights=None):
"""Calculate the weighted median of an array/list using numpy."""
import numpy as np
if weights is None:
return np.median(np.array(data).flatten())
data, weights = np.array(data).flatten(), | np.array(weights) | numpy.array |
import numpy as np
import json
class NCA():
def __init__(self, var_dims, learning_rate = 0.01, max_steps = 100, init_style = "normal", init_stddev = 0.1):
self.var_dims = var_dims
self.learning_rate = learning_rate
self.max_steps = max_steps
self.init_style = init_style
self.init_stddev = init_stddev
def transform(self, X):
halfM = np.dot(X, self.A)
return halfM
def fit_transform(self, X, Y):
self.fit(X, Y)
halfM = self.transform(X)
return halfM
def init_matrix(self, shape):
if self.init_style == "normal":
return self.init_stddev * np.random.standard_normal(size = shape)
elif self.init_style == "uniform":
return np.random.uniform(size = shape)
else:
print("error style!")
raise Exception
def fit(self, X, Y):
(n, d) = X.shape
self.n_samples = n
self.param_dims = d
self.A = self.init_matrix(shape = (self.param_dims, self.var_dims))
s = 0
target = 0
res = []
while s < self.max_steps:
if s >= 1:
res.append(target)
halfM = np.dot(X, self.A)
sum_row = np.sum(halfM ** 2, axis = 1)
xxt = np.dot(halfM, halfM.transpose())
#broadcast
dist_mat = sum_row + np.reshape(sum_row, (-1, 1)) - 2 * xxt
exp_neg_dist = | np.exp(-dist_mat) | numpy.exp |
# 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])
transformations.append((rot, trans_num, trans_den))
rot = N.array([1,0,0,0,1,0,0,0,1])
rot.shape = (3, 3)
trans_num = N.array([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([1,3,3])
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([1,3,3])
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([1,3,3])
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([1,3,3])
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([1,3,3])
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([1,3,3])
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([0,1,1])
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([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([1,3,3])
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([1,3,3])
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([1,3,3])
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([1,3,3])
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([1,3,3])
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([1,0,0,0,0,-1,0,1,0])
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([1,0,0,0,0,1,0,-1,0])
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([0,0,1,0,1,0,-1,0,0])
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([0,0,-1,0,1,0,1,0,0])
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([0,-1,0,1,0,0,0,0,1])
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([0,1,0,-1,0,0,0,0,1])
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([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([3,1,3])
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([3,1,3])
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([3,1,3])
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([3,1,3])
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([3,1,3])
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([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([1,0,0,0,0,-1,0,1,0])
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([1,0,0,0,0,1,0,-1,0])
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([0,0,1,0,1,0,-1,0,0])
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([0,0,-1,0,1,0,1,0,0])
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([0,-1,0,1,0,0,0,0,1])
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([0,1,0,-1,0,0,0,0,1])
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([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([3,3,1])
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([3,3,1])
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([3,3,1])
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([3,3,1])
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([3,3,1])
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([3,3,1])
trans_den = N.array([4,4,4])
transformations.append((rot, trans_num, trans_den))
sg = SpaceGroup(210, 'F 41 3 2', transformations)
space_groups[210] = sg
space_groups['F 41 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([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))
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([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([0,1,0,1,0,0,0,0,-1])
rot.shape = (3, 3)
trans_num = N.array([1,1,1])
trans_den = N.array([2,2,2])
transformations.append((rot, trans_num, trans_den))
rot = N.array([0,-1,0,-1,0,0,0,0,-1])
rot.shape = (3, 3)
trans_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(211, 'I 4 3 2', transformations)
space_groups[211] = sg
space_groups['I 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([3,3,1])
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([1,3,3])
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([1,3,3])
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([3,1,3])
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([3,1,3])
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([3,3,1])
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([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([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([0,0,1,0,-1,0,1,0,0])
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([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([3,1,3])
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))
sg = SpaceGroup(212, 'P 43 3 2', transformations)
space_groups[212] = sg
space_groups['P 43 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([1,1,3])
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([3,1,1])
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([3,1,1])
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([1,3,1])
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([1,3,1])
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([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([0,1,0,1,0,0,0,0,-1])
rot.shape = (3, 3)
trans_num = N.array([3,1,1])
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([3,3,3])
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([1,1,3])
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([3,3,3])
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([1,3,1])
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([3,3,3])
trans_den = N.array([4,4,4])
transformations.append((rot, trans_num, trans_den))
sg = SpaceGroup(213, 'P 41 3 2', transformations)
space_groups[213] = sg
space_groups['P 41 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([1,1,3])
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([1,3,3])
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([1,3,3])
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([1,3,1])
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([1,3,1])
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([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([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([0,0,1,0,-1,0,1,0,0])
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([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([1,3,1])
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([1,0,0,0,1,0,0,0,1])
rot.shape = (3, 3)
trans_num = N.array([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([3,3,5])
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([3,5,5])
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([3,5,5])
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([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([3,5,3])
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([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([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([0,0,1,0,-1,0,1,0,0])
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([0,0,-1,0,-1,0,-1,0,0])
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([-1,0,0,0,0,1,0,1,0])
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([-1,0,0,0,0,-1,0,-1,0])
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))
sg = SpaceGroup(214, 'I 41 3 2', transformations)
space_groups[214] = sg
space_groups['I 41 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))
sg = SpaceGroup(215, 'P -4 3 m', transformations)
space_groups[215] = sg
space_groups['P -4 3 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([-1,0,0,0,0,1,0,-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]) | numpy.array |
from CoaxialDrone import CoaxialCopter
from PIDcontroller import PIDController_with_ff
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
class DronewithPID(CoaxialCopter,PIDController_with_ff):
def __init__(self,
z_path,
z_dot_path,
z_dot_dot_path,
t,
dt,
Sensor
):
self.t = t
self.dt = dt
self.z_path = z_path
self.z_dot_path = z_dot_path
self.z_dot_dot_path = z_dot_dot_path
self.Sensor = Sensor
def PID_controller_with_measured_values(self,k_p,k_d,k_i,mass_err,sigma,use_measured_height=False):
# creating the co-axial drone object
Controlled_Drone=CoaxialCopter()
# array for recording the state history
drone_state_history = Controlled_Drone.X
# introducing a small error of the actual mass and the mass for which the path has been calculated
actual_mass = Controlled_Drone.m * mass_err
# creating the control system object
control_system = PIDController_with_ff(k_p,k_d,k_i)
# declaring the initial state of the drone with zero hight and zero velocity
Controlled_Drone.X = np.array([0.0,0.0,0.0,0.0])
Drone_Sensor = self.Sensor(Controlled_Drone.X, 0.95)
observation_history = Controlled_Drone.X[0]
# executing the flight
for i in range(1,self.z_path.shape[0]-1):
# condition to use height observation to control the drone or
# use the magically given true state
if use_measured_height:
z_observation = Drone_Sensor.measure(Controlled_Drone.X[0],sigma)
u_bar = control_system.control(self.z_path[i],
z_observation,
self.z_dot_path[i],
Controlled_Drone.X[2],
self.z_dot_dot_path[i],
self.dt)
observation_history = np.vstack((observation_history,z_observation))
else:
u_bar = control_system.control(self.z_path[i],
Controlled_Drone.X[0],
self.z_dot_path[i],
Controlled_Drone.X[2],
self.z_dot_dot_path[i],
self.dt)
observation_history = np.vstack((observation_history,self.z_path[i]))
Controlled_Drone.set_rotors_angular_velocities(u_bar,0.0)
# calculating the new state vector
drone_state = Controlled_Drone.advance_state(self.dt, actual_mass)
# generating a history of vertical positions for the drone
drone_state_history = np.vstack((drone_state_history, drone_state))
plt.subplot(211)
plt.plot(self.t,self.z_path,linestyle='-',marker='.',color='red')
plt.plot(self.t[1:],drone_state_history[:,0],linestyle='-',color='blue',linewidth=3)
if use_measured_height:
plt.scatter(self.t[1:],observation_history[:,0],color='black',marker='.',alpha=0.3)
plt.grid()
if use_measured_height:
plt.title('Change in height (using measured value)').set_fontsize(20)
else:
plt.title('Change in height (ideal case)').set_fontsize(20)
plt.xlabel('$t$ [sec]').set_fontsize(20)
plt.ylabel('$z-z_0$ [$m$]').set_fontsize(20)
plt.xticks(fontsize = 14)
plt.yticks(fontsize = 14)
if use_measured_height:
plt.legend(['Planned path','Executed path','Observed value'],fontsize = 14)
else:
plt.legend(['Planned path','Executed path'],fontsize = 14)
plt.show()
plt.subplot(212)
plt.plot(self.t[1:],abs(self.z_path[1:]-drone_state_history[:,0]),linestyle='-',marker='.',color='blue')
plt.grid()
plt.title('Error value ').set_fontsize(20)
plt.xlabel('$t$ [sec]').set_fontsize(20)
plt.ylabel('||$z_{target} - z_{actual}$|| [$m$]').set_fontsize(20)
plt.xticks(fontsize = 14)
plt.yticks(fontsize = 14)
plt.legend(['Error'],fontsize = 14)
plt.show()
def PID_controller_with_estimated_values(self,k_p,k_d,k_i,mass_err,sigma,alpha,use_estimated_height=False):
# creating the co-axial drone object
Controlled_Drone=CoaxialCopter()
# array for recording the state history
drone_state_history = Controlled_Drone.X
# introducing a small error of the actual mass and the mass for which the path has been calculated
actual_mass = Controlled_Drone.m * mass_err
# creating the control system object
control_system = PIDController_with_ff(k_p,k_d,k_i)
# declaring the initial state of the drone with zero hight and zero velocity
Controlled_Drone.X = np.array([0.0,0.0,0.0,0.0])
Drone_Sensor = self.Sensor(Controlled_Drone.X, alpha)
# recording the estimated height for each step
estimated_height_history = Drone_Sensor.x_hat
observation_history = Controlled_Drone.X[0]
# executing the flight
for i in range(1,self.z_path.shape[0]-1):
# condition to use height observation to control the drone or
# use the majically given true state
if use_estimated_height:
z_observation = Drone_Sensor.measure(Controlled_Drone.X[0],sigma)
z_estimated = Drone_Sensor.estimate(z_observation)
u_bar = control_system.control(self.z_path[i],
z_estimated,
self.z_dot_path[i],
Controlled_Drone.X[2],
self.z_dot_dot_path[i],
self.dt)
else:
z_observation = Drone_Sensor.measure(Controlled_Drone.X[0],sigma)
u_bar = control_system.control(self.z_path[i],
z_observation,
self.z_dot_path[i],
Controlled_Drone.X[2],
self.z_dot_dot_path[i],
self.dt)
Controlled_Drone.set_rotors_angular_velocities(u_bar,0.0)
# calculating the new state vector
drone_state = Controlled_Drone.advance_state(self.dt, actual_mass)
# generating a history of vertical positions for the drone
drone_state_history = np.vstack((drone_state_history, drone_state))
# generating the estimated height for each step
estimated_height_history = np.vstack((estimated_height_history,Drone_Sensor.x_hat))
observation_history = | np.vstack((observation_history,z_observation)) | numpy.vstack |
try:
from ulab import numpy as np
except ImportError:
import numpy as np
print(len(np.array([1, 2, 3, 4, 5], dtype=np.uint8)))
print(len(np.array([[1, 2, 3],[4, 5, 6]])))
print(~np.array([0, -1, -100], dtype=np.uint8))
print(~np.array([0, -1, -100], dtype=np.uint16))
print(~np.array([0, -1, -100], dtype=np.int8))
print(~np.array([0, -1, -100], dtype=np.int16))
print(abs(np.array([0, -1, -100], dtype=np.uint8)))
print(abs(np.array([0, -1, -100], dtype=np.uint16)))
print(abs(np.array([0, -1, -100], dtype=np.int8)))
print(abs(np.array([0, -1, -100], dtype=np.int16)))
print(abs(np.array([0, -1, -100], dtype=np.float)))
print(-(np.array([0, -1, -100], dtype=np.uint8)))
print(-(np.array([0, -1, -100], dtype=np.uint16)))
print(-(np.array([0, -1, -100], dtype=np.int8)))
print(-(np.array([0, -1, -100], dtype=np.int16)))
print(-(np.array([0, -1, -100], dtype=np.float)))
print(+(np.array([0, -1, -100], dtype=np.uint8)))
print(+( | np.array([0, -1, -100], dtype=np.uint16) | numpy.array |
# -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
import warnings
from typing import List
numeric_tuple = (int, float, np.float32, np.float64, np.longdouble)
def coupled_logarithm(value: [int, float, np.ndarray, tf.Tensor],
kappa: [int, float] = 0.0,
dim: int = 1
) -> [float, np.ndarray, tf.Tensor]:
"""
Generalization of the logarithm function, which defines smooth
transition to power functions.
Parameters
----------
value : Input variable in which the coupled logarithm is applied to.
Accepts int, float, np.ndarray and tf.Tensor data types.
kappa : Coupling parameter which modifies the coupled logarithm function.
Accepts int and float data types.
dim : The dimension (or rank) of value. If value is scalar, then dim = 1.
Accepts only int data type.
"""
# convert value into np.ndarray (if scalar) to keep consistency
value = | np.array(value) | numpy.array |
# -*- coding: utf-8 -*-
"""
Created on Thu May 30 20:03:50 2019
Finds Vg1 and Vg2 values above a threshold, determined by the ratio of the areas
of a Gaussian fit of the intensity histogram to the total area of the intensities
@author: <NAME>
"""
import numpy as np
import scipy.signal as ss
import scipy.optimize as opt
from scipy.signal import medfilt2d, savgol_filter
from scipy.ndimage import correlate
from sklearn.neighbors import KDTree
import stability as stab
def hist_data(z):
"""
Finds x and y data from histogram
:param z: input
:return: x and y
"""
data = np.histogram(z, bins='scott')
x = data[1]
x = np.array([(x[i] + x[i + 1]) / 2 for i in range(0, len(x) - 1)])
return x, np.array(data[0])
def gauss(x, *params):
return abs(params[2]) * np.exp(-(x - params[0]) ** 2 / (2 * params[1] ** 2))
def multi_gaussian(x, *params):
"""
Fits multiple Gaussian distributions, number of which determined by the number of parameters inputted
"""
y = np.zeros_like(x)
index = np.arange(0, len(params), 3)
if index.size > 1:
for i in range(0, len(params) // 3):
mu = params[i]
sig = params[i + len(params) // 3]
amp = params[i + 2 * len(params) // 3]
y = y + abs(amp) * np.exp(-(x - mu) ** 2 / (2 * sig ** 2))
else:
y = y + abs(params[2]) * np.exp(-(x - params[0]) ** 2 / (2 * params[1] ** 2))
return y
def multi_gauss_background(x, *params):
y = np.zeros_like(x)
index = np.arange(0, len(params) - 2, 3)
if index.size > 1:
y = y + params[0] * x + params[1]
for i in range(0, (len(params) - 2) // 3):
mu = params[i + 2]
sig = params[i + 2 + (len(params) - 2) // 3]
amp = params[i + 2 + 2 * (len(params) - 2) // 3]
y = y + abs(amp) * np.exp(-(x - mu) ** 2 / (2 * sig ** 2))
else:
y = y + params[0] * x + params[1] + abs(params[4]) * np.exp(-(x - params[2]) ** 2 / (2 * params[3] ** 2))
return y
def greedy_guess(guess, x, y):
n = (len(guess) - 2) // 3
m, sig, a = guess[2:n + 2], guess[n + 2:2 * n + 2], guess[2 * n + 2:]
chi = (y - multi_gauss_background(x, *guess)) / multi_gauss_background(x, *guess)
chi = savgol_filter(chi, 3, 2)
m, a = np.append(m, float(x[np.where(chi == np.max(chi))])), np.append(a, float(y[np.where(chi == np.max(chi))]))
sig = np.append(sig, sig[n - 1] / 2)
return np.append(guess[:2], np.append(m, np.append(sig, a)))
def gradient(x, y, z):
"""
Calculates gradient along x and y of intensities to reduce noise
@param x: x vales
@param y: y values
@param z: intensities
@return:
"""
m_z = np.reshape(z, (len(np.unique(y)), len(np.unique(x))))# Transform array into matrix
sg = savgol_filter(m_z, 5, 2) + savgol_filter(m_z, 5, 2, axis=0) # Savgol filter acts as a low pass band filter
signal = sg - np.mean(sg) + np.mean(m_z)
return np.reshape(signal, np.shape(x))
def gradient_exp(x, y, z):
"""
Calculates gradient along x and y of intensities to reduce noise
@param x: x vales
@param y: y values
@param z: intensities
@return:
"""
m_z = np.reshape(z, (len(np.unique(y)), len(np.unique(x))))# Transform array into matrix
diff = [[0, -1, 0], [-1, 5, -1], [0, -1, 0]]
z_diff = correlate(m_z, diff)
sg = savgol_filter(z_diff, 5, 2) + savgol_filter(z_diff, 5, 2, axis=0) # Savgol filter acts as a low pass band filter
signal = sg - np.mean(sg) + np.mean(m_z)
return np.reshape(signal, np.shape(x))
def filtering(x, y, z):
m_z = np.reshape(z, (len(np.unique(y)), len(np.unique(x)))) # Transform array into matrix
s = medfilt2d(m_z)
return np.reshape(s, (int(len(x)),))
def normalise(z):
"""
Unity-based normalisation function, such that all values range between 0 and 1
:param z: Raw data that needs normalising
:return: Normalised data
"""
return np.nan_to_num((z - np.min(z)) / (np.max(z) - np.min(z)))
def fit_gauss(z):
intensity = normalise(z)
x, y = hist_data(intensity)
guess = np.append(0, np.append(np.median(y), np.append(np.median(x[np.where(y == np.max(y))]),
np.append(np.std(x[np.where(y > np.median(y))]),
np.max(y)))))
fit_param, cov = opt.curve_fit(multi_gauss_background, x, y, guess)
if fit_param[2] > 0.5:
index = np.where(intensity<fit_param[2]-3*abs(fit_param[3]))
else:
index = np.where(intensity>fit_param[2]+3*abs(fit_param[3]))
return index
def curved_plane(x, y, param):
return param[0]*x + param[1]*x**2 + param[2]*y + param[3]*y**2 + param[4]*x*y + param[5]
def linear_plane(x, y, param):
return param[0]*x + param[1]*y + param[2]
def minimise_plane(param, x, y, z):
return np.sum((z - linear_plane(x, y, param))**2)
def linear(x, z):
return (np.median(z[np.where(x==np.min(x))])-np.median(z[np.where(x==np.max(x))]))/(np.min(x)-np.max(x))
def remove_background(x, y, z):
p = gradient_exp(x, y, z)
param = np.array((linear(x, z), linear(y,z), np.median(p)))
sol = opt.minimize(minimise_plane, param, args=(x, y, p))
p_n = normalise(p - linear_plane(x, y, sol.x))
return p_n*(np.max(z)-np.min(z)) + np.min(z)
def grad_exp(z, val_x, val_y):
val = z.reshape(val_y, val_x)
scharr = np.array([[ -3-3j, 0-10j, +3 -3j],
[-10+0j, 0+ 0j, +10 +0j],
[ -3+3j, 0+10j, +3 +3j]]) # Gx + j*Gy
grad = ss.convolve2d(val, scharr, boundary='symm', mode='same')
index = np.where(np.logical_or(abs(np.angle(grad).flatten())<=0.15, abs(np.angle(grad).flatten())>=np.pi-0.15))
z[index] = 0
return z
def get_klpq_div(p_probs, q_probs):
# Calcualtes the Kullback-Leibler divergence between pi and qi
kl_div = 0.0
for pi, qi in zip(p_probs, q_probs):
kl_div += pi*np.nan_to_num(np.log(pi/qi))
return kl_div
def D_KL(threshold, x, y):
# Finds best fit Gaussian distribution and calculates the corresponding Kullback-Leibler divergence
index = np.where(np.logical_and(x>=threshold[0], x<=threshold[1]))
xs, ys = x[index], y[index]
if np.trapz(ys)>0:
ys = ys/np.trapz(ys)
else:
return np.inf
guess = np.append(np.median(xs[np.where(ys == np.max(ys))]),
np.append(np.std(xs[np.where(ys > np.median(ys))]),
np.max(ys)))
bounds = ((np.min(x)-np.std(x), np.std(x)/10**4, np.mean(ys)), (np.max(x)+np.std(x), np.max(x)-np.min(x), 10*np.max(ys)))
fit_param, cov = opt.curve_fit(gauss, xs, ys, guess, bounds=bounds)
return get_klpq_div(ys+10**-7, gauss(xs, *fit_param)+10**-7) # Add small epsilon to ensure that we donn't devide by zero
def minimise_DKL(x, y):
# Estimate first guess and boundaries to use:
guess = np.append(np.median(x[np.where(y == np.max(y))]),
np.append(np.std(x[np.where(y > np.median(y))]),
np.max(y)))
b = ((np.min(x)-np.std(x), np.std(x)/10**4, np.mean(y)), (np.max(x)+np.std(x), np.max(x)-np.min(x), np.max(y)*10))
fit_param, cov = opt.curve_fit(gauss, x, y, guess, bounds=b)
x0 = [fit_param[0]-2*fit_param[1], fit_param[0]+2*fit_param[1]]
bound = ((np.min(x), fit_param[0]-fit_param[1]), (fit_param[0]+fit_param[1], np.max(x)))
# Find optimal bound solutions
sol = opt.minimize(D_KL, x0, jac=None, method='L-BFGS-B', options={'eps':1/len(x)}, args=(x, y), bounds=bound)
return sol.x
def threshold_DKL(z):
intensity = normalise(z)
x, y = hist_data(intensity)
y = y**0.5 # Broadens peak to allow to identify finer structure in the intensity
threshold = minimise_DKL(x, y)
if abs(np.max(z))>abs(np.min(z)):
index = np.where(intensity>=threshold[1])
else:
index = np.where(intensity<=threshold[0])
return index
def threshold(z, val):
if abs(np.max(z))>abs(np.min(z)):
v = abs(np.min(z))*0.9
else:
v = -abs(np.max(z))*0.9
val = np.append(val, v)
v = np.mean(abs(val))
m = np.where(np.logical_or(z > v, z < -v))
return m, val
def intense(z, index):
x, y = hist_data(z)
guess = np.append(np.median(x[np.where(y == np.max(y))]),
np.append(np.std(x[np.where(y > np.median(y))]),
np.max(y)))
fit_param, cov = opt.curve_fit(gauss, x, y, guess)
return z[index]-fit_param[0]
def threshold_experimental(vg1, vg2, i, q):
i_g, q_g = remove_background(vg1, vg2, i), remove_background(vg1, vg2, q)
m_i, m_q = threshold_DKL(i_g), threshold_DKL(q_g)
index = np.unique(np.append(m_i, m_q))
intensity = normalise(abs(intense(i, index)))+normalise(abs(intense(q, index)))
return vg1[index], vg2[index], intensity, i_g, q_g, index
def threshold_theoretical(vg1, vg2, i):
i_g = gradient(vg1, vg2, i)
x, y = hist_data(i_g)
x = normalise(x)
fit_param = [np.median(x[np.where(y == np.max(y))]), np.std(x[np.where(y > np.median(y))]), np.max(y)]
try:
fit_one, _ = opt.curve_fit(multi_gaussian, x, y, fit_param)
ind = np.where(x > fit_one[0] + fit_one[1])
ys = y[ind] - multi_gaussian(x[ind], *fit_one)
guess = [fit_one[0], np.median(x[ind][np.where(ys == np.max(ys))]),
fit_one[1], np.std(x[np.where(y > np.median(ys))]),
fit_one[2], np.max(ys)]
try:
fit_param, cov = opt.curve_fit(multi_gaussian, x, y, guess)
error = np.sqrt(np.diag(cov))
if error[1] * 10 > error[0]:
index = np.where(normalise(i) > fit_param[1])
else:
index = np.where(normalise(i) > 0.4)
except:
val = np.min(x[np.where(x > fit_one[0] + fit_one[1])])
index = np.where(normalise(i) > val)
except:
index = np.where(normalise(i) > 0.4)
return vg1[index], vg2[index], i[index], x, y, fit_param
def averaging_xy(x, y, intensity, leaf, n_neighbours):
"""
Uses KDTree to find n_neighbours and then calculates a weighted mean, resulting in thinning the data
:param x: threshold x values
:param y: threshold y values
:param intensity: corresponding intensities
:param leaf: determines how many neighbouring points to check, leaf > n_neighbours
:param n_neighbours: number of neighbours to average through
:return: thinned x and y values
"""
data = np.transpose(np.vstack([x, y]))
xs, ys, zs = [], [], []
tree = KDTree(data, leaf_size=leaf) # Finds relation between points
for i in range(0, len(data)):# // n_neighbours):
# Figure out which are the neighbouring points
# dist, ind = tree.query(np.reshape(data[i * n_neighbours, :], (1, -1)), k=n_neighbours)
dist, ind = tree.query(np.reshape(data[i, :], (1, -1)), k=n_neighbours)
# takes weighted average of x and y values of given point
x_m, y_m = np.average(x[ind], weights=intensity[ind]), np.average(y[ind], weights=intensity[ind])
z_m = np.average(intensity[ind])
xs, ys, zs = np.append(xs, x_m), np.append(ys, y_m), np.append(zs, z_m)
return xs, ys, zs
def thinning(Vg1, Vg2, i_g, q_g, ind):
val_x, val_y = len(np.unique(Vg1)), len(np.unique(Vg2))
# Set data points below threshold to zero
M = np.sqrt(i_g**2+q_g**2)
mask = np.ones(M.shape,dtype=bool)
mask[ind] = False
M[mask] = 0
M = grad_exp(M, val_x, val_y)
# Find peaks along x
if val_x > 100:
peaks, hight = ss.find_peaks(M, width=1, distance=val_x//100)
else:
peaks, hight = ss.find_peaks(M, width=1)
xs, ys, zs = Vg1[peaks], Vg2[peaks], M[peaks]
# Find peaks along y
xt = np.reshape(np.transpose(np.reshape(Vg1, (val_y, val_x))), np.shape(Vg1))
yt = np.reshape(np.transpose(np.reshape(Vg2, (val_y, val_x))), | np.shape(Vg2) | numpy.shape |
import argparse
import logging
import numpy as np
from obiwan import SimCatalog,BrickCatalog,utils,setup_logging
import settings
logger = logging.getLogger('preprocessing')
def isELG_colors(gflux=None, rflux=None, zflux=None, south=True, gmarg=0., grmarg=0., rzmarg=0., primary=None):
"""
Apply ELG selection with box enlarged by ``gmarg``, ``grmarg``, ``rzmarg``.
Base selection from https://github.com/desihub/desitarget/blob/master/py/desitarget/cuts.py.
"""
if primary is None:
primary = np.ones_like(rflux, dtype='?')
elg = primary.copy()
# ADM work in magnitudes instead of fluxes. NOTE THIS IS ONLY OK AS
# ADM the snr masking in ALL OF g, r AND z ENSURES positive fluxes.
g = 22.5 - 2.5*np.log10(gflux.clip(1e-16))
r = 22.5 - 2.5*np.log10(rflux.clip(1e-16))
z = 22.5 - 2.5*np.log10(zflux.clip(1e-16))
# ADM cuts shared by the northern and southern selections.
elg &= g > 20 - gmarg # bright cut.
elg &= r - z > 0.3 - rzmarg # blue cut.
elg &= r - z < 1.6 + rzmarg # red cut.
elg &= g - r < -1.2*(r - z) + 1.6 + grmarg # OII flux cut.
# ADM cuts that are unique to the north or south.
if south:
elg &= g < 23.5 + gmarg # faint cut.
# ADM south has the FDR cut to remove stars and low-z galaxies.
elg &= g - r < 1.15*(r - z) - 0.15 + grmarg
else:
elg &= g < 23.6 + gmarg # faint cut.
elg &= g - r < 1.15*(r - z) - 0.35 + grmarg # remove stars and low-z galaxies.
return elg
def get_truth(truth_fn, south=True):
"""Build truth table."""
truth = SimCatalog(truth_fn)
mask = isELG_colors(south=south,gmarg=0.5,grmarg=0.5,rzmarg=0.5,**{'%sflux' % b:utils.mag2nano(truth.get(b)) for b in ['g','r','z']})
logger.info('Target selection: %d/%d objects',mask.sum(),mask.size)
truth = truth[mask]
truth.rename('objid','id_truth')
truth.rename('rhalf','shape_r')
#truth.shape_r = 1e-5*truth.ones()
truth.rename('hsc_mizuki_photoz_best','redshift')
truth.sersic = truth.ones(dtype=int)
truth.sersic[truth.type=='DEV'] = 4
return truth
def sample_from_truth(randoms, truth, rng=None, seed=None):
"""Sample random photometry from truth table."""
if rng is None:
rng = np.random.RandomState(seed=seed)
ind = rng.randint(low=0,high=truth.size,size=randoms.size)
for field in ['id_truth','g','r','z','shape_r','sersic','redshift']:
randoms.set(field,truth.get(field)[ind])
for b in ['g','r','z']:
transmission = randoms.get_extinction(b,camera='DES')
flux = utils.mag2nano(randoms.get(b))*10**(-0.4*transmission)
randoms.set('flux_%s' % b,flux)
ba = rng.uniform(0.2,1.,size=randoms.size)
phi = rng.uniform(0,np.pi,size=randoms.size)
randoms.shape_e1,randoms.shape_e2 = utils.get_shape_e1_e2(ba,phi)
randoms.fill_obiwan()
return randoms
def write_randoms(truth_fn, randoms_fn, bricknames=None, density=1e3, seed=None, gen_in_brick=True):
"""Build Obiwan randoms from scratch and truth table."""
bricknames = bricknames or []
rng = np.random.RandomState(seed=seed)
bricks = BrickCatalog()
logger.info('Generating randoms in %s',bricknames)
if gen_in_brick:
randoms = 0
for brickname in bricknames:
brick = bricks.get_by_name(brickname)
radecbox = brick.get_radecbox()
size = rng.poisson(density*brick.get_area())
tmp = SimCatalog()
tmp.ra,tmp.dec = utils.sample_ra_dec(size,radecbox,rng=rng)
tmp.brickname = | np.full(tmp.size,brickname) | numpy.full |
import argparse
import copy
import os
import pickle
import sys
import time
import cv2
import numpy as np
from PIL import Image
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(BASE_DIR)
sys.path.append(ROOT_DIR)
import kitti_util as utils
from kitti_object import kitti_object
from draw_util import get_lidar_in_image_fov
from ops.pybind11.rbbox_iou import rbbox_iou_3d
from utils.box_util import box3d_iou
def extract_boxes(objects, type_whitelist, remove_diff=False):
boxes_2d = []
boxes_3d = []
filter_objects = []
for obj_idx in range(len(objects)):
obj = objects[obj_idx]
if obj.type not in type_whitelist:
continue
if remove_diff:
if obj.occlusion > 2 or obj.truncation > 0.5 or obj.ymax - obj.ymin < 25:
continue
boxes_2d += [obj.box2d]
l, w, h = obj.l, obj.w, obj.h
cx, cy, cz = obj.t
ry = obj.ry
cy = cy - h / 2
boxes_3d += [np.array([cx, cy, cz, l, w, h, ry])]
filter_objects += [obj]
if len(boxes_3d) != 0:
boxes_3d = np.stack(boxes_3d, 0)
boxes_2d = np.stack(boxes_2d, 0)
return filter_objects, boxes_2d, boxes_3d
def compute_box_3d_obj_array(obj_array):
'''
cx, cy, cz, l, w, h, ry
'''
cx, cy, cz, l, w, h, angle = obj_array
R = utils.roty(angle)
# 3d bounding box corners
x_corners = [l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2]
y_corners = [h / 2, h / 2, h / 2, h / 2, -h / 2, -h / 2, -h / 2, -h / 2]
z_corners = [w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2]
# rotate and translate 3d bounding box
corners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners]))
# print corners_3d.shape
corners_3d[0, :] = corners_3d[0, :] + cx
corners_3d[1, :] = corners_3d[1, :] + cy
corners_3d[2, :] = corners_3d[2, :] + cz
# print 'cornsers_3d: ', corners_3d
return np.transpose(corners_3d, (1, 0))
def compute_box_3d_obj(cx, cy, cz, l, w, h, ry):
''' Takes an object and a projection matrix (P) and projects the 3d
bounding box into the image plane.
Returns:
corners_2d: (8,2) array in left image coord.
corners_3d: (8,3) array in in rect camera coord.
'''
# compute rotational matrix around yaw axis
R = utils.roty(ry)
# 3d bounding box corners
x_corners = [l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2]
y_corners = [h / 2, h / 2, h / 2, h / 2, -h / 2, -h / 2, -h / 2, -h / 2]
z_corners = [w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2]
# rotate and translate 3d bounding box
corners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners]))
# print corners_3d.shape
corners_3d[0, :] = corners_3d[0, :] + cx
corners_3d[1, :] = corners_3d[1, :] + cy
corners_3d[2, :] = corners_3d[2, :] + cz
return np.transpose(corners_3d)
def single_overlap(box1, box2):
area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
x_w = min(box1[2], box2[2]) - max(box1[0], box2[0])
x_h = min(box1[3], box2[3]) - max(box1[1], box2[1])
if x_w <= 0 or x_h <= 0 or area1 <= 0 or area2 <= 0:
return 0
return (x_w * x_h) / (area1 + area2 - (x_w * x_h))
def in_hull(p, hull):
from scipy.spatial import Delaunay
if not isinstance(hull, Delaunay):
hull = Delaunay(hull)
return hull.find_simplex(p) >= 0
def extract_pc_in_box3d(pc, box3d):
''' pc: (N,3), box3d: (8,3) '''
box3d_roi_inds = in_hull(pc[:, 0:3], box3d)
return pc[box3d_roi_inds, :], box3d_roi_inds
def extract_pc_in_box2d(pc, box2d):
''' pc: (N,2), box2d: (xmin,ymin,xmax,ymax) '''
box2d_corners = np.zeros((4, 2))
box2d_corners[0, :] = [box2d[0], box2d[1]]
box2d_corners[1, :] = [box2d[2], box2d[1]]
box2d_corners[2, :] = [box2d[2], box2d[3]]
box2d_corners[3, :] = [box2d[0], box2d[3]]
box2d_roi_inds = in_hull(pc[:, 0:2], box2d_corners)
return pc[box2d_roi_inds, :], box2d_roi_inds
def random_shift_box2d(box2d, img_height, img_width, shift_ratio=0.1):
''' Randomly shift box center, randomly scale width and height
'''
r = shift_ratio
xmin, ymin, xmax, ymax = box2d
h = ymax - ymin
w = xmax - xmin
cx = (xmin + xmax) / 2.0
cy = (ymin + ymax) / 2.0
assert xmin < xmax and ymin < ymax
while True:
cx2 = cx + w * r * ( | np.random.random() | numpy.random.random |
from argparse import ArgumentParser
from itertools import starmap
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from fyne import blackscholes, heston
from matplotlib.patches import Patch
from scipy.stats import gaussian_kde
import settings
from align_settings import STARTTIME, ENDTIME
from utils import resample
def safe_xs(*args, **kwargs):
try:
return pd.Series.xs(*args, **kwargs)
except KeyError:
return np.nan
def get_tick_size(quote):
diffs = (quote['Ask'] + quote['Bid']).diff()
diffs = diffs[diffs > 1e-6]
return np.round(diffs.min(), 2)
def filter_tick_size(data, quote, size):
tick_size = quote.groupby('Strike').apply(get_tick_size)
return data.reindex(tick_size[tick_size == size].index, level='Strike')
def filter_trade_on_book(quote, trade):
max_expiry = np.max(quote.index.get_level_values('Expiry'))
trade = trade[trade.index.get_level_values('Expiry') <= max_expiry]
quote_aligned = trade.groupby(['Class', 'Expiry', 'Strike']
).apply(lambda o: resample(quote.xs(o.name),
o.xs(o.name).index))
valid_trades = ((trade['Price'] == quote_aligned['Bid']) |
(trade['Price'] == quote_aligned['Ask']))
filtered = trade[valid_trades]
quote_aligned = quote_aligned.loc[valid_trades]
filtered['Buy'] = filtered['Price'] == quote_aligned['Ask']
filtered['Half-spread'] = (quote_aligned['Ask'] - quote_aligned['Bid']).round(2)/2
return filtered
def compute_duration(quote):
quote = quote.copy()
quote['Half-spread'] = (quote['Ask'] - quote['Bid']).round(2)/2
time = quote.reset_index('Time'
).set_index('Half-spread', append=True)[['Time']]
time['Duration'] = time['Time'].groupby(['Class', 'Expiry', 'Strike']
).transform(lambda t: t.diff().shift(-1))
time['Time'] += time['Duration']/2
duration = time.set_index('Time', append=True)['Duration']
duration /= pd.to_timedelta('1s')
return duration
def compute_volume_duration(quote, trade):
trade = filter_trade_on_book(quote, trade)
volume = trade.set_index(['Half-spread', 'Buy'], append=True)['Volume']
duration = compute_duration(quote)
return volume, duration
def plot_arrival_rates_bubbles(volume, duration):
volume = volume.groupby(['Class', 'Expiry', 'Strike', 'Half-spread', 'Buy']
).sum()
duration = duration.groupby(['Class', 'Expiry', 'Strike',
'Half-spread']).sum()
duration = duration[duration > 300]
arrival_rate = volume.groupby(['Class', 'Expiry', 'Strike', 'Half-spread']
).transform(lambda d: d.xs(d.name
)/safe_xs(duration, d.name))
arrival_rate.name = 'Arrival rate'
fig, axes = plt.subplots(3, 2, sharey=True, sharex=True, figsize=(8, 10))
patches = [Patch(color='b', alpha=.5, label='Call'),
Patch(color='r', alpha=.5, label='Put')]
axes[0, 1].legend(handles=patches)
for row, (e, r_ex) in zip(axes, arrival_rate.groupby('Expiry')):
for bs in ['Buy', 'Sell']:
ax = row[0] if bs == 'Buy' else row[1]
ax.set_title("Expiry: {}, {}".format(
pd.to_datetime(e).strftime('%Y-%m-%d'), bs))
for cp, cl in [('C', 'b'), ('P', 'r')]:
r = r_ex.xs((cp, bs == 'Buy'), level=('Class', 'Buy'))
r.reset_index(['Strike', 'Half-spread']).plot.scatter(
x='Strike', y='Half-spread', s=20*r/r_ex.mean(), ax=ax,
xlim=(325, 550), ylim=(0, None), alpha=.5, color=cl)
return fig
def plot_arrival_rates(arrival_rate):
depths = arrival_rate.index.get_level_values('Half-spread')
arrival_rate = arrival_rate[depths > 0].dropna()
bandwidth = 0.25
levels = ['Class', 'Expiry', 'Buy']
kernel = arrival_rate.groupby(levels).apply(
lambda r: gaussian_kde(np.stack(r.xs(r.name, level=levels).index, axis=-1),
bandwidth, r.values))
xlen, ylen = 200, 150
xmin, xmax, ymin, ymax = -0.2, 0.15, 0.0, 0.3
x = np.linspace(xmin, xmax, xlen)
y = np.linspace(ymin, ymax, ylen)
x_b, y_b = | np.broadcast_arrays(x[:, None], y[None, :]) | numpy.broadcast_arrays |
import csv
import cv2
import numpy as np
from keras.models import Sequential
from keras.layers import Lambda
from keras.layers.core import Dense, Activation, Flatten, Dropout
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import MaxPooling2D
from keras.layers import Cropping2D
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from keras.callbacks import ModelCheckpoint
lines=[]
with open('driving_log.csv') as csvfile:
reader=csv.reader(csvfile)
for line in reader:
lines.append(line)
images=[]
measurements=[]
i=0;
images=[]
for line in lines[1:]:
measurement=float(line[3]) #get steering angle of the car in the image
col=np.random.choice([0,1,2]) # randomly select number
filename=line[col].split('/')[-1] #get filename from the column
# image=mpimg.imread("IMG/"+filename)
images.append(plt.imread("../data/IMG/"+filename)) #get image from the filepath
if(col==1):
measurements.append(measurement+0.25) #add 0.25 to steering angle for left images
elif(col==2):
measurements.append(measurement-0.25) #subtract 0.25 to steering angle for right images
else:
measurements.append(measurement)
X_train= | np.array(images) | numpy.array |
import unittest
from functools import partial
from scipy import stats
import numpy as np
from pyapprox.leja_sequences import \
leja_objective_and_gradient, compute_finite_difference_derivative, \
leja_objective, compute_coefficients_of_leja_interpolant, \
evaluate_tensor_product_function, gradient_of_tensor_product_function, \
get_leja_sequence_1d
from pyapprox.utilities import beta_pdf_derivative
from pyapprox.indexing import compute_hyperbolic_indices
from pyapprox.variable_transformations import \
define_iid_random_variable_transformation
from pyapprox.utilities import beta_pdf_on_ab
from pyapprox.multivariate_polynomials import PolynomialChaosExpansion, \
define_poly_options_from_variable_transformation
class TestLejaSequences(unittest.TestCase):
def setup(self, num_vars, alpha_stat, beta_stat):
def univariate_weight_function(x): return beta_pdf_on_ab(
alpha_stat, beta_stat, -1, 1, x)
def univariate_weight_function_deriv(x): return beta_pdf_derivative(
alpha_stat, beta_stat, (x+1)/2)/4
weight_function = partial(
evaluate_tensor_product_function,
[univariate_weight_function]*num_vars)
weight_function_deriv = partial(
gradient_of_tensor_product_function,
[univariate_weight_function]*num_vars,
[univariate_weight_function_deriv]*num_vars)
assert np.allclose(
(univariate_weight_function(0.5+1e-6) -
univariate_weight_function(0.5))/1e-6,
univariate_weight_function_deriv(0.5), atol=1e-6)
poly = PolynomialChaosExpansion()
var_trans = define_iid_random_variable_transformation(
stats.uniform(-2, 1), num_vars)
poly_opts = define_poly_options_from_variable_transformation(var_trans)
poly.configure(poly_opts)
return weight_function, weight_function_deriv, poly
def test_leja_objective_1d(self):
num_vars = 1
alpha_stat, beta_stat = [2, 2]
# alpha_stat,beta_stat = [1,1]
weight_function, weight_function_deriv, poly = self.setup(
num_vars, alpha_stat, beta_stat)
leja_sequence = np.array([[0.2, -1., 1.]])
degree = leja_sequence.shape[1]-1
indices = | np.arange(degree+1) | numpy.arange |
# Practice sites
#https://www.machinelearningplus.com/python/101-numpy-exercises-python/
#http://www.cs.umd.edu/~nayeem/courses/MSML605/files/04_Lec4_List_Numpy.pdf
#https://www.gormanalysis.com/blog/python-numpy-for-your-grandma/
#https://nickmccullum.com/advanced-python/numpy-indexing-assignment/
# 1. Import numpy as np and see the version
# Difficulty Level: L1
# Q. Import numpy as np and print the version number.
##? 1. Import numpy as np and see the version
# Difficulty Level: L1
# Q. Import numpy as np and print the version number.
import numpy as np
print(np.__version__)
##? 2. How to create a 1D array?
# Difficulty Level: L1
# Q. Create a 1D array of numbers from 0 to 9
arr = np.arange(10)
arr
##? 3. How to create a boolean array?
# Difficulty Level: L1
# Q. Create a 3×3 numpy array of all True’s
arr = np.full((3,3), True, dtype=bool)
arr
##? 4. How to extract items that satisfy a given condition from 1D array?
# Difficulty Level: L1
# Q. Extract all odd numbers from arr
arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
arr[arr % 2 == 1]
##? 5. How to replace items that satisfy a condition with another value in numpy array?
# Difficulty Level: L1
# Q. Replace all odd numbers in arr with -1
arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
arr[arr % 2 == 1] = -1
arr
##? 6. How to replace items that satisfy a condition without affecting the original array?
# Difficulty Level: L2
# Q. Replace all odd numbers in arr with -1 without changing arr
arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
#1 np.where
out = np.where(arr % 2 == 1, -1, arr)
out
#2 list comp
out = np.array([-1 if x % 2 == 1 else x for x in arr])
out
##? 7. How to reshape an array?
# Difficulty Level: L1
# Q. Convert a 1D array to a 2D array with 2 rows
arr = np.arange(10)
arr.reshape(2, -1)
# Setting y to -1 automatically decides number of columns.
# Could do the same with
arr.reshape(2, 5)
##? 8. How to stack two arrays vertically?
# Difficulty Level: L2
# Q. Stack arrays a and b vertically
a = np.arange(10).reshape(2, -1)
b = np.repeat(1, 10).reshape(2, -1)
#1
np.vstack([a, b])
#2
np.concatenate([a, b], axis=0)
#3
np.r_[a, b]
# 9. How to stack two arrays horizontally?
# Difficulty Level: L2
# Q. Stack the arrays a and b horizontally.
a = np.arange(10).reshape(2, -1)
b = np.repeat(1, 10).reshape(2, -1)
#1
np.hstack([a, b])
#2
np.concatenate([a, b], axis=1)
#3
np.c_[a, b]
##? 10. How to generate custom sequences in numpy without hardcoding?
# Difficulty Level: L2
# Q. Create the following pattern without hardcoding.
# Use only numpy functions and the below input array a.
a = np.array([1,2,3])
np.r_[np.repeat(a,3), np.tile(a, 3)]
##? 11. How to get the common items between two python numpy arrays?
# Difficulty Level: L2
# Q. Get the common items between a and b
a = np.array([1,2,3,2,3,4,3,4,5,6])
b = np.array([7,2,10,2,7,4,9,4,9,8])
np.intersect1d(a, b)
##? 12. How to remove from one array those items that exist in another?
# Difficulty Level: L2
# Q. From array a remove all items present in array b
a = np.array([1,2,3,4,5])
b = np.array([5,6,7,8,9])
# From 'a' remove all of 'b'
np.setdiff1d(a,b)
##? 13. How to get the positions where elements of two arrays match?
# Difficulty Level: L2
# Q. Get the positions where elements of a and b match
a = np.array([1,2,3,2,3,4,3,4,5,6])
b = np.array([7,2,10,2,7,4,9,4,9,8])
np.where(a==b)
# 14. How to extract all numbers between a given range from a numpy array?
# Difficulty Level: L2
# Q. Get all items between 5 and 10 from a.
a = np.array([2, 6, 1, 9, 10, 3, 27])
#1
idx = np.where((a>=5) & (a<=10))
a[idx]
#2
idx = np.where(np.logical_and(a >= 5, a <= 10))
a[idx]
#3
a[(a >= 5) & (a <= 10)]
##? 15. How to make a python function that handles scalars to work on numpy arrays?
# Difficulty Level: L2
# Q. Convert the function maxx that works on two scalars, to work on two arrays.
def maxx(x:np.array, y:np.array):
"""Get the maximum of two items"""
if x >= y:
return x
else:
return y
a = | np.array([5, 7, 9, 8, 6, 4, 5]) | numpy.array |
"""
@brief test log(time=120s)
"""
import unittest
import warnings
import sys
from logging import getLogger
from contextlib import redirect_stdout
from io import StringIO
import numpy
import onnx
from scipy.sparse import coo_matrix, csr_matrix, SparseEfficiencyWarning
from scipy.special import ( # pylint: disable=E0611
expit as logistic_sigmoid, erf)
from scipy.spatial.distance import cdist
from onnx import TensorProto, __version__ as onnx_version
from onnx.helper import make_sparse_tensor, make_tensor
from onnx.defs import onnx_opset_version
from onnx.numpy_helper import from_array
from pyquickhelper.pycode import ExtTestCase
from pyquickhelper.texthelper import compare_module_version
from sklearn.utils.extmath import softmax
try:
from sklearn.utils._testing import ignore_warnings
except ImportError:
from sklearn.utils.testing import ignore_warnings
from skl2onnx.algebra.onnx_ops import ( # pylint: disable=E0611
OnnxAbs, OnnxAdd, OnnxAnd,
OnnxArgMax_11, OnnxArgMax,
OnnxArgMin_11, OnnxArgMin,
OnnxBatchNormalization,
OnnxAcos, OnnxAcosh, OnnxAsin, OnnxAsinh, OnnxAtan, OnnxAtanh,
OnnxAveragePool,
OnnxCast, OnnxCeil, OnnxClip,
OnnxCompress,
OnnxConcat, OnnxConv, OnnxConvTranspose,
OnnxConstant, OnnxConstant_9, OnnxConstant_11,
OnnxConstant_12, OnnxConstant_13,
OnnxConstantOfShape,
OnnxCos, OnnxCosh,
OnnxCumSum,
OnnxDequantizeLinear,
OnnxDet, OnnxDiv,
OnnxDropout, OnnxDropout_7,
OnnxEinsum, OnnxEqual, OnnxErf, OnnxExp, OnnxEyeLike,
OnnxFlatten, OnnxFloor,
OnnxGreater, OnnxGreaterOrEqual, OnnxGemm, OnnxGlobalAveragePool,
OnnxIdentity, OnnxIsNaN,
OnnxLess, OnnxLessOrEqual,
OnnxLog, OnnxLpNormalization,
OnnxMatMul, OnnxMax, OnnxMaxPool, OnnxMean, OnnxMin, OnnxMod, OnnxMul,
OnnxNeg, OnnxNot,
OnnxOr,
OnnxPad, OnnxPow,
OnnxQLinearConv, OnnxQuantizeLinear,
OnnxRange,
OnnxReciprocal,
OnnxReduceL1, OnnxReduceL2,
OnnxReduceLogSumExp, OnnxReduceMax, OnnxReduceMean, OnnxReduceMin,
OnnxReduceProd,
OnnxReduceSum, OnnxReduceSumApi11, OnnxReduceSum_11, OnnxReduceSum_1,
OnnxReduceSumSquare,
OnnxRelu, OnnxReshape,
OnnxRound,
OnnxScatterElements, OnnxShape, OnnxSlice, OnnxSigmoid, OnnxSign,
OnnxSin, OnnxSinh,
OnnxSize, OnnxSoftmax,
OnnxSplit, OnnxSplitApi11,
OnnxSqrt, OnnxSub, OnnxSum,
OnnxSqueeze, OnnxSqueezeApi11,
OnnxTan, OnnxTanh, OnnxTopK, OnnxTranspose,
OnnxUnsqueeze, OnnxUnsqueezeApi11
)
try:
from skl2onnx.algebra.onnx_ops import OnnxCelu
except ImportError:
OnnxCelu = None
try:
from skl2onnx.algebra.onnx_ops import OnnxBatchNormalization_14
except ImportError:
OnnxBatchNormalization_14 = None
from skl2onnx import __version__ as skl2onnx_version, __max_supported_opset__
from mlprodict.onnxrt import OnnxInference
from mlprodict.tools.asv_options_helper import (
get_opset_number_from_onnx, get_ir_version_from_onnx)
from mlprodict.onnxrt.validate.validate_python import validate_python_inference
from mlprodict.onnxrt.ops_cpu.op_batch_normalization import (
_batchnorm_test_mode, _batchnorm_training_mode)
from mlprodict.onnxrt.ops_cpu.op_average_pool import (
_get_output_shape, _pool, _get_pad_shape)
from mlprodict.onnxrt.ops_cpu.op_global_average_pool import _global_average_pool
from mlprodict.onnxrt.ops_cpu._op_onnx_numpy import ( # pylint: disable=E0611,E0401
topk_element_min_double, topk_element_max_double,
topk_element_fetch_double,
topk_element_min_float, topk_element_max_float, topk_element_fetch_float,
topk_element_min_int64, topk_element_max_int64, topk_element_fetch_int64)
from mlprodict.onnxrt.ops_cpu.op_celu import _vcelu1, pycelu
from mlprodict.onnxrt.ops_cpu.op_topk import topk_sorted_implementation
from mlprodict.onnxrt.ops_cpu.op_pad import _pad_impl
from mlprodict.onnxrt.ops_cpu.op_max_pool import (
_pool_get_output_shape, _pool_impl)
from mlprodict.onnxrt.ops_cpu.op_dropout import _dropout
from mlprodict.onnxrt.ops_cpu._op_helper import proto2dtype
from mlprodict.onnx_tools.onnx2py_helper import (
guess_proto_dtype, _elem_type_as_str)
from mlprodict.tools.data_types import (
FloatTensorType, Int64TensorType, DoubleTensorType, StringTensorType,
Int32TensorType, BooleanTensorType, UInt8TensorType,
Int16TensorType, Int8TensorType, UInt16TensorType,
UInt32TensorType, UInt64TensorType, Float16TensorType)
from mlprodict.testing.test_utils.quantized_tensor import (
QuantizedTensor, QuantizedBiasTensor, test_qlinear_conv)
from mlprodict.onnxrt.ops_cpu.op_qlinear_conv_ import ( # pylint: disable=W0611,E0611,E0401
test_qgemm0, test_qgemm1)
from mlprodict.onnxrt.ops_cpu.op_constant import Constant_12, Constant_11, Constant_9
try:
numpy_str = numpy.str_
except ImportError:
numpy_str = str
try:
numpy_bool = numpy.bool_
except ImportError:
numpy_bool = bool
sparse_support = []
sparse_no_numpy = []
python_tested = []
def make_coo_matrix(*args, **kwargs):
coo = coo_matrix(*args, **kwargs)
coo.row = coo.row.astype(numpy.int64)
coo.col = coo.col.astype(numpy.int64)
return coo
def wraplog():
# from datetime import datetime
def wrapper(fct):
def call_f(self):
# no = datetime.now()
# print('BEGIN %s' % fct.__name__)
with warnings.catch_warnings(record=True):
warnings.simplefilter("always", DeprecationWarning)
fct(self)
# print('DONE %s - %r' % (fct.__name__, datetime.now() - no))
return call_f
return wrapper
class TestOnnxrtPythonRuntime(ExtTestCase): # pylint: disable=R0904
@classmethod
def setUpClass(cls):
pass
@classmethod
def tearDownClass(cls):
if __name__ == "__main__":
import pprint
print('-----------')
pprint.pprint(sparse_support)
print('-----------')
pprint.pprint(sparse_no_numpy)
print('-----------')
pprint.pprint(
list(sorted({_.__name__ for _ in python_tested})))
print('-----------')
def setUp(self):
logger = getLogger('skl2onnx')
logger.disabled = True
def test_opset_skl2onnx(self):
opset_mlprodict = get_opset_number_from_onnx()
opset_skl2onnx = __max_supported_opset__
self.assertGreater(opset_skl2onnx, opset_mlprodict)
def common_expected_shapes_types(self, oinf, inputs, got, onnx_cl, model_def,
raise_shape=False):
expected_types = oinf.infer_types()
self.assertEqual(set(got) & set(expected_types), set(got))
for k, v in got.items():
if expected_types[k] in (str, numpy.str_):
# Type mismatch: dtype('<U32') != <class 'str'>
continue
if v.dtype != expected_types[k]:
raise AssertionError(
"Type mismatch: %r != %r\nexpected_types=%r\ngot=%r"
"\n----\n%r" % (
v.dtype, expected_types[k], expected_types, got,
model_def))
try:
expected_shapes = oinf.infer_shapes()
self.assertEqual(set(got) & set(expected_shapes), set(got))
except RuntimeError as e:
if raise_shape:
raise e
warnings.warn("infer_shapes fails for operator %r." % onnx_cl)
res = oinf.infer_sizes(inputs)
self.assertIsInstance(res, dict)
@ignore_warnings(category=(RuntimeWarning, DeprecationWarning,
SparseEfficiencyWarning, PendingDeprecationWarning))
def common_test_onnxt_runtime_unary(self, onnx_cl, np_fct,
op_version=None,
outputs=None, debug=False,
do_sparse=True, raise_shape=False):
if op_version is None:
op_version = get_opset_number_from_onnx()
try:
onx = onnx_cl('X', output_names=['Y'], op_version=op_version)
except RuntimeError as e:
raise RuntimeError('onnx.opset={} op_version={}'.format(
get_opset_number_from_onnx(), op_version)) from e
X = numpy.array([[1, 2], [3, -4]], dtype=numpy.float64)
model_def = onx.to_onnx(
{'X': X.astype(numpy.float32)}, target_opset=op_version,
outputs=outputs)
if debug:
print(model_def)
python_tested.append(onnx_cl)
# python code
oinfpy = OnnxInference(model_def, runtime="python", inplace=True)
validate_python_inference(oinfpy, {'X': X.astype(numpy.float32)})
# no inplace
oinf = OnnxInference(model_def, inplace=False)
all_names = "\n".join(
"%s>=v%d" % (op.ops_.__class__.__name__,
op.ops_._schema.since_version) # pylint: disable=W0212
for op in oinf.sequence_)
if debug:
got = oinf.run({'X': X.astype(numpy.float32)},
verbose=1, fLOG=print)
else:
got = oinf.run({'X': X.astype(numpy.float32)})
self.assertEqual(list(sorted(got)), ['Y'])
self.common_expected_shapes_types(
oinf, {'X': X.astype(numpy.float32)}, got, onnx_cl,
model_def, raise_shape=raise_shape)
try:
self.assertEqualArray(np_fct(X), got['Y'], decimal=5)
except AssertionError as e:
raise AssertionError(
'onnx.opset={} op_version={}\n--ONNX--\n{}\n--NAMES--\n{}'.format(
get_opset_number_from_onnx(), op_version, model_def,
all_names)) from e
# inplace
oinf = OnnxInference(model_def, input_inplace=False, inplace=True)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(np_fct(X), got['Y'], decimal=5)
# inplace2
onx2 = OnnxIdentity(
onnx_cl('X', op_version=op_version),
output_names=['Y'], op_version=op_version)
model_def2 = onx2.to_onnx(
{'X': X.astype(numpy.float32)}, target_opset=op_version,
outputs=outputs)
oinf = OnnxInference(model_def2, input_inplace=False, inplace=True)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(np_fct(X), got['Y'], decimal=5)
# input inplace
expe = np_fct(X)
oinf = OnnxInference(model_def, input_inplace=True, inplace=True)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(expe, got['Y'], decimal=5)
# sparse
if do_sparse:
row = numpy.array([0, 0, 1, 3, 1])
col = numpy.array([0, 2, 1, 3, 1])
data = numpy.array([1, 1, 1, 1, 1])
X = make_coo_matrix((data, (row.astype(numpy.int64),
col.astype(numpy.int64))),
shape=(4, 4), dtype=numpy.float32)
try:
exp = np_fct(X)
except (TypeError, NotImplementedError, ValueError) as e:
# Function np_fct does not work on sparse data.
sparse_no_numpy.append((onnx_cl.__name__, op_version, e))
return
model_def_sparse = onx.to_onnx(
{'X': X.astype(numpy.float32)}, target_opset=op_version)
oinf = OnnxInference(
model_def_sparse, input_inplace=False, inplace=True)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualSparseArray(exp, got['Y'], decimal=5)
sparse_support.append(('UnOp', op_version, onnx_cl.__name__))
@ignore_warnings(category=(RuntimeWarning, DeprecationWarning,
SparseEfficiencyWarning, PendingDeprecationWarning))
def common_test_onnxt_runtime_binary(self, onnx_cl, np_fct,
dtype=numpy.float32,
op_version=None, debug=False,
raise_shape=False):
if op_version is None:
op_version = get_opset_number_from_onnx()
idi = numpy.identity(2, dtype=dtype)
onx = onnx_cl('X', idi, output_names=['Y'], op_version=op_version)
X = numpy.array([[1, 2], [3, -4]], dtype=numpy.float64)
model_def = onx.to_onnx({'X': X.astype(dtype)},
target_opset=op_version)
oinf = OnnxInference(model_def)
if debug:
got = oinf.run({'X': X.astype(dtype)}, verbose=1, fLOG=print)
else:
got = oinf.run({'X': X.astype(dtype)})
self.assertEqual(list(sorted(got)), ['Y'])
self.common_expected_shapes_types(
oinf, {'X': X.astype(dtype)}, got, onnx_cl, model_def,
raise_shape=raise_shape)
exp = np_fct(X, idi)
self.assertEqualArray(exp, got['Y'], decimal=5)
# python code
python_tested.append(onnx_cl)
oinfpy = OnnxInference(model_def, runtime="python", inplace=True)
validate_python_inference(oinfpy, {'X': X.astype(dtype)})
# sparse
idi = make_coo_matrix(numpy.identity(2)).astype(numpy.float32)
X = make_coo_matrix(numpy.array(
[[0, 2], [3, -4]], dtype=numpy.float32))
try:
exp = np_fct(X, idi)
except (TypeError, NotImplementedError, ValueError) as e:
# Function np_fct does not work on sparse data.
sparse_no_numpy.append((onnx_cl.__name__, op_version, e))
return
onx = onnx_cl('X', idi, output_names=['Y'], op_version=op_version)
model_def_sparse = onx.to_onnx({'X': X}, target_opset=op_version)
try:
oinf = OnnxInference(
model_def_sparse, input_inplace=False, inplace=True)
except RuntimeError as e:
raise RuntimeError(
"Unable to load sparse model\n{}".format(
model_def_sparse)) from e
if debug:
got = oinf.run({'X': X}, verbose=1, fLOG=print)
else:
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
if isinstance(exp, (coo_matrix, csr_matrix)):
self.assertEqualSparseArray(exp, got['Y'], decimal=5)
elif isinstance(exp, numpy.ndarray):
self.assertEqualArray(exp, got['Y'], decimal=5)
else:
self.assertEqual(exp, got['Y'])
sparse_support.append(('BinOp', op_version, onnx_cl.__name__))
@wraplog()
def test_onnxt_runtime_abs(self):
self.common_test_onnxt_runtime_unary(OnnxAbs, numpy.abs)
@wraplog()
def test_onnxt_runtime_abs_debug(self):
f = StringIO()
with redirect_stdout(f):
self.common_test_onnxt_runtime_unary(
OnnxAbs, numpy.abs, debug=True)
@wraplog()
def test_onnxt_runtime_acos(self):
self.common_test_onnxt_runtime_unary(OnnxAcos, numpy.arccos)
@wraplog()
def test_onnxt_runtime_acosh(self):
self.common_test_onnxt_runtime_unary(OnnxAcosh, numpy.arccosh)
@wraplog()
def test_onnxt_runtime_add(self):
self.common_test_onnxt_runtime_binary(OnnxAdd, numpy.add)
@wraplog()
def test_onnxt_runtime_and(self):
self.common_test_onnxt_runtime_binary(
OnnxAnd, numpy.logical_and, dtype=numpy.bool_)
@wraplog()
def test_onnxt_runtime_argmax(self):
opsets = list(range(11, get_opset_number_from_onnx() + 1))
opsets = ['11only'] + opsets
for opset in opsets:
with self.subTest(opset=opset):
X = numpy.array([[2, 1], [0, 1]], dtype=float)
if opset == '11only':
clarg = OnnxArgMax_11
opset = 11
br = True
else:
clarg = OnnxArgMax
br = False
onx = clarg('X', output_names=['Y'], keepdims=0,
op_version=opset)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
target_opset=opset)
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(numpy.argmax(
X, axis=0), got['Y'], decimal=5)
self.common_expected_shapes_types(
oinf, {'X': X}, got, clarg, model_def)
if br:
continue
oinfpy = OnnxInference(
model_def, runtime="python", inplace=True)
validate_python_inference(
oinfpy, {'X': X.astype(numpy.float32)})
onx = OnnxArgMax('X', output_names=['Y'], axis=1, keepdims=0,
op_version=opset)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
target_opset=opset)
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(numpy.argmax(X, axis=1).ravel(),
got['Y'].ravel())
onx = OnnxArgMax('X', output_names=['Y'], axis=1, keepdims=1,
op_version=opset)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
target_opset=opset)
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(numpy.argmax(X, axis=1).ravel(),
got['Y'].ravel())
# sparse
X = make_coo_matrix(X, dtype=numpy.float32)
try:
exp = numpy.argmax(X, axis=1)
except (TypeError, NotImplementedError, ValueError) as e:
# Function np_fct does not work on sparse data.
sparse_no_numpy.append((OnnxArgMax.__name__, None, e))
return
model_def_sparse = onx.to_onnx({'X': X},
target_opset=opset)
oinf = OnnxInference(model_def_sparse, input_inplace=False)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(exp, got['Y'], decimal=5)
X = numpy.array([[2, 1], [0, 1]], dtype=float)
sparse_support.append(('UnOp', None, OnnxArgMax.__name__))
python_tested.append(OnnxArgMax)
@unittest.skipIf(onnx_opset_version() < 12, reason="needs onnx 1.7.0")
@wraplog()
def test_onnxt_runtime_argmax_12(self):
self.assertGreater(onnx_opset_version(), 12)
from skl2onnx.algebra.onnx_ops import OnnxArgMax_12 # pylint: disable=E0611
X = numpy.array([[2, 2, 1], [0, 1, 1]], dtype=float)
onx = OnnxArgMax_12('X', output_names=['Y'], keepdims=0, axis=1,
select_last_index=1, op_version=12)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
target_opset=get_opset_number_from_onnx())
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(numpy.array([1, 2], dtype=numpy.int64),
got['Y'], decimal=5)
self.common_expected_shapes_types(
oinf, {'X': X}, got, OnnxArgMax_12, model_def)
@wraplog()
def test_onnxt_runtime_argmin(self):
opsets = list(range(11, get_opset_number_from_onnx() + 1))
opsets = ['11only'] + opsets
for opset in opsets:
with self.subTest(opset=opset):
if opset == '11only':
clarg = OnnxArgMin_11
opset = 11
br = True
else:
clarg = OnnxArgMin
br = False
X = numpy.array([[2, 1], [0, 1]], dtype=float)
onx = clarg('X', output_names=['Y'], keepdims=0,
op_version=opset)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
target_opset=opset)
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(numpy.argmin(
X, axis=0), got['Y'], decimal=5)
if br:
continue
oinfpy = OnnxInference(
model_def, runtime="python", inplace=True)
validate_python_inference(
oinfpy, {'X': X.astype(numpy.float32)})
self.common_expected_shapes_types(
oinfpy, {'X': X.astype(numpy.float32)},
got, clarg, model_def)
onx = OnnxArgMin('X', output_names=['Y'], axis=1, keepdims=0,
op_version=opset)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
target_opset=opset)
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(numpy.argmin(X, axis=1).ravel(),
got['Y'].ravel())
onx = OnnxArgMin('X', output_names=['Y'], axis=1, keepdims=1,
op_version=opset)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
target_opset=opset)
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(numpy.argmin(X, axis=1).ravel(),
got['Y'].ravel())
# sparse
X = make_coo_matrix(X, dtype=numpy.float32)
try:
exp = numpy.argmin(X, axis=1)
except (TypeError, NotImplementedError, ValueError) as e:
# Function np_fct does not work on sparse data.
sparse_no_numpy.append((OnnxArgMin.__name__, None, e))
return
model_def_sparse = onx.to_onnx({'X': X}, target_opset=opset)
oinf = OnnxInference(model_def_sparse, input_inplace=False)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(exp, got['Y'], decimal=5)
sparse_support.append(('UnOp', None, OnnxArgMin.__name__))
python_tested.append(OnnxArgMin)
@unittest.skipIf(onnx_opset_version() < 12, reason="needs onnx 1.7.0")
@wraplog()
def test_onnxt_runtime_argmin_12(self):
self.assertGreater(onnx_opset_version(), 12)
from skl2onnx.algebra.onnx_ops import OnnxArgMin_12 # pylint: disable=E0611
X = numpy.array([[2, 1, 1], [0, 0, 1]], dtype=float)
onx = OnnxArgMin_12('X', output_names=['Y'], keepdims=0, axis=1,
select_last_index=1, op_version=12)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)},
target_opset=get_opset_number_from_onnx())
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(numpy.array([2, 1], dtype=numpy.int64),
got['Y'], decimal=5)
self.common_expected_shapes_types(
oinf, {'X': X}, got, OnnxArgMin_12, model_def)
@wraplog()
def test_onnxt_runtime_asin(self):
self.common_test_onnxt_runtime_unary(OnnxAsin, numpy.arcsin)
@wraplog()
def test_onnxt_runtime_asinh(self):
self.common_test_onnxt_runtime_unary(OnnxAsinh, numpy.arcsinh)
@wraplog()
def test_onnxt_runtime_atan(self):
self.common_test_onnxt_runtime_unary(OnnxAtan, numpy.arctan)
@wraplog()
def test_onnxt_runtime_atanh(self):
self.common_test_onnxt_runtime_unary(OnnxAtanh, numpy.arctanh)
@wraplog()
def test_onnxt_runtime_atan2(self):
test_pairs = [[y, x]
for x in [3., -4., 0., -1., 1.]
for y in [5., -6., 0., -1., 1.]]
y_val = numpy.array([y for y, x in test_pairs], dtype=numpy.float32)
x_val = numpy.array([x for y, x in test_pairs], dtype=numpy.float32)
def atan2(y, x):
# size: 100000
# timeit arctan: 0.00205
# timeit arctan2: 0.00361
# timeit atan2: 0.00599
sx = numpy.sign(x)
sy = numpy.sign(y)
pi_part = (sy + sx * (sy ** 2 - 1)) * (sx - 1) * (-numpy.pi / 2)
atan_part = numpy.arctan(y / (x + (1 - sx ** 2))) * sx ** 2
return atan_part + pi_part
self.assertEqualArray(
numpy.arctan2(y_val, x_val), atan2(y_val, x_val), decimal=5)
def _expect_average_pool(self, node, inputs, outputs, opset=None):
if opset is None:
opset = get_opset_number_from_onnx()
ginputs = [
onnx.helper.make_tensor_value_info(
node.input[0], TensorProto.FLOAT, []), # pylint: disable=E1101,
]
goutputs = [
onnx.helper.make_tensor_value_info(
node.output[0], TensorProto.FLOAT, []), # pylint: disable=E1101,
]
model_def = onnx.helper.make_model(
opset_imports=[onnx.helper.make_operatorsetid('', opset)],
graph=onnx.helper.make_graph(
name='test_average_pool', inputs=ginputs, outputs=goutputs,
nodes=[node]))
oinf = OnnxInference(model_def)
got = oinf.run({n: v for n, v in zip(node.input, inputs)})
self.assertEqual(len(got), 1)
self.assertEqualArray(outputs[0], got['y'])
@wraplog()
def test_onnxt_runtime_average_pool(self):
node = onnx.helper.make_node(
'AveragePool', inputs=['x'], outputs=['y'],
kernel_shape=[2, 2], auto_pad='SAME_UPPER')
x = | numpy.random.randn(1, 3, 32, 32) | numpy.random.randn |
#!/usr/bin/env python3
# Copyright 2019 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
#
# @title :split_mnist.py
# @author :ch
# @contact :<EMAIL>
# @created :04/11/2019
# @version :1.0
# @python_version :3.6.7
"""
Split MNIST Dataset
^^^^^^^^^^^^^^^^^^^
The module :mod:`data.special.split_mnist` contains a wrapper for data
handlers for the SplitMNIST task.
"""
import numpy as np
from data.mnist_data import MNISTData
def get_split_MNIST_handlers(data_path, use_one_hot=True, validation_size=0,
steps=2):
"""This method instantiates 5 objects of the class :class:`SplitMNIST` which
will contain a disjoint set of labels.
The SplitMNIST task consists of 5 tasks corresponding to the images with
labels [0,1], [2,3], [4,5], [6,7], [8,9].
Args:
data_path: Where should the MNIST dataset be read from? If not existing,
the dataset will be downloaded into this folder.
use_one_hot: Whether the class labels should be represented in a one-hot
encoding.
validation_size: The size of the validation set of each individual
data handler.
steps: Number of classes to put into one data handler. If default
every data handler will include 2 digits, otherwise 1.
Returns:
A list of data handlers, each corresponding to a :class:`SplitMNIST`
object,
"""
print('Creating data handlers for SplitMNIST tasks ...')
handlers = []
assert (steps == 1 or steps == 2)
for i in range(0, 10, steps):
handlers.append(SplitMNIST(data_path, use_one_hot=use_one_hot,
validation_size=validation_size, labels=[i, i + steps - 1]))
print('Creating data handlers for SplitMNIST tasks ... Done')
return handlers
class SplitMNIST(MNISTData):
"""An instance of the class shall represent a SplitMNIST task.
Args:
data_path: Where should the dataset be read from? If not existing,
the dataset will be downloaded into this folder.
use_one_hot: Whether the class labels should be represented in a
one-hot encoding.
validation_size: The number of validation samples. Validation
samples will be taking from the training set (the first :math:`n`
samples).
labels: The labels that should be part of this task.
full_out_dim: Choose the original MNIST instead of the the new
task output dimension. This option will affect the attributes
:attr:`data.dataset.Dataset.num_classes` and
:attr:`data.dataset.Dataset.out_shape`.
"""
def __init__(self, data_path, use_one_hot=False, validation_size=1000,
labels=[0, 1], full_out_dim=False):
super().__init__(data_path, use_one_hot=use_one_hot, validation_size=0)
K = len(labels)
# assert(K == 2)
self._labels = labels
train_ins = self.get_train_inputs()
test_ins = self.get_test_inputs()
train_outs = self.get_train_outputs()
test_outs = self.get_test_outputs()
# Get labels.
if self.is_one_hot:
train_labels = self._to_one_hot(train_outs, reverse=True)
test_labels = self._to_one_hot(test_outs, reverse=True)
else:
train_labels = train_outs
test_labels = test_outs
train_labels = train_labels.squeeze()
test_labels = test_labels.squeeze()
train_mask = train_labels == labels[0]
test_mask = test_labels == labels[0]
for k in range(1, K):
train_mask = | np.logical_or(train_mask, train_labels == labels[k]) | numpy.logical_or |
""" Generate 2 group spectra of K modes """
import numpy as np
import matplotlib.pyplot as plt
def spectra2():
""" Return data set of 600 spectra.
Spectra consist of 20 cosine modes with uniform random frequencies
from .1 to 10 in arbitray frequency units. Amplitudes are sampled
from normal distribution centered on integer group number with standard
deviation of 0.2.
"""
#constants
nmode = 20 #number of modes
ngroup = 2 # number of groups
nobv = 600 # number of observations
minfreq = .01 # slowest mode frequency
maxfreq = .5 # fastest mode frequency
sigma = .2 # amplitude standard deviation
ngrid = 1000 #spectrum grid space
#assign random mode frequencies per group
omega = np.random.uniform(low=minfreq, high=maxfreq, size=(nmode,ngroup))
#assign random groups
group = np.random.randint(ngroup, size=(1,nobv))
#assign random mode amplitutes
eta = np.random.normal(loc=0, scale=sigma, size=(nmode, nobv))
#visualize distribution of ampl
#count, bins, ignored = plt.hist(eta, 30, density=True)
#plt.show()
#init spectra
spec = np.zeros((ngrid,nobv))
#loop over groups
for g in range(ngroup):
ingroup = | np.where(group[0,:]==g) | numpy.where |
# Class version of 1D Solver
from sys import dont_write_bytecode
import numpy as np
import matplotlib as mpl
import warnings
class FEM_Simulation:
'''
Object that represents a 1D FEM Simulation.
'''
def __init__(self, Element, verbose=False):
self.Element = Element
# get initialize data from the element
self.NoElementDim, \
self.NoElementNodes, \
self.ElementDofNames, \
self.NoElementHistory, \
self.ElementMaterialNames, \
self.ElementPostNames = Element.Elmt_Init()
self.NoElementMaterial = len(self.ElementMaterialNames)
self.NoNodeDofs = len(self.ElementDofNames)
# general program variables
self.verbose = verbose
self.verbose_system = True
self.state = 0
# general discretization variables
self.time = 0.0 # current time
self.dt = 1.0 # time increment gone frome last time
self.step = 0 # current step
self.lambda_load = 0 # global load multiplier
self.NoElements = 0 # number of elements
self.NoNodes = 0 # number of nodes
self.NoDofs = 0 # number of degrees of freedom
self.XI = 0 # nodal coordinates
self.ELEM = 0 # element connectivity
self.h_n = 0 # previous history field
self.h_t = 0 # current history field
# initialize fields for boundary conditions
self.NBC = [] # python list to collect natural boundary conditions before analysis
self.NBC_Indexes = 0 # vector of indexes to the external load vector where a nbc is present
self.NBC_Values = 0 # vector of values to be placed in the external load vector for each nbc index
self.EBC = [] # python list to collect essential boundary conditions before analysis
self.EBC_Indexes = 0 # vector of indexes of constrained degrees of freedom
self.EBC_Values = 0 # vector of values for each constrained degree of freedom
self.NoEquations = 0 # number of all unconstrained dofs
# element discretization parameter
self.ElementMaterial = [] # list of material parameter
self.h_n = 0 # vector of element history field of t=t (previous)
self.h_t = 0 # vector of element history field of t=t+1 (current)
self.DI = 0 # vector of degrees of freedom
self.R_ext = 0 # vector of external forces
# make some noise
print("FEM Solver Instance Created")
if (self.verbose): print("Simulation dimensions: ", self.NoElementDim)
if (self.verbose): print("Number of element nodes: ", self.NoElementNodes)
if (self.verbose): print("Names of nodal degrees of freedom: ", self.ElementDofNames)
if (self.verbose): print("Names of element parameters: ", self.ElementMaterialNames)
if (self.verbose): print("Names of available postprocessing fields: ", self.ElementPostNames)
def Add_Mesh(self, NodesList, ElementConnectivity, verbose=False):
'''
Add_Mesh(self, NodesList, ElementConnectivity, verbose=False) -> void
Sets a mesh based on a list of nodes and matrix of element connectivity.
Input :
NodeList -> List of nodal coordinates [... , [x,y], ...]
ElementConnectivity -> Matrix of nodal indexes per element [... , [n1, n2, n3], ...]
'''
# check input
if (NodesList.ndim == 1):
no_mesh_no, mesh_dim = len(NodesList), 1
else:
no_mesh_no, mesh_dim = NodesList.shape
no_mesh_el, mesh_no_el = ElementConnectivity.shape
if (self.verbose or verbose): print('Mesh NoNodes : ',no_mesh_no)
if (self.verbose or verbose): print('Mesh Dimension : ',mesh_dim)
if (self.verbose or verbose): print('Mesh NoElements : ',no_mesh_el)
if (self.verbose or verbose): print('Mesh Nodes per Element: ',mesh_no_el)
if (self.NoElementDim != mesh_dim): raise NameError('Mesh dimension is not the same as elements.')
if (self.NoElementNodes != mesh_no_el): raise NameError('Mesh is not compatible to element topology.')
# process infos
self.NoElements = no_mesh_el
self.NoNodes = no_mesh_no
self.NoDofs = no_mesh_no * self.NoNodeDofs
if (verbose): print('Mesh Total Dofs : ',self.NoDofs)
self.XI = np.array(NodesList, dtype=np.float64)
self.ELEM = np.array(ElementConnectivity, dtype=np.uint)
if (self.verbose or self.verbose): print(' Finite Elemenmt Mesh Read!')
self.state = 1
def Add_Material(self, MaterialList, Option=None):
'''Adds Material parameters as a list [....] for the next element without already specified material. With Option=All, all elements are set with the given list of parameters.'''
if len(MaterialList) != self.NoElementMaterial:
print(
'Error: Number of material parameter does not fit element requirements!')
print(' Requred parameters are :')
print(*self.ElementMaterialNames, sep=", ")
raise NameError('Error processing material parameters!')
if Option == "All":
self.ElementMaterial = []
for i in range(self.NoElements):
self.ElementMaterial.append(MaterialList)
if (self.verbose): print(' Material set for All Elements')
if Option == None:
self.ElementMaterial.append(MaterialList)
if (self.verbose): print(' Material set for Element %i' % len(self.ElementMaterial))
def Add_EBC(self, NodeSelector, DofSelector, Value):
'''Sets an essential boundary condition by NodeSelector, DofSelector, Value'''
NodeList = self.SelectNodes(NodeSelector)
AffectedDof = self.SelectDof(DofSelector)
if AffectedDof >= self.NoNodeDofs:
print("Error: Nodal degrees of freedom do not exceed %i" %
self.NoNodeDofs)
for node in NodeList:
self.EBC.append([node, AffectedDof, Value])
def Add_NBC(self, NodeSelector, DofSelector, Value):
'''Sets an essential boundary condition by NodeSelector, DofSelector, Value'''
NodeList = self.SelectNodes(NodeSelector)
AffectedDof = self.SelectDof(DofSelector)
if AffectedDof >= self.NoNodeDofs:
print("Error: Nodal degrees of freedom do not exceed %i" %
self.NoNodeDofs)
for node in NodeList:
self.NBC.append([node, AffectedDof, Value])
def SelectDof(self, Input):
'''Returns a single integer for the dof'''
if isinstance(Input, int):
return Input
if isinstance(Input, str):
for i, dofname in enumerate(self.ElementDofNames):
if dofname == Input:
return i
raise NameError('Error ! DOF name not supported by element')
return 100
def SelectNodes(self, Input):
'''
Returns a list containing the node number that fit the input.
Input can be:
A single node index SelectNodes(0)
A list of indexes node index SelectNodes([0,1,2])
A conditional based on the dimension 1D: SelectNodes("x==0") 2D: SelectNodes("x==0 && y==0")
'''
Outlist = []
# if input is a singe integer, check if there is a node for this integer and return it in a list
if isinstance(Input, int):
if Input <= self.NoElements:
Outlist.append(Input)
else:
print('Error: %i is not a valid node number!' % Input)
return
# if input is a list, we check each entry for being an integer and proceed as before. if the entry is
# an interger we append it to the output list
if isinstance(Input, list):
for i in range(len(Input)):
if isinstance(Input[i], int):
if Input[i] <= self.NoElements:
Outlist.append(Input[i])
else:
print('Error: %i is not a valid node number!' %
Input[i])
return
else:
print(
'Error: ', Input[i], " is not a valid node number! Integer required.")
# if input is a string, it is supposed to be a conditional
if isinstance(Input, str):
# 1D - condition is x only
if (self.NoElementDim==1):
conditional = eval("lambda x: "+Input)
Outlist = np.arange(self.NoNodes)[[conditional(x) for x in self.XI]]
# 2D - condition is x and y
elif (self.NoElementDim==2):
conditional = eval("lambda x, y: "+Input)
Outlist = np.arange(self.NoNodes)[[conditional(x,y) for x, y in self.XI]]
return Outlist
def Analysis(self):
'''Enters into the Analysis phase. At least there must be finite elements and Materials'''
if self.state < 1:
self.state_report()
return
elif self.NoElements < 1:
raise NameError('Error! No Elements! Use AddMesh.')
elif len(self.ElementMaterial) != self.NoElements:
raise NameError('Error! Not sufficent Material provided! Use AddMaterial.')
# initialize history
self.h_n = np.zeros(self.NoElements * self.NoElementHistory)
self.h_t = np.copy(self.h_n)
# initialize degrees of freedom
self.DI = np.zeros(self.NoNodes * self.NoNodeDofs)
# initialize external right hand side
self.R_ext = np.zeros(self.NoNodes * self.NoNodeDofs)
# consolidate boundary conditions
self.EBC_Indexes = np.array([ node*self.NoNodeDofs+dof for node, dof, value in self.EBC], dtype=np.uint)
self.EBC_Values = np.array([ value for node, dof, value in self.EBC], dtype=np.float64)
self.NBC_Indexes = np.array([ node*self.NoNodeDofs+dof for node, dof, value in self.NBC], dtype=np.uint)
self.NBC_Values = np.array([ value for node, dof, value in self.NBC], dtype=np.float64)
self.NoEquations = self.NoNodes * self.NoNodeDofs - len(self.NBC_Indexes)
print('Entering Analysis phase')
if (self.verbose_system):
print('---------------------------------')
print('FE Setup Summary :')
print('NoElementNodes :', self.NoElementNodes)
print('NoNodeDofs :', self.NoNodeDofs)
print('ElementDofNames :', self.ElementDofNames)
print('ElementPostNames :', self.ElementPostNames)
print('NoElementHistory :', self.NoElementHistory)
print('NoElements :', self.NoElements)
print('NoNodes :', self.NoNodes)
print('NoDofs :', self.NoDofs)
print('NoEssential BC :', len(self.EBC))
print('NoNatural BC :', len(self.NBC))
print('---------------------------------')
self.state = 100
def state_report(self):
'''Gives hints to the user what to do next, based on a standard procedure.'''
if self.state == 0:
print('Input required: Call the AddMesh() function.')
elif self.state == 1:
print('state is 1')
def NextStep(self, time=1, lambda_load=1):
# check requirements
if self.state < 100:
print('Error: Simulation has not entered analysis phase via Analysis().')
if (self.verbose_system):
print('\nCurrent Time : %f5.2' % time)
self.dt = time - self.time
if self.dt < 1e-16:
print(
'Error: Time given in NextStep is smaller than internal time: %f5.2' % time)
return
# time shift time dependent variables
self.time = time
self.step += 1
self.h_n = np.copy(self.h_t)
self.lambda_load = lambda_load
# apply EBC to DI
self.DI[self.EBC_Indexes] = self.lambda_load * self.EBC_Values
# apply NBC to
self.R_ext[self.NBC_Indexes] = self.lambda_load * self.NBC_Values
return
def CallElement(self, i, verbose=False):
if i > self.NoElements:
print('Error: Input exceeds number of elements. max is : %i8' %
self.NoElements)
elmt_nodes = self.ELEM[i]
elmt_dof_indexes = np.array([i * self.NoNodeDofs + d for i in elmt_nodes for d in range(self.NoNodeDofs)], dtype=np.uint)
elmt_hist_indexes = | np.arange(i * self.NoElementHistory,(i+1) * self.NoElementHistory) | numpy.arange |
import mmcv
import numpy as np
import torch
import cv2
import random
from skimage.util import random_noise
__all__ = [
'ImageTransform', 'BboxTransform', 'MaskTransform', 'SegMapTransform',
'Numpy2Tensor'
]
class ImageTransform(object):
"""Preprocess an image.
1. rescale the image to expected size
2. normalize the image
3. flip the image (if needed)
4. pad the image (if needed)
5. transpose to (c, h, w)
"""
def __init__(self,
mean=(0, 0, 0),
std=(1, 1, 1),
to_rgb=True,
size_divisor=None):
self.mean = np.array(mean, dtype=np.float32)
self.std = np.array(std, dtype=np.float32)
self.to_rgb = to_rgb
self.size_divisor = size_divisor
def opencv_blur(self, img, mode):
if mode == 'blur':
return cv2.blur(img, (5, 5))
elif mode == 'GaussianBlur':
return cv2.GaussianBlur(img, (5, 5), 0)
elif mode == 'medianBlur':
return cv2.medianBlur(img, 5)
elif mode == 'bilateralFilter':
return cv2.bilateralFilter(img, 9, 75, 75)
def add_noise(self, img, mode):
# modes = ['gaussian', 's&p', 'poisson', 'speckle']
# random_noise() method will convert image in [0, 255] to [0, 1.0],
# inherently it use np.random.normal() to create normal distribution
# and adds the generated noised back to image
if mode == 'gaussian':
noise_img = random_noise(img, mode=mode, var=0.05 ** 2)
else:
noise_img = random_noise(img, mode=mode)
return (255 * noise_img).astype(np.uint8)
def __call__(self, img, scale, flip=False, keep_ratio=True, hsv_h=0, hsv_s=0, hsv_v=0, noisy_mode=None, blur_mode=None):
# Augment colorspace
if hsv_h+hsv_s+hsv_v > 5:
# SV augmentation by 50%
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # hue, sat, val
H = img_hsv[:, :, 0].astype(np.float32) # hue
S = img_hsv[:, :, 1].astype(np.float32) # saturation
V = img_hsv[:, :, 2].astype(np.float32) # value
a = random.uniform(-1, 1) * hsv_h + 1
b = random.uniform(-1, 1) * hsv_s + 1
c = random.uniform(-1, 1) * hsv_v + 1
H *= a
S *= b
V *= c
img_hsv[:, :, 0] = H if a < 1 else H.clip(None, 255)
img_hsv[:, :, 1] = S if b < 1 else S.clip(None, 255)
img_hsv[:, :, 2] = V if c < 1 else V.clip(None, 255)
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
# Add noise
if noisy_mode is not None:
img = self.add_noise(img, noisy_mode)
# Blur
if blur_mode is not None:
img = self.opencv_blur(img, blur_mode)
if keep_ratio:
img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
else:
img, w_scale, h_scale = mmcv.imresize(
img, scale, return_scale=True)
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
pad_shape = img.shape
else:
pad_shape = img_shape
img = img.transpose(2, 0, 1)
return img, img_shape, pad_shape, scale_factor
def bbox_flip(bboxes, img_shape, direction='horizontal'):
"""Flip bboxes horizontally or vertically.
Args:
bboxes(ndarray): shape (..., 4*k)
img_shape(tuple): (height, width)
"""
assert bboxes.shape[-1] % 4 == 0
flipped = bboxes.copy()
if direction == 'horizontal':
w = img_shape[1]
flipped[..., 0::4] = w - bboxes[..., 2::4] - 1
flipped[..., 2::4] = w - bboxes[..., 0::4] - 1
else:
h = img_shape[0]
flipped[..., 1::4] = h - bboxes[..., 3::4] - 1
flipped[..., 3::4] = h - bboxes[..., 1::4] - 1
return flipped
class BboxTransform(object):
"""Preprocess gt bboxes.
1. rescale bboxes according to image size
2. flip bboxes (if needed)
3. pad the first dimension to `max_num_gts`
"""
def __init__(self, max_num_gts=None):
self.max_num_gts = max_num_gts
def __call__(self, bboxes, img_shape, scale_factor, flip=False):
gt_bboxes = bboxes * scale_factor
if flip:
gt_bboxes = bbox_flip(gt_bboxes, img_shape)
gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_shape[1] - 1)
gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_shape[0] - 1)
if self.max_num_gts is None:
return gt_bboxes
else:
num_gts = gt_bboxes.shape[0]
padded_bboxes = np.zeros((self.max_num_gts, 4), dtype=np.float32)
padded_bboxes[:num_gts, :] = gt_bboxes
return padded_bboxes
class MaskTransform(object):
"""Preprocess masks.
1. resize masks to expected size and stack to a single array
2. flip the masks (if needed)
3. pad the masks (if needed)
"""
def __call__(self, masks, pad_shape, scale_factor, flip=False):
# aspect ratio unchanged
if isinstance(scale_factor, float):
masks = [
mmcv.imrescale(mask, scale_factor, interpolation='nearest')
for mask in masks
]
# aspect ratio changed
else:
w_ratio, h_ratio = scale_factor[:2]
if masks:
h, w = masks[0].shape[:2]
new_h = int(np.round(h * h_ratio))
new_w = int(np.round(w * w_ratio))
new_size = (new_w, new_h)
masks = [
mmcv.imresize(mask, new_size, interpolation='nearest')
for mask in masks
]
if flip:
masks = [mask[:, ::-1] for mask in masks]
padded_masks = [
mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
]
padded_masks = np.stack(padded_masks, axis=0)
return padded_masks
class SegMapTransform(object):
"""Preprocess semantic segmentation maps.
1. rescale the segmentation map to expected size
3. flip the image (if needed)
4. pad the image (if needed)
"""
def __init__(self, size_divisor=None):
self.size_divisor = size_divisor
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img = mmcv.imrescale(img, scale, interpolation='nearest')
else:
img = mmcv.imresize(img, scale, interpolation='nearest')
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
return img
class Numpy2Tensor(object):
def __init__(self):
pass
def __call__(self, *args):
if len(args) == 1:
return torch.from_numpy(args[0])
else:
return tuple([torch.from_numpy( | np.array(array) | numpy.array |
##script for finding the overlap in the top 100 most significant gene sets from msigdb for good and bad genes
##load necessary modules
import pylab as plt
import numpy as np
import math
import os
BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
##I did not write this function, from http://depts.washington.edu/clawpack/clawpack-4.6.3/python/pyclaw/plotters/colormaps.py
##-------------------------
def make_colormap(colors):
##-------------------------
"""
Define a new color map based on values specified in the dictionary
colors, where colors[z] is the color that value z should be mapped to,
with linear interpolation between the given values of z.
The z values (dictionary keys) are real numbers and the values
colors[z] can be either an RGB list, e.g. [1,0,0] for red, or an
html hex string, e.g. "#ff0000" for red.
"""
from matplotlib.colors import LinearSegmentedColormap, ColorConverter
from numpy import sort
z = sort(colors.keys())
n = len(z)
z1 = min(z)
zn = max(z)
x0 = (z - z1) / (zn - z1)
CC = ColorConverter()
R = []
G = []
B = []
for i in range(n):
#i'th color at level z[i]:
Ci = colors[z[i]]
if type(Ci) == str:
# a hex string of form '#ff0000' for example (for red)
RGB = CC.to_rgb(Ci)
else:
# assume it's an RGB triple already:
RGB = Ci
R.append(RGB[0])
G.append(RGB[1])
B.append(RGB[2])
cmap_dict = {}
cmap_dict['red'] = [(x0[i],R[i],R[i]) for i in range(len(R))]
cmap_dict['green'] = [(x0[i],G[i],G[i]) for i in range(len(G))]
cmap_dict['blue'] = [(x0[i],B[i],B[i]) for i in range(len(B))]
mymap = LinearSegmentedColormap('mymap',cmap_dict)
return mymap
##get the 100 most enriched protective and harmful gene sets for each cancer
f=open(os.path.join(BASE_DIR,'cox_regression','BLCA','good_overlap'))
BLCA_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
BLCA_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','BLCA','bad_overlap'))
BLCA_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
BLCA_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','LGG','good_overlap'))
LGG_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
LGG_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','LGG','bad_overlap'))
LGG_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
LGG_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','BRCA','good_overlap'))
BRCA_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
BRCA_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','BRCA','bad_overlap'))
BRCA_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
BRCA_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','CESC','good_overlap'))
CESC_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
CESC_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','CESC','bad_overlap'))
CESC_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
CESC_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','COAD','good_overlap'))
COAD_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
COAD_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','COAD','bad_overlap'))
COAD_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
COAD_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','GBM','good_overlap'))
GBM_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
GBM_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','GBM','bad_overlap'))
GBM_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
GBM_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','HNSC','good_overlap'))
HNSC_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
HNSC_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','HNSC','bad_overlap'))
HNSC_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
HNSC_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','KIRC','good_overlap'))
KIRC_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
KIRC_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','KIRC','bad_overlap'))
KIRC_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
KIRC_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','KIRP','good_overlap'))
KIRP_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
KIRP_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','KIRP','bad_overlap'))
KIRP_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
KIRP_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','LAML','good_overlap'))
LAML_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
LAML_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','LAML','bad_overlap'))
LAML_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
LAML_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','LIHC','good_overlap'))
LIHC_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
LIHC_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','LIHC','bad_overlap'))
LIHC_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
LIHC_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','LUAD','good_overlap'))
LUAD_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
LUAD_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','LUAD','bad_overlap'))
LUAD_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
LUAD_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','LUSC','good_overlap'))
LUSC_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
LUSC_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','LUSC','bad_overlap'))
LUSC_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
LUSC_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','SKCM','good_overlap'))
SKCM_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
SKCM_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','SKCM','bad_overlap'))
SKCM_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
SKCM_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','OV','good_overlap'))
OV_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
OV_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','OV','bad_overlap'))
OV_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
OV_bad.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','STAD','good_overlap'))
STAD_good=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
STAD_good.append(x.split('\t')[0])
x=f.readline()
f=open(os.path.join(BASE_DIR,'cox_regression','STAD','bad_overlap'))
STAD_bad=[]
x=f.readline()
while x!='':
x=f.readline()
if x=='Gene Set Name\t# Genes in Gene Set (K)\tDescription\t# Genes in Overlap (k)\tk/K\tp-value\tFDR q-value\n':
x=f.readline()
while x!='\n':
STAD_bad.append(x.split('\t')[0])
x=f.readline()
all_cancers1=[BLCA_good,BRCA_good,CESC_good,COAD_good,GBM_good,\
HNSC_good,KIRC_good,KIRP_good,LAML_good,LGG_good,LIHC_good,\
LUAD_good,LUSC_good,OV_good,SKCM_good,STAD_good]
all_cancers2=[BLCA_bad,BRCA_bad,CESC_bad,COAD_bad,GBM_bad,\
HNSC_bad,KIRC_bad,KIRP_bad,LAML_bad,LGG_bad,LIHC_bad,\
LUAD_bad,LUSC_bad,OV_bad,SKCM_bad,STAD_bad]
##create a list of lists of the overlaps, use all_cancers1 for good overlaps, all_cancers2 for bad overlaps
final_array=[]
for i in all_cancers2[::-1]:
temp=[]
for j in all_cancers2[::-1]:
temp.append(len([k for k in j if k in i]))
final_array.append(temp)
##plotting, use blue_yellow_red1 cmap for good overlaps, blue_yellow_red2 for bad overlaps
blue_yellow_red1 = make_colormap({0:'#00005C',.05:'#0000D0',.14:'#01BBCF',.15:'#33CC33',.2:'#FFFF00',.27:'#FF9900',.33:'#B47603',.35:'#A32900',1:'#751E00'})
blue_yellow_red2 = make_colormap({0:'#00005C',.05:'#0000D0',.14:'#01BBCF',.15:'#33CC33',.25:'#FFFF00',.3:'#FF9900',.38:'#B47603',.45:'#A32900',1:'#751E00'})
Z= | np.array(final_array) | numpy.array |
# Copyright (c) 2015, <NAME> (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from GPy.inference.latent_function_inference.var_dtc import VarDTC
from GPy.util.linalg import jitchol, tdot, dtrtri, dtrtrs, backsub_both_sides,\
dpotrs, dpotri, symmetrify, mdot
from GPy.core.parameterization.variational import VariationalPosterior
from GPy.util import diag
from GPy.inference.latent_function_inference.posterior import Posterior
log_2_pi = np.log(2*np.pi)
import logging, itertools
logger = logging.getLogger('vardtc')
class VarDTCFixedCov(VarDTC):
"""
An object for inference when the likelihood is Gaussian, but we want to do sparse inference.
The function self.inference returns a Posterior object, which summarizes
the posterior.
For efficiency, we sometimes work with the cholesky of Y*Y.T. To save repeatedly recomputing this, we cache it.
save_per_dim:
save the log likelihood per output dimension, this is for testing the differential gene expression analysis using BGPLVM and MRD
"""
const_jitter = 1e-6
def __init__(self, limit=1, save_per_dim=False):
#self._YYTfactor_cache = caching.cache()
from paramz.caching import Cacher
self.limit = limit
self.get_trYYT = Cacher(self._get_trYYT, limit)
self.get_YYTfactor = Cacher(self._get_YYTfactor, limit)
self.save_per_dim = save_per_dim
def set_limit(self, limit):
self.get_trYYT.limit = limit
self.get_YYTfactor.limit = limit
def _get_trYYT(self, Y):
return np.einsum("ij,ij->", Y, Y)
# faster than, but same as:
# return np.sum(np.square(Y))
def __getstate__(self):
# has to be overridden, as Cacher objects cannot be pickled.
return self.limit
def __setstate__(self, state):
# has to be overridden, as Cacher objects cannot be pickled.
self.limit = state
from paramz.caching import Cacher
self.get_trYYT = Cacher(self._get_trYYT, self.limit)
self.get_YYTfactor = Cacher(self._get_YYTfactor, self.limit)
def _get_YYTfactor(self, Y):
"""
find a matrix L which satisfies LLT = YYT.
Note that L may have fewer columns than Y.
"""
N, D = Y.shape
if (N>=D):
return Y.view(np.ndarray)
else:
return jitchol(tdot(Y))
def compute_lik_per_dim(self, psi0, A, LB, _LBi_Lmi_psi1, beta, Y):
lik_1 = (-0.5 * Y.shape[0] * (np.log(2. * np.pi) - np.log(beta)) - 0.5 * beta * np.einsum('ij,ij->j',Y,Y))
lik_2 = -0.5 * (np.sum(beta * psi0) - np.trace(A)) * np.ones(Y.shape[1])
lik_3 = -(np.sum(np.log(np.diag(LB))))
lik_4 = .5* beta**2 * ((_LBi_Lmi_psi1.dot(Y).T)**2).sum(1)
return lik_1 + lik_2 + lik_3 + lik_4
def get_VVTfactor(self, Y, prec):
return Y * prec # TODO chache this, and make it effective
def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None, Lm=None, dL_dKmm=None, fixed_covs_kerns=None, **kw):
_, output_dim = Y.shape
uncertain_inputs = isinstance(X, VariationalPosterior)
#see whether we've got a different noise variance for each datum
beta = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6)
# VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
#self.YYTfactor = self.get_YYTfactor(Y)
#VVT_factor = self.get_VVTfactor(self.YYTfactor, beta)
het_noise = beta.size > 1
if het_noise:
raise(NotImplementedError("Heteroscedastic noise not implemented, should be possible though, feel free to try implementing it :)"))
if beta.ndim == 1:
beta = beta[:, None]
# do the inference:
num_inducing = Z.shape[0]
num_data = Y.shape[0]
# kernel computations, using BGPLVM notation
Kmm = kern.K(Z).copy()
diag.add(Kmm, self.const_jitter)
if Lm is None:
Lm = jitchol(Kmm)
# The rather complex computations of A, and the psi stats
if uncertain_inputs:
psi0 = kern.psi0(Z, X)
psi1 = kern.psi1(Z, X)
if het_noise:
psi2_beta = np.sum([kern.psi2(Z,X[i:i+1,:]) * beta_i for i,beta_i in enumerate(beta)],0)
else:
psi2_beta = kern.psi2(Z,X) * beta
LmInv = dtrtri(Lm)
A = LmInv.dot(psi2_beta.dot(LmInv.T))
else:
psi0 = kern.Kdiag(X)
psi1 = kern.K(X, Z)
if het_noise:
tmp = psi1 * (np.sqrt(beta))
else:
tmp = psi1 * (np.sqrt(beta))
tmp, _ = dtrtrs(Lm, tmp.T, lower=1)
A = tdot(tmp)
# factor B
B = np.eye(num_inducing) + A
LB = jitchol(B)
# back substutue C into psi1Vf
#tmp, _ = dtrtrs(Lm, psi1.T.dot(VVT_factor), lower=1, trans=0)
#_LBi_Lmi_psi1Vf, _ = dtrtrs(LB, tmp, lower=1, trans=0)
#tmp, _ = dtrtrs(LB, _LBi_Lmi_psi1Vf, lower=1, trans=1)
#Cpsi1Vf, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
# data fit and derivative of L w.r.t. Kmm
#delit = tdot(_LBi_Lmi_psi1Vf)
# Expose YYT to get additional covariates in (YYT + Kgg):
tmp, _ = dtrtrs(Lm, psi1.T, lower=1, trans=0)
_LBi_Lmi_psi1, _ = dtrtrs(LB, tmp, lower=1, trans=0)
tmp, _ = dtrtrs(LB, _LBi_Lmi_psi1, lower=1, trans=1)
Cpsi1, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
# TODO: cache this:
# Compute fixed covariates covariance:
if fixed_covs_kerns is not None:
K_fixed = 0
for name, [cov, k] in fixed_covs_kerns.iteritems():
K_fixed += k.K(cov)
#trYYT = self.get_trYYT(Y)
YYT_covs = (tdot(Y) + K_fixed)
data_term = beta**2 * YYT_covs
trYYT_covs = np.trace(YYT_covs)
else:
data_term = beta**2 * tdot(Y)
trYYT_covs = self.get_trYYT(Y)
#trYYT = self.get_trYYT(Y)
delit = mdot(_LBi_Lmi_psi1, data_term, _LBi_Lmi_psi1.T)
data_fit = np.trace(delit)
DBi_plus_BiPBi = backsub_both_sides(LB, output_dim * np.eye(num_inducing) + delit)
if dL_dKmm is None:
delit = -0.5 * DBi_plus_BiPBi
delit += -0.5 * B * output_dim
delit += output_dim * np.eye(num_inducing)
# Compute dL_dKmm
dL_dKmm = backsub_both_sides(Lm, delit)
# derivatives of L w.r.t. psi
dL_dpsi0, dL_dpsi1, dL_dpsi2 = _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm,
data_term, Cpsi1, DBi_plus_BiPBi,
psi1, het_noise, uncertain_inputs)
# log marginal likelihood
log_marginal = _compute_log_marginal_likelihood(likelihood, num_data, output_dim, beta, het_noise,
psi0, A, LB, trYYT_covs, data_fit, Y)
if self.save_per_dim:
self.saved_vals = [psi0, A, LB, _LBi_Lmi_psi1, beta]
# No heteroscedastics, so no _LBi_Lmi_psi1Vf:
# For the interested reader, try implementing the heteroscedastic version, it should be possible
_LBi_Lmi_psi1Vf = None # Is just here for documentation, so you can see, what it was.
#noise derivatives
dL_dR = _compute_dL_dR(likelihood,
het_noise, uncertain_inputs, LB,
_LBi_Lmi_psi1Vf, DBi_plus_BiPBi, Lm, A,
psi0, psi1, beta,
data_fit, num_data, output_dim, trYYT_covs, Y, None)
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR,Y_metadata)
#put the gradients in the right places
if uncertain_inputs:
grad_dict = {'dL_dKmm': dL_dKmm,
'dL_dpsi0':dL_dpsi0,
'dL_dpsi1':dL_dpsi1,
'dL_dpsi2':dL_dpsi2,
'dL_dthetaL':dL_dthetaL}
else:
grad_dict = {'dL_dKmm': dL_dKmm,
'dL_dKdiag':dL_dpsi0,
'dL_dKnm':dL_dpsi1,
'dL_dthetaL':dL_dthetaL}
if fixed_covs_kerns is not None:
# For now, we do not take the gradients, we can compute them,
# but the maximum likelihood solution is to switch off the additional covariates....
dL_dcovs = beta * np.eye(K_fixed.shape[0]) - beta**2*tdot(_LBi_Lmi_psi1.T)
grad_dict['dL_dcovs'] = -.5 * dL_dcovs
#get sufficient things for posterior prediction
#TODO: do we really want to do this in the loop?
if 1:
woodbury_vector = (beta*Cpsi1).dot(Y)
else:
import ipdb; ipdb.set_trace()
psi1V = np.dot(Y.T*beta, psi1).T
tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
tmp, _ = dpotrs(LB, tmp, lower=1)
woodbury_vector, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
Bi, _ = dpotri(LB, lower=1)
symmetrify(Bi)
Bi = -dpotri(LB, lower=1)[0]
diag.add(Bi, 1)
woodbury_inv = backsub_both_sides(Lm, Bi)
#construct a posterior object
post = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector, K=Kmm, mean=None, cov=None, K_chol=Lm)
return post, log_marginal, grad_dict
def _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm, data_term, Cpsi1, DBi_plus_BiPBi, psi1, het_noise, uncertain_inputs):
dL_dpsi0 = -0.5 * output_dim * (beta* np.ones([num_data, 1])).flatten()
dL_dpsi1 = np.dot(data_term, Cpsi1.T)
dL_dpsi2_beta = 0.5 * backsub_both_sides(Lm, output_dim * np.eye(num_inducing) - DBi_plus_BiPBi)
if het_noise:
if uncertain_inputs:
dL_dpsi2 = beta[:, None] * dL_dpsi2_beta[None, :, :]
else:
dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, (psi1 * beta).T).T
dL_dpsi2 = None
else:
dL_dpsi2 = beta * dL_dpsi2_beta
if not uncertain_inputs:
# subsume back into psi1 (==Kmn)
dL_dpsi1 += 2.*np.dot(psi1, dL_dpsi2)
dL_dpsi2 = None
return dL_dpsi0, dL_dpsi1, dL_dpsi2
def _compute_dL_dR(likelihood, het_noise, uncertain_inputs, LB, _LBi_Lmi_psi1Vf, DBi_plus_BiPBi, Lm, A, psi0, psi1, beta, data_fit, num_data, output_dim, trYYT, Y, VVT_factr=None):
# the partial derivative vector for the likelihood
if likelihood.size == 0:
# save computation here.
dL_dR = None
elif het_noise:
if uncertain_inputs:
raise(NotImplementedError, "heteroscedatic derivates with uncertain inputs not implemented")
else:
#from ...util.linalg import chol_inv
#LBi = chol_inv(LB)
LBi, _ = dtrtrs(LB,np.eye(LB.shape[0]))
Lmi_psi1, nil = dtrtrs(Lm, psi1.T, lower=1, trans=0)
_LBi_Lmi_psi1, _ = dtrtrs(LB, Lmi_psi1, lower=1, trans=0)
dL_dR = -0.5 * beta + 0.5 * VVT_factr**2
dL_dR += 0.5 * output_dim * (psi0 - np.sum(Lmi_psi1**2,0))[:,None] * beta**2
dL_dR += 0.5*np.sum(mdot(LBi.T,LBi,Lmi_psi1)*Lmi_psi1,0)[:,None]*beta**2
dL_dR += -np.dot(_LBi_Lmi_psi1Vf.T,_LBi_Lmi_psi1).T * Y * beta**2
dL_dR += 0.5*np.dot(_LBi_Lmi_psi1Vf.T,_LBi_Lmi_psi1).T**2 * beta**2
else:
# likelihood is not heteroscedatic
dL_dR = -0.5 * num_data * output_dim * beta + 0.5 * trYYT * beta ** 2
dL_dR += 0.5 * output_dim * (psi0.sum() * beta ** 2 - np.trace(A) * beta)
dL_dR += beta * (0.5 * np.sum(A * DBi_plus_BiPBi) - data_fit)
return dL_dR
def _compute_log_marginal_likelihood(likelihood, num_data, output_dim, beta, het_noise, psi0, A, LB, trYYT_covs, data_fit, Y):
#compute log marginal likelihood
if het_noise:
lik_1 = -0.5 * num_data * output_dim * np.log(2. * np.pi) + 0.5 * output_dim * np.sum(np.log(beta)) - 0.5 * np.sum(beta.ravel() * np.square(Y).sum(axis=-1))
lik_2 = -0.5 * output_dim * (np.sum(beta.flatten() * psi0) - np.trace(A))
else:
lik_1 = -0.5 * num_data * output_dim * (np.log(2. * np.pi) - | np.log(beta) | numpy.log |
import random
import pandas as pd
import numpy as np
from multiprocessing import Pool
from scipy.spatial import distance
from scipy.spatial.distance import cdist
from src.configs import GENERAL, PREPROCESSING, MODELING
N_RANDOM_OBS = None
N_POTENTIAL_EL = 3
DISTANCE_TYPE = MODELING['distance_type']
class TripletGenerator:
def __init__(self, n_jobs=5):
self.n_jobs = n_jobs
self.paired_nodes = []
@staticmethod
def map_parallel(func, iterable_args, n_jobs=1):
if n_jobs==1:
return map(func, iterable_args)
with Pool(n_jobs) as pool:
result = pool.starmap(func, iterable_args)
return result
@staticmethod
def corrected_cosine(x, y, corr):
x, y, corr = np.array(x), np.array(y), np.array(corr)
corrected_x = x - corr
corrected_y = y - corr
return distance.cosine(corrected_x, corrected_y)
def choose_pos_x_hard(self, X, y, anchor_x, anchor_y, n_random_objects=N_RANDOM_OBS, distance_type=DISTANCE_TYPE):
"""
choose the pos label with attention on the most remote examples
"""
X = X[y==anchor_y]
y = y[y==anchor_y]
if n_random_objects is not None:
n_random_objects = n_random_objects if n_random_objects < X.shape[0] else X.shape[0]
else:
n_random_objects = X.shape[0]
indices = np.random.choice(X.shape[0], n_random_objects, replace=False)
X, y = X[indices], y[indices]
y = np.array(y)
if distance_type == 'euclidean':
d = self.map_parallel(
lambda x, y: distance.euclidean(x, y)/distance.cosine(x, y),
[(anchor_x, ex) for ex in X])
elif distance_type == 'cosine':
d = self.map_parallel(distance.cosine, [(anchor_x, ex) for ex in X])
elif distance_type == 'minkowski':
d = self.map_parallel(
lambda x, y: distance.minkowski(x, y)/distance.cosine(x, y),
[(anchor_x, ex) for ex in X])
elif distance_type == 'chebyshev':
d = self.map_parallel(
lambda x, y: distance.chebyshev(x, y)/distance.cosine(x, y),
[(anchor_x, ex) for ex in X])
elif distance_type == 'cityblock':
d = self.map_parallel(
lambda x, y: distance.cityblock(x, y)/distance.cosine(x, y),
[(anchor_x, ex) for ex in X])
else:
raise KeyError('Unknown distance metric!')
#print('pos', d.shape, X.shape)
pos_x = X[np.argmax(d)]
return pos_x
def choose_neg_x_hard(self, X, y, anchor_x, pos_x, anchor_y, n_random_objects=N_RANDOM_OBS, distance_type=DISTANCE_TYPE):
"""
choose the neg label with attention on the closest exaples
"""
X = X[y!=anchor_y]
y = y[y!=anchor_y]
if n_random_objects is not None:
n_random_objects = n_random_objects if n_random_objects < X.shape[0] else X.shape[0]
else:
n_random_objects = X.shape[0]
indices = np.random.choice(X.shape[0], n_random_objects, replace=False)
X, y = X[indices], y[indices]
y = np.array(y)
if distance_type == 'euclidean':
d = self.map_parallel(
lambda x, y: distance.euclidean(x, y)/self.corrected_cosine(pos_x, y, anchor_x),
[(anchor_x, ex) for ex in X])
elif distance_type == 'cosine':
d = self.map_parallel(distance.cosine, [(anchor_x, ex) for ex in X])
elif distance_type == 'minkowski':
d = self.map_parallel(
lambda x, y: distance.minkowski(x, y)/self.fixed_cosine(pos_x, y, anchor_x),
[(anchor_x, ex) for ex in X])
elif distance_type == 'chebyshev':
d = self.map_parallel(
lambda x, y: distance.chebyshev(x, y)/self.fixed_cosine(pos_x, y, anchor_x),
[(anchor_x, ex) for ex in X])
elif distance_type == 'cityblock':
d = self.map_parallel(
lambda x, y: distance.cityblock(x, y)/self.fixed_cosine(pos_x, y, anchor_x),
[(anchor_x, ex) for ex in X])
else:
raise KeyError('Unknown distance metric!')
#print('neg', d.shape, X.shape)
neg_x = X[np.argmin(d)]
return neg_x
def get_triplet(self, X, y):
# choose random class
probs = np.array([y[y==cls].shape[0]/y.shape[0] for cls in y])
probs = probs/sum(probs)
anchor_y = np.random.choice(y, p=probs)
anchor_x_idx = np.random.choice(X[y==anchor_y].shape[0])
anchor_x = X[y==anchor_y][anchor_x_idx]
if y[y==anchor_y].shape[0] == 1:
pos_x = anchor_x
else:
pos_x = self.choose_pos_x_hard(X, y, anchor_x, anchor_y)
neg_x = self.choose_neg_x_hard(X, y, anchor_x, pos_x, anchor_y)
return anchor_x, pos_x, neg_x
def generate_triplets(self, X, y, batch_size):
while 1:
list_a = []
list_p = []
list_n = []
for i in range(batch_size):
a, p, n = self.get_triplet(X, y)
list_a.append(a)
list_p.append(p)
list_n.append(n)
A = np.array(list_a, dtype='float32')
P = np.array(list_p, dtype='float32')
N = np.array(list_n, dtype='float32')
# a "dummy" label which will come in to our identity loss
# function below as y_true. We'll ignore it.
label = | np.ones(batch_size) | numpy.ones |
from __future__ import print_function
import string
import sys
import os
from collections import deque
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.switch_backend('Agg')
import tensorflow as tf
import keras
keras.backend.image_data_format()
from keras import backend as K
from keras import regularizers
from keras.layers import Input, Dense, Reshape, Lambda, Conv1D, Flatten, MaxPooling1D, UpSampling1D, GlobalMaxPooling1D
from keras.layers import LSTM, Bidirectional, BatchNormalization, Dropout, Concatenate, Embedding, Activation, Dot, dot
from keras.models import Model, clone_model, Sequential
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping,ModelCheckpoint
from keras.constraints import unitnorm
from keras_layer_normalization import LayerNormalization
tf.keras.backend.set_floatx('float32')
import sklearn as sk
from sklearn.base import BaseEstimator, _pprint
from sklearn.utils import check_array, check_random_state
from sklearn.utils.validation import check_is_fitted
from sklearn.preprocessing import StandardScaler
from sklearn.manifold import LocallyLinearEmbedding, MDS, Isomap, TSNE
from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA, SparsePCA, TruncatedSVD, FastICA, NMF, MiniBatchDictionaryLearning
from sklearn.random_projection import GaussianRandomProjection, SparseRandomProjection
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import KFold, GroupKFold, train_test_split
from sklearn.metrics import mean_squared_error, explained_variance_score, mean_absolute_error, median_absolute_error, r2_score
from sklearn.metrics import average_precision_score, precision_score, recall_score, f1_score, roc_auc_score, matthews_corrcoef
from sklearn.metrics import roc_curve, precision_recall_curve, RocCurveDisplay, PrecisionRecallDisplay
from sklearn.metrics import roc_auc_score,accuracy_score,matthews_corrcoef
from scipy import stats
from scipy.stats import multivariate_normal, kurtosis, skew, pearsonr, spearmanr
import processSeq
from processSeq import load_seq_1, kmer_dict, load_signal_1, load_seq_2, load_seq_2_kmer, load_seq_altfeature
import xgboost
import pickle
import os.path
from optparse import OptionParser
import time
from timeit import default_timer as timer
import utility_1
from utility_1 import mapping_Idx
import h5py
import json
# generate sequences
# idx_sel_list: chrom, serial
# seq_list: relative positions
def generate_sequences(idx_sel_list, gap_tol=5, region_list=[]):
chrom = idx_sel_list[:,0]
chrom_vec = np.unique(chrom)
chrom_vec = np.sort(chrom_vec)
seq_list = []
print(len(chrom),chrom_vec)
for chrom_id in chrom_vec:
b1 = np.where(chrom==chrom_id)[0]
t_serial = idx_sel_list[b1,1]
prev_serial = t_serial[0:-1]
next_serial = t_serial[1:]
distance = next_serial-prev_serial
b2 = np.where(distance>gap_tol)[0]
if len(b2)>0:
if len(region_list)>0:
# print('region_list',region_list,len(b2))
b_1 = np.where(region_list[:,0]==chrom_id)[0]
# print(b2)
t_serial = idx_sel_list[b2,1]
if len(b_1)>0:
# b2 = np.setdiff1d(b2,region_list[b_1,1])
# print(region_list,region_list[b_1,1],len(b2))
t_id1 = utility_1.mapping_Idx(t_serial,region_list[b_1,1])
t_id1 = t_id1[t_id1>=0]
t_id2 = b2[t_id1]
b2 = np.setdiff1d(b2,t_id2)
# print(len(b2))
# print(idx_sel_list[b2])
# return
# print('gap',len(b2))
if len(b2)>0:
t_seq = list(np.vstack((b2[0:-1]+1,b2[1:])).T)
t_seq.insert(0,np.asarray([0,b2[0]]))
t_seq.append(np.asarray([b2[-1]+1,len(b1)-1]))
else:
t_seq = [np.asarray([0,len(b1)-1])]
# print(t_seq)
# print(chrom_id,len(t_seq),max(distance))
seq_list.extend(b1[np.asarray(t_seq)])
return np.asarray(seq_list)
# select sample
def sample_select2a1(x_mtx, y, idx_sel_list, seq_list, tol=5, L=5):
num_sample = len(idx_sel_list)
num1 = len(seq_list)
size1 = 2*L+1
print(num_sample,num1,size1)
feature_dim = x_mtx.shape[1]
vec1_local = np.zeros((num_sample,size1),dtype=int)
vec1_serial = np.zeros((num_sample,size1),dtype=int)
feature_mtx = np.zeros((num_sample,size1,feature_dim),dtype=np.float32)
signal_mtx = np.zeros((num_sample,size1))
ref_serial = idx_sel_list[:,1]
id_vec = np.zeros(num_sample,dtype=np.int8)
for i in range(0,num1):
s1, s2 = seq_list[i][0], seq_list[i][1]+1
serial = ref_serial[s1:s2]
id_vec[s1:s2] = 1
# print('start stop',s1,s2,serial)
num2 = len(serial)
t1 = np.outer(list(range(s1,s2)),np.ones(size1))
t2 = t1 + np.outer(np.ones(num2),list(range(-L,L+1)))
t2[t2<s1] = s1
t2[t2>=s2] = s2-1
idx = np.int64(t2)
# print(idx)
vec1_local[s1:s2] = idx
vec1_serial[s1:s2] = ref_serial[idx]
feature_mtx[s1:s2] = x_mtx[idx]
signal_mtx[s1:s2] = y[idx]
# if i%10000==0:
# print(i,num2,vec1_local[s1],vec1_serial[s1])
id1 = np.where(id_vec>0)[0]
num2 = len(id1)
if num2<num_sample:
feature_mtx, signal_mtx = feature_mtx[id1], signal_mtx[id1]
# vec1_serial, vec1_local = vec1_serial[id1], vec1_local[id1]
vec1_serial = vec1_serial[id1]
id_1 = -np.ones(sample_num,dtype=np.int64)
id_1[id1] = np.arange(num2)
vec1_local = id_1[vec1_local]
b1 = np.where(vec1_local<0)[0]
if len(b1)>0:
print('error!',b1)
return -1
# signal_mtx = signal_mtx[:,np.newaxis]
signal_mtx = np.expand_dims(signal_mtx, axis=-1)
# signal_mtx = np.expand_dims(signal_ntx, axis=-1)
return feature_mtx, signal_mtx, vec1_serial, vec1_local
def score_2a(y, y_predicted):
score1 = mean_squared_error(y, y_predicted)
score2 = pearsonr(y, y_predicted)
score3 = explained_variance_score(y, y_predicted)
score4 = mean_absolute_error(y, y_predicted)
score5 = median_absolute_error(y, y_predicted)
score6 = r2_score(y, y_predicted)
score7, pvalue = spearmanr(y,y_predicted)
# vec1 = [score1, score2[0], score2[1], score3, score4, score5, score6]
vec1 = [score1, score2[0], score2[1], score3, score4, score5, score6, score7, pvalue]
return vec1
def read_phyloP(species_name):
path1 = './'
filename1 = '%s/estimate_rt/estimate_rt_%s.txt'%(path1,species_name)
# filename2a = 'test_seq_%s.1.txt'%(species_name)
file1 = pd.read_csv(filename1,sep='\t')
col1, col2, col3 = '%s.chrom'%(species_name), '%s.start'%(species_name), '%s.stop'%(species_name)
chrom_ori, start_ori, stop_ori, serial_ori = np.asarray(file1[col1]), np.asarray(file1[col2]), np.asarray(file1[col3]), np.asarray(file1['serial'])
num_sample = len(chrom_ori)
chrom_vec = np.unique(chrom_ori)
chrom_vec = ['chr22']
for chrom_id in chrom_vec:
filename1 = '%s/phyloP/hg19.phyloP100way.%s.bedGraph'%(path1,chrom_id)
data1 = pd.read_csv(filename1,header=None,sep='\t')
chrom, start, stop, score = data1[0], data1[1], data1[2], data1[3]
len1 = stop-start
b = np.where(chrom_ori==chrom_id)[0]
num_sample1 = len(b)
vec1 = np.zeros((num_sample1,16))
print(chrom_id,len(chrom),len(b))
cnt = 0
b1 = [-1]
for i in b:
t1 = b1[-1]+1
b1 = np.where((start[t1:]>=start_ori[i])&(stop[t1:]<stop_ori[i]))[0]+t1
if len(b1)==0:
b1 = [-1]
continue
t_len1, t_score = np.asarray(len1[b1]), np.asarray(score[b1])
s1 = 0
s2 = np.sum(t_len1)
i1 = cnt
for j in range(0,12):
temp1 = (j-8)*2.5
b2 = np.where((t_score<temp1+2.5)&(t_score>=temp1))[0]
print(b2)
vec1[i1,j] = np.sum(t_len1[b2])*1.0/s2
s1 = s1+temp1*vec1[i1,j]
vec1[i1,12] = s1 # average
vec1[i1,13] = np.median(t_score)
vec1[i1,14] = np.max(t_score)
vec1[i1,15] = np.min(t_score)
cnt += 1
if cnt%1000==0:
print(cnt,len(b1),s2,vec1[i1,12:16])
break
# dict1 = dict()
# dict1['vec'], dict1['index'] = vec1,b
# np.save('phyloP_%s'%(chrom_id),dict1,allow_pickle=True)
fields = ['index']
for j in range(0,12):
temp1 = (j-8)*2.5
fields.append('%s-%s'%(temp1,temp1+2.5))
fields.extend(range(0,4))
data1 = pd.DataFrame(data = np.hstack((b[:,np.newaxis],vec1)),columns=fields)
data1.to_csv('phyloP_%s.txt'%(chrom_id),sep='\t',index=False)
return vec1
def read_phyloP_1(ref_filename,header,file_path,chrom_vec,n_level=15,offset=10,magnitude=2):
file1 = pd.read_csv(ref_filename,header=header,sep='\t')
# col1, col2, col3 = '%s.chrom'%(species_name), '%s.start'%(species_name), '%s.stop'%(species_name)
colnames = list(file1)
col1, col2, col3, col4 = colnames[0], colnames[1], colnames[2], colnames[3]
chrom_ori, start_ori, stop_ori, serial_ori = np.asarray(file1[col1]), np.asarray(file1[col2]), np.asarray(file1[col3]), np.asarray(file1[col4])
num_sample = len(chrom_ori)
# chrom_vec = np.unique(chrom_ori)
# chrom_vec = [chrom_id]
# n_level, offset, magnitude = 15, 10, 2
score_max = (n_level-offset)*magnitude
for chrom_id in chrom_vec:
# filename1 = '%s/hg19.phyloP100way.%s.bedGraph'%(file_path,chrom_id)
filename1 = '%s/chr%s.phyloP100way.bedGraph'%(file_path,chrom_id)
data1 = pd.read_csv(filename1,header=None,sep='\t')
chrom, start, stop, score = data1[0], data1[1], data1[2], data1[3]
len1 = stop-start
chrom_id1 = 'chr%s'%(chrom_id)
b = np.where(chrom_ori==chrom_id1)[0]
num_sample1 = len(b)
vec1 = np.zeros((num_sample1,n_level+4))
print(chrom_id,len(chrom),len(b))
cnt = 0
m_idx = len(start)-1
start_idx = 0
print("number of regions", len(b))
for i in b:
t_start, t_stop = start_ori[i], stop_ori[i] # position of zero region
position = [t_start,t_stop]
if start_idx<=m_idx:
b1, start_idx = utility_1.search_region_include(position, start, stop, m_idx, start_idx)
# print(count,t_start,t_stop,t_stop-t_start,start_idx,len(id3))
if len(b1)==0:
continue
t_len1, t_score = np.asarray(len1[b1]), np.asarray(score[b1])
t_score[t_score>score_max] = score_max-1e-04
s1 = 0
s2 = np.sum(t_len1)
for j in range(0,n_level):
temp1 = (j-offset)*magnitude
b2 = np.where((t_score<temp1+magnitude)&(t_score>=temp1))[0]
# print(b2)
vec1[cnt,j] = np.sum(t_len1[b2])*1.0/s2
s1 = s1+temp1*vec1[cnt,j]
vec1[cnt,n_level:n_level+4] = [s1,np.median(t_score),np.max(t_score),np.min(t_score)]
cnt += 1
pre_b1 = b1
if cnt%1000==0:
print(chrom_id,cnt,len(b1),s2,vec1[cnt,-4:])
# break
# dict1 = dict()
# dict1['vec'], dict1['index'] = vec1,b
# np.save('phyloP_%s'%(chrom_id),dict1,allow_pickle=True)
fields = ['index']
for j in range(0,n_level):
temp1 = (j-offset)*magnitude
fields.append('%s-%s'%(temp1,temp1+magnitude))
fields.extend(range(0,4))
idx = serial_ori[b]
data1 = pd.DataFrame(data = np.hstack((idx[:,np.newaxis],vec1)),columns=fields)
data1.to_csv('phyloP_%s.txt'%(chrom_id),sep='\t',index=False)
return vec1
def read_motif_1(filename,output_filename=-1):
data1 = pd.read_csv(filename,sep='\t')
colnames = list(data1)
col1, col2, col3 = colnames[0], colnames[1], colnames[2]
chrom, start, stop = np.asarray(data1[col1]), np.asarray(data1[col2]), np.asarray(data1[col3])
region_len = stop-start
m1, m2, median_len = np.max(region_len), np.min(region_len), np.median(region_len)
b1 = np.where(region_len!=median_len)[0]
print(m1,m2,median_len,len(b1))
bin_size = median_len
motif_name = colnames[3:]
mtx1 = np.asarray(data1.loc[:,motif_name])
mtx1 = mtx1*1000.0/np.outer(region_len,np.ones(mtx1.shape[1]))
print('motif',len(motif_name))
print(mtx1.shape)
print(np.max(mtx1),np.min(mtx1),np.median(mtx1))
if output_filename!=-1:
fields = colnames
data1 = pd.DataFrame(columns=fields)
data1[colnames[0]], data1[colnames[1]], data1[colnames[2]] = chrom, start, stop
num1 = len(fields)-3
for i in range(0,num1):
data1[colnames[i+3]] = mtx1[:,i]
data1.to_csv(output_filename,header=True,index=False,sep='\t')
print(output_filename, data1.shape)
return mtx1, chrom, start, stop, colnames
def read_gc_1(ref_filename,header,filename,output_filename):
sel_idx = []
file1 = pd.read_csv(ref_filename,header=header,sep='\t')
f_list = load_seq_altfeature(filename,sel_idx)
# col1, col2, col3 = '%s.chrom'%(species_name), '%s.start'%(species_name), '%s.stop'%(species_name)
colnames = list(file1)
col1, col2, col3, col4 = colnames[0], colnames[1], colnames[2], colnames[3]
chrom_ori, start_ori, stop_ori, serial_ori = np.asarray(file1[col1]), np.asarray(file1[col2]), np.asarray(file1[col3]), np.asarray(file1[col4])
num_sample = len(chrom_ori)
if num_sample!=f_list.shape[0]:
print('error!',num_sample,f_list.shape[0])
fields = ['chrom','start','stop','serial','GC','GC_N','GC_skew']
file2 = pd.DataFrame(columns=fields)
file2['chrom'], file2['start'], file2['stop'], file2['serial'] = chrom_ori, start_ori, stop_ori, serial_ori
for i in range(0,3):
file2[fields[i+4]] = f_list[:,i]
file2.to_csv(output_filename,index=False,sep='\t')
return f_list
def generate_serial(filename1,chrom,start,stop):
# chrom_vec = np.sort(np.unique(chrom))
# print(chrom_vec)
chrom_vec = []
for i in range(1,23):
chrom_vec.append('chr%d'%(i))
chrom_vec += ['chrX']
chrom_vec += ['chrY']
print(chrom_vec)
# print(chrom)
print(len(chrom))
data1 = pd.read_csv(filename1,header=None,sep='\t')
ref_chrom, chrom_size = np.asarray(data1[0]), np.asarray(data1[1])
serial_start = 0
serial_vec = np.zeros(len(chrom))
bin_size = stop[1]-start[1]
print(bin_size)
for chrom_id in chrom_vec:
b1 = np.where(ref_chrom==chrom_id)[0]
t_size = chrom_size[b1[0]]
b2 = np.where(chrom==chrom_id)[0]
if len(b1)>0:
size1 = int(np.ceil(t_size*1.0/bin_size))
serial = np.int64(start[b2]/bin_size)+serial_start
serial_vec[b2] = serial
print(chrom_id,b2,len(serial),serial_start,size1)
serial_start = serial_start+size1
else:
print("error!")
return
return np.int64(serial_vec)
def generate_serial_local(filename1,chrom,start,stop,chrom_num):
# chrom_vec = np.sort(np.unique(chrom))
# print(chrom_vec)
chrom_vec = []
for i in range(1,chrom_num+1):
chrom_vec.append('chr%d'%(i))
chrom_vec += ['chrX']
chrom_vec += ['chrY']
chrom_vec += ['chrM']
print(chrom_vec)
print(chrom)
print(len(chrom))
t_chrom = np.unique(chrom)
data1 = pd.read_csv(filename1,header=None,sep='\t')
ref_chrom, chrom_size = np.asarray(data1[0]), np.asarray(data1[1])
# serial_start = np.zeros(len(chrom))
serial_start = 0
serial_start_1 = dict()
serial_vec = np.zeros(len(chrom))
bin_size = stop[1]-start[1]
print(bin_size)
for chrom_id in chrom_vec:
b1 = np.where(ref_chrom==chrom_id)[0]
t_size = chrom_size[b1[0]]
serial_start_1[chrom_id] = serial_start
size1 = int(np.ceil(t_size*1.0/bin_size))
serial_start = serial_start+size1
for chrom_id in t_chrom:
b2 = np.where(chrom==chrom_id)
serial = np.int64(start[b2]/bin_size)+serial_start_1[chrom_id]
serial_vec[b2] = serial
return np.int64(serial_vec)
def generate_serial_start(filename1,chrom,start,stop,chrom_num=19):
# chrom_vec = np.sort(np.unique(chrom))
# print(chrom_vec)
chrom_vec = []
for i in range(1,chrom_num+1):
chrom_vec.append('chr%d'%(i))
chrom_vec += ['chrX']
chrom_vec += ['chrY']
print(chrom_vec)
print(chrom)
print(len(chrom))
data1 = pd.read_csv(filename1,header=None,sep='\t')
ref_chrom, chrom_size = np.asarray(data1[0]), np.asarray(data1[1])
serial_start = 0
serial_vec = -np.ones(len(chrom))
bin_size = stop[1]-start[1]
print(bin_size)
start_vec = dict()
for chrom_id in chrom_vec:
start_vec[chrom_id] = serial_start
b1 = np.where(ref_chrom==chrom_id)[0]
t_size = chrom_size[b1[0]]
b2 = np.where(chrom==chrom_id)[0]
if len(b1)>0:
size1 = int(np.ceil(t_size*1.0/bin_size))
serial = np.int64(start[b2]/bin_size)+serial_start
serial_vec[b2] = serial
print(chrom_id,b2,len(serial),serial_start,size1)
serial_start = serial_start+size1
else:
print("error!")
return
return np.int64(serial_vec), start_vec
def shuffle_array(vec):
num1 = len(vec)
idx = np.random.permutation(num1)
vec = vec[idx]
return vec, idx
# input: estimated attention, type_id: training, validation, or test data
# output: ranking of attention
def select_region1_sub(filename,type_id):
data1 = pd.read_csv(filename,sep='\t')
colnames = list(data1)
# chrom start stop serial signal predicted_signal predicted_attention
chrom, start, serial = data1['chrom'], data1['start'], data1['serial']
chrom, start, serial = np.asarray(chrom), np.asarray(start), np.asarray(serial)
predicted_attention = data1['predicted_attention']
predicted_attention = np.asarray(predicted_attention)
ranking = stats.rankdata(predicted_attention,'average')/len(predicted_attention)
rank1 = np.zeros((len(predicted_attention),2))
rank1[:,0] = ranking
chrom_vec = np.unique(chrom)
for t_chrom in chrom_vec:
b1 = np.where(chrom==t_chrom)[0]
t_attention = predicted_attention[b1]
t_ranking = stats.rankdata(t_attention,'average')/len(t_attention)
rank1[b1,1] = t_ranking
data1['Q1'] = rank1[:,0] # rank across all the included chromosomes
data1['Q2'] = rank1[:,1] # rank by each chromosome
data1['typeId'] = np.int8(type_id*np.ones(len(rank1)))
return data1,chrom_vec
# merge estimated attention from different training/test splits
# type_id1: chromosome order; type_id2: training: 0, test: 1, valid: 2
def select_region1_merge(filename_list,output_filename,type_id1=0,type_id2=1):
list1 = []
chrom_numList = []
# b1 = np.where((self.chrom!='chrX')&(self.chrom!='chrY'))[0]
# ref_chrom, ref_start, ref_serial = self.chrom[b1], self.start[b1], self.serial[b1]
# num_sameple = len(ref_chrom)
i = 0
serial1 = []
num1 = len(filename_list)
vec1 = list(range(num1))
if type_id1==1:
vec1 = list(range(num1-1,-1,-1))
for i in vec1:
filename1 = filename_list[i]
# data1: chrom, start, stop, serial, signal, predicted_signal, predicted_attention, Q1, Q2, typeId
# typeId: training: 0, test: 1, valid: 2
data1, chrom_vec = select_region1_sub(filename1,type_id2)
print(filename1,len(data1))
# list1.append(data1)
# if i==0:
# serial1 = np.asarray(data1['serial'])
t_serial = np.asarray(data1['serial'],dtype=np.int64)
t_serial2 = np.setdiff1d(t_serial,serial1)
serial1 = np.union1d(serial1,t_serial)
id1 = mapping_Idx(t_serial,t_serial2)
colnames = list(data1)
data1 = data1.loc[id1,colnames]
list1.append(data1)
chrom_numList.append(chrom_vec)
data2 = pd.concat(list1, axis=0, join='outer', ignore_index=True,
keys=None, levels=None, names=None, verify_integrity=False, copy=True)
print('sort')
data2 = data2.sort_values(by=['serial'])
data2.to_csv(output_filename,index=False,sep='\t')
return data2, chrom_numList
class Reader(object):
def __init__(self, ref_filename, feature_idvec = [1,1,1,1]):
# Initializes RepliSeq
self.ref_filename = ref_filename
self.feature_idvec = feature_idvec
def generate_serial(self,filename1,filename2,output_filename,header=None):
data1 = pd.read_csv(filename2, header=header, sep='\t')
colnames = list(data1)
chrom, start, stop = np.asarray(data1[colnames[0]]), np.asarray(data1[colnames[1]]), np.asarray(data1[colnames[2]])
serial_vec, start_vec = generate_serial_start(filename1,chrom,start,stop)
if output_filename!=None:
colnames2 = colnames[0:3]+['serial']+colnames[3:]
data2 = pd.DataFrame(columns=colnames2)
data2['serial'] = serial_vec
for colname1 in colnames:
data2[colname1] = data1[colname1]
flag = False
if header!=None:
flag = True
data2.to_csv(output_filename,header=flag,index=False,sep='\t')
return serial_vec, start_vec
def load_motif(self,filename1,motif_filename,output_filename):
# output_filename = None
# ref_filename = 'hg38.5k.serial.bed'
# motif_filename = 'hg38.motif.count.txt'
# output_filename1 = None
mtx1, chrom, start, stop, colnames = read_motif_1(motif_filename)
serial_vec, start_vec = generate_serial_start(filename1,chrom,start,stop)
if output_filename!=None:
colnames2 = ['chrom','start','stop','serial']
data2 = pd.DataFrame(columns=colnames2)
data2['chrom'], data2['start'], data2['stop'], data2['serial'] = chrom, start, stop, serial_vec
data3 = pd.DataFrame(columns=colnames[3:],data=mtx1)
data1 = pd.concat([data2,data3], axis=1, join='outer', ignore_index=True,
keys=None, levels=None, names=None, verify_integrity=False, copy=True)
data1.to_csv(output_filename,header=True,index=False,sep='\t')
print('data1',data1.shape)
return True
class ConvergenceMonitor(object):
_template = "{iter:>10d} {logprob:>16.4f} {delta:>+16.4f}"
def __init__(self, tol, n_iter, verbose):
self.tol = tol
self.n_iter = n_iter
self.verbose = verbose
self.history = deque(maxlen=2)
self.iter = 0
def __repr__(self):
class_name = self.__class__.__name__
params = dict(vars(self), history=list(self.history))
return "{0}({1})".format(
class_name, _pprint(params, offset=len(class_name)))
def report(self, logprob):
if self.verbose:
delta = logprob - self.history[-1] if self.history else np.nan
message = self._template.format(
iter=self.iter + 1, logprob=logprob, delta=delta)
print(message, file=sys.stderr)
self.history.append(logprob)
self.iter += 1
@property
def converged(self):
return (self.iter == self.n_iter or
(len(self.history) == 2 and
self.history[1] - self.history[0] < self.tol))
class _Base1(BaseEstimator):
def __init__(self, file_path, species_id, resolution, run_id, generate,
chromvec,test_chromvec,
featureid,type_id,cell,method,ftype,ftrans,tlist,
flanking,normalize,
config,
attention=1,feature_dim_motif=1,
kmer_size=[6,5]):
# Initializes RepliSeq
self.run_id = run_id
self.cell = cell
self.generate = generate
self.train_chromvec = chromvec
self.chromosome = chromvec[0]
print('train_chromvec',train_chromvec)
print('test_chromvec',test_chromvec)
self.test_chromvec = test_chromvec
self.config = config
self.n_epochs = config['n_epochs']
self.species_id = species_id
self.type_id = type_id
self.cell_type = cell
self.cell_type1 = config['celltype_id']
self.method = method
self.ftype = ftype
self.ftrans = ftrans[0]
self.ftrans1 = ftrans[1]
self.t_list = tlist
self.flanking = flanking
self.flanking1 = 3
self.normalize = normalize
self.batch_size = config['batch_size']
# config = dict(output_dim=hidden_unit,fc1_output_dim=fc1,fc2_output_dim=fc2,units1=units1[0],
# units2=units1[1],n_epochs=n_epochs,batch_size=batch_size)
# config['feature_dim_vec'] = units1[2:]
self.tol = config['tol']
self.attention = attention
self.attention_vec = [12,17,22,32,51,52,58,60]
self.attention_vec1 = [1]
self.lr = config['lr']
self.step = config['step']
self.feature_type = -1
self.kmer_size = kmer_size
self.activation = config['activation']
self.min_delta = config['min_delta']
self.chromvec_sel = chromvec
self.feature_dim_transform = config['feature_dim_transform']
feature_idvec = [1,1,1,1]
# ref_filename = 'hg38_5k_serial.bed'
if 'ref_filename' in config:
ref_filename = config['ref_filename']
else:
ref_filename = 'hg38_5k_serial.bed'
self.reader = Reader(ref_filename, feature_idvec)
self.predict_type_id = 0
self.method = method
self.train = self.config['train_mode']
self.path = file_path
self.model_path = '%s/test_%d.h5'%(self.path,run_id)
self.pos_code = config['pos_code']
self.feature_dim_select1 = config['feature_dim_select']
self.method_vec = [[11,31],[22,32,52,17,51,58,60],[56,62]]
self.resolution = resolution
# if self.species_id=='mm10':
# self.cell_type1 = config['cell_type1']
if 'cell_type1' in self.config:
self.cell_type1 = config['cell_type1']
if ('load_type' in self.config) and (self.config['load_type']==1):
self.load_type = 1
else:
self.load_type = 0
if (method>10) and not(method in [56]) :
self.predict_context = 1
else:
self.predict_context = 0
if ftype[0]==-5:
self.feature_idx1= -5 # full dimensions
elif ftype[0]==-6:
self.feature_idx1 = -6 # frequency dimensions
else:
self.feature_idx1 = ftype
if 'est_attention_type1' in self.config:
self.est_attention_type1 = self.config['est_attention_type1']
else:
self.est_attention_type1 = 1
if 'est_attention_sel1' in self.config:
self.est_attention_sel1 = self.config['est_attention_sel1']
else:
self.est_attention_sel1 = 0
# self.feature_idx = [0,2]
self.feature_idx = featureid
self.x, self.y = dict(), dict() # feature matrix and signals
self.vec = dict() # serial
self.vec_local = dict()
if self.species_id.find('hg')>=0:
self.chrom_num = 22
elif self.species_id.find('mm')>=0:
self.chrom_num = 19
else:
self.chrom_num = -1
self.region_list_test, self.region_list_train, self.region_list_valid = [],[],[]
if 'region_list_test' in config:
self.region_list_test = config['region_list_test']
if 'region_list_train' in config:
self.region_list_train = config['region_list_train']
if 'region_list_valid' in config:
self.region_list_valid = config['region_list_valid']
flag = False
if 'scale' in config:
flag = True
self.scale = config['scale']
else:
self.scale = [0,1]
if ('activation_basic' in config) and (config['activation_basic']=='tanh'):
if (flag==True) and (self.scale[0]>=0):
flag = False
if flag==False:
self.scale = [-1,1]
self.region_boundary = []
self.serial_vec = []
self.f_mtx = []
print('scale',self.scale)
print(self.test_chromvec)
filename1 = '%s_chr%s-chr%s_chr%s-chr%s'%(self.cell_type, self.train_chromvec[0], self.train_chromvec[-1], self.test_chromvec[0], self.test_chromvec[-1])
self.filename_load = filename1
print(self.filename_load,self.method,self.predict_context,self.attention)
self.set_generate(generate,filename1)
def load_ref_serial(self, ref_filename, header=None):
if header==None:
file1 = pd.read_csv(ref_filename,header=header,sep='\t')
else:
file1 = pd.read_csv(ref_filename,sep='\t')
colnames = list(file1)
# col1, col2, col3 = '%s.chrom'%(species_name), '%s.start'%(species_name), '%s.stop'%(species_name)
col1, col2, col3, col_serial = colnames[0], colnames[1], colnames[2], colnames[3]
self.chrom_ori, self.start_ori, self.stop_ori, self.serial_ori = np.asarray(file1[col1]), np.asarray(file1[col2]), np.asarray(file1[col3]), np.asarray(file1[col_serial])
print('load ref serial', self.serial_ori.shape)
return self.serial_ori
# load local serial and signal
def load_local_serial(self, filename1, header=None, region_list=[], type_id2=1, signal_normalize=1,region_list_1=[]):
if header==None:
file2 = pd.read_csv(filename1,header=header,sep='\t')
else:
file2 = pd.read_csv(filename1,sep='\t')
colnames = list(file2)
col1, col2, col3, col_serial = colnames[0], colnames[1], colnames[2], colnames[3]
# sort the table by serial
file2 = file2.sort_values(by=[col_serial])
self.chrom, self.start, self.stop, self.serial = np.asarray(file2[col1]), np.asarray(file2[col2]), np.asarray(file2[col3]), np.asarray(file2[col_serial])
b = np.where((self.chrom!='chrX')&(self.chrom!='chrY')&(self.chrom!='chrM'))[0]
self.chrom, self.start, self.stop, self.serial = self.chrom[b], self.start[b], self.stop[b], self.serial[b]
if self.chrom_num>0:
chrom_num = self.chrom_num
else:
chrom_num = len(np.unique(self.chrom))
chrom_vec = [str(i) for i in range(1,chrom_num+1)]
print('chrom_vec', chrom_vec)
self.bin_size = self.stop[1]-self.start[1]
scale = self.scale
if len(colnames)>=5:
col_signal = colnames[4]
self.signal = np.asarray(file2[col_signal])
self.signal = self.signal[b]
self.signal_pre = self.signal.copy()
if signal_normalize==1:
if self.run_id>10:
# self.signal = signal_normalize(self.signal,[0,1]) # normalize signals
self.signal_pre1, id1, signal_vec1 = self.signal_normalize_chrom(self.chrom,self.signal,chrom_vec,scale)
if not('train_signal_update' in self.config) or (self.config['train_signal_update']==1):
train_signal, id2, signal_vec2 = self.signal_normalize_chrom(self.chrom,self.signal,self.train_chromvec,scale)
id_1 = mapping_Idx(id1,id2)
self.signal = self.signal_pre1.copy()
self.signal[id_1] = train_signal
else:
self.signal = self.signal_pre1.copy()
else:
print('signal_normalize_bychrom')
self.signal, id1, signal_vec = self.signal_normalize_bychrom(self.chrom,self.signal,chrom_vec,scale)
else:
self.signal = np.ones(len(b))
# print(self.signal.shape)
print('load local serial', self.serial.shape, self.signal.shape, np.max(self.signal), np.min(self.signal))
if 'tol_region_search' in self.config:
tol = self.config['tol_region_search']
else:
tol = 2
# only train or predict on some regions
print('load_local_serial',len(self.chrom))
if len(region_list_1)>0:
num1 = len(region_list_1)
list1 = []
for i in range(num1):
t_region = region_list_1[i]
t_chrom, t_start, t_stop = 'chr%d'%(t_region[0]), t_region[1], t_region[2]
t_id1 = np.where((self.chrom==t_chrom)&(self.start<t_stop)&(self.stop>t_start))[0]
list1.extend(t_id1)
b1 = np.asarray(list1)
self.chrom, self.start, self.stop, self.serial = self.chrom[b1], self.start[b1], self.stop[b1], self.serial[b1]
print('load_local_serial',num1,len(self.chrom))
print(region_list_1)
if len(region_list)>0:
# print('load_local_serial',region_list)
# id1, region_list = self.region_search_1(chrom,start,stop,serial,region_list)
id1, region_list = self.region_search_1(self.chrom,self.start,self.stop,self.serial,region_list,type_id2,tol)
self.chrom, self.start, self.stop, self.serial, self.signal = self.chrom[id1], self.start[id1], self.stop[id1], self.serial[id1], self.signal[id1]
id2 = self.region_search_boundary(self.chrom,self.start,self.stop,self.serial,region_list)
# print('region_search_boundary', id2[:,0], self.start[id2[:,1:3]],self.stop[id2[:,1:3]])
self.region_boundary = id2
# print(self.serial[id2[:,1:3]])
print('region_boundary',id2)
# return
else:
print('load_local_serial',region_list)
# assert len(region_list)>0
# return
return self.serial, self.signal
# training, validation and test data index
def prep_training_test(self,train_sel_list_ori):
train_id1, test_id1, y_signal_train1, y_signal_test, train1_sel_list, test_sel_list = self.generate_train_test_1(train_sel_list_ori)
self.idx_list = {'test':test_id1}
self.y_signal = {'test':y_signal_test}
if len(y_signal_test)>0:
print('y_signal_test',np.max(y_signal_test),np.min(y_signal_test))
if len(y_signal_train1)>0:
print('y_signal_train',np.max(y_signal_train1),np.min(y_signal_train1))
self.idx_list.update({'train':[],'valid':[]})
else:
return
# y_signal_test_ori = signal_normalize(y_signal_test,[0,1])
# shuffle array
# x_test_trans, shuffle_id2 = shuffle_array(x_test_trans)
# test_sel_list = test_sel_list[shuffle_id2]
# x_train1_trans, shuffle_id1 = shuffle_array(x_train1_trans)
# train_sel_list = train_sel_list[shuffle_id1]
print(train1_sel_list[0:5])
# split training and validation data
if 'ratio1' in self.config:
ratio = self.config['ratio1']
else:
ratio = 0.95
if 'type_id1' in self.config:
type_id_1 = self.config['type_id1']
else:
type_id_1 = 0
idx_train, idx_valid, idx_test = self.generate_index_1(train1_sel_list, test_sel_list, ratio, type_id_1)
print('idx_train,idx_valid,idx_test', len(idx_train), len(idx_valid), len(idx_test))
if (len(self.region_list_train)>0) or (len(self.region_list_valid)>0):
idx_train, idx_valid = self.generate_train_test_2(train1_sel_list,idx_train,idx_valid)
print('idx_train,idx_valid', len(idx_train), len(idx_valid))
train_sel_list, val_sel_list = train1_sel_list[idx_train], train1_sel_list[idx_valid]
self.idx_list.update({'train':train_id1[idx_train],'valid':train_id1[idx_valid]})
self.idx_train_val = {'train':idx_train,'valid':idx_valid}
self.y_signal.update({'train':y_signal_train1[idx_train],'valid':y_signal_train1[idx_valid]})
return train_sel_list, val_sel_list, test_sel_list
# prepare data from predefined features: kmer frequency feature and motif feature
def prep_data_sub2(self,path1,file_prefix,type_id2,feature_dim1,feature_dim2,flag_1):
species_id = self.species_id
celltype_id = self.cell_type1
if species_id=='mm10':
kmer_dim_ori, motif_dim_ori = 100, 50
filename1 = '%s/%s_%d_%d_%d.npy'%(path1,file_prefix,type_id2,kmer_dim_ori,motif_dim_ori)
# filename2 = 'test_%s_genome%d_kmer7.h5'%(species_id,celltype_id)
filename2 = '%s_%d_kmer7_0_200_trans.h5'%(species_id,celltype_id)
else:
kmer_dim_ori, motif_dim_ori = 50, 50
filename1 = '%s/%s_%d_%d_%d.npy'%(path1,file_prefix,type_id2,kmer_dim_ori,motif_dim_ori)
# filename2 = 'test_%s_kmer7.h5'%(species_id)
filename2 = '%s_kmer7_0_200_trans.h5'%(species_id)
kmer_size1, kmer_size2, kmer_size3 = 5,6,7
x_train1_trans, train_sel_list_ori = [], []
flag1, flag2 = 0, 0
flag3 = True
# if kmer_size2 in self.kmer_size:
if flag3==True:
if os.path.exists(filename1)==True:
print("loading data...")
data1 = np.load(filename1,allow_pickle=True)
data_1 = data1[()]
x_train1_trans_ori, train_sel_list_ori = np.asarray(data_1['x1']), np.asarray(data_1['idx'])
print('train_sel_list',train_sel_list_ori.shape)
print('x_train1_trans',x_train1_trans_ori.shape)
if kmer_size2 in self.kmer_size:
flag1 = 1
serial1 = train_sel_list_ori[:,1]
dim1 = x_train1_trans_ori.shape[1]
if (self.feature_dim_motif==0) or (flag_1==True):
x_train1_trans = x_train1_trans_ori[:,0:-motif_dim_ori]
else:
# d1 = np.min((dim1-motif_dim_ori+feature_dim2,d1))
# d2 = dim1-motif_dim_ori
# sel_id1 = list(range(21))+list(range(21,21+feature_dim1))
# x_train1_trans_1 = x_train1_trans[:,sel_id1]
# x_train1_trans_2 = x_train1_trans[:,d2:d1]
x_train1_trans_1 = x_train1_trans_ori[:,0:dim1-motif_dim_ori]
x_train1_trans_2 = x_train1_trans_ori[:,dim1-motif_dim_ori:]
else:
print('data not found!')
print(filename1)
return x_train1_trans, trans_sel_list_ori
if kmer_size3 in self.kmer_size:
with h5py.File(filename2,'r') as fid:
serial2 = fid["serial"][:]
feature_mtx = fid["vec"][:]
# feature_mtx = feature_mtx[:,0:kmer_dim_ori]
print(serial2)
print(len(serial2),feature_mtx.shape)
flag2 = 1
if flag1==1:
if flag2==1:
t_serial = np.intersect1d(serial1,serial2)
id1 = mapping_Idx(serial1,t_serial)
id2 = mapping_Idx(serial2,t_serial)
if 'feature_dim_transform_1' in self.config:
sel_idx = self.config['feature_dim_transform_1']
sel_id1, sel_id2 = list(0,21)+list(range(sel_idx[0])), range(sel_idx[1])
else:
sel_id1 = list(0,21)+list(range(10))
sel_id2 = range(feature_dim1-sel_idx1)
if (self.feature_dim_motif==0) or (flag_1==True):
x_train1_trans = np.hstack((x_train1_trans[id1,sel_id1],feature_mtx[id2,sel_id2]))
else:
x_train1_trans = np.hstack((x_train1_trans_1[id1,sel_id1],feature_mtx[id2,sel_id2],x_train1_trans_2[id1,0:feature_dim2]))
train_sel_list_ori = train_sel_list_ori[id1]
else:
pass
elif flag2==1:
t_serial = np.intersect1d(serial1,serial2)
id1 = mapping_Idx(serial1,t_serial)
id2 = mapping_Idx(serial2,t_serial)
x_train1_trans = np.hstack((x_train1_trans_ori[id1,0:2],feature_mtx[id2,0:feature_dim1]))
train_sel_list_ori = train_sel_list_ori[id1]
self.feature_dim_select1 = -1
if (self.feature_dim_motif==1) and (flag_1==False):
x_train1_trans = np.hstack((x_train1_trans,x_train1_trans_2[id1,0:feature_dim2]))
# id1 = mapping_Idx(self.serial_ori,serial2)
# b1 = (id1>=0)
# id1 = id1[b1]
# serial2, feature_mtx = serial2[b1], feature_mtx[b1]
# chrom1 = self.chrom_ori[id1]
# chrom2 = np.zeros(len(serial2),dtype=np.int32)
# chrom_vec = np.unique(chrom1)
# for chrom_id in chrom_vec:
# b2 = np.where(chrom1==chrom_id)[0]
# chrom_id1 = int(chrom_id[3:])
# chrom2[b2] = chrom_id1
# x_train1_trans = feature_mtx[:,0:feature_dim1]
# trans_sel_list_ori = np.vstack((chrom2,serial2)).T
else:
print('data not found!')
return x_train1_trans, train_sel_list_ori
# prepare data from predefined features
def prep_data_sub1(self,path1,file_prefix,type_id2,feature_dim_transform,load_type=0):
self.feature_dim_transform = feature_dim_transform
# map_idx = mapping_Idx(serial_ori,serial)
sub_sample_ratio = 1
shuffle = 0
normalize, flanking, attention, run_id = self.normalize, self.flanking, self.attention, self.run_id
config = self.config
vec2 = dict()
tol = self.tol
L = flanking
# np.save(filename1)
print("feature transform")
# filename1 = '%s/%s_%d_%d_%d.npy'%(path1,file_prefix,type_id2,feature_dim_transform[0],feature_dim_transform[1])
print(self.species_id)
t_featuredim1, t_featuredim2 = feature_dim_transform[0], feature_dim_transform[1]
flag1 = False
if self.species_id=='hg38':
if 'motif_trans_typeid' in self.config:
flag1 = True
if (self.species_id=='mm10'):
flag1 = True
if (t_featuredim1>0) or (flag1==False):
x_train1_trans, train_sel_list_ori = self.prep_data_sub2(path1,file_prefix,type_id2,t_featuredim1,t_featuredim2,flag1)
if len(x_train1_trans)==0:
print('data not found!')
return -1
if t_featuredim2>0:
print('train_sel_list',train_sel_list_ori.shape)
print('x_train1_trans',x_train1_trans.shape)
if (self.feature_dim_motif>=1) and (flag1==True):
if self.species_id=='mm10':
annot1 = '%s_%d_motif'%(self.species_id,self.cell_type1)
else:
annot1 = '%s_motif'%(self.species_id)
motif_trans_typeid = self.config['motif_trans_typeid']
motif_featuredim = self.config['motif_featuredim']
motif_filename = '%s_%d_%d_trans.h5'%(annot1,motif_trans_typeid,motif_featuredim)
if motif_featuredim<t_featuredim2:
print('error! %d %d',motif_featuredim,t_featuredim2)
t_featuredim2 = motif_featuredim
with h5py.File(motif_filename,'r') as fid:
serial_1 = fid["serial"][:]
motif_data = fid["vec"][:]
print(len(serial_1),motif_data.shape)
serial1 = train_sel_list_ori[:,1]
serial2 = serial_1
t_serial = np.intersect1d(serial1,serial2)
id1 = mapping_Idx(serial1,t_serial)
id2 = mapping_Idx(serial2,t_serial)
x_train1_trans = np.hstack((x_train1_trans[id1],motif_data[id2,0:t_featuredim2]))
train_sel_list_ori = train_sel_list_ori[id1]
# train_sel_list_ori2 = serial_1[id2]
else:
print("data not found!")
return
x_train1_trans = self.feature_dim_select(x_train1_trans,feature_dim_transform)
# feature loaded not specific to cell type
if load_type==1:
return x_train1_trans, train_sel_list_ori
list1 = ['motif_feature','feature2']
for t_feature in list1:
if (t_feature in self.config) and (self.config[t_feature]==1):
if t_feature=='feature2':
pre_config = self.config['pre_config']
if self.chrom_num>0:
chrom_num = self.chrom_num
else:
chrom_num = len(np.unique(self.chrom))
chrom_vec = list(range(1,chrom_num+1))
feature_mtx2, serial_2 = self.prep_data_sequence_3(pre_config,chrom_vec)
else:
x = 1
x_train1_trans_ori1 = x_train1_trans.copy()
train_sel_list_ori1 = train_sel_list_ori.copy()
serial1 = train_sel_list_ori[:,1]
serial2 = serial_2[:,1]
t_serial = np.intersect1d(serial1,serial2)
id1 = mapping_Idx(serial1,t_serial)[0]
id2 = mapping_Idx(serial2,t_serial)[0]
x_train1_trans = np.hstack((x_train1_trans[id1],feature_mtx2[id2]))
train_sel_list_ori = train_sel_list_ori[id1]
train_sel_list_ori2 = serial_2[id2]
b1 = np.where(train_sel_list_ori[:,0]!=train_sel_list_ori2[:,0])[0]
if len(b1)>0:
print('error! train_sel_list_ori',len(b1))
if ('centromere' in self.config) and (self.config['centromere']==1):
regionlist_filename = 'hg38.centromere.bed'
serial1 = train_sel_list_ori[:,1]
serial_list1, centromere_serial = self.select_region(serial1, regionlist_filename)
id1 = mapping_Idx(serial1,serial_list1)
id1 = id1[id1>=0]
x_train1_trans = x_train1_trans[id1]
train_sel_list_ori = train_sel_list_ori[id1]
print(x_train1_trans.shape,train_sel_list_ori.shape)
print('positional encoding', self.pos_code)
print('feature dim',x_train1_trans.shape)
self.feature_dim = x_train1_trans.shape[1]
start = time.time()
if self.pos_code ==1:
x_train1_trans = self.positional_encoding1(x_train1_trans,train_sel_list_ori,self.feature_dim)
print(x_train1_trans.shape)
stop = time.time()
print('positional encoding', stop-start)
## shuffle array
if ('shuffle' in self.config) and (self.config['shuffle']==1):
x_train1_trans, shuffle_id1 = shuffle_array(x_train1_trans)
print('array shuffled')
# np.random.shuffle(x_tran1_trans)
# train_sel_list = train_sel_list[shuffle_id1]
elif ('noise' in self.config) and (self.config['noise']>0):
if self.config['noise']==1:
x_train1_trans = np.zeros_like(x_train1_trans)
print('x_train1_trans, noise 1', x_train1_trans[0:5])
elif self.config['noise']==2:
x_train1_trans = np.random.uniform(0,1,x_train1_trans.shape)
else:
x_train1_trans = np.random.normal(0,1,x_train1_trans.shape)
else:
pass
if 'sub_sample_ratio' in self.config:
sub_sample_ratio = self.config['sub_sample_ratio']
num_sample = len(train_sel_list_ori)
sub_sample = int(num_sample*sub_sample_ratio)
train_sel_list_ori = train_sel_list_ori[0:sub_sample]
x_train1_trans = x_train1_trans[0:sub_sample]
# align train_sel_list_ori and serial
print(train_sel_list_ori.shape,len(self.serial))
id1 = mapping_Idx(train_sel_list_ori[:,1],self.serial)
id2 = (id1>=0)
print('mapping',len(self.serial),np.sum(id2),len(self.serial),len(id2))
# self.chrom, self.start, self.stop, self.serial, self.signal = self.chrom[id2], self.start[id2], self.stop[id2], self.serial[id2], self.signal[id2]
self.local_serial_1(id2)
id1 = id1[id2]
train_sel_list_ori = train_sel_list_ori[id1]
x_train1_trans = x_train1_trans[id1]
self.x_train1_trans = x_train1_trans
self.train_sel_list = train_sel_list_ori
return x_train1_trans, train_sel_list_ori
def output_generate_sequences(self,idx_sel_list,seq_list):
num1 = len(seq_list)
t_serial1 = idx_sel_list[:,1]
seq_list = np.asarray(seq_list)
t_serial = t_serial1[seq_list]
id1 = mapping_Idx(self.serial,t_serial[:,0])
chrom1, start1, stop1 = self.chrom[id1], self.start[id1], self.stop[id1]
id2 = mapping_Idx(self.serial,t_serial[:,1])
chrom2, start2, stop2 = self.chrom[id2], self.start[id2], self.stop[id2]
fields = ['chrom','start','stop','serial1','serial2']
data1 = pd.DataFrame(columns=fields)
data1['chrom'], data1['start'], data1['stop'] = chrom1, start1, stop2
data1['serial1'], data1['serial2'] = t_serial[:,0], t_serial[:,1]
data1['region_len'] = t_serial[:,1]-t_serial[:,0]+1
output_filename = 'test_seqList_%d_%d.txt'%(idx_sel_list[0][0],idx_sel_list[0][1])
data1.to_csv(output_filename,index=False,sep='\t')
return True
# prepare data from predefined features
def prep_data(self,path1,file_prefix,type_id2,feature_dim_transform):
x_train1_trans, train_sel_list_ori = self.prep_data_sub1(path1,file_prefix,type_id2,feature_dim_transform)
train_sel_list, val_sel_list, test_sel_list = self.prep_training_test(train_sel_list_ori)
# keys = ['train','valid','test']
keys = ['train','valid']
# self.idx_sel_list = {'train':train1_sel_list,'valid':val_sel_list,'test':test_sel_list}
idx_sel_list = {'train':train_sel_list,'valid':val_sel_list,'test':test_sel_list}
# self.idx_sel_list = idx_sel_list
# seq_list_train, seq_list_valid: both locally calculated
self.seq_list = dict()
start = time.time()
for i in keys:
self.seq_list[i] = generate_sequences(idx_sel_list[i],region_list=self.region_boundary)
print(len(self.seq_list[i]))
self.output_generate_sequences(idx_sel_list[i],self.seq_list[i])
stop = time.time()
print('generate_sequences', stop-start)
# generate initial state index
self.init_id = dict()
self.init_index(keys)
# training and validation data
# x_train1_trans = self.x_train1_trans
for i in keys:
idx = self.idx_list[i]
if self.method<5 or self.method in [56]:
self.x[i] = x_train1_trans[idx]
self.y[i] = self.y_signal[i]
print(self.x[i].shape, self.y[i].shape)
else:
idx_sel_list = self.train_sel_list[idx]
start = time.time()
x, y, self.vec[i], self.vec_local[i] = sample_select2a1(x_train1_trans[idx],self.y_signal[i],
idx_sel_list, self.seq_list[i], self.tol, self.flanking)
stop = time.time()
print('sample_select2a1',stop-start)
# concate context for baseline methods
if self.method<=10:
# x_train, x_valid, y_train, y_valid = train_test_split(x_train1, y_train1, test_size=0.2, random_state=42)
x = x.reshape(x.shape[0],x.shape[1]*x.shape[-1])
y = y[:,self.flanking]
self.x[i], self.y[i] = x, y
print(self.x[i].shape, self.y[i].shape)
return True
# prepare data from predefined features
def prep_data_1(self,path1,file_prefix,type_id2,feature_dim_transform,
n_fold=5, ratio=0.9, type_id=1):
x_train1_trans, train_sel_list_ori = self.prep_data_sub1(path1,file_prefix,type_id2,feature_dim_transform)
print(train_sel_list_ori)
id1 = mapping_Idx(train_sel_list_ori[:,1],self.serial)
id2 = (id1>=0)
print('mapping',len(self.serial),np.sum(id2))
self.chrom, self.start, self.stop, self.serial, self.signal = self.chrom[id2], self.start[id2], self.stop[id2], self.serial[id2], self.signal[id2]
id1 = id1[id2]
train_sel_list_ori = train_sel_list_ori[id1]
self.x_train1_trans = self.x_train1_trans[id1]
print(train_sel_list_ori.shape,self.x_train1_trans.shape)
id_vec = self.generate_index_2(train_sel_list_ori, n_fold=n_fold, ratio=ratio, type_id=type_id)
return id_vec
def find_serial_ori_1_local(self,chrom_vec,type_id2=1):
# filename1 = 'mm10_%d_%s_encoded1.h5'%(self.config['cell_type1'],chrom_id1)
self.species_id = 'mm10'
self.cell_type1 = self.config['cell_type1']
file_path1 = '/work/magroup/yy3/data1/replication_timing3/mouse'
# filename1 = '%s/mm10_5k_seq_genome%d_1.txt'%(file_path1,self.config['cell_type1'])
chrom_id1 = 'chr1'
filename1 = '%s_%d_%s_encoded1.h5'%(self.species_id,self.cell_type1,chrom_id1)
list1, list2 = [], []
serial_vec = []
print(filename1)
if os.path.exists(filename1)==False:
# prepare data from predefined features
# one hot encoded feature vectors for each chromosome
self.prep_data_sequence_ori()
print('prep_data_sequence_ori',filename1)
for chrom_id in chrom_vec:
# if chrom_id<22:
# continue
chrom_id1 = 'chr%s'%(chrom_id)
# if self.config['species_id']==0:
# filename2 = 'mm10_%d_%s_encoded1.h5'%(self.config['cell_type1'],chrom_id1)
# else:
# filename2 = '%s_%s_encoded1.h5'%(self.species_id,chrom_id1)
filename2 = '%s_%d_%s_encoded1.h5'%(self.species_id,self.cell_type1,chrom_id1)
with h5py.File(filename2,'r') as fid:
serial1 = fid["serial"][:]
if type_id2==1:
seq1 = fid["vec"][:]
list2.extend(seq1)
list1.extend([chrom_id]*len(serial1))
serial_vec.extend(serial1)
print(chrom_id,len(serial1))
list1, serial_vec = np.asarray(list1), np.asarray(serial_vec)
serial_vec = np.hstack((list1[:,np.newaxis],serial_vec))
f_mtx = np.asarray(list2)
# data_1 = pd.read_csv(filename1,sep='\t')
# colnames = list(data_1)
# local_serial = np.asarray(data_1['serial'])
# local_seq = np.asarray(data_1['seq'])
# print('local_seq', local_seq.shape)
# serial_vec = local_serial
# f_mtx = local_seq
# filename2 = '%s/mm10_5k_serial.bed'%(file_path1)
# file2 = pd.read_csv(filename2,header=None,sep='\t')
# ref_chrom, ref_start, ref_stop, ref_serial = np.asarray(file2[0]), np.asarray(file2[1]), np.asarray(file2[2]), np.asarray(file2[3])
# # assert list(local_serial==list(ref_serial))
# id_vec1 = []
# for chrom_id in chrom_vec:
# # if chrom_id<22:
# # continue
# # chrom_id1 = 'chr%s'%(chrom_id)
# id1 = np.where(ref_chrom=='chr%d'%(chrom_id))[0]
# id_vec1.extend(id1)
# print(chrom_id,len(id1))
# id_vec1 = np.asarray(id_vec1)
# ref_chrom_1, ref_serial_1 = ref_chrom[id_vec1], ref_serial[id_vec1]
# print('ref chrom local', len(ref_chrom_1), len(ref_serial_1))
# id1 = utility_1.mapping_Idx(ref_serial_1,local_serial)
# id2 = np.where(id1>=0)[0]
# id1 = id1[id2]
# # assert len(id2)==len(id1)
# chrom1 = ref_chrom_1[id1]
# local_chrom = [int(chrom1[3:]) for chrom1 in ref_chrom_1]
# local_chrom = np.asarray(local_chrom)
# local_serial, local_seq = local_serial[id2], local_seq[id2]
# serial_vec = np.column_stack((local_chrom,local_serial))
# f_mtx = np.asarray(local_seq)
return serial_vec, f_mtx
# find serial and feature vectors
# input: type_id1: load sequence feature or kmer frequency feature, motif feature
# type_id2: load serial or feature vectors
def find_serial_ori_1(self,file_path,file_prefix,chrom_vec,type_id1=0,type_id2=0,select_config={}):
# load the sequences
if type_id1==0:
# list2 = np.zeros((interval,region_unit_size,4),dtype=np.int8)
filename1 = '%s_serial_2.txt'%(self.species_id)
list1, list2 = [], []
serial_vec = []
if (os.path.exists(filename1)==False) or (type_id2==1):
if self.config['species_id']==0:
serial_vec, list2 = self.find_serial_ori_1_local(chrom_vec)
else:
for chrom_id in chrom_vec:
# if chrom_id<22:
# continue
chrom_id1 = 'chr%s'%(chrom_id)
filename2 = '%s_%s_encoded1.h5'%(self.species_id,chrom_id1)
with h5py.File(filename2,'r') as fid:
serial1 = fid["serial"][:]
if type_id2==1:
seq1 = fid["vec"][:]
list2.extend(seq1)
list1.extend([chrom_id]*len(serial1))
serial_vec.extend(serial1)
print(chrom_id,len(serial1))
list1, serial_vec = np.asarray(list1), np.asarray(serial_vec)
serial_vec = np.hstack((list1[:,np.newaxis],serial_vec))
np.savetxt(filename1,serial_vec,fmt='%d',delimiter='\t')
else:
serial_vec = np.loadtxt(filename1,dtype=np.int64)
if serial_vec.shape[-1]>2:
cnt1 = serial_vec[:,-1]
b1 = np.where(cnt1>0)[0]
ratio1 = len(b1)/len(serial_vec)
print('sequence with N', len(b1),len(serial_vec),ratio1)
# serial_vec = serial_vec[:,0]
f_mtx = np.asarray(list2)
elif type_id1==2:
filename1 = select_config['input_filename1']
layer_name = select_config['layer_name']
with h5py.File(filename1,'r') as fid:
f_mtx = np.asarray(fid[layer_name][:],dtype=np.float32)
print(f_mtx.shape)
serial_vec = fid["serial"][:]
assert len(serial_vec )==f_mtx.shape[0]
print(serial_vec[0:5])
else:
# load kmer frequency features and motif features
load_type_id2 = 0
x_train1_trans, train_sel_list_ori = self.prep_data_sub1(file_path,file_prefix,load_type_id2,self.feature_dim_transform,load_type=1)
# serial_vec = train_sel_list_ori[:,1]
serial_vec = np.asarray(train_sel_list_ori)
f_mtx = np.asarray(x_train1_trans)
return serial_vec, f_mtx
def find_serial_ori(self,file_path,file_prefix,type_id1=0,type_id2=0,select_config={}):
chrom_vec = np.unique(self.chrom)
chrom_vec1 = []
for chrom_id in chrom_vec:
try:
id1 = chrom_id.find('chr')
if id1>=0:
chrom_id1 = int(chrom_id[3:])
chrom_vec1.append(chrom_id1)
except:
continue
chrom_vec1 = np.sort(chrom_vec1)
serial_vec, f_mtx = self.find_serial_ori_1(file_path,file_prefix,chrom_vec1,
type_id1=type_id1,type_id2=type_id2,
select_config=select_config)
self.serial_vec = serial_vec
self.f_mtx = f_mtx
# list2 = np.zeros((interval,region_unit_size,4),dtype=np.int8)
print(len(self.chrom),len(self.serial))
# cnt1 = serial_vec[:,1]
# b1 = np.where(cnt1>0)[0]
# ratio1 = len(b1)/len(serial_vec)
# print(len(b1),len(serial_vec),ratio1)
id1 = mapping_Idx(serial_vec[:,1],self.serial)
b1 = np.where(id1>=0)[0]
self.local_serial_1(b1,type_id=0)
print(len(self.chrom),len(self.serial))
return True
def prep_data_2(self,file_path,file_prefix,seq_len_thresh=50):
self.find_serial_ori(file_path,file_prefix)
chrom_vec = np.unique(self.chrom)
chrom_vec1 = []
for chrom_id in chrom_vec:
try:
id1 = chrom_id.find('chr')
if id1>=0:
chrom_id1 = int(chrom_id[3:])
chrom_vec1.append(chrom_id1)
except:
continue
chrom_vec1 = np.sort(chrom_vec1)
sample_num = len(self.chrom)
idx_sel_list = -np.ones((sample_num,2),dtype=np.int64)
for chrom_id in chrom_vec1:
chrom_id1 = 'chr%d'%(chrom_id)
b1 = np.where(self.chrom==chrom_id1)[0]
idx_sel_list[b1,0] = [chrom_id]*len(b1)
idx_sel_list[b1,1] = self.serial[b1]
id1 = idx_sel_list[:,0]>=0
idx_sel_list = idx_sel_list[id1]
sample_num = len(id1)
y = self.signal[id1]
x_mtx = idx_sel_list[id1]
seq_list = generate_sequences(idx_sel_list, gap_tol=5, region_list=[])
seq_len = seq_list[:,1]-seq_list[:,0]+1
thresh1 = seq_len_thresh
b1 = np.where(seq_len>thresh1)[0]
print(len(seq_list),len(b1))
seq_list = seq_list[b1]
seq_len1 = seq_list[:,1]-seq_list[:,0]+1
print(sample_num,np.sum(seq_len1),seq_list.shape,np.max(seq_len),np.min(seq_len),np.median(seq_len),np.max(seq_len1),np.min(seq_len1),np.median(seq_len1))
self.output_generate_sequences(idx_sel_list,seq_list)
t_mtx, signal_mtx, vec1_serial, vec1_local = sample_select2a1(x_mtx, y, idx_sel_list, seq_list, tol=self.tol, L=self.flanking)
t_serial = vec1_serial[:,self.flanking]
context_size = vec1_serial.shape[1]
id1 = mapping_Idx(idx_sel_list[:,1],t_serial)
b1 = np.where(id1>=0)[0]
if len(b1)!=len(vec1_serial):
print('error!',len(b1),len(vec1_serial))
return -1
sel_id1 = id1[b1]
# idx_sel_list1 = idx_sel_list[sel_id1]
# label1 = y[sel_id1]
t_chrom = idx_sel_list[sel_id1,0]
print(t_chrom,t_serial)
print(t_chrom.shape,t_serial.shape)
print(vec1_serial.shape)
list_ID = []
cnt1 = 0
interval = 200
list1, list2 = [],[]
list3 = []
# region_unit_size = 5000
# list2 = np.zeros((interval,region_unit_size,4),dtype=np.int8)
for chrom_id in chrom_vec1:
# if chrom_id<22:
# continue
chrom_id1 = 'chr%s'%(chrom_id)
filename1 = '%s_%s_encoded1.h5'%(self.species_id,chrom_id1)
t_id1 = np.where(t_chrom==chrom_id)[0]
t_serial1 = t_serial[t_id1] # serial by chromosome
sample_num1 = len(t_serial1)
num_segment = np.int(np.ceil(sample_num1/interval))
print(chrom_id1,num_segment,interval,sample_num1)
with h5py.File(filename1,'r') as fid:
serial1 = fid["serial"][:]
seq1 = fid["vec"][:]
serial1 = serial1[:,0]
print(serial1.shape, seq1.shape)
id1 = utility_1.mapping_Idx(serial1,t_serial1)
id2 = np.where(id1>=0)[0]
num1 = len(id2)
segment_id = 0
t_signal_mtx = signal_mtx[t_id1[id2]]
list3.extend(t_signal_mtx)
for i in range(num1):
cnt2 = i+1
t_id2 = id2[i]
label_serial = t_serial1[t_id2]
t_vec1_serial = vec1_serial[t_id1[t_id2]]
id_1 = mapping_Idx(serial1,t_vec1_serial)
b1 = np.where(id_1>=0)[0]
if len(b1)!=context_size:
b2 = np.where(id_1<0)[0]
print('error!',chrom_id1,label_serial,t_vec1_serial[b2],len(b1),context_size)
np.savetxt('temp1.txt',serial1,fmt='%d',delimiter='\t')
np.savetxt('temp2.txt',t_vec1_serial,fmt='%d',delimiter='\t')
return -1
t_mtx = seq1[id_1[b1]]
list1.append(t_vec1_serial)
list2.append(t_mtx)
local_id = cnt2%interval
label_id = cnt1
output_filename = 'test1_%s_%s_%d.h5'%(self.cell,chrom_id1,segment_id)
if (cnt2%interval==0) or (cnt2==num1):
output_filename1 = '%s/%s'%(file_path,output_filename)
list1 = np.asarray(list1)
list2 = np.asarray(list2,dtype=np.int8)
print(chrom_id1,segment_id,local_id,label_id,label_serial,list1.shape,list2.shape)
# with h5py.File(output_filename1,'w') as fid:
# fid.create_dataset("serial", data=list1, compression="gzip")
# fid.create_dataset("vec", data=list2, compression="gzip")
# dict1 = {'serial':list1.tolist(),'vec':list2.tolist()}
# np.save(output_filename,dict1,allow_pickle=True)
# with open(output_filename, "w") as fid:
# json.dump(dict1,fid)
# with open(output_filename,"w",encoding='utf-8') as fid:
# json.dump(dict1,fid,separators=(',', ':'), sort_keys=True, indent=4)
list1, list2 = [], []
segment_id += 1
cnt1 = cnt1+1
list_ID.append([label_id,label_serial,output_filename,local_id])
# if cnt2%interval==0:
# break
# with open(output_filename, "r") as fid:
# dict1 = json.load(fid)
# serial1, vec1 = np.asarray(dict1['serial']), np.asarray(dict1['vec'])
# print(serial1.shape,vec1.shape)
# with h5py.File(output_filename1,'r') as fid:
# serial1 = fid["serial"][:]
# vec1 = fid["vec"][:]
# print(serial1.shape,vec1.shape)
fields = ['label_id','label_serial','filename','local_id']
list_ID = np.asarray(list_ID)
data1 = pd.DataFrame(columns=fields,data=list_ID)
output_filename = '%s/%s_label_ID_1'%(file_path,self.cell)
data1.to_csv(output_filename+'.txt',index=False,sep='\t')
# np.save(output_filename,data1,allow_pickle=True)
output_filename = '%s/%s_label.h5'%(file_path,self.cell)
list3 = np.asarray(list3)
print(list3.shape)
with h5py.File(output_filename,'w') as fid:
fid.create_dataset("vec", data=np.asarray(list3), compression="gzip")
return list_ID
# find serial for training and validation data
def prep_data_2_sub1(self,file_path,file_prefix,type_id1=0,type_id2=0,gap_tol=5,seq_len_thresh=5,select_config={}):
if type_id1>=0:
self.find_serial_ori(file_path,file_prefix,
type_id1=type_id1,type_id2=type_id2,
select_config=select_config)
chrom_vec = np.unique(self.chrom)
chrom_vec1 = []
for chrom_id in chrom_vec:
try:
id1 = chrom_id.find('chr')
if id1>=0:
chrom_id1 = int(chrom_id[3:])
chrom_vec1.append(chrom_id1)
except:
continue
chrom_vec1 = np.sort(chrom_vec1)
sample_num = len(self.chrom)
idx_sel_list = -np.ones((sample_num,2),dtype=np.int64)
if 'gap_thresh' in self.config:
gap_tol = self.config['gap_thresh']
if 'seq_len_thresh' in self.config:
seq_len_thresh = self.config['seq_len_thresh']
for chrom_id in chrom_vec1:
chrom_id1 = 'chr%d'%(chrom_id)
b1 = np.where(self.chrom==chrom_id1)[0]
idx_sel_list[b1,0] = [chrom_id]*len(b1)
idx_sel_list[b1,1] = self.serial[b1]
id1 = idx_sel_list[:,0]>=0
idx_sel_list = idx_sel_list[id1]
sample_num = len(id1)
y = self.signal[id1]
x_mtx = idx_sel_list[id1]
self.train_sel_list_ori = idx_sel_list
self.y_signal_1 = self.signal[id1]
ref_serial = idx_sel_list[:,1]
# train_sel_list, val_sel_list = train1_sel_list[idx_train], train1_sel_list[idx_valid]
# self.idx_list.update({'train':train_id1[idx_train],'valid':train_id1[idx_valid]})
# self.idx_train_val = {'train':idx_train,'valid':idx_valid}
# self.y_signal.update({'train':y_signal_train1[idx_train],'valid':y_signal_train1[idx_valid]})
train_sel_list, val_sel_list, test_sel_list = self.prep_training_test(idx_sel_list)
print(len(train_sel_list),len(val_sel_list),len(test_sel_list))
keys = ['train','valid','test']
# keys = ['train','valid']
# self.idx_sel_list = {'train':train1_sel_list,'valid':val_sel_list,'test':test_sel_list}
self.idx_sel_list_ori = {'train':train_sel_list,'valid':val_sel_list,'test':test_sel_list}
# self.idx_sel_list = idx_sel_list
# seq_list_train, seq_list_valid: both locally calculated
self.seq_list = dict()
start = time.time()
# seq_len_thresh = 20
self.local_serial_dict = dict()
for i in keys:
# self.seq_list[i] = generate_sequences(idx_sel_list1[i],region_list=self.region_boundary)
# print(len(self.seq_list[i]))
# self.output_generate_sequences(idx_sel_list[i],self.seq_list[i])
idx_sel_list1 = self.idx_sel_list_ori[i]
# region_list_id = 'region_list_%s'%(i)
# if region_list_id in self.config:
# region_list = self.config[region_list_id]
# else:
# region_list = []
# region_list = np.asarray(region_list)
# print(region_list_id,region_list)
# if i=='test':
# region_boundary = self.region_boundary
# else:
# region_boundary = []
region_boundary = self.region_boundary
print('region_boundary',region_boundary)
# assert len(region_boundary)==0
seq_list = generate_sequences(idx_sel_list1, gap_tol=gap_tol, region_list=region_boundary)
# seq_len = seq_list[:,1]-seq_list[:,0]+1
# thresh1 = seq_len_thresh
# b1 = np.where(seq_len>thresh1)[0]
# print(len(seq_list),len(b1))
# seq_list = seq_list[b1]
# seq_len1 = seq_list[:,1]-seq_list[:,0]+1
# print(sample_num,np.sum(seq_len1),len(seq_list),np.max(seq_len),np.min(seq_len),np.median(seq_len),np.max(seq_len1),np.min(seq_len1),np.median(seq_len1))
# reselect the regions according to the subsequence length
# recalculate seq_list
idx_sel_list1, seq_list = self.select_region_local_1(idx_sel_list1,seq_list,
gap_tol=gap_tol,
seq_len_thresh=seq_len_thresh,
region_list=[])
self.idx_sel_list_ori[i] = idx_sel_list1
self.seq_list[i] = seq_list
x1 = idx_sel_list1
sel_id = utility_1.mapping_Idx(ref_serial,idx_sel_list1[:,1])
y1 = self.y_signal_1[sel_id]
x, y, t_vec_serial, t_vec_local = sample_select2a1(x1,y1,
idx_sel_list1, seq_list, self.tol, self.flanking)
t_serial1 = t_vec_serial[:,self.flanking]
# if np.sum(t_serial1!=sel_idx_list1[:,1])>0:
# print('error!',i)
# return
id1 = utility_1.mapping_Idx(idx_sel_list1[:,1],t_serial1)
b1 = np.where(id1>=0)[0]
if len(b1)!=len(t_serial1):
print('error!',i)
return
idx_sel_list1 = idx_sel_list1[id1[b1]]
self.local_serial_dict[i] = [idx_sel_list1,y1,y,t_vec_serial,t_vec_local]
print(i,t_serial1.shape,y.shape)
stop = time.time()
print('generate_sequences', stop-start)
return self.local_serial_dict
# load feature
def load_feature_local(self,chrom_vec,type_id=0,select_config={}):
# load sequences
if type_id==0:
serial_vec = []
list1, list2 = [],[]
# list2 = np.zeros((interval,region_unit_size,4),dtype=np.int8)
if self.config['species_id']==0:
serial_vec, f_mtx = self.find_serial_ori_1_local(chrom_vec)
else:
for chrom_id in chrom_vec:
# if chrom_id<22:
# continue
chrom_id1 = 'chr%s'%(chrom_id)
filename1 = '%s_%s_encoded1.h5'%(self.species_id,chrom_id1)
with h5py.File(filename1,'r') as fid:
serial1 = fid["serial"][:]
seq1 = fid["vec"][:]
serial_vec.extend(serial1)
list1.extend([chrom_id]*len(serial1))
list2.extend(seq1)
print(len(serial1),seq1.shape)
list1 = np.asarray(list1)
serial_vec = np.hstack((list1[:,np.newaxis],serial_vec))
f_mtx = np.asarray(list2)
# kmer frequency and motif feature
elif type_id==1:
if len(self.serial_vec)>0 and (len(self.f_mtx)>0):
serial_vec = self.serial_vec
f_mtx = self.f_mtx
else:
type_id2 = 0
x_train1_trans, train_sel_list_ori = self.prep_data_sub1(self.file_path,self.file_prefix,type_id2,self.feature_dim_transform,load_type=1)
# serial_vec = train_sel_list_ori[:,1]
serial_vec = np.asarray(train_sel_list_ori)
f_mtx = np.asarray(x_train1_trans)
else:
filename1 = select_config['input_filename1']
layer_name = select_config['layer_name']
with h5py.File(filename1,'r') as fid:
f_mtx = np.asarray(fid[layer_name][:],dtype=np.float32)
print(f_mtx.shape)
serial_vec = fid["serial"][:]
assert len(serial_vec )==f_mtx.shape[0]
print(serial_vec[0:5])
return serial_vec, f_mtx
# find serial
def find_serial_local(self,ref_serial,vec_serial_ori,sel_id):
serial_1 = vec_serial_ori[:,self.flanking]
# print(len(ref_serial),ref_serial)
# print(len(serial_1),serial_1)
assert np.max(np.abs(ref_serial-serial_1))==0
t_vec_serial = np.ravel(vec_serial_ori[sel_id])
serial1 = np.unique(t_vec_serial)
id1 = mapping_Idx(ref_serial,serial1)
b1 = np.where(id1<0)[0]
if len(b1)>0:
print('error!',len(b1))
print(serial1[b1])
b_1 = np.where(id1>=0)[0]
id1 = id1[b_1]
sample_num = len(ref_serial)
id2 = np.setdiff1d(np.arange(sample_num),id1)
if len(id2)>0:
t_serial2 = ref_serial[id2]
id_2 = mapping_Idx(serial_1,t_serial2)
sel_id = list(sel_id)+list(id_2)
sel_id = np.unique(sel_id)
print('find serial local',len(sel_id),len(id_2))
return sel_id
# load training and validation data
def prep_data_2_sub2(self,type_id1=0,keys=['train','valid'],stride=1,type_id=0,select_config={}):
chrom1 = []
for i in range(0,len(keys)):
key1 = keys[i]
idx_sel_list, y_ori, y, vec_serial, vec_local = self.local_serial_dict[key1]
chrom1.extend(idx_sel_list[:,0])
chrom_vec1 = np.sort(np.unique(chrom1))
serial_vec, f_mtx = self.load_feature_local(chrom_vec1,type_id=type_id1,select_config=select_config)
print('load feature local', serial_vec.shape, f_mtx.shape)
if serial_vec.shape[1]>2:
cnt1 = serial_vec[:,-1]
b1 = np.where(cnt1>0)[0]
ratio1 = len(b1)/len(serial_vec)
print(len(b1),len(serial_vec),ratio1)
ref_serial = serial_vec[:,1]
for i in range(0,len(keys)):
key1 = keys[i]
idx_sel_list, y_ori, y, vec_serial, vec_local = self.local_serial_dict[key1]
num1 = len(idx_sel_list)
if stride>1:
id1 = list(range(0,num1,stride))
# the windows cover the positions
print(num1,stride)
if type_id==1:
id1 = self.find_serial_local(idx_sel_list[:,1],vec_serial,id1)
y, vec_serial, vec_local = y[id1], vec_serial[id1], vec_local[id1]
self.local_serial_dict[key1] = [idx_sel_list, y_ori, y, vec_serial, vec_local]
id2 = mapping_Idx(ref_serial,idx_sel_list[:,1])
print(key1,len(ref_serial),len(idx_sel_list))
print(ref_serial[0:5])
print(idx_sel_list[0:5,1])
b1 = np.where(id2<0)[0]
if len(b1)>0:
print('error!',len(b1),key1)
# return
print('mapping',len(id2))
# update
b_1 = np.where(id2>=0)[0]
id2 = id2[b_1]
idx_sel_list, y_ori = idx_sel_list[b_1], y_ori[b_1]
y, vec_serial, vec_local = y[b_1], vec_serial[b_1], vec_local[b_1]
self.local_serial_dict[key1] = [idx_sel_list, y_ori, y, vec_serial, vec_local]
self.x[key1] = f_mtx[id2]
self.idx[key1] = id2
return True
# training and predition with sequences
def control_pre_test1_repeat(self,path1,file_prefix,run_id_load=-1):
self.prep_data_2_sub1(path1,file_prefix)
config = self.config.copy()
units1=[50,50,32,25,50,25,0,0]
flanking = 50
context_size = 2*flanking+1
n_step_local_ori = 5000
region_unit_size = 1
feature_dim = 4
local_conv_list1 = []
regularizer2, bnorm, activation = 1e-04, 1, 'relu'
if self.run_id==110001:
config_vec1 = [[64, 15, 5, 1, 2, 2, 0.2, 0],
[32, 5, 1, 1, 10, 10, 0.2, 0],
[32, 3, 1, 1, 5, 5, 0.2, 0]]
for t1 in config_vec1:
n_filters, kernel_size1, stride, dilation_rate1, pool_length1, stride1, drop_out_rate, boundary = t1
conv_1 = [n_filters, kernel_size1, stride, regularizer2, dilation_rate1, boundary, bnorm, activation, pool_length1, stride1, drop_out_rate]
local_conv_list1.append(conv_1)
config['local_conv_list1'] = local_conv_list1
print(local_conv_list1)
feature_dim1, feature_dim2, return_sequences_flag1, sample_local, pooling_local = 32, 25, True, 0, 0
n_step_local1 = 10
feature_dim3 = []
local_vec_1 = [feature_dim1, feature_dim2, feature_dim3, return_sequences_flag1, sample_local, pooling_local]
attention2_local = 0
select2 = 1
concatenate_1, concatenate_2 = 0, 1
hidden_unit = 32
regularizer2_2 = 1e-04
config.update({'attention1':0,'attention2':1,'select2':select2,'context_size':context_size,'n_step_local':n_step_local1,'n_step_local_ori':n_step_local_ori})
config.update({'local_vec_1':local_vec_1,'attention2_local':attention2_local})
config['feature_dim_vec'] = units1[2:]
config['feature_dim_vec_basic'] = units1[2:]
config.update({'local_conv_list1':local_conv_list1,'local_vec_1':local_vec_1})
config.update({'attention1':0,'attention2':1,'context_size':context_size,
'n_step_local_ori':n_step_local_ori})
config.update({'select2':select2,'attention2_local':attention2_local})
config.update({'concatenate_1':concatenate_1,'concatenate_2':concatenate_2})
config.update({'feature_dim':feature_dim,'output_dim':hidden_unit,'regularizer2_2':regularizer2_2})
model = utility_1.get_model2a1_attention_1_2_2_sample5(config)
# find feature vectors with the serial
self.x = dict()
self.idx = dict()
self.prep_data_2_sub2(type_id1=0,keys=['train','valid'],stride=1)
mtx_train = self.x['train']
idx_sel_list_train, y_train_ori_1, y_train_ori, vec_serial_train, vec_local_train = self.local_serial_dict['train']
mtx_valid = self.x['valid']
idx_sel_list_valid, y_valid_ori_1, y_valid_ori, vec_serial_valid, vec_local_valid = self.local_serial_dict['valid']
train_num1, valid_num1 = len(y_train_ori), len(y_valid_ori)
print('train',len(idx_sel_list_train),len(y_train_ori),mtx_train.shape)
print('valid',len(idx_sel_list_valid),len(y_valid_ori),mtx_valid.shape)
x_valid = mtx_valid[vec_local_valid]
y_valid = y_valid_ori
print(x_valid.shape,y_valid.shape)
type_id2 = 2
MODEL_PATH = 'test%d.h5'%(self.run_id)
n_epochs = 1
BATCH_SIZE = 32
n_step_local = n_step_local_ori
earlystop = EarlyStopping(monitor='val_loss', min_delta=self.min_delta, patience=self.step, verbose=1, mode='auto')
checkpointer = ModelCheckpoint(filepath=MODEL_PATH, monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=False)
num_sample1 = 1
interval = 2500
select_num = np.int(np.ceil(train_num1/interval))
# select_num1 = select_num*interval
# print(num_sample1,select_num,interval,select_num1)
if select_num>1:
t1 = np.arange(0,train_num1,interval)
pos = np.vstack((t1,t1+interval)).T
pos[-1][1] = train_num1
print(train_num1,select_num,interval)
print(pos)
else:
pos = [[0,train_num1]]
start2 = time.time()
train_id_1 = np.arange(train_num1)
valid_id_1 = np.arange(valid_num1)
np.random.shuffle(valid_id_1)
cnt1 = 0
mse1 = 1e5
decay_rate = 0.95
decay_step = 1
init_lr = self.config['lr']
for i1 in range(50):
self.config['lr'] = init_lr*((decay_rate)**(int(i1/decay_step)))
np.random.shuffle(train_id_1)
start1 = time.time()
valid_num2 = 2500
num2 = np.min([valid_num1,valid_num2])
valid_id2 = valid_id_1[0:num2]
x_valid1, y_valid1 = x_valid[valid_id2], y_valid[valid_id2]
for l in range(select_num):
s1, s2 = pos[l]
print(l,s1,s2)
sel_id = train_id_1[s1:s2]
x_train = mtx_train[vec_local_train[sel_id]]
y_train = y_train_ori[sel_id]
x_train, y_train = np.asarray(x_train), np.asarray(y_train)
print(x_train.shape,y_train.shape)
n_epochs = 1
train_num = x_train.shape[0]
print('x_train, y_train', x_train.shape, y_train.shape)
print('x_valid, y_valid', x_valid1.shape, y_valid1.shape)
# model.fit(x_train,y_train,epochs = n_epochs,batch_size = BATCH_SIZE,validation_data = [x_valid,y_valid],callbacks=[earlystop,checkpointer])
model.fit(x_train,y_train,epochs = n_epochs, batch_size = BATCH_SIZE, validation_data = [x_valid1,y_valid1],
callbacks=[earlystop,checkpointer])
# model.load_weights(MODEL_PATH)
model_path2 = '%s/model_%d_%d_%d_%d.h5'%(self.path,self.run_id,type_id2,context_size,i1)
model.save(model_path2)
# model_path2 = MODEL_PATH
if l%5==0:
print('loading weights... ', MODEL_PATH)
model.load_weights(MODEL_PATH) # load model with the minimum training error
y_predicted_valid1 = model.predict(x_valid)
y_predicted_valid = np.ravel(y_predicted_valid1[:,flanking])
temp1 = score_2a(np.ravel(y_valid[:,flanking]), y_predicted_valid)
print(temp1)
print('loading weights... ', model_path2)
model.load_weights(model_path2) # load model with the minimum training error
print('loading weights... ', model_path2)
model.load_weights(model_path2) # load model with the minimum training error
y_predicted_valid1 = model.predict(x_valid)
y_predicted_valid = np.ravel(y_predicted_valid1[:,flanking])
temp1 = score_2a(np.ravel(y_valid[:,flanking]), y_predicted_valid)
print([i1,l]+list(temp1))
t_mse1 = temp1[0]
if np.abs(t_mse1-mse1)<self.min_delta:
cnt1 += 1
else:
cnt1 = 0
if t_mse1 < mse1:
mse1 = t_mse1
if cnt1>=self.step:
break
stop1 = time.time()
print(stop1-start1)
print('loading weights... ', MODEL_PATH)
model.load_weights(MODEL_PATH) # load model with the minimum training error
y_predicted_valid1 = model.predict(x_valid)
y_predicted_valid = np.ravel(y_predicted_valid1[:,flanking])
temp1 = score_2a( | np.ravel(y_valid[:,flanking]) | numpy.ravel |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import numpy as np
import pickle
import gzip
from inspect import getargspec
from env import env
import theano
import theano.tensor as TT
import logging as loggers
from tensor_conversion import neural_computation
from disconnected_grad import disconnected_grad
from deepy.utils import Scanner
logging = loggers.getLogger(__name__)
class GraphBuilder(object):
"""
Tool for creating computational graph in deepy.
"""
def __init__(self):
self._default_block = self.new_block("default_block")
def default_block(self):
"""
Return the default block.
"""
return self._default_block
def collect_parameters(self):
"""
Return the default block, as all parameters will be registered to the default one.
"""
return self._default_block
def new_block(self, *layers, **kwargs):
"""
Create a parameters block.
:param layers: register some layers in the block
:param name: specify the name of this block
"""
from deepy.layers.block import Block
block = Block(*layers, **kwargs)
return block
def var(self, tensor_type, last_dim=0, test_shape=None):
"""
An alias of deepy.tensor.var.
"""
from deepy.tensor import var
return var(tensor_type, last_dim=last_dim, test_shape=test_shape)
def create_vars_from_data(self, dataset, split="train"):
"""
Create vars given a dataset and set test values.
Useful when dataset is already defined.
"""
from deepy.core.neural_var import NeuralVariable
vars = []
if split == "valid":
data_split = dataset.valid_set()
elif split == "test":
data_split = dataset.test_set()
else:
data_split = dataset.train_set()
first_data_piece = list(data_split)[0]
for i, numpy_tensor in enumerate(first_data_piece):
if numpy_tensor.dtype == "int64":
numpy_tensor = numpy_tensor.astype("int32")
if numpy_tensor.dtype == "float64":
numpy_tensor = numpy_tensor.astype(env.FLOATX)
type_map = {
0: "scalar",
1: "vector",
2: "matrix",
3: "tensor3",
4: "tensor4",
5: "tensor5",
}
tensor_type = type_map[numpy_tensor.ndim] if numpy_tensor.ndim in type_map else type_map[0]
if numpy_tensor.dtype.kind == "i":
tensor_type = "i" + tensor_type
theano_tensor = getattr(TT, tensor_type)("input_{}_{}".format(i + 1, tensor_type))
last_dim = numpy_tensor.shape[-1]
var = NeuralVariable(theano_tensor, dim=last_dim)
var.set_test_value(numpy_tensor)
vars.append(var)
return vars
@neural_computation
def scan(self, func, sequences=None, outputs=None, non_sequences=None, block=None, **kwargs):
"""
A loop function, the usage is identical with the theano one.
:type block: deepy.layers.Block
"""
results, updates = Scanner(func, sequences, outputs, non_sequences, neural_computation=True, **kwargs).compute()
if block and updates:
if type(updates) == dict:
updates = updates.items()
block.register_updates(*updates)
return results
def loop(self, sequences=None, outputs=None, non_sequences=None, block=None, **kwargs):
"""
Start a loop.
Usage:
```
with deepy.graph.loop(sequences={"x": x}, outputs={"o": None}) as vars:
vars.o = vars.x + 1
loop_outputs = deepy.graph.loop_outputs()
result = loop_outputs.o
```
"""
from loop import Loop
return Loop(sequences, outputs, non_sequences, block, **kwargs)
def get_trainer(self, model, method='sgd', config=None, annealer=None, validator=None):
"""
Get a trainer to optimize given model.
:rtype: deepy.trainers.GeneralNeuralTrainer
"""
from deepy.trainers import GeneralNeuralTrainer
return GeneralNeuralTrainer(model, method=method, config=config, annealer=annealer, validator=validator)
@neural_computation
def shared(self, value, name=None):
"""
Create a shared theano scalar value.
"""
if type(value) == int:
final_value = np.array(value, dtype="int32")
elif type(value) == float:
final_value = | np.array(value, dtype=env.FLOATX) | numpy.array |
"""Generating templates of ECG and PPG complexes"""
import numpy as np
from scipy.special import erf
from sklearn.preprocessing import MinMaxScaler
from scipy import signal
import scipy
from scipy.signal import argrelextrema
from scipy.integrate import solve_ivp
from vital_sqi.preprocess.preprocess_signal import squeeze_template
def ppg_dual_double_frequency_template(width):
"""
EXPOSE
Generate a PPG template by using 2 sine waveforms.
The first waveform double the second waveform frequency
:param width: the sample size of the generated waveform
:return: a 1-D numpy array of PPG waveform
having diastolic peak at the low position
"""
t = np.linspace(0, 1, width, False) # 1 second
sig = np.sin(2 * np.pi * 2 * t - np.pi / 2) + \
np.sin(2 * np.pi * 1 * t - np.pi / 6)
sig_scale = MinMaxScaler().fit_transform( | np.array(sig) | numpy.array |
import sys
import numpy as np
from mpi4py import MPI
from rvs import *
from scheduler import *
from modeling import *
def eval_wmpi(rank):
log(INFO, "starting;", rank=rank)
sys.stdout.flush()
if rank == 0:
blog(sinfo_m=sinfo_m)
sys.stdout.flush()
schingi__sl_E_std_l = []
for i, sching_m in enumerate(sching_m_l):
for p in range(1, num_mpiprocs):
eval_i = np.array([i], dtype='i')
comm.Send([eval_i, MPI.INT], dest=p)
Esl_l, sl_std_l = [], []
# cum_sl_l = []
for p in range(1, num_mpiprocs):
sl_E_std = np.empty(2, dtype=np.float64)
comm.Recv(sl_E_std, source=p)
Esl_l.append(sl_E_std[0] )
sl_std_l.append(sl_E_std[1] )
# sl_l = np.empty(T, dtype=np.float64)
# comm.Recv(sl_l, source=p)
# cum_sl_l += sl_l.tolist()
log(INFO, "", i=i, sching_m=sching_m, Esl=np.mean(Esl_l), sl_std=np.mean(sl_std_l) )
sys.stdout.flush()
schingi__sl_E_std_l.append(sl_E_std)
# x_l = numpy.sort(cum_sl_l)[::-1]
# y_l = numpy.arange(x_l.size)/x_l.size
# plot.step(x_l, y_l, label=sching_m['name'], color=next(dark_color), marker=next(marker), linestyle=':')
# plot.xscale('log')
# plot.yscale('log')
# plot.legend()
# plot.xlabel(r'Slowdown', fontsize=13)
# plot.ylabel(r'Tail distribution', fontsize=13)
# plot.savefig("sltail_ar{0:.2f}.png".format(ar) )
# plot.gcf().clear()
for p in range(1, num_mpiprocs):
eval_i = np.array([-1], dtype='i')
comm.Send([eval_i, MPI.INT], dest=p)
print("Sent req eval_i= {} to p= {}".format(eval_i, p) )
return schingi__sl_E_std_l
else:
while True:
eval_i = np.empty(1, dtype='i')
comm.Recv([eval_i, MPI.INT], source=0)
eval_i = eval_i[0]
if eval_i == -1:
return
scher = Scher(mapping_m, sching_m_l[eval_i] )
# log(INFO, "simulating;", rank=rank, eval_i=eval_i, scher=scher)
sys.stdout.flush()
t_s_l, t_a_l, t_r_l, t_sl_l, load_mean, droprate_mean = sample_traj(sinfo_m, scher, use_lessreal_sim)
print("rank= {}, eval_i= {}, a_mean= {}, sl_mean= {}, load_mean= {}, droprate_mean= {}".format(rank, eval_i, np.mean(t_a_l), np.mean(t_sl_l), load_mean, droprate_mean) )
sl_E_std = np.array([np.mean(t_sl_l), np.std(t_sl_l) ], dtype=np.float64)
comm.Send([sl_E_std, MPI.FLOAT], dest=0)
sys.stdout.flush()
def learn_wmpi(rank):
scher = RLScher(sinfo_m, mapping_m, sching_m)
N, T, s_len = scher.N, scher.T, scher.s_len
log(INFO, "starting;", rank=rank, scher=scher)
sys.stdout.flush()
if rank == 0:
blog(sinfo_m=sinfo_m)
for i in range(nlearningsteps):
scher.save(i)
n_t_s_l, n_t_a_l, n_t_r_l, n_t_sl_l = np.zeros((N, T, s_len)), np.zeros((N, T, 1)), np.zeros((N, T, 1)), np.zeros((N, T, 1))
for n in range(N):
p = n % (num_mpiprocs-1) + 1
sim_step = np.array([i], dtype='i')
comm.Send([sim_step, MPI.INT], dest=p)
for n in range(N):
p = n % (num_mpiprocs-1) + 1
t_s_l = | np.empty(T*s_len, dtype=np.float64) | numpy.empty |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.11.4
# kernelspec:
# display_name: wtte-dev
# language: python
# name: wtte-dev
# ---
# %% [markdown]
# # WTTE-RNN in PyTorch
#
# <NAME>
#
# Based on original Keras version written by <NAME>:
# https://github.com/ragulpr/wtte-rnn/blob/master/examples/keras/simple_example.ipynb
# MIT license
#
# For details, check out
# https://ragulpr.github.io/2016/12/22/WTTE-RNN-Hackless-churn-modeling/
# https://github.com/ragulpr/wtte-rnn
# %%
# %matplotlib inline
import sys
import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import TensorDataset, DataLoader
import matplotlib.pyplot as plt
sys.path.append("..")
from torch_wtte import losses
np.random.seed(11)
torch.manual_seed(11)
# %%
def get_data(n_timesteps, every_nth, n_repeats, noise_level, n_features, use_censored=True):
def get_equal_spaced(n, every_nth):
# create some simple data of evenly spaced events recurring every_nth step
# Each is on (time,batch)-format
events = np.array([np.array(range(n)) for _ in range(every_nth)])
events = events + np.array(range(every_nth)).reshape(every_nth, 1) + 1
tte_actual = every_nth - 1 - events % every_nth
was_event = (events % every_nth == 0) * 1.0
was_event[:, 0] = 0.0
events = tte_actual == 0
is_censored = (events[:, ::-1].cumsum(1)[:, ::-1] == 0) * 1
tte_censored = is_censored[:, ::-1].cumsum(1)[:, ::-1] * is_censored
tte_censored = tte_censored + (1 - is_censored) * tte_actual
events = np.copy(events.T * 1.0)
tte_actual = np.copy(tte_actual.T * 1.0)
tte_censored = np.copy(tte_censored.T * 1.0)
was_event = np.copy(was_event.T * 1.0)
not_censored = 1 - np.copy(is_censored.T * 1.0)
return tte_censored, not_censored, was_event, events, tte_actual
tte_censored, not_censored, was_event, events, tte_actual = get_equal_spaced(
n=n_timesteps, every_nth=every_nth
)
# From https://keras.io/layers/recurrent/
# input shape rnn recurrent if return_sequences: (nb_samples, timesteps, input_dim)
u_train = not_censored.T.reshape(n_sequences, n_timesteps, 1)
x_train = was_event.T.reshape(n_sequences, n_timesteps, 1)
tte_censored = tte_censored.T.reshape(n_sequences, n_timesteps, 1)
y_train = np.append(tte_censored, u_train, axis=2) # (n_sequences,n_timesteps,2)
u_test = np.ones(shape=(n_sequences, n_timesteps, 1))
x_test = np.copy(x_train)
tte_actual = tte_actual.T.reshape(n_sequences, n_timesteps, 1)
y_test = np.append(tte_actual, u_test, axis=2) # (n_sequences,n_timesteps,2)
if not use_censored:
x_train = np.copy(x_test)
y_train = np.copy(y_test)
# Since the above is deterministic perfect fit is feasible.
# More noise->more fun so add noise to the training data:
x_train = np.tile(x_train.T, n_repeats).T
y_train = np.tile(y_train.T, n_repeats).T
# Try with more than one feature TODO
x_train_new = np.zeros([x_train.shape[0], x_train.shape[1], n_features])
x_test_new = np.zeros([x_test.shape[0], x_test.shape[1], n_features])
for f in range(n_features):
x_train_new[:, :, f] = x_train[:, :, 0]
x_test_new[:, :, f] = x_test[:, :, 0]
x_train = x_train_new
x_test = x_test_new
# xtrain is signal XOR noise with probability noise_level
noise = np.random.binomial(1, noise_level, size=x_train.shape)
x_train = x_train + noise - x_train * noise
return y_train, x_train, y_test, x_test, events
# %% [markdown]
# ### Generate some data
#
# * The true event-sequence is evenly spaced points (but we start anywhere in the sequence)
# * The true feature is (binary) if there was an event in last step
# * In the training data the feature has added noise
# * Training TTE is censored. Testing TTE is uncensored.
# %%
n_timesteps = 200
n_sequences = every_nth = 80
n_features = 1
n_repeats = 1000
noise_level = 0.005
use_censored = True
y_train, x_train, y_test, x_test, events = get_data(
n_timesteps, every_nth, n_repeats, noise_level, n_features, use_censored
)
# %%
#### Plots
print("test shape", x_test.shape, y_test.shape)
plt.imshow(x_test[:, :, :].sum(axis=2) > 0, interpolation="none", cmap="Accent", aspect="auto")
plt.title("x_test (lagged/deterministic event indicator)")
plt.show()
plt.imshow(y_test[:, :, 0], interpolation="none", cmap="jet", aspect="auto")
plt.title("y_test[:,:,0] actual tte")
plt.show()
print("train shape", x_train.shape, y_train.shape)
plt.imshow(
x_train[:every_nth, :, :].mean(axis=2), interpolation="none", cmap="Accent", aspect="auto"
)
plt.title("x_train[:every_nth,:,0] (lagged/noisy event indicator)")
plt.show()
plt.imshow(y_train[:every_nth, :, 0], interpolation="none", cmap="jet", aspect="auto")
plt.title("y_train[:every_nth,:,0] censored tte")
plt.show()
plt.imshow(y_train[:every_nth, :, 1], interpolation="none", cmap="Accent", aspect="auto")
plt.title("y_train[:every_nth,:,1] u (non-censoring indicator)")
plt.show()
## Example TTE:
print("Example TTEs")
plt.plot(
y_train[every_nth // 4, :, 0],
label="censored tte (train)",
color="black",
linestyle="dashed",
linewidth=2,
drawstyle="steps-post",
)
plt.plot(
y_test[every_nth // 4, :, 0],
label="actual tte (test)",
color="black",
linestyle="solid",
linewidth=2,
drawstyle="steps-post",
)
plt.xlim(0, n_timesteps)
plt.xlabel("time")
plt.ylabel("time to event")
plt.title("Example TTEs")
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
plt.show()
# %% [markdown]
# # Train a WTTE-RNN
# ### Things to try out:
# * have fun with data paramaters:
# * every_nth to control event frequency
# * noise_level to make it more noisy
# * n_timesteps
# * n_features to get more noisy input
# * Generate more interesting temporal relationships
# * Here we use the smallest possible GRU. Try different learning rates, network architectures, initializations.
# * Try Implementing multivariate distributions, other distributions, data pipelines etc.
# * Invent better output activation layer
# * Invent ways to overcome instability with lots of censoring
# * ETC and have fun!
# %%
# Paramaeters for output activation layer initialization.
# Start at naive geometric (beta=1) MLE:
tte_mean_train = | np.nanmean(y_train[:, :, 0]) | numpy.nanmean |
# Sound Source locate
#
# @Time : 2019-10-9 19:03
# @Author : xyzhao
# @File : generateGcc.py
# @Description: process wav file into features
import numpy as np
import math
import pickle
import wave
import collections
import os
import random
import copy
import sys
import matplotlib.pyplot as plt
'''
This function computes the offset between the signal sig and the reference signal refsig
using the Generalized Cross Correlation - Phase Transform (GCC-PHAT)method.
'''
def gcc_phat(sig, refsig, fs=1, max_tau=None, interp=1):
if isinstance(sig, list):
sig = np.array(sig)
if isinstance(refsig, list):
refsig = np.array(refsig)
# make sure the length for the FFT is larger or equal than len(sig) + len(refsig)
n = sig.shape[0] + refsig.shape[0]
# Generalized Cross Correlation Phase Transform
SIG = np.fft.rfft(sig, n=n)
REFSIG = np.fft.rfft(refsig, n=n)
R = SIG * np.conj(REFSIG)
cc = np.fft.irfft(R / np.abs(R), n=(interp * n))
max_shift = int(interp * n / 2)
if max_tau:
max_shift = np.minimum(int(interp * fs * max_tau), max_shift)
cc = | np.concatenate((cc[-max_shift:], cc[:max_shift + 1])) | numpy.concatenate |
"""
ndt.py
File containing class definitions of NDT approximation for Consensus NDT SLAM
Also contains helper NDT functions
Author: <NAME>
Date created: 15th April 2019
Last modified: 13th November 2019
"""
import numpy as np
import pptk
import utils
import transforms3d
from scipy.optimize import check_grad
from scipy.optimize import minimize
import odometry
import diagnostics
import integrity
import numpy_indexed
import itertools
import time
from scipy.interpolate import RegularGridInterpolator as RGI
"""
Importing base libraries
"""
class NDTCloudBase:
"""
A class to store the sparse grid center points, means and covariances for grid points that are full.
This class will be the de facto default for working with NDT point clouds.
After refactoring for multiscale NDT with different methods, has become parent class for all NDT approximation methods
"""
def __init__(self, xlim, ylim, zlim, input_horiz_grid_size, input_vert_grid_size, cloud_type):
"""
A method to initialize a member of the NDTCloud class. When initializing a member of the class, grid limits are
given along with the grid sizes. Using these values a sparse grid is created and corresponding zero mean and
covariance lists are also created.
Since the first grid is highly dependent on the user it is for, there is no default initialization
:param xlim: Limit of the grid along the x-axis
:param ylim: Limit of the grid along the y-axis
:param zlim: Limit of the grid along the z-axis
:param input_horiz_grid_size: User entered
:param input_vert_grid_size:
"""
# Don't really need to store the limits of the space spanned by the NDT cloud. They will be needed to find if
# the origin is a grid center though
# When initializing the cloud, the origin is either going to be a grid center or not.
self.horiz_grid_size = np.float(input_horiz_grid_size)
self.vert_grid_size = np.float(input_vert_grid_size)
# Create NDT map for reference grid
# Initialize empty lists to store means and covariance matrices
self.stats = {} # Create an empty dictionary for mu and sigma corresponding to each voxel
"""
Dictionary structure is {<key = center point>, {<key = 'mu'>, [mu value], <key='sigma'>, [sigma_value]
, <key='no_points'>, int, <key='integrity'>, float}, ...}
NOTE: key must be a tuple not a ndarray
"""
self.local_to_global = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
self.max_no_points = 0
self.first_center = np.empty([1, 3])
self.max_no_voxels = -1
self.cloud_type = cloud_type
def update_displacement(self, odometry_vector):
"""
A function to update the displacement of the current local frame of reference from the global reference
:param odometry_vector: A vector of [x, y, z, phi, theta, psi] measuring the affine transformation of the
current local frame of reference (LiDAR origin) to the global frame of reference (map origin)
:return: None
"""
# TODO:
# Update translation vector
self.local_to_global[:3] += odometry_vector[:3]
# Update euler angle vector
phi_local = np.deg2rad(self.local_to_global[3])
theta_local = np.deg2rad(self.local_to_global[4])
psi_local = np.deg2rad(self.local_to_global[5])
R_local = transforms3d.euler.euler2mat(phi_local, theta_local, psi_local)
phi_delta = np.deg2rad(odometry_vector[3])
theta_delta = np.deg2rad(odometry_vector[4])
psi_delta = np.deg2rad(odometry_vector[5])
R_delta = transforms3d.euler.euler2mat(phi_delta, theta_delta, psi_delta, 'rxyz')
R_new = np.matmul(R_delta, R_local)
phi_rad, theta_rad, psi_rad = transforms3d.euler.mat2euler(R_new, 'rxyz')
angle_new = np.rad2deg(np.array([phi_rad, theta_rad, psi_rad]))
self.local_to_global[3:] = angle_new
return None
def find_voxel_center(self, ref_pointcloud, tol=1.0e-7):
"""
A function to return grid indices for a given set of 3D points. The input may be a set of (x, y, z) Nx3 or
(x, y, z, int) Nx4. This function is written to be agnostic to either form of the array
This function also checks if points on a edge of the grid upto a tolerance level. If they are, it assigns them
a value to ensure that no calculations involve that point
:param ref_pointcloud: Nx3 or Nx4 numpy array for which binning is required
:param tol: Tolerance for picking center. Default values used for overlapping
:return: grid_centers: Matrix containing center coordinates corresponding to the given points Nx3
"""
# Used an array over a tuple as there is a small possibility that the coordinates might change
ref_points = np.array(ref_pointcloud[:, :3]) # to remove intensity if it has been passed accidentally
grid_size = np.array([self.horiz_grid_size, self.horiz_grid_size, self.vert_grid_size])
number_row = np.shape(self.first_center)[0]
points_repeated = np.tile(ref_points, (number_row, 1))
N = ref_points.shape[0]
voxel_centers = np.zeros_like(points_repeated)
for i in range(number_row):
pre_voxel_number = (ref_points + self.first_center[i, :]) / grid_size
pre_voxel_center = np.round(pre_voxel_number).astype(int) * grid_size
first_grid_edge = self.first_center[i, :] - 0.5*np.array([self.horiz_grid_size, self.horiz_grid_size,
self.vert_grid_size])
line_check = np.abs(np.mod(ref_points, grid_size) + first_grid_edge)
pre_voxel_center[line_check < tol] = np.nan
pre_voxel_center[np.abs(line_check - 1) < tol] = np.nan
voxel_centers[i*N:(i+1)*N, :] = np.multiply(np.sign(ref_points), np.abs(pre_voxel_center) -
np.sign(ref_points)*np.broadcast_to(self.first_center[i, :],
(N, 3)))
return points_repeated, voxel_centers
def bin_in_voxels(self, points_to_bin):
"""
Function to bin given points into voxels in a dictionary approach
:param points_to_bin: The points that are to be binned into the voxel clusters indexed by the voxel center tuple
:return: points_in_voxel: A dictionary indexed by the tuple of the center of the bin
"""
points_repeated, voxel_centers = self.find_voxel_center(points_to_bin)
dummy = numpy_indexed.group_by(voxel_centers, points_repeated)
points_in_voxels = {}
for i in range(np.shape(dummy[0])[0]):
voxel_key = tuple(dummy[0][i])
points_in_voxels[voxel_key] = dummy[1][i]
return points_in_voxels
def find_likelihood(self, transformed_pc):
"""
Function to return likelihood for a given transformed point cloud w.r.t NDT point cloud
Slightly different from reference papers in that 1/2det(sigma) is also included while calculating the likelihood
The likelihood is increased if a corresponding Gaussian is found. If not, 0 is added
:param transformed_pc: Point cloud that has been passed through a candidate affine transformation
:return: likelihood: Scalar value representing the likelihood of the given
"""
transformed_xyz = transformed_pc[:, :3]
likelihood = 0
points_in_voxels = self.bin_in_voxels(transformed_xyz)
for key, val in points_in_voxels.items():
if key in self.stats:
sigma = self.stats[key]['sigma']
sigma_inv = np.linalg.inv(sigma)
diff = np.atleast_2d(val - self.stats[key]['mu'])
likelihood += np.sum(np.exp(-0.5 * np.diag(np.matmul(np.matmul(diff, sigma_inv), diff.T))))
return likelihood
def display(self, plot_density=1.0):
"""
Function to display the single NDT approximation
:param fig: The figure object on which the probability function has to be plotted
:param plot_density: The density of the plot (as a int scalar) the higher the density, the more points per grid
:return: plot_points: The points sampled from the distribution that are to be plotted like any other PC
"""
base_num_pts = 48 # 3 points per vertical and 4 per horizontal
plot_points = np.empty([3, 0])
plot_integrity = np.empty(0)
for key, value in self.stats.items():
sigma = self.stats[key]['sigma']
mu = self.stats[key]['mu']
measure_num = self.stats[key]['no_points']
num_pts = np.int(3 * measure_num / self.max_no_points * plot_density * base_num_pts )
if num_pts < 2:
num_pts = 2
if 'integrity' in self.stats[key]:
voxel_score = self.stats[key]['integrity'] * np.ones(num_pts)
else:
voxel_score = np.ones(num_pts)
center_pt = np.array(key)
grid_lim = np.zeros([2, 3])
grid_lim[0, 0] = center_pt[0] - self.horiz_grid_size
grid_lim[1, 0] = center_pt[0] + self.horiz_grid_size
grid_lim[0, 1] = center_pt[1] - self.horiz_grid_size
grid_lim[1, 1] = center_pt[1] + self.horiz_grid_size
grid_lim[0, 2] = center_pt[2] - self.vert_grid_size
grid_lim[1, 2] = center_pt[2] + self.vert_grid_size
grid_plot_points = np.random.multivariate_normal(mu, sigma, num_pts)
# Ensure that all selected points are inside the grid
for i in range(3):
grid_plot_points[grid_plot_points[:, i] < grid_lim[0, i], i] = grid_lim[0, i]
grid_plot_points[grid_plot_points[:, i] > grid_lim[1, i], i] = grid_lim[1, i]
plot_points = np.hstack((plot_points, grid_plot_points.T))
plot_integrity = np.append(plot_integrity, voxel_score)
print('The maximum number of points per voxel is ', self.max_no_points)
return plot_points.T, plot_integrity
def update_stats(self, points_in_voxels):
"""
Function to update the statistics of the NDT cloud given points and the center of the grid that they belong to
:param points_in_voxels: A dictionary indexed by the center of the grid and containing corresponding values
:return: None
"""
for k, v in points_in_voxels.items():
no_in_voxel = v.size/3 # to prevent a single row vector from being counted as 3
if k in self.stats:
# Use update methodology from 3D NDT Scan Matching Paper Eq 4 and 5
m_old = self.stats[k]['no_points']*self.stats[k]['mu'] # row vector
s_old = self.stats[k]['no_points']*self.stats[k]['sigma'] + \
np.matmul(np.reshape(self.stats[k]['mu'], [3, 1]), np.reshape(m_old, [1, 3]))
m_new = m_old + np.sum(v, axis=0)
s_new = s_old + np.matmul(v.T, v)
self.stats[k]['no_points'] += no_in_voxel
self.stats[k]['mu'] = m_new/self.stats[k]['no_points']
self.stats[k]['sigma'] = (s_new - np.matmul(np.reshape(self.stats[k]['mu'], [3, 1]),
np.reshape(m_new, [1, 3])))/self.stats[k]['no_points']
if self.stats[k]['no_points'] > self.max_no_points:
self.max_no_points = self.stats[k]['no_points']
else:
if no_in_voxel >= 5 and np.sum(np.isnan(np.array(k))) == 0:
self.stats[k] = {} # Initialize empty dictionary before populating with values
self.stats[k]['mu'] = np.mean(v, axis=0)
self.stats[k]['sigma'] = np.cov(v, rowvar=False)
self.stats[k]['no_points'] = no_in_voxel
self.stats[k]['idx'] = self.pairing_cent2int(np.atleast_2d(np.array(k)))
self.max_no_voxels += 1
if self.stats[k]['no_points'] > self.max_no_points:
self.max_no_points = self.stats[k]['no_points']
return None
def eig_check(self):
"""
Function to perform an eigenvalue based consistency check on the covariance matrix and adjust values accordingly
Algorithm based on 3d NDT Scan Matching and Biber's NDT paper
Using an SVD approach here. For covariance matrices, SVD and eigen decomposition should be the same. SVD
implementations are often more stable
:return: None
"""
scale_param = 0.0001
for key, val in self.stats.items():
u, s_diag, v = np.linalg.svd(val['sigma']) # np.svd naturally returns a diagonal
s_diag[s_diag < scale_param*s_diag.max()] = scale_param*s_diag.max()
val['sigma'] = np.matmul(np.matmul(u, np.diag(s_diag)), v)
return None
def update_cloud(self, pc_points):
"""
Function to add points to current NDT approximation. This function adds both, new centers and points to
existing grid points.
:param pc_points: The points that are to be added to the NDT approximation. Might be Nx3 or Nx4.
Function agnostic to that
:return: None
"""
# This function should be used to update an empty NDT cloud as well using the given points
# Find grid centers corresponding to given points
update_points = pc_points[:, :3]
# Dictionary approach here as well
points_in_voxels = self.bin_in_voxels(update_points)
# Update the NDT approximation with these binned points
self.update_stats(points_in_voxels)
self.eig_check()
return None
def find_integrity(self, points):
"""
Given a set of points and the underlying NDT Cloud, find the integrity of each voxel and the combined navigation
solution
:param points: Transformed points for which the integrity is required
:return: Im: The integrity of the navigation solution obtained using the transformed points given
:return: iscore: Voxel integrity score corresponding to the voxel center
"""
test_xyz = points[:, :3]
binned_points = self.bin_in_voxels(test_xyz)
N = len(self.stats)
iscore_array = np.zeros(N)
loop_index = 0
mu_points = np.zeros([N, 3])
for key, val in self.stats.items():
if key in binned_points:
mu_points[loop_index, :] = val['mu']
iscore_array[loop_index] = integrity.voxel_integrity(val, binned_points[key])
self.stats[key]['integrity'] = iscore_array[loop_index]
if np.isnan(iscore_array[loop_index]):
print('NaN detected!')
loop_index += 1
else:
self.stats[key]['integrity'] = 0
iscore_array[iscore_array == 0] = 1e-9
Im, iscore_sum = integrity.solution_score(mu_points[:loop_index, :], iscore_array[:loop_index], points)
# The loop index is added to ensure that only points that have a corresponding voxel are used for IDOP
return Im, iscore_sum
def optimization_integrity(self, points):
"""
Given a set of points and the underlying NDT Cloud, find the integrity of each voxel and the combined navigation
solution
:param points: Transformed points for which the integrity is required
:return: Im: The integrity of the navigation solution obtained using the transformed points given
:return: iscore: Voxel integrity score corresponding to the voxel center
"""
test_xyz = points[:, :3]
binned_points = self.bin_in_voxels(test_xyz)
N = len(self.stats)
iscore_dict = {}
rbar_dict = {}
k_dict = {}
loop_index = 0
mu_points = np.zeros([N, 3])
for key, val in self.stats.items():
if key in binned_points:
mu_points[loop_index, :] = val['mu']
iscore_dict[key], rbar_dict[key], k_dict[key] = integrity.voxel_int_opt(val, binned_points[key])
if np.isnan(iscore_dict[key]):
print('NaN detected!')
loop_index += 1
iscore_dict[iscore_dict == 0] = 1e-9
# The loop index is added to ensure that only points that have a corresponding voxel are used for IDOP
return iscore_dict, rbar_dict, k_dict
def filter_voxels_integrity(self, integrity_limit=0.7):
"""
Function to trim an ndt_cloud based on the integrity values of its voxels
:param self: The NDT approximation to be trimmed
:param integrity_limit: The minimum valid voxel integrity value
:return: ndt_cloud: The same NDT approximation, but now with all voxels below an integrity limit removed
"""
delete_index = []
for key in self.stats.keys():
if self.stats[key]['integrity'] < integrity_limit:
delete_index.append(key)
for del_key in delete_index:
del self.stats[del_key]
return None
def pairing_cent2int(self, point_centers):
"""
:param point_centers: Nx3 numpy array containing coordinates under consideration
:return:
"""
"""
1. Using voxel size, convert each center to a coordinate with only integer values
2. Implement a standard pairing function to bind said coordinate to an index
"""
assert(point_centers.shape[1] == 3) # Checking that the matrix is all row vectors
# Assign unique positive value to each integer
pt_centers_temp = np.copy(point_centers)
pt_centers_temp = (pt_centers_temp + self.first_center[0, :])/np.array([self.horiz_grid_size, self.horiz_grid_size, self.vert_grid_size])
pt_centers_temp[pt_centers_temp > 0] = 2*pt_centers_temp[pt_centers_temp > 0]
pt_centers_temp[pt_centers_temp < 0] = -2*pt_centers_temp[pt_centers_temp < 0] - 1
x = np.atleast_2d(pt_centers_temp[:, 0])
y = np.atleast_2d(pt_centers_temp[:, 1])
z = np.atleast_2d(pt_centers_temp[:, 2])
assert(np.min(x) > -1)
assert(np.min(y) > -1)
assert(np.min(z) > -1)
pair_1 = np.atleast_2d(0.5*(x + y)*(x + y + 1) + y)
int_pairing = np.atleast_2d(0.5*(pair_1 + z)*(pair_1 + z + 1) + z)
int_pairing = np.reshape(int_pairing, [-1, 1])
assert(int_pairing.shape == (point_centers.shape[0], 1))
return int_pairing
def pair_check(self):
"""
Checking that the number of voxels and the number of unique index assignments is the same
:return: None
"""
voxels = []
number = 0
for key in self.stats:
voxels.append(self.stats[key]['idx'][0][0])
number += 1
voxels = np.array(voxels)
unique_voxels, unique_counts, case_counts = np.unique(voxels, return_index=True, return_counts=True)
unique_no = np.size(unique_voxels)
print('The number of voxels is ', number)
print('The number of maximum voxels is ', self.max_no_voxels)
print('The number of unique voxels is ', unique_no)
assert(np.size(unique_voxels) == self.max_no_voxels)
return None
def prune_pc(self, pc):
"""
Remove all points that don't overlap with NDT Cloud
:param pc: Point cloud
:return pruned_pc: Unique points that overlap with NDT Cloud
"""
pruned_pc = np.zeros([0, 3])
center_dict = self.bin_in_voxels(pc)
keys = np.zeros([0, 3])
binned_keys = np.zeros([0, 3])
original_keys = np.zeros([0, 3])
for key in self.stats:
original_keys = np.vstack((original_keys, key))
for key in center_dict:
binned_keys = | np.vstack((binned_keys, key)) | numpy.vstack |
import streamlit as st
import numpy as np
import pandas as pd
import torch
import copy
from sklearn import decomposition
import plotly.express as px
import plotly.graph_objects as go
import altair as alt
import graphviz
from graphviz import Digraph
import nltk
from nltk.tokenize import RegexpTokenizer
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
from random import sample
import pickle
from scipy.special import softmax
import time
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.nn.functional import pad
import zipfile
import os
from os import listdir
from zipfile import ZipFile
from os.path import isfile, join
from urllib.request import urlopen
from word_highlight import get_highlight_text
from train_vis import get_train_content,get_train_content_local, loss_acc_plot, params_plot
MODEL_PATH = 'https://github.com/CMU-IDS-2020/fp-good_or_bad/raw/main/models/xentropy_adam_lr0.0001_wd0.0005_bs128'
EMBEDDING_URL = "https://github.com/CMU-IDS-2020/fp-good_or_bad/raw/main/sample_embeddings/sample_words_embeddings.pt"
AMAZON_EMBEDDING_URL = 'https://github.com/CMU-IDS-2020/fp-good_or_bad/blob/main/sample_embeddings/100d/amazon_products_sample_embeddings.pt'
MOVIE_EMBEDDING_URL = 'https://github.com/CMU-IDS-2020/fp-good_or_bad/blob/main/sample_embeddings/100d/movie_review_sample_embeddings.pt'
YELP_EMBEDDING_URL = 'https://github.com/CMU-IDS-2020/fp-good_or_bad/blob/main/sample_embeddings/100d/yelp_restaurant_sample_embeddings.pt'
MODEL_PATH_PT = 'https://github.com/CMU-IDS-2020/fp-good_or_bad/raw/main/models/xentropy_adam_lr0.0001_wd0.0005_bs128.pt'
EPOCH = 30
SAMPLE_LIMIT = 5000
EPOCH_SAMPLE_LIMIT = SAMPLE_LIMIT // EPOCH
MOVIE_DATASET = 'Movie reviews'
AMAZON_DATASET = 'Amazon products'
YELP_DATASET = 'Yelp restaurants'
OVERVIEW = '1) Overview'
PREPROCESS = '2) Dataset & Input Preprocessing'
TRAIN = '3) Training'
PREDICT = '4) Predicting'
ADAM = 'ADAM'
SGD = 'SGD with Momentum'
preprocesse_exed = False
train_exed = False
@st.cache(ttl=60 * 20)
def download_stopword():
nltk.download('stopwords')
@st.cache(ttl=60 * 20)
def download_wordnet():
nltk.download('wordnet')
class Model:
def __init__(self, dataset, learning_rate, batch_size, weight_decay, optimizer):
self.dataset = dataset
self.learning_rate = learning_rate
self.batch_size = batch_size
self.weight_decay = weight_decay
self.optimizer = optimizer
self.model_url = None
self.model_name = None
self.mapped_dataset = None
self.mapped_optimizer = None
self.mapped_weight_decay = None
self.max_length = 0
dataset_map = { 'Movie reviews':'movie_reviews','Amazon products' : "amazon_products", 'Yelp restaurants':"yelp_restaurants"}
optimizer_map = {'ADAM':"adam",'SGD with Momentum':"sgdmomentum"}
self.mapped_dataset = dataset_map[self.dataset]
self.mapped_optimizer = optimizer_map[self.optimizer]
if self.weight_decay == "5e-4":
self.mapped_weight_decay = "0.0005"
else:
self.mapped_weight_decay = self.weight_decay
url = "https://github.com/CMU-IDS-2020/fp-good_or_bad/raw/main/models/" + self.mapped_dataset + "/model_state_dict/"
self.model_name = "xentropy_{}_lr{}_wd{}_bs{}.pt".format(self.mapped_optimizer, self.learning_rate, self.mapped_weight_decay, self.batch_size)
self.model_url = url + self.model_name
if self.mapped_dataset == 'movie_reviews':
self.max_len = 29
elif self.mapped_dataset == "yelp_restaurants":
self.max_len = 245
else:
self.max_len = 721
def main():
download_stopword()
download_wordnet()
st.sidebar.header('Navigation')
page = st.sidebar.radio('', (OVERVIEW, PREPROCESS, TRAIN, PREDICT))
if page == OVERVIEW:
_, center_emb_col, _ = st.beta_columns([1, 3, 1])
with center_emb_col:
st.markdown("<h1 style='text-align: center; color: Black;'>Good or Bad? Visualizing Neural Networks on Sentiment Analysis</h1>", unsafe_allow_html=True)
# st.write("")
st.write("")
st.write("")
st.write("")
st.write("")
st.subheader("Who is this app for?")
st.write("")
st.write("")
# st.write("")
st.markdown(" <b><font color='blue'>Our app is especially useful for curious machine learning laymen. With our app, you will be able to visualize the full process of sentiment analysis using a neural network, as well as the interaction of training data, hyperparameters and the model itself. </font></b>", unsafe_allow_html=True)
st.markdown("<b><font color='blue'>We hope that this app can demystify the magic of neural networks.</font></b>", unsafe_allow_html=True)
st.write("")
# st.write("")
# st.write("")
st.title("Overview")
st.write("")
st.write("")
st.write("In this age of social media, **personal opinions** are expressed ubiquitously in the public. \
Behind these opinions are sentiments and emotions. \
Gaining an understanding into sentiments regarding a topic can be beneficial in many ways, be it in the case of a business trying to know its customers or the case of a politician trying to know the electorate. \
This age has also witnessed a rise of artificial intelligence and machine learning, which enables a quick capture of the sentiments behind numerous opinions existing on social media.")
st.image('https://www.kdnuggets.com/images/sentiment-fig-1-689.jpg', caption = 'Sentiment Analysis (reference: https://www.kdnuggets.com/2018/03/5-things-sentiment-analysis-classification.html)', use_column_width=True)
st.write('''**Machine learning** methods can be highly accurate and efficient for various tasks. \
However, machine learning models, especially neural networks, are still a “black box” for many people, even experienced experts in the field (for example, considering the poorly understood nature of generalization of neural networks). \
Given this problem, we built this visualization application to help people understand internal mechanisms of a neural network. \
We use the task of sentiment analysis as a case study in our application to walk users through the neural network’s training and decision making process.''')
st.write('''To effectively capture, classify and predict sentiments, we design, utilize and demonstrate a convolutional neural network (CNN) [1], which is known for its excellent performance in computer vision tasks, as well as natural language processing tasks recently. \
Specifically, CNNs have been shown to be able to model inherent syntactic and semantic features of sentimental expressions [2]. \
Finally, another advantage of using CNNs (and neural networks in general) is no requirement of deep domain knowledge, in this case linguistics [2]. ''')
st.image('https://miro.medium.com/max/726/1*Y4aATgaQ8OO_gxLFTy3rQg.png', caption = 'Neural Networks for Sentiment Analysis (reference: https://medium.com/nlpython/sentiment-analysis-analysis-part-3-neural-networks-3768dd088f71)', use_column_width=True)
st.write("")
st.write("")
st.write("")
st.title("User Instructions")
st.write("Our app would first take into user's input sentences and preprocess into tokens. Tokens are then converted into embedding vectors to pass in to the neural network. See more details in section 'Dataset & Preprocessing'.")
st.write("During training, our model would use the target rating to learn the optimal parameters, mainly weights and biases. See more details in section 'Training'.")
st.write("During prediction, the same preprocessing process will be applied to the newly input sentence and we'll use the optimal parameters we got from training to map the embedding vectors to the predicted rating. See more details in section 'Predicting'.")
st.markdown("<font color='blue'><b>To start using our app:</b></font>", unsafe_allow_html=True)
st.write(" 1. Use the sidebar on the left to navigate to the next section: **dataset & input preprocessing**.")
st.write(" 2. Select a specific **dataset** and feel free to **write something emotional**!")
st.write(" 3. In Training section, adjust the **training hyperparameters**, or selection **two different sets of hyperparameters** to see the entire training process!")
st.write(" 4. In predicting section, check out how a neural net can understand your sentiment!")
st.write("")
st.write("")
st.write("")
st.markdown('''
### References
[1]
<NAME> and <NAME>. "An introduction to convolutional neural networks." arXiv preprint arXiv:1511.08458 (2015).
[2]
<NAME> and <NAME> (2019) - "Sentiment Classification Using Convolutional Neural Networks."
Applied Sciences, 2019, 9, 2347.
''')
st.markdown('''
### Authors (ranked by first name):
<NAME>
<NAME>
<NAME>
<NAME>
''')
elif page == PREPROCESS:
_, center_emb_col, _ = st.beta_columns([1, 3, 1])
with center_emb_col:
st.title("Dataset & Input Preprocessing")
# st.header("Model Description")
# st.write("Our model has the following architecture: ")
# st.write("- 3 layers of 1-Dimensional CNN with kernel sizes (2,3,4) for extracting features")
# st.write("- Max Pooling Layer for retaining prominent features")
# st.write("- Dropout Layer with probability 0.5 for better model generalization")
# st.write("- Linear Layer with output dimension 5 for sentiment classification")
st.write("")
st.write("")
st.header("Dataset Description")
st.write("We trained our model on three relevant datasets, including Rotten Tomato movie reviews, Yelp restaurant reviews and Amazon product reviews, each with various hyperparameter values.")
st.write("[Rotten Tomato movie reviews](https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data) contains more than 15,5000 movie reviews and ratings from 1 to 5.")
st.write("[Yelp restaurant reviews](https://www.kaggle.com/omkarsabnis/yelp-reviews-dataset) contains more than 11,000 retaurant reviews and ratings from 1 to 5.")
st.write("[Amazon product reviews](https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products?select=Datafiniti_Amazon_Consumer_Reviews_of_Amazon_Products.csv) contains more than 5,000 electronic product reviews and ratings from 1 to 5.")
st.write("")
st.write("")
st.header("Choose a dataset and explore the preprocessing!")
elif page == TRAIN:
_, center_emb_col, _ = st.beta_columns([1, 3, 1])
with center_emb_col:
st.title("Training Neural Network")
elif page == PREDICT:
_, center_emb_col, _ = st.beta_columns([1, 3, 1])
with center_emb_col:
st.title("Predict Sentiment")
if page != OVERVIEW:
_, center_emb_col, _ = st.beta_columns([1, 3, 1])
with center_emb_col:
dataset = st.selectbox('Choose a dataset', (MOVIE_DATASET, AMAZON_DATASET, YELP_DATASET))
if dataset == MOVIE_DATASET:
user_input = st.text_input('Write something emotional and hit enter!',
"I absolutely love this romantic movie! It's such an interesting film!")
elif dataset == AMAZON_DATASET:
user_input = st.text_input('Write something emotional and hit enter!', "Great device! It's easy to use!")
else:
user_input = st.text_input('Write something emotional and hit enter!',
"Delicious food! Best place to have lunch with a friend!")
if page != OVERVIEW and page != PREPROCESS:
models = []
st.sidebar.header("Adjust Model Hyper-Parameters")
learning_rate = st.sidebar.select_slider("Learning rate", options=[0.1, 0.01, 0.001, 0.0001], value=0.001)
# st.sidebar.text('learning rate={}'.format(learning_rate))
weight_decay = st.sidebar.select_slider("Weight decay", options=[0, 5e-7, 5e-6, 5e-5, 5e-4], value=5e-5)
# st.sidebar.text('weight decay={}'.format(weight_decay))
batch_size = st.sidebar.select_slider("Batch_size", options=[32, 64, 128, 256, 512], value=512)
# st.sidebar.text('batch size={}'.format(batch_size))
optimizer = st.sidebar.radio('Optimizer', (ADAM, SGD))
models.append(Model(dataset, learning_rate, batch_size, weight_decay, optimizer))
two_models = st.sidebar.checkbox('Compare with another set of model parameters')
if two_models:
learning_rate2 = st.sidebar.select_slider("Learning rate of second model", options=[0.1, 0.01, 0.001, 0.0001], value=0.001)
# st.sidebar.text('learning rate={}'.format(learning_rate))
weight_decay2 = st.sidebar.select_slider("Weight decay of second model", options=[0, 5e-7, 5e-6, 5e-5, 5e-4], value=5e-5)
# st.sidebar.text('weight decay={}'.format(weight_decay))
batch_size2 = st.sidebar.select_slider("Batch_size of second model", options=[32, 64, 128, 256, 512], value=512)
# st.sidebar.text('batch size={}'.format(batch_size))
optimizer2 = st.sidebar.radio('Optimizer of second model', (ADAM, SGD))
models.append(Model(dataset, learning_rate2, batch_size2, weight_decay2, optimizer2))
if page == PREPROCESS:
models = []
models.append(Model(dataset, 0.001, 512, 5e-5, ADAM))
preprocessed = run_preprocess(models[0], user_input)
elif page == TRAIN:
run_train(models)
elif page == PREDICT:
preprocessed = run_preprocess(models[0], user_input, False)
run_predict(preprocessed, models)
class Network(nn.Module):
def __init__(self, input_channel, out_channel, kernel_sizes, output_dim):
super().__init__()
self.convs = nn.ModuleList([
nn.Conv1d(in_channels = input_channel,
out_channels = out_channel,
kernel_size = ks)
for ks in kernel_sizes
])
self.linear = nn.Linear(len(kernel_sizes) * out_channel, output_dim)
self.dropout = nn.Dropout(0.5)
def forward(self, embedded):
embedded = embedded.permute(0, 2, 1)
conved = [F.relu(conv(embedded)) for conv in self.convs]
pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved]
cat = self.dropout(torch.cat(pooled, dim = 1))
return self.linear(cat)
input_channel = 100
out_channel = 50
kernel_sizes = [2,3,4]
output_dim = 5
def run_preprocess(model, input, visible=True):
# tokenize -> lowercase -> remove stopwords -> lemmatize
def tokenize_text(text):
tokenizer = RegexpTokenizer(r'\w+')
return tokenizer.tokenize(text)
def lowercase_text(tokens):
return [token.lower() for token in tokens]
def remove_stopwords(tokens):
english_stopwords = stopwords.words('english')
return [token if token not in english_stopwords and token in word2vec_dict else None for token in tokens]
def lemmatize(tokens):
lemmatizer = WordNetLemmatizer()
return [lemmatizer.lemmatize(token) if token else None for token in tokens]
dataset = model.dataset
if visible:
_, center_emb_col, _ = st.beta_columns([1, 3, 1])
with center_emb_col:
st.write("How can neural networks read text like humans? You might wonder. Actually, they cannot; they can only read numbers.\
This section walks you through every step that we must perform up to the conversion of text to numbers.")
st.write("_**Tips**_")
st.markdown('''
1. Try to change dataset and view different word cloud.
2. Change your input text as well!
''')
st.subheader("WordCloud & Word Importance")
st.write("Before we head into text preprocessing, let's check out the words that are particularly important, or frequent, in your selected dataset. We highlight your \
input text based on the term frequency in the chosen dataset. ")
if dataset == AMAZON_DATASET:
st.image('https://github.com/CMU-IDS-2020/fp-good_or_bad/raw/main/static_pictures/amazon_wordcloud.png', use_column_width=True)
get_highlight_text(input, "top_frequent_words/amazon_products_top1000.pt")
elif dataset == MOVIE_DATASET:
st.image('https://github.com/CMU-IDS-2020/fp-good_or_bad/raw/main/static_pictures/movie_wordcloud.png', use_column_width=True)
get_highlight_text(input, "top_frequent_words/rotten_tomato_top1000.pt")
elif dataset == YELP_DATASET:
st.image('https://github.com/CMU-IDS-2020/fp-good_or_bad/raw/main/static_pictures/yelp_wordcloud.png', use_column_width=True)
get_highlight_text(input, "top_frequent_words/yelp_restaurant_top1000.pt")
if visible:
_, center_emb_col, _ = st.beta_columns([1, 3, 1])
with center_emb_col:
st.subheader("Preprocessing")
st.write('''Let's see all that happens before the step of converting text to numbers, as promised. Now, a very natural question might come to your mind,\
"Do you convert on a sentence/word/character level? Would it be too simplified if we convert a whole sentence into a single number?" Indeed, \
sentence-level mapping could be meaningless, given that we want to read every word or character in a sentence. Thus, what we usually do in practice \
is word or character level mapping. In this app, for the purpose of easy interpretation and demonstration, we choose a word-level mapping for text-to-number conversion.''')
st.write("Now, the need for breaking sentences into words becomes clear. As you can see in the following figure, our first step is splitting sentences into word tokens by spaces.")
st.write('''Is that all? Probably not, as the word tokens need some standardization. Consider the tokens "love" and "LOVE." We want them to be considered as the same word, but due to \
different letter cases, they are understood as different words by a machine. Thus, the next step that follows is making all word tokens have a consistent letter case; we choose to convert all to lowercase.''')
st.write('''The next step we perform is removing the so-called "stopwords." In English, there are some extremely common yet barely meaningful words, for example, articles. To prevent from diluting, we remove them from our set of word tokens!''')
st.write('''One last step before text-to-number conversion is lemmatization, which is a further step of standardization. Consider the tokens "cat" and "cats." We want them to be considered as the same word, don't we? Thus, in this last step, we reduce every word token to its stem form.''')
tokens = tokenize_text(input)
lowercase_tokens = lowercase_text(tokens)
removed_stopwords = remove_stopwords(lowercase_tokens)
lemmatized = lemmatize(removed_stopwords)
if visible:
g = Digraph()
i = 0
g.node(input)
for token, lc_token, r_token, l_token in zip(reversed(tokens), reversed(lowercase_tokens), reversed(removed_stopwords), reversed(lemmatized)):
g.node(token+"token"+str(i), label = token)
g.edge(input, token+"token"+str(i))
g.node(lc_token+"lc_token"+str(i), label = lc_token)
g.edge(token+"token"+str(i), lc_token+"lc_token"+str(i))
if r_token:
g.node(r_token+"r_token"+str(i), label = r_token)
g.edge(lc_token+"lc_token"+str(i), r_token+"r_token"+str(i))
g.node(l_token+"l_token"+str(i), label = l_token)
g.edge(r_token+"r_token"+str(i), l_token+"l_token"+str(i))
i += 1
with g.subgraph(name='cluster_1') as c:
c.attr(color='white')
c.node_attr['style'] = 'filled'
c.node(input)
c.attr(label='Original Input')
with g.subgraph(name='cluster_2') as c:
c.attr(color='white')
c.node_attr['style'] = 'filled'
for i, token in enumerate(reversed(tokens)):
c.node(token+"token"+str(i))
c.attr(label='Word Tokens')
with g.subgraph(name='cluster_3') as c:
c.attr(color='white')
c.node_attr['style'] = 'filled'
for i, token in enumerate(reversed(lowercase_tokens)):
c.node(token+"lc_token"+str(i))
c.attr(label='Lowercase Tokens')
with g.subgraph(name='cluster_4') as c:
c.attr(color='white')
c.node_attr['style'] = 'filled'
for i, token in enumerate(reversed(removed_stopwords)):
if token:
c.node(token+"r_token"+str(i))
c.attr(label='Stopwords Removed')
with g.subgraph(name='cluster_5') as c:
c.attr(color='white')
c.node_attr['style'] = 'filled'
for i, token in enumerate(reversed(lemmatized)):
if token:
c.node(token+"l_token"+str(i))
c.attr(label='Lemmatized Tokens')
st.graphviz_chart(g, use_container_width=True)
_, center_emb_col, _ = st.beta_columns([1, 3, 1])
with center_emb_col:
st.subheader('Word Embeddings')
st.markdown('''
Word embeddings are dense vector representations of words. Word Embeddings have their dimensional distance correlated to the semantic similarity of the underlying words.
We use [Glove Embeddings](https://nlp.stanford.edu/projects/glove/) with 1.9 million vocabulary to translate each word into a vector of its postion in the embedding space.
To help you visualize how word embeddings are used in this sentiment analysis project, we plot the word embeddings of your
input sentence with some common words which has straightforward sentiment tendencies.
Note that although word embeddings are dense, the embedding space is still high dimensional. In our case, the embedding vector of
each word is of dimension 100. We perform dimensionality reduction trick to map the word embeddings to a 3D space while keeping
their relative positions.
In the plot below, **blue dots** represents word embeddings of some common words in this dataset. The **red diamonds** are
word embeddings of words in your input sentence. All data points are labeled with their corresponding words.
''')
st.write("_**Tips**_")
st.markdown('''
The distances among points can be deceptive when looking from only one angle.
1. By moving your mouse on a specific data point,
lines will be displayed connecting to the axes to show you the exact position.
2. You can click and drag on the plot to rotate it.
3. Use two fingers on your touchpad to zoom in and out; you can also
click on the **zoom** tool on the top right corner of the graph, and then click and drag to zoom the plot.
''')
sentence = [token for token in lemmatized if token is not None]
if visible:
embedding_for_plot = {}
for word in sentence:
embedding_for_plot[word] = word2vec_dict[word]
_, center_emb_col, _ = st.beta_columns([1, 3, 1])
with center_emb_col:
run_embedding(model.mapped_dataset, embedding_for_plot)
st.markdown("<b><font color='blue'>Now, use the sidebar to navigate to the next section: training, to further explore the training process of neural nets.</font></b>", unsafe_allow_html=True)
return sentence
@st.cache(ttl=60*10,allow_output_mutation=True)
def load_word2vec_dict(word2vec_urls, word2vec_dir):
word2vec_dict = []
for i in range(len(word2vec_urls)):
url = word2vec_urls[i]
# torch.hub.download_url_to_file(url, word2vec_dir+"word2vec_dict"+str(i)+".pt")
word2vec = pickle.load(open(word2vec_dir+"word2vec_dict"+str(i)+".pt", "rb" ))
word2vec = list(word2vec.items())
word2vec_dict += word2vec
return dict(word2vec_dict)
@st.cache(ttl=60*10,allow_output_mutation=True)
def load_word2vec_dict_local(word2vec_dir):
word2vec_dict = []
for f in listdir(word2vec_dir):
word2vec = pickle.load(open(join(word2vec_dir,f), "rb"))
word2vec = list(word2vec.items())
word2vec_dict += word2vec
return dict(word2vec_dict)
def tokenize_sentence(sentence, word2vec_dict):
tokenizer = RegexpTokenizer(r'\w+')
lemmatizer = WordNetLemmatizer()
english_stopwords = stopwords.words('english')
sentence = sentence.strip()
tokenized_sentence = [lemmatizer.lemmatize(token.lower()) for token in tokenizer.tokenize(sentence) if token.lower() in word2vec_dict and token.lower() not in english_stopwords]
return tokenized_sentence
def run_predict(input, models):
def predict(sentence, self_model, max_seq_length = 29):
#tokenized_sentence = tokenize_sentence(sentence,word2vec_dict)
embedding_for_plot = {}
for word in sentence:
embedding_for_plot[word] = word2vec_dict[word]
embedding = np.array([word2vec_dict[word] for word in sentence])
model = Network(input_channel, out_channel, kernel_sizes, output_dim)
# torch.hub.download_url_to_file(model_url, "./cur_model.pt")
# state_dict = torch.load("./cur_model.pt",map_location=torch.device('cpu'))
state_dict = torch.load("./models/" + self_model.mapped_dataset + "/model_state_dict/" + self_model.model_name, map_location=torch.device('cpu'))
model.load_state_dict(state_dict)
# model.load_state_dict(torch.hub.load_state_dict_from_url(model_url, progress=False, map_location=torch.device('cpu')))
model.eval()
embedding = np.expand_dims(embedding,axis=0)
embedding = pad(torch.FloatTensor(embedding), (0, 0, 0, max_seq_length - len(embedding)))
outputs = model(embedding)
_, predicted = torch.max(outputs.data, 1)
return softmax(outputs.data), predicted.item() + 1, embedding_for_plot
_, center_emb_col, _ = st.beta_columns([1, 3, 1])
with center_emb_col:
st.subheader('Predicted Result')
st.write("Our model will generate five probabilities for each input. This step is accomplished by performing [softmax](https://developers.google.com/machine-learning/crash-course/multi-class-neural-networks/softmax#:~:text=Softmax%20extends%20this%20idea%20into,quickly%20than%20it%20otherwise%20would.) on the outputs of the final linear layer. It assigns probabilities to multiple classes and makes sure they sum to 1.")
st.write("Now let's see what results our neural net gives for your input text. The bar chart below shows the predicted probability that your text contains a certain type of sentiment.")
st.write("_**Tips**_")
st.write("1. Move your mouse over the bars to see the exact predicted probabilities.")
st.write("2. Also try different hyperparameters in the sidebar and see if they predict the same outcome!")
st.write("")
st.write("")
probs_list = []
for i in range(len(models)):
probs, _, embedding = predict(input, models[i], models[i].max_len)
probs = probs[0].numpy()
probs_list.append(probs)
if len(models) == 2:
re_columns = st.beta_columns(len(models))
for i in range(len(models)):
d = {'Sentiment': ["negative", "somewhat negative", "neutral", "somewhat positive", "positive"], 'Probability': probs_list[i]}
max_sentiment = d["Sentiment"][np.argmax(d["Probability"])]
source = pd.DataFrame(d)
highlight = alt.selection_single(on='mouseover', fields=['Probability'], nearest=False, clear="mouseout")
c = alt.Chart(source).mark_bar().encode(
alt.X('Probability:Q', axis=alt.Axis(format='.0%')),
alt.Y('Sentiment:N', sort=d['Sentiment']),
color=alt.condition(~highlight,
alt.Color('Probability:Q', scale=alt.Scale(scheme='greens'), legend=None),
alt.value('orange'), ), tooltip=['Probability:Q']).properties(width=400, height=200).add_selection(
highlight).interactive()
with re_columns[i]:
st.write(c, use_column_width=True)
st.write("Our model predicts that your input text contains " + max_sentiment + " sentiment!")
else:
_, center_result_col, _ = st.beta_columns([1, 2, 1])
d = {'Sentiment': ["negative", "somewhat negative", "neutral", "somewhat positive", "positive"], 'Probability': probs_list[0]}
max_sentiment = d["Sentiment"][ | np.argmax(d["Probability"]) | numpy.argmax |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 26 17:34:11 2017
@author: Patricio
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
from numba import jit,float64,vectorize,int64
#import Wavelets
@vectorize([float64(float64)])
def alphan(v):
return -0.01*(v+34)/(np.exp(-0.1*(v+34))-1) # ok RH
@vectorize([float64(float64)])
def betan(v):
return 0.125*np.exp(-(v+44)/80) # ok RH
@vectorize([float64(float64)])
def alpham(v):
return -0.1*(v+35)/(np.exp(-0.1*(v+35))-1) # ok RH
@vectorize([float64(float64)])
def betam(v):
return 4*np.exp(-(v+60)/18) # ok RH
@vectorize([float64(float64)])
def alphah(v):
return 0.07*np.exp(-(v+58)/20) # ok RH
@vectorize([float64(float64)])
def betah(v):
return 1/(np.exp(-0.1*(v+28))+1) # ok RH
def expnorm(tau1,tau2):
if tau1>tau2:
t2=tau2; t1=tau1
else:
t2=tau1; t1=tau2
tpeak = t1*t2/(t1-t2)*np.log(t1/t2)
return (np.exp(-tpeak/t1) - np.exp(-tpeak/t2))/(1/t2-1/t1)
# Neurons Parameters
gNa = 35.0; gK = 9.0; gL=0.1 #mS/cm^2
ENa = 55.0; EK = -90.0; EL = -65.0 #mV
phi = 5.0
VsynE = 0; VsynI = -80 #reversal potential
tau1E = 3; tau2E = 1
tau1I = 4; tau2I = 1
theta=-20 #threshold for detecting spikes
Iapp = 0; # uA/cm^2, injected current
#Synaptic parameters
mGsynE = 5; mGsynI = 200; mGsynExt = 3 #mean
sGsynE = 1; sGsynI = 10; sGsynExt = 1
Pe=0.3; Pi=0.3
iRate = 3.5 #Rate of external input
mdelay=1.5; sdelay = 0.1 #ms synaptic delays, mean and SD
dt = 0.02 #ms
#Network parameters
Ne=100 #Numero de neuronas excitatorias
Ni=25 #Numero de neuronas inhibitorias
def genRandomCM(mode='all', AdjMe=None, AdjMi=None):
global CMe,CMi,GsynExt,N
if mode not in ('exc','inh','excinh','ext','all'):
raise ValueError("mode has to be one of ['exc','inh','excinh','ext','all']")
N=Ne+Ni
factE = 1000*dt*expnorm(tau1E,tau2E)
factI = 1000*dt*expnorm(tau1I,tau2I)
if mode in ('exc','excinh','all'):
GsynE = np.random.normal(mGsynE,sGsynE,size=(N,Ne))
GsynE = GsynE*(GsynE>0) # remove negative values
if AdjMe is None:
AdjMe=np.random.binomial(1,Pe,size=(N,Ne))
elif AdjMe.shape!=(N,Ne):
raise ValueError("Check dimensions of AdjMe. It has to be N x Ne")
CMe= AdjMe * GsynE / factE
if mode in ('inh','excinh','all'):
GsynI = np.random.normal(mGsynI,sGsynI,size=(N,Ni))
GsynI = GsynI*(GsynI>0) # remove negative values
if AdjMi is None:
AdjMi=np.random.binomial(1,Pi,size=(N,Ni))
elif AdjMi.shape!=(N,Ni):
raise ValueError("Check dimensions of AdjMe. It has to be N x Ni")
CMi= AdjMi* GsynI / factI
if mode in ('ext','all'):
#Weigths for external random input
GsynExt = np.random.normal(mGsynExt,sGsynExt,size=N)
GsynExt = GsynExt*(GsynExt>0) / factE # remove negative values and normalize
genDelays()
def genDelays():
global delay,delay_dt
delay = np.random.normal(mdelay,sdelay,size=N)
delay_dt=(delay/dt).astype(int)
genRandomCM()
Ggj=0.001 # not so big gap junction conductance
CMelec=Ggj * np.random.binomial(1,0.3,(Ni,Ni)) #mock electric connectivity
#firing=np.zeros(N)
@jit(float64[:,:](float64[:,:],int64[:],int64),nopython=True)
def WB_network(X,ls,i):
v=X[0,:]
h=X[1,:]
n=X[2,:]
sex=X[3,:]
sey=X[4,:]
six=X[5,:]
siy=X[6,:]
sexe=X[7,:]
seye=X[8,:]
minf=alpham(v)/(betam(v)+alpham(v))
INa=gNa*minf**3*h*(v-ENa)
IK=gK*n**4*(v-EK)
IL=gL*(v-EL)
ISyn= (sey + seye) * (v - VsynE) + siy * (v - VsynI)
Igj = np.zeros(N)
Igj[Ne:] = np.sum(CMelec * (np.expand_dims(v[Ne:],1) - v[Ne:]),-1)
firingExt = np.random.binomial(1,iRate*dt,size=N)
firing=1.*(ls==(i-delay_dt))
return np.vstack((-INa-IK-IL-ISyn-Igj+Iapp,
phi*(alphah(v)*(1-h) - betah(v)*h),
phi*(alphan(v)*(1-n) - betan(v)*n),
-sex*(1/tau1E + 1/tau2E) - sey/(tau1E*tau2E) + np.dot(CMe,firing[0:Ne]),
sex,
-six*(1/tau1I + 1/tau2I) - siy/(tau1I*tau2I) + np.dot(CMi,firing[Ne:]),
six,
-sexe*(1/tau1E + 1/tau2E) - seye/(tau1I*tau2I) + firingExt*GsynExt,
sexe))
equil=400
Trun=2000
#Total=Trun + equil #ms
#nsteps=len(Time)
def initVars(v=None):
if v is None:
v_init=np.random.uniform(-80,-60,size=N) #-70.0 * np.ones(N) # -70 is the one used in brian simulation
h=1/(1+betah(v_init)/alphah(v_init))
n=1/(1+betan(v_init)/alphan(v_init))
sex=np.zeros_like(v_init)
sey=np.zeros_like(v_init)
six=np.zeros_like(v_init)
siy=np.zeros_like(v_init)
sexe=np.zeros_like(v_init)
seye=np.zeros_like(v_init)
return np.array([v_init,h,n,sex,sey,six,siy,sexe,seye])
#X=initVars()
def runSim(v_init=None,output='spikes'):
global firing
if v_init is None:
X=initVars()
elif len(v_init)==N:
X=initVars(v_init)
else:
raise ValueError("v_init has to be None or an array of length N")
if output not in ('spikes','LFP','allV'):
raise ValueError("output has to be one of ['spikes','LFP','allV']")
firing=np.zeros(N)
#adaptation simulation - not stored
equil_dt=int(equil/dt) #equilibrium time - in samples
bufferl=100*(np.max(delay_dt)//100+1)
V_t=np.zeros((bufferl,N))
lastSpike=equil_dt*np.ones(N,dtype=np.int64)
for i in range(equil_dt):
ib=i%bufferl
X+=dt*WB_network(X,lastSpike,i)
# firing=1*(V_t[ib-delay_dt,range(N)]>theta)*(V_t[ib-delay_dt-1,range(N)]<theta)
Time = np.arange(0,Trun,dt)
if output=='spikes':
spikes=[]
bufferl=100*(np.max(delay_dt)//100+1)
V_t=np.zeros((bufferl,N))
lastSpike=lastSpike-equil_dt
lastSpike[lastSpike==0]=int(Trun/dt)
for i,t in enumerate(Time):
ib=i%bufferl
V_t[ib]=X[0]
if np.any((V_t[ib]>theta)*(V_t[ib-1]<theta)):
for idx in np.where((V_t[ib]>theta)*(V_t[ib-1]<theta))[0]:
spikes.append([idx,t])
lastSpike[idx]=i
X+=dt*WB_network(X,lastSpike,i)
return np.array(spikes)
elif output=='LFP':
spikes=[]
bufferl=100*(np.max(delay_dt)//100+1)
V_t=np.zeros((bufferl,N))
LFP_t=np.zeros(len(Time))
lastSpike=lastSpike-equil_dt
lastSpike[lastSpike==0]=int(Trun/dt)
for i,t in enumerate(Time):
ib=i%bufferl
V_t[ib]=X[0]
LFP_t[i]=np.mean(X[0])
if np.any((V_t[ib]>theta)*(V_t[ib-1]<theta)):
for idx in np.where((V_t[ib]>theta)*(V_t[ib-1]<theta))[0]:
spikes.append([idx,t])
lastSpike[idx]=i
X+=dt*WB_network(X,lastSpike,i)
return np.array(spikes),LFP_t,Time
elif output=='allV':
spikes=[]
V_t=np.zeros((len(Time),N))
lastSpike=lastSpike-equil_dt
lastSpike[lastSpike==0]=int(Trun/dt)
for i,t in enumerate(Time):
V_t[i]=X[0]
if np.any((V_t[i]>theta)*(V_t[i-1]<theta)):
for idx in np.where((V_t[i]>theta)*(V_t[i-1]<theta))[0]:
spikes.append([idx,t])
lastSpike[idx]=i
X+=dt*WB_network(X,lastSpike,i)
return np.array(spikes),V_t,Time
def ParamsNode():
pardict={}
for var in ('gNa','gK','gL','ENa','EK','EL','phi','theta','Iapp'):
pardict[var]=eval(var)
return pardict
def ParamsSyn():
pardict={}
for var in ('VsynE','VsynI','tau1E','tau2E','tau1I','tau2I','mdelay','sdelay',
'factE','factI'):
pardict[var]=eval(var)
return pardict
def ParamsNet():
pardict={}
for var in ('Ne','Ni','N','Pe','Pi','iRate'):
pardict[var]=eval(var)
return pardict
def ParamsNetMatrix():
pardict={}
for var in ('mGsynE','mGsynI','mGsynExt','sGsynE','sGsynI','sGsynExt',
'GsynE','GsynI','GsynExt'):
pardict[var]=eval(var)
return pardict
def ParamsSim():
pardict={}
for var in ('equil','Trun','dt'):
pardict[var]=eval(var)
return pardict
# V_t = np.zeros((nsteps,N))
# for i in range(nsteps):
# V_t[i]=X[0]
# X+=dt*WB_network(X,i)
#%%
if __name__=='__main__':
Pi=0.3
iRate = 3.
genRandomCM()
Ggj=0.1 # not so big gap junction conductance
CMelec=Ggj * np.random.binomial(1,0.3,(Ni,Ni)) #mock electric connectivity
WB_network.recompile()
spikes=runSim()
# spikes,V_t,Time=runSim(output='allV')
binsize = 0.5 # bin size for population activity in ms
tbase = np.arange(0,Trun, binsize) # raster time base
kernel=signal.gaussian(10*2/binsize+1,2/binsize)
kernel/=np.sum(kernel)
#spikes=[(np.diff(1*(V_t[:,i]>-20))==1).nonzero()[0] for i in range(N)]
#pop_spikes = np.asarray([item for sublist in spikes for item in sublist]) # todas las spikes de la red
pop_spikes = spikes[:,1]
popact,binedge = | np.histogram(pop_spikes, tbase) | numpy.histogram |
import numpy as np
import argparse
import tensorflow as tf
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import time
import pickle
NUM_LABELS = 47
rnd = | np.random.RandomState(123) | numpy.random.RandomState |
import math
from math import pi
import numpy as np
import open3d as o3d
import matplotlib.pyplot as plt
import cv2
import toml
from .cameraparam import CameraParam
from .fitted_line import FittedLine
from .ransac_fit import ransac_line_fit, ransac_ground_fit
from .util import check_all_false
# TODO: output random seed used in ransac and open3d
# PCL pre-processing (the unit of these numerics is [m])
DOWNSAMPLE_VOXEL_SIZE = 0.003
DOWNSAMPLE_VOXEL_SIZE_GROUND = 0.005
# Ground fit
X_MIN = 0.
X_MAX = +1.2
Y_MIN = -0.8
Y_MAX = +0.8
GRID_SIZE = 0.080
GROUND_SEED_Z_MAX = 0.
GROUND_SEED_MARGIN = 0.080
GROUND_MARGIN = 0.030
SMOOTHING_KERNEL = GRID_SIZE * 0.5
# Clustering
# DBSCAN_EPS : Density parameter that is used to find neighbouring points
# DBSCAN_MINPOINTS : Minimum number of points to form a cluster
DBSCAN_EPS = 0.016
DBSCAN_MINPOINTS = 10
CLUSTER_MINPOINTS = 50
CMAP_CLUSTER = plt.get_cmap("tab20")
def set_pcl_fitter(toml_path):
dict_toml = toml.load(open(toml_path))
set_roll = float(dict_toml['General']['set_roll'])
set_pitch = float(dict_toml['General']['set_pitch'])
set_yaw = float(dict_toml['General']['set_yaw'])
camera_set_param = CameraParam()
camera_set_param.set_tf_rot_and_trans([set_roll, set_pitch, set_yaw], [0., 0., 0.])
return PCLFitter(camera_set_param, dict_toml)
class PCLFitter(object):
def __init__(self, camera_set_param=None, target_attribute=None):
self.depth_img = None
self.camera_param = None
self.grid_xyzw = None
if camera_set_param is None:
self.camera_set_param = CameraParam()
else:
self.camera_set_param = camera_set_param
if target_attribute is None:
self.set_parameters()
else:
self.set_target_attribute(target_attribute)
def set_target_attribute(self, dict_toml):
self.pcl_cutoff_dist = float(dict_toml['Selection']['pcl_cutoff_dist'])
self.target_max_dist = float(dict_toml['Selection']['target_max_dist'])
self.target_min_dist = float(dict_toml['Selection']['target_min_dist'])
self.target_max_len = float(dict_toml['Selection']['target_max_len'])
self.target_min_len = float(dict_toml['Selection']['target_min_len'])
self.target_max_tilt = float(dict_toml['Selection']['target_max_tilt'])
def set_parameters(self):
self.pcl_cutoff_dist = 1.1
self.target_max_dist = 0.85
self.target_min_dist = 0.3
self.target_min_len = 0.25
self.target_max_len = 0.40
self.target_max_tilt = 30.
def get_pcd_from_depth_img(self, depth_img, camera_param):
self.depth_img = depth_img
self.camera_param = camera_param
pcl_raw = self.tfm_pcl_cam2global(self.cvt_depth2pcl(self.depth_img, self.camera_param), camera_param)
pcd = self.downsample(pcl_raw, voxel_size=DOWNSAMPLE_VOXEL_SIZE)
return pcd
def fit_pcd(self, pcd, cluster_eps=DBSCAN_EPS, cluster_min_points=DBSCAN_MINPOINTS, verbose=True):
pcd_list = []
fitgeom_list = []
pcd_array = np.array(pcd.points, dtype=np.float32)
bflg_above_ground, xy_binidx, grid_xyzw, pcd_grounds_list = self.ground_fit(pcd_array)
pcd_grounds_ary_pre_downsample = np.asarray(pcd_grounds_list[2].points) # pcd_grounds = [pcd_out_of_bin, pcd_groundseed, pcd_ground]
pcd_grounds = self.downsample(pcd_grounds_ary_pre_downsample, voxel_size=DOWNSAMPLE_VOXEL_SIZE_GROUND)
ground_points_ary = np.asarray(pcd_grounds.points)
pcd_list += [ground_points_ary]
fitgeom_list.append(self.get_mesh_ground())
# TODO debug.error() send to cloud if above ground is all false
if check_all_false(bflg_above_ground):
return [], pcd_list, fitgeom_list, pcd_array, ground_points_ary
labels, cluster_pcd = self.clustering(pcd_array[bflg_above_ground],
eps=cluster_eps, min_points=cluster_min_points)
pcd_list.append(cluster_pcd)
line_list = self.line_fit(pcd_array[bflg_above_ground], labels)
self.merge_lines(line_list)
self.mark_multiline_clusters(line_list)
self.extend_lines_to_ground(line_list, grid_xyzw)
self.check_line_truncation(line_list)
self.final_selection(line_list)
if verbose:
self.print_line_info(line_list)
self.bkg_postprocess(line_list)
self.remove_noise_lines(line_list, grid_xyzw)
mesh_cylinders = self.get_line_fit_geometry(line_list)
fitgeom_list += mesh_cylinders
return line_list, pcd_list, fitgeom_list, pcd_array, ground_points_ary
def cvt_depth2pcl(self, depth_img, camera_param):
cx, cy = camera_param.center_xy
fx, fy = camera_param.focal_xy
DEPTH_MIN = 1e-3
arr_y = np.arange(depth_img.shape[0], dtype=np.float32)
arr_x = np.arange(depth_img.shape[1], dtype=np.float32)
val_x, val_y = np.meshgrid(arr_x, arr_y)
# TODO: rewrite axis convertion explicitly (i.e. zense clockwise rotation)
tmp_x = +depth_img
tmp_y = +depth_img * (val_y - cy) * (1. / fy)
tmp_z = -depth_img * (val_x - cx) * (1. / fx)
filled = (depth_img > DEPTH_MIN) * (depth_img < self.pcl_cutoff_dist + 0.2)
filled_x = tmp_x[filled]
filled_y = tmp_y[filled]
filled_z = tmp_z[filled]
pcl = np.stack([filled_x, filled_y, filled_z], axis=-1)
return pcl
def tfm_pcl_cam2global(self, pcl_camframe, camera_param):
pcl_tmp = np.dot(pcl_camframe, camera_param.rot_mtx.transpose()) + camera_param.translation
pcl_global = np.dot(pcl_tmp, self.camera_set_param.rot_mtx.transpose())
return pcl_global
def cvt_to_2d_image_xyd(self, input_points, camera_param):
points = input_points.reshape(-1, 3)
points_tmp = np.dot(points, self.camera_set_param.inv_rot_mtx.transpose())
points_camframe = np.dot(points_tmp - camera_param.translation, camera_param.inv_rot_mtx.transpose())
cx, cy = camera_param.center_xy
fx, fy = camera_param.focal_xy
depth = +points_camframe[:, 0]
val_y = +points_camframe[:, 1] / depth * fy + cy
val_x = -points_camframe[:, 2] / depth * fx + cx
xyd = np.stack([val_x, val_y, depth], axis=-1)
return xyd.reshape(input_points.shape)
def downsample(self, pcl_raw, voxel_size):
pcd_raw = self.cvt_numpy2open3d(pcl_raw, color=[0., 0., 1.])
pcd = pcd_raw.voxel_down_sample(voxel_size=voxel_size)
return pcd
def cvt_numpy2open3d(self, pcl, color=None):
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(pcl.astype(np.float64))
if not color is None:
pcd.paint_uniform_color(color)
return pcd
def ground_fit(self, pcl):
x_nbin = int( (X_MAX - X_MIN) / float(GRID_SIZE) + 1e-3 )
y_nbin = int( (Y_MAX - Y_MIN) / float(GRID_SIZE) + 1e-3 )
x_edge = np.linspace(X_MIN, X_MIN + GRID_SIZE * x_nbin, x_nbin + 1).reshape(1, -1)
y_edge = np.linspace(Y_MIN, Y_MIN + GRID_SIZE * y_nbin, y_nbin + 1).reshape(1, -1)
x_ctr = (x_edge[0, 1:] + x_edge[0, :-1]) * 0.5
y_ctr = (y_edge[0, 1:] + y_edge[0, :-1]) * 0.5
pcl_tmp = pcl.reshape(-1, 1, 3)
x_binflg = (pcl_tmp[:, :, 0] >= x_edge[:, :-1]) * (pcl_tmp[:, :, 0] < x_edge[:, 1:])
y_binflg = (pcl_tmp[:, :, 1] >= y_edge[:, :-1]) * (pcl_tmp[:, :, 1] < y_edge[:, 1:])
x_binidx = np.argmax(x_binflg, axis=-1)
y_binidx = np.argmax(y_binflg, axis=-1)
x_binidx[(x_binflg.sum(axis=-1) == 0)] = -1
y_binidx[(y_binflg.sum(axis=-1) == 0)] = -1
xy_binidx = np.concatenate([x_binidx.reshape(-1,1), y_binidx.reshape(-1,1)], axis=-1)
bflg_out_of_bin = (xy_binidx == -1).sum(-1).astype(np.bool)
bflg_in_bin = (bflg_out_of_bin == False)
grid_xyzw = np.zeros([x_nbin, y_nbin, 4], dtype=np.float64)
for i_x in range(x_nbin):
for i_y in range(y_nbin):
in_bin = (x_binidx == i_x) * (y_binidx == i_y)
pcl_in_bin = pcl[in_bin]
valid = (pcl_in_bin[:, 2] < GROUND_SEED_Z_MAX)
pcl_valid = pcl_in_bin[valid]
if pcl_valid.shape[0] == 0:
z_val = 0.
wgt = 0.1
else:
z_val = pcl_valid[:, 2].min()
wgt = 1.
grid_xyzw[i_x, i_y] = [x_ctr[i_x], y_ctr[i_y], z_val, wgt]
grid_xyzw = self.fill_empy_gridz(grid_xyzw, w_thres=0.1)
pcd_groundseed = self.cvt_numpy2open3d(grid_xyzw.reshape(-1, 4)[:, :3], color=[1., 0., 1.])
pcl_ground_seed_z = grid_xyzw[x_binidx, y_binidx, 2]
bflg_ground_seed = (pcl[:, 2] < (pcl_ground_seed_z + GROUND_SEED_MARGIN)) * bflg_in_bin
grid_xyzw = ransac_ground_fit(pcl[bflg_ground_seed], xy_binidx[bflg_ground_seed], grid_xyzw)
grid_xyzw = self.fill_empy_gridz(grid_xyzw, w_thres=1.)
grid_xyzw = self.smooth_ground(grid_xyzw, kernel_size=SMOOTHING_KERNEL)
self.grid_xyzw = grid_xyzw
bflg_in_range = (np.linalg.norm(pcl[:,:2], axis=-1) < self.pcl_cutoff_dist)
bflg_valid_points = bflg_in_range * bflg_in_bin
pcl_ground_z = grid_xyzw[x_binidx, y_binidx, 2]
bflg_ground = (pcl[:, 2] < (pcl_ground_z + GROUND_MARGIN)) * bflg_valid_points
bflg_above_ground = (bflg_ground == False) * bflg_valid_points
pcd_out_of_bin = self.cvt_numpy2open3d(pcl[bflg_valid_points == False], color=[0.3, 0., 0.5])
pcd_ground = self.cvt_numpy2open3d(pcl[bflg_ground], color=[0., 0., 0.5])
pcd_all = [pcd_out_of_bin, pcd_groundseed, pcd_ground]
return bflg_above_ground, xy_binidx, grid_xyzw, pcd_all
def fill_empy_gridz(self, grid_xyzw, w_thres=0.1):
filled = (grid_xyzw[:,:,3] > w_thres)
empty = (filled == False)
# print 'filled ', filled.shape, filled.sum()
# print 'empty ', empty.shape, empty.sum()
filled_xyzw = grid_xyzw[filled].reshape(-1, 1, 4)
empty_xyzw = grid_xyzw[empty].reshape(1, -1, 4)
# print 'filled_xyzw ', filled_xyzw.shape
# print 'empty_xyzw ', empty_xyzw.shape
dist_array = np.linalg.norm(filled_xyzw[:,:,:2] - empty_xyzw[:,:,:2], axis=-1)
# print 'dist_array ', dist_array.shape
if dist_array.shape[0] != 0:
nearest_filled = np.argmin(dist_array, axis=0)
grid_xyzw[empty, 2] = filled_xyzw[nearest_filled, 0, 2]
return grid_xyzw
def smooth_ground(self, grid_xyzw, kernel_size):
vect = grid_xyzw[:,:,:2].reshape(1, -1, 2) - grid_xyzw[:,:,:2].reshape(-1, 1, 2)
dsq = (vect ** 2).sum(axis=-1)
z_orig = grid_xyzw[:,:,2].reshape(-1)
wgt = grid_xyzw[:,:,3].reshape(-1)
coeff = 0.5 / kernel_size ** 2
fill_wgt = wgt * np.exp(-dsq * coeff)
z_smooth = (z_orig * fill_wgt).sum(axis=-1) / fill_wgt.sum(axis=-1)
grid_xyzw[:,:,2].reshape(-1)[:] = z_smooth
return grid_xyzw
def get_mesh_ground(self):
return self.cvt_gridvtx2mesh(self.grid_xyzw) if self.grid_xyzw is not None else None
def cvt_gridvtx2mesh(self, grid_vtx, double_sided=True):
ngrid_x = grid_vtx.shape[0]
ngrid_y = grid_vtx.shape[1]
vertices = np.array(grid_vtx[:,:,:3].reshape(-1,3))
triangles = []
for i_x in range(grid_vtx.shape[0] - 1):
for i_y in range(grid_vtx.shape[1] - 1):
ivert_base = i_x * ngrid_y + i_y
triangles.append([ivert_base, ivert_base+ngrid_y, ivert_base+1])
triangles.append([ivert_base+ngrid_y+1, ivert_base+1, ivert_base+ngrid_y])
triangles = np.array(triangles)
if double_sided:
triangles = | np.concatenate([triangles, triangles[:,::-1]], axis=0) | numpy.concatenate |
import spiderman as sp
import numpy as np
import matplotlib.pyplot as plt
import time as timing
def plot_test():
spider_params = sp.ModelParams(brightness_model='zhang')
spider_params.n_layers= 20
spider_params.t0= 200 # Central time of PRIMARY transit [days]
spider_params.per= 0.81347753 # Period [days]
spider_params.a_abs= 0.01526 # The absolute value of the semi-major axis [AU]
spider_params.inc= 82.33 # Inclination [degrees]
spider_params.ecc= 0.0 # Eccentricity
spider_params.w= 90 # Argument of periastron
spider_params.rp= 0.1594 # Planet to star radius ratio
spider_params.a= 4.855 # Semi-major axis scaled by stellar radius
spider_params.p_u1= 0 # Planetary limb darkening parameter
spider_params.p_u2= 0 # Planetary limb darkening parameter
spider_params.xi= 0.3 # Ratio of radiative to advective timescale
spider_params.T_n= 1128 # Temperature of nightside
spider_params.delta_T= 942 # Day-night temperature contrast
spider_params.T_s = 5000 # Temperature of the star
spider_params.l1 = 1.3e-6 # start of integration channel in microns
spider_params.l2 = 1.6e-6 # end of integration channel in microns
t= spider_params.t0 + np.linspace(0, + spider_params.per,100)
lc = sp.lightcurve(t,spider_params)
plt.plot(t,lc)
plt.show()
def time_test(nlayers=5,tpoints=100,nreps=1000):
spider_params = sp.ModelParams(brightness_model='zhang')
# spider_params = sp.ModelParams(brightness_model='uniform brightness')
spider_params.n_layers= nlayers
spider_params.t0= 200 # Central time of PRIMARY transit [days]
spider_params.per= 0.81347753 # Period [days]
spider_params.a_abs= 0.01526 # The absolute value of the semi-major axis [AU]
spider_params.inc= 82.33 # Inclination [degrees]
spider_params.ecc= 0.0 # Eccentricity
spider_params.w= 90 # Argument of periastron
spider_params.rp= 0.1594 # Planet to star radius ratio
spider_params.a= 4.855 # Semi-major axis scaled by stellar radius
spider_params.p_u1= 0 # Planetary limb darkening parameter
spider_params.p_u2= 0 # Planetary limb darkening parameter
spider_params.xi= 0.3 # Ratio of radiative to advective timescale
spider_params.T_n= 1128 # Temperature of nightside
spider_params.delta_T= 942 # Day-night temperature contrast
spider_params.T_s = 4500 # Temperature of the star
spider_params.l1 = 1.3e-6 # start of integration channel in microns
spider_params.l2 = 1.6e-6 # end of integration channel in microns
spider_params.pb = 0.01 # planet relative brightness
t= spider_params.t0 + | np.linspace(0, + spider_params.per,tpoints) | numpy.linspace |
import os
import unittest
from io import StringIO
from numpy import array, allclose, cross
import numpy as np
import pyNastran
from pyNastran.bdf.bdf import BDF
from pyNastran.bdf.bdf import CORD2C, GRID, FORCE
from pyNastran.bdf.mesh_utils.loads import sum_forces_moments, sum_forces_moments_elements
model_path = os.path.join(pyNastran.__path__[0], '..', 'models')
log = None
class TestLoadSum(unittest.TestCase):
def test_loads_sum_01(self):
"""tests FORCE"""
model = BDF(log=log, debug=False)
bdf_filename = os.path.join(model_path, 'solid_bending', 'solid_bending.bdf')
model.read_bdf(bdf_filename)
loadcase_id = 1
#print("keys1", model.loads.keys())
p0 = array([0., 0., 0.])
F_expected = array([23000., 0., 0.])
M_expected = array([0., 33209.869, -22803.951])
eids = None
nids = None
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
def test_loads_sum_02(self):
"""tests FORCE"""
model = BDF(log=log, debug=False)
bdf_filename = os.path.join(model_path, 'sol_101_elements', 'static_solid_shell_bar.bdf')
model.read_bdf(bdf_filename)
loadcase_id = 10000
#print("keys2", model.loads.keys())
p0 = array([0., 0., 0.])
F_expected = array([0., 0., 10000.])
M_expected = array([5000., -5000., 0.])
eids = None
nids = None
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
loadcase_id = 123458
p0 = array([0., 0., 0.])
F_expected = array([0., 0., 10000.])
M_expected = array([5000., -5000., 0.])
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
def test_loads_sum_03(self):
"""tests N/A"""
if 0: # pragma: no cover
model = BDF(log=log, debug=False)
bdf_filename = os.path.join(model_path, 'iSat', 'ISat_Launch_Sm_4pt.dat')
model.read_bdf(bdf_filename)
loadcase_id = 1
#print("keys3", model.loads.keys())
p0 = array([0., 0., 0.])
F_expected = array([0., 0., 1.])
M_expected = array([0., 0., 0.])
eids = None
nids = None
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
def test_loads_sum_04(self):
"""
tests:
- 1=FORCE
- 2=LOAD/FORCE
- 3=LOAD/PLOAD4
- 4=LOAD/PLOAD4
- 5=LOAD/PLOAD4
- 6=LOAD/PLOAD4
- 10=PLOAD4
- 11=PLOAD4
"""
p0 = array([0., 0., 0.])
model = BDF(log=log, debug=False)
bdf_filename = os.path.join(model_path, 'plate', 'plate.bdf')
#print(bdf_filename)
model.read_bdf(bdf_filename)
#print("keys4", model.loads.keys())
loadcase_id = 1
F_expected = array([600., 0., 0.])
M_expected = array([0., 0., -3000.])
eids = None
nids = None
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
loadcase_id = 2
F_expected = array([600., 0., 0.])
M_expected = array([0., 0., -3000.])
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
#---------
loadcase_id = 3
A = 0.
for e, element in model.elements.items():
A += element.Area()
A_expected = 100.
self.assertTrue(allclose(A, A_expected), 'loadcase_id=%s A_expected=%s A=%s' % (loadcase_id, A_expected, A))
p = 3.
Fi = p * A
eids = None
nids = None
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(p*A, F[2]), 'loadcase_id=%s p*A=%s F=%s' % (loadcase_id, p*A, F))
F_expected = array([0., 0., 300.])
M_expected = array([1500., -1500., 0.])
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
#---
loadcase_id = 10
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
#---
loadcase_id = 4
F_expected = array([0., 0., 300.])
M_expected = array([1500., -1500., 0.])
F_expected *= 5.
M_expected *= 5.
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
#print('F =', F)
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
loadcase_id = 5
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
F_expected = array([0., 0., 300.])
M_expected = array([1500., -1500., 0.])
F_expected *= 7.
M_expected *= 7.
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
loadcase_id = 6
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
F_expected = array([0., 0., 300.])
M_expected = array([1500., -1500., 0.])
F_expected *= 7. * 5.
M_expected *= 7. * 5.
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
#---------
loadcase_id = 11
A_expected = 4.
A = 4.
p = 3.
Fi = p * A
element = model.elements[1]
normal = element.Normal()
normal_expected = array([0., 0., 1.])
self.assertTrue(allclose(normal_expected, normal), 'loadcase_id=%s normal_expected=%s normal=%s' % (loadcase_id, normal_expected, normal))
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(p*A, F[2]), 'loadcase_id=%s p*A=%s F=%s' % (loadcase_id, p*A, F))
F_expected = array([0., 0., 12.])
M_expected = array([12., -12., 0.])
self.assertTrue(allclose(F_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F_expected, F))
self.assertTrue(allclose(M_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M_expected, M))
def test_loads_sum_05(self):
"""
tests:
- 1=LOAD/PLOAD4
- 2=LOAD/PLOAD4/FORCE
- 5=PLOAD4
- 6=PLOAD4
- 1001=PLOAD4
- 1002=1002
- 1003=PLOAD
"""
model = BDF(log=log, debug=False)
bdf_filename = os.path.join(model_path, 'real', 'loads', 'loads.bdf')
model.read_bdf(bdf_filename)
p = 3.
A = 1.
n = array([0., 0., 1.])
F1001_expected = p * A * n
r = array([0.5, 1.5, 0.])
p0 = array([0., 0., 0.])
M1001_expected = cross(r, F1001_expected)
loadcase_id = 1001
eids = None
nids = None
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F1001_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F1001_expected, F))
self.assertTrue(allclose(M1001_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M1001_expected, M))
loadcase_id = 1002
r = array([4., 2., 0.])
F1002_expected = array([0., 0., 1.])
M1002_expected = cross(r, F1002_expected)
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
self.assertTrue(allclose(F1002_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F1002_expected, F))
self.assertTrue(allclose(M1002_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M1002_expected, M))
loadcase_id = 1
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F1001_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F1001_expected, F))
self.assertTrue(allclose(M1001_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M1001_expected, M))
loadcase_id = 2
F2_expected = F1001_expected + F1002_expected
M2_expected = M1001_expected + M1002_expected
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F2_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F2_expected, F))
self.assertTrue(allclose(M2_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M2_expected, M))
F6_expected = 2. * (3. * F1001_expected + 13. * F1002_expected)
M6_expected = 2. * (3. * M1001_expected + 13. * M1002_expected)
F7_expected = 7. * 11. * F6_expected
M7_expected = 7. * 11. * M6_expected
if 0: # pragma: no cover
loadcase_id = 6
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F6_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F6_expected, F))
self.assertTrue(allclose(M6_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M6_expected, M))
loadcase_id = 7
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F7_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F7_expected, F))
self.assertTrue(allclose(M7_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M7_expected, M))
loadcase_id = 5
p = 2.
A = 1.
n = array([0., 1., 1.]) / np.sqrt(2.)
F5_expected = p * A * n
r = array([0.5, 0.5, 0.])
M5_expected = cross(r, F5_expected)
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F5_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F5_expected, F))
self.assertTrue(allclose(M5_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M5_expected, M))
#print('loadcase_id=%s F=%s M=%s' % (loadcase_id, F, M))
loadcase_id = 6
p = 2.
A = 1.
n = array([0., 0., 0.5]) / 0.5
F6_expected = p * A * n
r = array([0.5, 0.5, 0.])
M6_expected = cross(r, F6_expected)
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F6_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F6_expected, F))
self.assertTrue(allclose(M6_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M6_expected, M))
#print('loadcase_id=%s F=%s M=%s' % (loadcase_id, F, M))
loadcase_id = 1003
p = 9.
A = 1.
n = array([0., 0., 1.])
F1003_expected = p * A * n
r = array([0.5, 0.5, 0.])
M1003_expected = cross(r, F1003_expected)
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F1003_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F1003_expected, F))
self.assertTrue(allclose(M1003_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M1003_expected, M))
loadcase_id = 8
F8_expected = 2. * (3. * F7_expected + 2. * F1003_expected)
M8_expected = 2. * (3. * M7_expected + 2. * M1003_expected)
if 0: # pragma: no cover
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F8_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F8_expected, F))
self.assertTrue(allclose(M8_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M8_expected, M))
loadcase_id = 800
p = 3.5
A = 1.
n = array([0., 0., 1.])
F800_expected = p * A * n
r = array([3.5, 1.5, 0.])
M800_expected = cross(r, F800_expected)
if 0: # pragma: no cover
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F800_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F800_expected, F))
self.assertTrue(allclose(M800_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M800_expected, M))
loadcase_id = 801
F801_expected = F800_expected
M801_expected = M800_expected
if 0: # pragma: no cover
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F801_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F801_expected, F))
self.assertTrue(allclose(M801_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M801_expected, M))
loadcase_id = 802
p = 3.5
A = 0.5
n = array([0., 0., 1.])
F802_expected = p * A * n
rx = (3. + 4. + 4.) / 3.
ry = (1. + 1. + 2.) / 3.
r = array([rx, ry, 0.])
M802_expected = cross(r, F802_expected)
if 0: # pragma: no cover
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F802_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F802_expected, F))
self.assertTrue(allclose(M802_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M802_expected, M))
bdf_file = StringIO()
model.write_bdf(bdf_file, close=False)
bdf_file.seek(0)
model.write_bdf(bdf_file, size=16)
def _test_loads_sum_06(self):
model = BDF(log=log, debug=False)
bdf_filename = os.path.join(model_path, 'real', 'loads', 'bars.bdf')
model.read_bdf(bdf_filename)
p0 = array([0., 0., 0.])
loadcase_id = 1
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
if 0: # pragma: no cover
r = array([0., 0., 0.])
F1_expected = array([0., 0., 1.])
M1_expected = cross(r, F1_expected)
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids, include_grav=False)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
self.assertTrue(allclose(F1_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F1_expected, F))
self.assertTrue(allclose(M1_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M1_expected, M))
def test_loads_sum_radial_01(self):
model = BDF(debug=False)
model.nodes[1] = GRID(1, cp=1, xyz=[0., 0., 0.], cd=0, ps='', seid=0,
comment='')
cid = 1
origin = [0., 0., 0.]
zaxis = [0., 0., 1.]
xaxis = [1., 0., 0.]
model.add_cord2c(cid, rid=0, origin=origin, zaxis=zaxis, xzplane=xaxis,
comment='')
sid = 1
node = 1
cid = 1
mag = 1.1
xyz = [1., 0., 0.]
radial_force = model.add_force(sid, node, mag, xyz, cid=cid, comment='')
sid = 2
xyz = [1., 90., 0.]
mag = 2.2
theta_force = model.add_force(sid, node, mag, xyz, cid=cid, comment='')
model.cross_reference()
p0 = 1
eids = None
nids = None
loadcase_id = 1
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False,
xyz_cid0=None)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids,
include_grav=False,
xyz_cid0=None)
assert np.allclose(F, F2), 'F=%s F2=%s' % (F, F2)
assert np.allclose(M, M2), 'M=%s M2=%s' % (M, M2)
F1_expected = np.array([1.1, 0., 0.])
M1_expected = np.array([0., 0., 0.])
self.assertTrue(allclose(F1_expected, F), 'loadcase_id=%s F_expected=%s F=%s' % (loadcase_id, F1_expected, F))
self.assertTrue(allclose(M1_expected, M), 'loadcase_id=%s M_expected=%s M=%s' % (loadcase_id, M1_expected, M))
loadcase_id = 2
F, M = sum_forces_moments(model, p0, loadcase_id, include_grav=False,
xyz_cid0=None)
F2, M2 = sum_forces_moments_elements(model, p0, loadcase_id, eids, nids,
include_grav=False,
xyz_cid0=None)
assert | np.allclose(F, F2) | numpy.allclose |
import numpy as np
import sys, os, subprocess
import OpenEXR
import Imath
from PIL import Image, ImageChops
from plyfile import PlyData, PlyElement
def trim(im):
bg = Image.new(im.mode, im.size, im.getpixel((0,0)))
diff = ImageChops.difference(im, bg)
diff = ImageChops.add(diff, diff)
bbox = diff.getbbox()
if bbox:
return im.crop(bbox)
# PATH_TO_MITSUBA2 = "/home/tolga/Codes/mitsuba2/build/dist/mitsuba" # mitsuba exectuable
PATH_TO_MITSUBA2 = "/home/wangyida/Documents/gitfarm/mitsuba2/build/dist/mitsuba" # mitsuba exectuable
# replaced by command line arguments
# PATH_TO_NPY = 'pcl_ex.npy' # the tensor to load
# note that sampler is changed to 'independent' and the ldrfilm is changed to hdrfilm
xml_head = \
"""
<scene version="0.6.0">
<integrator type="path">
<integer name="maxDepth" value="-1"/>
</integrator>
<sensor type="perspective">
<float name="farClip" value="100"/>
<float name="nearClip" value="0.1"/>
<transform name="toWorld">
<lookat origin="3,3,3" target="0,0,0" up="0,0,1"/>
</transform>
<float name="fov" value="25"/>
<sampler type="independent">
<integer name="sampleCount" value="256"/>
</sampler>
<film type="hdrfilm">
<integer name="width" value="720"/>
<integer name="height" value="720"/>
<rfilter type="gaussian"/>
</film>
</sensor>
<bsdf type="roughplastic" id="surfaceMaterial">
<string name="distribution" value="ggx"/>
<float name="alpha" value="0.05"/>
<float name="intIOR" value="1.46"/>
<rgb name="diffuseReflectance" value="1,1,1"/> <!-- default 0.5 -->
</bsdf>
"""
# I also use a smaller point size
xml_ball_segment = \
"""
<shape type="sphere">
<float name="radius" value="0.015"/>
<transform name="toWorld">
<translate x="{}" y="{}" z="{}"/>
</transform>
<bsdf type="diffuse">
<rgb name="reflectance" value="{},{},{}"/>
</bsdf>
</shape>
"""
# material for plastic
"""
<bsdf type="diffuse">
<rgb name="reflectance" value="{},{},{}"/>
</bsdf>
"""
# material for dielectric
"""
<bsdf type="roughdielectric">
<string name="distribution" value="beckmann"/>
<float name="alpha" value="0.1"/>
<string name="int_ior" value="bk7"/>
<string name="ext_ior" value="air"/>
<rgb name="specular_reflectance" value="{},{},{}"/>
</bsdf>
"""
# material for metal
"""
<bsdf type="roughconductor">
<string name="material" value="Ag"/>
<string name="distribution" value="beckmann"/>
<float name="alpha" value="0.4"/>
<rgb name="specular_reflectance" value="{},{},{}"/>
</bsdf>
"""
obj_mesh = \
"""
<shape type="obj">
<string name="filename" value="{}"/>
<bsdf type="diffuse">
</bsdf>
</shape>
"""
# A rectangular bottom plane
"""
<shape type="rectangle">
<ref name="bsdf" id="surfaceMaterial"/>
<transform name="toWorld">
<scale x="10" y="10" z="1"/>
<translate x="0" y="0" z="{}"/>
</transform>
</shape>
"""
xml_tail = \
"""
<shape type="rectangle">
<ref name="bsdf" id="surfaceMaterial"/>
<transform name="toWorld">
<scale x="10" y="10" z="1"/>
<translate x="0" y="0" z="{}"/>
</transform>
</shape>
<shape type="rectangle">
<transform name="toWorld">
<scale x="10" y="10" z="1"/>
<lookat origin="-4,4,20" target="0,0,0" up="0,0,1"/>
</transform>
<emitter type="area">
<rgb name="radiance" value="7,7,7"/>
</emitter>
</shape>
</scene>
"""
def colormap(x, y, z):
vec = np.array([x, y, z])
vec = np.clip(vec, 0.001, 1.0)
norm = np.sqrt(np.sum(vec ** 2))
vec /= norm
return [vec[0], vec[1], vec[2]]
def standardize_bbox(pcl, points_per_object):
pt_indices = np.random.choice(pcl.shape[0], points_per_object, replace=False)
np.random.shuffle(pt_indices)
pcl = pcl[pt_indices] # n by 3
mins = np.amin(pcl, axis=0)
maxs = np.amax(pcl, axis=0)
center = (mins + maxs) / 2.
scale = | np.amax(maxs - mins) | numpy.amax |
"""
miscelallaneous functions and classes to extract connectivity metrics
Author: <NAME>, PhD [<EMAIL>], https://twitter.com/davemomi
"""
import numpy as np
import pandas as pd
from math import pi
import glob
import seaborn as sns
import matplotlib.pyplot as plt
import bct as bct
class Connectivity_metrics(object):
def __init__(self, matrices_files, net_label_txt, labels_dic):
self.matrices_files = matrices_files
self.net_label_txt = net_label_txt
self.labels_dic = labels_dic
def nodes_overall_conn(self, make_symmetric=True, upper_threshold=None,
lower_threshold=None):
'''
computing the overall connectivity of each node
regardless of network affiliation
Parameters
----------
make_symmetric: Boolean|
True indicate that the matrix is either upper
or lower triangular and need to be symmetrize
False indicate that the matrix is a full matrix already
upper_threshold: int |
an integer value ranging from 0 to 100 representing the
percentage of values as respect to maximum. The value
under that threshold will be 0 (Default is None)
lower_threshold: int |
an integer value ranging from 0 to 100 representing the
percentage of values as respect to maximum. The value
above that threshold will be 0 (Default is None)
Returns
-------
float data : numpy array |
numpy array (dim number of subject X number of node)
representing the connectivity of each node regardless
of network affiliation
'''
self.nodes_conn = []
for subj in range(len(self.matrices_files)):
self.matrix = pd.read_csv(self.matrices_files[subj], sep= ' ', header=None)
self.matrix = np.array(self.matrix)
if make_symmetric==True:
self.matrix = self.matrix + self.matrix.T - np.diag(self.matrix.diagonal())
else:
self.matrix = self.matrix
self.max=np.max(self.matrix.flatten())
if upper_threshold==None:
self.matrix= self.matrix
else:
self.matrix[self.matrix < upper_threshold*self.max/100 ] = 0
if lower_threshold==None:
self.matrix= self.matrix
else:
self.matrix[self.matrix > lower_threshold*self.max/100 ] = 0
np.fill_diagonal(self.matrix,0)
for nodes in range(self.matrix.shape[0]):
self._node_conn = np.sum(self.matrix[nodes])
self.nodes_conn.append(self._node_conn)
self.nodes_conn = np.array(self.nodes_conn)
self.nodes_conn = self.nodes_conn.reshape(len(self.matrices_files), self.matrix.shape[0])
return self.nodes_conn
def node_inner_conn(self, sbj_number, nodes_number, make_symmetric=True,
upper_threshold=None, lower_threshold=None):
'''
computing the connectivity of each node with its own network
Parameters
----------
sbj_number: int |
number of subjects
nodes_number: int|
number of nodes
make_symmetric: Boolean|
True indicate that the matrix is either upper
or lower triangular and need to be symmetrize
False indicate that the matrix is a full matrix already
upper_threshold: int |
an integer value ranging from 0 to 100 representing the
percentage of values as respect to maximum. The value
under that threshold will be 0 (Default is None)
lower_threshold: int |
an integer value ranging from 0 to 100 representing the
percentage of values as respect to maximum. The value
above that threshold will be 0 (Default is None)
Returns
-------
float data : numpy array |
numpy array (dim number of subject X number of node)
representing the connectivity of each node with its own
network
'''
with open(self.net_label_txt) as f:
net=f.read().splitlines()
self.all_conn = np.zeros([sbj_number, nodes_number])
for subj in range(len(self.matrices_files)):
self.matrix = pd.read_csv(self.matrices_files[subj], sep= ' ', header=None)
self.matrix = np.array(self.matrix)
if make_symmetric==True:
self.matrix = self.matrix + self.matrix.T - np.diag(self.matrix.diagonal())
else:
self.matrix = self.matrix
self.max=np.max(self.matrix.flatten())
if upper_threshold==None:
self.matrix= self.matrix
else:
self.matrix[self.matrix < upper_threshold*self.max/100 ] = 0
if lower_threshold==None:
self.matrix= self.matrix
else:
self.matrix[self.matrix > lower_threshold*self.max/100 ] = 0
np.fill_diagonal(self.matrix,0)
for network in net:
for nodes in self.labels_dic[network]:
self.sub_matrix =self.matrix[nodes]
self.streamlines_sum = np.sum(self.sub_matrix[self.labels_dic[network]])
self.all_conn[subj, nodes] = self.streamlines_sum/self.labels_dic[network].shape[0]
return self.all_conn
def node_outer_conn(self, sbj_number, nodes_number, make_symmetric=True,
upper_threshold=None, lower_threshold=None):
'''
computing the connectivity of each node with the other nodes
which don't belong to the same network
Parameters
----------
sbj_number: int |
number of subjects
nodes_number: int|
number of nodes
make_symmetric: Boolean|
True indicate that the matrix is either upper
or lower triangular and need to be symmetrize
False indicate that the matrix is a full matrix already
upper_threshold: int |
an integer value ranging from 0 to 100 representing the
percentage of values as respect to maximum. The value
under that threshold will be 0 (Default is None)
lower_threshold: int |
an integer value ranging from 0 to 100 representing the
percentage of values as respect to maximum. The value
above that threshold will be 0 (Default is None)
Returns
-------
float data : numpy array |
numpy array (dim number of subject X number of node)
representing the connectivity of each node with regions that
are outsite the node's network
'''
with open(self.net_label_txt) as f:
net=f.read().splitlines()
self.all_conn = np.zeros([sbj_number, nodes_number])
for subj in range(len(self.matrices_files)):
self.matrix = pd.read_csv(self.matrices_files[subj], sep= ' ', header=None)
self.matrix = np.array(self.matrix)
if make_symmetric==True:
self.matrix = self.matrix + self.matrix.T - np.diag(self.matrix.diagonal())
else:
self.matrix = self.matrix
self.max=np.max(self.matrix.flatten())
if upper_threshold==None:
self.matrix= self.matrix
else:
self.matrix[self.matrix < upper_threshold*self.max/100 ] = 0
if lower_threshold==None:
self.matrix= self.matrix
else:
self.matrix[self.matrix > lower_threshold*self.max/100 ] = 0
| np.fill_diagonal(self.matrix,0) | numpy.fill_diagonal |
import isopy
import numpy as np
import pytest
# calculate_mass_fractionation_factor, remove_mass_fractionation, add_mass_fractionation
def test_mass_fractionation1():
# Testing with input as isotope array
# Using default reference values
mass_ref = isopy.refval.isotope.mass_W17
fraction_ref = isopy.refval.isotope.best_measurement_fraction_M16
unfractionated = isopy.random(100, (1, 0.001), keys=isopy.refval.element.isotopes['pd'], seed = 46)
unfractionated = unfractionated * fraction_ref
unfractionated['108pd'] = fraction_ref.get('108pd/105pd') * unfractionated['105pd']
mf_factor = isopy.random(100, (0, 2), seed=47)
c_fractionated1 = isopy.tb.add_mass_fractionation(unfractionated, mf_factor, '105pd')
c_fractionated2 = isopy.tb.add_mass_fractionation(unfractionated, mf_factor)
assert c_fractionated1.keys == unfractionated.keys
assert c_fractionated1.size == unfractionated.size
assert c_fractionated2.keys == unfractionated.keys
assert c_fractionated2.size == unfractionated.size
c_unfractionated1 = isopy.tb.remove_mass_fractionation(c_fractionated1, mf_factor, '105pd')
c_unfractionated2 = isopy.tb.remove_mass_fractionation(c_fractionated2, mf_factor)
assert c_unfractionated1.keys == unfractionated.keys
assert c_unfractionated1.size == unfractionated.size
assert c_unfractionated2.keys == unfractionated.keys
assert c_unfractionated2.size == unfractionated.size
c_mf_factor2 = isopy.tb.calculate_mass_fractionation_factor(c_fractionated1, '108pd/105pd')
np.testing.assert_allclose(c_mf_factor2, mf_factor)
for key in unfractionated.keys:
mass_diff = mass_ref.get(key/'105pd')
fractionated = unfractionated[key] * (mass_diff ** mf_factor)
np.testing.assert_allclose(c_fractionated1[key], fractionated)
np.testing.assert_allclose(c_unfractionated1[key], unfractionated[key])
np.testing.assert_allclose(c_unfractionated2[key], unfractionated[key])
#Changing reference values
mass_ref = isopy.refval.isotope.mass_number
fraction_ref = isopy.refval.isotope.initial_solar_system_fraction_L09
unfractionated = isopy.random(100, (1, 0.001), keys=isopy.refval.element.isotopes['pd'],
seed=46)
unfractionated = unfractionated * fraction_ref
unfractionated['108pd'] = fraction_ref.get('108pd/105pd') * unfractionated['105pd']
unfractionated2 = unfractionated.ratio('105pd')
mf_factor = isopy.random(100, (0, 2), seed=47)
c_fractionated1 = isopy.tb.add_mass_fractionation(unfractionated, mf_factor, '105pd', isotope_masses=mass_ref)
c_fractionated2 = isopy.tb.add_mass_fractionation(unfractionated, mf_factor, isotope_masses=mass_ref)
assert c_fractionated1.keys == unfractionated.keys
assert c_fractionated1.size == unfractionated.size
assert c_fractionated2.keys == unfractionated.keys
assert c_fractionated2.size == unfractionated.size
c_unfractionated1 = isopy.tb.remove_mass_fractionation(c_fractionated1, mf_factor, '105pd', isotope_masses=mass_ref)
c_unfractionated2 = isopy.tb.remove_mass_fractionation(c_fractionated2, mf_factor, isotope_masses=mass_ref)
assert c_unfractionated1.keys == unfractionated.keys
assert c_unfractionated1.size == unfractionated.size
assert c_unfractionated2.keys == unfractionated.keys
assert c_unfractionated2.size == unfractionated.size
c_mf_factor2 = isopy.tb.calculate_mass_fractionation_factor(c_fractionated1, '108pd/105pd',
isotope_masses=mass_ref, isotope_fractions=fraction_ref)
np.testing.assert_allclose(c_mf_factor2, mf_factor)
for key in unfractionated.keys:
mass_diff = mass_ref.get(key / '105pd')
fractionated = unfractionated[key] * (mass_diff ** mf_factor)
np.testing.assert_allclose(c_fractionated1[key], fractionated)
np.testing.assert_allclose(c_unfractionated1[key], unfractionated[key])
np.testing.assert_allclose(c_unfractionated2[key], unfractionated[key])
# calculate_mass_fractionation_factor, remove_mass_fractionation, add_mass_fractionation
def test_mass_fractionation2():
# Testing with input as ratio array
# Using default reference values
mass_ref = isopy.refval.isotope.mass_W17
fraction_ref = isopy.refval.isotope.best_measurement_fraction_M16
unfractionated = isopy.random(100, (1, 0.001), keys=isopy.refval.element.isotopes['pd'],
seed=46)
unfractionated = unfractionated * fraction_ref
unfractionated['108pd'] = fraction_ref.get('108pd/105pd') * unfractionated['105pd']
unfractionated = unfractionated.ratio('105pd')
mf_factor = isopy.random(100, (0, 2), seed=47)
c_fractionated2 = isopy.tb.add_mass_fractionation(unfractionated, mf_factor)
assert c_fractionated2.keys == unfractionated.keys
assert c_fractionated2.size == unfractionated.size
c_unfractionated2 = isopy.tb.remove_mass_fractionation(c_fractionated2, mf_factor)
assert c_unfractionated2.keys == unfractionated.keys
assert c_unfractionated2.size == unfractionated.size
c_mf_factor2 = isopy.tb.calculate_mass_fractionation_factor(c_fractionated2, '108pd/105pd')
np.testing.assert_allclose(c_mf_factor2, mf_factor)
for key in unfractionated.keys:
mass_diff = mass_ref.get(key)
fractionated = unfractionated[key] * (mass_diff ** mf_factor)
np.testing.assert_allclose(c_fractionated2[key], fractionated)
np.testing.assert_allclose(c_unfractionated2[key], unfractionated[key])
# Changing reference values
mass_ref = isopy.refval.isotope.mass_number
fraction_ref = isopy.refval.isotope.initial_solar_system_fraction_L09
unfractionated = isopy.random(100, (1, 0.001), keys=isopy.refval.element.isotopes['pd'],
seed=46)
unfractionated = unfractionated * fraction_ref
unfractionated['108pd'] = fraction_ref.get('108pd/105pd') * unfractionated['105pd']
unfractionated = unfractionated.ratio('105pd')
mf_factor = isopy.random(100, (0, 2), seed=47)
c_fractionated2 = isopy.tb.add_mass_fractionation(unfractionated, mf_factor, isotope_masses=mass_ref)
assert c_fractionated2.keys == unfractionated.keys
assert c_fractionated2.size == unfractionated.size
c_unfractionated2 = isopy.tb.remove_mass_fractionation(c_fractionated2, mf_factor, isotope_masses=mass_ref)
assert c_unfractionated2.keys == unfractionated.keys
assert c_unfractionated2.size == unfractionated.size
c_mf_factor2 = isopy.tb.calculate_mass_fractionation_factor(c_fractionated2, '108pd/105pd',
isotope_masses=mass_ref, isotope_fractions=fraction_ref)
np.testing.assert_allclose(c_mf_factor2, mf_factor)
for key in unfractionated.keys:
mass_diff = mass_ref.get(key)
fractionated = unfractionated[key] * (mass_diff ** mf_factor)
np.testing.assert_allclose(c_fractionated2[key], fractionated)
np.testing.assert_allclose(c_unfractionated2[key], unfractionated[key])
class Test_MassIndependentCorrection:
def test_one(self):
# Default reference values
mass_ref = isopy.refval.isotope.mass_W17
fraction_ref = isopy.refval.isotope.best_measurement_fraction_M16
unfractionated1 = isopy.random(100, (1, 0.001), keys=isopy.refval.element.isotopes['pd'],
seed=46)
unfractionated1 = unfractionated1 * fraction_ref
unfractionated1['108pd'] = fraction_ref.get('108pd/105pd') * unfractionated1['105pd']
unfractionated2 = unfractionated1.ratio('105pd')
n_unfractionated2 = (unfractionated2 / fraction_ref - 1) * 10000
mf_factor = isopy.random(100, (0, 2), seed=47)
fractionated1 = isopy.tb.add_mass_fractionation(unfractionated2, mf_factor)
fractionated2 = fractionated1.deratio(unfractionated1['105pd'])
self.run(fractionated1, unfractionated2, '108pd/105pd')
self.run(fractionated2, unfractionated2, '108pd/105pd')
self.run(fractionated1, n_unfractionated2, '108pd/105pd', factor=10_000)
self.run(fractionated2, n_unfractionated2, '108pd/105pd', factor=10_000)
self.run(fractionated1, n_unfractionated2, '108pd/105pd', factor='epsilon')
self.run(fractionated2, n_unfractionated2, '108pd/105pd', factor='epsilon')
# Different reference values
mass_ref = isopy.refval.isotope.mass_number
fraction_ref = isopy.refval.isotope.initial_solar_system_fraction_L09
unfractionated1 = isopy.random(100, (1, 0.001), keys=isopy.refval.element.isotopes['pd'],
seed=46)
unfractionated1 = unfractionated1 * fraction_ref
unfractionated1['108pd'] = fraction_ref.get('108pd/105pd') * unfractionated1['105pd']
unfractionated2 = unfractionated1.ratio('105pd')
n_unfractionated2 = (unfractionated2 / fraction_ref - 1) * 10000
mf_factor = isopy.random(100, (0, 2), seed=47)
fractionated1 = isopy.tb.add_mass_fractionation(unfractionated2, mf_factor,
isotope_masses=mass_ref)
fractionated2 = fractionated1.deratio(unfractionated1['105pd'])
self.run(fractionated1, unfractionated2, '108pd/105pd', mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated2, unfractionated2, '108pd/105pd', mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated1, n_unfractionated2, '108pd/105pd', factor=10_000, mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated2, n_unfractionated2, '108pd/105pd', factor=10_000, mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated1, n_unfractionated2, '108pd/105pd', factor='epsilon', mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated2, n_unfractionated2, '108pd/105pd', factor='epsilon', mass_ref=mass_ref, fraction_ref=fraction_ref)
def test_two(self):
# With interference correctionn
# We wont get an exact match here so we have to lower the tolerance.
# Default reference values
mass_ref = isopy.refval.isotope.mass_W17
fraction_ref = isopy.refval.isotope.best_measurement_fraction_M16
mf_factor = isopy.random(100, (0, 2), seed=47)
data = isopy.random(100, (1, 0.1), keys='101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd'.split(), seed=46)
data = data * fraction_ref
data['108pd'] = fraction_ref.get('108pd/105pd') * data['105pd']
fractionated = data.copy()
fractionated = isopy.tb.add_mass_fractionation(fractionated, mf_factor)
for key in fractionated.keys.filter(element_symbol='pd'):
if (ru:=fraction_ref.get(f'ru{key.mass_number}/ru101', 0)) > 0:
ru *= fractionated['101ru'] * (mass_ref.get(f'ru{key.mass_number}/ru101', 0) ** mf_factor)
fractionated[key] += ru
if (cd:=fraction_ref.get(f'cd{key.mass_number}/cd111', 0)) > 0:
cd *= fractionated['111cd'] * (mass_ref.get(f'cd{key.mass_number}/cd111', 0) ** mf_factor)
fractionated[key] += cd
correct1 = data.copy(element_symbol = 'pd').ratio('105pd')
correct2 = (correct1 / fraction_ref - 1)
correct3 = (correct1 / fraction_ref - 1) * 10_000
self.run(fractionated, correct1, '108pd/105pd')
self.run(fractionated, correct2, '108pd/105pd', factor=1)
self.run(fractionated, correct3, '108pd/105pd', factor=10_000)
self.run(fractionated, correct3, '108pd/105pd', factor='epsilon')
# Different reference values
mass_ref = isopy.refval.isotope.mass_number
fraction_ref = isopy.refval.isotope.initial_solar_system_fraction_L09
mf_factor = isopy.random(100, (0, 2), seed=47)
data = isopy.random(100, (1, 0.1), keys='101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd'.split(), seed=46)
data = data * fraction_ref
data['108pd'] = fraction_ref.get('108pd/105pd') * data['105pd']
fractionated = data.copy()
fractionated = isopy.tb.add_mass_fractionation(fractionated, mf_factor, isotope_masses=mass_ref)
for key in fractionated.keys.filter(element_symbol='pd'):
if (ru := fraction_ref.get(f'ru{key.mass_number}/ru101', 0)) > 0:
ru *= fractionated['101ru'] * (
mass_ref.get(f'ru{key.mass_number}/ru101', 0) ** mf_factor)
fractionated[key] += ru
if (cd := fraction_ref.get(f'cd{key.mass_number}/cd111', 0)) > 0:
cd *= fractionated['111cd'] * (
mass_ref.get(f'cd{key.mass_number}/cd111', 0) ** mf_factor)
fractionated[key] += cd
correct1 = data.copy(element_symbol='pd').ratio('105pd')
correct2 = (correct1 / fraction_ref - 1)
correct3 = (correct1 / fraction_ref - 1) * 10_000
self.run(fractionated, correct1, '108pd/105pd', mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct2, '108pd/105pd', factor=1, mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct3, '108pd/105pd', factor=10_000, mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct3, '108pd/105pd', factor='epsilon', mass_ref=mass_ref, fraction_ref=fraction_ref)
def test_three(self):
# Normalisations
# Default reference values
mass_ref = isopy.refval.isotope.mass_W17
fraction_ref = isopy.refval.isotope.best_measurement_fraction_M16
mf_factor = isopy.random(100, (0, 2), seed=47)
data = isopy.random(100, (1, 0.1), keys='102pd 104pd 105pd 106pd 108pd 110pd'.split(), seed=46)
data = data * fraction_ref
data['108pd'] = fraction_ref.get('108pd/105pd') * data['105pd']
fractionated = data.copy()
fractionated = isopy.tb.add_mass_fractionation(fractionated, mf_factor)
correct1 = data.copy(element_symbol='pd').ratio('105pd')
correct2 = (correct1 / fraction_ref - 1)
correct3 = correct2 * 1000
correct4 = correct2 * 10_000
correct5 = correct2 * 1_000_000
self.run(fractionated, correct1, '108pd/105pd')
self.run(fractionated, correct2, '108pd/105pd', factor=1)
self.run(fractionated, correct3, '108pd/105pd', factor=1000)
self.run(fractionated, correct3, '108pd/105pd', factor='ppt')
self.run(fractionated, correct3, '108pd/105pd', factor='permil')
self.run(fractionated, correct4, '108pd/105pd', factor=10_000)
self.run(fractionated, correct4, '108pd/105pd', factor='epsilon')
self.run(fractionated, correct5, '108pd/105pd', factor=1_000_000)
self.run(fractionated, correct5, '108pd/105pd', factor='mu')
self.run(fractionated, correct5, '108pd/105pd', factor='ppm')
# Single value
std1 = isopy.random(100, (1, 0.1), keys='102pd 104pd 105pd 106pd 108pd 110pd'.split(), seed=48)
std1 = std1 * fraction_ref
rstd1 = std1.ratio('pd105')
correct1 = data.copy(element_symbol='pd').ratio('105pd')
correct2 = (correct1 / np.mean(rstd1) - 1)
correct3 = correct2 * 1000
correct4 = correct2 * 10_000
correct5 = correct2 * 1_000_000
self.run(fractionated, correct2, '108pd/105pd', norm_val=rstd1)
self.run(fractionated, correct2, '108pd/105pd', factor=1, norm_val=rstd1)
self.run(fractionated, correct3, '108pd/105pd', factor=1000, norm_val=rstd1)
self.run(fractionated, correct3, '108pd/105pd', factor='ppt', norm_val=rstd1)
self.run(fractionated, correct3, '108pd/105pd', factor='permil', norm_val=rstd1)
self.run(fractionated, correct4, '108pd/105pd', factor=10_000, norm_val=rstd1)
self.run(fractionated, correct4, '108pd/105pd', factor='epsilon', norm_val=rstd1)
self.run(fractionated, correct5, '108pd/105pd', factor=1_000_000, norm_val=rstd1)
self.run(fractionated, correct5, '108pd/105pd', factor='mu', norm_val=rstd1)
self.run(fractionated, correct5, '108pd/105pd', factor='ppm', norm_val=rstd1)
std1 = np.mean(std1)
rstd1 = np.mean(rstd1)
self.run(fractionated, correct2, '108pd/105pd', norm_val=rstd1)
self.run(fractionated, correct2, '108pd/105pd', factor=1, norm_val=rstd1)
self.run(fractionated, correct3, '108pd/105pd', factor=1000, norm_val=rstd1)
self.run(fractionated, correct3, '108pd/105pd', factor='ppt', norm_val=rstd1)
self.run(fractionated, correct3, '108pd/105pd', factor='permil', norm_val=rstd1)
self.run(fractionated, correct4, '108pd/105pd', factor=10_000, norm_val=rstd1)
self.run(fractionated, correct4, '108pd/105pd', factor='epsilon', norm_val=rstd1)
self.run(fractionated, correct5, '108pd/105pd', factor=1_000_000, norm_val=rstd1)
self.run(fractionated, correct5, '108pd/105pd', factor='mu', norm_val=rstd1)
self.run(fractionated, correct5, '108pd/105pd', factor='ppm', norm_val=rstd1)
# Multiple
std1 = isopy.random(100, (1, 0.1), keys='102pd 104pd 105pd 106pd 108pd 110pd'.split(),
seed=48)
std1 = std1 * fraction_ref
rstd1 = std1.ratio('pd105')
std2 = isopy.random(50, (1, 0.1), keys='102pd 104pd 105pd 106pd 108pd 110pd'.split(),
seed=49)
std2 = std2 * fraction_ref
rstd2 = std2.ratio('pd105')
correct1 = data.copy(element_symbol='pd').ratio('105pd')
correct2 = (correct1 / (np.mean(rstd1)/2 + np.mean(rstd2)/2) - 1)
correct3 = correct2 * 1000
correct4 = correct2 * 10_000
correct5 = correct2 * 1_000_000
self.run(fractionated, correct2, '108pd/105pd', norm_val=(rstd1, rstd2))
self.run(fractionated, correct2, '108pd/105pd', factor=1, norm_val=(rstd1, rstd2))
self.run(fractionated, correct3, '108pd/105pd', factor=1000, norm_val=(rstd1, rstd2))
self.run(fractionated, correct3, '108pd/105pd', factor='ppt', norm_val=(rstd1, rstd2))
self.run(fractionated, correct3, '108pd/105pd', factor='permil', norm_val=(rstd1, rstd2))
self.run(fractionated, correct4, '108pd/105pd', factor=10_000, norm_val=(rstd1, rstd2))
self.run(fractionated, correct4, '108pd/105pd', factor='epsilon', norm_val=(rstd1, rstd2))
self.run(fractionated, correct5, '108pd/105pd', factor=1_000_000, norm_val=(rstd1, rstd2))
self.run(fractionated, correct5, '108pd/105pd', factor='mu', norm_val=(rstd1, rstd2))
self.run(fractionated, correct5, '108pd/105pd', factor='ppm', norm_val=(rstd1, rstd2))
std1 = np.mean(std1)
rstd1 = np.mean(rstd1)
self.run(fractionated, correct2, '108pd/105pd', norm_val=(rstd1, rstd2))
self.run(fractionated, correct2, '108pd/105pd', factor=1, norm_val=(rstd1, rstd2))
self.run(fractionated, correct3, '108pd/105pd', factor=1000, norm_val=(rstd1, rstd2))
self.run(fractionated, correct3, '108pd/105pd', factor='ppt', norm_val=(rstd1, rstd2))
self.run(fractionated, correct3, '108pd/105pd', factor='permil', norm_val=(rstd1, rstd2))
self.run(fractionated, correct4, '108pd/105pd', factor=10_000, norm_val=(rstd1, rstd2))
self.run(fractionated, correct4, '108pd/105pd', factor='epsilon', norm_val=(rstd1, rstd2))
self.run(fractionated, correct5, '108pd/105pd', factor=1_000_000, norm_val=(rstd1, rstd2))
self.run(fractionated, correct5, '108pd/105pd', factor='mu', norm_val=(rstd1, rstd2))
self.run(fractionated, correct5, '108pd/105pd', factor='ppm', norm_val=(rstd1, rstd2))
std2 = np.mean(std2)
rstd2 = np.mean(rstd2)
self.run(fractionated, correct2, '108pd/105pd', norm_val=(rstd1, rstd2))
self.run(fractionated, correct2, '108pd/105pd', factor=1, norm_val=(rstd1, rstd2))
self.run(fractionated, correct3, '108pd/105pd', factor=1000, norm_val=(rstd1, rstd2))
self.run(fractionated, correct3, '108pd/105pd', factor='ppt', norm_val=(rstd1, rstd2))
self.run(fractionated, correct3, '108pd/105pd', factor='permil', norm_val=(rstd1, rstd2))
self.run(fractionated, correct4, '108pd/105pd', factor=10_000, norm_val=(rstd1, rstd2))
self.run(fractionated, correct4, '108pd/105pd', factor='epsilon', norm_val=(rstd1, rstd2))
self.run(fractionated, correct5, '108pd/105pd', factor=1_000_000, norm_val=(rstd1, rstd2))
self.run(fractionated, correct5, '108pd/105pd', factor='mu', norm_val=(rstd1, rstd2))
self.run(fractionated, correct5, '108pd/105pd', factor='ppm', norm_val=(rstd1, rstd2))
# Different reference values
mass_ref = isopy.refval.isotope.mass_number
fraction_ref = isopy.refval.isotope.initial_solar_system_fraction_L09
mf_factor = isopy.random(100, (0, 2), seed=47)
data = isopy.random(100, (1, 0.1), keys='102pd 104pd 105pd 106pd 108pd 110pd'.split(),
seed=46)
data = data * fraction_ref
data['108pd'] = fraction_ref.get('108pd/105pd') * data['105pd']
fractionated = data.copy()
fractionated = isopy.tb.add_mass_fractionation(fractionated, mf_factor, isotope_masses=mass_ref)
correct1 = data.copy(element_symbol='pd').ratio('105pd')
correct2 = (correct1 / fraction_ref - 1)
correct3 = correct2 * 1000
correct4 = correct2 * 10_000
correct5 = correct2 * 1_000_000
self.run(fractionated, correct1, '108pd/105pd', mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct2, '108pd/105pd', factor=1, mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct3, '108pd/105pd', factor=1000, mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct3, '108pd/105pd', factor='ppt', mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct3, '108pd/105pd', factor='permil', mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct4, '108pd/105pd', factor=10_000, mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct4, '108pd/105pd', factor='epsilon', mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct5, '108pd/105pd', factor=1_000_000, mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct5, '108pd/105pd', factor='mu', mass_ref=mass_ref, fraction_ref=fraction_ref)
self.run(fractionated, correct5, '108pd/105pd', factor='ppm', mass_ref=mass_ref, fraction_ref=fraction_ref)
def run(self, data, correct, mb_ratio, factor = None, mass_ref = None, fraction_ref=None, norm_val = None):
if type(factor) is str:
func = getattr(isopy.tb.internal_normalisation, factor)
factor2 = None
else:
factor2 = factor
func = isopy.tb.internal_normalisation
kwargs = {}
if factor2 is not None: kwargs['extnorm_factor'] = factor2
if mass_ref is not None: kwargs['isotope_masses'] = mass_ref
if fraction_ref is not None: kwargs['isotope_fractions'] = fraction_ref
if norm_val is not None: kwargs['extnorm_value'] = norm_val
corrected = func(data, mb_ratio, **kwargs)
assert corrected.keys == correct.keys - mb_ratio
assert corrected.size == correct.size
assert corrected.ndim == correct.ndim
for key in corrected.keys:
np.testing.assert_allclose(corrected[key], correct[key])
# mass independent correction
if type(factor) is str:
func = getattr(isopy.tb.mass_independent_correction, factor)
factor2 = None
else:
factor2 = factor
func = isopy.tb.mass_independent_correction
kwargs = {}
if factor2 is not None: kwargs['normalisation_factor'] = factor2
if mass_ref is not None: kwargs['isotope_masses'] = mass_ref
if fraction_ref is not None: kwargs['isotope_fractions'] = fraction_ref
if norm_val is not None: kwargs['normalisation_value'] = norm_val
corrected = func(data, mb_ratio, **kwargs)
assert corrected.keys == correct.keys - mb_ratio
assert corrected.size == correct.size
assert corrected.ndim == correct.ndim
for key in corrected.keys:
np.testing.assert_allclose(corrected[key], correct[key])
class Test_IsobaricInterferences:
def test_one(self):
# No mass fractionation factor
# Single interference isotope
# Default reference values
fraction_ref = isopy.refval.isotope.best_measurement_fraction_M16
base_data = isopy.random(100, (1, 0.01), keys='101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd'.split())
base_data = base_data * fraction_ref
data = base_data.copy()
for key in data.keys.filter(element_symbol='pd'):
data[key] += fraction_ref.get(f'ru{key.mass_number}/ru101', 0) * data['101ru']
data[key] += fraction_ref.get(f'cd{key.mass_number}/cd111', 0) * data['111cd']
interferences1 = {'ru': ('102pd', '104pd'), 'cd': ('106pd', '108pd', '110pd')}
correct1 = base_data.copy()
correct1['101ru', '111cd'] = 0
interferences2 = {'ru': ('104pd',), 'cd': ('106pd', '108pd')}
correct2 = base_data.copy()
correct2['101ru', '111cd'] = 0
correct2['102pd'] = data['102pd']
correct2['110pd'] = data['110pd']
self.run(data, data, correct1, correct2, interferences1, interferences2, '105pd')
# Different reference values
fraction_ref = isopy.refval.isotope.initial_solar_system_fraction_L09
base_data = isopy.random(100, (1, 0.01), keys='101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd'.split())
base_data = base_data * fraction_ref
data = base_data.copy()
for key in data.keys.filter(element_symbol='pd'):
data[key] += fraction_ref.get(f'ru{key.mass_number}/ru101', 0) * data['101ru']
data[key] += fraction_ref.get(f'cd{key.mass_number}/cd111', 0) * data['111cd']
interferences1 = {'ru': ('102pd', '104pd'), 'cd': ('106pd', '108pd', '110pd')}
correct1 = base_data.copy()
correct1['101ru', '111cd'] = 0
interferences2 = {'ru': ('104pd',), 'cd': ('106pd', '108pd')}
correct2 = base_data.copy()
correct2['101ru', '111cd'] = 0
correct2['102pd'] = data['102pd']
correct2['110pd'] = data['110pd']
self.run(data, data, correct1, correct2, interferences1, interferences2, '105pd',
fraction_ref=fraction_ref)
def test_two(self):
# No mass fractionation factor
# Multiple interference isotopes
# Default reference values
fraction_ref = isopy.refval.isotope.best_measurement_fraction_M16
base_data = isopy.random(100, (1, 0.01), keys='99ru 101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd 112cd'.split())
# 112cd > 111cd, 101ru > 99ru
base_data = base_data * fraction_ref
data1 = base_data.copy()
data1['99ru', '111cd'] = -1 # so that we dont accidentally make this the largest isotope
for key in data1.keys.filter(key_neq = '<KEY>'.split()):
data1[key] += fraction_ref.get(f'ru{key.mass_number}/ru101', 0) * data1['101ru']
data1[key] += fraction_ref.get(f'cd{key.mass_number}/cd112', 0) * data1['112cd']
interferences1 = {'ru': ('102pd', '104pd'), 'cd': ('106pd', '108pd', '110pd')}
correct1 = base_data.copy()
correct1['101ru', '112cd'] = 0
correct1['99ru', '111cd'] = -1
interferences2 = {'ru99': ('104pd',), 'cd111': ('106pd', '108pd')}
data2 = base_data.copy()
data2['ru101', 'cd112'] = -1 # so that we dont accidentally make this the largest isotope
for key in data2.keys.filter(key_neq='ru99 cd111 102pd 110pd'.split()):
data2[key] += fraction_ref.get(f'ru{key.mass_number}/ru99', 0) * data2['99ru']
data2[key] += fraction_ref.get(f'cd{key.mass_number}/cd111', 0) * data2['111cd']
correct2 = base_data.copy()
correct2['99ru', '111cd'] = 0
correct2['101ru', '112cd'] = -1
self.run(data1, data2, correct1, correct2, interferences1, interferences2, '105pd')
# Different reference values
fraction_ref = isopy.refval.isotope.initial_solar_system_fraction_L09
base_data = isopy.random(100, (1, 0.01),
keys='99ru 101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd 112cd'.split())
# 112cd > 111cd, 101ru > 99ru
base_data = base_data * fraction_ref
data1 = base_data.copy()
data1['99ru', '111cd'] = -1 # so that we dont accidentally make this the largest isotope
for key in data1.keys.filter(key_neq='<KEY>'.split()):
data1[key] += fraction_ref.get(f'ru{key.mass_number}/ru101', 0) * data1['101ru']
data1[key] += fraction_ref.get(f'cd{key.mass_number}/cd112', 0) * data1['112cd']
interferences1 = {'ru': ('102pd', '104pd'), 'cd': ('106pd', '108pd', '110pd')}
correct1 = base_data.copy()
correct1['101ru', '112cd'] = 0
correct1['99ru', '111cd'] = -1
interferences2 = {'ru99': ('104pd',), 'cd111': ('106pd', '108pd')}
data2 = base_data.copy()
data2['ru101', 'cd112'] = -1 # so that we dont accidentally make this the largest isotope
for key in data2.keys.filter(key_neq='<KEY>'.split()):
data2[key] += fraction_ref.get(f'ru{key.mass_number}/ru99', 0) * data2['99ru']
data2[key] += fraction_ref.get(f'cd{key.mass_number}/cd111', 0) * data2['111cd']
correct2 = base_data.copy()
correct2['99ru', '111cd'] = 0
correct2['101ru', '112cd'] = -1
self.run(data1, data2, correct1, correct2, interferences1, interferences2, '105pd',
fraction_ref=fraction_ref)
def test_three(self):
#Mass fractionation
#Single interference isotope
mass_ref = isopy.refval.isotope.mass_W17
fraction_ref = isopy.refval.isotope.best_measurement_fraction_M16
base_data = isopy.random(100, (1, 0.01),
keys='<KEY>'.split())
mf_factor = isopy.random(100, (0,2))
base_data = base_data * fraction_ref
data = base_data.copy()
for key in data.keys.filter(element_symbol='pd'):
if (ru:=fraction_ref.get(f'ru{key.mass_number}/ru101', 0)) > 0:
ru *= data['101ru'] * (mass_ref.get(f'ru{key.mass_number}/ru101', 0) ** mf_factor)
data[key] += ru
if (cd:=fraction_ref.get(f'cd{key.mass_number}/cd111', 0)) > 0:
cd *= data['111cd'] * (mass_ref.get(f'cd{key.mass_number}/cd111', 0) ** mf_factor)
data[key] += cd
interferences1 = {'ru': ('102pd', '104pd'), 'cd': ('106pd', '108pd', '110pd')}
correct1 = base_data.copy()
correct1['101ru', '111cd'] = 0
interferences2 = {'ru': ('104pd',), 'cd': ('106pd', '108pd')}
correct2 = base_data.copy()
correct2['101ru', '111cd'] = 0
correct2['102pd'] = data['102pd']
correct2['110pd'] = data['110pd']
self.run(data, data, correct1, correct2, interferences1, interferences2, '105pd',
mf_factor=mf_factor)
#M Multiple interference isotopes
# Different reference values
mass_ref = isopy.refval.isotope.mass_number
fraction_ref = isopy.refval.isotope.initial_solar_system_fraction_L09
base_data = isopy.random(100, (1, 0.01),
keys='99ru 101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd 112cd'.split())
# 112cd > 111cd, 101ru > 99ru
base_data = base_data * fraction_ref
data1 = base_data.copy()
data1['99ru', '111cd'] = -1 # so that we dont accidentally make this the largest isotope
for key in data1.keys.filter(key_neq='<KEY>'.split()):
if (ru:=fraction_ref.get(f'ru{key.mass_number}/ru101', 0)) > 0:
ru *= data1['101ru'] * (mass_ref.get(f'ru{key.mass_number}/ru101', 0) ** mf_factor)
data1[key] += ru
if (cd:=fraction_ref.get(f'cd{key.mass_number}/cd112', 0)) > 0:
cd *= data1['cd112'] * (mass_ref.get(f'cd{key.mass_number}/cd112', 0) ** mf_factor)
data1[key] += cd
interferences1 = {'ru': ('102pd', '104pd'), 'cd': ('106pd', '108pd', '110pd')}
correct1 = base_data.copy()
correct1['101ru', '112cd'] = 0
correct1['99ru', '111cd'] = -1
interferences2 = {'ru99': ('104pd',), 'cd111': ('106pd', '108pd')}
data2 = base_data.copy()
data2['ru101', 'cd112'] = -1 # so that we dont accidentally make this the largest isotope
for key in data2.keys.filter(key_neq='ru99 cd111 102pd 110pd'.split()):
if (ru:=fraction_ref.get(f'ru{key.mass_number}/ru99', 0)) > 0:
ru *= data2['ru99'] * (mass_ref.get(f'ru{key.mass_number}/ru99', 0) ** mf_factor)
data2[key] += ru
if (cd:=fraction_ref.get(f'cd{key.mass_number}/cd111', 0)) > 0:
cd *= data2['111cd'] * (mass_ref.get(f'cd{key.mass_number}/cd111', 0) ** mf_factor)
data2[key] += cd
correct2 = base_data.copy()
correct2['99ru', '111cd'] = 0
correct2['101ru', '112cd'] = -1
self.run(data1, data2, correct1, correct2, interferences1, interferences2, '105pd',
fraction_ref=fraction_ref, mass_ref=mass_ref, mf_factor=mf_factor)
def run(self, data1, data2, correct1, correct2, interferences1, interferences2, denom=None,
mf_factor=None, fraction_ref=None, mass_ref=None):
interferences = isopy.tb.find_isobaric_interferences('pd', data1)
assert len(interferences) == len(interferences)
for key in interferences1:
assert key in interferences
assert interferences[key] == interferences1[key]
corrected1 = isopy.tb.remove_isobaric_interferences(data1, interferences,
mf_factor=mf_factor,
isotope_fractions=fraction_ref,
isotope_masses=mass_ref)
assert corrected1.keys == correct1.keys
assert corrected1.size == correct1.size
for key in corrected1.keys:
np.testing.assert_allclose(corrected1[key], correct1[key])
corrected2 = isopy.tb.remove_isobaric_interferences(data2, interferences2,
mf_factor=mf_factor,
isotope_fractions=fraction_ref,
isotope_masses=mass_ref)
assert corrected2.keys == correct2.keys
assert corrected2.size == correct2.size
for key in corrected2.keys:
np.testing.assert_allclose(corrected2[key], correct2[key])
#Ratio test data
if denom is not None:
data1 = data1.ratio(denom)
data2 = data2.ratio(denom)
correct1 = correct1.ratio(denom)
correct2 = correct2.ratio(denom)
interferences = isopy.tb.find_isobaric_interferences('pd', data1)
assert len(interferences) == len(interferences)
for key in interferences1:
assert key in interferences
assert interferences[key] == interferences1[key]
corrected1 = isopy.tb.remove_isobaric_interferences(data1, interferences,
mf_factor=mf_factor,
isotope_fractions=fraction_ref,
isotope_masses=mass_ref)
assert corrected1.keys == correct1.keys
assert corrected1.size == correct1.size
for key in corrected1.keys:
np.testing.assert_allclose(corrected1[key], correct1[key])
corrected2 = isopy.tb.remove_isobaric_interferences(data2, interferences2,
mf_factor=mf_factor,
isotope_fractions=fraction_ref,
isotope_masses=mass_ref)
assert corrected2.keys == correct2.keys
assert corrected2.size == correct2.size
for key in corrected2.keys:
np.testing.assert_allclose(corrected2[key], correct2[key])
def test_find(self):
interferences = isopy.tb.find_isobaric_interferences('pd', ('ru', 'cd'))
assert len(interferences) == 2
assert 'ru' in interferences
assert interferences['ru'] == ('102Pd', '104Pd')
assert 'cd' in interferences
assert interferences['cd'] == ('106Pd', '108Pd', '110Pd')
interferences = isopy.tb.find_isobaric_interferences('pd', ('ru', 'rh', 'ag', 'cd'))
assert len(interferences) == 2
assert 'ru' in interferences
assert interferences['ru'] == ('102Pd', '104Pd')
assert 'cd' in interferences
assert interferences['cd'] == ('106Pd', '108Pd', '110Pd')
interferences = isopy.tb.find_isobaric_interferences('ce')
assert len(interferences) == 4
assert 'xe' in interferences
assert interferences['xe'] == ('136Ce',)
assert 'ba' in interferences
assert interferences['ba'] == ('136Ce', '138Ce')
assert 'la' in interferences
assert interferences['la'] == ('138Ce', )
assert 'nd' in interferences
assert interferences['nd'] == ('142Ce',)
interferences = isopy.tb.find_isobaric_interferences('138ce')
assert len(interferences) == 2
assert 'ba' in interferences
assert interferences['ba'] == ('138Ce',)
assert 'la' in interferences
assert interferences['la'] == ('138Ce',)
interferences = isopy.tb.find_isobaric_interferences('zn', ('ni', 'ge', 'ba++'))
assert len(interferences) == 3
assert 'ni' in interferences
assert interferences['ni'] == ('64Zn',)
assert 'ge' in interferences
assert interferences['ge'] == ('70Zn',)
assert 'ba++' in interferences
assert interferences['ba++'] == ('66Zn', '67Zn', '68Zn')
class Test_rDelta():
def test_rDelta1(self):
# Data is a single value
data = isopy.refval.isotope.fraction.to_array(element_symbol='pd')
# Dict
reference = isopy.refval.isotope.fraction
correct1 = isopy.zeros(None, data.keys)
correct2 = isopy.ones(None, data.keys)
self.run(data, data, reference, correct1, correct2)
# Single array
reference = isopy.random(100, keys=data.keys)
correct1 = data / np.mean(reference) - 1
correct2 = data / np.mean(reference)
self.run(data, data, reference, correct1, correct2)
self.run(data, data, np.mean(reference), correct1, correct2)
correct1 = correct1 * 10_000
correct2 = correct2 * 10_000
self.run(data, data, reference, correct1, correct2, 10_000)
self.run(data, data, np.mean(reference), correct1, correct2, 10_000)
# Multiple values
reference1 = isopy.random(100, keys=data.keys)
reference2 = isopy.random(100, keys=data.keys)
meanmean = np.mean(reference1)/2 + np.mean(reference2)/2
correct1 = data / meanmean - 1
correct2 = data / meanmean
self.run(data, data, (reference1, reference2), correct1, correct2)
self.run(data, data, (np.mean(reference1), reference2), correct1, correct2)
self.run(data, data, (np.mean(reference1), np.mean(reference2)), correct1, correct2)
correct1 = correct1 * 10_000
correct2 = correct2 * 10_000
self.run(data, data, (reference1, reference2), correct1, correct2, 10_000)
self.run(data, data, (np.mean(reference1), reference2), correct1, correct2, 10_000)
self.run(data, data, (np.mean(reference1), np.mean(reference2)), correct1, correct2, 10_000)
# Keys that do not match
data2 = data.copy()
data2['105pd', '106pd'] = np.nan
reference1 = isopy.random(100, keys='101ru 102pd 104pd 105pd 108pd 110pd 111cd'.split())
reference2 = isopy.random(100, keys='101ru 102pd 104pd 106pd 108pd 110pd 111cd'.split())
meanmean = np.mean(reference1) / 2 + np.mean(reference2) / 2
correct1 = data / meanmean - 1
correct2 = data / meanmean
self.run(data, data2, (reference1, reference2), correct1, correct2)
self.run(data, data2, (np.mean(reference1), reference2), correct1, correct2)
self.run(data, data2, (np.mean(reference1), np.mean(reference2)), correct1, correct2)
correct1 = correct1 * 10_000
correct2 = correct2 * 10_000
self.run(data, data2, (reference1, reference2), correct1, correct2, 10_000)
self.run(data, data2, (np.mean(reference1), reference2), correct1, correct2, 10_000)
self.run(data, data2, (np.mean(reference1), np.mean(reference2)), correct1, correct2, 10_000)
def test_rDelta2(self):
data = isopy.random(100, keys=isopy.refval.element.isotopes['pd'])
data = data * isopy.refval.isotope.fraction
# Dict
reference = isopy.refval.isotope.fraction
correct1 = data / reference - 1
correct2 = data / reference
self.run(data, data, reference, correct1, correct2)
# Single array
reference = isopy.random(100, keys=data.keys)
correct1 = data / np.mean(reference) - 1
correct2 = data / np.mean(reference)
self.run(data, data, reference, correct1, correct2)
self.run(data, data, np.mean(reference), correct1, correct2)
correct1 = correct1 * 10_000
correct2 = correct2 * 10_000
self.run(data, data, reference, correct1, correct2, 10_000)
self.run(data, data, np.mean(reference), correct1, correct2, 10_000)
# Multiple values
reference1 = isopy.random(100, keys=data.keys)
reference2 = isopy.random(100, keys=data.keys)
meanmean = np.mean(reference1)/2 + np.mean(reference2)/2
correct1 = data / meanmean - 1
correct2 = data / meanmean
self.run(data, data, (reference1, reference2), correct1, correct2)
self.run(data, data, (np.mean(reference1), reference2), correct1, correct2)
self.run(data, data, (np.mean(reference1), np.mean(reference2)), correct1, correct2)
correct1 = correct1 * 10_000
correct2 = correct2 * 10_000
self.run(data, data, (reference1, reference2), correct1, correct2, 10_000)
self.run(data, data, (np.mean(reference1), reference2), correct1, correct2, 10_000)
self.run(data, data, (np.mean(reference1), np.mean(reference2)), correct1, correct2, 10_000)
# Keys that do not match
data2 = data.copy()
data2['105pd', '106pd'] = np.nan
reference1 = isopy.random(100, keys='101ru 102pd 104pd 105pd 108pd 110pd 111cd'.split())
reference2 = isopy.random(100, keys='101ru 102pd 104pd 106pd 108pd 110pd 111cd'.split())
meanmean = np.mean(reference1) / 2 + np.mean(reference2) / 2
correct1 = data / meanmean - 1
correct2 = data / meanmean
self.run(data, data2, (reference1, reference2), correct1, correct2)
self.run(data, data2, (np.mean(reference1), reference2), correct1, correct2)
self.run(data, data2, (np.mean(reference1), np.mean(reference2)), correct1, correct2)
correct1 = correct1 * 10_000
correct2 = correct2 * 10_000
self.run(data, data2, (reference1, reference2), correct1, correct2, 10_000)
self.run(data, data2, (np.mean(reference1), reference2), correct1, correct2, 10_000)
self.run(data, data2, (np.mean(reference1), np.mean(reference2)), correct1, correct2, 10_000)
def test_presets(self):
data = isopy.random(100, keys=isopy.refval.element.isotopes['pd'])
data = data * isopy.refval.isotope.fraction
reference = isopy.refval.isotope.fraction
correct = (data / reference - 1) * 1000
normalised = isopy.tb.rDelta.ppt(data, reference)
denormalised = isopy.tb.inverse_rDelta.ppt(normalised, reference)
self.compare(correct, normalised)
self.compare(data, denormalised)
correct = (data / reference - 1) * 1000
normalised = isopy.tb.rDelta.permil(data, reference)
denormalised = isopy.tb.inverse_rDelta.permil(normalised, reference)
self.compare(correct, normalised)
self.compare(data, denormalised)
correct = (data / reference - 1) * 10_000
normalised = isopy.tb.rDelta.epsilon(data, reference)
denormalised = isopy.tb.inverse_rDelta.epsilon(normalised, reference)
self.compare(correct, normalised)
self.compare(data, denormalised)
correct = (data / reference - 1) * 1_000_000
normalised = isopy.tb.rDelta.mu(data, reference)
denormalised = isopy.tb.inverse_rDelta.mu(normalised, reference)
self.compare(correct, normalised)
self.compare(data, denormalised)
correct = (data / reference - 1) * 1_000_000
normalised = isopy.tb.rDelta.ppm(data, reference)
denormalised = isopy.tb.inverse_rDelta.ppm(normalised, reference)
self.compare(correct, normalised)
self.compare(data, denormalised)
def run(self, data1, data2, reference_value, correct1, correct2, factor=1):
normalised = isopy.tb.rDelta(data1, reference_value, factor=factor)
assert normalised.keys == data1.keys
assert normalised.size == data1.size
assert normalised.ndim == data1.ndim
for key in normalised.keys:
np.testing.assert_allclose(normalised[key], correct1[key])
denormalised = isopy.tb.inverse_rDelta(normalised, reference_value, factor=factor)
assert denormalised.keys == data1.keys
assert denormalised.size == data1.size
assert denormalised.ndim == data1.ndim
for key in denormalised.keys:
np.testing.assert_allclose(denormalised[key], data2[key])
normalised = isopy.tb.rDelta(data1, reference_value, factor=factor, deviations=0)
assert normalised.keys == data1.keys
assert normalised.size == data1.size
assert normalised.ndim == data1.ndim
for key in normalised.keys:
np.testing.assert_allclose(normalised[key], correct2[key])
denormalised = isopy.tb.inverse_rDelta(normalised, reference_value, factor=factor, deviations=0)
assert denormalised.keys == data1.keys
assert denormalised.size == data1.size
assert denormalised.ndim == data1.ndim
for key in denormalised.keys:
np.testing.assert_allclose(denormalised[key], data2[key])
def compare(self, correct, calculated):
assert calculated.keys == correct.keys
assert calculated.size == correct.size
assert calculated.ndim == correct.ndim
for key in calculated.keys:
np.testing.assert_allclose(calculated[key], correct[key])
class Test_OutliersLimits:
def test_limits(self):
data = isopy.random(100, (1,1), keys=isopy.refval.element.isotopes['pd'])
median = np.median(data)
mean = np.mean(data)
mad3 = isopy.mad3(data)
sd2 = isopy.sd2(data)
upper = isopy.tb.upper_limit(data)
assert upper == median + mad3
upper = isopy.tb.upper_limit(data, np.mean, isopy.sd2)
assert upper == mean + sd2
upper = isopy.tb.upper_limit.sd2(data)
assert upper == mean + sd2
upper = isopy.tb.upper_limit(data, 1, isopy.sd2)
assert upper == 1 + sd2
upper = isopy.tb.upper_limit(data, np.mean, 1)
assert upper == mean + 1
upper = isopy.tb.upper_limit(data, 1, 1)
assert upper == 2
lower = isopy.tb.lower_limit(data)
assert lower == median - mad3
lower = isopy.tb.lower_limit.sd2(data)
assert lower == mean - sd2
lower = isopy.tb.lower_limit(data, np.mean, isopy.sd2)
assert lower == mean - sd2
lower = isopy.tb.lower_limit(data, 1, isopy.sd2)
assert lower == 1 - sd2
lower = isopy.tb.lower_limit(data, np.mean, 1)
assert lower == mean - 1
lower = isopy.tb.lower_limit(data, 1, 1)
assert lower == 0
def test_find_outliers1(self):
#axis = 0
data = isopy.random(100, (1, 1), keys=isopy.refval.element.isotopes['pd'])
median = np.median(data)
mean = np.mean(data)
mad3 = isopy.mad3(data)
sd = isopy.sd(data)
median_outliers = (data > (median + mad3)) + (data < (median - mad3))
mean_outliers = (data > (mean + sd)) + (data < (mean - sd))
mean_outliers1 = (data > (1 + sd)) + (data < (1 - sd))
mean_outliers2 = (data > (mean + 1)) + (data < (mean - 1))
mean_outliers3 = (data > (1 + 1)) + (data < (1 - 1))
outliers = isopy.tb.find_outliers(data)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], median_outliers[key])
outliers = isopy.tb.find_outliers(data, np.mean, isopy.sd)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], mean_outliers[key])
outliers = isopy.tb.find_outliers.sd(data)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], mean_outliers[key])
outliers = isopy.tb.find_outliers(data, 1, isopy.sd)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], mean_outliers1[key])
outliers = isopy.tb.find_outliers(data, np.mean, 1)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], mean_outliers2[key])
outliers = isopy.tb.find_outliers(data, 1, 1)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], mean_outliers3[key])
# invert
median_outliers = np.invert(median_outliers)
mean_outliers = np.invert(mean_outliers)
mean_outliers1 = np.invert(mean_outliers1)
mean_outliers2 = np.invert(mean_outliers2)
mean_outliers3 = np.invert(mean_outliers3)
outliers = isopy.tb.find_outliers(data, invert=True)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], median_outliers[key])
outliers = isopy.tb.find_outliers(data, np.mean, isopy.sd, invert=True)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], mean_outliers[key])
outliers = isopy.tb.find_outliers.sd(data, invert=True)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], mean_outliers[key])
outliers = isopy.tb.find_outliers(data, 1, isopy.sd, invert=True)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], mean_outliers1[key])
outliers = isopy.tb.find_outliers(data, np.mean, 1, invert=True)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], mean_outliers2[key])
outliers = isopy.tb.find_outliers(data, 1, 1, invert=True)
assert outliers.keys == data.keys
assert outliers.size == data.size
for key in outliers.keys:
np.testing.assert_allclose(outliers[key], mean_outliers3[key])
def test_find_outliers2(self):
# axis = 0
data = isopy.random(100, (1, 1), keys=isopy.refval.element.isotopes['pd'])
median = np.median(data)
mean = np.mean(data)
mad3 = isopy.mad3(data)
sd = isopy.sd2(data)
median_outliers = np.any((data > (median + mad3)) + (data < (median - mad3)), axis=1)
mean_outliers = np.any((data > (mean + sd)) + (data < (mean - sd)), axis=1)
mean_outliers1 = np.any((data > (1 + sd)) + (data < (1 - sd)), axis=1)
mean_outliers2 = np.any((data > (mean + 1)) + (data < (mean - 1)), axis=1)
mean_outliers3 = np.any((data > (1 + 1)) + (data < (1 - 1)), axis=1)
outliers = isopy.tb.find_outliers(data, axis=1)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, median_outliers)
outliers = isopy.tb.find_outliers(data, np.mean, isopy.sd2, axis=1)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, mean_outliers)
outliers = isopy.tb.find_outliers.sd2(data, axis=1)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, mean_outliers)
outliers = isopy.tb.find_outliers(data, 1, isopy.sd2, axis=1)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, mean_outliers1)
outliers = isopy.tb.find_outliers(data, np.mean, 1, axis=1)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, mean_outliers2)
outliers = isopy.tb.find_outliers(data, 1, 1, axis=1)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, mean_outliers3)
# invert
median_outliers = np.invert(median_outliers)
mean_outliers = np.invert(mean_outliers)
mean_outliers1 = np.invert(mean_outliers1)
mean_outliers2 = np.invert(mean_outliers2)
mean_outliers3 = np.invert(mean_outliers3)
outliers = isopy.tb.find_outliers(data, axis=1, invert=True)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, median_outliers)
outliers = isopy.tb.find_outliers(data, np.mean, isopy.sd2, axis=1, invert=True)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, mean_outliers)
outliers = isopy.tb.find_outliers.sd2(data, axis=1, invert=True)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, mean_outliers)
outliers = isopy.tb.find_outliers(data, 1, isopy.sd2, axis=1, invert=True)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, mean_outliers1)
outliers = isopy.tb.find_outliers(data, np.mean, 1, axis=1, invert=True)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, mean_outliers2)
outliers = isopy.tb.find_outliers(data, 1, 1, axis=1, invert=True)
assert len(outliers) == data.size
np.testing.assert_allclose(outliers, mean_outliers3)
class Test_Make:
def test_make_array1(self):
# No mass fractionation
mass_ref = isopy.refval.isotope.mass_W17
fraction_ref = isopy.refval.isotope.best_measurement_fraction_M16
correct = isopy.ones(None, keys='102pd 104pd 105pd 106pd 108pd 110pd'.split())
correct = correct * fraction_ref
correct10 = correct.normalise(10, '106pd')
self.compare(correct, isopy.tb.make_ms_array('pd'))
self.compare(correct10, isopy.tb.make_ms_beams('pd', integrations=None))
self.compare(correct10, isopy.tb.make_ms_sample('pd', integrations=None))
correct = isopy.ones(None, keys='101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd'.split())
correct = correct * fraction_ref
for key in correct.keys.filter(key_neq = '<KEY>'.split()):
correct[key] += fraction_ref.get(f'ru{key.mass_number}/ru101', 0) * correct['101ru']
correct[key] += fraction_ref.get(f'cd{key.mass_number}/cd111', 0) * correct['111cd']
correct10 = correct.normalise(10, isopy.keymax)
self.compare(correct, isopy.tb.make_ms_array('pd', '101ru', '111cd'))
self.compare(correct10, isopy.tb.make_ms_beams('pd', '101ru', '111cd', integrations=None))
correct = isopy.ones(None, keys='101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd'.split())
correct['101ru'] *= 0.1
correct['111cd'] *= 0.01
correct = correct * fraction_ref
for key in correct.keys.filter(key_neq='<KEY>'.split()):
correct[key] += fraction_ref.get(f'ru{key.mass_number}/ru101', 0) * correct['101ru']
correct[key] += fraction_ref.get(f'cd{key.mass_number}/cd111', 0) * correct['111cd']
correct10 = correct.normalise(10, '106pd')
self.compare(correct, isopy.tb.make_ms_array('pd', **{'101ru': 0.1, '111cd':0.01}))
self.compare(correct, isopy.tb.make_ms_array('pd', ru101 = 0.1, cd111=0.01))
self.compare(correct10, isopy.tb.make_ms_beams('pd', ru101 = 0.1, cd111=0.01, integrations=None))
self.compare(correct10, isopy.tb.make_ms_sample('pd', ru101 = 0.1, cd111=0.01, integrations=None))
correct = isopy.ones(None, keys='99ru 101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd 112cd'.split())
correct['101ru'] *= 0.1
correct['99ru'] *= 0.1
correct['111cd'] *= 0.01
correct['112cd'] *= 0
correct2 = correct * fraction_ref
correct = correct2.copy()
for key in correct.keys.filter(key_neq='ru99 ru101 cd111'.split()):
correct[key] += fraction_ref.get(f'ru{key.mass_number}/ru101', 0) * correct['101ru']
correct[key] += fraction_ref.get(f'ru{key.mass_number}/ru99', 0) * correct['99ru']
correct[key] += fraction_ref.get(f'cd{key.mass_number}/cd111', 0) * correct['111cd']
correct['ru99'] += fraction_ref.get(f'ru99/ru101', 0) * correct2['101ru']
correct['101ru'] += fraction_ref.get(f'ru101/ru99', 0) * correct2['ru99']
correct10 = correct.normalise(10, '106pd')
self.compare(correct, isopy.tb.make_ms_array('pd', **{'101ru': 0.1, '111cd': 0.01, '99ru': 0.1, '112cd': 0}))
self.compare(correct, isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01, ru99=0.1, cd112=0))
self.compare(correct10,
isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, ru99=0.1, cd112=0, integrations=None))
self.compare(correct10,
isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, ru99=0.1, cd112=0, integrations=None))
# Different reference values
mass_ref = isopy.refval.isotope.mass_number
fraction_ref = isopy.refval.isotope.initial_solar_system_fraction_L09
correct = isopy.ones(None, keys='102pd 104pd 105pd 106pd 108pd 110pd'.split())
correct = correct * fraction_ref
correct10 = correct.normalise(10, '106pd')
self.compare(correct, isopy.tb.make_ms_array('pd', isotope_fractions=fraction_ref, isotope_masses=mass_ref))
self.compare(correct10, isopy.tb.make_ms_beams('pd', integrations=None, isotope_fractions=fraction_ref, isotope_masses=mass_ref))
self.compare(correct10, isopy.tb.make_ms_sample('pd', integrations=None, isotope_fractions=fraction_ref, isotope_masses=mass_ref))
correct = isopy.ones(None, keys='101ru 102pd 104pd 105pd 106pd 108pd 110pd 111cd'.split())
correct = correct * fraction_ref
for key in correct.keys.filter(key_neq='<KEY>'.split()):
correct[key] += fraction_ref.get(f'ru{key.mass_number}/ru101', 0) * correct['101ru']
correct[key] += fraction_ref.get(f'cd{key.mass_number}/cd111', 0) * correct['111cd']
correct10 = correct.normalise(10, isopy.keymax)
self.compare(correct, isopy.tb.make_ms_array('pd', '101ru', '111cd', isotope_fractions=fraction_ref, isotope_masses=mass_ref))
self.compare(correct10, isopy.tb.make_ms_beams('pd', '101ru', '111cd', integrations=None, isotope_fractions=fraction_ref, isotope_masses=mass_ref))
correct = isopy.ones(None, keys='101ru 102pd 104pd 105pd 106pd 108pd <KEY>'.split())
correct['101ru'] *= 0.1
correct['111cd'] *= 0.01
correct = correct * fraction_ref
for key in correct.keys.filter(key_neq='<KEY>'.split()):
correct[key] += fraction_ref.get(f'ru{key.mass_number}/ru101', 0) * correct['101ru']
correct[key] += fraction_ref.get(f'cd{key.mass_number}/cd111', 0) * correct['111cd']
correct10 = correct.normalise(10, '106pd')
self.compare(correct, isopy.tb.make_ms_array('pd', **{'101ru': 0.1, '111cd': 0.01}, isotope_fractions=fraction_ref, isotope_masses=mass_ref))
self.compare(correct, isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01, isotope_fractions=fraction_ref, isotope_masses=mass_ref))
self.compare(correct10, isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, integrations=None, isotope_fractions=fraction_ref, isotope_masses=mass_ref))
self.compare(correct10, isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, integrations=None, isotope_fractions=fraction_ref, isotope_masses=mass_ref))
def test_make_array2(self):
# At this stage we know that the functions correctly create the arrays.
# So we only need to make sure that what we create can be reversed using the
# mass independent correction.
# Default reference values
mass_ref = isopy.refval.isotope.mass_W17
fraction_ref = isopy.refval.isotope.best_measurement_fraction_M16
correct = isopy.ones(None, keys='102pd 104pd 105pd 106pd 110pd'.split())
correct = correct * fraction_ref
correct = correct.ratio('105pd')
result = isopy.tb.make_ms_array('pd', 'ru', 'cd')
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd')
self.compare(correct, corrected)
result = isopy.tb.make_ms_beams('pd', 'ru', 'cd', integrations=None)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd')
self.compare(correct, corrected)
result = isopy.tb.make_ms_sample('pd', ru=1, cd=1, integrations=None)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd')
self.compare(correct, corrected)
result = isopy.tb.make_ms_array('pd', ru101=0.1, cd111 = 0.01)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd')
self.compare(correct, corrected)
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111 = 0.01, integrations=None)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd')
self.compare(correct, corrected)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111 = 0.01, integrations=None)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd')
self.compare(correct, corrected)
result = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01, ru99=0.1)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd')
self.compare(correct, corrected)
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, ru99=0.1, cd112=0, integrations=None)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd')
self.compare(correct, corrected)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, ru99=0.1, cd112=0, integrations=None)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd')
self.compare(correct, corrected)
# Different default values
mass_ref = isopy.refval.isotope.mass_number
fraction_ref = isopy.refval.isotope.initial_solar_system_fraction_L09
correct = isopy.ones(None, keys='102pd 104pd 105pd 106pd 110pd'.split())
correct = correct * fraction_ref
correct = correct.ratio('105pd')
result = isopy.tb.make_ms_array('pd', 'ru', 'cd', isotope_masses=mass_ref, isotope_fractions=fraction_ref)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd', isotope_masses=mass_ref, isotope_fractions=fraction_ref)
self.compare(correct, corrected)
result = isopy.tb.make_ms_beams('pd', 'ru', 'cd', integrations=None, isotope_masses=mass_ref, isotope_fractions=fraction_ref)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd', isotope_masses=mass_ref, isotope_fractions=fraction_ref)
self.compare(correct, corrected)
result = isopy.tb.make_ms_sample('pd', ru=1, cd=1, integrations=None, isotope_masses=mass_ref, isotope_fractions=fraction_ref)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd', isotope_masses=mass_ref, isotope_fractions=fraction_ref)
self.compare(correct, corrected)
result = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01, isotope_masses=mass_ref, isotope_fractions=fraction_ref)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd', isotope_masses=mass_ref, isotope_fractions=fraction_ref)
self.compare(correct, corrected)
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, integrations=None, isotope_masses=mass_ref, isotope_fractions=fraction_ref)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd', isotope_masses=mass_ref, isotope_fractions=fraction_ref)
self.compare(correct, corrected)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, integrations=None, isotope_masses=mass_ref, isotope_fractions=fraction_ref)
corrected = isopy.tb.internal_normalisation(result, '108pd/105pd', isotope_masses=mass_ref, isotope_fractions=fraction_ref)
self.compare(correct, corrected)
def test_make_array3(self):
correct = isopy.tb.make_ms_array('pd', 'ru', 'cd').normalise(10, isopy.keymax)
result = isopy.tb.make_ms_beams('pd', 'ru', 'cd', random_seed=46)
self.compare_sd(correct, 100, result)
result = isopy.tb.make_ms_sample('pd', ru=1, cd=1, random_seed=46)
self.compare_sd(correct, 100, result)
correct = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01).normalise(10, isopy.keymax)
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, random_seed=46)
self.compare_sd(correct, 100, result)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, random_seed=46)
self.compare_sd(correct, 100, result)
correct = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01, ru99=0.1, cd112=0).normalise(10, isopy.keymax)
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, ru99=0.1, cd112=0, random_seed=46)
self.compare_sd(correct, 100, result)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, ru99=0.1, cd112=0, random_seed=46)
self.compare_sd(correct, 100, result)
# Integrations
correct = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01).normalise(10, isopy.keymax)
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, random_seed=46, integrations=200)
self.compare_sd(correct, 200, result)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, random_seed=46, integrations=200)
self.compare_sd(correct, 200, result)
# Fixed Key & Value
correct = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01).normalise(10)
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key=None)
self.compare_sd(correct, 200, result)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key=None)
self.compare_sd(correct, 200, result)
correct = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01).normalise(1)
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key=None, fixed_voltage=1)
self.compare_sd(correct, 200, result)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key=None, fixed_voltage=1)
self.compare_sd(correct, 200, result)
correct = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01).normalise(10, '102pd')
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key='102pd')
self.compare_sd(correct, 200, result)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key='102pd')
self.compare_sd(correct, 200, result)
correct = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01).normalise(5, '102pd')
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key='102pd', fixed_voltage=5)
self.compare_sd(correct, 200, result)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key='102pd', fixed_voltage=5)
self.compare_sd(correct, 200, result)
correct = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01).normalise(10, ('102pd', '104pd'))
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key=('102pd', '104pd'))
self.compare_sd(correct, 200, result)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key=('102pd', '104pd'))
self.compare_sd(correct, 200, result)
correct = isopy.tb.make_ms_array('pd', ru101=0.1, cd111=0.01).normalise(100,
('102pd', '104pd'))
result = isopy.tb.make_ms_beams('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key=('102pd', '104pd'), fixed_voltage=100)
self.compare_sd(correct, 200, result)
result = isopy.tb.make_ms_sample('pd', ru101=0.1, cd111=0.01, random_seed=46,
integrations=200, fixed_key=('102pd', '104pd'), fixed_voltage=100)
self.compare_sd(correct, 200, result)
def test_spike(self):
spike = isopy.array(pd104 = 1, pd106=0, pd108=1, pd110=0)
spike = spike.normalise(1)
sample = isopy.refval.isotope.fraction.to_array(element_symbol='pd')
sample = sample.normalise(1, spike.keys)
correct = isopy.add(sample * 0.5, spike * 0.5, 0)
correct = correct.normalise(10, isopy.keymax)
result = isopy.tb.make_ms_sample('pd', spike=spike, integrations=None)
self.compare(correct, result)
result = isopy.tb.make_ms_sample('pd', spike=spike)
self.compare_sd(correct, 100, result)
correct = isopy.add(sample * 0.1, spike * 0.9, 0)
correct = correct.normalise(10, isopy.keymax)
result = isopy.tb.make_ms_sample('pd', spike=spike, integrations=None, spike_fraction=0.9)
self.compare(correct, result)
result = isopy.tb.make_ms_sample('pd', spike=spike, spike_fraction=0.9)
self.compare_sd(correct, 100, result)
def test_blank(self):
sample = isopy.refval.isotope.fraction.to_array(element_symbol='pd')
blank = isopy.ones(None, sample.keys)
blank = blank + isopy.refval.isotope.fraction
blank = blank.normalise(1)
blank2 = blank.normalise(0.01, '106pd')
correct = sample.normalise(10-0.01, '106pd')
correct = correct + blank2
result = isopy.tb.make_ms_sample('pd', blank=blank, integrations=None)
self.compare(correct, result)
result = isopy.tb.make_ms_sample('pd', blank=blank)
self.compare_sd(correct, 100, result)
blank2 = blank.normalise(0.1, '106pd')
correct = sample.normalise(10 - 0.1, '106pd')
correct = correct + blank2
result = isopy.tb.make_ms_sample('pd', blank=blank, blank_fixed_voltage=0.1, integrations=None)
self.compare(correct, result)
result = isopy.tb.make_ms_sample('pd', blank=blank, blank_fixed_voltage=0.1)
self.compare_sd(correct, 100, result)
blank2 = blank.normalise(0.1, '102pd')
correct = sample.normalise(10 - blank2['106pd'], '106pd')
correct = correct + blank2
result = isopy.tb.make_ms_sample('pd', blank=blank, blank_fixed_voltage=0.1,
blank_fixed_key='102pd', integrations=None)
self.compare(correct, result)
result = isopy.tb.make_ms_sample('pd', blank=blank, blank_fixed_voltage=0.1,
blank_fixed_key='102pd')
self.compare_sd(correct, 100, result)
blank2 = blank.normalise(0.1, ('102pd', '104pd'))
correct = sample.normalise(10 - blank2['106pd'], '106pd')
correct = correct + blank2
result = isopy.tb.make_ms_sample('pd', blank=blank, blank_fixed_voltage=0.1,
blank_fixed_key=('102pd', '104pd'), integrations=None)
self.compare(correct, result)
result = isopy.tb.make_ms_sample('pd', blank=blank, blank_fixed_voltage=0.1,
blank_fixed_key=('102pd', '104pd'))
self.compare_sd(correct, 100, result)
blank2 = blank.normalise(0.01, '106pd')
correct = sample.normalise(10 - blank2['102pd'], '102pd')
correct = correct + blank2
result = isopy.tb.make_ms_sample('pd', blank=blank, integrations=None, fixed_key='102pd')
self.compare(correct, result)
result = isopy.tb.make_ms_sample('pd', blank=blank, fixed_key='102pd')
self.compare_sd(correct, 100, result)
blank2 = blank.normalise(0.01, '106pd')
correct = sample.normalise(10 - blank2[('102pd', '104pd')].sum(axis=None), ('102pd', '104pd'))
correct = correct + blank2
result = isopy.tb.make_ms_sample('pd', blank=blank, integrations=None, fixed_key=('102pd', '104pd'))
self.compare(correct, result)
result = isopy.tb.make_ms_sample('pd', blank=blank, fixed_key=('102pd', '104pd'))
self.compare_sd(correct, 100, result)
blank2 = blank.normalise(0.01, '106pd')
correct = sample.normalise(10 - blank2.sum(axis=None), None)
correct = correct + blank2
result = isopy.tb.make_ms_sample('pd', blank=blank, integrations=None, fixed_key=None)
self.compare(correct, result)
result = isopy.tb.make_ms_sample('pd', blank=blank, fixed_key=None)
self.compare_sd(correct, 100, result)
def compare(self, correct, result):
assert result.keys == correct.keys
assert result.size == correct.size
assert result.ndim == correct.ndim
for key in result.keys:
np.testing.assert_allclose(result[key], correct[key])
def compare_sd(self, correct, integrations, result):
assert result.keys == correct.keys
assert result.size == integrations
assert result.ndim == 1
for key in result.keys:
np.testing.assert_allclose(np.mean(result[key]), correct[key], rtol=0, atol=isopy.sd(result[key]))
class Test_JohnsonNyquistNoise:
def test_one(self):
self.run(10)
self.run(10, 1E12)
self.run(10, time=4.1)
self.run(10, T=400)
self.run(10, 1E10)
self.run(10, cpv=1E7)
voltages = isopy.refval.isotope.fraction.to_array(element_symbol='pd').normalise(10, isopy.keymax)
resistors = isopy.full(None, 1E11, voltages.keys)
resistors['102pd'] = 1E13
resistors['106pd'] = 1E10
self.run(voltages)
self.run(voltages, 1E12)
self.run(voltages, resistors)
self.run(voltages, time=4.1)
self.run(voltages, T=400)
self.run(voltages, 1E10)
self.run(voltages, cpv=1E7)
def test2(self):
Os187 = [0.000052, 0.000522, 0.001044, 0.002088, 0.003132, 0.004176, 0.005220, 0.007830, 0.010439, 0.026099, 0.052197]
Os188 = [0.000324, 0.003244, 0.006487, 0.012974, 0.019462, 0.025949, 0.032436, 0.048654, 0.064872, 0.162180, 0.324359]
jk_correct = [0.0140985, 0.0014082, 0.0007042, 0.0003521, 0.0002347, 0.0001760, 0.0001408, 0.0000939, 0.0000704, 0.0000282, 0.0000141]
combined_correct = [0.0141373, 0.0014467, 0.0007422, 0.0003892, 0.0002709, 0.0002115, 0.0001756, 0.0001270, 0.0001022, 0.0000547, 0.0000360]
Os_data = isopy.array(os187=Os187, os188=Os188)
jk_result = isopy.tb.johnson_nyquist_noise(Os_data, 1E12, include_counting_statistics=False)
| np.testing.assert_allclose(jk_result, 0.00004510199454/10) | numpy.testing.assert_allclose |
'''
Common methods for beamtools package
Created Fri May 12
@author: cpkmanchee
'''
import numpy as np
from beamtools.file_formats import file_formats
import dill
__all__ = ['saveObj','loadObj','normalize','rmbg','gaussian','sech2','lorentzian',
'gaussian2D','rk4','moments','d4sigma','roi','alias_dict']
class Func:
def __init__(self, value=None, index=None):
self.val = value
self.ind = index
def at(self,x):
return np.interp(x, self.ind, self.val)
def diff(self):
self.gradient = np.gradient(self.val)/np.gradient(self.ind)
def diff_at(self,x):
return np.interp(x,self.ind,self.gradient)
class FitResult():
def __init__(self, ffunc, ftype, popt, pcov=0, indep_var='time', bgform='constant'):
self.ffunc = ffunc
self.ftype = ftype
self.popt = popt
self.pcov = pcov
self.iv = indep_var
self.bgform = bgform
def subs(self,x):
return self.ffunc(x,*self.popt)
def get_args(self):
return inspect.getargspec(self.ffunc)
class DataObj(dict):
def __init__(self,d):
self.__dict__ = d
def fields(self):
return self.__dict__.keys()
def properties(self):
[print(k,v) for k,v in file_formats[self.filetype].items()]
return
def saveObj(obj, filename):
with open(filename, 'wb') as output:
dill.dump(obj, output, -1)
def loadObj(filename):
with open(filename, 'rb') as input:
obj = dill.load(input)
return obj
def normalize(f, offset=0, method='normal'):
'''Normalize array of data. Optional offset.
'''
norm = (f-f.min())/(f.max()-f.min()) + offset
if method.lower() in ['area']:
norm = norm/np.sum(norm)
return norm
def rmbg(data, fit=None, form='constant'):
'''Removes background from data
data = [x,y]
if sending poly fit params: p[0]*x**(N-1) + ... + p[N-1]
return --> y - background
'''
if fit is None:
#estimate background from given form
if form.lower() in alias_dict['constant']:
p = min(y)
elif form.lower() in alias_dict['linear']:
p = np.linalg.solve([[1,x[0]],[1,x[-1]]], [y[0],y[-1]])
p = np.flipud(p)
elif form.lower() in alias_dict['quadratic']:
index = np.argmin(y)
if index == 0:
x3 = 2*x[0]-x[-1]
y3 = y[-1]
elif index == len(y)-1:
x3 = 2*x[-1]-x[0]
y3 = y[0]
else:
x3 = x[index]
y3 = y[index]
a = [[1,x[0],x[0]**2],[1,x[-1],x[-1]**2],[1,x3,x3**2]]
b = [y[0],y[-1],y3]
p = np.linalg.solve(a,b)
p = np.flipud(p)
else:
print('Unknown background form')
p = np.zeros((3))
elif isinstance(fit,FitResult):
#get background from FitResult object
if fit.bgform.lower() in alias_dict['constant']:
p=1
elif fit.bgform.lower() in alias_dict['linear']:
p=2
elif fit.bgform.lower() in alias_dict['quadratic']:
p=3
else:
p=1
bg = np.polyval(fit.popt[-p:], data[0])
elif any([type(fit) is z for z in [list,np.array]]):
#background polynomial parameters supplied
bg = np.polyval(fit,data[0])
else:
#Unknown or error
print('Unknown fit argument.')
bg = 0
return data[1]-bg
def gaussian(x,sigma,amp=1,x0=0,const=0,chirp=0,sg=1, fr=True):
'''Gaussian distribution.
x = independent variable
sigma = sd (width parameter)
x0 = centre position
amp = amplitude
const = y-offset
chirp = chirp parameter
sg = supergaussian number
fr = Force Real
- if chirp == 0 and fr the output is cast as real.
- used for curve fitting (complex # dont work)
Note: can be used for either field or intensity. Be careful of sigma definition.
'''
f = amp*np.exp(-(1+1j*chirp)*((x-x0)**2/(2*sigma**2))**sg) + const
if chirp==0 and fr:
return np.real(f)
else:
return f
def sech2(x,sigma,amp=1,x0=0,const=0,chirp=0,fr=True):
'''Hyperbolic secant-squared distribution.
x = independent variable
sigma = width parameter
x0 = centre position
amp = amplitude
const = y-offset
chirp = chirp parameter ** check consistency
fr = Force Real
- if chirp == 0 and fr the output is cast as real.
- used for curve fitting (complex # dont work)
Note: this may be used to represent intensity of sech2 pulse.
'''
f = amp*((1/np.cosh((x-x0)/sigma))**2)*np.exp(1j*chirp*(x-x0)**2/(2*sigma**2)) + const
if chirp==0 and fr:
return np.real(f)
else:
return f
def sech(x,sigma,amp=1,x0=0,const=0,chirp=0,fr=True):
'''Hyperbolic secant distribution.
x = independent variable
sigma = width parameter
x0 = centre position
amp = amplitude
const = y-offset
chirp = chirp parameter
fr = Force Real
- if chirp == 0 and fr the output is cast as real.
- used for curve fitting (complex # dont work)
Note: this may be used to represent electric field of sech2 pulse.
'''
f = amp*(1/np.cosh((x-x0)/sigma))*np.exp(1j*chirp*(x-x0)**2/(2*sigma**2)) + const
if chirp==0 and fr:
return np.real(f)
else:
return f
def lorentzian(x,sigma,amp=1,x0=0,const=0):
'''Lorentzian distribution.
x = independent variable
sigma = width parameter
x0 = centre position
amp = amplitude
const = y-offset
'''
return amp*(sigma**2/((x-x0)**2+sigma**2)) + const
def gaussian2D(xy_meshgrid,x0,y0,sigx,sigy,amp,const,theta=0):
'''Generates a 2D gaussian surface of size (n x m).
Inputs:
xy_meshgrid = [x,y]
x = meshgrid of x array
y = meshgrid of y array
where x and y are of size (n x m)
n = y.shape[0] (or x.) = number of rows
m = x.shape[1] (or y.) = number of columns
x0,y0 = peak location
sig_ = standard deviation in x and y, gaussian 1/e radius
amp = amplitude
const = offset (constant)
theta = rotation parameter, 0 by default
Output:
g.ravel() = flattened array of gaussian amplitude data
where g is the 2D array of gaussian amplitudes of size (n x m)
'''
x = xy_meshgrid[0]
y = xy_meshgrid[1]
a = np.cos(theta)**2/(2*sigx**2) + np.sin(theta)**2/(2*sigy**2)
b = -np.sin(2*theta)/(4*sigx**2) + np.sin(2*theta)/(4*sigy**2)
c = np.sin(theta)**2/(2*sigx**2) + np.cos(theta)**2/(2*sigy**2)
g = amp*np.exp(-(a*(x-x0)**2 -b*(x-x0)*(y-y0) + c*(y-y0)**2)) + const
return g.ravel()
def rk4(f, x, y0, const_args=[], abs_x=False):
'''
functional form
y'(x) = f(x,y,constants)
f must be function, f(x,y,const_args)
x = array
y0 = initial condition,
cont_args = additional constants required for f
returns y, integrated array
'''
N = | np.size(x) | numpy.size |
import numpy as np
from numpy.testing._private.utils import assert_
import pytest
def test_sum_single_vector(backend):
from csdl.examples.valid.ex_sum_single_vector import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
v1 = np.arange(n)
desired_vector_sum = np.sum(v1)
np.testing.assert_almost_equal(sim['single_vector_sum'],
desired_vector_sum)
assert sim['v1'].shape == (n, )
assert sim['single_vector_sum'].shape == (1, )
partials_error_vector_sum = sim.check_partials(
includes=['comp_single_vector_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_vector_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_single_matrix(backend):
from csdl.examples.valid.ex_sum_single_matrix import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
desired_matrix_sum = np.sum(M1)
np.testing.assert_almost_equal(sim['single_matrix_sum'],
desired_matrix_sum)
assert sim['M1'].shape == (n, m)
assert sim['single_matrix_sum'].shape == (1, )
partials_error_vector_sum = sim.check_partials(
includes=['comp_single_matrix_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_vector_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_single_tensor(backend):
from csdl.examples.valid.ex_sum_single_tensor import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 4
p = 5
q = 6
T1 = np.arange(n * m * p * q).reshape((n, m, p, q))
desired_tensor_sum = np.sum(T1)
np.testing.assert_almost_equal(sim['single_tensor_sum'],
desired_tensor_sum)
assert sim['single_tensor_sum'].shape == (1, )
partials_error_tensor_sum = sim.check_partials(
includes=['comp_single_tensor_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_tensor_sum,
atol=1.e-5,
rtol=1.e-5)
def test_sum_multiple_vector(backend):
from csdl.examples.valid.ex_sum_multiple_vector import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
v1 = np.arange(n)
v2 = np.arange(n, 2 * n)
desired_vector_sum = v1 + v2
np.testing.assert_almost_equal(sim['multiple_vector_sum'],
desired_vector_sum)
assert sim['multiple_vector_sum'].shape == (n, )
partials_error_vector_sum = sim.check_partials(
includes=['comp_multiple_vector_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_vector_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_matrix(backend):
from csdl.examples.valid.ex_sum_multiple_matrix import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
M2 = np.arange(n * m, 2 * n * m).reshape((n, m))
desired_matrix_sum = M1 + M2
np.testing.assert_almost_equal(sim['multiple_matrix_sum'],
desired_matrix_sum)
assert sim['multiple_matrix_sum'].shape == (n, m)
partials_error_matrix_sum = sim.check_partials(
includes=['comp_multiple_matrix_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_matrix_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_tensor(backend):
from csdl.examples.valid.ex_sum_multiple_tensor import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
p = 7
q = 10
T1 = np.arange(n * m * p * q).reshape((n, m, p, q))
T2 = np.arange(n * m * p * q, 2 * n * m * p * q).reshape(
(n, m, p, q))
desired_tensor_sum = T1 + T2
np.testing.assert_almost_equal(sim['multiple_tensor_sum'],
desired_tensor_sum)
assert sim['multiple_tensor_sum'].shape == (n, m, p, q)
partials_error_tensor_sum = sim.check_partials(
includes=['comp_multiple_tensor_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_tensor_sum,
atol=1.e-5,
rtol=1.e-5)
def test_sum_single_matrix_along0(backend):
from csdl.examples.valid.ex_sum_single_matrix_along0 import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
desired_single_matrix_sum_axis_0 = np.sum(M1, axis=0)
np.testing.assert_almost_equal(sim['single_matrix_sum_along_0'],
desired_single_matrix_sum_axis_0)
assert sim['single_matrix_sum_along_0'].shape == (m, )
partials_error_single_matrix_axis_0 = sim.check_partials(
includes=['comp_single_matrix_sum_along_0'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_single_matrix_axis_0,
atol=1.e-6,
rtol=1.e-6)
def test_sum_single_matrix_along1(backend):
from csdl.examples.valid.ex_sum_single_matrix_along1 import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
desired_single_matrix_sum_axis_1 = np.sum(M1, axis=1)
np.testing.assert_almost_equal(sim['single_matrix_sum_along_1'],
desired_single_matrix_sum_axis_1)
assert sim['single_matrix_sum_along_1'].shape == (n, )
partials_error_single_matrix_axis_1 = sim.check_partials(
includes=['comp_single_matrix_sum_along_1'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_single_matrix_axis_1,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_matrix_along0(backend):
from csdl.examples.valid.ex_sum_multiple_matrix_along0 import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
M2 = np.arange(n * m, 2 * n * m).reshape((n, m))
desired_multiple_matrix_sum_axis_0 = np.sum(M1 + M2, axis=0)
np.testing.assert_almost_equal(sim['multiple_matrix_sum_along_0'],
desired_multiple_matrix_sum_axis_0)
partials_error_multiple_matrix_axis_0 = sim.check_partials(
includes=['comp_multiple_matrix_sum_along_0'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_multiple_matrix_axis_0,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_matrix_along1(backend):
from csdl.examples.valid.ex_sum_multiple_matrix_along1 import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
M2 = np.arange(n * m, 2 * n * m).reshape((n, m))
desired_multiple_matrix_sum_axis_1 = np.sum(M1 + M2, axis=1)
np.testing.assert_almost_equal(sim['multiple_matrix_sum_along_1'],
desired_multiple_matrix_sum_axis_1)
partials_error_multiple_matrix_axis_1 = sim.check_partials(
includes=['comp_multiple_matrix_sum_along_1'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_multiple_matrix_axis_1,
atol=1.e-6,
rtol=1.e-6)
def test_sum_concatenate_sums(backend):
from csdl.examples.valid.ex_sum_concatenate import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
x = np.array([np.sum(np.arange(5)), np.sum(np.arange(4)), 0])
| np.testing.assert_almost_equal(sim['sum_vector'], x) | numpy.testing.assert_almost_equal |
import random
from typing import Optional, Tuple
import arcade
import numpy as np
from triple_vision import Settings as s
from triple_vision.entities import Spike
from triple_vision.utils import tile_to_pixels
random.seed(1)
class Map:
def __init__(self, view: arcade.View, shape: Tuple[int, int]) -> None:
self.view = view
self.shape = shape
self.AIR = 0
self.WALL = 1
self.FLOOR = 2
self.SPIKE = 3
self.GENERATIONS = 6
self.FILL_PROBABILITY = 0.2
self.SPIKE_PROBABILITY = 0.01
self.sprites: Optional[arcade.SpriteList] = None
def generate(self) -> np.array:
map_ = np.ones(self.shape)
for i in range(self.shape[0]):
for j in range(self.shape[1]):
choice = random.uniform(0, 1)
map_[i][j] = self.WALL \
if choice < self.FILL_PROBABILITY else self.FLOOR
for gen in range(self.GENERATIONS):
for i in range(self.shape[0]):
for j in range(self.shape[1]):
# Get walls that are 1 away from each index
submap = map_[
max(i - 1, 0): min(i + 2, map_.shape[0]),
max(j - 1, 0): min(j + 2, map_.shape[1])
]
flat_submap = submap.flatten()
wall_count_1_away = len(np.where(flat_submap == self.WALL)[0])
floor_count_1_away = len( | np.where((flat_submap == self.FLOOR) | (flat_submap == self.SPIKE)) | numpy.where |
from typing import Any
import numpy as np
import tree
class UniformBuffer(object):
def __init__(self, min_size: int, max_size: int, traj_len: int):
self._min_size = min_size
self._max_size = max_size
self._traj_len = traj_len
self._timestep_storage = None
self._n = 0
self._idx = 0
def extend(self, timesteps: Any):
if self._timestep_storage is None:
sample_timestep = tree.map_structure(lambda t: t[0], timesteps)
self._timestep_storage = self._preallocate(sample_timestep)
num_steps = timesteps.observation.shape[0]
indices = np.arange(self._idx, self._idx + num_steps) % self._max_size
tree.map_structure(lambda a, x: assign(a, indices, x), self._timestep_storage, timesteps)
self._idx = (self._idx + num_steps) % self._max_size
self._n = min(self._n + num_steps, self._max_size)
def sample(self, batch_size: int):
if batch_size + self._traj_len > self._n:
return None
start_indices = | np.random.choice(self._n - self._traj_len, batch_size, replace=False) | numpy.random.choice |
"""
Mask R-CNN
Display and Visualization Functions.
Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by <NAME>
"""
import math
import random
import itertools
import colorsys
import numpy as np
import IPython.display
import tensorflow as tf
import keras.backend as KB
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.lines as lines
import skimage.util
from skimage.measure import find_contours
from PIL import Image
from matplotlib.patches import Polygon
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import mrcnn.utils as utils
from mrcnn.datagen import load_image_gt
############################################################
# Visualization
############################################################
def get_ax(rows=1, cols=1, size=8):
"""Return a Matplotlib Axes array to be used in
all visualizations in the notebook. Provide a
central point to control graph sizes.
Change the default size attribute to control the size
of rendered images
"""
_, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))
return ax
def random_colors(N, bright=True):
"""
Generate random colors.
To get visually distinct colors, generate them in HSV space then
convert to RGB.
"""
brightness = 1.0 if bright else 0.7
hsv = [(i / N, 1, brightness) for i in range(N)]
colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
random.shuffle(colors)
return colors
def apply_mask(image, mask, color, alpha=0.5):
"""Apply the given mask to the image.
"""
for c in range(3):
image[:, :, c] = np.where(mask == 1,
image[:, :, c] * (1 - alpha) + alpha * color[c] * 255,
image[:, :, c])
return image
##----------------------------------------------------------------------
## display_image
## figsize : tuple of integers, optional: (width, height) in inches
## default: None
## If not provided, defaults to rc figure.figsize.
##----------------------------------------------------------------------
def display_image(image, title='', cmap=None, norm=None,
interpolation=None, figsize=(10,10), ax=None):
"""
Display one image, optionally with titles.
image: list or array of image tensors in HWC format.
title: optional. A list of titles to display with each image.
cols: number of images per row
cmap: Optional. Color map to use. For example, "Blues".
norm: Optional. A Normalize instance to map values to colors.
interpolation: Optional. Image interporlation to use for display.
"""
plt.figure(figsize=figsize)
# if title is None:
title += "H x W={}x{}".format(image.shape[0], image.shape[1])
plt.title(title, fontsize=12)
plt.imshow(image, cmap=cmap,
norm=norm, interpolation=interpolation)
##----------------------------------------------------------------------
## display_image
## figsize : tuple of integers, optional: (width, height) in inches
## default: None
## If not provided, defaults to rc figure.figsize.
##----------------------------------------------------------------------
def display_image_bw(image, title="B/W Display" , cmap=None, norm=None,
interpolation=None, figsize=(10,10), ax=None):
"""
Display one image, optionally with titles.
image: list or array of image tensors in HWC format.
title: optional. A list of titles to display with each image.
cols: number of images per row
cmap: Optional. Color map to use. For example, "Blues".
norm: Optional. A Normalize instance to map values to colors.
interpolation: Optional. Image interporlation to use for display.
"""
plt.figure(figsize=figsize)
plt.title(title, fontsize=12)
arr = np.asarray(image)
# print(type(image), image.shape)
# print(type(arr), arr.shape)
# plt.imshow(image.astype(np.uint8), cmap=cmap,
# norm=norm, interpolation=interpolation)
plt.imshow(arr, cmap='gray')
##----------------------------------------------------------------------
## display_images
##----------------------------------------------------------------------
def display_images(images, titles=None, cols=4, cmap=None, norm=None,
interpolation=None, width=14):
"""
Display the given set of images, optionally with titles.
images: list or array of image tensors in HWC format.
titles: optional. A list of titles to display with each image.
cols: number of images per row
cmap: Optional. Color map to use. For example, "Blues".
norm: Optional. A Normalize instance to map values to colors.
interpolation: Optional. Image interporlation to use for display.
"""
titles = titles if titles is not None else [""] * len(images)
rows = len(images) // cols + 1
plt.figure(figsize=(width, width * rows // cols))
i = 1
for image, title in zip(images, titles):
title += " H x W={}x{}".format(image.shape[0], image.shape[1])
plt.subplot(rows, cols, i)
plt.title(title, fontsize=9)
plt.axis('off')
plt.imshow(image.astype(np.uint8), cmap=cmap,
norm=norm, interpolation=interpolation)
i += 1
plt.show()
##------------------------------------------------------------------------------------
## display_training_batch()
##------------------------------------------------------------------------------------
def display_image_gt(dataset, config, image_ids, masks= False, only_classes = None, size=12):
'''
display images in a mrcnn train_batch
'''
from mrcnn.datagen import data_gen_simulate
if not isinstance(image_ids, list):
image_ids = [image_ids]
for image_id in image_ids:
image = dataset.load_image(image_id)
# molded_image, image_meta, class_ids, bbox = load_image_gt(dataset, config, image_id)
_, image_meta, _, _ = load_image_gt(dataset, config, image_id)
mask, class_ids = dataset.load_mask(image_id)
bbox = utils.extract_bboxes(mask)
class_names = [str(dataset.class_names[class_id]) for class_id in class_ids]
print(' Image_id : ', image_id, ' Reference: ', dataset.image_reference(image_id) , 'Coco Id:', dataset.image_info[image_id]['id'])
print(' Image meta : ', image_meta[:10])
print(' Class ids : ', class_ids.shape, ' ' , class_ids)
print(' Class Names : ', class_names)
# display_top_masks(image, mask, class_ids, dataset.class_names)
if masks:
display_instances_with_mask(image, bbox, mask, class_ids, dataset.class_names, size =size)
else:
display_instances(image, bbox, class_ids, dataset.class_names, only_classes = only_classes, size=size)
return
##------------------------------------------------------------------------------------
## display_training_batch()
##------------------------------------------------------------------------------------
def display_training_batch(dataset, batch_x, masks= False):
'''
display images in a mrcnn train_batch
'''
# replaced following two lines with next line to avoid the need to pass model to this fuction
# imgmeta_idx = mrcnn_model.keras_model.input_names.index('input_image_meta')
# img_meta = train_batch_x[imgmeta_idx]
img_meta = batch_x[1]
for img_idx in range(img_meta.shape[0]):
image_id = img_meta[img_idx,0]
print('image id : ', image_id)
image = dataset.load_image(image_id)
mask, class_ids = dataset.load_mask(image_id)
bbox = utils.extract_bboxes(mask)
class_names = [str(dataset.class_names[class_id]) for class_id in class_ids]
print(' Image_id : ', image_id, ' Reference: ', dataset.image_reference(image_id) , 'Coco Id:', dataset.image_info[image_id]['id'])
print(' Image meta : ', img_meta[img_idx, :8])
print(' Class ids : ', class_ids.shape, ' ' , class_ids)
print(' Class Names : ', class_names) # print('Classes (1: circle, 2: square, 3: triangle ): ',class_ids)
if masks:
display_top_masks(image, mask, class_ids, dataset.class_names)
display_instances_with_mask(image, bbox, mask, class_ids, dataset.class_names)
else:
display_instances(image, bbox, class_ids, dataset.class_names)
return
##----------------------------------------------------------------------
## display_instances
##----------------------------------------------------------------------
def display_instances(image, boxes, class_ids, class_names,
scores=None, title="", only_classes = None,
figsize=(16, 16), ax=None, score_range = (-1.0, 1.0), size = 16):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [num_instances, height, width]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
figsize: (optional) the size of the image.
max_score: show instances with score less than this
"""
# Number of instances
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == class_ids.shape[0], " boxes.shape[0]: {:d} must be ==class_ids.shape[0]: {:d}".format(boxes.shape[0], class_ids.shape[0])
# assert boxes.shape[0] == class_ids.shape[0]
# print(' display_instances() : Image shape: ', image.shape)
if not ax:
ax = get_ax(rows =1, cols = 1, size= size)
# _, ax = plt.subplots(1, figsize=figsize)
# Generate random colors
colors = random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
# ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
class_id = class_ids[i]
if only_classes is not None:
if class_id not in only_classes:
continue
if scores is not None:
# print(' boxes ' ,i,' ' , boxes[i], 'score: ', scores[i], ' ', score_range)
if scores[i] <= score_range[0] or scores[i] >= score_range[1]:
continue
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Label
score = scores[i] if scores is not None else None
if class_id >= 0 :
label = "{:2d}-{:2d} {}".format(i,class_id, class_names[class_id])
else:
label = "{:2d}-{:2d} {}".format(i,class_id, class_names[-class_id]) + ' (CROWD)'
x = random.randint(x1, (x1 + x2) // 2)
caption = "{} {:.3f}".format(label, score) if score else label
t = ax.text(x1, y1 + 8, caption, color='k', size=8, backgroundcolor="w")
t.set_bbox(dict(facecolor='w', alpha=0.5, edgecolor='w'))
ax.imshow(masked_image.astype(np.uint8))
plt.show()
return
##----------------------------------------------------------------------
## display_instances_with_mask
##----------------------------------------------------------------------
def display_instances_with_mask(image, boxes, masks, class_ids, class_names,
scores=None, title="",
figsize=(16, 16), ax=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [num_instances, height, width]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
figsize: (optional) the size of the image.
max_score: show instances with score less than this
"""
# Number of instances
# print(' display_instances WITH MASK() : Image shape: ', image.shape)
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
if not ax:
_, ax = plt.subplots(1, figsize=figsize)
# Generate random colors
colors = random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
# ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Label
score = scores[i] if scores is not None else None
class_id = class_ids[i]
# label = class_names[class_id]
# if class_id >= 0 :
# label = class_names[class_id]
# else:
# label = class_names[-class_id] + ' (CROWD)'
# x = random.randint(x1, (x1 + x2) // 2)
# caption = "{} {:.3f}".format(label, score) if score else label
# ax.text(x1, y1 + 8, caption, color='k', size=11, backgroundcolor="w")
if class_id >= 0 :
label = "{:2d}-{:2d} {}".format(i,class_id, class_names[class_id])
else:
label = "{:2d}-{:2d} {}".format(i,class_id, class_names[-class_id]) + ' (CROWD)'
x = random.randint(x1, (x1 + x2) // 2)
caption = "{} {:.3f}".format(label, score) if score else label
t = ax.text(x1, y1 + 8, caption, color='k', size=8, backgroundcolor="w")
t.set_bbox(dict(facecolor='w', alpha=0.5, edgecolor='w'))
# Mask
mask = masks[:, :, i]
masked_image = apply_mask(masked_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros((mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p)
ax.imshow(masked_image.astype(np.uint8))
plt.show()
return
##----------------------------------------------------------------------
## display_instances from pr_scores
##----------------------------------------------------------------------
def display_instances_from_prscores(image, pr_scores, class_names,
title="", only_classes = None,
figsize=(16, 16), ax=None, score_range = (0.0, 1.0)):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [num_instances, height, width]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
figsize: (optional) the size of the image.
max_score: show instances with score less than this
"""
# Number of instances
boxes = pr_scores[:,:4]
class_ids = pr_scores[:,4].astype(int)
scores = pr_scores[:,5]
det_ind = pr_scores[:,6].astype(int)
sequences = pr_scores[:,7].astype(int)
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == class_ids.shape[0], " boxes.shape[0]: {:d} must be ==class_ids.shape[0]: {:d}".format(boxes.shape[0], class_ids.shape[0])
print(' display_instances() : Image shape: ', image.shape)
if not ax:
_, ax = plt.subplots(1, figsize=figsize)
# Generate random colors
colors = random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
# ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
class_id = class_ids[i]
if only_classes is not None:
if class_id not in only_classes:
continue
if scores is not None:
if scores[i] <= score_range[0] or scores[i] >= score_range[1]:
continue
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Label
score = scores[i] if scores is not None else None
if det_ind[i] == -1:
det_ttl = ' ADDED FP'
else:
det_ttl = ''
if class_id >= 0 :
label = class_names[class_id] + det_ttl
else:
label = class_names[-class_id] + ' (CROWD)'
x = random.randint(x1, (x1 + x2) // 2)
caption = "{:2d}-{} {:.4f}".format(class_id, label, score) if score else label
ax.text(x1, y1 - 8, caption, color='k', size=9, backgroundcolor="w")
ax.imshow(masked_image.astype(np.uint8))
plt.show()
return
##----------------------------------------------------------------------
## display_instances
##----------------------------------------------------------------------
def display_instances_two_scores(image, boxes, class_ids, class_names,
scores1=None, scores2= None , title="", only_classes = None,
figsize=(16, 16), ax=None, score_range = (-1.0, 1.0), size = 16):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [num_instances, height, width]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
figsize: (optional) the size of the image.
max_score: show instances with score less than this
"""
# Number of instances
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == class_ids.shape[0]
if scores2 is not None :
assert scores2.shape == scores1.shape
# print(' display_instances() : Image shape: ', image.shape)
if not ax:
ax = get_ax(rows =1, cols = 1, size= size)
# _, ax = plt.subplots(1, figsize=figsize)
# Generate random colors
colors = random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
# ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
class_id = class_ids[i]
if only_classes is not None:
if class_id not in only_classes:
continue
# if scores1 is not None:
# print(' boxes ' ,i,' ' , boxes[i], 'score: ', scores[i], ' ', score_range)
# if scores1[i] <= score_range[0] or scores1[i] >= score_range[1]:
# continue
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Label
score1 = scores1[i] if scores1 is not None else None
score2 = scores2[i] if scores2 is not None else None
if class_id >= 0 :
label = "{:2d}-{:2d} {}".format(i,class_id, class_names[class_id])
else:
label = "{:2d}-{:2d} {}".format(i,class_id, class_names[-class_id]) + ' (CROWD)'
x = random.randint(x1, (x1 + x2) // 2)
caption = "{} {:5.4f}".format(label, score1) if score1 else label
caption += "--> {:5.4f}".format(score2) if score2 else ''
t = ax.text(x1, y1 + 8, caption, color='k', size=8, backgroundcolor="w")
t.set_bbox(dict(facecolor='w', alpha=0.5, edgecolor='w'))
ax.imshow(masked_image.astype(np.uint8))
plt.show()
return
##----------------------------------------------------------------------
## draw_rois (along with the refined_rois)
##----------------------------------------------------------------------
# def draw_rois_with_refinements(image, rois, refined_rois, mask, class_ids, class_names, limit=10):
def draw_rois_with_refinements(image, rois, refined_rois, class_ids, class_names, limit=0,
ids = None, random = False, size = 16):
"""
rois: [n, 4 : {y1, x1, y2, x2}] list of anchors in image coordinates.
refined_rois: [n, 4 : {y1, x1, y2, x2}] the same anchors but refined to fit objects better.
"""
masked_image = image.copy()
# Pick random anchors in case there are too many.
print(' rois.shape[0]: ',rois.shape[0], ' limit = ', limit)
if limit == 0 :
limit = max(rois.shape[0], limit)
print(' limit : ', limit)
if ids is None:
ids = np.arange(limit, dtype=np.int32)
if random:
ids = np.random.choice(ids, limit, replace=False) if ids.shape[0] > limit else ids
print(' ids : ', ids)
fig, ax = plt.subplots(1,1, figsize=(size, size))
if rois.shape[0] > limit:
plt.title("Showing {} random ROIs out of {}".format(
len(ids), rois.shape[0]))
else:
plt.title("{} ROIs".format(len(ids)))
# Show area outside image boundaries.
ax.set_ylim(image.shape[0] + 20, -20)
ax.set_xlim(-50, image.shape[1] + 20)
# ax.axis('off')
for i, id in enumerate(ids):
# print('i: ', i, 'id :', id)
color = np.random.rand(3)
class_id = class_ids[id]
# ROI
y1, x1, y2, x2 = rois[id]
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
edgecolor=color if class_id else "gray",
facecolor='none', linestyle="dashed")
ax.add_patch(p)
# Refined ROI
if class_id:
ry1, rx1, ry2, rx2 = refined_rois[id]
p = patches.Rectangle((rx1, ry1), rx2 - rx1, ry2 - ry1, linewidth=2,
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Connect the top-left corners of the anchor and proposal for easy visualization
ax.add_line(lines.Line2D([x1, rx1], [y1, ry1], color=color))
# Label
label = class_names[class_id]
ax.text(rx1, ry1 + 8, "{}".format(label),
color='w', size=11, backgroundcolor="none")
# Mask
# m = utils.unmold_mask(mask[id], rois[id]
# [:4].astype(np.int32), image.shape)
# masked_image = apply_mask(masked_image, m, color)
ax.imshow(masked_image)
# Print stats
print("Positive ROIs: ", class_ids[class_ids > 0].shape[0])
print("Negative ROIs: ", class_ids[class_ids == 0].shape[0])
print("Positive Ratio: {:.2f}".format(
class_ids[class_ids > 0].shape[0] / class_ids.shape[0]))
##----------------------------------------------------------------------
## draw rois proposals (w/o refinements)
##----------------------------------------------------------------------
def draw_rois(image, rois, class_ids, class_names, bbox_ids = None , limit=0, random = False, display_bg = False):
"""
anchors: [n, (y1, x1, y2, x2)] list of anchors in image coordinates.
proposals: [n, 4] the same anchors but refined to fit objects better.
bbox_ids : list of bbox ids that will be displayed. If not specified will use limit
"""
masked_image = image.copy()
# Pick random anchors in case there are too many.
print(' rois.shape[0]: ',rois.shape[0], ' limit = ', limit)
if bbox_ids:
pass
else:
bbox_ids = np.arange(rois.shape[0])
print(' num of bbox_ids : ', len(bbox_ids))
print(' limit to display : ', limit)
if limit == 0 :
limit = len(bbox_ids)
else:
limit = min(len(bbox_ids), limit)
print(' limit to display : ', limit)
# bbox_ids = np.arange(limit, dtype=np.int32)
if random:
bbox_ids = | np.random.choice(bbox_ids, limit, replace=False) | numpy.random.choice |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 11:43:06 2021
@author: student
"""
import pandas as pd
import numpy as np
import argparse
import os
import random
import matplotlib.pyplot as plt
from stellargraph.mapper import PaddedGraphGenerator
from stellargraph.layer import DeepGraphCNN, GCNSupervisedGraphClassification
from stellargraph import StellarGraph
from sklearn import model_selection
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Dense, Conv1D, MaxPooling1D, MaxPooling2D, Dropout, Flatten, BatchNormalization
from tensorflow.keras.utils import plot_model
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
import pickle
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from tensorflow.keras.utils import to_categorical
# results directory
RES_DIR = 'results/gcn'
if not os.path.exists(RES_DIR):
os.makedirs(RES_DIR)
MODEL_DIR = 'models/gcn/'
os.makedirs(MODEL_DIR, exist_ok=True)
SEED = 5000
np.random.seed(SEED)
random.seed(SEED)
tf.random.set_seed(SEED)
def _info(s):
print('---')
print(s)
print('---')
def threshold_proportional(W, p, copy=True):
"""
Convert values less than the threshold value to 0
Parameters
----------
W : 2D array, connevtivity matrix to be thresholded.
p : float value between 0 and 1, Cell Value less than threshold value will be set to 0.
copy : boolean, optional, The default is True.
Raises
------
ValueError, If the threshold is not within 0 and 1.
Returns
-------
W : Thresholded 2D array, A matrix that does not contains negative values.
"""
if p >= 1 or p <= 0:
raise ValueError("Threshold value should be between 0 and 1")
if copy:
W = W.copy()
n = len(W) # number of nodes
np.fill_diagonal(W, 0) # clear diagonal
if np.all(W == W.T): # if symmetric matrix
W[np.tril_indices(n)] = 0 # ensure symmetry is preserved
ud = 2 # halve number of removed links
else:
ud = 1
ind = | np.where(W) | numpy.where |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 20 09:24:50 2020
@author: Hugo
Source for the sim
https://medium.com/analytics-vidhya/modeling-the-simplest-biological-neuron-with-python-adda892c8384
https://neuronaldynamics.epfl.ch/online/Ch1.S3.html
http://tips.vhlab.org/techniques-and-tricks/matlab/integrate-and-fire
Choice for the default params
http://neuralensemble.org/docs/PyNN/reference/neuronmodels.html
"""
import stim
import plots
import LIF
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from mpl_toolkits.mplot3d import Axes3D
import fit
def get_3d_quantifs(data):
mean_spiketrains = []
std_spiketrains = []
for trial in data :
mean_spiketrains.append(np.mean([len(x) for x in trial]))
std_spiketrains.append(np.std([len(x) for x in trial]))
tc_pars = fit.fit_gaussian(mean_spiketrains)
fit_tc = fit.gaussian(np.linspace(-3, 3, 1000),
tc_pars['mu'], tc_pars['sig'], tc_pars['scale'])
return tc_pars['sig'], np.max(fit_tc)
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
# Parameters
## Simulation parameters
T = 50 # total simtime ; ms
dt = 0.01 #timestep ; ms
n_repeat = 2 # number of time we rerun the whole stimulation set, used for trial-to-trial variance
## LIF parameters, note that we scale everything later to speed up computations
## You probably don't want to touch this
Rm = 1 # resistance (kOhm)
Cm = 10 # capacitance (uF)
tau_m = Rm*Cm # time constant (msec)
refrac_time = 1. # refractory period (msec)
Vth = 1. # spike threshold (V)
## Stimulation parameters
n_pars = 6 #number of parameters, either contrast or bandwidth
contrasts = np.linspace(1., 8., n_pars) #stimulation contrast, max = 5 is a good idea
bandwidths = np.linspace(.3, .8, n_pars) # stimulation bandwidth, it's sigma of gaussian
## Finn parameters
k = 3.5 # power law scale
a = -.5 # power law exponent
loc = .8 # noise normal law center
scale = .5 # noise normal law var
## Bandwidth parameters
k_bw = 3.5 # other neurons' power law scale
a_bw = -0 # multiplier of bw on other neurons
## Plotting parameters
labels = bandwidths #rescale for actual stim values
colors = plt.cm.inferno(np.linspace(.9, .2, len(bandwidths))) #tc colormap
# Initialization
tot_steps = int(T/dt) # length of the time vector
time = np.linspace(0, T+dt, tot_steps) # time vector
# Stimulation, contrasts and bandwidths
pwlaw = stim.power_law(k = k, x = contrasts, a = a)
input_tcs = np.zeros((n_pars, n_pars), dtype = object)
for i0, max_amp in enumerate(contrasts) :
for i1, bw in enumerate(bandwidths) :
inp = stim.generate_stim(mu = 0., sig = bw, max_amp = max_amp)
inp *= pwlaw[i0]
new_pwlaw = stim.power_law(k = | np.max(inp) | numpy.max |
"""
Module to build timelines, delta series from aggregated transactions
This module provides the foundation types needed for real time analytics.
It also includes logic to compute timeline statistics from aggreagated transactions
Author: <NAME>, <NAME>
"""
import time
import numpy
import logging
from collections import OrderedDict
from xpedite.types.probe import compareProbes
from xpedite.types.route import conflateRoutes
LOGGER = logging.getLogger(__name__)
TSC_EVENT_NAME = 'wall time'
class Timeline(object):
"""A timeline is a sequence of events happening as time progresses"""
def __init__(self, txn):
"""
Creates an instance of Timeline for the given transaction
:param txn: Source transaction for this timeline
:type data: xpedite.transaction.Transaction
"""
self.txn = txn
self.tsc = txn[0].tsc
self.txnId = txn.txnId
self.points = []
self.endpoint = None
self.inception = None
def addTimePoint(self, timePoint):
"""
Adds a time point to this time line
:param timePoint: A time point for an event hapenning at a specific point in time
:type timePoint: xpedite.analytics.timeline.TimePoint
"""
self.points.append(timePoint)
@property
def duration(self):
"""Elapsed wall time (in micro seconds) for this timeline"""
return self.endpoint.duration
def __getitem__(self, index):
"""Returns a time point at a given index in this time line"""
return self.points[index]
def __len__(self):
"""
Returns the length of this time line.
The length of a time line counts the number of timepoints in the line
"""
return len(self.points)
def __repr__(self):
"""Returns str representation of a timeline"""
pointStr = '\n\t'.join((str(point) for point in self.points))
return 'Timeline: id {} | ({})\n\t'.format(self.txnId, pointStr)
def __eq__(self, other):
return self.__dict__ == other.__dict__
class TimePoint(object):
"""A time point marks a specific instance of time in a time line"""
def __init__(self, name, point=None, duration=None, pmcNames=None, deltaPmcs=None, topdownValues=None, data=None):
"""
Creates an instance of TimePoint
:param name: The name of this time point
:type name: str
:param point: The absolute point in time, when an event occurred
:type point: double
:param duration: The total duration (in micro seconds) spanned by this time point
:type duration: double
:param pmcNames: The list of pmu event names captured by this timepoint
:param deltaPmcs: The list of pmu event values captured by this timepoint
:param topdownValues: The list of topdown values computed for this timepoint
:param data: The 128 bit raw data captured by this timepoint
"""
self.name = name
self.point = point
self.duration = duration
self.pmcNames = pmcNames
self.deltaPmcs = deltaPmcs
self.topdownValues = topdownValues
self.data = data
def __repr__(self):
"""Returns str representation of a TimePoint"""
rep = 'TimePoint {0}: point {1:4,.3f} | duration {2:4,.3f}'.format(self.name, self.point, self.duration)
if self.deltaPmcs:
rep += ' | pmc {}'.format({self.pmcNames[i]: self.deltaPmcs[i] for i in range(len(self.deltaPmcs))})
return rep
def __eq__(self, other):
return self.__dict__ == other.__dict__
class DeltaSeries(object):
"""A series of duration (micro seconds) and pmu counter values"""
def __init__(self, beginProbeName, endProbeName):
"""
Creates an instance of Duration Series
A duration is a measure of time or pmu events, expended to execute code between
a pair of probes
:param beginProbeName: Name of the probe, that marks the beginning of this time period
:type beginProbeName: str
:param endProbeName: Name of the probe, that marks the end of this time period
:type endProbeName: str
"""
self.beginProbeName = beginProbeName
self.endProbeName = endProbeName
self.series = []
self._count = 0
self._min = None
self._max = None
self._median = None
self._mean = None
self._standardDeviation = None
self.numpyArray = None
def _computeStats(self):
"""Computes statistics for a series of druation/counter values"""
if self.series and self._count != len(self.series):
self._count = len(self.series)
self._min = min(self.series)
self._max = max(self.series)
self._median = numpy.median(self.series)
self._mean = numpy.mean(self.series)
self._standardDeviation = numpy.std(self.series)
self.numpyArray = | numpy.array(self.series) | numpy.array |
import unittest
import numpy as np
from . import plot
import funcsfa
class TestInvalidInputs(unittest.TestCase):
def setUp(self):
self.rand = np.random.RandomState(1968486074)
self.n_factors = 9
self.f = funcsfa.SFA()
self.n_samples = 221
self.n_features = 37
self.X_a = self.rand.normal(0, 1, (self.n_samples, 30))
self.X_b = self.rand.normal(0, 1, (self.n_samples, 7))
self.data_one = funcsfa.DataMatrix(self.X_a)
self.data_two = funcsfa.StackedDataMatrix([
funcsfa.DataMatrix(self.X_a),
funcsfa.DataMatrix(self.X_b)])
def test_l1_penalty_length_one_dt(self):
self.f.fit(self.data_one, self.n_factors, max_iter=0, l1=0.0)
self.f.fit(self.data_one, self.n_factors, max_iter=0, l1=0.0, l2=0.0)
self.f.fit(self.data_one, self.n_factors, max_iter=0, l1=[0.0], l2=0.0)
self.f.fit(self.data_one, self.n_factors, max_iter=0, l1=[0.0],
l2=[0.0])
with self.assertRaises(Exception):
self.f.fit(self.data_one, self.n_factors, max_iter=0,
l1=[0.0, 0.1])
with self.assertRaises(Exception):
self.f.fit(self.data_one, self.n_factors, max_iter=0,
l1=[])
with self.assertRaises(Exception):
self.f.fit(self.data_one, self.n_factors, max_iter=0,
l1=[0.1, 0.2], l2=[0.1])
with self.assertRaises(Exception):
self.f.fit(self.data_one, self.n_factors, max_iter=0,
l1=[0.1, 0.2], l2=0.1)
def test_l2_penalty_length_one_dt(self):
self.f.fit(self.data_one, self.n_factors, max_iter=0, l1=0.0, l2=[0.0])
with self.assertRaises(Exception):
self.f.fit(self.data_one, self.n_factors, max_iter=0,
l2=[0.0, 0.1])
with self.assertRaises(Exception):
self.f.fit(self.data_one, self.n_factors, max_iter=0,
l2=[])
with self.assertRaises(Exception):
self.f.fit(self.data_one, self.n_factors, max_iter=0,
l2=[0.1, 0.2], l1=[0.1])
with self.assertRaises(Exception):
self.f.fit(self.data_one, self.n_factors, max_iter=0,
l2=[0.1, 0.2], l1=0.1)
def test_more_factors_than_features(self):
with self.assertRaises(Exception):
self.f.fit(self.data_two, self.data_two.dt_n_features[0]+1,
max_iter=0)
with self.assertRaises(Exception):
self.f.fit(self.data_two, self.data_two.dt_n_features[1]+1,
max_iter=0)
def test_invalid_transform(self):
f = funcsfa.SFA()
with self.assertRaises(Exception):
f.transform(self.data_one)
f.fit(self.data_one, self.n_factors, max_iter=10)
f.transform(self.data_one)
f.transform(self.data_one)
with self.assertRaises(Exception):
f.transform(self.data_two)
with self.assertRaises(Exception):
f.transform(self.data_two.data)
class TestSingleDatatypeReproduceRandom(unittest.TestCase):
def setUp(self):
self.n_samples = 400
self.n_features = 2000
self.n_factors = 10
self.rand = np.random.RandomState(1968486074)
self.B = self.rand.normal(0, 1, (self.n_features, self.n_factors))
Zvar = np.linspace(10, 1, self.n_factors)
Zvar = Zvar / np.mean(Zvar)
self.Z = self.rand.normal(0, np.sqrt(Zvar),
(self.n_samples, self.n_factors))
self.X = | np.dot(self.Z, self.B.T) | numpy.dot |
"""
Title: io_helper.py
Author: <NAME>
Mail: <EMAIL>.tomko __at__ fmph.uniba.sk
"""
import gzip
import json
import numpy as np
import multiprocessing
import os
import pickle
def prepare_recording_vector(rec_vec, i):
"""
Prepares a recording vectors for saving.
Parameters
----------
rec_vec : neuron.hoc.HocObject
the recording vector
Returns
-------
vec : dict
the recording vector as a dictionary
"""
vec = {
'section': rec_vec.section,
'segment_x': rec_vec.segment_x,
'vector': np.array(rec_vec.vector)
}
return vec
class IOHelper:
"""
A class used to load and save data
...
Attributes
----------
path_saving : str
the path to the directory where the data is stored or will be saved
path_settings : str
the path to the synapses .json file
npool : str, optional
the number of pool processes (default multiprocessing.cpu_count() - 1)
Methods
-------
save_recordings(synapses, tw_vec, v_soma_vec, t_vec, dend_vecs, p_vec, d_vec, alpha_scount_vec, ta_vec,
apc_vec, cai_vecs, cal_ica_vecs, ina_vecs, nmda_ica_vecs)
Saves the recorded data
prepare_dict_recording_vectors(vecs)
Prepares a dictionary of recording vectors for saving.
load_synapses()
Loads setting from the synapses.json file.
save_synapses(synapses)
Saves synapses to a .json file.
load_setting()
Loads setting from the setting.json file.
save_setting(setting)
Saves setting to the setting.json file.
"""
def __init__(self, path_saving, path_settings):
"""
Parameters
----------
path_saving : str
the path to the directory where the data will be saved
path_settings : str
the path to the directory with settings
"""
self.path_saving = path_saving
self.path_settings = path_settings
self.npool = multiprocessing.cpu_count() - 1
try:
if not os.path.exists(self.path_saving):
os.makedirs(self.path_saving)
except OSError as e:
if e.errno != 17:
raise
pass
def save_recordings(self, synapses, tw_vec, v_soma_vec, t_vec, dend_vecs, p_vec, d_vec, alpha_scount_vec, ta_vec,
apc_vec, cai_vecs, cal_ica_vecs, ina_vecs, nmda_ica_vecs):
"""
Saves the recorded data to a dictionary structures in binary files.
Parameters
----------
synapses : dict
the dictionary of synapses
tw_vec : neuron.hoc.HocObject
the time vector for synaptic weights
v_soma_vec : neuron.hoc.HocObject
the somatic voltage vector
t_vec : neuron.hoc.HocObject
the time vector for voltage
dend_vecs : dict
the dictionary containing voltage vectors from dendrites
p_vec : neuron.hoc.HocObject
the potentiation amplitude vector
d_vec : neuron.hoc.HocObject
the depression amplitude vector
alpha_scount_vec : neuron.hoc.HocObject
the integrated spike count vector
ta_vec : neuron.hoc.HocObject
the time vector for amplitudes
apc_vec : neuron.hoc.HocObject
the vector of times of fired action potentials
cai_vecs : dict
the dictionary containing intracellular calcium concentration vectors
cal_ica_vecs : dict
the dictionary containing CaL channel-mediated calcium current vectors
ina_vecs : dict
the dictionary containing sodium current vectors
nmda_ica_vecs : dict
the dictionary of NMDAR channel-mediated calcium current vectors
"""
# a dictionary of synapses
synapses_dict = {}
for sec in synapses:
synapses_list = []
for syn in synapses[sec]:
s = {
'name': str(syn.synapse),
'section': str(syn.section),
'segment_x': syn.segment_x,
'distance': syn.distance,
'weight': np.array(syn.weight_vec),
'input_spikes': np.array(syn.input_spikes),
'stimulated': syn.stimulated,
'receptor': syn.receptor,
'pathway': syn.pathway
}
synapses_list.append(s)
synapses_dict[sec] = synapses_list
print('Saving recordings...')
# saving of synapses
weights = {}
weights['T'] = np.array(tw_vec)
weights['synapses'] = synapses_dict
pickle.dump(weights, gzip.GzipFile(self.path_saving + 'synapses.p', 'wb'))
print('The synapses were saved in the directory: ' + self.path_saving)
# saving of voltages
voltages = {}
voltages['T'] = np.array(t_vec)
voltages['V_soma'] = np.array(v_soma_vec)
voltages['APs'] = np.array(apc_vec)
voltages['V_dends'] = self.prepare_dict_recording_vectors(vecs=dend_vecs)
pickle.dump(voltages, gzip.GzipFile(self.path_saving + 'voltages.p', 'wb'))
print('The voltages were saved in the directory: ' + self.path_saving)
# saving of currents
currents = {}
currents['T'] = | np.array(t_vec) | numpy.array |
import copy
import sys
import os
import numpy as np
import pdb
sys.path.append('/home/acauligi/Software')
from casadi import *
from astrobee_strips import STRIPS, Node, Queue, get_plan
from scp import update_f, update_A, update_B, slerp
from double_integrator import compute_Ak, compute_Bk
class AstrobeeTAMP:
def __init__(self, Xi, Xref, mode='double_integrator'):
self.operators = ['dock_objA_dockA', 'dock_objA_dockB', 'dock_objA_dockC', \
'dock_objB_dockA', 'dock_objB_dockB', 'dock_objB_dockC', \
'undock_objA_dockA', 'undock_objA_dockB', 'undock_objA_dockC', \
'undock_objB_dockA', 'undock_objB_dockB', 'undock_objB_dockC', \
'grasp_objA', 'grasp_objB']
self.N = 10
self.mode = mode
self.Xi = Xi
self.Xref = Xref
if mode == 'double_integrator':
self.n, self.m = 4, 2
else:
self.n, self.m = 13, 6
self.dh = 0.05
self.Xprev, self.Uprev = None, None
self.R = np.eye(self.m)
# environment parameters
self.dock_loc = {}
self.dock_loc['A'] = np.array([10., 15.])
self.dock_loc['B'] = np.array([20., 15.])
self.dock_loc['C'] = np.array([30., 15.])
# robot parameters
J = np.array([[0.1083, 0.0, 0.0],
[0.0, 0.1083, 0.0],
[0.0, 0.0, 0.1083]])
Jxx, Jyy, Jzz = np.diag(J)
Jinv = np.linalg.inv(J)
mass = 7.0
hard_limit_vel = 5000. # 0.50
hard_limit_accel = 1000. # 0.10
hard_limit_omega = 45*np.pi/180
hard_limit_alpha = 50*np.pi/180
self.arm_length = 0.1
self.params = {}
self.params['mass'] = mass
self.params['J'] = J
self.params['hard_limit_vel'] = hard_limit_vel
self.params['hard_limit_accel'] = hard_limit_accel
self.params['hard_limit_omega'] = hard_limit_omega
self.params['hard_limit_alpha'] = hard_limit_alpha
self.params['dh'] = self.dh
# state box constraints
if self.mode == 'double_integrator':
self.Xlb = np.array([-np.inf,-np.inf,
-hard_limit_vel/np.sqrt(2),-hard_limit_vel/np.sqrt(2)])
self.Xub = np.array([np.inf,np.inf,
hard_limit_vel/np.sqrt(2),hard_limit_vel/np.sqrt(2)])
# control box constraints
Jmin = np.min(np.diag(J))
self.Ulb = np.array([-mass*hard_limit_accel/np.sqrt(2), -mass*hard_limit_accel/np.sqrt(2)])
self.Uub = np.array([mass*hard_limit_accel/np.sqrt(2), mass*hard_limit_accel/np.sqrt(2)])
else:
self.Xlb = np.array([-np.inf,-np.inf,-np.inf,
-1.0,-1.0,-1.0,-1.0,
-hard_limit_vel/np.sqrt(3),-hard_limit_vel/np.sqrt(3),-hard_limit_vel/np.sqrt(3),
-hard_limit_omega/np.sqrt(3),-hard_limit_omega/np.sqrt(3),-hard_limit_omega/np.sqrt(3)])
self.Xub = np.array([np.inf,np.inf,np.inf,
1.0,1.0,1.0,1.0,
hard_limit_vel/np.sqrt(3),hard_limit_vel/np.sqrt(3),hard_limit_vel/np.sqrt(3),
hard_limit_omega/np.sqrt(3),hard_limit_omega/np.sqrt(3),hard_limit_omega/np.sqrt(3)])
# control box constraints
Jmin = np.min(np.diag(J))
self.Ulb = np.array([-mass*hard_limit_accel/np.sqrt(3), -mass*hard_limit_accel/np.sqrt(3), -mass*hard_limit_accel/np.sqrt(3), -Jmin*hard_limit_alpha/np.sqrt(3), -Jmin*hard_limit_alpha/np.sqrt(3), -Jmin*hard_limit_alpha/np.sqrt(3)])
self.Uub = np.array([mass*hard_limit_accel/np.sqrt(3), mass*hard_limit_accel/np.sqrt(3), mass*hard_limit_accel/np.sqrt(3), Jmin*hard_limit_alpha/np.sqrt(3), Jmin*hard_limit_alpha/np.sqrt(3), Jmin*hard_limit_alpha/np.sqrt(3)])
self.cost = 0.
self.w, self.lbw, self.ubw = None, None, None
self.g, self.lbg, self.ubg = None, None, None
def init_straightline(self):
N_plan = len(self.X)
self.Xprev, self.Uprev = np.zeros((self.n, N_plan)), np.zeros((self.m, N_plan-1))
if self.mode == 'double_integrator':
for ii in range(self.n):
self.Xprev[ii,:] = np.linspace(self.Xi[ii], np.array(self.Xref)[ii], num=N_plan).flatten()
else:
for ii in range(3):
self.Xprev[ii,:] = np.linspace(self.Xi[ii], np.array(self.Xref)[ii], num=N_plan).flatten()
self.Xprev[7+ii,:] = np.linspace(self.Xi[7+ii], np.array(self.Xref)[7+ii], num=N_plan).flatten()
self.Xprev[10+ii,:] = np.linspace(self.Xi[10+ii], np.array(self.Xref)[10+ii], num=N_plan).flatten()
qi = self.Xi[3:7].flatten()
qf = | np.array(self.Xref) | numpy.array |
#=============================================================================
# Project: SoPHI
# File: phi_gen.py
# Author: <NAME> (<EMAIL>)
# Contributors:
#-----------------------------------------------------------------------------
# Description:
#-----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import fftconvolve, tukey, savgol_filter
from itertools import combinations
from .tools import *
import SPGPylibs.GENtools.plot_lib as plib
# __all__ = ['bar', 'baz']
def shift(matrix, shift=[0, 0], fill_value=0):
'''Shift operator
Shift an image in 2D naively as in SOLO-PHI instrument.
Faster and more efficient methods can be used in normal CPU.
Input is a vector shift=[x,y] of x and y displacement
+x -> positive; +y -> positive
fill_value = float.
This method does not have any boundary condition.
'''
try:
dimy, dimx = matrix.shape
except:
raise ValueError("Input is not 2D matrix")
try:
nx = shift[1]
ny = shift[0]
except:
raise ValueError("Provided shift not in rigth format 'shift=[0, 0]' of not present")
e = np.empty_like(matrix)
if nx > 0:
e[:nx, :] = fill_value
e[nx:, :] = matrix[:-nx, :]
elif nx < 0:
e[nx:, :] = fill_value
e[:nx, :] = matrix[-nx:, :]
else:
e = matrix
s = | np.empty_like(matrix) | numpy.empty_like |
from __future__ import division
from distanceclosure.distance import pairwise_proximity, _jaccard_coef_scipy, _jaccard_coef_binary, _jaccard_coef_set, _jaccard_coef_weighted_numpy
import numpy as np
from scipy.sparse import csr_matrix
B = np.array([
[1,1,1,1],
[1,1,1,0],
[1,1,0,0],
[1,0,0,0],
])
N = np.array([
[2,3,4,2],
[2,3,4,2],
[2,3,3,2],
[2,1,3,4]
])
W = np.array([
[4,3,2,1],
[3,2,1,0],
[2,1,0,0],
[1,0,0,0],
])
def test_jaccard_scipy():
""" Test Jaccard: scipy.spatial.dist.jaccard """
u = np.array([2,3,4,5])
v = np.array([2,3,4,2])
d = _jaccard_coef_scipy(u,v,min_support=1)
assert (d == 0.75)
def test_jaccard_binary():
""" Test Jaccard: binary (bitwise) coef """
u = np.array([1,1,1,1])
v = np.array([1,1,1,0])
d = _jaccard_coef_binary(u,v,min_support=1)
assert (d == 0.75)
def test_jaccard_set():
""" Test Jaccard: set coef """
u = np.array([4,3,2,1])
v = np.array([3,2,1,0])
d = _jaccard_coef_set(u,v,min_support=1)
assert (d == 0.6)
def test_jaccard_weighted():
""" Test Jaccard: weighted coef """
u = np.array([4,3,2,1])
v = | np.array([3,2,1,0]) | numpy.array |
import os
import numpy as np
from colorama import Back, Fore
from config import cfg
from dataset import detection_set
from dataset.voc.pascal_voc import PascalVoc
from dataset.coco.coco import COCO
def get_dataset(dataset_sequence, params, mode='train', only_classes=False):
only_cls_str = 'classes for ' if only_classes else ''
print(Back.WHITE + Fore.BLACK + 'Loading {}image dataset...'.format(only_cls_str))
dataset_name = dataset_sequence.split('_')[0]
if dataset_name == 'detect':
dataset = detection_set.DetectionSet(params)
short_name = 'det_set'
print('Loaded Detection dataset.')
elif dataset_name == 'voc':
year = dataset_sequence.split('_')[1]
image_set = dataset_sequence[(len(dataset_name) + len(year) + 2):]
if 'devkit_path' in params:
params['devkit_path'] = os.path.join(cfg.DATA_DIR, params['devkit_path'])
else:
print(Back.YELLOW + Fore.BLACK + 'WARNING! '
+ 'Cannot find "devkit_path" in additional parameters. '
+ 'Try to use default path (./data/VOCdevkit)...')
params['devkit_path'] = os.path.join(cfg.DATA_DIR, 'VOCdevkit'+year)
dataset = PascalVoc(image_set, year, params, only_classes)
short_name = dataset_name + '_' + year
print('Loaded {} PascalVoc {} {} dataset.'.format(only_cls_str, year, image_set))
elif dataset_name == 'coco':
year = dataset_sequence.split('_')[1]
image_set = dataset_sequence[(len(dataset_name) + len(year) + 2):]
if 'data_path' in params:
params['data_path'] = os.path.join(cfg.DATA_DIR, params['data_path'])
else:
print(Back.YELLOW + Fore.BLACK + 'WARNING! '
+ 'Cannot find "data_path" in additional parameters. '
+ 'Try to use default path (./data/COCO)...')
params['data_path'] = os.path.join(cfg.DATA_DIR, 'COCO')
params['dev_path'] = os.path.abspath(cfg.DATA_DIR)
dataset = COCO(image_set, year, params, only_classes)
short_name = dataset_name + '_' + year
print('Loaded {}COCO {} {} dataset.'.format(only_cls_str, year, image_set))
else:
raise NotImplementedError(Back.RED + 'Not implement for "{}" dataset!'.format(dataset_name))
if not only_classes:
if mode == 'train' and cfg.TRAIN.USE_FLIPPED:
print(Back.WHITE + Fore.BLACK + 'Appending horizontally-flipped '
+ 'training examples...')
dataset = _append_flipped_images(dataset)
print('Done.')
print(Back.WHITE + Fore.BLACK + 'Preparing image data...')
dataset = _prepare_data(dataset)
print('Done.')
if mode == 'train':
print(Back.WHITE + Fore.BLACK + 'Filtering image data '
+ '(remove images without boxes)...')
dataset = _filter_data(dataset)
print('Done.')
return dataset, short_name
def _append_flipped_images(dataset):
for i in range(len(dataset)):
img = dataset.image_data[i].copy()
img['index'] = len(dataset)
img['id'] += '_f'
img['flipped'] = True
boxes = img['boxes'].copy()
oldx1 = boxes[:, 0].copy()
oldx2 = boxes[:, 2].copy()
boxes[:, 0] = img['width'] - oldx2 - 1
boxes[:, 2] = img['width'] - oldx1 - 1
assert (boxes[:, 2] >= boxes[:, 0]).all()
img['boxes'] = boxes
""" do the same for selective search boxes """
ss_boxes = img['ss_boxes'].copy()
oldx1 = ss_boxes[:, 0].copy()
oldx2 = ss_boxes[:, 2].copy()
ss_boxes[:, 0] = img['width'] - oldx2 - 1
ss_boxes[:, 2] = img['width'] - oldx1 - 1
assert (ss_boxes[:, 2] >= ss_boxes[:, 0]).all()
img['ss_boxes'] = ss_boxes
dataset.image_data.append(img)
dataset._image_index.append(img['id'])
return dataset
def _prepare_data(dataset):
for i in range(len(dataset)):
# TODO: is this really need!?
# max overlap with gt over classes (columns)
max_overlaps = dataset.image_data[i]['gt_overlaps'].max(axis=1)
# gt class that had the max overlap
max_classes = dataset.image_data[i]['gt_overlaps'].argmax(axis=1)
dataset.image_data[i]['max_classes'] = max_classes
dataset.image_data[i]['max_overlaps'] = max_overlaps
# sanity checks
# max overlap of 0 => class should be zero (background)
zero_inds = np.where(max_overlaps == 0)[0]
assert all(max_classes[zero_inds] == 0)
# max overlap > 0 => class should not be zero (must be a fg class)
nonzero_inds = | np.where(max_overlaps > 0) | numpy.where |
"""
This file contains the methods used for estimating aberration prevalence in a
two-echelon supply chain. See descriptions for particular inputs.
"""
######### NEED TO ADD CAPACITY TO HANDLE DIFFERENT DIAGNOSTIC DEVICES @ DIFFERENT DATA POINTS
import numpy as np
import scipy.optimize as spo
import scipy.stats as spstat
import scipy.special as sps
import time
# THESE IMPORTS ARE FOR DEVELOPING NEW CODE, ETC.;
# NEED TO BE CHANGED BACK TO THOSE BELOW BEFORE UPLOADING TO GITHUB
# todo: Change these import references before submitting a new version of logistigate
import sys
import os
SCRIPT_DIR = os.path.dirname(os.path.realpath(os.path.join(os.getcwd(), os.path.expanduser(__file__))))
sys.path.append(os.path.normpath(os.path.join(SCRIPT_DIR, 'logistigate','mcmcsamplers')))
import adjustedNUTS as adjnuts
import lmc as langevinMC
import metrohastings as mh
# THESE ARE FOR THE ACTUAL PACKAGE
# todo: Use the below import references
#import logistigate.mcmcsamplers.adjustedNUTS as adjnuts
#import logistigate.mcmcsamplers.lmc as langevinMC
#import logistigate.mcmcsamplers.metrohastings as mh
#import nuts
########################### PRIOR CLASSES ###########################
class prior_laplace:
"""
Defines the class instance of Laplace priors, with an associated mu (mean)
and scale in the logit-transfomed [0,1] range, and the following methods:
rand: generate random draws from the distribution
lpdf: log-likelihood of a given vector
lpdf_jac: Jacobian of the log-likelihood at the given vector
lpdf_hess: Hessian of the log-likelihood at the given vector
beta inputs may be a Numpy array of vectors
"""
def __init__(self, mu=sps.logit(0.1), scale=np.sqrt(5/2)):
self.mu = mu
self.scale = scale
def rand(self, n=1):
return np.random.laplace(self.mu, self.scale, n)
def expitrand(self, n=1): # transformed to [0,1] space
return sps.expit(np.random.laplace(self.mu, self.scale, n))
def lpdf(self,beta):
if beta.ndim == 1: # reshape to 2d
beta = np.reshape(beta,(1,-1))
lik = -(1/self.scale) * np.sum(np.abs(beta - self.mu),axis=1)
return np.squeeze(lik)
def lpdf_jac(self,beta):
if beta.ndim == 1: # reshape to 2d
beta = np.reshape(beta,(1,-1))
jac = -(1/self.scale) * np.squeeze(1*(beta>=self.mu) - 1*(beta<=self.mu))
return np.squeeze(jac)
def lpdf_hess(self,beta):
if beta.ndim == 1: # reshape to 2d
beta = np.reshape(beta,(1,-1))
k,n = len(beta[:,0]),len(beta[0])
hess = np.tile(np.zeros(shape=(n,n)),(k,1,1))
return np.squeeze(hess)
class prior_normal:
"""
Defines the class instance of Normal priors, with an associated mu (mean)
and var (variance) in the logit-transfomed [0,1], i.e. unbounded, range,
and the following methods:
rand: generate random draws from the distribution
lpdf: log-likelihood of a given vector
lpdf_jac: Jacobian of the log-likelihood at the given vector
lpdf_hess: Hessian of the log-likelihood at the given vector
beta inputs may be a Numpy array of vectors
"""
def __init__(self,mu=sps.logit(0.1),var=5):
self.mu = mu
self.var = var
def rand(self, n=1):
return np.random.normal(self.mu, np.sqrt(self.var), n)
def expitrand(self, n=1): # transformed to [0,1] space
return sps.expit(np.random.normal(self.mu, np.sqrt(self.var), n))
def lpdf(self,beta):
if beta.ndim == 1: # reshape to 2d
beta = np.reshape(beta,(1,-1))
lik = -(1/(2*self.var)) * np.sum((beta - (self.mu))**2,axis=1)
return np.squeeze(lik)
def lpdf_jac(self,beta):
if beta.ndim == 1: # reshape to 2d
beta = np.reshape(beta,(1,-1))
jac = -(1/self.var) * (beta - self.mu)
return np.squeeze(jac)
def lpdf_hess(self,beta):
if beta.ndim == 1: # reshape to 2d
beta = np.reshape(beta,(1,-1))
k,n = len(beta[:,0]),len(beta[0])
hess = np.tile(np.zeros(shape=(n,n)),(k,1,1))
for i in range(k):
hess[i] = np.diag( -(1/self.var) * beta[i])
return np.squeeze(hess)
########################### END PRIOR CLASSES ###########################
########################## UNTRACKED FUNCTIONS ##########################
def Untracked_LogLike(beta,numVec,posVec,sens,spec,transMat):
# for array of beta; beta should be [importers, outlets]
if beta.ndim == 1: # reshape to 2d
beta = np.reshape(beta,(1,-1))
n,m = transMat.shape
th, py = sps.expit(beta[:,:m]), sps.expit(beta[:,m:])
pMat = py + (1-py)*np.matmul(th,transMat.T)
pMatTilde = sens*pMat+(1-spec)*(1-pMat)
L = np.sum(np.multiply(posVec,np.log(pMatTilde))+np.multiply(np.subtract(numVec,posVec),\
np.log(1-pMatTilde)),axis=1)
return np.squeeze(L)
def Untracked_LogLike_Jac(beta,numVec,posVec,sens,spec,transMat):
# betaVec should be [importers, outlets]; can be used with array beta
if beta.ndim == 1: # reshape to 2d
beta = np.reshape(beta,(1,-1))
n,m = transMat.shape
k = beta.shape[0]
th, py = sps.expit(beta[:,:m]), sps.expit(beta[:,m:])
pMat = py + (1-py)*np.matmul(th,transMat.T)
pMatTilde = sens*pMat+(1-spec)*(1-pMat)
#Grab importers partials first, then outlets
impPartials = (sens+spec-1)*np.sum( np.reshape([transMat]*k,(k,n,m))*\
np.reshape((th-th**2),(k,1,m))*np.tile(np.reshape((1-py),(k,n,1)),(m))*\
np.reshape((posVec[:,None]/pMatTilde.T-(numVec-posVec)[:,None]/(1-pMatTilde).T).T,(k,n,1)),axis=1)
outletPartials = (sens+spec-1)*(1-np.matmul(transMat,th.T)).T*(py-py**2)*\
(posVec/pMatTilde-(numVec-posVec)/(1-pMatTilde))
return np.squeeze(np.concatenate((impPartials,outletPartials),axis=1))
def Untracked_LogLike_Hess(betaVec,numVec,posVec,sens,spec,transMat):
# betaVec should be [importers, outlets]; NOT for array beta
n,m = transMat.shape
th = betaVec[:m]
py = betaVec[m:]
zVec = sps.expit(py)+(1-sps.expit(py))*np.matmul(transMat,sps.expit(th))
zVecTilde = sens*zVec+(1-spec)*(1-zVec)
sumVec = np.matmul(transMat,sps.expit(th))
#initialize a Hessian matrix
hess = np.zeros((n+m,n+m))
# get off-diagonal entries first; importer-outlet entries
for triRow in range(n):
for triCol in range(m):
outBeta,impBeta = py[triRow],th[triCol]
outP,impP = sps.expit(outBeta),sps.expit(impBeta)
s,r=sens,spec
c1 = transMat[triRow,triCol]*(s+r-1)*(sps.expit(impBeta)-sps.expit(impBeta)**2)
yDat,nSam = posVec[triRow],numVec[triRow]
elem = c1*(1-outP)*(yDat*( (s+r-1)*(-sumVec[triRow]*(outP**2-outP) - outP + outP**2) )\
/( s*(sumVec[triRow]*(1 - outP) + outP) +\
(1-r)*(-sumVec[triRow]*(1 - outP) + 1 - outP) )**2 -\
(nSam - yDat)*((-r + 1-s)*(-sumVec[triRow]*(-outP + outP**2)-outP+outP**2))\
/(-s*(sumVec[triRow]*(1 - outP) + outP) - (1-r)*(-sumVec[triRow]*(1 - outP) +\
1 - outP) + 1)**2) +\
c1*(yDat/(s*(sumVec[triRow]*(1 - outP) + outP) + (-r + 1)*(-sumVec[triRow]*(1 - outP) +\
1 - outP)) - (nSam - yDat)/( -s*(sumVec[triRow]*(1 - outP) +\
outP) - (1-r)*(-sumVec[triRow]*(1 - outP) + 1 - outP) + 1))*( outP**2 - outP)
hess[m+triRow,triCol] = elem
hess[triCol,m+triRow] = elem
# get off-diagonals for importer-importer entries
for triCol in range(m-1):
for triCol2 in range(triCol+1,m):
elem = 0
for i in range(n):
nextPart = (sens+spec-1)*transMat[i,triCol]*(1-sps.expit(py[i]))*(sps.expit(th[triCol])-sps.expit(th[triCol])**2)*\
(-posVec[i]*(sens+spec-1)*(1-sps.expit(py[i]))*transMat[i,triCol2]*(sps.expit(th[triCol2]) - sps.expit(th[triCol2])**2) /\
(zVecTilde[i]**2)
- (numVec[i]-posVec[i])*(sens+spec-1)*(1-sps.expit(py[i]))*transMat[i,triCol2]*(sps.expit(th[triCol2]) - sps.expit(th[triCol2])**2) /\
((1-zVecTilde[i])**2) )
elem += nextPart
hess[triCol,triCol2] = elem
hess[triCol2,triCol] = elem
# importer diagonals next
impPartials = np.zeros(m)
for imp in range(m):
currPartial = 0
for outlet in range(n):
outBeta,impBeta = py[outlet],th[imp]
outP,impP = sps.expit(outBeta),sps.expit(impBeta)
s,r=sens,spec
c1 = transMat[outlet,imp]*(s+r-1)*(1-outP)
c3 = (1-outP)*transMat[outlet,imp]
yDat,nSam = posVec[outlet],numVec[outlet]
currElem = c1*(yDat/(zVecTilde[outlet]) - (nSam - yDat)/(1-zVecTilde[outlet]))\
*(impP - 3*(impP**2) + 2*(impP**3)) +\
c1*(impP - impP**2)*(yDat*((s+r-1)*c3*(\
(impP**2)-impP) )/(zVecTilde[outlet])**2 -\
(nSam - yDat)*((s+r-1)*(c3*impP - c3*(impP**2)))/\
(1-zVecTilde[outlet])**2)
currPartial += currElem
impPartials[imp] = currPartial
# outlet diagonals next
outletPartials = np.zeros(n)
for outlet in range(n):
outBeta = py[outlet]
outP = sps.expit(outBeta)
s,r=sens,spec
c1 = sumVec[outlet]
c2 = (r + s - 1)
yDat,nSam = posVec[outlet],numVec[outlet]
currPartial = (1-c1)*(yDat/(zVecTilde[outlet]) -\
(nSam - yDat)/(1-zVecTilde[outlet]))*c2*(outP -\
3*(outP**2) + 2*(outP**3)) + \
(1-c1)*(outP - outP**2 )*(yDat*(-c2*(c1*(-outP + outP**2 )+ outP -outP**2 ) )/\
(zVecTilde[outlet])**2 - (nSam - yDat)*(c2*(c1*(-outP + outP**2) +\
outP - outP**2 ))/( -s*(c1*(1 - outP) +\
outP) - (1-r)*(1-c1*(1 - outP) - outP) + 1 )**2)*c2
outletPartials[outlet] = currPartial
diags = np.diag(np.concatenate((impPartials,outletPartials)))
hess = (hess + diags)
return hess
def Untracked_NegLogLike(betaVec,numVec,posVec,sens,spec,transMat):
return -1*Untracked_LogLike(betaVec,numVec,posVec,sens,spec,transMat)
def Untracked_NegLogLike_Jac(betaVec,numVec,posVec,sens,spec,transMat):
return -1*Untracked_LogLike_Jac(betaVec,numVec,posVec,sens,spec,transMat)
def Untracked_NegLogLike_Hess(betaVec,numVec,posVec,sens,spec,transMat):
return -1*Untracked_LogLike_Hess(betaVec,numVec,posVec,sens,spec,transMat)
def Untracked_LogPost(beta,numVec,posVec,sens,spec,transMat,prior):
return prior.lpdf(beta)\
+Untracked_LogLike(beta,numVec,posVec,sens,spec,transMat)
def Untracked_LogPost_Grad(beta, nsamp, ydata, sens, spec, A,prior):
return prior.lpdf_jac(beta)\
+Untracked_LogLike_Jac(beta,nsamp,ydata,sens,spec,A)
def Untracked_LogPost_Hess(beta, nsamp, ydata, sens, spec, A,prior):
return prior.lpdf_hess(beta)\
+Untracked_LogLike_Hess(beta,nsamp,ydata,sens,spec,A)
def Untracked_NegLogPost(betaVec,numVec,posVec,sens,spec,transMat,prior):
return -1*Untracked_LogPost(betaVec,numVec,posVec,sens,spec,transMat,prior)
def Untracked_NegLogPost_Grad(beta, nsamp, ydata, sens, spec, A,prior):
return -1*Untracked_LogPost_Grad(beta, nsamp, ydata, sens, spec, A,prior)
def Untracked_NegLogPost_Hess(beta, nsamp, ydata, sens, spec, A,prior):
return -1*Untracked_LogPost_Hess(beta, nsamp, ydata, sens, spec, A,prior)
######################## END UNTRACKED FUNCTIONS ########################
########################### TRACKED FUNCTIONS ###########################
def Tracked_LogLike(beta,numMat,posMat,sens,spec):
# betaVec should be [importers, outlets]; can be used with array beta
if beta.ndim == 1: # reshape to 2d
beta = np.reshape(beta,(1,-1))
n,m = numMat.shape
k = beta.shape[0]
th, py = sps.expit(beta[:,:m]), sps.expit(beta[:,m:])
pMat = np.reshape(np.tile(th,(n)),(k,n,m)) + np.reshape(np.tile(1-th,(n)),(k,n,m)) *\
np.transpose(np.reshape(np.tile(py,(m)),(k,m,n)),(0,2,1))
#each term is a k-by-n-by-m array
pMatTilde = sens*pMat+(1-spec)*(1-pMat)
L = np.sum(np.multiply(posMat,np.log(pMatTilde))+np.multiply(np.subtract(numMat,posMat),\
np.log(1-pMatTilde)),axis=(1,2))
#each term is a k-by-n-by-m array, with the n-by-m matrices then summed
return np.squeeze(L)
def Tracked_LogLike_Jac(beta, numMat, posMat, sens, spec):
# betaVec should be [importers, outlets]; can be used with array beta
if beta.ndim == 1: # reshape to 2d
beta = np.reshape(beta,(1,-1))
n,m = numMat.shape
k = beta.shape[0]
th, py = sps.expit(beta[:,:m]), sps.expit(beta[:,m:])
pMat = np.reshape(np.tile(th,(n)),(k,n,m)) + np.reshape(np.tile(1-th,(n)),(k,n,m)) *\
np.transpose(np.reshape(np.tile(py,(m)),(k,m,n)),(0,2,1))
pMatTilde = sens*pMat+(1-spec)*(1-pMat)
#Grab importers partials first, then outlets
impPartials = (sens+spec-1)*np.sum( | np.reshape((th-th**2),(k,1,m)) | numpy.reshape |
# -*- coding: utf-8 -*-
r"""Define an instrument for resolution calculations
"""
import numpy as np
from scipy.linalg import block_diag as blkdiag
from ..crystal import Sample
from ..energy import Energy
from .analyzer import Analyzer
from .exceptions import ScatteringTriangleError
from .general import GeneralInstrument
from .monochromator import Monochromator
from .plot import PlotInstrument
from .tools import GetTau, _CleanArgs, _Dummy, _modvec, _scalar, _star, _voigt
class TripleAxisInstrument(GeneralInstrument, PlotInstrument):
u"""An object that represents a Triple Axis Spectrometer (TAS) instrument
experimental configuration, including a sample.
Parameters
----------
efixed : float, optional
Fixed energy, either ei or ef, depending on the instrument
configuration. Default: 14.7
sample : obj, optional
Sample lattice constants, parameters, mosaic, and orientation
(reciprocal-space orienting vectors). Default: A crystal with
a,b,c = 6,7,8 and alpha,beta,gamma = 90,90,90 and orientation
vectors u=[1 0 0] and v=[0 1 0].
hcol : list(4)
Horizontal Soller collimations in minutes of arc starting from the
neutron guide. Default: [40 40 40 40]
vcol : list(4), optional
Vertical Soller collimations in minutes of arc starting from the
neutron guide. Default: [120 120 120 120]
mono_tau : str or float, optional
The monochromator reciprocal lattice vector in Å\ :sup:`-1`,
given either as a float, or as a string for common monochromator types.
Default: 'PG(002)'
mono_mosaic : float, optional
The mosaic of the monochromator in minutes of arc. Default: 25
ana_tau : str or float, optional
The analyzer reciprocal lattice vector in Å\ :sup:`-1`,
given either as a float, or as a string for common analyzer types.
Default: 'PG(002)'
ana_mosaic : float, optional
The mosaic of the monochromator in minutes of arc. Default: 25
Attributes
----------
method
moncor
mono
ana
hcol
vcol
arms
efixed
sample
orient1
orient2
infin
beam
detector
monitor
Smooth
guide
description_string
Methods
-------
calc_resolution
calc_resolution_in_Q_coords
calc_projections
get_angles_and_Q
get_lattice
get_resolution_params
get_resolution
plot_projections
plot_ellipsoid
plot_instrument
resolution_convolution
resolution_convolution_SMA
plot_slice
"""
def __init__(self, efixed=14.7, sample=None, hcol=None, vcol=None, mono='PG(002)',
mono_mosaic=25, ana='PG(002)', ana_mosaic=25, **kwargs):
if sample is None:
sample = Sample(6, 7, 8, 90, 90, 90)
sample.u = [1, 0, 0]
sample.v = [0, 1, 0]
if hcol is None:
hcol = [40, 40, 40, 40]
if vcol is None:
vcol = [120, 120, 120, 120]
self.mono = Monochromator(mono, mono_mosaic)
self.ana = Analyzer(ana, ana_mosaic)
self.hcol = np.array(hcol)
self.vcol = np.array(vcol)
self.efixed = efixed
self.sample = sample
self.orient1 = np.array(sample.u)
self.orient2 = np.array(sample.v)
self.detector = _Dummy('Detector')
self.monitor = _Dummy('Monitor')
self.guide = _Dummy('Guide')
for key, value in kwargs.items():
setattr(self, key, value)
def __repr__(self):
return "Instrument('tas', engine='neutronpy', efixed={0})".format(self.efixed)
def __eq__(self, right):
self_parent_keys = sorted(list(self.__dict__.keys()))
right_parent_keys = sorted(list(right.__dict__.keys()))
if not np.all(self_parent_keys == right_parent_keys):
return False
for key, value in self.__dict__.items():
right_parent_val = getattr(right, key)
if not np.all(value == right_parent_val):
print(value, right_parent_val)
return False
return True
def __ne__(self, right):
return not self.__eq__(right)
@property
def mono(self):
u"""A structure that describes the monochromator.
Attributes
----------
tau : str or float
The monochromator reciprocal lattice vector in Å\ :sup:`-1`.
Instead of a numerical input one can use one of the following
keyword strings:
+------------------+--------------+-----------+
| String | τ | |
+==================+==============+===========+
| Be(002) | 3.50702 | |
+------------------+--------------+-----------+
| Co0.92Fe0.08(200)| 3.54782 | (Heusler) |
+------------------+--------------+-----------+
| Cu(002) | 3.47714 | |
+------------------+--------------+-----------+
| Cu(111) | 2.99913 | |
+------------------+--------------+-----------+
| Cu(220) | 4.91642 | |
+------------------+--------------+-----------+
| Cu2MnAl(111) | 1.82810 | (Heusler) |
+------------------+--------------+-----------+
| Ge(111) | 1.92366 | |
+------------------+--------------+-----------+
| Ge(220) | 3.14131 | |
+------------------+--------------+-----------+
| Ge(311) | 3.68351 | |
+------------------+--------------+-----------+
| Ge(511) | 5.76968 | |
+------------------+--------------+-----------+
| Ge(533) | 7.28063 | |
+------------------+--------------+-----------+
| PG(002) | 1.87325 | |
+------------------+--------------+-----------+
| PG(004) | 3.74650 | |
+------------------+--------------+-----------+
| PG(110) | 5.49806 | |
+------------------+--------------+-----------+
| Si(111) | 2.00421 | |
+------------------+--------------+-----------+
mosaic : int
The monochromator mosaic in minutes of arc.
vmosaic : int
The vertical mosaic of monochromator in minutes of arc. If
this field is left unassigned, an isotropic mosaic is assumed.
dir : int
Direction of the crystal (left or right, -1 or +1, respectively).
Default: -1 (left-handed coordinate frame).
rh : float
Horizontal curvature of the monochromator in cm.
rv : float
Vertical curvature of the monochromator in cm.
"""
return self._mono
@mono.setter
def mono(self, value):
self._mono = value
@property
def ana(self):
u"""A structure that describes the analyzer and contains fields as in
:attr:`mono` plus optional fields.
Attributes
----------
thickness: float
The analyzer thickness in cm for ideal-crystal reflectivity
corrections (Section II C 3). If no reflectivity corrections are to
be made, this field should remain unassigned or set to a negative
value.
Q : float
The kinematic reflectivity coefficient for this correction. It is
given by
.. math:: Q = \\frac{4|F|**2}{V_0} \\frac{(2\\pi)**3}{\\tau**3},
where V0 is the unit cell volume for the analyzer crystal, F is the
structure factor of the analyzer reflection, and τ is the analyzer
reciprocal lattice vector. For PG(002) Q = 0.1287. Leave this field
unassigned or make it negative if you don’t want the correction
done.
horifoc : bool
A flag that is set to 1 if a horizontally focusing analyzer is used
(Section II D). In this case ``hcol[2]`` (see below) is the angular
size of the analyzer, as seen from the sample position. If the
field is unassigned or equal to -1, a flat analyzer is assumed.
Note that this option is only available with the Cooper-Nathans
method.
dir : int
Direction of the crystal (left or right, -1 or +1, respectively).
Default: -1 (left-handed coordinate frame).
rh : float
Horizontal curvature of the analyzer in cm.
rv : float
Vertical curvature of the analyzer in cm.
"""
return self._ana
@ana.setter
def ana(self, value):
self._ana = value
@property
def method(self):
"""Selects the computation method.
If ``method=0`` or left undefined, a Cooper-Nathans calculation is
performed. For a Popovici calculation set ``method=1``.
"""
return self._method
@method.setter
def method(self, value):
self._method = value
@property
def moncor(self):
"""Selects the type of normalization used to calculate ``R0``
If ``moncor=1`` or left undefined, ``R0`` is calculated in
normalization to monitor counts (Section II C 2). 1/k\ :sub:`i` monitor
efficiency correction is included automatically. To normalize ``R0`` to
source flux (Section II C 1), use ``moncor=0``.
"""
return self._moncar
@moncor.setter
def moncor(self, value):
self._moncar = value
@property
def hcol(self):
r""" The horizontal Soller collimations in minutes of arc (FWHM beam
divergence) starting from the in-pile collimator. In case of a
horizontally-focusing analyzer ``hcol[2]`` is the angular size of the
analyzer, as seen from the sample position. If the beam divergence is
limited by a neutron guide, the corresponding element of :attr:`hcol`
is the negative of the guide’s *m*-value. For example, for a 58-Ni
guide ( *m* = 1.2 ) before the monochromator, ``hcol[0]`` should be
-1.2.
"""
return self._hcol
@hcol.setter
def hcol(self, value):
self._hcol = value
@property
def vcol(self):
"""The vertical Soller collimations in minutes of arc (FWHM beam
divergence) starting from the in-pile collimator. If the beam
divergence is limited by a neutron guide, the corresponding element of
:attr:`vcol` is the negative of the guide’s *m*-value. For example, for
a 58-Ni guide ( *m* = 1.2 ) before the monochromator, ``vcol[0]``
should be -1.2.
"""
return self._vcol
@vcol.setter
def vcol(self, value):
self._vcol = value
@property
def arms(self):
"""distances between the source and monochromator, monochromator
and sample, sample and analyzer, analyzer and detector, and
monochromator and monitor, respectively. The 5th element is only needed
if ``moncor=1``
"""
return self._arms
@arms.setter
def arms(self, value):
self._arms = value
@property
def efixed(self):
"""the fixed incident or final neutron energy, in meV.
"""
return self._efixed
@efixed.setter
def efixed(self, value):
self._efixed = value
@property
def sample(self):
"""A structure that describes the sample.
Attributes
----------
mosaic
FWHM sample mosaic in the scattering plane
in minutes of arc. If left unassigned, no sample
mosaic corrections (section II E) are performed.
vmosaic
The vertical sample mosaic in minutes of arc.
If left unassigned, isotropic mosaic is assumed.
dir
The direction of the crystal (left or right, -1 or +1,
respectively). Default: -1 (left-handed coordinate frame).
"""
return self._sample
@sample.setter
def sample(self, value):
self._sample = value
@property
def orient1(self):
"""Miller indexes of the first reciprocal-space orienting vector for
the S coordinate system, as explained in Section II G.
"""
return self._sample.u
@orient1.setter
def orient1(self, value):
self._sample.u = np.array(value)
@property
def orient2(self):
"""Miller indexes of the second reciprocal-space orienting vector
for the S coordinate system, as explained in Section II G.
"""
return self._sample.v
@orient2.setter
def orient2(self, value):
self._sample.v = np.array(value)
@property
def infin(self):
"""a flag set to -1 or left unassigned if the final energy is fixed, or
set to +1 in a fixed-incident setup.
"""
return self._infin
@infin.setter
def infin(self, value):
self._infin = value
@property
def guide(self):
r"""A structure that describes the source
"""
return self._guide
@guide.setter
def guide(self, value):
self._guide = value
@property
def detector(self):
"""A structure that describes the detector
"""
return self._detector
@detector.setter
def detector(self, value):
self._detector = value
@property
def monitor(self):
"""A structure that describes the monitor
"""
return self._monitor
@monitor.setter
def monitor(self, value):
self._monitor = value
@property
def Smooth(self):
u"""Defines the smoothing parameters as explained in Section II H. Leave this
field unassigned if you don’t want this correction done.
* ``Smooth.E`` is the smoothing FWHM in energy (meV). A small number
means “no smoothing along this direction”.
* ``Smooth.X`` is the smoothing FWHM along the first orienting vector
(x0 axis) in Å\ :sup:`-1`.
* ``Smooth.Y`` is the smoothing FWHM along the y axis in Å\ :sup:`-1`.
* ``Smooth.Z`` is the smoothing FWHM along the vertical direction in
Å\ :sup:`-1`.
"""
return self._Smooth
@Smooth.setter
def Smooth(self, value):
self._Smooth = value
def get_lattice(self):
r"""Extracts lattice parameters from EXP and returns the direct and
reciprocal lattice parameters in the form used by _scalar.m, _star.m,
etc.
Returns
-------
[lattice, rlattice] : [class, class]
Returns the direct and reciprocal lattice sample classes
Notes
-----
Translated from ResLib 3.4c, originally authored by <NAME>,
1999-2007, Oak Ridge National Laboratory
"""
lattice = Sample(self.sample.a,
self.sample.b,
self.sample.c,
np.deg2rad(self.sample.alpha),
np.deg2rad(self.sample.beta),
np.deg2rad(self.sample.gamma))
rlattice = _star(lattice)[-1]
return [lattice, rlattice]
def _StandardSystem(self):
r"""Returns rotation matrices to calculate resolution in the sample view
instead of the instrument view
Attributes
----------
EXP : class
Instrument class
Returns
-------
[x, y, z, lattice, rlattice] : [array, array, array, class, class]
Returns the rotation matrices and real and reciprocal lattice
sample classes
Notes
-----
Translated from ResLib 3.4c, originally authored by <NAME>,
1999-2007, Oak Ridge National Laboratory
"""
[lattice, rlattice] = self.get_lattice()
orient1 = self.orient1
orient2 = self.orient2
modx = _modvec(orient1, rlattice)
x = orient1 / modx
proj = _scalar(orient2, x, rlattice)
y = orient2 - x * proj
mody = _modvec(y, rlattice)
if len(np.where(mody <= 0)[0]) > 0:
raise ScatteringTriangleError('Orienting vectors are colinear')
y /= mody
z = np.array([ x[1] * y[2] - y[1] * x[2],
x[2] * y[0] - y[2] * x[0],
-x[1] * y[0] + y[1] * x[0]], dtype=np.float64)
proj = _scalar(z, x, rlattice)
z -= x * proj
proj = _scalar(z, y, rlattice)
z -= y * proj
modz = _modvec(z, rlattice)
z /= modz
return [x, y, z, lattice, rlattice]
def calc_resolution_in_Q_coords(self, Q, W):
r"""For a momentum transfer Q and energy transfers W, given experimental
conditions specified in EXP, calculates the Cooper-Nathans or Popovici
resolution matrix RM and resolution prefactor R0 in the Q coordinate
system (defined by the scattering vector and the scattering plane).
Parameters
----------
Q : ndarray or list of ndarray
The Q vectors in reciprocal space at which resolution should be
calculated, in inverse angstroms
W : float or list of floats
The energy transfers at which resolution should be calculated in meV
Returns
-------
[R0, RM] : list(float, ndarray)
Resolution pre-factor (R0) and resolution matrix (RM) at the given
reciprocal lattice vectors and energy transfers
Notes
-----
Translated from ResLib 3.4c, originally authored by <NAME>,
1999-2007, Oak Ridge National Laboratory
"""
CONVERT1 = np.pi / 60. / 180. / np.sqrt(8 * np.log(2))
CONVERT2 = 2.072
[length, Q, W] = _CleanArgs(Q, W)
RM = np.zeros((length, 4, 4), dtype=np.float64)
R0 = np.zeros(length, dtype=np.float64)
RM_ = np.zeros((4, 4), dtype=np.float64)
# the method to use
method = 0
if hasattr(self, 'method'):
method = self.method
# Assign default values and decode parameters
moncor = 1
if hasattr(self, 'moncor'):
moncor = self.moncor
alpha = np.array(self.hcol) * CONVERT1
beta = np.array(self.vcol) * CONVERT1
mono = self.mono
etam = np.array(mono.mosaic) * CONVERT1
etamv = np.copy(etam)
if hasattr(mono, 'vmosaic') and (method == 1 or method == 'Popovici'):
etamv = np.array(mono.vmosaic) * CONVERT1
ana = self.ana
etaa = np.array(ana.mosaic) * CONVERT1
etaav = np.copy(etaa)
if hasattr(ana, 'vmosaic'):
etaav = np.array(ana.vmosaic) * CONVERT1
sample = self.sample
infin = -1
if hasattr(self, 'infin'):
infin = self.infin
efixed = self.efixed
monitorw = 1.
monitorh = 1.
beamw = 1.
beamh = 1.
monow = 1.
monoh = 1.
monod = 1.
anaw = 1.
anah = 1.
anad = 1.
detectorw = 1.
detectorh = 1.
sshapes = np.repeat(np.eye(3, dtype=np.float64)[np.newaxis].reshape((1, 3, 3)), length, axis=0)
sshape_factor = 12.
L0 = 1.
L1 = 1.
L1mon = 1.
L2 = 1.
L3 = 1.
monorv = 1.e6
monorh = 1.e6
anarv = 1.e6
anarh = 1.e6
if hasattr(self, 'guide'):
beam = self.guide
if hasattr(beam, 'width'):
beamw = beam.width ** 2 / 12.
if hasattr(beam, 'height'):
beamh = beam.height ** 2 / 12.
bshape = np.diag([beamw, beamh])
if hasattr(self, 'monitor'):
monitor = self.monitor
if hasattr(monitor, 'width'):
monitorw = monitor.width ** 2 / 12.
monitorh = monitorw
if hasattr(monitor, 'height'):
monitorh = monitor.height ** 2 / 12.
monitorshape = np.diag([monitorw, monitorh])
if hasattr(self, 'detector'):
detector = self.detector
if hasattr(detector, 'width'):
detectorw = detector.width ** 2 / 12.
if hasattr(detector, 'height'):
detectorh = detector.height ** 2 / 12.
dshape = np.diag([detectorw, detectorh])
if hasattr(mono, 'width'):
monow = mono.width ** 2 / 12.
if hasattr(mono, 'height'):
monoh = mono.height ** 2 / 12.
if hasattr(mono, 'depth'):
monod = mono.depth ** 2 / 12.
mshape = np.diag([monod, monow, monoh])
if hasattr(ana, 'width'):
anaw = ana.width ** 2 / 12.
if hasattr(ana, 'height'):
anah = ana.height ** 2 / 12.
if hasattr(ana, 'depth'):
anad = ana.depth ** 2 / 12.
ashape = np.diag([anad, anaw, anah])
if hasattr(sample, 'shape_type'):
if sample.shape_type == 'cylindrical':
sshape_factor = 16.
elif sample.shape_type == 'rectangular':
sshape_factor = 12.
if hasattr(sample, 'width') and hasattr(sample, 'depth') and hasattr(sample, 'height'):
_sshape = np.diag([sample.depth, sample.width, sample.height]).astype(np.float64) ** 2 / sshape_factor
sshapes = np.repeat(_sshape[np.newaxis].reshape((1, 3, 3)), length, axis=0)
elif hasattr(sample, 'shape'):
_sshape = sample.shape.astype(np.float64) / sshape_factor
if len(_sshape.shape) == 2:
sshapes = np.repeat(_sshape[np.newaxis].reshape((1, 3, 3)), length, axis=0)
else:
sshapes = _sshape
if hasattr(self, 'arms') and method == 1:
arms = self.arms
L0, L1, L2, L3 = arms[:4]
L1mon = np.copy(L1)
if len(arms) > 4:
L1mon = np.copy(arms[4])
if hasattr(mono, 'rv'):
monorv = mono.rv
if hasattr(mono, 'rh'):
monorh = mono.rh
if hasattr(ana, 'rv'):
anarv = ana.rv
if hasattr(ana, 'rh'):
anarh = ana.rh
taum = GetTau(mono.tau)
taua = GetTau(ana.tau)
horifoc = -1
if hasattr(self, 'horifoc'):
horifoc = self.horifoc
if horifoc == 1:
alpha[2] = alpha[2] * np.sqrt(8. * np.log(2.) / 12.)
sm = self.mono.dir
ss = self.sample.dir
sa = self.ana.dir
for ind in range(length):
sshape = sshapes[ind, :, :]
# Calculate angles and energies
w = W[ind]
q = Q[ind]
ei = efixed
ef = efixed
if infin > 0:
ef = efixed - w
else:
ei = efixed + w
ki = np.sqrt(ei / CONVERT2)
kf = np.sqrt(ef / CONVERT2)
thetam = np.arcsin(taum / (2. * ki)) * sm
thetaa = np.arcsin(taua / (2. * kf)) * sa
s2theta = np.arccos(np.complex((ki ** 2 + kf ** 2 - q ** 2) / (2. * ki * kf))) * ss
if np.abs(np.imag(s2theta)) > 1e-12:
raise ScatteringTriangleError(
'KI,KF,Q triangle will not close. Change the value of KFIX,FX,QH,QK or QL.')
else:
s2theta = np.real(s2theta)
# correct sign of curvatures
monorh = monorh * sm
monorv = monorv * sm
anarh = anarh * sa
anarv = anarv * sa
thetas = s2theta / 2.
phi = np.arctan2(-kf * np.sin(s2theta), ki - kf * np.cos(s2theta))
# Calculate beam divergences defined by neutron guides
alpha[alpha < 0] = -alpha[alpha < 0] * 0.1 * 60. * (2. * np.pi / ki) / 0.427 / np.sqrt(3.)
beta[beta < 0] = -beta[beta < 0] * 0.1 * 60. * (2. * np.pi / ki) / 0.427 / np.sqrt(3.)
# Redefine sample geometry
psi = thetas - phi # Angle from sample geometry X axis to Q
rot = np.matrix([[np.cos(psi), np.sin(psi), 0],
[-np.sin(psi), np.cos(psi), 0],
[0, 0, 1]], dtype=np.float64)
# sshape=rot'*sshape*rot
sshape = np.matrix(rot) * np.matrix(sshape) * np.matrix(rot).H
# Definition of matrix G
G = np.matrix(
np.diag(1. / np.array([alpha[:2], beta[:2], alpha[2:], beta[2:]], dtype=np.float64).flatten() ** 2))
# Definition of matrix F
F = np.matrix(np.diag(1. / np.array([etam, etamv, etaa, etaav], dtype=np.float64) ** 2))
# Definition of matrix A
A = np.matrix([[ki / 2. / np.tan(thetam), -ki / 2. / np.tan(thetam), 0, 0, 0, 0, 0, 0],
[0, ki, 0, 0, 0, 0, 0, 0],
[0, 0, 0, ki, 0, 0, 0, 0],
[0, 0, 0, 0, kf / 2. / np.tan(thetaa), -kf / 2. / np.tan(thetaa), 0, 0],
[0, 0, 0, 0, kf, 0, 0, 0],
[0, 0, 0, 0, 0, 0, kf, 0]], dtype=np.float64)
# Definition of matrix C
C = np.matrix([[0.5, 0.5, 0, 0, 0, 0, 0, 0],
[0., 0., 1. / (2. * np.sin(thetam)), -1. / (2. * np.sin(thetam)), 0, 0, 0, 0],
[0, 0, 0, 0, 0.5, 0.5, 0, 0],
[0, 0, 0, 0, 0, 0, 1. / (2. * np.sin(thetaa)), -1. / (2. * np.sin(thetaa))]],
dtype=np.float64)
# Definition of matrix Bmatrix
Bmatrix = np.matrix([[np.cos(phi), np.sin(phi), 0, -np.cos(phi - s2theta), -np.sin(phi - s2theta), 0],
[-np.sin(phi), np.cos(phi), 0, np.sin(phi - s2theta), -np.cos(phi - s2theta), 0],
[0, 0, 1, 0, 0, -1],
[2. * CONVERT2 * ki, 0, 0, -2. * CONVERT2 * kf, 0, 0]], dtype=np.float64)
# Definition of matrix S
Sinv = np.matrix(blkdiag(np.array(bshape, dtype=np.float64), mshape, sshape, ashape, dshape)) # S-1 matrix
S = Sinv.I
# Definition of matrix T
T = np.matrix([[-1. / (2. * L0), 0, np.cos(thetam) * (1. / L1 - 1. / L0) / 2.,
np.sin(thetam) * (1. / L0 + 1. / L1 - 2. / (monorh * np.sin(thetam))) / 2., 0,
np.sin(thetas) / (2. * L1), np.cos(thetas) / (2. * L1), 0, 0, 0, 0, 0, 0],
[0, -1. / (2. * L0 * np.sin(thetam)), 0, 0,
(1. / L0 + 1. / L1 - 2. * np.sin(thetam) / monorv) / (2. * np.sin(thetam)), 0, 0,
-1. / (2. * L1 * np.sin(thetam)), 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, np.sin(thetas) / (2. * L2), -np.cos(thetas) / (2. * L2), 0,
np.cos(thetaa) * (1. / L3 - 1. / L2) / 2.,
np.sin(thetaa) * (1. / L2 + 1. / L3 - 2. / (anarh * np.sin(thetaa))) / 2., 0,
1. / (2. * L3), 0],
[0, 0, 0, 0, 0, 0, 0, -1. / (2. * L2 * np.sin(thetaa)), 0, 0,
(1. / L2 + 1. / L3 - 2. * np.sin(thetaa) / anarv) / (2. * np.sin(thetaa)), 0,
-1. / (2. * L3 * np.sin(thetaa))]], dtype=np.float64)
# Definition of matrix D
# Lots of index mistakes in paper for matrix D
D = np.matrix([[-1. / L0, 0, -np.cos(thetam) / L0, np.sin(thetam) / L0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, np.cos(thetam) / L1, np.sin(thetam) / L1, 0, np.sin(thetas) / L1, np.cos(thetas) / L1,
0, 0, 0, 0, 0, 0],
[0, -1. / L0, 0, 0, 1. / L0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, -1. / L1, 0, 0, 1. / L1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, np.sin(thetas) / L2, -np.cos(thetas) / L2, 0, -np.cos(thetaa) / L2,
np.sin(thetaa) / L2, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, np.cos(thetaa) / L3, np.sin(thetaa) / L3, 0, 1. / L3, 0],
[0, 0, 0, 0, 0, 0, 0, -1. / L2, 0, 0, 1. / L2, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1. / L3, 0, 1. / L3]], dtype=np.float64)
# Definition of resolution matrix M
if method == 1 or method == 'popovici':
K = S + T.H * F * T
H = np.linalg.inv(D * np.linalg.inv(K) * D.H)
Ninv = A * np.linalg.inv(H + G) * A.H
else:
H = G + C.H * F * C
Ninv = A * np.linalg.inv(H) * A.H
# Horizontally focusing analyzer if needed
if horifoc > 0:
Ninv = np.linalg.inv(Ninv)
Ninv[3:5, 3:5] = np.matrix([[(np.tan(thetaa) / (etaa * kf)) ** 2, 0],
[0, (1 / (kf * alpha[2])) ** 2]], dtype=np.float64)
Ninv = np.linalg.inv(Ninv)
Minv = Bmatrix * Ninv * Bmatrix.H
M = np.linalg.inv(Minv)
RM_ = np.copy(M)
# Calculation of prefactor, normalized to source
Rm = ki ** 3 / np.tan(thetam)
Ra = kf ** 3 / np.tan(thetaa)
R0_ = Rm * Ra * (2. * np.pi) ** 4 / (64. * np.pi ** 2 * np.sin(thetam) * np.sin(thetaa))
if method == 1 or method == 'popovici':
# Popovici
R0_ = R0_ * np.sqrt(np.linalg.det(F) / np.linalg.det(H + G))
else:
# Cooper-Nathans (popovici Eq 5 and 9)
R0_ = R0_ * np.sqrt(np.linalg.det(F) / np.linalg.det(H))
# Normalization to flux on monitor
if moncor == 1:
g = G[:4, :4]
f = F[:2, :2]
c = C[:2, :4]
t = np.matrix([[-1. / (2. * L0), 0, np.cos(thetam) * (1. / L1mon - 1. / L0) / 2.,
np.sin(thetam) * (1. / L0 + 1. / L1mon - 2. / (monorh * np.sin(thetam))) / 2., 0, 0,
1. / (2. * L1mon)],
[0, -1. / (2. * L0 * np.sin(thetam)), 0, 0,
(1. / L0 + 1. / L1mon - 2. * np.sin(thetam) / monorv) / (2. * np.sin(thetam)), 0, 0]],
dtype=np.float64)
sinv = blkdiag(np.array(bshape, dtype=np.float64), mshape, monitorshape) # S-1 matrix
s = np.linalg.inv(sinv)
d = np.matrix([[-1. / L0, 0, -np.cos(thetam) / L0, np.sin(thetam) / L0, 0, 0, 0],
[0, 0, np.cos(thetam) / L1mon, np.sin(thetam) / L1mon, 0, 0, 1. / L1mon],
[0, -1. / L0, 0, 0, 1. / L0, 0, 0],
[0, 0, 0, 0, -1. / L1mon, 0, 0]], dtype=np.float64)
if method == 1 or method == 'popovici':
# Popovici
Rmon = Rm * (2 * np.pi) ** 2 / (8 * np.pi * np.sin(thetam)) * np.sqrt(
np.linalg.det(f) / np.linalg.det(np.linalg.inv(d * np.linalg.inv(s + t.H * f * t) * d.H) + g))
else:
# Cooper-Nathans
Rmon = Rm * (2 * np.pi) ** 2 / (8 * np.pi * np.sin(thetam)) * np.sqrt(
np.linalg.det(f) / np.linalg.det(g + c.H * f * c))
R0_ = R0_ / Rmon
R0_ = R0_ * ki # 1/ki monitor efficiency
# Transform prefactor to Chesser-Axe normalization
R0_ = R0_ / (2. * np.pi) ** 2 * np.sqrt(np.linalg.det(RM_))
# Include kf/ki part of cross section
R0_ = R0_ * kf / ki
# Take care of sample mosaic if needed
# [<NAME> & <NAME>, J. Appl. Phys. 42, 4736, (1971), eq 19]
if hasattr(sample, 'mosaic'):
etas = sample.mosaic * CONVERT1
etasv = np.copy(etas)
if hasattr(sample, 'vmosaic'):
etasv = sample.vmosaic * CONVERT1
R0_ = R0_ / np.sqrt((1 + (q * etas) ** 2 * RM_[2, 2]) * (1 + (q * etasv) ** 2 * RM_[1, 1]))
Minv[1, 1] = Minv[1, 1] + q ** 2 * etas ** 2
Minv[2, 2] = Minv[2, 2] + q ** 2 * etasv ** 2
RM_ = np.linalg.inv(Minv)
# Take care of analyzer reflectivity if needed [<NAME>, BNL]
if hasattr(ana, 'thickness') and hasattr(ana, 'Q'):
KQ = ana.Q
KT = ana.thickness
toa = (taua / 2.) / np.sqrt(kf ** 2 - (taua / 2.) ** 2)
smallest = alpha[3]
if alpha[3] > alpha[2]:
smallest = alpha[2]
Qdsint = KQ * toa
dth = (np.arange(1, 201) / 200.) * np.sqrt(2. * np.log(2.)) * smallest
wdth = np.exp(-dth ** 2 / 2. / etaa ** 2)
sdth = KT * Qdsint * wdth / etaa / np.sqrt(2. * np.pi)
rdth = 1. / (1 + 1. / sdth)
reflec = sum(rdth) / sum(wdth)
R0_ = R0_ * reflec
R0[ind] = R0_
RM[ind] = RM_.copy()
return [R0, RM]
def calc_resolution(self, hkle):
r"""For a scattering vector (H,K,L) and energy transfers W, given
experimental conditions specified in EXP, calculates the Cooper-Nathans
resolution matrix RMS and Cooper-Nathans Resolution prefactor R0 in a
coordinate system defined by the crystallographic axes of the sample.
Parameters
----------
hkle : list
Array of the scattering vector and energy transfer at which the
calculation should be performed
Notes
-----
Translated from ResLib, originally authored by <NAME>, 1999-2007,
Oak Ridge National Laboratory
"""
self.HKLE = hkle
[H, K, L, W] = hkle
[length, H, K, L, W] = _CleanArgs(H, K, L, W)
self.H, self.K, self.L, self.W = H, K, L, W
[x, y, z, sample, rsample] = self._StandardSystem()
del z, sample
Q = _modvec([H, K, L], rsample)
uq = np.vstack((H / Q, K / Q, L / Q))
xq = _scalar(x, uq, rsample)
yq = _scalar(y, uq, rsample)
tmat = np.array(
[np.array([[xq[i], yq[i], 0, 0], [-yq[i], xq[i], 0, 0], [0, 0, 1., 0], [0, 0, 0, 1.]], dtype=np.float64) for i in range(len(xq))])
RMS = np.zeros((length, 4, 4), dtype=np.float64)
rot = np.zeros((3, 3), dtype=np.float64)
# Sample shape matrix in coordinate system defined by scattering vector
sample = self.sample
if hasattr(sample, 'shape'):
samples = []
for i in range(length):
rot = tmat[i, :3, :3]
samples.append(np.matrix(rot) * np.matrix(sample.shape) * np.matrix(rot).H)
self.sample.shape = np.array(samples)
[R0, RM] = self.calc_resolution_in_Q_coords(Q, W)
for i in range(length):
RMS[i] = | np.matrix(tmat[i]) | numpy.matrix |
"""
Created by: 6/1/17
On:jesseclark
"""
import pandas as pd
import numpy as np
from itertools import combinations
import argparse
import logging
import copy
logging.info('Starting logger for...')
LOGGER = logging.getLogger(__name__)
LOGGER.setLevel(logging.DEBUG)
def invert_dict(dict_in, append_list=False):
"""
Invert the key:values of a dict
:param dict_in: dict to invert
:param append_list: append to a list? (for non-uniqueness)
:return: inverted dict
"""
if not append_list:
return {val:key for key,val in dict_in.items()}
else:
dict_out = {val:[] for key,val in dict_in.items()}
for key, val in dict_in.items():
dict_out[val].append(key)
return dict_out
def load_data(fname='SeatTest_New.xlsx'):
"""
Load the xlsx using pandas.
:param fname: string location of the file to load
:return: pandas object
"""
return pd.ExcelFile(fname)
def get_names_teams_cur_seats(file_in, names='names'):
"""
Process the xlsx sheet, extracting the names and seats
:param file_in: pandas object for the file, use load_data(fname)
:param names: the name of the tab that contains the names
:return: list of names, names:seats dict, names:teams dict
"""
# load the names portion of the sheet
names_df = file_in.parse(names)
# rename if nec
if 'Full Name' in names_df.columns:
names_df = names_df.rename(columns={'Full Name':'Names'})
if 'Seat' in names_df.columns:
names_df = names_df.rename(columns={'Seat':'current seat'})
if 'Team' in names_df.columns:
names_df = names_df.rename(columns={'Team':'Teams'})
# we sort here to set an order for constructing the X and dij
names = sorted(names_df.Names.values.tolist())
# make the init names_Seats_dict
names_seats_dict = {row[1]['Names']: row[1]['current seat'] for row in
names_df[['Names', 'current seat']].reset_index(drop=True).iterrows()}
# get the names-teams dict
names_teams_dict = {row[1]['Names']: str(row[1]['Teams']) for row in
names_df[['Names', 'Teams']].reset_index(drop=True).iterrows()}
return names, names_seats_dict, names_teams_dict
def create_seating_graph(seats_arr, excludes=('nan',0), inc_self=True):
"""
Create the graph of seats from the layout.
:param seats_arr: np array of physical seating arrangement
:param excludes: ignore entries in seats_arr that take these values (i.e. use 0 or nan for aisles)
:param inc_self: include the seat number as a neighbiour to itself?
:return: seats graph as a dict
"""
# which chairs are neighbours
ni, nj = seats_arr.shape
seats_graph = {}
# loop through each seat
for indi in range(ni):
for indj in range(nj):
# get current seat
seat = seats_arr[indi, indj]
if seat not in excludes:
# now get the neighbours of the seat
# here we consider the diagonals to be a neighbour
ii = np.array([-1, 0, 1]) + indi
jj = np.array([-1, 0, 1]) + indj
# keep the indices within the bounds
ii = ii[(ii >= 0) & (ii < ni)]
jj = jj[(jj >= 0) & (jj < nj)]
# loop through the indices
inds = [(i, j) for i in ii for j in jj]
neighbours = [seats_arr[ind] for ind in inds if seats_arr[ind] not in [seat]+list(excludes)]
if inc_self:
neighbours.append(seat)
seats_graph[seat] = neighbours
return seats_graph
def get_seat_locations(file_in, seats='seat_map', more_connected=True):
"""
Process the seat locations portion of the xlsx sheet
:param file_in: pandas object for the file, use load_data(fname)
:param seats: name of the tab that contains the sheets
:param more_connected: bool, remove aisles from seats when constructing connection graph?
:return: list of seats, dict of seat locations (tuples), graph of seat connections,
dict of seat-seat distances, numpy array of seat map
"""
# get seat locations from map and also all available seats (not just those occupied)
seat_map_df = file_in.parse(seats, header=None)
seats_arr = np.nan_to_num(np.array(seat_map_df, dtype=float)).astype(int)
seats = sorted(list(seats_arr[np.where(seats_arr.astype(float) != 0)]))
# a dict of the seat number and location
seat_locations = {seat: (np.where(seats_arr == seat)[0][0], np.where(seats_arr == seat)[1][0]) for seat in seats}
# we can make the seats have more neighbours by removing the aisles
if more_connected:
_seats_arr = seats_arr[np.where(seats_arr.sum(1) != 0), :].squeeze()
_seats_arr = _seats_arr[:, np.where(_seats_arr.sum(0) != 0)].squeeze()
else:
_seats_arr = seats_arr
seats_graph = create_seating_graph(_seats_arr, inc_self=False)
# we want the distance from each seat to every other seat
seat_distances = {}
for seat1 in seats:
distances = {}
for seat2 in seats:
p1 = np.array(seat_locations[seat1])
p2 = np.array(seat_locations[seat2])
distances[seat2] = abs(p1 - p2).sum()
seat_distances[seat1] = distances
return seats, seat_locations, seats_graph, seat_distances, seats_arr
def get_person_person_distance(names_seats_dict, seat_distances, names):
"""
get person to person distances
used for getting the cost of how far people have moved from each other
can get names_seats_dict = X_to_names_seats_dict(X, names, seats)
:param names_seats_dict: names:seats dict
:param seat_distances: dict of seat-seat distances, indexed by seat name
:param names: list of names
:return: numpy array of person-person distances
"""
pij = np.zeros((len(names), len(names)))
# loop through people and seats
# use the sorted names list, this dictates the ordering for the matrix
for ind1, name1 in enumerate(names):
# get seat
seat1 = names_seats_dict[name1]
# get distances to all other people
for ind2, name2 in enumerate(names):
seat2 = names_seats_dict[name2]
pij[ind1, ind2] = seat_distances[seat1][seat2]
return pij
def calc_dij(names, seats, seat_distances, names_seats_dict):
"""
calc the person-seat distances.
:param names: list of names
:param seats: list of seats
:param seat_distances: dict of seat-seat distances
:param names_seats_dict: names:seats dict of current arrangement
:return: numpy array of person-seat distances
"""
# calc the dij matrix - ordered! 0 important
dij = np.zeros((len(names), len(seats)))
for ind1, name in enumerate(names):
# get cur seat for name
cur_seat = names_seats_dict[name]
# get the distance to all other seats
dists = [seat_distances[cur_seat][ind] for ind in seats]
dij[ind1, :] = dists
return dij
def names_seats_dict_to_X(names_seats_dict, names=None, seats=None):
"""
Convert the dictionary of names:seats into allocation matrix Xij
:param names_seats_dict: names:seats dict
:param names: list of names, ordering dictates ordering of X, if none provided, defaults to sorted keys
:param seats: list of seats
:return: numpy array allocating person i to seat j Xij
"""
if names is None:
names = sorted(names_seats_dict.keys())
if seats is None:
seats = sorted(names_seats_dict.values())
X = np.zeros((len(names), len(seats)))
for ind1, name in enumerate(names):
# get the index of the name in seat
ind2 = seats.index(names_seats_dict[name])
X[ind1, ind2] = 1
return X
def X_to_names_seats_dict(X, names, seats):
"""
Inverse operation of names_seats_dict_to_X, create a names:seats dict from numpy allocation array Xij
:param X: numpy array allocating person i to seat j Xij
:param names: list of names, required for keys
:param seats: list of seats
:return: names:seats dict for current allocation
"""
# use normal dict so we still get key errors
names_seats_dict = {}
for ind1, name in enumerate(names):
cur_seat_ind = | np.where(X[ind1, :] == 1) | numpy.where |
import numpy as np
import pycuda.driver as cuda
import pycuda.autoinit
from cuda_functions_sp import cu_matrix_kernel
from image_functions import convolve_undersample
import sys
def numpy3d_to_array(np_array, allow_surface_bind=False, layered=True):
d, h, w = np_array.shape
descr = cuda.ArrayDescriptor3D()
descr.width = w
descr.height = h
descr.depth = d
descr.format = cuda.dtype_to_array_format(np_array.dtype)
descr.num_channels = 1
descr.flags = 0
if allow_surface_bind:
descr.flags = cuda.array3d_flags.SURFACE_LDST
if layered:
descr.flags = cuda.array3d_flags.ARRAY3D_LAYERED
device_array = cuda.Array(descr)
copy = cuda.Memcpy3D()
copy.set_src_host(np_array)
copy.set_dst_array(device_array)
copy.width_in_bytes = copy.src_pitch = np_array.strides[1]
copy.src_height = copy.height = h
copy.depth = d
copy()
return device_array
def array_to_numpy3d(cuda_array):
descriptor = cuda_array.get_descriptor_3d()
w = descriptor.width
h = descriptor.height
d = descriptor.depth
shape = d, h, w
dtype = array_format_to_dtype(descriptor.format)
numpy_array = np.zeros(shape, dtype)
copy = cuda.Memcpy3D()
copy.set_src_array(cuda_array)
copy.set_dst_host(numpy_array)
itemsize = numpy_array.dtype.itemsize
copy.width_in_bytes = copy.src_pitch = w * itemsize
copy.src_height = copy.height = h
copy.depth = d
copy()
return numpy_array
def compute_matrix_and_vector_cuda(R, RB, T, Vinv, mask, kernelIndex,
extendedBasis, kernelRadius, params,
stamp_positions=None):
# Import CUDA function to compute the matrix
cu_compute_matrix = cu_matrix_kernel.get_function('cu_compute_matrix')
cu_compute_vector = cu_matrix_kernel.get_function('cu_compute_vector')
cu_compute_matrix_stamps = cu_matrix_kernel.get_function(
'cu_compute_matrix_stamps')
cu_compute_vector_stamps = cu_matrix_kernel.get_function(
'cu_compute_vector_stamps')
# Copy the reference, target and inverse variance images to
# GPU texture memory
RTV = np.array([R, RB, T, Vinv, mask]).astype(np.float32).copy()
RTV_cuda = numpy3d_to_array(RTV)
texref = cu_matrix_kernel.get_texref("tex")
texref.set_array(RTV_cuda)
texref.set_filter_mode(cuda.filter_mode.POINT)
# Create a numpy array for matrix H
dp = (params.pdeg + 1) * (params.pdeg + 2) / 2
ds = (params.sdeg + 1) * (params.sdeg + 2) / 2
db = (params.bdeg + 1) * (params.bdeg + 2) / 2
hs = (kernelIndex.shape[0] - 1) * ds + dp + db
H = np.zeros([hs, hs]).astype(np.float32).copy()
V = np.zeros(hs).astype(np.float32).copy()
# Fill the elements of H
print
hs, ' * ', hs, ' elements'
blockDim = (256, 1, 1)
gridDim = (hs, hs, 1)
k0 = kernelIndex[:, 0].astype(np.int32).copy()
k1 = kernelIndex[:, 1].astype(np.int32).copy()
if params.use_stamps:
posx = np.float32(stamp_positions[:params.nstamps, 0].copy() - 1.0)
posy = np.float32(stamp_positions[:params.nstamps, 1].copy() - 1.0)
cu_compute_matrix_stamps(np.int32(params.pdeg), np.int32(params.sdeg),
np.int32(params.bdeg), np.int32(R.shape[1]),
np.int32(R.shape[0]),
np.int32(params.nstamps),
np.int32(params.stamp_half_width),
cuda.In(posx), cuda.In(posy), cuda.In(k0),
cuda.In(k1), cuda.In(extendedBasis),
np.int32(kernelIndex.shape[0]),
np.int32(kernelRadius), cuda.Out(H),
block=blockDim, grid=gridDim,
texrefs=[texref])
else:
cu_compute_matrix(np.int32(params.pdeg), np.int32(params.sdeg),
np.int32(params.bdeg), np.int32(R.shape[1]),
np.int32(R.shape[0]), cuda.In(k0), cuda.In(k1),
cuda.In(extendedBasis),
np.int32(kernelIndex.shape[0]),
np.int32(kernelRadius), cuda.Out(H), block=blockDim,
grid=gridDim, texrefs=[texref])
# Fill the elements of V
blockDim = (256, 1, 1)
gridDim = (hs, 1, 1)
if params.use_stamps:
cu_compute_vector_stamps(np.int32(params.pdeg), np.int32(params.sdeg),
np.int32(params.bdeg), np.int32(R.shape[1]),
np.int32(R.shape[0]),
np.int32(params.nstamps),
np.int32(params.stamp_half_width),
cuda.In(posx), cuda.In(posy), cuda.In(k0),
cuda.In(k1), cuda.In(extendedBasis),
np.int32(kernelIndex.shape[0]),
np.int32(kernelRadius), cuda.Out(V),
block=blockDim, grid=gridDim,
texrefs=[texref])
else:
cu_compute_vector(np.int32(params.pdeg), np.int32(params.sdeg),
np.int32(params.bdeg), np.int32(R.shape[1]),
np.int32(R.shape[0]), cuda.In(k0), cuda.In(k1),
cuda.In(extendedBasis),
np.int32(kernelIndex.shape[0]),
np.int32(kernelRadius), cuda.Out(V), block=blockDim,
grid=gridDim, texrefs=[texref])
return H, V, texref
def compute_model_cuda(image_size, texref, c, kernelIndex, extendedBasis,
params):
# Import CUDA function to perform the convolution
cu_compute_model = cu_matrix_kernel.get_function('cu_compute_model')
# Create a numpy array for the model M
M = np.zeros(image_size).astype(np.float32).copy()
# Call the cuda function to perform the convolution
blockDim = (256, 1, 1)
gridDim = (image_size[1], image_size[0]) + (1,)
k0 = kernelIndex[:, 0].astype(np.int32).copy()
k1 = kernelIndex[:, 1].astype(np.int32).copy()
cu_compute_model(np.int32(params.pdeg), np.int32(params.sdeg),
np.int32(params.bdeg), cuda.In(k0), cuda.In(k1),
cuda.In(extendedBasis), np.int32(kernelIndex.shape[0]),
cuda.In(c), cuda.Out(M), block=blockDim, grid=gridDim,
texrefs=[texref])
return M
def photom_all_stars(diff, inv_variance, positions, psf_image, c, kernelIndex,
extendedBasis, kernelRadius, params,
star_group_boundaries=None,
detector_mean_positions_x=None,
detector_mean_positions_y=None):
from astropy.io import fits
# Read the PSF
psf, psf_hdr = fits.getdata(psf_image, 0, header='true')
print
'CIF psf_shape', psf.shape
print
'CIF psf_sum = ', np.sum(psf)
psf_height = psf_hdr['PSFHEIGH']
psf_x = psf_hdr['PSFX']
psf_y = psf_hdr['PSFY']
psf_size = psf.shape[1]
psf_fit_rad = params.psf_fit_radius
if params.psf_profile_type == 'gaussian':
psf_sigma_x = psf_hdr['PAR1'] * 0.8493218
psf_sigma_y = psf_hdr['PAR2'] * 0.8493218
psf_parameters = np.array(
[psf_size, psf_height, psf_sigma_x, psf_sigma_y, psf_x, psf_y,
psf_fit_rad, params.gain]).astype(np.float32)
profile_type = 0
elif params.psf_profile_type == 'moffat25':
print
'params.psf_profile_type moffat25 not working yet. Exiting.'
sys.exit(0)
psf_sigma_x = psf_hdr['PAR1']
psf_sigma_y = psf_hdr['PAR2']
psf_sigma_xy = psf_hdr['PAR3']
psf_parameters = np.array(
[psf_size, psf_height, psf_sigma_x, psf_sigma_y, psf_x, psf_y,
psf_fit_rad, params.gain, psf_sigma_xy]).astype(np.float32)
print
'psf_parameters', psf_parameters
profile_type = 1
else:
print
'params.psf_profile_type undefined'
sys.exit(0)
# Copy the difference and inverse variance images into GPU texture memory
RR = np.array([diff, inv_variance]).astype(np.float32).copy()
diff_cuda = numpy3d_to_array(RR)
texref = cu_matrix_kernel.get_texref("tex")
texref.set_array(diff_cuda)
texref.set_filter_mode(cuda.filter_mode.POINT)
# Call the CUDA function to perform the photometry.
# Each block is one star.
# Each thread is one column of the PSF, but 32 threads per warp
nstars = positions.shape[0]
gridDim = (int(nstars), 1, 1)
blockDim = (16, 16, 1)
k0 = kernelIndex[:, 0].astype(np.int32).copy()
k1 = kernelIndex[:, 1].astype(np.int32).copy()
positions = positions.reshape(-1, 2)
if params.star_file_is_one_based:
posx = np.float32(positions[:, 0].copy() - 1.0)
posy = np.float32(positions[:, 1].copy() - 1.0)
else:
posx = np.float32(positions[:, 0].copy())
posy = np.float32(positions[:, 1].copy())
# psf_0 = convolve_undersample(psf[0]).astype(np.float32).copy()
# psf_xd = convolve_undersample(psf[1]).astype(np.float32).copy()*0.0
# psf_yd = convolve_undersample(psf[2]).astype(np.float32).copy()*0.0
# psf_0 = psf[0].astype(np.float32).copy()
# psf_xd = psf[1].astype(np.float32).copy()*0.0
# psf_yd = psf[2].astype(np.float32).copy()*0.0
psf_0 = psf.astype(np.float32).copy()
psf_xd = psf.astype(np.float32).copy() * 0.0
psf_yd = psf.astype(np.float32).copy() * 0.0
flux = np.float32(posy.copy() * 0.0);
dflux = np.float32(posy.copy() * 0.0);
cu_photom = cu_matrix_kernel.get_function('cu_photom')
try:
cu_photom(np.int32(profile_type), np.int32(diff.shape[0]),
np.int32(diff.shape[1]), np.int32(params.pdeg),
np.int32(params.sdeg), np.int32(c.shape[0]),
np.int32(kernelIndex.shape[0]), np.int32(kernelRadius),
cuda.In(k0), cuda.In(k1), cuda.In(extendedBasis),
cuda.In(psf_parameters), cuda.In(psf_0), cuda.In(psf_xd),
cuda.In(psf_yd), cuda.In(posx), cuda.In(posy), cuda.In(c),
cuda.Out(flux), cuda.Out(dflux), block=blockDim,
grid=gridDim, texrefs=[texref])
except:
print
'Call to cu_photom failed.'
print
'psf_parameters', psf_parameters
print
'size of posx, posy:', posx.shape, posy.shape
print
'Parameters:'
for par in dir(params):
print
par, getattr(params, par)
print
return flux, dflux
def convolve_image_with_psf(psf_image, image1, image2, c, kernelIndex,
extendedBasis, kernelRadius, params):
from astropy.io import fits
# Read the PSF
psf, psf_hdr = fits.getdata(psf_image, 0, header='true')
psf_height = psf_hdr['PSFHEIGH']
psf_x = psf_hdr['PSFX']
psf_y = psf_hdr['PSFY']
psf_size = psf.shape[1]
psf_fit_rad = params.psf_fit_radius
if params.psf_profile_type == 'gaussian':
psf_sigma_x = psf_hdr['PAR1'] * 0.8493218
psf_sigma_y = psf_hdr['PAR2'] * 0.8493218
psf_parameters = np.array(
[psf_size, psf_height, psf_sigma_x, psf_sigma_y, psf_x, psf_y,
psf_fit_rad, params.gain]).astype(np.float32)
profile_type = 0
elif params.psf_profile_type == 'moffat25':
print
'params.psf_profile_type moffat25 not working yet. Exiting.'
sys.exit(0)
psf_sigma_x = psf_hdr['PAR1']
psf_sigma_y = psf_hdr['PAR2']
psf_sigma_xy = psf_hdr['PAR3']
psf_parameters = np.array(
[psf_size, psf_height, psf_sigma_x, psf_sigma_y, psf_x, psf_y,
psf_fit_rad, params.gain, psf_sigma_xy]).astype(np.float32)
profile_type = 1
else:
print
'params.psf_profile_type undefined'
sys.exit(0)
# Copy the images into GPU texture memory
nx, ny = image1.shape
RR = np.array([image1, image2]).astype(np.float32).copy()
image_cuda = numpy3d_to_array(RR)
texref = cu_matrix_kernel.get_texref("tex")
texref.set_array(image_cuda)
texref.set_filter_mode(cuda.filter_mode.POINT)
# Call the CUDA function to perform the double convolution.
# Each block is one image section.
# Each thread is one pixel of the PSF, but 32 threads per warp
cu_convolve = cu_matrix_kernel.get_function('convolve_image_psf')
k0 = kernelIndex[:, 0].astype(np.int32).copy()
k1 = kernelIndex[:, 1].astype(np.int32).copy()
# psf_0 = convolve_undersample(psf[0]).astype(np.float32).copy()
# psf_xd = convolve_undersample(psf[1]).astype(np.float32).copy()*0.0
# psf_yd = convolve_undersample(psf[2]).astype(np.float32).copy()*0.0
psf_0 = psf.astype(np.float32).copy()
psf_xd = psf.astype(np.float32).copy() * 0.0
psf_yd = psf.astype(np.float32).copy() * 0.0
image_section_size = 32
convolved_image1 = (0.0 * image1).astype(np.float32)
convolved_image2 = (0.0 * image1).astype(np.float32)
gridDim = (int((nx - 1) / image_section_size + 1),
int((ny - 1) / image_section_size + 1), 1)
blockDim = (16, 16, 1)
cu_convolve(np.int32(profile_type), np.int32(nx), np.int32(ny),
np.int32(image_section_size), np.int32(image_section_size),
np.int32(params.pdeg), np.int32(params.sdeg),
np.int32(c.shape[0]), | np.int32(kernelIndex.shape[0]) | numpy.int32 |
from sparse_cnn_tensorflow.sparse_data_value import SparseDataValue
import numpy as np
def test_simple_dense_int_array():
dense = np.array(
[
[[1, 2], [0, 0], [0, 0]],
[[0, 0], [5, 6], [6, 7]],
[[0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [16, 17]]
]
)
sparse = SparseDataValue(dense)
assert sparse.dense_shape == (4, 3, 2)
np.testing.assert_array_equal(sparse.ground_state, np.zeros(2, dtype=np.int64))
np.testing.assert_array_equal(sparse.H, np.array([[0, 0], [1, 1], [1, 2], [3, 2]]))
np.testing.assert_array_equal(sparse.M, np.array([[1, 2], [5, 6], [6, 7], [16, 17]]))
def test_simple_dense_float32_array():
dense = np.array(
[
[[1.432, 2.654], [0, 0], [0, 0]],
[[0, 0], [5.327, 6.777], [6.112, 7.123]],
[[0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [16.853, 17.352]]
]
).astype(np.float32)
sparse = SparseDataValue(dense)
assert sparse.dense_shape == (4, 3, 2)
np.testing.assert_array_equal(sparse.ground_state, np.zeros(2, dtype=np.int64))
np.testing.assert_array_equal(sparse.H, np.array([[0, 0], [1, 1], [1, 2], [3, 2]]))
np.testing.assert_array_almost_equal(
sparse.M, np.array([[1.432, 2.654], [5.327, 6.777], [6.112, 7.123], [16.853, 17.352]])
)
def test_simple_sparse_to_dense():
dense = np.array(
[
[[1.432, 2.654], [0, 0], [0, 0]],
[[0, 0], [5.327, 6.777], [6.112, 7.123]],
[[0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [16.853, 17.352]]
]
).astype(np.float32)
sparse = SparseDataValue(dense)
np.testing.assert_almost_equal(dense, sparse.to_dense())
def test_large_dense_to_dense():
| np.random.seed(1) | numpy.random.seed |
"""Sky brightnes approzimation using Zernike polynomials
The form and notation used here follow:
<NAME>., <NAME>., <NAME>., <NAME>. & VSIA
Standards Taskforce Members. Vision science and its
applications. Standards for reporting the optical aberrations of
eyes. J Refract Surg 18, S652-660 (2002).
"""
# imports
from math import factorial
import logging
import os
import warnings
from glob import glob
from functools import lru_cache
import numpy as np
import pandas as pd
from numexpr import NumExpr
from sklearn.linear_model import LinearRegression
import scipy.optimize
from scipy.interpolate import interp1d
import palpy
import healpy
import rubin_sim.utils as utils
from rubin_sim.data import get_data_dir
# constants
logging.basicConfig(format="%(asctime)s %(message)s")
LOGGER = logging.getLogger(__name__)
TELESCOPE = utils.Site("LSST")
SIDEREAL_TIME_SAMPLES_RAD = np.radians(np.arange(361, dtype=float))
BANDS = ("u", "g", "r", "i", "z", "y")
# exception classes
# interface functions
def fit_pre(npy_fname, npz_fname, *args, **kwargs):
"""Fit Zernike coefficients to a pre-computed data set
Parameters
----------
npy_fname : `str`
File name of the SkyBrightessPre <MJD>_<MDJ>.npy file
npz_fname : `str`
File name of the SkyBrightessPre <MJD>_<MDJ>.npz file
other arguments are passed to the ZernikeSky constructor.
Returns
-------
zernike_coeffs : `pd.DataFrame`
A DataFrame with the coefficients, indexed by band and mjd.
"""
# Load the pre-computed data
npz = np.load(npz_fname, allow_pickle=True)
npz_hdr = npz["header"][()]
npz_data = npz["dict_of_lists"][()]
pre_sky = np.load(npy_fname, allow_pickle=True)
mjds = npz_data["mjds"]
alt = npz_hdr["alt"]
az = npz_hdr["az"]
zernike_coeffs_by_band = []
zernike_sky = ZernikeSky(*args, **kwargs)
for band in pre_sky.dtype.fields.keys():
LOGGER.info("Starting %s band", band)
zernike_coeff_arrays = []
for mjd_idx, mjd in enumerate(mjds):
zernike_coeff_arrays.append(
zernike_sky.fit_coeffs(alt, az, pre_sky[band][mjd_idx], mjd)
)
if mjd_idx % 1000 == 0:
msg = f"Finished {mjd_idx*100.0/float(len(mjds)):.2f}%"
LOGGER.debug(msg)
zernike_coeffs_by_band.append(
pd.DataFrame(
zernike_coeff_arrays,
columns=np.arange(len(zernike_coeff_arrays[0])),
index=pd.MultiIndex.from_arrays(
[np.full_like(mjds, band, dtype=type(band)), mjds],
names=["band", "mjd"],
),
)
)
zernike_coeffs = pd.concat(zernike_coeffs_by_band)
return zernike_coeffs
def bulk_zernike_fit(data_dir, out_fname, *args, **kwargs):
"""Fit Zernike coeffs to all SkyBrightnessPre files in a directory.
Parameters
----------
data_dir : `str`
Name of the directory in which to look for SkyBrightnessPre
data files.
out_fname: `str`
Name of the file in which to save fit coefficients.
other arguments are passed to the ZernikeSky constructor.
Returns
-------
zernike_coeffs : `pd.DataFrame`
A DataFrame with the coefficients, indexed by band and mjd.
"""
zernike_coeff_batches = []
for npz_fname in glob(os.path.join(data_dir, "?????_?????.npz")):
LOGGER.info("Processing %s", npz_fname)
npy_fname = os.path.splitext(npz_fname)[0] + ".npy"
zernike_coeff_batch = fit_pre(npy_fname, npz_fname, *args, **kwargs)
zernike_coeff_batches.append(zernike_coeff_batch)
zernike_coeffs = pd.concat(zernike_coeff_batches)
zernike_coeffs.sort_index(level="mjd", inplace=True)
if out_fname is not None:
zernike_coeffs.to_hdf(out_fname, "zernike_coeffs", complevel=6)
zernike_sky = ZernikeSky(*args, **kwargs)
zernike_metadata = pd.Series(
{"order": zernike_sky.order, "max_zd": zernike_sky.max_zd}
)
zernike_metadata.to_hdf(out_fname, "zernike_metadata")
return zernike_coeffs
# classes
class ZernikeSky:
"""Zernike sky approximator.
Parameters
----------
order : `int`, optional
The order of the Zernike polynomial to use. Default is 6.
nside : `int`, optional
The nside of the healpix array to pre-compute Zernike Z terms for.
Default is 32.
max_zd : `float`, optional
The maximum zenith distance, in degrees. This value will correspond
to rho=1 in the Thibos et al. (2002) notation.
Default is 67.
dtype : `type`: optional
The numpy type to use for all calculations. Default is `np.float64`.
"""
def __init__(self, order=6, nside=32, max_zd=67, dtype=np.float64):
self.order = order
self.dtype = dtype
self.nside = nside
# Sets the value of zd where rho (radial coordinate of the
# unit disk in which Zernike polynomials are orthogonal) = 1
self.max_zd = max_zd
# a list of functions to calculate big Z given rho, phi,
# following eqn 1 of Thibos et al. (2002). The jth element of
# the list returns the jth Z, following the indexing
# convertions of Thibos et al. eqn 4.
#
# Should switch to using functools.cached_property in python 3.8
self._z_function = self._build_z_functions()
# A function that calculates the full Zernike approximation,
# taking rho and phi as arguments.
#
# numexpr can only compile functions with a limited number of
# arguments. If the order is too high, sum the terms
# separately
if order <= 7:
self._zern_function = self._build_zern_function()
else:
self._zern_function = self._compute_sky_by_sum
# big Z values for all m,n at all rho, phi in the
# pre-defined healpix coordinate, following eqn 1 of Thibos et
# al. (2002) The array returned should be indexed with j,
# Should switch to using functools.cached_property in python 3.8
self.healpix_z = self._compute_healpix_z()
self._interpolate_healpix_z = interp1d(
SIDEREAL_TIME_SAMPLES_RAD, self.healpix_z, axis=0, kind="nearest"
)
# A pd.DataFrame of zernike coeffs, indexed by mjd, providing the
# Zernike polynomial coefficients for the approximation of the
# sky at that time. That is, self._coeffs[5, 3] is the
# j=3 coefficient of the approximation of the sky at
# mjd=self.mjds[5], where j is defined as in Thibos et al. eqn 4.
self._coeffs = pd.DataFrame()
def load_coeffs(self, fname, band):
"""Load Zernike coefficients from a file.
Parameters
----------
fname : `str`
The file name of the hdf5 file with the Zernike coeffs.
band : `str`
The band to load.
"""
zernike_metadata = pd.read_hdf(fname, "zernike_metadata")
assert self.order == zernike_metadata["order"]
assert self.max_zd == zernike_metadata["max_zd"]
all_zernike_coeffs = pd.read_hdf(fname, "zernike_coeffs")
self._coeffs = all_zernike_coeffs.loc[band]
self._coeff_calc_func = interp1d(
self._coeffs.index.values, self._coeffs.values, axis=0
)
def compute_sky(self, alt, az, mjd=None):
"""Estimate sky values
Parameters
----------
alt : `np.ndarray`, (N)
An array of altitudes above the horizon, in degrees
az : `np.ndarray`, (N)
An array of azimuth coordinates, in degrees
mjd : `float`
The time (floating point MJD) at which to estimate the sky.
Returns
-------
`np.ndarray` (N) of sky brightnesses (mags/asec^2)
"""
rho = self._calc_rho(alt)
phi = self._calc_phi(az)
result = self._zern_function(rho, phi, *tuple(self.coeffs(mjd)))
return result
def _compute_sky_by_sum(self, rho, phi, *coeffs):
z = self._compute_z(rho, phi)
if len(z.shape) == 2:
result = np.sum(np.array(coeffs) * z, axis=1)
else:
result = np.sum(np.array(coeffs) * z)
return result
def compute_healpix(self, hpix, mjd=None):
"""Estimate sky values
Parameters
----------
hpix : `int`, (N)
Array of healpix indexes of the desired coordinates.
mjd : `float`
The time (floating point MJD) at which to estimate the sky.
Returns
-------
`np.ndarray` (N) of sky brightnesses (mags/asec^2)
"""
interpolate_healpix_z = self._interpolate_healpix_z
gmst = palpy.gmst(mjd)
mjd_healpix_z = interpolate_healpix_z(gmst)
# mjd_healpix_z = self.healpix_z[int(np.degrees(gmst))]
if hpix is None:
result = np.sum(self.coeffs(mjd) * mjd_healpix_z, axis=1)
else:
result = np.sum(self.coeffs(mjd) * mjd_healpix_z[hpix], axis=1)
return result
def coeffs(self, mjd):
"""Zerinke coefficients at a time
Parameters
----------
mjd : `float`
The time (floating point MJD) at which to estimate the sky.
Returns
-------
`np.ndarray` of Zernike coefficients following the OSA/ANSI
indexing convention described in Thibos et al. (2002).
"""
if len(self._coeffs) == 1:
these_coeffs = self._coeffs.loc[mjd]
else:
calc_these_coeffs = self._coeff_calc_func
these_coeffs = calc_these_coeffs(mjd)
return these_coeffs
def fit_coeffs(self, alt, az, sky, mjd, min_moon_sep=10, maxdiff=False):
"""Fit Zernike coefficients to a set of points
Parameters
----------
alt : `np.ndarray`, (N)
An array of altitudes above the horizon, in degrees
az : `np.ndarray`, (N)
An array of azimuth coordinates, in degrees
sky : `np.ndarray`, (N)
An array of sky brightness values (mags/asec^2)
mjd : `float`
The time (floating point MJD) at which to estimate the sky.
maxdiff : `bool`
Minimize the maximum difference between the estimate and data,
rather than the default RMS.
"""
# Do not fit too close to the moon
alt_rad, az_rad = np.radians(alt), np.radians(az)
gmst_rad = palpy.gmst(mjd)
lst_rad = gmst_rad + TELESCOPE.longitude_rad
moon_ra_rad, moon_decl_rad, moon_diam = palpy.rdplan(
mjd, 3, TELESCOPE.longitude_rad, TELESCOPE.latitude_rad
)
moon_ha_rad = lst_rad - moon_ra_rad
moon_az_rad, moon_el_rad = palpy.de2h(
moon_ha_rad, moon_decl_rad, TELESCOPE.latitude_rad
)
moon_sep_rad = palpy.dsepVector(
np.full_like(az_rad, moon_az_rad),
np.full_like(alt_rad, moon_el_rad),
az_rad,
alt_rad,
)
moon_sep = np.degrees(moon_sep_rad)
rho = self._calc_rho(alt)
phi = self._calc_phi(az)
good_points = np.logical_and(rho <= 1.0, moon_sep > min_moon_sep)
rho = rho[good_points]
phi = phi[good_points]
sky = sky[good_points]
alt = alt[good_points]
az = az[good_points]
num_points = len(alt)
assert len(az) == num_points
assert len(sky) == num_points
z = np.zeros((num_points, self._number_of_terms), dtype=self.dtype)
for j in np.arange(self._number_of_terms):
compute_z = self._z_function[j]
z[:, j] = compute_z(rho, phi)
# If the points being fit were evenly distributed across the sky,
# we might be able to get away with a multiplication rather than
# a linear regression, but we might be asked to fit masked data
zern_fit = LinearRegression(fit_intercept=False).fit(z, sky)
fit_coeffs = zern_fit.coef_
if maxdiff:
def max_abs_diff(test_coeffs):
max_resid = np.max(
np.abs(np.sum(test_coeffs * z, axis=1) - sky)
)
return max_resid
min_fit = scipy.optimize.minimize(max_abs_diff, fit_coeffs)
fit_coeffs = min_fit.x
self._coeffs = pd.DataFrame(
[fit_coeffs],
columns=np.arange(len(fit_coeffs)),
index=pd.Index([mjd], name="mjd"),
)
return fit_coeffs
def _compute_healpix_z(self):
# Compute big Z values for all m,n at all rho, phi in the
# pre-defined healpix coordinate, following eqn 1 of Thibos et
# al. (2002) The array returned should be indexed with j,
# following the conventions of eqn 4.
sphere_npix = healpy.nside2npix(self.nside)
sphere_ipix = np.arange(sphere_npix)
ra, decl = healpy.pix2ang(self.nside, sphere_ipix, lonlat=True)
num_st = len(SIDEREAL_TIME_SAMPLES_RAD)
healpix_z = np.full(
[num_st, sphere_npix, self._number_of_terms], np.nan
)
for st_idx, gmst_rad in enumerate(SIDEREAL_TIME_SAMPLES_RAD):
lst_rad = gmst_rad + TELESCOPE.longitude_rad
ha_rad = lst_rad - np.radians(ra)
az_rad, alt_rad = palpy.de2hVector(
ha_rad, np.radians(decl), TELESCOPE.latitude_rad
)
sphere_az, sphere_alt = np.degrees(az_rad), np.degrees(alt_rad)
# We only need the half sphere above the horizen
visible_ipix = sphere_ipix[sphere_alt > 0]
alt, az = sphere_alt[visible_ipix], sphere_az[visible_ipix]
rho = self._calc_rho(alt)
phi = self._calc_phi(az)
healpix_z[st_idx, visible_ipix] = self._compute_z(rho, phi)
return healpix_z
def _compute_horizan_healpix_z(self):
# Compute big Z values for all m,n at all rho, phi in the
# pre-defined healpix coordinate, following eqn 1 of Thibos et
# al. (2002) The array returned should be indexed with j,
# following the conventions of eqn 4.
sphere_npix = healpy.nside2npix(self.nside)
sphere_ipix = np.arange(sphere_npix)
sphere_az, sphere_alt = healpy.pix2ang(
self.nside, sphere_ipix, lonlat=True
)
# We only need the half sphere above the horizen
ipix = sphere_ipix[sphere_alt > 0]
alt, phi_deg = sphere_alt[ipix], sphere_az[ipix]
rho = self._calc_rho(alt)
rho, phi = (90.0 - alt) / self.max_zd, np.radians(phi_deg)
healpix_z = self._compute_z(rho, phi)
return healpix_z
def _compute_z(self, rho, phi):
# Compute big Z values for all m,n at rho, phi
# following eqn 1 of Thibos et al. (2002)
# The array returned should be indexed with j,
# following the conventions of eqn 4.
try:
npix = len(rho)
z = np.zeros((npix, self._number_of_terms), dtype=self.dtype)
for j in np.arange(self._number_of_terms):
compute_z = self._z_function[j]
z[:, j] = compute_z(rho, phi)
except TypeError:
z = np.zeros(self._number_of_terms, dtype=self.dtype)
for j in np.arange(self._number_of_terms):
compute_z = self._z_function[j]
z[j] = compute_z(rho, phi)
return z
def _build_z_functions(self):
z_functions = []
for j in np.arange(self._number_of_terms):
z_functions.append(self._make_z_function(j))
return z_functions
def _build_zern_function(self):
coeffs = [f"c{j}" for j in np.arange(self._number_of_terms)]
expression = ""
for j, coeff in enumerate(coeffs):
zern_z_expr = self._make_z_expression(j)
if zern_z_expr == "(1)":
term = f"{coeff}"
else:
term = f"{coeff}*({zern_z_expr})"
if expression == "":
expression = term
else:
expression += f" + {term}"
arg_types = []
if expression.find("rho") >= 0:
arg_types.append(("rho", self.dtype),)
if expression.find("phi") >= 0:
arg_types.append(("phi", self.dtype),)
for coeff in coeffs:
arg_types.append((coeff, self.dtype),)
arg_types = tuple(arg_types)
zern_function = NumExpr(expression, arg_types)
return zern_function
@property
def _number_of_terms(self):
n_terms = np.sum(np.arange(self.order) + 1)
return n_terms
def _make_r_expression(self, m, n):
if (n - m) % 2 == 1:
return 0
assert n >= m
assert m >= 0
m = int(m)
n = int(n)
num_terms = 1 + (n - m) // 2
expression = "("
for k in range(num_terms):
# From eqn 2 of Thibos et al. (2002)
coeff = (((-1) ** k) * factorial(n - k)) / (
factorial(k)
* factorial(int((n + m) / 2 - k))
* factorial(int((n - m) / 2 - k))
)
assert coeff == int(coeff)
coeff = int(coeff)
power = n - 2 * k
if len(expression) > 1:
expression += " + "
if power == 0:
expression += f"{coeff}"
elif power == 1:
expression += f"{coeff}*rho"
else:
expression += f"{coeff}*rho**{power}"
expression += ")"
return expression
def _make_z_expression(self, j=None, mprime=None, n=None):
if j is None:
assert mprime is not None
assert n is not None
else:
assert mprime is None
assert n is None
# From eqn 5 in Thibos et al. (2002)
n = np.ceil((-3 + np.sqrt(9 + 8 * j)) / 2).astype(int)
# From eqn 6 in Thibos et al. (2002)
mprime = 2 * j - n * (n + 2)
m = np.abs(mprime)
r = self._make_r_expression(m, n)
# From eqn. 3 of Thibos et al. 2002, again
delta = 1 if m == 0 else 0
big_nsq = 2 * (n + 1) / (1 + delta)
assert int(big_nsq) == big_nsq
big_nsq = int(big_nsq)
if mprime == 0:
expression = f"sqrt({big_nsq})*{r}"
elif mprime > 0:
expression = f"sqrt({big_nsq})*{r}*cos({m}*phi)"
elif mprime < 0:
expression = f"sqrt({big_nsq})*{r}*sin({m}*phi)"
else:
assert False
return expression
def _make_z_function(self, j=None, mprime=None, n=None):
expression = self._make_z_expression(j, mprime, n)
arg_types = []
if expression.find("rho") >= 0:
arg_types.append(("rho", self.dtype),)
if expression.find("phi") >= 0:
arg_types.append(("phi", self.dtype),)
arg_types = tuple(arg_types)
raw_z_function = NumExpr(expression, arg_types)
# Create functions with dummy arguments so that
# terms that do not require both phi and rho can
# still accept them, such that all z_functions
# can be called in the same way.
if len(arg_types) == 0:
def z_function(rho=None, phi=None):
return raw_z_function()
elif len(arg_types) == 1:
def z_function(rho, phi=None):
return raw_z_function(rho)
else:
z_function = raw_z_function
return z_function
def _calc_rho(self, alt):
zd = 90.0 - alt
if np.isscalar(alt) and zd > self.max_zd:
return np.nan
rho = zd / self.max_zd
if not np.isscalar(alt):
rho[zd > self.max_zd] = np.nan
return rho
def _calc_phi(self, az):
phi = np.radians(az)
return phi
class SkyBrightnessPreData:
"""Manager for raw pre-computed sky brightness data
Parameters
----------
base_fname : `str`
Base name for data files to load.
bands: `List` [`str`]
Name of bands to read.
pre_data_dir : `str`
Name of source directory for pre-computed sky brightness data.
max_num_mjds : `int`
If there are more than this number of MJDs in the requested
data files, sample this many out of the total.
"""
def __init__(
self, fname_base, bands, pre_data_dir=None, max_num_mjds=None
):
if pre_data_dir is None:
try:
self.pre_data_dir = os.environ["SIMS_SKYBRIGHTNESS_DATA"]
except KeyError:
self.pre_data_dir = "."
else:
self.pre_data_dir = pre_data_dir
self.fname_base = fname_base
self.max_num_mjds = max_num_mjds
self.times = None
self.sky = None
self.metadata = {}
self.load(fname_base, bands)
def load(self, fname_base, bands="ugrizy"):
"""Load pre-computed sky values.
Parameters
----------
base_fname : `str`
Base name for data files to load.
bands: `List` [`str`]
Name of bands to read.
"""
npz_fname = os.path.join(self.pre_data_dir, fname_base + "." + "npz")
npy_fname = os.path.join(self.pre_data_dir, fname_base + "." + "npy")
npz = np.load(npz_fname, allow_pickle=True)
npz_hdr = npz["header"][()]
npz_data = npz["dict_of_lists"][()]
pre_sky = np.load(npy_fname, allow_pickle=True)
alt = npz_hdr["alt"]
az = npz_hdr["az"]
alt_rad, az_rad = np.radians(alt), np.radians(az)
self.metadata = npz_hdr
self.times = pd.DataFrame(
{
k: npz_data[k]
for k in npz_data.keys()
if npz_data[k].shape == npz_data["mjds"].shape
}
)
read_mjds = len(self.times)
if self.max_num_mjds is not None:
read_mjd_idxs = pd.Series(np.arange(read_mjds))
mjd_idxs = read_mjd_idxs.sample(self.max_num_mjds)
else:
mjd_idxs = np.arange(read_mjds)
skies = []
for mjd_idx in mjd_idxs:
mjd = npz_data["mjds"][mjd_idx]
gmst_rad = palpy.gmst(mjd)
lst_rad = gmst_rad + TELESCOPE.longitude_rad
ha_rad, decl_rad = palpy.dh2eVector(
az_rad, alt_rad, TELESCOPE.latitude_rad
)
ra_rad = (lst_rad - ha_rad) % (2 * np.pi)
moon_ra_rad = npz_data["moonRAs"][mjd_idx]
moon_decl_rad = npz_data["moonDecs"][mjd_idx]
moon_ha_rad = lst_rad - moon_ra_rad
moon_az_rad, moon_el_rad = palpy.de2h(
moon_ha_rad, moon_decl_rad, TELESCOPE.latitude_rad
)
moon_sep = palpy.dsepVector(
np.full_like(az_rad, moon_az_rad),
np.full_like(alt_rad, moon_el_rad),
az_rad,
alt_rad,
)
for band in bands:
skies.append(
pd.DataFrame(
{
"band": band,
"mjd": npz_data["mjds"][mjd_idx],
"gmst": np.degrees(gmst_rad),
"lst": np.degrees(lst_rad),
"alt": alt,
"az": az,
"ra": np.degrees(ra_rad),
"decl": np.degrees(decl_rad),
"moon_ra": np.degrees(
npz_data["moonRAs"][mjd_idx]
),
"moon_decl": np.degrees(
npz_data["moonDecs"][mjd_idx]
),
"moon_alt": np.degrees(
npz_data["moonAlts"][mjd_idx]
),
"moon_az": np.degrees(moon_az_rad),
"moon_sep": np.degrees(moon_sep),
"sun_ra": np.degrees(npz_data["sunRAs"][mjd_idx]),
"sun_decl": np.degrees(
npz_data["sunDecs"][mjd_idx]
),
"sun_alt": np.degrees(
npz_data["sunAlts"][mjd_idx]
),
"sky": pre_sky[band][mjd_idx],
}
)
)
self.sky = pd.concat(skies).set_index(
["band", "mjd", "alt", "az"], drop=False
)
self.sky.sort_index(inplace=True)
if self.max_num_mjds is not None:
self.times = self.times.iloc[mjd_idxs]
def __getattr__(self, name):
return self.metadata[name]
class SkyModelZernike:
"""Interface to zernike sky that is more similar to SkyModelPre
Parameters
----------
data_file : `str`, optional
File name from which to load Zernike coefficients. Default None uses default data directory.
"""
def __init__(self, data_file=None, **kwargs):
if data_file is None:
if "SIMS_SKYBRIGHTNESS_DATA" in os.environ:
data_dir = os.environ["SIMS_SKYBRIGHTNESS_DATA"]
else:
data_dir = os.path.join(get_data_dir(),
"sims_skybrightness_pre")
data_file = os.path.join(data_dir, "zernike", "zernike.h5")
zernike_metadata = pd.read_hdf(data_file, "zernike_metadata")
order = int(zernike_metadata["order"])
if 'order' in kwargs:
assert order == kwargs['order']
else:
kwargs['order'] = order
max_zd = zernike_metadata["max_zd"]
if 'max_zd' in kwargs:
assert max_zd == kwargs['max_zd']
else:
kwargs['max_zd'] = max_zd
self.zernike_model = {}
for band in BANDS:
sky = ZernikeSky(**kwargs)
sky.load_coeffs(data_file, band)
self.zernike_model[band] = sky
self.nside = sky.nside
def returnMags(
self,
mjd,
indx=None,
badval=healpy.UNSEEN,
filters=["u", "g", "r", "i", "z", "y"],
extrapolate=False,
):
"""
Return a full sky map or individual pixels for the input mjd
Parameters
----------
mjd : float
Modified Julian Date to interpolate to
indx : List of int(s) (None)
indices to interpolate the sky values at. Returns full sky if None. If the class was
instatiated with opsimFields, indx is the field ID, otherwise it is the healpix ID.
badval : float (-1.6375e30)
Mask value. Defaults to the healpy mask value.
filters : list
List of strings for the filters that should be returned.
extrapolate : bool (False)
In indx is set, extrapolate any masked pixels to be the same as the nearest non-masked
value from the full sky map.
Returns
-------
sbs : dict
A dictionary with filter names as keys and np.arrays as values which
hold the sky brightness maps in mag/sq arcsec.
"""
sky_brightness = {}
sun_el = _calc_sun_el(mjd)
if sun_el > 0:
warnings.warn('Requested MJD between sunrise and sunset')
if indx is None:
nside = self.zernike_model[filters[0]].nside
npix = healpy.nside2npix(nside)
else:
npix = len(indx)
for band in filters:
sky_brightness[band] = np.full(npix, badval)
return sky_brightness
if extrapolate:
raise NotImplementedError
for band in filters:
band_brightness = self.zernike_model[band].compute_healpix(
indx, mjd
)
badval_idxs = np.where(~ | np.isfinite(band_brightness) | numpy.isfinite |
# 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]) | numpy.array |
import numpy as np
import os
import argparse
from tqdm import tqdm
import textwrap
from src.video import get_video_info
from src.frame_generator import FrameGenerator
from src.grid_optical_flow import get_grid_flow, get_grid_centres
import pandas as pd
import logging
from report_segmentation import render_report
import cv2
logging.basicConfig(level=logging.INFO, format="%(levelname)s:%(message)s")
def parseargs():
parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter,
description=textwrap.dedent(
'''classify video frames stationary or moving based on optical flow'''))
parser.add_argument('--video', '-v', type=str, help="path to the videofile")
parser.add_argument('--threshold', '-t', type=float, help="The threshold parameter for segmentation")
parser.add_argument("--output-dir", "-o", type=str,
help="Folder where the segmentation and plots are saved")
parser.add_argument(
"--grid-size",
"-g",
nargs="+",
type=int,
help="A touple representing the ncols and nrows of the grid.",
)
args = parser.parse_args()
return args
def segment(video, output_dir, grid_size, threshold):
_, _, fps, _, h, w = get_video_info(video)
fg = FrameGenerator(video, show_video_info=True, use_rgb=False)
# center and normalize grid centres
frame_iterator = iter(fg)
p_frame = next(frame_iterator)
p_frame = cv2.cvtColor(p_frame, cv2.COLOR_BGR2GRAY)
segmentation = pd.DataFrame()
logging.info("Segment video {}".format(video))
out_file = os.path.join(output_dir, "segmented.mp4")
fourcc = cv2.VideoWriter_fourcc(*'XVID')
writer = cv2.VideoWriter(out_file, fourcc, 25.0, (200,200))
total_optical_flow = None
for i, frame in tqdm(enumerate(frame_iterator), desc="playing video", unit="frame", total=len(fg) - 1):
original_frame = np.array(frame)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
optical_flow = get_grid_flow(p_frame, frame, grid_size)
if total_optical_flow is None:
total_optical_flow = optical_flow
else:
total_optical_flow += optical_flow
magnitudes = np.sqrt(np.sum( | np.power(total_optical_flow, 2) | numpy.power |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 12 15:31:55 2017
@author: <NAME>, <NAME>, <NAME>
"""
from __future__ import division, print_function, absolute_import
import numpy as np
import matplotlib.pyplot as plt
from scipy.linalg import sqrtm
from pyUSID.io.hdf_utils import get_auxiliary_datasets
from pyUSID.viz.plot_utils import set_tick_font_size
def do_bayesian_inference(i_meas, bias, freq, num_x_steps=251, r_extra=110, gam=0.03, e=10.0, sigma=10., sigmaC=1.,
num_samples=2E3, show_plots=False, econ=False):
"""
this function accepts a Voltage vector and current vector
and returns a Bayesian inferred result for R(V) and capacitance
Used for solving the situation I = V/R(V) + CdV/dt
to recover R(V) and C, where C is constant.
Parameters
----------
i_meas : 1D array or list
current values, should be in nA
bias : 1D array or list
voltage values
freq : float
frequency of applied waveform
num_x_steps : unsigned int (Optional, Default = 251)
Number of steps in x vector (interpolating V)
r_extra : float (Optional, default = 220 [Ohms])
Extra resistance in the RC circuit that will provide correct current and resistance values
gam : float (Optional, Default = 0.03)
gamma value for reconstruction
e : float (Optional, Default = 10.0)
Ask Kody
sigma : float (Optional, Default = 10.0)
Ask Kody
sigmaC : float (Optional, Default = 1.0)
Ask Kody
num_samples : unsigned int (Optional, Default = 1E4)
Number of samples. 1E+4 is more than sufficient
show_plots : Boolean (Optional, Default = False)
Whether or not to show plots
econ : Boolean (Optional, Default = False)
Whether or not extra datasets are returned. Turn this on when running on multiple datasets
Returns
-------
results_dict : Dictionary
Dictionary iterms are
'x' : 1D float array. Voltage vector interpolated with num_samples number of points
'm' : Ask Kody
'mR' : 1D float array. Bayesian inference of the resistance. This is the one you want
'vR' : 2D float array. varaiance ? of inferred resistance
'Irec' : 1D array or float. Reconstructed current without capacitance
'Sigma' : Ask Kody
'cValue' : float. Capacitance value
'm2R' : Ask Kody
'SI' : Ask Kody
Written by <NAME> (Matlab) and translated to Python by <NAME>
"""
num_samples = int(num_samples)
num_x_steps = int(num_x_steps)
if num_x_steps % 2 == 0:
num_x_steps += 1 # Always keep it odd
# Organize, set up the problem
t_max = 1. / freq
t = np.linspace(0, t_max, len(bias))
dt = t[2] - t[1]
dv = np.diff(bias) / dt
dv = np.append(dv, dv[-1])
max_volts = max(bias)
# num_x_steps = int(round(2 * round(max_volts / dx, 1) + 1, 0))
x = np.linspace(-max_volts, max_volts, num_x_steps)
dx = x[1] - x[0]
# M = len(x)
num_volt_points = len(bias)
# Build A
A = np.zeros(shape=(num_volt_points, num_x_steps + 1))
for j in range(num_volt_points):
ix = int(round(np.floor((bias[j] + max_volts) / dx) + 1))
ix = min(ix, len(x) - 1)
ix = max(ix, 1)
A[j, ix] = bias[j] * (bias[j] - x[ix - 1]) / (x[ix] - x[ix - 1])
A[j, ix - 1] = bias[j] * (1. - (bias[j] - x[ix - 1]) / (x[ix] - x[ix - 1]))
A[:, num_x_steps] = dv + r_extra * bias
# generate simulated observations
Lapt = (-1. * np.diag((t[:-1]) ** 0, -1) - np.diag(t[:-1] ** 0, 1) + 2. * np.diag(t ** 0, 0)) / dt / dt
Lapt[0, 0] = 1. / dt / dt
Lapt[-1, -1] = 1. / dt / dt
O = (1. / gam ** 2) * (np.eye(num_volt_points))
# noise_term = np.linalg.lstsq(sqrtm(O),np.random.randn(N,1))[0]
# y = IV_point
# Itrue + noise_term.ravel()
Lap = (-1. * np.diag((x[:-1]) ** 0, -1) - np.diag(x[:-1] ** 0, 1) + 2. * np.diag(x ** 0, 0)) / dx / dx
Lap[0, 0] = 1. / dx / dx
Lap[-1, -1] = 1. / dx / dx
m0 = 3. * np.ones((num_x_steps, 1))
m0 = np.append(m0, 0)
P0 = np.zeros(shape=(num_x_steps + 1, num_x_steps + 1))
P0[:num_x_steps, :num_x_steps] = 1. / sigma ** 2 * (1. * np.eye(num_x_steps) + np.linalg.matrix_power(Lap, 3))
P0[num_x_steps, num_x_steps] = 1. / sigmaC ** 2
"""
There is a SERIOUS problem with numpy, especially linear algebra. Parallelism is wasted on just this one line!
See single_rank_single_node log in the output folder
https://github.com/joblib/joblib/issues/575
"""
Sigma = np.linalg.inv(np.dot(A.T, np.dot(O, A)) + P0)
m = np.dot(Sigma, (np.dot(A.T, np.dot(O, i_meas)) + np.dot(P0, m0)))
# Reconstructed current
Irec = np.dot(A, m) # This includes the capacitance
# Draw samples from S
# SI = (np.matlib.repmat(m[:M], num_samples, 1).T) + np.dot(sqrtm(Sigma[:M, :M]), np.random.randn(M, num_samples))
SI = np.tile(m[:num_x_steps], (num_samples, 1)).T + np.dot(sqrtm(Sigma[:num_x_steps, :num_x_steps]),
np.random.randn(num_x_steps, num_samples))
# approximate mean and covariance of R
mR = 1. / num_samples * np.sum(1. / SI, 1)
m2R = 1. / num_samples * np.dot(1. / SI, (1. / SI).T)
# m2R=1./num_samples*(1./SI)*(1./SI).T
# vR=m2R-np.dot(mR,mR.T)
vR = m2R - mR * mR.T
cValue = m[-1]
if econ:
results_dict = {'x': x, 'mR': mR, 'vR': np.diag(vR), 'Irec': Irec, 'cValue': cValue}
else:
results_dict = {'x': x, 'm': m, 'mR': mR, 'vR': vR, 'Irec': Irec, 'Sigma': Sigma, 'cValue': cValue, 'm2R': m2R,
'SI': SI}
if show_plots:
# Do some plotting
plt.figure(101)
plt.plot(x, mR, 'b', linewidth=3)
plt.plot(x, mR + np.sqrt(np.diag(vR)), 'r-', linewidth=3)
plt.plot(x, mR - np.sqrt( | np.diag(vR) | numpy.diag |
from keras.models import load_model
import glob
import os
from skimage.io import imread, imsave
from skimage.transform import resize
import cv2
import matplotlib.pyplot as plt
from create_individual_lettuce_train_data import construct_ground_truth, fix_noise
from skimage.color import rgb2grey, grey2rgb
from skimage.draw import circle, line, set_color
from skimage.util.shape import view_as_windows
import numpy as np
# write function to load the images.
def load_field_data():
dataset_name = '20160823_Gs_NDVI_1000ft_2-148_1/'
#dataset_name = '20160816_Gs_Wk33_NDVI_1000ft_Shippea_Hill_211-362'
image_path = '../AirSurf/Jennifer Manual Counts/ground_truth/Processed for Batch Analysis/' + dataset_name
ground_truth_path = '../AirSurf/Jennifer Manual Counts/ground_truth/' + dataset_name
names = []
train_X = []
position_Y = []
files = glob.glob(ground_truth_path + "*.txt")
for ind, textfile in enumerate(files):
image_Y = ground_truth_path
image = image_path
for txt in os.path.splitext(os.path.basename(textfile))[:-1]:
image += txt
image_Y += txt
image += '.txt_sub_img.tif'
if not os.path.isfile(image):
continue
img = fix_noise(cv2.cvtColor(cv2.imread(image), cv2.COLOR_BGR2RGB))
img = rgb2grey(img)
name = "./CONVERTED/"+os.path.basename(textfile)+".tif"
img_y = imread(image_Y + ".tif")
img = resize(img, (img_y.shape[0], img_y.shape[1], 1))
positions = construct_ground_truth(img_y)
names.append(name)
train_X.append(img)
position_Y.append(positions)
return names, np.array(train_X), np.array(position_Y)
#given the img, and the model. Slide along the image, extracting plots and counting the lettuces.
def sliding_window_count(img, model, stride=10):
img = img.reshape(img.shape[:2])
img = np.pad(img, stride+1, mode='constant')
todraw = grey2rgb(img.copy()) ##reshape it from 900,900,1 to 900,900
plt.imshow(todraw)
plt.show()
img = img.reshape((img.shape[0], img.shape[1], 1))
print(img.shape)
w, h = img.shape[:2]
l = 20
#count the number of predicted ones.
lettuce_count = 0
kernel = 9
for x in range(kernel, w-l, stride):
for y in range(kernel, h-l, stride):
regions = []
inds = []
for x1 in range(x-kernel, x+kernel):
for y1 in range(y-kernel, y+kernel):
regions.append(img[x1:x1 + l, y1:y1 + l])
inds.append((x1, y1))
print(x1)
print(y1)
inds = np.array(inds)
pred = model.predict(np.array(regions), verbose=0)
#count lettuce predictions in this kernel region.
args = np.argmax(pred, axis=1)
#count the number of 1's, in the arg list.
count = np.count_nonzero(args)
#75% of preds are for a lettuce.
if count >= float(inds.shape[0]) * 0.75:
#find the index of the best pred
best_arg = np.argmax(pred[:1])
x_1, y_1 = inds[best_arg]
todraw[circle(x_1,y_1,5,shape=todraw.shape)] = (1,0,0)
lettuce_count += 1
return lettuce_count, todraw
#given the img, and the model. Slide along the image, extracting plots and counting the lettuces.
def sliding_window_count_vectorised(img, model, length=20, stride=3, probability_threshold = 0.95):
#img = img.reshape(img.shape[:2])
#img = np.pad(img, stride, mode='constant')
img = img.reshape((img.shape[0], img.shape[1], 1))
#count the number of predicted ones.
lettuce_count = 0
boxes = []
probs = []
if min(img.shape[:2]) < length:
return np.array(boxes), np.array(probs)
im4D = view_as_windows(img, (length,length,1), step=(stride,stride,1))
im3d = im4D.reshape(-1,length,length,1)
#from a given index, we should be able to convert it back into a 2d co-ord.
preds = model.predict(im3d, verbose=0)
xs = np.arange(0, img.shape[0]-length+1, step=stride)
ys = np.arange(0, img.shape[1]-length+1, step=stride)
#unravel the predictions, and construct the bounding boxes from the indexes.
for index, pred in enumerate(preds):
if np.argmax(pred) == 1:
probability = np.max(pred)
if probability < probability_threshold:
continue
probs.append(probability)
#deconstruct index into x,y.
x,y = np.unravel_index(index, im4D.shape[:2])
#need to then map back to the stride params from original image.
x = xs[x]
y = ys[y]
boxes.append([x,y,x+length,y+length])
return np.array(boxes), np.array(probs)
#given the img, and the model. Slide along the image, extracting plots and counting the lettuces.
def sliding_window_count_simple(img, model, stride=5):
img = img.reshape(img.shape[:2])
img = np.pad(img, stride, mode='constant')
img = img.reshape((img.shape[0], img.shape[1], 1))
w, h = img.shape[:2]
l = 20
#count the number of predicted ones.
lettuce_count = 0
boxes = []
probs = []
for x in range(stride, w-l, stride):
for y in range(stride, h-l, stride):
pred = model.predict(np.array([img[x:x+l,y:y+l]]), verbose=0)
if np.argmax(pred) == 1:
probs.append(np.max(pred))
boxes.append([x,y,x+l,y+l])
return boxes, probs
# Malisiewicz et al.
def non_max_suppression_fast(boxes, probabilities, overlapThresh):
# if there are no boxes, return an empty list
if len(boxes) == 0:
return []
# if the bounding boxes integers, convert them to floats --
# this is important since we'll be doing a bunch of divisions
if boxes.dtype.kind == "i":
boxes = boxes.astype("float")
# initialize the list of picked indexes
pick = []
# grab the coordinates of the bounding boxes
x1 = boxes[:,0]
y1 = boxes[:,1]
x2 = boxes[:,2]
y2 = boxes[:,3]
# compute the area of the bounding boxes and sort the bounding
# boxes by the bottom-right y-coordinate of the bounding box
area = (x2 - x1 + 1) * (y2 - y1 + 1)
idxs = np.argsort(probabilities) # sort bounding box based on predictions.
# keep looping while some indexes still remain in the indexes
# list
while len(idxs) > 0:
# grab the last index in the indexes list and add the
# index value to the list of picked indexes
last = len(idxs) - 1
i = idxs[last]
pick.append(i)
# find the largest (x, y) coordinates for the start of
# the bounding box and the smallest (x, y) coordinates
# for the end of the bounding box
xx1 = np.maximum(x1[i], x1[idxs[:last]])
yy1 = np.maximum(y1[i], y1[idxs[:last]])
xx2 = np.minimum(x2[i], x2[idxs[:last]])
yy2 = np.minimum(y2[i], y2[idxs[:last]])
# compute the width and height of the bounding box
w = np.maximum(0, xx2 - xx1 + 1)
h = np.maximum(0, yy2 - yy1 + 1)
# compute the ratio of overlap
overlap = (w * h) / area[idxs[:last]]
# delete all indexes from the index list that have
idxs = np.delete(idxs, np.concatenate(([last],
np.where(overlap > overlapThresh)[0])))
# return only the bounding boxes that were picked using the
# integer data type
return boxes[pick].astype("int"), probabilities[pick]
def draw_boxes(im, boxs, color=(1,0,0)):
for (x1, y1, x2, y2) in boxs:
set_color(im, circle(abs((x2+x1))/2.0, abs(y2+y1)/2.0, radius=(abs(y2 - y1) + 1.0) / 2.0), color)
return im
def draw_boxes_please(im, boxs, color=(1,0,0), width=0):
for (x1, y1, x2, y2) in boxs:
up_thick_line(im, x1, y1, x1, y2, color, width)
horizontal_thick_line(im, x1, y1, x2, y1, color, width)
up_thick_line(im, x2, y2, x2, y1, color, width)
horizontal_thick_line(im, x1, y2, x2, y2, color, width)
return im
def up_thick_line(im, x1,y1,x2,y2, color, width=5):
if width == 0:
set_color(im, line(x1, y1, x2, y2), color)
else:
for i in range(-width, width):
set_color(im, line(x1+i, y1, x2+i, y2), color)
def horizontal_thick_line(im, x1,y1,x2,y2, color, width=5):
if width == 0:
set_color(im, line(x1, y1, x2, y2), color)
for i in range(-width, width):
set_color(im, line(x1, y1+i, x2, y2+i), color)
def draw_circles(im, boxs, radius=10):
print(boxs)
for (x1, y1) in boxs:
set_color(im, circle((10 + x1), int(10 + y1), radius=radius), (1, 0, 0))
return im
def test_model():
loaded_model = load_model('./trained_model_new2.h5')
names, train_X, position_Y, = load_field_data()
all_data = []
stride = 5
length = 20
print("loaded")
overlap = 0.18
y_hat = []
y = []
for name, train, positions in zip(names, train_X, position_Y):
boxes, probs = sliding_window_count_vectorised(train, loaded_model, length, stride)
boxes, _ = non_max_suppression_fast(boxes, probs, overlap) # 18%
#all_data.append((boxes, probs))
y_hat.append(boxes.shape[0])
y.append(positions.shape[0])
print(name, positions.shape[0], boxes.shape[0])
'''for name, train, positions, (boxes, probs) in zip(names, train_X, position_Y, all_data):
boxes,_ = non_max_suppression_fast(boxes, probs, overlap) # 18%
img = np.pad(train.copy().reshape(train.shape[:2]), stride, mode='constant')
img = grey2rgb(img) ##reshape it from 900,900,1 to 900,900
img = draw_boxes(img, boxes)
plt.imshow(img)
plt.show()'''
from sklearn.metrics import r2_score
score = r2_score(y, y_hat)
print(score)
plt.figure(figsize=(10, 10))
plt.title("Cumulative mean of the average across all sub images for both manual and automatic \nR2 = " + str(score))
plt.scatter(y, y_hat, s=24)
plt.xlabel("Manual counts")
plt.ylabel("Automatic counts")
plt.savefig("train_data_pairwise.png")
plt.close()
plt.show()
'''
overlap = 0.2
old_overlap = 0.2
learning_rate = 0.1
changed = True
while changed:
zipped = list(zip(train_X, position_Y, all_data))
random.shuffle(zipped)
for train, positions, (boxes, probs) in zipped:
boxes = non_max_suppression_fast(boxes, probs, overlap) #20%
img = np.pad(train.copy().reshape(train.shape[:2]), stride, mode='constant')
img = grey2rgb(img) ##reshape it from 900,900,1 to 900,900
img = draw_boxes(img, boxes)
plt.imshow(img)
plt.show()
y_hat = boxes.shape[0]
y = positions.shape[0]
error = y_hat - y
sigmoid_error = 1/(1+math.e**-error)
sign = np.sign(error) * -1
##learningrate * our current overlap* propotional to the sigmoid error* with the direction we want to change
overlap += (learning_rate*overlap*sigmoid_error*sign)
print(overlap)
if overlap == old_overlap:
changed = False
old_overlap = overlap
'''
'''
print(image.shape[:2])
for i, (x,y,radius) in enumerate(position_Y[index]):
im = image[x - radius:x + radius, y - radius:y + radius]
if im.shape[0] == 20 and im.shape[1] == 20:
print(loaded_model.predict_classes(np.array([im])))
plt.imshow(im.reshape(im.shape[:2]))
plt.show()
break
'''
def create_bounding_box_figure():
loaded_model = load_model('./trained_model_new2.h5')
all_data = []
stride = 2
length = 20
img = imread("C:/Users/bostroma/Documents/LettuceProject/CONVERTED_positives/20160823_Gs_NDVI_1000ft_2-148_1_modified.tif_934_2177_1234_2477.txt.tif")[0:8,12:27]
plt.imshow(img)
plt.show()
train = resize(img, (24,45,1))
boxes, probs = sliding_window_count_vectorised(train, loaded_model, length, stride)
box, prob = non_max_suppression_fast(boxes, probs, 0.18)
train = grey2rgb(train.reshape(train.shape[:2]))
#draw_boxes_please(train.copy(), boxes)
#plt.axis("off")
plt.imshow(train)
plt.show()
#plt.axis("off")
import matplotlib.patches as mpatches
N = len(boxes)
import colorsys
def hsv2rgb(h, s, v):
return tuple(i for i in colorsys.hsv_to_rgb(h, s, v))
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N - 1, -1, -1)] # blue,green,red
colors = np.array(list(map(lambda x: hsv2rgb(*x), HSV_tuples)))
print(colors)
im = train.copy()
width = 0
for (x1, y1, x2, y2), color in zip(boxes, colors):
up_thick_line(im, x1, y1, x1, y2, color, width)
horizontal_thick_line(im, x1, y1, x2, y1, color, width)
up_thick_line(im, x2, y2, x2, y1, color, width)
horizontal_thick_line(im, x1, y2, x2, y2, color, width)
plt.imshow(im)
legend = []
for color, p in zip(colors,probs):
legend.append(mpatches.Patch(color=color, label="%.4f"%p))
plt.legend(handles=legend)
plt.show()
im = train.copy()
width = 0
colors = [colors[2]]
box = [boxes[2]]
probs = [probs[2]]
for (x1, y1, x2, y2), color in zip(box, colors):
up_thick_line(im, x1, y1, x1, y2, color, width)
horizontal_thick_line(im, x1, y1, x2, y1, color, width)
up_thick_line(im, x2, y2, x2, y1, color, width)
horizontal_thick_line(im, x1, y2, x2, y2, color, width)
plt.imshow(im)
legend = []
for color, p in zip(colors,probs):
legend.append(mpatches.Patch(color=color, label="%.4f"%p))
plt.legend(handles=legend)
plt.show()
return
def create_bounding_box_quadrant():
file_name = "bottom_field_cropped"
whole_field = imread("greyscale_images/"+file_name + ".png")[:, :, :3]
l = 250
stride = 5
box_length = 20
h, w = whole_field.shape[:2]
boxes = []
for x in range(0, h, l-box_length):
for y in range(0, w, l-box_length):
boxes.append((x,y,x+l,y+l))
print(boxes)
whole_field = draw_boxes_please(grey2rgb(whole_field), np.array(boxes), color=(255,255,0), width=5)
plt.imshow(whole_field)
plt.show()
imsave("quadrants.png", resize(whole_field, np.divide(whole_field.shape,(10,10,1)).astype(np.int)))
#construct sub image, and do sliding window quadrant.
l = 60
index = 5
s1 = slice(boxes[index][0],boxes[index][2])
s2 = slice(boxes[index][1],boxes[index][3])
whole_field = whole_field[s1,s2,:]
whole_field = resize(whole_field, (whole_field.shape[0]*3, whole_field.shape[1]*3))
plt.imshow(whole_field)
plt.show()
boxes = []
h, w = whole_field.shape[:2]
for x in range(0, h, 9):
for y in range(0, w, 9):
boxes.append((x,y,x+l,y+l))
whole_field = draw_boxes_please(whole_field, | np.array(boxes) | numpy.array |
# encoding: utf-8
#
# @Author: <NAME>, <NAME>
# @Date: Nov 15, 2021
# @Filename: ism.py
# @License: BSD 3-Clause
# @Copyright: <NAME>, <NAME>
import os.path
from astropy import units as u
from astropy import constants as c
import numpy as np
from astropy.io import fits, ascii
from astropy.table import Table
from scipy.special import sph_harm
from astropy.wcs import WCS
from astropy.wcs.utils import proj_plane_pixel_scales
from astropy.coordinates import SkyCoord
from astropy.modeling.models import Sersic2D
from dataclasses import dataclass
import sys
if (sys.version_info[0]+sys.version_info[1]/10.) < 3.8:
from backports.cached_property import cached_property
else:
from functools import cached_property
from scipy.ndimage.interpolation import map_coordinates
from scipy.interpolate import interp1d, interp2d
import lvmdatasimulator
from lvmdatasimulator import log
import progressbar
from joblib import Parallel, delayed
from astropy.convolution import convolve_fft, kernels
from lvmdatasimulator.utils import calc_circular_mask, convolve_array, set_default_dict_values, \
ism_extinction, check_overlap, assign_units
fluxunit = u.erg / (u.cm ** 2 * u.s * u.arcsec ** 2)
velunit = u.km / u.s
def brightness_inhomogeneities_sphere(harm_amplitudes, ll, phi_cur, theta_cur, rho, med, radius, thickness):
"""
Auxiliary function producing the inhomogeneities on the brightness distribution for the Cloud of Bubble objects
using the spherical harmonics.
"""
brt = theta_cur * 0
for m in np.arange(-ll, ll + 1):
brt += (harm_amplitudes[m + ll * (ll + 1) - 1] * sph_harm(m, ll, phi_cur, theta_cur).real * med *
(1 - np.sqrt(abs(rho.value ** 2 / radius.value ** 2 - (1 - thickness / 2) ** 2))))
return brt
def sphere_brt_in_line(brt_3d, rad_3d, rad_model, flux_model):
"""
Auxiliary function computing the brightness of the Cloud or Bubble at given radii and in given line
according to the Cloudy models
"""
p = interp1d(rad_model, flux_model, fill_value='extrapolate', assume_sorted=True)
return p(rad_3d) * brt_3d
def interpolate_sphere_to_cartesian(spherical_array, x_grid=None, y_grid=None, z_grid=None,
rad_grid=None, theta_grid=None, phi_grid=None, pxscale=1. * u.pc):
"""
Auxiliary function to project the brightness or velocities from the spherical to cartesian coordinates
"""
x, y, z = np.meshgrid(x_grid, y_grid, z_grid, indexing='ij')
phi_c, theta_c, rad_c = xyz_to_sphere(x, y, z, pxscale=pxscale)
ir = interp1d(rad_grid, np.arange(len(rad_grid)), bounds_error=False)
ith = interp1d(theta_grid, np.arange(len(theta_grid)))
iphi = interp1d(phi_grid, np.arange(len(phi_grid)))
new_ir = ir(rad_c.ravel())
new_ith = ith(theta_c.ravel())
new_iphi = iphi(phi_c.ravel())
cart_data = map_coordinates(spherical_array, np.vstack([new_ir, new_ith, new_iphi]),
order=1, mode='constant', cval=0)
return cart_data.reshape([len(x_grid), len(y_grid), len(z_grid)]).T
def limit_angle(value, bottom_limit=0, top_limit=np.pi):
"""
Auxiliary function to limit the angle values to the range of [0, pi]
"""
value[value < bottom_limit] += (top_limit - bottom_limit)
value[value > top_limit] -= (top_limit - bottom_limit)
return value
def xyz_to_sphere(x, y, z, pxscale=1. * u.pc):
"""
Auxiliary function to map the coordinates from cartesian to spherical system
"""
phi_c = np.arctan2(y, x)
rad_c = (np.sqrt(x ** 2 + y ** 2 + z ** 2))
rad_c[rad_c == 0 * u.pc] = 1e-3 * pxscale
theta_c = (np.arccos(z / rad_c))
phi_c = limit_angle(phi_c, 0 * u.radian, 2 * np.pi * u.radian)
theta_c = limit_angle(theta_c, 0 * u.radian, np.pi * u.radian)
return phi_c, theta_c, rad_c
def find_model_id(file=lvmdatasimulator.CLOUDY_MODELS,
check_id=None, params=lvmdatasimulator.CLOUDY_SPEC_DEFAULTS['id']):
"""
Checks the input parameters of the pre-computed Cloudy model and return corresponding index in the grid
"""
with fits.open(file) as hdu:
if check_id is None:
if params is None:
check_id = lvmdatasimulator.CLOUDY_SPEC_DEFAULTS['id']
log.warning(f'Default Cloudy model will be used (id = {check_id})')
else:
summary_table = Table(hdu['Summary'].data)
indexes = np.arange(len(summary_table)).astype(int)
rec_table = np.ones(shape=len(summary_table), dtype=bool)
def closest(rec, prop, val):
unique_col = np.unique(summary_table[prop][rec])
if isinstance(val, str):
res = unique_col[unique_col == val]
if len(res) == 0:
return ""
return res
else:
return unique_col[np.argsort(np.abs(unique_col - val))[0]]
for p in params:
if p not in summary_table.colnames or params[p] is None or \
((isinstance(params[p], float) or isinstance(params[p], int)) and ~np.isfinite(params[p])):
continue
rec_table = rec_table & (summary_table[p] == closest(indexes, p, params[p]))
indexes = np.flatnonzero(rec_table)
if len(indexes) == 0:
break
if len(indexes) == 0 or len(indexes) == len(summary_table):
check_id = lvmdatasimulator.CLOUDY_SPEC_DEFAULTS['id']
log.warning('Input parameters do not correspond to any pre-computed Cloudy model.'
'Default Cloudy model will be used (id = {0})'.format(check_id))
elif len(indexes) == 1:
check_id = summary_table['Model_ID'][indexes[0]]
for p in params:
if p not in summary_table.colnames or params[p] is None or \
((isinstance(params[p], float) or
isinstance(params[p], int)) and ~np.isfinite(params[p])):
continue
if params[p] != summary_table[p][indexes[0]]:
log.warning(f'Use the closest pre-computed Cloudy model with id = {check_id}')
break
else:
check_id = summary_table['Model_ID'][indexes[0]]
log.warning(f'Select one of the closest pre-computed Cloudy model with id = {check_id}')
#
# for cur_ext in range(len(hdu)):
# if cur_ext == 0:
# continue
# found = False
# for p in params:
# if p == 'id':
# continue
# precision = 1
# if p == 'Z':
# precision = 2
# if np.round(params[p], precision) != np.round(hdu[cur_ext].header[p], precision):
# break
# else:
# found = True
# if found:
# return cur_ext, check_id
# check_id = lvmdatasimulator.CLOUDY_SPEC_DEFAULTS['id']
# log.warning('Input parameters do not correspond to any pre-computed Cloudy model.'
# 'Default Cloudy model will be used (id = {0})'.format(check_id))
extension_index = None
while extension_index is None:
extension_index = [cur_ext for cur_ext in range(len(hdu)) if (
check_id == hdu[cur_ext].header.get('MODEL_ID'))]
if len(extension_index) == 0:
if check_id == lvmdatasimulator.CLOUDY_SPEC_DEFAULTS['id']:
log.warning('Model_ID = {0} is not found in the Cloudy models grid. '
'Use the first one in the grid instead'.format(check_id))
extension_index = 1
else:
log.warning('Model_ID = {0} is not found in the Cloudy models grid. '
'Use default ({1}) instead'.format(check_id,
lvmdatasimulator.CLOUDY_SPEC_DEFAULTS['id']))
check_id = lvmdatasimulator.CLOUDY_SPEC_DEFAULTS['id']
extension_index = None
else:
extension_index = extension_index[0]
return extension_index, check_id
@dataclass
class Nebula:
"""
Base class defining properties of every nebula type.
By itself it describes the rectangular nebula (e.g. DIG)
Constructed nebula has 4 dimensions, where 4th derive its appearance in different lines
(if spectrum_id is None, or if it is dark nebula => only one line)
"""
xc: int = None # Center of the region in the field of view, pix
yc: int = None # Center of the region in the field of view, pix
x0: int = 0 # Coordinates of the bottom-left corner in the field of view, pix
y0: int = 0 # Coordinates of the bottom-left corner in the field of view, pix
pix_width: int = None # full width of cartesian grid, pix (should be odd)
pix_height: int = None # full height of cartesian grid, pix (should be odd)
width: u.pc = 0 * u.pc # width of the nebula in pc (not used if pix_width is set up)
height: u.pc = 0 * u.pc # height of the nebula in pc (not used if pix_height is set up)
pxscale: u.pc = 0.01 * u.pc # pixel size in pc
spectrum_id: int = None # ID of a template Cloudy emission spectrum for this nebula
n_brightest_lines: int = None # limit the number of the lines to the first N brightest
sys_velocity: velunit = 0 * velunit # Systemic velocity
turbulent_sigma: velunit = 10 * velunit # Velocity dispersion due to turbulence; included in calculations of LSF
max_brightness: fluxunit = 1e-15 * fluxunit
max_extinction: u.mag = 0 * u.mag
perturb_scale: int = 0 * u.pc # Spatial scale of correlated perturbations
perturb_amplitude: float = 0.1 # Maximal amplitude of perturbations
_npix_los: int = 1 # full size along line of sight in pixels
nchunks: int = -1 # number of chuncks to use for the convolution. If negative, select automatically
vel_gradient: (velunit / u.pc) = 0 # velocity gradient along the nebula
vel_pa: u.degree = 0 # Position angle of the kinematical axis (for the velocity gradient or rotation velocity)
def __post_init__(self):
self._assign_all_units()
self._assign_position_params()
self._ref_line_id = 0
self.linerat_constant = True # True if the ratio of line fluxes shouldn't change across the nebula
def _assign_all_units(self):
whole_list_properties = ['pxscale', 'sys_velocity', 'turbulent_sigma', 'max_brightness', 'max_extinction',
'perturb_scale', 'radius', 'PA', 'length', 'width', 'vel_gradient', 'r_eff',
'vel_rot', 'expansion_velocity', 'spectral_axis', 'vel_pa']
whole_list_units = [u.pc, velunit, velunit, fluxunit, u.mag, u.pc, u.pc, u.degree, u.pc, u.pc,
(velunit / u.pc), u.kpc, velunit, velunit, velunit, u.degree]
cur_list_properties = []
cur_list_units = []
for prp, unit in zip(whole_list_properties, whole_list_units):
if hasattr(self, prp):
cur_list_properties.append(prp)
cur_list_units.append(unit)
assign_units(self, cur_list_properties, cur_list_units)
def _assign_position_params(self, conversion_type='rect'):
if conversion_type == 'rect':
for v in ['height', 'width']:
if self.__getattribute__(f'pix_{v}') is None:
val = np.round((self.__getattribute__(v) / self.pxscale).value / 2.).astype(int) * 2 + 1
else:
val = np.round(self.__getattribute__(f'pix_{v}') / 2.).astype(int) * 2 + 1
setattr(self, f'pix_{v}', val)
elif conversion_type == 'ellipse':
self.pix_width = (np.round(np.abs(self.radius / self.pxscale * np.sin(self.PA)) +
np.abs(self.radius / self.pxscale *
self.ax_ratio * np.cos(self.PA))).astype(int) * 2 + 1).value
self.pix_height = (np.round(np.abs(self.radius / self.pxscale * np.cos(self.PA)) +
np.abs(self.radius / self.pxscale *
self.ax_ratio * np.sin(self.PA))).astype(int) * 2 + 1).value
elif conversion_type == 'galaxy':
self.pix_width = (np.round(np.abs(self.r_max * np.sin(self.PA)) +
np.abs(self.r_max * self.ax_ratio * np.cos(self.PA))).astype(int) * 2 + 1).value
self.pix_height = (np.round(np.abs(self.r_max * np.cos(self.PA)) +
np.abs(self.r_max * self.ax_ratio * np.sin(self.PA))).astype(int) * 2 + 1).value
elif conversion_type == 'cylinder':
self.pix_width = (np.ceil((self.length * np.abs(np.sin(self.PA)) +
self.width * np.abs(np.cos(self.PA))) / self.pxscale / 2.
).astype(int) * 2 + 1).value
self.pix_height = (np.ceil((self.length * np.abs(np.cos(self.PA)) +
self.width * np.abs(np.sin(self.PA))) / self.pxscale / 2.
).astype(int) * 2 + 1).value
if (self.xc is not None) and (self.yc is not None):
self.x0 = self.xc - np.round((self.pix_width - 1) / 2).astype(int)
self.y0 = self.yc - np.round((self.pix_height - 1) / 2).astype(int)
elif (self.x0 is not None) and (self.y0 is not None):
self.xc = self.x0 + np.round((self.pix_width - 1) / 2).astype(int)
self.yc = self.y0 + np.round((self.pix_height - 1) / 2).astype(int)
@cached_property
def _cartesian_x_grid(self):
return np.arange(self.pix_width) * self.pxscale
@cached_property
def _cartesian_y_grid(self):
return np.arange(self.pix_height) * self.pxscale
@cached_property
def _cartesian_z_grid(self):
return np.arange(self._npix_los) * self.pxscale
@cached_property
def _max_density(self):
return self.max_extinction * (1.8e21 / (u.cm ** 2 * u.mag))
@cached_property
def _brightness_3d_cartesian(self):
"""
Method to obtain the brightness (or density) distribution of the nebula in cartesian coordinates
"""
brt = np.ones(shape=(self.pix_height, self.pix_width, self._npix_los), dtype=float) / self._npix_los
if (self.perturb_scale > 0) and (self.perturb_amplitude > 0):
pertscale = (self.perturb_scale / self.pxscale).value
perturb = np.random.uniform(-1, 1, (self.pix_height, self.pix_width)
) * self.perturb_amplitude / self._npix_los
xx, yy = np.meshgrid(np.arange(self.pix_width), np.arange(self.pix_height))
f = np.exp(-2 * (xx ** 2 + yy ** 2) / pertscale)
perturb = 4 / np.sqrt(np.pi) / pertscale * np.fft.ifft2(np.fft.fft2(perturb) * np.fft.fft2(f)).real
brt += (perturb[:, :, None] - np.median(perturb))
return brt
@cached_property
def _brightness_4d_cartesian(self):
"""
Derive the brightness (or density) distribution of the nebula for each emission line in cartesian coordinates
"""
if self.spectrum_id is None or self.linerat_constant:
flux_ratios = np.array([1.])
else:
with fits.open(lvmdatasimulator.CLOUDY_MODELS) as hdu:
flux_ratios = hdu[self.spectrum_id].data[1:, 1]
index_ha = np.flatnonzero(hdu[self.spectrum_id].data[1:, 0] == 6562.81)
if self.n_brightest_lines is not None and \
(self.n_brightest_lines > 0) and (self.n_brightest_lines < len(flux_ratios)):
indexes_sorted = np.argsort(flux_ratios)[::-1]
flux_ratios = flux_ratios[indexes_sorted[: self.n_brightest_lines]]
index_ha = np.flatnonzero(hdu[self.spectrum_id].data[1:, 0][indexes_sorted] == 6562.81)
if len(index_ha) == 1:
self._ref_line_id = index_ha[0]
return self._brightness_3d_cartesian[None, :, :, :] * flux_ratios[:, None, None, None]
@cached_property
def brightness_skyplane(self):
"""
Project the 3D nebula onto sky plane (for emission or continuum sources)
"""
if self.max_brightness > 0:
norm_max = self.max_brightness
else:
norm_max = 1
map2d = np.nansum(self._brightness_3d_cartesian, 2)
return map2d / np.nanmax(map2d) * norm_max
@cached_property
def brightness_skyplane_lines(self):
"""
Project the 3D emission nebula line onto sky plane (return images in each emission line)
"""
if self.max_brightness > 0:
map2d = np.nansum(self._brightness_4d_cartesian, 3)
return map2d / np.nanmax(map2d[self._ref_line_id, :, :]) * self.max_brightness
else:
return None
@cached_property
def extinction_skyplane(self):
"""
Project the 3D nebula onto sky plane (for dark clouds)
"""
if self.max_extinction > 0:
map2d = np.nansum(self._brightness_3d_cartesian, 2)
return map2d / np.nanmax(map2d) * self._max_density / (1.8e21 / (u.cm ** 2 * u.mag))
else:
return None
@cached_property
def vel_field(self):
return self._get_2d_velocity()
# if vel_field is None:
# return np.atleast_1d(self.sys_velocity)
# else:
# return vel_field + self.sys_velocity
def _get_2d_velocity(self):
if hasattr(self, 'vel_gradient') and (self.vel_gradient is not None) and (self.vel_gradient != 0):
xx, yy = np.meshgrid(np.arange(self.pix_width), np.arange(self.pix_height))
vel_field = (- (xx - (self.pix_width - 1) / 2) * np.sin(self.vel_pa) +
(yy - (self.pix_height - 1) / 2) * np.cos(self.vel_pa)) * self.pxscale * self.vel_gradient
return vel_field
else:
return None
# @cached_property
# def line_profile(self):
# lprf = np.zeros(shape=len(self.los_velocity), dtype=float)
# lprf[np.floor(len(lprf) / 2.).astype(int)] = 1.
# return lprf
@dataclass
class Rectangle(Nebula):
"""
Class defining a simple rectangular component.
This is equal to Nebula, but no perturbations and turbulence by default
"""
perturb_amplitude: float = 0.0 # Maximal amplitude of perturbations
turbulent_sigma: velunit = 0 * velunit # Velocity dispersion due to turbulence; included in calculations of LSF
def __post_init__(self):
self._assign_all_units()
self._assign_position_params()
self._ref_line_id = 0
self.linerat_constant = True # True if the ratio of line fluxes shouldn't change across the nebula
@dataclass
class Ellipse(Nebula):
"""
Class defining a simple elliptical component.
No perturbations and turbulence by default
"""
perturb_amplitude: float = 0.0 # Maximal amplitude of perturbations
turbulent_sigma: velunit = 0 * velunit # Velocity dispersion due to turbulence; included in calculations of LSF
radius: u.pc = 1.0 * u.pc # Radius along the major axis of the ellipse (or radius of the circle)
PA: u.degree = 90 * u.degree # position angle of the major axis
ax_ratio: float = 1. # ratio of minor/major axes
def __post_init__(self):
self._assign_all_units()
self._npix_los = 1
self._assign_position_params(conversion_type='ellipse')
self._ref_line_id = 0
self.linerat_constant = True # True if the ratio of line fluxes shouldn't change across the nebula
@cached_property
def _brightness_3d_cartesian(self):
"""
Method to obtain the brightness (or density) distribution of the nebula in cartesian coordinates
"""
xx, yy = np.meshgrid(np.arange(self.pix_width), np.arange(self.pix_height))
brt = np.ones(shape=(self.pix_height, self.pix_width), dtype=np.float32)
angle = (self.PA + 90 * u.degree).to(u.radian).value
xct = (xx - (self.pix_width - 1) / 2) * np.cos(angle) + \
(yy - (self.pix_height - 1) / 2) * np.sin(angle)
yct = (xx - (self.pix_width - 1) / 2) * np.sin(angle) - \
(yy - (self.pix_height - 1) / 2) * np.cos(angle)
rmaj = (self.radius.to(u.pc) / self.pxscale.to(u.pc)).value
rmin = (self.radius.to(u.pc) / self.pxscale.to(u.pc)).value * self.ax_ratio
rec = (xct ** 2 / rmaj ** 2) + (yct ** 2 / rmin ** 2) >= 1
brt[rec] = 0
brt = brt.reshape((self.pix_height, self.pix_width, 1))
return brt
@dataclass
class Circle(Ellipse):
"""
Class defining a simple circular component.
"""
def __post_init__(self):
self._assign_all_units()
self.ax_ratio = 1.
self._npix_los = 1
self._assign_position_params(conversion_type='ellipse')
self._ref_line_id = 0
self.linerat_constant = True # True if the ratio of line fluxes shouldn't change across the nebula
@dataclass
class Filament(Nebula):
"""
Class of an isotropic cylindrical shape filament.
Defined by its position, length, PA, radius, maximal optical depth.
If it is emission-type filament, then also maximal brightness is required.
Velocity gradient also can be set up
"""
PA: u.degree = 90 * u.degree # position angle of the filament
length: u.pc = 10 * u.pc # full length of the filament
width: u.pc = 0.1 * u.pc # full width (diameter) of the filament
def __post_init__(self):
self._assign_all_units()
self._assign_position_params(conversion_type='cylinder')
self._npix_los = 1
self._ref_line_id = 0
self.linerat_constant = True # True if the ratio of line fluxes shouldn't change across the nebula
@cached_property
def _brightness_3d_cartesian(self):
"""
Method to obtain the brightness (or density) distribution of the nebula in cartesian coordinates
"""
xx, yy = np.meshgrid(np.arange(self.pix_width), np.arange(self.pix_height))
brt = np.zeros_like(xx, dtype=np.float32)
xct = (xx - (self.pix_width - 1) / 2) * np.cos(self.PA + 90 * u.degree) + \
(yy - (self.pix_height - 1) / 2) * np.sin(self.PA + 90 * u.degree)
yct = (xx - (self.pix_width - 1) / 2) * np.sin(self.PA + 90 * u.degree) - \
(yy - (self.pix_height - 1) / 2) * np.cos(self.PA + 90 * u.degree)
rad = ((self.width / self.pxscale).value / 2.)
len_px = ((self.length / self.pxscale).value / 2.)
rec = (np.abs(yct) <= rad) & (np.abs(xct) <= len_px)
brt[rec] = np.sqrt(1. - (yct[rec] / rad) ** 2)
brt = brt.reshape((self.pix_height, self.pix_width, 1))
return brt
@dataclass
class _ObsoleteFilament(Nebula):
"""
Class of an isotropic cylindrical shape filament.
Defined by its position, length, PA, radius, maximal optical depth
if it is emission-type filament, then maximal brightness
NB: this class is obsolete, but might be considered later in case of implementation of varying line ratios
"""
PA: u.degree = 90 * u.degree # position angle of the filament
length: u.pc = 10 * u.pc # full length of the filament
width: u.pc = 0.1 * u.pc # full width (diameter) of the filament
vel_gradient: (velunit / u.pc) = 0 # velocity gradient along the filament (to be added)
_theta_bins: int = 50
_rad_bins: int = 0
_h_bins: int = 2
_npix_los: int = 101
def __post_init__(self):
self._assign_all_units()
if self._rad_bins == 0:
self._rad_bins = np.ceil(self.width.to(u.pc).value / self.pxscale.to(u.pc).value * 5).astype(int)
if (self.xc is not None) and (self.yc is not None):
self.x0 = self.xc - np.round((len(self._cartesian_y_grid) - 1) / 2).astype(int)
self.y0 = self.yc - np.round((len(self._cartesian_z_grid) - 1) / 2).astype(int)
elif (self.x0 is not None) and (self.y0 is not None):
self.xc = self.x0 + np.round((len(self._cartesian_y_grid) - 1) / 2).astype(int)
self.yc = self.y0 + np.round((len(self._cartesian_z_grid) - 1) / 2).astype(int)
self._ref_line_id = 0
self.linerat_constant = True # True if the ratio of line fluxes shouldn't change across the nebula
@cached_property
def _theta_grid(self):
return np.linspace(0, 2 * np.pi, self._theta_bins)
@cached_property
def _h_grid(self):
return np.linspace(0, self.length, self._h_bins)
@cached_property
def _rad_grid(self):
return np.linspace(0, self.width / 2, self._rad_bins)
@cached_property
def _cartesian_y_grid(self):
npix = np.ceil(1.01 * (self.length * np.abs(np.sin(self.PA)) +
self.width * np.abs(np.cos(self.PA))) / self.pxscale).astype(int)
npix_l = npix / 2 - np.ceil(self.length / 2 * np.sin(-self.PA) / self.pxscale).astype(int)
return (np.linspace(0, npix, npix + 1) - npix_l) * self.pxscale
@cached_property
def _cartesian_z_grid(self):
npix = np.ceil(1.01 * (self.length * np.abs(np.cos(self.PA)) +
self.width * np.abs(np.sin(self.PA))) / self.pxscale).astype(int)
npix_l = npix / 2 - np.ceil(self.length / 2 * np.cos(-self.PA) / self.pxscale).astype(int)
return (np.linspace(0, npix, npix + 1) - npix_l) * self.pxscale
@cached_property
def _cartesian_x_grid(self):
return np.linspace(-1.01, 1.01, self._npix_los) * self.width / 2
@cached_property
def _brightness_3d_cylindrical(self):
"""
Method to calculate brightness (or opacity) of the cloud at given theta, phi and radii
theta: float -- azimuthal angle [0, 2 * np.pi]
rad: float -- radius [0, self.width / 2]
h: float -- height [0, self.length]
Returns:
3D cube of normalized brightness in theta-rad-h grid; total brightness = 1
"""
rho, theta, h = np.meshgrid(self._rad_grid, self._theta_grid, self._h_grid, indexing='ij')
brt = np.ones_like(theta)
brt[rho > (self.width / 2)] = 0
norm = np.sum(brt)
if norm > 0:
brt = brt / np.sum(brt)
return brt
@cached_property
def _brightness_3d_cartesian(self):
x, y, z = np.meshgrid(self._cartesian_x_grid, self._cartesian_y_grid,
self._cartesian_z_grid, indexing='ij')
h_c = -y * np.sin(self.PA) + z * np.cos(self.PA)
theta_c = np.arctan2(y * np.cos(self.PA) + z * np.sin(self.PA), x)
rad_c = np.sqrt(x ** 2 + (y * np.cos(self.PA) + z * np.sin(self.PA)) ** 2)
rad_c[rad_c == 0 * u.pc] = 1e-3 * self.pxscale
theta_c = limit_angle(theta_c, 0 * u.radian, 2 * np.pi * u.radian)
ir = interp1d(self._rad_grid, np.arange(self._rad_bins), bounds_error=False)
ith = interp1d(self._theta_grid, np.arange(self._theta_bins))
ih = interp1d(self._h_grid, np.arange(self._h_bins), bounds_error=False)
new_ir = ir(rad_c.ravel())
new_ith = ith(theta_c.ravel())
new_ih = ih(h_c.ravel())
cart_data = map_coordinates(self._brightness_3d_cylindrical,
np.vstack([new_ir, new_ith, new_ih]),
order=1, mode='constant', cval=0)
return cart_data.reshape([len(self._cartesian_x_grid),
len(self._cartesian_y_grid),
len(self._cartesian_z_grid)]).T
@dataclass
class Galaxy(Nebula):
"""
Class defining the galaxy object (set up it as Sersic2D profile assuming it has continuum and emission components)
"""
PA: u.degree = 90 * u.degree # position angle of the major axis
ax_ratio: float = 0.7 # ratio of minor/major axes
r_eff: u.kpc = 1 * u.kpc # Effective radius in kpc
rad_lim: float = 3. # Maximum radius for calculations (in R_eff)
n: float = 1. # Sersic index
vel_rot: velunit = 0 * velunit # Rotational velocity (not implemented yet)
def __post_init__(self):
self._assign_all_units()
self._npix_los = 1
self.r_max = self.r_eff.to(u.pc).value / self.pxscale.to(u.pc).value * self.rad_lim
self._assign_position_params(conversion_type='galaxy')
self._ref_line_id = 0
self.linerat_constant = True # True if the ratio of line fluxes shouldn't change across the nebula
@cached_property
def _brightness_3d_cartesian(self):
"""
Method to obtain the brightness (or density) distribution of the nebula in cartesian coordinates
"""
xx, yy = np.meshgrid(np.arange(self.pix_width), np.arange(self.pix_height))
angle = (self.PA + 90 * u.degree).to(u.radian).value
mod = Sersic2D(amplitude=1, r_eff=(self.r_eff.to(u.pc) / self.pxscale.to(u.pc)).value,
n=self.n, x_0=(self.pix_width - 1) / 2, y_0=(self.pix_height - 1) / 2,
ellip=1 - self.ax_ratio, theta=angle)
brt = mod(xx, yy)
xct = (xx - (self.pix_width - 1) / 2) * np.cos(angle) + \
(yy - (self.pix_height - 1) / 2) * np.sin(angle)
yct = (xx - (self.pix_width - 1) / 2) * np.sin(angle) - \
(yy - (self.pix_height - 1) / 2) * np.cos(angle)
rmaj = self.rad_lim * (self.r_eff.to(u.pc) / self.pxscale.to(u.pc)).value
rmin = self.rad_lim * (self.r_eff.to(u.pc) / self.pxscale.to(u.pc)).value * self.ax_ratio
mask = np.ones_like(brt, dtype=np.float32)
rec = (xct ** 2 / rmaj ** 2) + (yct ** 2 / rmin ** 2) >= 1
mask[rec] = 0
mask = convolve_fft(mask, kernels.Gaussian2DKernel(3.), fill_value=0, allow_huge=True)
brt = brt * mask
brt = brt.reshape(self.pix_height, self.pix_width, 1)
return brt
def _get_2d_velocity(self):
if hasattr(self, 'vel_rot') and (self.vel_rot is not None) and (self.vel_rot != 0):
xx, yy = np.meshgrid(np.arange(self.pix_width), np.arange(self.pix_height))
angle = (self.PA + 90 * u.degree).to(u.radian).value
xct = (xx - (self.pix_width - 1) / 2) * np.cos(angle) + \
(yy - (self.pix_height - 1) / 2) * np.sin(angle)
yct = (xx - (self.pix_width - 1) / 2) * np.sin(angle) - \
(yy - (self.pix_height - 1) / 2) * np.cos(angle)
rad = np.sqrt(xct ** 2 + yct ** 2)
vel_field = np.zeros_like(xx, dtype=np.float32) * velunit
rec = rad > 0
vel_field[rec] = self.vel_rot * np.sqrt(1 - self.ax_ratio ** 2) * xct[rec] / rad[rec]
return vel_field
else:
return None
@dataclass
class DIG(Nebula):
"""
Class defining the DIG component. For now it is defined just by its brightness (constant)
"""
max_brightness: fluxunit = 1e-17 * fluxunit
vel_gradient: (velunit / u.pc) = 0
@dataclass
class Cloud(Nebula):
"""Class of an isotropic spherical gas cloud without any ionization source.
Defined by its position, radius, density, maximal optical depth"""
radius: u.pc = 1.0 * u.pc
max_brightness: fluxunit = 0 * fluxunit
max_extinction: u.mag = 2.0 * u.mag
thickness: float = 1.0
perturb_degree: int = 0 # Degree of perturbations (max. degree of spherical harmonics for cloud)
linerat_constant: bool = False # True if the ratio of line fluxes shouldn't change across the nebula
_phi_bins: int = 90
_theta_bins: int = 90
_rad_bins: int = 0
_npix_los: int = 100
def __post_init__(self):
self._assign_all_units()
if self._rad_bins == 0:
self._rad_bins = np.ceil(self.radius.to(u.pc).value / self.pxscale.to(u.pc).value * 3).astype(int)
delta = np.round((len(self._cartesian_y_grid) - 1) / 2).astype(int)
if (self.xc is not None) and (self.yc is not None):
self.x0 = self.xc - delta
self.y0 = self.yc - delta
elif (self.x0 is not None) and (self.y0 is not None):
self.xc = self.x0 + delta
self.yc = self.y0 + delta
self._ref_line_id = 0
@cached_property
def _theta_grid(self):
return np.linspace(0, np.pi, self._theta_bins)
@cached_property
def _phi_grid(self):
return np.linspace(0, 2 * np.pi, self._phi_bins)
@cached_property
def _rad_grid(self):
return np.linspace(0, self.radius, self._rad_bins)
@cached_property
def _cartesian_z_grid(self):
npix = np.ceil(1.02 * self.radius / self.pxscale).astype(int)
return np.linspace(-npix, npix, 2 * npix + 1) * self.pxscale
@cached_property
def _cartesian_y_grid(self):
return self._cartesian_z_grid.copy()
@cached_property
def _cartesian_x_grid(self):
return np.linspace(-1.02, 1.02, self._npix_los) * self.radius
@cached_property
def _brightness_3d_spherical(self):
"""
Method to calculate brightness (or opacity) of the cloud at given theta, phi and radii
theta: float -- polar angle [0, np.pi]
phi: float -- azimuthal angle [0, 2 * np.pi]
rad: float -- radius [0, self.radius]
Returns:
3D cube of normalized brightness in theta-phi-rad grid; total brightness = 1
"""
rho, theta, phi = np.meshgrid(self._rad_grid, self._theta_grid, self._phi_grid, indexing='ij')
brt = np.ones_like(theta)
brt[rho < (self.radius * (1 - self.thickness))] = 0
brt[rho > self.radius] = 0
med = np.median(brt[brt > 0])
if self.perturb_degree > 0:
phi_cur = limit_angle(phi + np.random.uniform(0, 2 * np.pi, 1), 0, 2 * np.pi)
theta_cur = limit_angle(theta + np.random.uniform(0, np.pi, 1), 0, np.pi)
harm_amplitudes = self.perturb_amplitude * np.random.randn(self.perturb_degree * (self.perturb_degree + 2))
brt += np.nansum(Parallel(n_jobs=lvmdatasimulator.n_process)(delayed(brightness_inhomogeneities_sphere)
(harm_amplitudes, ll, phi_cur, theta_cur,
rho, med, self.radius, self.thickness)
for ll in np.arange(1,
self.perturb_degree + 1)),
axis=0)
brt[brt < 0] = 0
if med > 0:
brt = brt / np.nansum(brt)
return brt
@cached_property
def _brightness_4d_spherical(self):
"""
Method to calculate brightness of the cloud at given theta, phi and radii for each line
theta: float -- polar angle [0, np.pi]
phi: float -- azimuthal angle [0, 2 * np.pi]
rad: float -- radius [0, self.radius]
Returns:
4D cube of brightness in line-theta-phi-rad grid; normalized to the total brightness in Halpha
"""
s = self._brightness_3d_spherical.shape
if self.spectrum_id is None or self.linerat_constant:
return self._brightness_3d_spherical.reshape((1, s[0], s[1], s[2]))
rho, _, _ = np.meshgrid(self._rad_grid, self._theta_grid, self._phi_grid, indexing='ij')
with fits.open(lvmdatasimulator.CLOUDY_MODELS) as hdu:
radius = hdu[self.spectrum_id].data[0, 2:] * (self.thickness * self.radius) + \
self.radius * (1 - self.thickness)
fluxes = hdu[self.spectrum_id].data[1:, 2:]
radius = np.insert(radius, 0, self.radius * (1 - self.thickness))
fluxes = | np.insert(fluxes, 0, fluxes[:, 0], axis=1) | numpy.insert |
# license: Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
# Licensed under the CC BY-NC-SA 4.0 license
# (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
# this code simulate the approximate motion required
# all time unit are picoseconds (1 picosec = 1e-12 sec)
import sys
sys.path.insert(0,'../pipe/')
import numpy as np
import os, json, glob
import imageio
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from utils import *
from tof_class import *
import pdb
import pickle
import time
import scipy.misc
from scipy import sparse
import scipy.interpolate
from copy import deepcopy
import multiprocessing
from kinect_spec import *
import cv2
from numpy import linalg as LA
from tensorflow.contrib import learn
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
tf.logging.set_verbosity(tf.logging.INFO)
from vis_flow import *
from kinect_init import *
PI = 3.14159265358979323846
raw_depth_new = 0
flg = False
dtype = tf.float32
def gen_approx_motion(scene_ns, array_dir, tof_cam, text_flg = False, do_vis = True):
global flg
# first loading each scene, and we will combine them then
meass = []
depths = []
msks = []
vs = []
v_flg = False
while (v_flg == False):
v_flg = True
# first loading each scene, and we will combine them then
meass = []
depths = []
msks = []
vs = []
Ps = []
for scene_n in scene_ns:
print('Augmenting scene', scene_n)
## load all data
# if the raw file does not exist, just find one and use
if not os.path.exists(array_dir+scene_n[-16:]+'.pickle'):
scenes = glob.glob(array_dir+'*.pickle')
with open(scenes[0],'rb') as f:
data = pickle.load(f)
cam = data['cam']
# separately read the true depth and true rendering
with open(scene_n[0:-16]+'gt/'+scene_n[-16::],'rb') as f:
gt=np.fromfile(f, dtype=np.float32)
depth_true = np.reshape(gt,(cam['dimy']*4,cam['dimx']*4))
with open(scene_n[0:-16]+'ideal/'+scene_n[-16::],'rb') as f:
meas_gt=np.fromfile(f, dtype=np.int32)
meas_gt = np.reshape(meas_gt,(cam['dimy'],cam['dimx'],9)).astype(np.float32)
else:
with open(array_dir+scene_n[-16::]+'.pickle','rb') as f:
data = pickle.load(f)
program = data['program']
cam = data['cam']
cam_t = data['cam_t']
scene = data['scene']
depth_true = data['depth_true']
prop_idx = data['prop_idx']
prop_s = data['prop_s']
res_gt = tof_cam.process_gt_delay_vig_dist_surf_mapmax(cam, prop_idx, prop_s, scene, depth_true)
meas_gt = res_gt['meas']
# directly read pregenerate raw measurement
with open(scene_n[0:-16]+'full/'+scene_n[-16::],'rb') as f:
meas=np.fromfile(f, dtype=np.int32)
meas = np.reshape(meas,(cam['dimy'],cam['dimx'],9)).astype(np.float32)
msk = kinect_mask().astype(np.float32)
meas = [meas[:,:,i]*msk for i in range(meas.shape[2])]
meas = np.stack(meas,-1)
meas = meas / tof_cam.cam['map_max']
# meas = meas[::-1,:,:]
meas_gt = [meas_gt[:,:,i]*msk for i in range(meas_gt.shape[2])]
meas_gt = np.stack(meas_gt,-1)
meas_gt = meas_gt / tof_cam.cam['map_max']
# reduce the resolution of the depth
depth_true[np.where(depth_true==0)] = np.nan # deal with the mix problem at edge
depth_true_s = scipy.misc.imresize(\
depth_true,\
meas.shape[0:2],\
mode='F'\
)
depth_true_s = tof_cam.dist_to_depth(depth_true_s)
depth_true_s[np.where(np.isnan(depth_true_s))] = 0
# load the mask and classification
with open(scene_n[0:-16]+'msk'+'/'+scene_n[-16:],'rb') as f:
msk_array=np.fromfile(f, dtype=np.float32)
msk_array = np.reshape(msk_array,(cam['dimy'],cam['dimx'],4))
msk = {}
msk['background'] = msk_array[:,:,0]
msk['edge'] = msk_array[:,:,1]
msk['noise'] = msk_array[:,:,2]
msk['reflection'] = msk_array[:,:,3]
# compute mask
msk_true_s = msk['background'] * msk['edge']
true = np.stack([depth_true_s,msk_true_s],2)
true = np.concatenate([true, meas_gt], 2)
msk = msk_true_s
if text_flg == True:
# add textures (simply multiply a ratio)
# WARNING: IF YOU WANT TO USE TEXTURES
# CREATE A DIRECTORY:
# ../FLAT/kinect/list/textures-curet/
# PUT THE TEXTURE IMAGES (.png format) INTO IT
# add textures (simply multiply a ratio)
texts = glob.glob('../FLAT/kinect/list/textures-curet/'+'*.png')
idx = np.random.choice(len(texts),1,replace=False)[0]
im_text = cv2.imread(texts[idx],0).astype(np.float32)
im_text /= 255.
lo = np.random.uniform(0,1) # random range
hi = np.random.uniform(lo,1)
im_text = im_text * (hi-lo) + lo
im_text = scipy.misc.imresize(im_text,meas.shape[0:2],mode='F')
im_text = np.expand_dims(im_text,-1)
# apply the texture
meas = meas * im_text
meas_gt = meas_gt * im_text
# compute the camera matrix
xx,yy = np.meshgrid(np.arange(depth_true_s.shape[1]), np.arange(depth_true_s.shape[0]))
ratio = depth_true_s.shape[1]
fov = 0.7
xx = (xx.flatten() - (xx.shape[1]-1)/2)/ratio
yy = (yy.flatten() - (yy.shape[0]-1)/2)/ratio
xx = xx * fov
yy = yy * fov
depth_f = depth_true_s.flatten()
idx = np.where(depth_f != 0)
xx = xx[idx]
yy = yy[idx]
depth_f = depth_f[idx]
idx = np.random.choice(len(depth_f),2000,replace=False)
xx = xx[idx]
yy = yy[idx]
depth_f = depth_f[idx]
pts_3d = np.stack([xx*depth_f, yy*depth_f, depth_f, np.ones(depth_f.shape)],-1)
pts_2d = np.stack([xx, yy, np.ones(depth_f.shape)],-1)
# use the DLT algorithm
a00 = np.zeros(pts_3d.shape)
a01 = -pts_2d[:,2:3]*pts_3d
a02 = pts_2d[:,1:2]*pts_3d
a10 = -a01
a11 = np.zeros(pts_3d.shape)
a12 = -pts_2d[:,0:1]*pts_3d
a20 = -a02
a21 = -a12
a22 = np.zeros(pts_3d.shape)
a0 = np.concatenate([a00, a01, a02],1)
a1 = np.concatenate([a10, a11, a12],1)
a2 = np.concatenate([a20, a21, a22],1)
A = np.concatenate([a0, a1, a2], 0)
U,s,vh=np.linalg.svd(A, full_matrices =False)
v = vh.T
P = np.reshape(v[:,-1],[3,4])
pts_2d_reproj = np.matmul(pts_3d,P.T)
pts_2d_reproj /= pts_2d_reproj[:,-1::]
reproj_err = np.sum(np.abs(pts_2d_reproj - pts_2d))
print('Reprojection error:',reproj_err)
# randomly generating the 6 affine transform parameters
max_pix = 5
max_mov_m = 0.03
mov = 10
while (np.abs(mov).max() >= max_mov_m):
th1 = np.random.normal(0.0,0.01,[3,3])
th1[0,0]+=1
th1[1,1]+=1
th1[2,2]+=1
th2 = np.random.normal(0.0,.01,[3,1])
th3 = np.array([[0,0,0,1]])
th = np.concatenate([th1,th2],1)
th = np.concatenate([th,th3],0)
Y = pts_3d[:,0]
X = pts_3d[:,1]
Z = pts_3d[:,2]
pts_3d_new = np.matmul(pts_3d, th.T)
mov = np.sqrt(np.sum((pts_3d_new - pts_3d)**2,1))
# append the data
meass.append(meas)
depths.append(depth_true_s)
msks.append(msk)
vs.append(th)
Ps.append(P)
# move the object and combine them by channel
y = np.arange(meass[0].shape[0])
x = np.arange(meass[0].shape[1])
xx, yy = np.meshgrid(x,y)
meass_new = []
meass_old = []
vys_new = []
vxs_new = []
vys_inv = []
vxs_inv = []
msks_new = []
depths_new = []
mid = 4
for i in range(9):
meas_v = []
meas_old_v = []
depth_v = []
msk_v = []
depth_old_v = []
vy_v = []
vx_v = []
vy_inv = []
vx_inv = []
for j in range(len(meass)):
# constant transformation
# notice that the velocity is inversed here
th = vs[j]
th = LA.matrix_power(th, i-mid)
#
xx_p = (xx - (xx.shape[1]-1)/2)/ratio
yy_p = (yy - (yy.shape[0]-1)/2)/ratio
zz_p = depths[j]
xx_p = xx_p * fov * zz_p
yy_p = yy_p * fov * zz_p
xx_p = xx_p.flatten()
yy_p = yy_p.flatten()
zz_p = zz_p.flatten()
idx = np.where(zz_p != 0)
yy_p = yy_p[idx]
xx_p = xx_p[idx]
zz_p = zz_p[idx]
# prepare teh data
meas_f = meass[j][:,:,i].flatten()
meas_f = meas_f[idx]
depth_f = depths[j].flatten()
depth_f = depth_f[idx]
msk_f = msks[j].flatten()
msk_f = msk_f[idx]
# do the transformation
pts_3d = np.stack([yy_p, xx_p, zz_p, np.ones(xx_p.shape)],-1)
pts_2d_raw = np.stack([(yy.flatten())[idx], (xx.flatten())[idx]],-1)
pts_2d = np.stack([yy_p / zz_p, xx_p / zz_p],-1)
pts_3d_new = np.matmul(pts_3d, th.T)
P = Ps[j]
pts_2d_new = np.matmul(pts_3d_new,P.T)
pts_2d_new = pts_2d_new[:,0:2]/pts_2d_new[:,2:3]
y_p = pts_2d_new[:,0] / fov * ratio + (xx.shape[0]-1)/2
x_p = pts_2d_new[:,1] / fov * ratio + (xx.shape[1]-1)/2
pts_2d_new_raw = np.stack([y_p, x_p],-1)
pts = np.stack([yy.flatten(), xx.flatten()],-1)
# cut off the regions outside
idx = np.where((y_p<(yy.shape[0]-1))*(y_p>0)*(x_p<(xx.shape[1]-1))*(x_p>0))
y_pc = y_p[idx]
x_pc = x_p[idx]
# add a map of zeros
zero_map = np.zeros(xx.shape)
zero_map[(np.floor(y_pc).astype(np.int32),np.floor(x_pc).astype(np.int32))] = 1
zero_map[(np.ceil(y_pc).astype(np.int32),np.floor(x_pc).astype(np.int32))] = 1
zero_map[(np.floor(y_pc).astype(np.int32),np.ceil(x_pc).astype(np.int32))] = 1
zero_map[(np.ceil(y_pc).astype(np.int32),np.ceil(x_pc).astype(np.int32))] = 1
y_zero = yy[np.where(zero_map==0)]
x_zero = xx[np.where(zero_map==0)]
val_nan = np.nan*x_zero
pts_2d_zero = np.stack([y_zero, x_zero],-1)
pts_2d_new_full = np.concatenate([pts_2d_new_raw, pts_2d_zero],0)
meas_f = np.concatenate([meas_f, val_nan],0)
depth_f = np.concatenate([depth_f, val_nan],0)
msk_f = np.concatenate([msk_f, val_nan],0)
f1 = scipy.interpolate.griddata(pts_2d_new_full,meas_f,pts)
meas_v.append(np.reshape(f1, xx.shape))
meas_old_v.append(meass[j][:,:,i])
f2 = scipy.interpolate.griddata(pts_2d_new_full,depth_f,pts)
depth_v.append(np.reshape(f2, xx.shape))
depth_old_v.append(depths[j])
f3 = scipy.interpolate.griddata(pts_2d_new_full,msk_f,pts)
msk_v.append(np.reshape(f3, xx.shape))
# add the velocity
vy_v.append(np.zeros(yy.shape))
vy_v[-1][(pts_2d_raw[:,0],pts_2d_raw[:,1])] = pts_2d_new_raw[:,0] - pts_2d_raw[:,0]
vx_v.append(np.ones(xx.shape))
vx_v[-1][(pts_2d_raw[:,0],pts_2d_raw[:,1])] = pts_2d_new_raw[:,1] - pts_2d_raw[:,1]
# mask out those regions that interpolates with the background
msk_v[-1][np.where(msk_v[-1]<0.999)] = 0
# combine the raw measurement based on depth
msk_v = np.stack(msk_v, -1)
meas_v = np.stack(meas_v, -1)
meas_old_v = np.stack(meas_old_v, -1)
depth_v = np.stack(depth_v, -1)
depth_old_v = np.stack(depth_old_v, -1)
vy_v = np.stack(vy_v, -1)
vx_v = np.stack(vx_v, -1)
# combine
depth_v[np.where(np.isnan(depth_v))] = 999999999
idx = np.argmin(depth_v, -1)
pts = [yy.flatten(), xx.flatten(), idx.flatten()]
meas_new = np.reshape(meas_v[pts], xx.shape)
vy_new = np.reshape(vy_v[pts], xx.shape)
vx_new = np.reshape(vx_v[pts], xx.shape)
msk_new = np.reshape(msk_v[pts], xx.shape)
depth_new = np.reshape(depth_v[pts], xx.shape)
# remove the
msk_new[np.where(np.isnan(msk_new))] = 0
meas_new[np.where(np.isnan(meas_new))] = 0
depth_old_v[np.where(depth_old_v == 0)] = 999999999
idx = np.nanargmin(depth_old_v, -1)
pts = [yy.flatten(), xx.flatten(), idx.flatten()]
vy_inv = np.reshape(vy_v[pts], xx.shape)
vx_inv = np.reshape(vx_v[pts], xx.shape)
meas_old = np.reshape(meas_old_v[pts], xx.shape)
meass_new.append(meas_new)
vys_new.append(vy_new)
vxs_new.append(vx_new)
msks_new.append(msk_new)
depths_new.append(depth_new)
vys_inv.append(vy_inv)
vxs_inv.append(vx_inv)
meass_old.append(meas_old)
meas_all = np.stack(meass_new, -1)
meas_all = meas_all[20:-20,:,:]
meas_old_all = | np.stack(meass_old, -1) | numpy.stack |
#!/usr/bin/env python
# encoding: utf-8
# The MIT License (MIT)
# Copyright (c) 2018 CNRS
# 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.
# AUTHORS
# <NAME> - http://herve.niderb.fr
"""Speech activity detection"""
import numpy as np
from .base import LabelingTask
from .base import LabelingTaskGenerator
class SpeechActivityDetectionGenerator(LabelingTaskGenerator):
"""Batch generator for training speech activity detection
Parameters
----------
precomputed : `pyannote.audio.features.Precomputed`
Precomputed features
overlap : bool, optional
Switch to 3 classes "non-speech vs. one speaker vs. 2+ speakers".
Defaults to 2 classes "non-speech vs. speech".
duration : float, optional
Duration of sub-sequences. Defaults to 3.2s.
batch_size : int, optional
Batch size. Defaults to 32.
per_epoch : float, optional
Total audio duration per epoch, in seconds.
Defaults to one hour (3600).
parallel : int, optional
Number of prefetching background generators. Defaults to 1.
Each generator will prefetch enough batches to cover a whole epoch.
Set `parallel` to 0 to not use background generators.
Usage
-----
# precomputed features
>>> from pyannote.audio.features import Precomputed
>>> precomputed = Precomputed('/path/to/mfcc')
# instantiate batch generator
>>> batches = SpeechActivityDetectionGenerator(precomputed)
# evaluation protocol
>>> from pyannote.database import get_protocol
>>> protocol = get_protocol('Etape.SpeakerDiarization.TV')
# iterate over training set
>>> for batch in batches(protocol, subset='train'):
>>> # batch['X'] is a (batch_size, n_samples, n_features) numpy array
>>> # batch['y'] is a (batch_size, n_samples, 1) numpy array
>>> pass
"""
def __init__(self, precomputed, overlap=False, **kwargs):
super(SpeechActivityDetectionGenerator, self).__init__(
precomputed, exhaustive=True, **kwargs)
self.overlap = overlap
def postprocess_y(self, Y):
"""Generate labels for speech activity detection
Parameters
----------
Y : (n_samples, n_speakers) numpy.ndarray
Discretized annotation returned by `pyannote.audio.util.to_numpy`.
Returns
-------
y : (n_samples, 1) numpy.ndarray
See also
--------
`pyannote.audio.util.to_numpy`
"""
# number of speakers for each frame
speaker_count = np.sum(Y, axis=1, keepdims=True)
# mark speech regions as such
speech = | np.int64(speaker_count > 0) | numpy.int64 |
import logging
import time
import unittest
import numpy as np
import os
from neurolib.models.aln import ALNModel
from neurolib.models.fhn import FHNModel
from neurolib.optimize.exploration import BoxSearch
from neurolib.utils.parameterSpace import ParameterSpace
import neurolib.utils.functions as func
from neurolib.utils.loadData import Dataset
import neurolib.optimize.exploration.explorationUtils as eu
import neurolib.utils.pypetUtils as pu
import neurolib.utils.paths as paths
import string
import random
def randomString(stringLength=10):
"""Generate a random string of fixed length """
letters = string.ascii_lowercase
return "".join(random.choice(letters) for i in range(stringLength))
class TestExplorationSingleNode(unittest.TestCase):
"""
ALN single node exploration.
"""
def test_single_node(self):
start = time.time()
model = ALNModel()
parameters = ParameterSpace({"mue_ext_mean": np.linspace(0, 3, 2), "mui_ext_mean": np.linspace(0, 3, 2)})
search = BoxSearch(model, parameters, filename="test_single_nodes.hdf")
search.run()
search.loadResults()
for i in search.dfResults.index:
search.dfResults.loc[i, "max_r"] = np.max(
search.results[i]["rates_exc"][:, -int(1000 / model.params["dt"]) :]
)
end = time.time()
logging.info("\t > Done in {:.2f} s".format(end - start))
class TestExplorationBrainNetwork(unittest.TestCase):
"""
FHN brain network simulation with BOLD simulation.
"""
def test_fhn_brain_network_exploration(self):
ds = Dataset("hcp")
model = FHNModel(Cmat=ds.Cmat, Dmat=ds.Dmat)
model.params.duration = 10 * 1000 # ms
model.params.dt = 0.2
model.params.bold = True
parameters = ParameterSpace(
{
"x_ext": [ | np.ones((model.params["N"],)) | numpy.ones |
import collections
import numpy as np
from .base import ClassifierModule, LMModule, NERModule, MRCModule
from ..model.base import ClsDecoder, BinaryClsDecoder, SeqClsDecoder, SeqClsCrossDecoder, MRCDecoder
from ..model.bert import BERTEncoder, BERTDecoder, BERTConfig, create_instances_from_document, create_masked_lm_predictions, get_decay_power
from ..model.crf import CRFDecoder, viterbi_decode
from ..token import WordPieceTokenizer
from ..third import tf
from .. import com
class BERTClassifier(ClassifierModule):
""" Single-label classifier on BERT. """
_INFER_ATTRIBUTES = {
"max_seq_length": "An integer that defines max sequence length of input tokens",
"label_size": "An integer that defines number of possible labels of outputs",
"init_checkpoint": "A string that directs to the checkpoint file used for initialization",
}
def __init__(
self,
config_file,
vocab_file,
max_seq_length=128,
label_size=None,
init_checkpoint=None,
output_dir=None,
gpu_ids=None,
drop_pooler=False,
do_lower_case=True,
truncate_method="LIFO",
):
self.__init_args__ = locals()
super(ClassifierModule, self).__init__(init_checkpoint, output_dir, gpu_ids)
self.batch_size = 0
self.max_seq_length = max_seq_length
self.label_size = label_size
self.truncate_method = truncate_method
self._drop_pooler = drop_pooler
self._id_to_label = None
self.bert_config = BERTConfig.from_json_file(config_file)
self.tokenizer = WordPieceTokenizer(vocab_file, do_lower_case)
self.decay_power = get_decay_power(self.bert_config.num_hidden_layers)
if "[CLS]" not in self.tokenizer.vocab:
self.tokenizer.add("[CLS]")
self.bert_config.vocab_size += 1
tf.logging.info("Add necessary token `[CLS]` into vocabulary.")
if "[SEP]" not in self.tokenizer.vocab:
self.tokenizer.add("[SEP]")
self.bert_config.vocab_size += 1
tf.logging.info("Add necessary token `[SEP]` into vocabulary.")
def convert(self, X=None, y=None, sample_weight=None, X_tokenized=None, is_training=False, is_parallel=False):
self._assert_legal(X, y, sample_weight, X_tokenized)
if is_training:
assert y is not None, "`y` can't be None."
if is_parallel:
assert self.label_size, "Can't parse data on multi-processing when `label_size` is None."
n_inputs = None
data = {}
# convert X
if X or X_tokenized:
tokenized = False if X else X_tokenized
input_ids, input_mask, segment_ids = self._convert_X(X_tokenized if tokenized else X, tokenized=tokenized)
data["input_ids"] = np.array(input_ids, dtype=np.int32)
data["input_mask"] = np.array(input_mask, dtype=np.int32)
data["segment_ids"] = np.array(segment_ids, dtype=np.int32)
n_inputs = len(input_ids)
if n_inputs < self.batch_size:
self.batch_size = max(n_inputs, len(self._gpu_ids))
# convert y
if y:
label_ids = self._convert_y(y)
data["label_ids"] = np.array(label_ids, dtype=np.int32)
# convert sample_weight
if is_training or y:
sample_weight = self._convert_sample_weight(sample_weight, n_inputs)
data["sample_weight"] = np.array(sample_weight, dtype=np.float32)
return data
def _convert_X(self, X_target, tokenized):
# tokenize input texts
segment_input_tokens = []
for idx, sample in enumerate(X_target):
try:
segment_input_tokens.append(self._convert_x(sample, tokenized))
except Exception:
raise ValueError("Wrong input format (line %d): \"%s\". " % (idx, sample))
input_ids = []
input_mask = []
segment_ids = []
for idx, segments in enumerate(segment_input_tokens):
_input_tokens = ["[CLS]"]
_input_ids = []
_input_mask = [1]
_segment_ids = [0]
com.truncate_segments(segments, self.max_seq_length - len(segments) - 1, truncate_method=self.truncate_method)
for s_id, segment in enumerate(segments):
_segment_id = min(s_id, 1)
_input_tokens.extend(segment + ["[SEP]"])
_input_mask.extend([1] * (len(segment) + 1))
_segment_ids.extend([_segment_id] * (len(segment) + 1))
_input_ids = self.tokenizer.convert_tokens_to_ids(_input_tokens)
# padding
for _ in range(self.max_seq_length - len(_input_ids)):
_input_ids.append(0)
_input_mask.append(0)
_segment_ids.append(0)
input_ids.append(_input_ids)
input_mask.append(_input_mask)
segment_ids.append(_segment_ids)
return input_ids, input_mask, segment_ids
def _convert_x(self, x, tokenized):
if not tokenized:
# deal with general inputs
if isinstance(x, str):
return [self.tokenizer.tokenize(x)]
# deal with multiple inputs
return [self.tokenizer.tokenize(seg) for seg in x]
# deal with tokenized inputs
if isinstance(x[0], str):
return [x]
# deal with tokenized and multiple inputs
return x
def _convert_y(self, y):
label_set = set(y)
# automatically set `label_size`
if self.label_size:
assert len(label_set) <= self.label_size, "Number of unique `y`s exceeds `label_size`."
else:
self.label_size = len(label_set)
# automatically set `id_to_label`
if not self._id_to_label:
self._id_to_label = list(label_set)
try:
# Allign if user inputs continual integers.
# e.g. [2, 0, 1]
self._id_to_label = list(sorted(self._id_to_label))
except Exception:
pass
if len(self._id_to_label) < self.label_size:
self._id_to_label = list(range(self.label_size))
# automatically set `label_to_id` for prediction
self._label_to_id = {label: index for index, label in enumerate(self._id_to_label)}
label_ids = [self._label_to_id[label] for label in y]
return label_ids
def _set_placeholders(self, target, on_export=False, **kwargs):
self.placeholders = {
"input_ids": com.get_placeholder(target, "input_ids", [None, self.max_seq_length], tf.int32),
"input_mask": com.get_placeholder(target, "input_mask", [None, self.max_seq_length], tf.int32),
"segment_ids": com.get_placeholder(target, "segment_ids", [None, self.max_seq_length], tf.int32),
"label_ids": com.get_placeholder(target, "label_ids", [None], tf.int32),
}
if not on_export:
self.placeholders["sample_weight"] = com.get_placeholder(target, "sample_weight", [None], tf.float32)
def _forward(self, is_training, split_placeholders, **kwargs):
encoder = BERTEncoder(
bert_config=self.bert_config,
is_training=is_training,
input_ids=split_placeholders["input_ids"],
input_mask=split_placeholders["input_mask"],
segment_ids=split_placeholders["segment_ids"],
drop_pooler=self._drop_pooler,
**kwargs,
)
encoder_output = encoder.get_pooled_output()
decoder = ClsDecoder(
is_training=is_training,
input_tensor=encoder_output,
label_ids=split_placeholders["label_ids"],
label_size=self.label_size,
sample_weight=split_placeholders.get("sample_weight"),
scope="cls/seq_relationship",
**kwargs,
)
return decoder.get_forward_outputs()
def _get_fit_ops(self, as_feature=False):
ops = [self._tensors["preds"], self._tensors["losses"]]
if as_feature:
ops.extend([self.placeholders["label_ids"]])
return ops
def _get_fit_info(self, output_arrays, feed_dict, as_feature=False):
if as_feature:
batch_labels = output_arrays[-1]
else:
batch_labels = feed_dict[self.placeholders["label_ids"]]
# accuracy
batch_preds = output_arrays[0]
accuracy = | np.mean(batch_preds == batch_labels) | numpy.mean |
import logging
import numpy as np
from matplotlib.patches import Ellipse, FancyArrow
from mot.common.state import Gaussian
logging.getLogger("matplotlib").setLevel(logging.WARNING)
class BasicPlotter:
@staticmethod
def plot_point(
ax,
x,
y,
label=None,
marker="o",
color="b",
marker_size=50,
):
scatter = ax.scatter(x, y, marker=marker, color=color, label=label, s=marker_size, edgecolors="k")
return scatter
@staticmethod
def plot_covariance_ellipse(ax, mean, covariance, color="b"):
assert mean.shape == (2,), f"mean has {mean.shape} shape"
covariance = covariance[:2, :2]
assert covariance.shape == (2, 2), f"covariance has {covariance.shape} shape"
lambda_, v = np.linalg.eig(covariance)
lambda_ = np.sqrt(lambda_)
ell_width, ell_height = lambda_[0] * 2, lambda_[1] * 2
ell_angle = np.rad2deg( | np.arccos(v[0, 0]) | numpy.arccos |
from atm import reference
import numpy as np
from utils import geo
def calc_atm_loss(freq_hz, gas_path_len_m=0, rain_path_len_m=0, cloud_path_len_m=0, atmosphere=None, pol_angle=0,
el_angle=0):
"""
Ref:
ITU-R P.676-11(09/2016) Attenuation by atmospheric gases
ITU-R P.840-6 (09/2013) Attenuation due to clouds and fog
ITU-R P.838-3 (03/2005) Specific attenuation model for rain for use in
prediction methods
Ported from MATLAB Code
<NAME>
16 March 2021
:param freq_hz: Frequency [Hz]
:param gas_path_len_m: Path length for gas loss [m] [default = 0]
:param rain_path_len_m: Path length for rain loss [m] [default = 0]
:param cloud_path_len_m: Path length for cloud loss [m] [default = 0]
:param atmosphere: atm.reference.Atmosphere object (if not provided, standard atmosphere will be generated)
:param pol_angle: Polarization angle [radians], 0 for Horizontal, pi/2 for Vertical, between 0 and pi for slant.
[default = 0]
:param el_angle: Elevation angle of the path under test [default = 0]
:return: loss along the path due to atmospheric absorption [dB, one-way]
"""
if atmosphere is None:
# Default atmosphere is the standard atmosphere at sea level, with no
# fog/clouds or rain.
atmosphere = reference.get_standard_atmosphere(0)
# Compute loss coefficients
if np.any(gas_path_len_m > 0):
coeff_ox, coeff_water = get_gas_loss_coeff(freq_hz, atmosphere.press, atmosphere.water_vapor_press,
atmosphere.temp)
coeff_gas = coeff_ox + coeff_water
else:
coeff_gas = 0
if np.any(rain_path_len_m > 0) and np.any(atmosphere.rainfall) > 0:
coeff_rain = get_rain_loss_coeff(freq_hz, pol_angle, el_angle, atmosphere.rainfall)
else:
coeff_rain = 0
if np.any(cloud_path_len_m > 0) and np.any(atmosphere.cloud_dens) > 0:
coeff_cloud = get_fog_loss_coeff(freq_hz, atmosphere.cloud_dens, atmosphere.temp)
else:
coeff_cloud = 0
# Compute loss components
loss_gass_db = coeff_gas * gas_path_len_m / 1.0e3
loss_rain_db = coeff_rain * rain_path_len_m / 1.0e3
loss_cloud_db = coeff_cloud * cloud_path_len_m / 1.0e3
return loss_gass_db + loss_rain_db + loss_cloud_db
def calc_zenith_loss(freq_hz, alt_start_m=0, zenith_angle_deg=0):
"""
# Computes the cumulative loss from alt_start [m] to zenith (100 km
# altitude), for the given frequencies (freq) in Hz and angle from zenith
# zenith_angle, in degrees.
#
# Does not account for refraction of the signal as it travels through the
# atmosphere; assumes a straight line propagation at the given zenith
# angle.
Ported from MATLAB Code
<NAME>
17 March 2021
:param freq_hz: Carrier frequency [Hz]
:param alt_start_m: Starting altitude [m]
:param zenith_angle_deg: Angle between line of sight and zenith (straight up) [deg]
:return zenith_loss: Cumulative loss to the edge of the atmosphere [dB]
:return zenith_loss_o: Cumulative loss due to dry air [dB]
:return zenith_loss_w: Cumulative loss due to water vapor [dB]
"""
# Add a new first dimension to all the inputs (if they're not scalar)
if np.size(freq_hz) > 1:
freq_hz = np.expand_dims(freq_hz, axis=0)
if np.size(alt_start_m) > 1:
alt_start_m = np.expand_dims(alt_start_m, axis=0)
if np.size(zenith_angle_deg) > 1:
zenith_angle_deg = np.expand_dims(zenith_angle_deg, axis=0)
# Make Altitude Layers
# From ITU-R P.676-11(12/2017), layers should be set at exponential intervals
num_layers = 922 # Used for ceiling of 100 km
layer_delta = .0001*np.exp(np.arange(num_layers)/100) # Layer thicknesses [km], eq 21
layer_delta = np.reshape(layer_delta, (num_layers, 1))
layer_top = np.cumsum(layer_delta) # [km]
layer_bottom = layer_top - layer_delta # [km]
layer_mid = (layer_top+layer_bottom)/2
# Drop layers below alt_start
alt_start_km = alt_start_m / 1e3
layer_mask = layer_top >= min(alt_start_km)
layer_bottom = layer_bottom[layer_mask]
layer_mid = layer_mid[layer_mask]
layer_top = layer_top[layer_mask]
# Lookup standard atmosphere for each band
atmosphere = reference.get_standard_atmosphere(layer_mid*1e3)
# Compute loss coefficient for each band
ao, aw = get_gas_loss_coeff(freq_hz, atmosphere.P, atmosphere.e, atmosphere.T)
# Account for off-nadir paths and partial layers
el_angle_deg = 90 - zenith_angle_deg
layer_delta_eff = geo.compute_slant_range(max(layer_bottom, alt_start_km), layer_top, el_angle_deg, True)
np.place(layer_delta_eff, layer_top <= alt_start_km, 0) # Set all layers below alt_start_km to zero
# Zenith Loss by Layer (loss to pass through each layer)
zenith_loss_by_layer_oxygen = ao*layer_delta_eff
zenith_loss_by_layer_water = aw*layer_delta_eff
# Cumulative Zenith Loss
# Loss from ground to the bottom of each layer
zenith_loss_o = np.squeeze( | np.sum(zenith_loss_by_layer_oxygen, axis=0) | numpy.sum |
"""
Tests for dcmstack.dcmstack
"""
import sys
from os import path
from glob import glob
from hashlib import sha256
from nose.tools import ok_, eq_, assert_raises
from copy import deepcopy
import numpy as np
import dicom
from dicom import datadict
import nibabel as nb
from nibabel.orientations import aff2axcodes
test_dir = path.dirname(__file__)
src_dir = path.normpath(path.join(test_dir, '../src'))
sys.path.insert(0, src_dir)
import dcmstack
def test_key_regex_filter():
filt = dcmstack.make_key_regex_filter(['test', 'another'],
['2', 'another test'])
ok_(filt('test', 1))
ok_(filt('test another', 1))
ok_(filt('another tes', 1))
ok_(not filt('test2', 1))
ok_(not filt('2 another', 1))
ok_(not filt('another test', 1))
class TestReorderVoxels(object):
def setUp(self):
self.vox_array = np.arange(16).reshape((2, 2, 2, 2))
self.affine = np.eye(4)
def test_invalid_vox_order(self):
assert_raises(ValueError,
dcmstack.reorder_voxels,
self.vox_array,
self.affine,
'lra',
)
assert_raises(ValueError,
dcmstack.reorder_voxels,
self.vox_array,
self.affine,
'rpil',
)
assert_raises(ValueError,
dcmstack.reorder_voxels,
self.vox_array,
self.affine,
'lrz',
)
def test_invalid_vox_array(self):
assert_raises(ValueError,
dcmstack.reorder_voxels,
| np.eye(2) | numpy.eye |
import os
import pathlib
from datetime import datetime
from functools import wraps
from pathos.multiprocessing import Pool
import numpy as np
from scipy import linalg as splin
from scipy import sparse as spsparse
from scipy.optimize import minimize
import h5py
import cupy as cp
from geoist import gridder
from geoist.pfm import prism
from geoist.inversion.mesh import PrismMesh
from geoist.others import walsh
from geoist.others import toeplitz as tptz
from geoist.others import utils
print_level = -1 # control indentation of prints.
last_print_level = -2
# A helper decorator print time consumption of f.
def timeit(f):
@wraps(f)
def wrap(*args,**kwargs):
global print_level
global last_print_level
print_level += 1
if print_level == last_print_level:
print('')
print(' '*4*print_level+'calling {}'.format(f.__name__))
st = datetime.now()
res = f(*args,**kwargs)
ed = datetime.now()
print(' '*4*print_level+'{} completed in {}'.format(f.__name__,ed-st))
last_print_level = print_level
print_level -= 1
return res
return wrap
def free_gpu():
'''free up gpu memory consumption'''
mempool = cp.get_default_memory_pool()
pinned_mempool = cp.get_default_pinned_memory_pool()
mempool.free_all_blocks()
pinned_mempool.free_all_blocks()
class SmoothOperator:
def __init__(self,reverse=False):
self.axis = {'x':-1,'y':-2,'z':-3}
if reverse:
self.axis = {'x':-3,'y':-2,'z':-1}
def diff(self,v,along='dx'):
for axis_i in axis_list[1:]:
slices = [slice(None)]*v.ndim
slices[self.axis[axis_i]] = slice(-1,None,-1)
return np.diff(v[tuple(slices)],axis=self.axis[axis_i])
def rdiff(self,v,along='dx'):
for axis_i in axis_list[1:]:
slices = [slice(None)]*v.ndim
slices[self.axis[axis_i]] = 0
shape = [-1]*v.ndim
shape[self.axis[axis_i]] = 1
prepend=np.zeros_like(v[tuple(slices)].reshape(tuple(shape)))
append=np.zeros_like(v[tuple(slices)].reshape(tuple(shape)))
return np.diff(v,
axis=self.axis[axis_i],
prepend=prepend,
append=append)
class AbicLSQOperator(tptz.LSQOperator):
'''An operator doing matrix vector multiplication. The matrix is:
$\alpha_g G^TG + \sum \alpha_i W^TB_i^TB_iW$. Where $\alpha$'s are
weights, $G$ is kernel matrix, $W$ is depth constraint, $B_i$'s are
other constrains.
'''
def __init__(self,
toep,
depth_constraint=None,
dxyz_constraint=None,
refer_constraint=None,
weights=None):
super().__init__(toep)
self.weights = weights
self.depth_constraint = depth_constraint
self.refer_constraint = refer_constraint
self.dxyz_constraint = dxyz_constraint
if self.weights is None:
self.weights = {'bound':1,'obs':1,'depth':1,'refer':1,'dx':1,'dy':1,'dz':1}
def matvec(self,v):
tmp = self.gtoep.matvec(v)
tmp = self.weights['obs']*self.gtoep.rmatvec(tmp)
if 'depth' in self.weights.keys():
v = self.depth_constraint*v
if 'refer' in self.weights.keys():
tmp += self.weights['refer']*self.weights['depth']*self.depth_constraint*self.refer_constraint**2*v
if not self.dxyz_constraint is None:
spaces = {'dz':self.nx*self.ny*(self.nz-1),
'dy':self.nx*(self.ny-1),
'dx':self.nx-1}
for key,constraint in self.dxyz_constraint.items():
if not key in self.weights.keys():
continue
tmp2 = v.reshape(-1,*constraint.shape)
fft_comp = list(range(tmp2.ndim))[1:]
tmp2 = self.xp.fft.ifftn(self.xp.fft.fftn(tmp2,axes=fft_comp)*constraint,axes=fft_comp).real
slices = [slice(None)]*tmp2.ndim
slices[-1] = slice(spaces[key],None)
tmp2[tuple(slices)] = 0
tmp2 = self.xp.real(self.xp.fft.ifftn(self.xp.fft.fftn(tmp2,axes=fft_comp)*self.xp.conj(constraint),axes=fft_comp))
if v.ndim == 1:
tmp += self.weights[key]*self.weights['depth']*self.depth_constraint*tmp2.ravel()
else:
tmp += self.weights[key]*self.weights['depth']*self.depth_constraint*tmp2.reshape(v.shape[0],-1)
return tmp
class GravInvAbicModel:
def __init__(self,
nzyx=[4,4,4],
smooth_components=['dx','dy','dz'],
depth_constraint=None,
model_density=None,
refer_density=None,
weights=None,
source_volume=None,
smooth_on='m',
data_dir='/data/gravity_inversion'):
self._nz,self._ny,self._nx = nzyx
self.smooth_on = smooth_on
self.dxyz_shapes = {'dx':(self._nz,self._ny,self._nx),
'dy':(self._nz,self._nx*self._ny),
'dz':(self._nx*self._ny*self._nz,)}
self.dxyz_spaces = {'dx':self._nx-1,
'dy':self._nx*(self._ny-1),
'dz':self._nx*self._ny*(self._nz-1)}
self.data_dir = data_dir
self.gen_model_name()
self.nobsx = nzyx[2]
self.nobsy = nzyx[1]
self.source_volume = source_volume
if model_density is None:
self._model_density = None
else:
self._model_density = model_density.ravel()
self._smooth_components = smooth_components
self.constraints = dict()
self.constraints_val = dict()
if depth_constraint is None:
self.constraints['depth'] = np.ones(np.prod(nzyx))
self.constraints_val['depth'] = None
else:
self.constraints['depth'] = (depth_constraint.reshape(-1,1)*np.ones((1,self._nx*self._ny))).ravel()
self.constraints_val['depth'] = 0
if refer_density is None:
self.constraints['refer'] = None
self.constraints_val['refer'] = None
else:
self.constraints['refer'] = np.ones(self._nx*self._ny*self._nz)
self.constraints_val['refer'] = refer_density.ravel()
self._weights = weights
if not 'depth' in self._weights.keys():
self._weights['depth'] = 1.0
self._gen_dxyz_constraint()
self.kernel_op = None
self.abic_val = 0
self.log_total_det_val = 0
self.log_prior_det_val = 0
self.log_obs_det_val = 0
self.min_u_val = 0
self.min_density = -1.0e4
self.max_density = 1.0e4
@property
def source_volume(self):
return self._source_volume
@source_volume.setter
def source_volume(self,value):
self._source_volume = value
self.gen_mesh()
def gen_model_name(self):
self.model_name = '{}x{}x{}'.format(self._nx,self._ny,self._nz)
self.fname = pathlib.Path(self.data_dir)/pathlib.Path(self.model_name+'.h5')
@property
def weights(self):
return self._weights
@weights.setter
def weights(self,values):
self._weights = values
if not self.kernel_op is None:
self.kernel_op.weights = self._weights
@property
def smooth_components(self):
return self._smooth_components
@smooth_components.setter
def smooth_components(self,values):
self._smooth_components = values
self._gen_dxyz_constraint()
if not self.kernel_op is None:
self.kernel_op.dxyz_constraint = self.dxyz_constraint
@timeit
def _gen_dxyz_constraint(self):
'''first generate multi-level circulant matrix, constraint of dx is a part of it. then calculate it's eigenvalues.
self._dx stores the eigenvalues finally. When multiply it with a vector, specific element should be discarded'''
self.dxyz_constraint = dict()
for component in self._smooth_components:
tmp = np.zeros(self.nx*self.ny*self.nz)
tmp[0] = 1
tmp[self.dxyz_spaces[component]] = -1
tmp = tmp.reshape(self.dxyz_shapes[component])
self.dxyz_constraint[component] = np.fft.fftn(tmp)
self.constraints[component] = self.dxyz_constraint[component]
@property
def refer_density(self):
return self.constraints_val['refer'].reshape(self._nz,self._ny,self._nx)
@refer_density.setter
def refer_density(self,value):
self.constraints_val['refer'] = value.ravel()
@property
def nx(self):
return self._nx
@nx.setter
def nx(self,value):
self._nx = value
self.nobsx = self._nx
self.gen_model_name()
if not self.constraints['depth'] is None:
self.constraints['depth'] = self.constraints['depth'].reshape(self._nz,-1)[:,0]*np.ones((1,self._nx*self._ny))
self.constraints['depth'] = self.constraints['depth'].ravel()
self.constraints['refer'] = np.ones(self._nx*self._ny*self._nz)
@property
def ny(self):
return self._ny
@ny.setter
def ny(self,value):
self._ny = value
self.nobsy = self._ny
self.gen_model_name()
if not self.constraints['depth'] is None:
self.constraints['depth'] = self.constraints['depth'].reshape(self._nz,-1)[:,0]*np.ones((1,self._nx*self._ny))
self.constraints['depth'] = self.constraints['depth'].ravel()
self.constraints['refer'] = np.ones(self._nx*self._ny*self._nz)
@property
def nz(self):
return self._nz
@nz.setter
def nz(self,value):
self._nz = value
self.gen_model_name()
self.constraints['refer'] = np.ones(self._nx*self._ny*self._nz)
print("Warning: nz changed. \nDon't forget setting depth constraints.")
@property
def model_density(self):
return(self._model_density.reshape(self.nz,self.ny,self.nx))
@model_density.setter
def model_density(self,value):
self._model_density = value.ravel()
def gen_mesh(self,height = -1):
shape = (self._nz, self._ny, self._nx)
self.mesh = PrismMesh(self._source_volume, shape)
density = np.ones(shape)*1.0e3
self.mesh.addprop('density', density.ravel())
# generate obs grid
# coordinate: x North-South,y East-West
# gridder is in the order: (nx,ny)
self.obs_area = (self._source_volume[0]+0.5*self.mesh.dims[0],
self._source_volume[1]-0.5*self.mesh.dims[0],
self._source_volume[2]+0.5*self.mesh.dims[1],
self._source_volume[3]-0.5*self.mesh.dims[1])
obs_shape = (self.nobsx, self.nobsy)
self.xp, self.yp, self.zp = gridder.regular(self.obs_area, obs_shape, z=height)
def _gen_walsh_matrix(self):
print('generating walsh_matrix')
if os.path.exists(self.fname):
with h5py.File(self.fname,mode='r') as f:
if not 'walsh_matrix' in f.keys():
have_walsh_matrix = False
else:
have_walsh_matrix = True
else:
have_walsh_matrix = False
if have_walsh_matrix:
return
walsh_matrix = walsh.walsh_matrix(self._nx*self._ny*self._nz,
normalized=True,
ordering='sequence2',
nxyz=(self._nx,self._ny,self._nz))
walsh_matrix = walsh_matrix.astype(np.float32)
step = self._nx*self._ny*self._nz//4
components = ['0','1','2','3']
with h5py.File(self.fname,mode='a') as f:
fgroup = f.create_group('walsh_matrix')
for i in range(4):
fgroup.create_dataset(components[i],data=walsh_matrix[i*step:(i+1)*step,:])
def gen_kernel(self):
def calc_kernel(i):
return prism.gz(self.xp[0:1],self.yp[0:1],self.zp[0:1],[self.mesh[i]])
with Pool(processes=16) as pool:
kernel0 = pool.map(calc_kernel,range(len(self.mesh)))
self.kernel0 = np.array(kernel0).reshape(self.nz,self.ny,self.nx)
self.kernel_op = AbicLSQOperator(self.kernel0,
depth_constraint=self.constraints['depth'],
dxyz_constraint=self.dxyz_constraint,
refer_constraint=self.constraints['refer'],
weights=self._weights)
def _dxyzvec(self,vec=None,key=None):
res = vec.reshape(-1,*self.dxyz_shapes[key])
axes = np.arange(1,res.ndim)
res = np.fft.ifftn(np.fft.fftn(res,axes=axes)*self.dxyz_constraint[key],axes=axes).real
slices = [slice(None)]*res.ndim
slices[-1] = slice(0,self.dxyz_spaces[key])
if vec.ndim == 1:
return res[tuple(slices)].ravel()
else:
return res[tuple(slices)].reshape(vec.shape[0],-1)
def _diagvec(self,vec=None,diag=None):
if vec.ndim == 1:
return vec * diag
else:
return vec * diag.reshape(1,-1)
@timeit
def walsh_transform(self,keys=None):
if keys is None:
keys = ['kernel'] + list(self.constraints.keys())
else:
keys = keys
is_stored = dict()
for key in keys:
is_stored[key] = False
if os.path.exists(self.fname):
with h5py.File(self.fname,mode='r') as f:
for key in keys:
try:
if '3' in f[key].keys():
is_stored[key] = True
if key == 'depth':
res = f['depth']['constraint'][:] - self.constraints['depth']
res = np.linalg.norm(res)/np.linalg.norm(self.constraints['depth'])
if res > 1.0e-3:
is_stored[key] = False
except KeyError:
continue
self._gen_walsh_matrix()
logn = int(np.ceil(np.log2(self._nx*self._ny*self._nz)))
norm_walsh = 1./(np.sqrt(2)**logn)
blocks = ['0','1','2','3']
matvec_op = {'kernel':self.kernel_op.gtoep.matvec,
'dx': lambda x: self._dxyzvec(x,key='dx'),
'dy': lambda x: self._dxyzvec(x,key='dy'),
'dz': lambda x: self._dxyzvec(x,key='dz'),
'refer': lambda x: self._diagvec(x,diag=self.constraints['refer']),
'depth': lambda x: self._diagvec(x,diag=np.sqrt(self.constraints['depth']))
}
is_stored['refer'] = True
for key in keys:
if is_stored[key]:
print('walsh transformation of {} already exists.'.format(key))
continue
print('performing walsh transformation on {}.'.format(key))
step = self.nx*self.ny*self.nz // 4
if key == 'depth':
step = self._nz
with h5py.File(self.fname,mode='a') as f:
try:
del f[key]
except KeyError:
pass
dxyz_group = f.create_group(key)
walsh_group = f['walsh_matrix']
for i in range(4):
print("\t progress {}/4".format(i))
part_walsh = walsh_group[blocks[i]][:]
if key == 'depth':
part_walsh = walsh_group[blocks[i]][:self._nz]
part_walsh = matvec_op[key](part_walsh)
with cp.cuda.Device(2):
res = cp.zeros((step,step))
j = 0
while j*step < part_walsh.shape[1]:
tmp_block_gpu = cp.asarray(part_walsh[:,j*step:(j+1)*step])
res += tmp_block_gpu @ tmp_block_gpu.T
j += 1
res = cp.asnumpy(res)
if key in self._smooth_components:
res[np.abs(res)<1.0e-1*norm_walsh] = 0.
tmp_block_gpu = None
mempool = cp.get_default_memory_pool()
pinned_mempool = cp.get_default_pinned_memory_pool()
mempool.free_all_blocks()
pinned_mempool.free_all_blocks()
dxyz_group.create_dataset(blocks[i],data=res)
if ('depth' in keys) and (not is_stored['depth']):
with h5py.File(self.fname,mode='a') as f:
try:
del f['depth_constraint']
except KeyError:
pass
dxyz_group = f['depth']
dxyz_group.create_dataset('constraint',data=self.constraints['depth'])
@property
def depth_constraint(self):
return(self.constraints['depth'].reshape(self._nz,-1)[:,0])
@depth_constraint.setter
def depth_constraint(self,value):
self.constraints['depth'] = (value.reshape(-1,1)*np.ones((1,self._nx*self._ny))).ravel()
@timeit
def forward(self,model_density=None):
if model_density is None:
model_density = self._model_density
else:
model_density = model_density.ravel()
self.obs_data = self.kernel_op.gtoep.matvec(model_density)
def _gen_rhs(self):
self.rhs = self._weights['obs']*self.kernel_op.gtoep.rmatvec(self.obs_data)
if 'depth' in self._weights.keys():
v = self.constraints['depth']*self.constraints_val['refer']
if 'refer' in self._weights.keys():
self.rhs += (self._weights['refer']
*self._weights['depth']
*self.constraints['depth']
*v)
if self.smooth_on == 'm-m0':
if not self.dxyz_constraint is None:
for key,constraint in self.dxyz_constraint.items():
if not key in self._weights.keys():
continue
tmp2 = v.reshape(-1,*constraint.shape)
fft_comp = list(range(tmp2.ndim))[1:]
tmp2 = np.fft.ifftn(np.fft.fftn(tmp2,axes=fft_comp)*constraint,axes=fft_comp).real
slices = [slice(None)]*tmp2.ndim
slices[-1] = slice(self.dxyz_spaces[key],None)
tmp2[tuple(slices)] = 0
tmp2 = np.real(np.fft.ifftn(np.fft.fftn(tmp2,axes=fft_comp)*np.conj(constraint),axes=fft_comp))
if v.ndim == 1:
self.rhs += self._weights[key]*self._weights['depth']*self.constraints['depth']*tmp2.ravel()
else:
self.rhs += self._weights[key]*self._weights['depth']*self.constraints['depth']*tmp2.reshape(v.shape[0],-1)
@timeit
def do_linear_solve(self):
self._gen_rhs()
self.solution = spsparse.linalg.cg(self.kernel_op,self.rhs,tol=1.0e-5)[0]
@timeit
def calc_min_u(self,solved=False,x=None):
if x is None:
if not solved:
self.do_linear_solve()
x = self.solution
self.min_u_val = self._weights['obs']*np.linalg.norm(self.kernel_op.gtoep.matvec(x) - self.obs_data)**2
if ('refer' in self._weights.keys()) and (self.smooth_on == 'm-m0'):
v = x - self.constraints_val['refer']
else:
v = x
if 'depth' in self._weights.keys():
v = np.sqrt(self._weights['depth'])*self.constraints['depth']*v
if not self.dxyz_constraint is None:
for key,constraint in self.dxyz_constraint.items():
if not key in self._weights.keys():
continue
tmp2 = np.fft.ifftn(
np.fft.fftn(v.reshape(constraint.shape))*constraint
).real
slices = [slice(None)]*constraint.ndim
slices[-1] = slice(0,self.dxyz_spaces[key])
self.min_u_val += self._weights[key]*np.linalg.norm(tmp2[tuple(slices)].ravel())**2
if 'refer' in self._weights.keys():
v = x - self.constraints_val['refer']
if 'depth' in self._weights.keys():
v = np.sqrt(self._weights['depth'])*self.constraints['depth']*v
self.min_u_val += self._weights['refer'] *np.linalg.norm(v)**2
return self.min_u_val
def bound_constraint_u(self,x=None):
self.calc_min_u(x=x,solved=True)
log_barrier = np.sum(np.log(x-self.min_density) + np.log(self.max_density-x))
return self.min_u_val - 2.*self._weights['bound']*log_barrier
def bound_jac_u(self,x=None):
res = 0.
res += self._weights['obs']*(self.kernel_op.gtoep.matvec(x) - self.obs_data)
if ('refer' in self._weights.keys()) and (self.smooth_on == 'm-m0'):
v = x - self.constraints_val['refer']
else:
v = x
if 'depth' in self._weights.keys():
v = self._weights['depth']*self.constraints['depth']*v
if not self.dxyz_constraint is None:
for key,constraint in self.dxyz_constraint.items():
if not key in self._weights.keys():
continue
tmp2 = np.fft.ifftn(
np.fft.fftn(v.reshape(constraint.shape))*constraint
).real
slices = [slice(None)]*constraint.ndim
slices[-1] = slice(0,self.dxyz_spaces[key])
res += self._weights[key]*tmp2[tuple(slices)].ravel()
if 'refer' in self._weights.keys():
v = x - self.constraints_val['refer']
if 'depth' in self._weights.keys():
v = self._weights['depth']*self.constraints['depth']*v
res += self._weights['refer'] *v
res += self._weights['bound']*(1./(self.max_density-x) - 1./(x-self.min_density))
return 2.*res
def bound_hessp_u(self,x,v):
res = self.kernel_op.matvec(v)
hess_diag = 1./(self.max_density-x)**2 + 1./(x-self.min_density)**2
res += self._weights['bound']*hess_diag*v
return 2.*res
def bound_optimize(self,x0=None):
if x0 is None:
if 'refer' in self._weights.keys():
x0 = self.constraints_val['refer']
else:
x0 = np.zeros(self._nx*self._ny*self._nz)
self.solution = minimize(self.bound_constraint_u,
x0,
method='Newton-CG',
jac=self.bound_jac_u,
hessp=self.bound_hessp_u)
def calc_res(self):
self.residuals = dict()
self.stds = dict()
self.residuals['obs'] = np.linalg.norm(self.kernel_op.gtoep.matvec(self.solution)-self.obs_data)**2
self.stds['obs'] = np.std(self.kernel_op.gtoep.matvec(self.solution)-self.obs_data)
for key in self.dxyz_constraint.keys():
try:
tmp2 = self.solution.reshape(self.dxyz_constraint[key].shape)
if ('refer' in self.constraints_val.keys()) and (self.smooth_on == 'm-m0'):
tmp2 -= self.constraints_val['refer'].reshape(self.dxyz_constraint[key].shape)
tmp2 = np.fft.ifftn( | np.fft.fftn(tmp2) | numpy.fft.fftn |
import numpy as np
from tidepool_data_science_models.models.simple_metabolism_model import SimpleMetabolismModel
def get_bgri(bg_df):
# Calculate LBGI and HBGI using equation from
# <NAME>., & <NAME>. (2009)
bgs = bg_df.copy()
bgs[bgs < 1] = 1 # this is added to take care of edge case BG <= 0
transformed_bg = 1.509 * ((np.log(bgs) ** 1.084) - 5.381)
risk_power = 10 * (transformed_bg) ** 2
low_risk_bool = transformed_bg < 0
high_risk_bool = transformed_bg > 0
rlBG = risk_power * low_risk_bool
rhBG = risk_power * high_risk_bool
LBGI = np.mean(rlBG)
HBGI = np.mean(rhBG)
BGRI = LBGI + HBGI
return LBGI, HBGI, BGRI
def lbgi_risk_score(lbgi):
if lbgi > 10:
risk = 4
elif lbgi > 5:
risk = 3
elif lbgi > 2.5:
risk = 2
elif lbgi > 0:
risk = 1
else:
risk = 0
return risk
def hbgi_risk_score(hbgi):
if hbgi > 18:
risk = 4
elif hbgi > 9:
risk = 3
elif hbgi > 4.5:
risk = 2
elif hbgi > 0:
risk = 1
else:
risk = 0
return risk
def get_dka_risk_hours(temp_basals, iob_array, sbr):
# Use refactor of metabolism model
metab_model = SimpleMetabolismModel(
insulin_sensitivity_factor=0, carb_insulin_ratio=0
)
steady_state_iob = metab_model.get_steady_state_iob_from_sbr(
sbr, use_fda_submission_constant=True
)
fifty_percent_steady_state_iob = steady_state_iob / 2
indices_with_less_50percent_sbr_iob = iob_array < fifty_percent_steady_state_iob
hours_with_less_50percent_sbr_iob = (
| np.sum(indices_with_less_50percent_sbr_iob) | numpy.sum |
import math
from math import log2, exp
import numpy as np
import torch
from torch import nn
from torch.nn.functional import softplus
import torch.nn.functional as F
from torch.autograd import grad
from typing import List, Callable, Union, Any, TypeVar, Tuple
# from torch import tensor as Tensor
Tensor = TypeVar('torch.tensor')
from CALAE.loss.hessian_penalty import hessian_penalty
from CALAE.metrics.perceptual import PerceptualLoss
import lpips
import piq
def zero_centered_gradient_penalty(real_samples, real_prediction):
"""
Computes zero-centered gradient penalty for E, D
"""
grad_outputs = torch.ones_like(real_prediction, requires_grad=True)
squared_grad_wrt_x = grad(outputs=real_prediction, inputs=real_samples, grad_outputs=grad_outputs,\
create_graph=True, retain_graph=True)[0].pow(2)
return squared_grad_wrt_x.view(squared_grad_wrt_x.shape[0], -1).sum(dim=1).mean()
def loss_discriminator(E, D, alpha, real_samples, fake_samples, gamma=10, use_bce=False,
enable_hessian_real=False, enable_hessian_fake=False,
hessian_layers_fake=[-2], hessian_layers_real=[-2]):
E_r = E(real_samples, alpha)
E_f = E(fake_samples, alpha)
real_prediction, fake_prediction = D(E_r), D(E_f)
if use_bce:
loss = adv_loss(real_prediction, 1)
loss += adv_loss(fake_prediction, 0)
else:
# Minimize negative = Maximize positive (Minimize incorrect D predictions for real data,
# minimize incorrect D predictions for fake data)
loss = (F.softplus(-real_prediction) + F.softplus(fake_prediction)).mean()
if gamma > 0:
loss += zero_centered_gradient_penalty(real_samples, real_prediction).mul(gamma/2)
return loss
def loss_discriminator_img(D, real_samples, fake_samples, gamma=10, use_bce=False):
real_prediction = D(real_samples)
fake_prediction = D(fake_samples)
if use_bce:
loss = adv_loss(real_prediction, 1)
loss += adv_loss(fake_prediction, 0)
else:
# Minimize negative = Maximize positive (Minimize incorrect D predictions for real data,
# minimize incorrect D predictions for fake data)
loss = (F.softplus(-real_prediction) + F.softplus(fake_prediction)).mean()
if gamma > 0:
loss += zero_centered_gradient_penalty(real_samples, real_prediction).mul(gamma/2)
return loss
def loss_generator(E, D, alpha, fake_samples, enable_hessian=True, hessian_layers=[-1,-2], current_layer=[-1], hessian_weight=0.01):
# Hessian applied to E here
# Minimize negative = Maximize positive (Minimize correct D predictions for fake data)
E_z = E(fake_samples, alpha)
loss = softplus(-D(E_z)).mean()
if enable_hessian:
for layer in hessian_layers:
h_loss = hessian_penalty(E, z=fake_samples, alpha=alpha, return_norm=layer) * hessian_weight
if layer in current_layer:
h_loss = h_loss * alpha
loss += h_loss
return loss
def loss_avg_generator(G, G_avg, F_z, scale, alpha, loss_fn, bbox=None):
# Hessian applied to G here
G_z = G(F_z, scale, alpha, bbox=bbox)
G_avg_z = G_avg(F_z, scale, alpha, bbox=bbox)
loss = loss_fn(G_z, G_avg_z)
return loss
def loss_generator_consistency(fake, real, loss_fn=None, use_perceptual=False,
use_ssim=True, ssim_weight=1, use_ssim_tv=False,
use_sobel=True, sobel_weight=1,
use_sobel_tv=False, sobel_fn=None):
if loss_fn:
if use_perceptual:
scale = fake.shape[2]
p_scale = scale if scale < 32 else 32
p_func = perceptual_loss[p_scale]
if p_func is None:
p_func = PerceptualLoss(ilayer=percep_layer_lookup[p_scale])
perceptual_loss[scale] = p_func
loss = loss_fn(p_func(fake), p_func(real))
else:
loss = loss_fn(fake, real)
else:
loss = 0
if use_ssim:
s_loss = ssim_loss(fake, real) * ssim_weight
if use_ssim_tv:
s_loss = s_loss / total_variation(fake)
loss *= s_loss
if use_sobel:
sobel_real = sobel(real)
sobel_fake = sobel(fake)
if use_sobel_tv:
sobel_real = sobel_real / total_variation(fake)
sobel_fake = sobel_fake / total_variation(fake)
if sobel_fn:
sobel_loss = sobel_fn(sobel_real, sobel_fake)
else:
sim, cs = ssim(sobel_real, sobel_fake, window_size=11, size_average=True, full=True, val_range=2)
sim = (1 - sim) / 2
cs = (1 - cs) / 2
sobel_loss = (sim + cs) ** cs
loss += sobel_loss * sobel_weight
return loss
def loss_autoencoder(F, G, E, scale, alpha, z, loss_fn,
labels=None, use_tv=False, tv_weight=0.001,
permute_regularize=False, bbox=None):
# Hessian applied to G here
F_z = F(z, scale, z2=None, p_mix=0)
# Autoencoding loss in latent space
G_z = G(F_z, scale, alpha, bbox=bbox)
E_z = E(G_z, alpha)
#E_z = E_z.reshape(E_z.shape[0], 1, E_z.shape[1]).repeat(1, F_z.shape[1], 1)
F_x = F_z[:,0,:]
if labels is not None:
if permute_regularize:
perm = torch.randperm(E_z.shape[0], device=E_z.device)
E_z_hat = torch.index_select(E_z, 0, perm)
F_x_hat = torch.index_select(F_x, 0, perm)
F_hat = torch.cat([F_x, F_x_hat], 0)
E_hat = torch.cat([E_z, E_z_hat], 0)
loss = loss_fn(F_hat, E_hat, labels)
else:
loss = loss_fn(F_x, E_z, labels)
else:
loss = loss_fn(F_x, E_z)
if use_tv:
loss += total_variation(G_z) * tv_weight
return loss
################################################################################
#### H E S S I A N #############################################################
###################-------------------------------------------------------------
# GENERATOR
def loss_generator_hessian(G, F, z, scale, alpha,
scale_alpha=False,
hessian_layers=[3],
current_layer=[0],
hessian_weight=0.01):
loss = hessian_penalty(G, z=F(z, scale, z2=None, p_mix=0), scale=scale, alpha=alpha, return_norm=hessian_layers)
if current_layer in hessian_layers or scale_alpha:
loss = loss * alpha
return loss * hessian_weight
# ENCODER
def loss_encoder_hessian(E, samples, alpha, scale_alpha=False,
hessian_layers=[-1,-2], current_layer=[-1],
hessian_weight=0.01):
loss = hessian_penalty(E, z=samples, alpha=alpha, return_norm=hessian_layers)
if current_layer in hessian_layers or scale_alpha:
loss = loss * alpha
return loss * hessian_weight
################################################################################
#### F O U R I E R #############################################################
###################-------------------------------------------------------------
def fft_loss(x, y, dim=2, diff_fn=lambda x,y: torch.abs(x-y)):
xf = torch.rfft(x, 3)
yf = torch.rfft(y, 3)
diff = diff_fn(xf[dim], yf[dim])
loss = diff.mean()
return loss
################################################################################
#### S T A N D A R D ###########################################################
#####################-----------------------------------------------------------
# Generally applicable losses?
def msle(x, y):
return (torch.log(x) - torch.log(y)).pow(2).mean()
def mse(x, y):
return (x - y).pow(2).mean()
def mae(x, y):
return torch.abs(x - y).mean()
def logcosh(x, y):
diff = x - y
loss = (diff + 1e-12).cosh().log()
return loss.mean()
def xtanh(x, y):
diff = x - y
loss = diff.tanh() * diff
return loss.mean()
def xsigmoid(x, y):
diff = x - y
loss = 1 + (-diff).exp()
loss = loss - diff
loss = 2 * diff / loss
return loss.mean()
#return torch.mean(2 * diff / (1 + torch.exp(-diff)) - diff)
def correlation(x, y):
delta = torch.abs(x - y)
loss = torch.mean((delta[:-1] * delta[1:]).sum(1))
return loss
# Simple BCE Discriminator target
def adv_loss(logits, target):
assert target in [1, 0]
targets = torch.full_like(logits, fill_value=target)
loss = F.binary_cross_entropy_with_logits(logits, targets)
return loss
####################################################################################
#### P E R C E P T U A L ###########################################################
#########################-----------------------------------------------------------
## Perceptual Loss
percep_layer_lookup = {
4: 4,
8: 9,
16: 16,
32: 23
}
perceptual_loss = {
4: None,
8: None,
16: None,
32: None,
}
def percep_loss(x, y, scale):
p_scale = scale if scale < 32 else 32
p_func = perceptual_loss[p_scale]
if p_func is None:
p_func = PerceptualLoss(ilayer=percep_layer_lookup[p_scale])
perceptual_loss[scale] = p_func
loss = p_func(x) - p_func(y)
loss = loss.pow(2)
loss = loss.mean()
return loss
######################################################################################
### FAMOS losses - https://github.com/zalandoresearch/famos/blob/master/utils.py #####
##some image level content loss
def contentLoss(a, b, netR, loss_type):
def nr(x):
return (x**2).mean()
return x.abs().mean()
if loss_type==0:
a = avgG(a)
b = avgG(b)
return nr(a.mean(1) - b.mean(1))
if loss_type==1:
a = avgP(a)
b = avgP(b)
return nr(a.mean(1) - b.mean(1))
if loss_type==10:
return nr(netR(a)-netR(b))
if loss_type==100:
return nr(netR(a)-b)
if loss_type == 101:
return nr(avgG(netR(a)) - avgG(b))
if loss_type == 102:
return nr(avgP(netR(a)) - avgP(b))
if loss_type == 103:
return nr(avgG(netR(a)).mean(1) - avgG(b).mean(1))
raise Exception("NYI")
def GaussKernel(sigma,wid=None):
if wid is None:
wid =2 * 2 * sigma + 1+10
def gaussian(x, mu, sigma):
return np.exp(-(float(x) - float(mu)) ** 2 / (2 * sigma ** 2))
def make_kernel(sigma):
# kernel radius = 2*sigma, but minimum 3x3 matrix
kernel_size = max(3, int(wid))
kernel_size = min(kernel_size,150)
mean = np.floor(0.5 * kernel_size)
kernel_1d = np.array([gaussian(x, mean, sigma) for x in range(kernel_size)])
# make 2D kernel
np_kernel = np.outer(kernel_1d, kernel_1d).astype(dtype=np.float32)
# normalize kernel by sum of elements
kernel = np_kernel / np.sum(np_kernel)
return kernel
ker = make_kernel(sigma)
a = np.zeros((3,3,ker.shape[0],ker.shape[0])).astype(dtype=np.float32)
for i in range(3):
a[i,i] = ker
return a
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
gsigma=1.##how much to blur - larger blurs more ##+"_sig"+str(gsigma)
gwid=61
kernel = torch.FloatTensor(GaussKernel(gsigma,wid=gwid)).to(device)
def avgP(x):
return nn.functional.avg_pool2d(x,int(16))
def avgG(x):
pad=nn.functional.pad(x,(gwid//2,gwid//2,gwid//2,gwid//2),'reflect')##last 2 dimensions padded
return nn.functional.conv2d(pad,kernel)##reflect pad should avoid border artifacts
########################################################################################
#### T O T A L - V A R I A T I O N #####################################################
###################################-----------------------------------------------------
def tv_loss(x, y, loss_fn):
loss = loss_fn(total_variation(x), total_variation(y))
return loss
#absolute difference in X and Y directions
def total_variation(y):
return torch.mean(torch.abs(y[:, :, :, :-1] - y[:, :, :, 1:])) + torch.mean(torch.abs(y[:, :, :-1, :] - y[:, :, 1:, :]))
##2D array of the edges of C channels image
def tvArray(x):
border1 = x[:, :, :-1] - x[:, :, 1:]
border1 = torch.cat([border1.abs().sum(1).unsqueeze(1), x[:, :1, :1] * 0], 2) ##so square with extra 0 line
border2 = x[:, :, :, :-1] - x[:, :, :, 1:]
border2 = torch.cat([border2.abs().sum(1).unsqueeze(1), x[:, :1, :, :1] * 0], 3)
border = torch.cat([border1, border2], 1)
return border
##########################################################################################
#### G R A M #############################################################################
#############-----------------------------------------------------------------------------
def gram_loss(x, y):
loss = gramMatrix(x, x).exp() - gramMatrix(y, y).exp()
loss = loss.abs()
loss = loss.mean()
return loss
def gram_matrix(y):
(b, ch, h, w) = y.size()
features = y.view(b, ch, w * h)
features_t = features.transpose(1, 2)
gram = features.bmm(features_t) / (ch * h * w)
return gram
##negative gram matrix
def gramMatrix(x,y=None,sq=True,bEnergy=False):
if y is None:
y = x
B, CE, width, height = x.size()
hw = width * height
energy = torch.bmm(x.permute(2, 3, 0, 1).view(hw, B, CE),
y.permute(2, 3, 1, 0).view(hw, CE, B), )
energy = energy.permute(1, 2, 0).view(B, B, width, height)
if bEnergy:
return energy
sqX = (x ** 2).sum(1).unsqueeze(0)
sqY = (y ** 2).sum(1).unsqueeze(1)
d=-2 * energy + sqX + sqY
if not sq:
return d##debugging
gram = -torch.clamp(d, min=1e-10)#.sqrt()
return gram
##########################################################################################
#### P E A K - S I G N A L - N O I S E - R A T I O #######################################
###################################################---------------------------------------
## PSNR
def psnr(img1, img2):
diff = img1 - img2
mse = np.mean(diff ** 2 )
if mse == 0:
return 100
PIXEL_MAX = 255.0
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
##########################################################################################
#### S S I M #############################################################################
#############-----------------------------------------------------------------------------
## SSIM
def ssim_loss(x, y):
loss = 1 - ssim(x, y)
loss = loss / 2
return loss
def ssim_yuv_loss(x, y):
loss = 1 - ssim(x, y)
loss = loss / 2
return loss
def msssim_loss(x, y):
loss = 1 - ms_ssim(x, y)
loss = loss / 2
return loss
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
def create_window(window_size, channel=1):
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
return window
def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=2):
# Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
if val_range is None:
if torch.max(img1) > 128:
max_val = 255
else:
max_val = 1
if torch.min(img1) < -0.5:
min_val = -1
else:
min_val = 0
L = max_val - min_val
else:
L = val_range
padd = 0
(_, channel, height, width) = img1.size()
if window is None:
real_size = min(window_size, height, width)
window = create_window(real_size, channel=channel).to(img1.device)
mu1 = F.conv2d(img1, window, padding=padd, groups=channel)
mu2 = F.conv2d(img2, window, padding=padd, groups=channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1 * mu2
sigma1_sq = F.conv2d(img1 * img1, window, padding=padd, groups=channel) - mu1_sq
sigma2_sq = F.conv2d(img2 * img2, window, padding=padd, groups=channel) - mu2_sq
sigma12 = F.conv2d(img1 * img2, window, padding=padd, groups=channel) - mu1_mu2
C1 = (0.01 * L) ** 2
C2 = (0.03 * L) ** 2
v1 = 2.0 * sigma12 + C2
v2 = sigma1_sq + sigma2_sq + C2
cs = torch.mean(v1 / v2) # contrast sensitivity
ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
if size_average:
ret = ssim_map.mean()
else:
ret = ssim_map.mean(1).mean(1).mean(1)
if full:
return ret, cs
return ret
def ms_ssim(X_a, X_b, window_size=11, size_average=True, C1=0.01**2, C2=0.03**2):
"""
Taken from Po-Hsun-Su/pytorch-ssim
"""
channel = X_a.size(1)
def gaussian(sigma=1.5):
gauss = torch.Tensor(
[math.exp(-(x - window_size // 2) **
2 / float(2 * sigma ** 2)) for x in range(window_size)])
return gauss / gauss.sum()
def create_window():
_1D_window = gaussian(window_size).unsqueeze(1)
_2D_window = _1D_window.mm(
_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = torch.Tensor(
_2D_window.expand(channel, 1, window_size,
window_size).contiguous())
return window.cuda()
window = create_window()
mu1 = torch.nn.functional.conv2d(X_a, window,
padding=window_size // 2, groups=channel)
mu2 = torch.nn.functional.conv2d(X_b, window,
padding=window_size // 2, groups=channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1 * mu2
sigma1_sq = torch.nn.functional.conv2d(
X_a * X_a, window, padding=window_size // 2, groups=channel) - mu1_sq
sigma2_sq = torch.nn.functional.conv2d(
X_b * X_b, window, padding=window_size // 2, groups=channel) - mu2_sq
sigma12 = torch.nn.functional.conv2d(
X_a * X_b, window, padding=window_size // 2, groups=channel) - mu1_mu2
ssim_map = (((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) /
((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)))
if size_average:
return ssim_map.mean()
else:
return ssim_map.mean(1).mean(1).mean(1)
##########################################################################################
#### S O B E L ###########################################################################
###############---------------------------------------------------------------------------
## Sobel
def ssim_sobel_loss(x, y, window_size=11, size_average=True, val_range=2, normalize=True):
x_sobel = sobel(x)
y_sobel = sobel(y)
sim, cs = ssim(x_sobel, y_sobel, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
sim = (1 - sim) / 2
cs = (1 - cs) / 2
loss = (sim + cs) ** cs
return loss
def ssim_sobel_loss_broke(x, y, window_size=11, size_average=True, val_range=2, normalize=True):
x_sobel = sobel(x)
y_sobel = sobel(y)
mssim = []
mcs = []
sim, cs = ssim(x, y, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
mssim.append(((sim + 1) / 2))
mcs.append(((cs + 1) / 2))
sim, cs = ssim(x_sobel, y_sobel, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
mssim.append(((sim + 1) / 2))
mcs.append(((cs + 1) / 2))
x_sobel_0 = x * x_sobel[:, 0, ...].reshape(x_sobel.shape[0], 1, x_sobel.shape[2], x_sobel.shape[3])
y_sobel_0 = y * y_sobel[:, 0, ...].reshape(y_sobel.shape[0], 1, y_sobel.shape[2], y_sobel.shape[3])
sim, cs = ssim(x_sobel_0, y_sobel_0, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
mssim.append(((sim + 1) / 2))
mcs.append(((cs + 1) / 2))
x_sobel_1 = x * x_sobel[:, 1, ...].reshape(x_sobel.shape[0], 1, x_sobel.shape[2], x_sobel.shape[3])
y_sobel_1 = y * y_sobel[:, 1, ...].reshape(y_sobel.shape[0], 1, y_sobel.shape[2], y_sobel.shape[3])
sim, cs = ssim(x_sobel_1, y_sobel_1, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
mssim.append(((sim + 1) / 2))
mcs.append(((cs + 1) / 2))
x_sobel_3 = x - (x_sobel_0 * x_sobel_1)
y_sobel_3 = y - (y_sobel_0 * y_sobel_1)
sim, cs = ssim(x_sobel_3, y_sobel_3, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
mssim.append(((sim + 1) / 2))
mcs.append(((cs + 1) / 2))
mssim = torch.stack(mssim)
mcs = torch.stack(mcs)
loss = torch.prod(mssim ** mcs)
return loss
def sobel_correlation_loss(x, y):
x_sobel = sobel(x)
y_sobel = sobel(y)
return correlation(x_sobel, y_sobel)
def sobel(img):
#N,C,_,_ = img.size()
grad_y, grad_x = sobel_grad(img)
return torch.cat((grad_y, grad_x), dim=1)
def sobel_grad(img, stride=1, padding=1):
img = torch.mean(img, 1, True)
fx = | np.array([[1,0,-1],[2,0,-2],[1,0,-1]]) | numpy.array |
from PIL import ImageGrab, Image
from collections import Counter
import numpy as np
import re
import pytesseract
import cv2
from imutils.contours import sort_contours
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract"
def snap_shot_to_file(rect, file):
pic = ImageGrab.grab((rect.left, rect.top, rect.right, rect.bottom))
pic.save(file)
def snap_shot(rect):
return np.asarray(ImageGrab.grab((rect.left, rect.top, rect.right, rect.bottom)))
def draw_bbox(img_array, bbox, out_img):
#img_array = np.asarray(Image.open(img))
height, width = img_array.shape[:2]
left, top, right, bottom = bbox
right = min(right, width - 1)
bottom = min(bottom, height - 1)
region_img = np.zeros((height, width, 3), dtype="uint8")
for row in range(height):
for col in range(width):
region_img[row][col] = img_array[row][col]
# 画线
for col in range(left, right):
region_img[top][col] = (255, 0, 0)
region_img[bottom][col] = (255, 0, 0)
for row in range(top, bottom):
region_img[row][left] = (255, 0, 0)
region_img[row][right] = (255, 0, 0)
Image.fromarray(region_img).save(out_img)
LEFT_MOST = 20
RIGHT_MOST = -20
MAX_SEARCH_ROW = 100
def _is_possible_bg(pixel):
return pixel[0] == pixel[1] and pixel[1] == pixel[2] and pixel[0] > 200\
and pixel[0] != 255
def get_comment_bg(img_array):
height, width = img_array.shape[:2]
col = 10
counter = Counter()
for row in range(height-50, height-5):
if _is_possible_bg(img_array[row][col]):
counter[tuple(img_array[row][col])] += 1
top = counter.most_common(1)
if top:
return top[0][0]
else:
return None
def locate_start_row(img_array, debug_fn=None, bg_color=None):
if bg_color is None:
bg_color = [255, 255, 255]
height, width = img_array.shape[:2]
col = 10
found = False
for row in range(5, height-5):
if not np.all(img_array[row, col] == bg_color):
found = True
break
if not found:
return 200
row += 5
if debug_fn:
draw_bbox(img_array, (0, row, width-1, row+1), debug_fn + "-start-row.png")
return row
def locate_content_bottom(img_array, start_row, debug_fn=None, bg_color=None, bg_color2=None):
if bg_color is None:
bg_color = [255, 255, 255]
if bg_color2 is None:
bg_color2 = [242, 242, 242]
height, width = img_array.shape[:2]
col = 10
has_content = False
for row in range(start_row, height-5):
if np.all(img_array[row, col] == bg_color):
has_content = True
elif np.all(img_array[row, col] == bg_color2):
break
if debug_fn:
draw_bbox(img_array, (0, row, width-1, row+1), debug_fn + ".png")
if not has_content:
return -1
return row
def ocr(img):
options = "-l {} --psm {}".format("chi_sim", "7")
text = pytesseract.image_to_string(img, config=options)
return text
def _extract_template(img, thrshold=200, kernel=12, debug_fn=None):
bgr_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
gray = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2GRAY)
ret, thresh1 = cv2.threshold(gray, thrshold, 255, cv2.THRESH_BINARY_INV)
rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel, kernel))
# Appplying dilation on the threshold image
dilation = cv2.dilate(thresh1, rect_kernel, iterations=1)
# Finding contours
contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
contours = sort_contours(contours, method="left-to-right")[0]
template = None
for i, cnt in enumerate(contours):
x, y, w, h = cv2.boundingRect(cnt)
if debug_fn:
cv2.rectangle(bgr_img, (x, y), (x + w, y + h), (0, 255, 0), 2)
region = img[y:y+h, x:x+w]
text = _clear_text(ocr(region))
if debug_fn:
print(text)
if '分享' in text:
if i > 0:
prev_region = contours[i - 1]
else:
prev_region = contours[i]
x_prev, y_prev, w_prev, h_prev = cv2.boundingRect(prev_region)
y_min = min(y, y_prev)
y_max = max(y + h, y_prev + h_prev)
x_min = x_prev
x_max = x + w
template = img[y_min:y_max, x_min:x_max]
print("x, {}:{}; y, {}:{}".format(x_min, x_max, y_min, y_max))
loc_x, loc_y = find_img(img, template, reverse=True)
print("loc_x={}, loc_y={}".format(loc_x, loc_y))
break
if debug_fn:
cv2.imwrite(debug_fn+"-template.png", bgr_img)
return template
def find_img(large_array, small_array, reverse=False,
start_x=None, end_x=None, start_y=None, end_y=None):
"""
在大图(large_array)中搜索小图(small_array)的位置，精确的像素级匹配
:param large_array: 待搜索的大图的numpyarray
:param small_array: 被搜索的小图的numpyarray
:param reverse: 是否从后(右下往左上)往前搜索
:param start_x: 搜索大图的x(width)坐标的起点(包括)
:param end_x: 搜索大图的x坐标的终点(不包含)
:param start_y: 搜索大图的y坐标的起点(包括)
:param end_y: 搜索大图的y坐标的终点(不包含)
:return: (x, y) tuple，找到的坐标(width, height)。如果找不到
返回(-1, -1)
"""
small_height, small_width = small_array.shape[:2]
large_height, large_width = large_array.shape[:2]
search_end_x = large_width - small_width + 1
search_end_y = large_height - small_height + 1
if end_x is not None and end_x <= large_width:
search_end_x = end_x - small_width + 1
if end_y is not None and end_y <= large_height:
search_end_y = end_y - small_height + 1
search_start_x = 0 if start_x is None else start_x
search_start_y = 0 if start_y is None else start_y
if reverse:
for x in range(search_end_x - 1, search_start_x - 1, -1):
for y in range(search_end_y - 1, search_start_y - 1, -1):
x2 = x + small_width
y2 = y + small_height
pic = large_array[y:y2, x:x2]
test = (pic == small_array)
if test.all():
return x, y
else:
for x in range(search_start_x, search_end_x):
for y in range(search_start_y, search_end_y):
x2 = x + small_width
y2 = y + small_height
pic = large_array[y:y2, x:x2]
test = (pic == small_array)
if test.all():
return x, y
return -1, -1
def _process_share_template(img_array, template_img, bottom, debug_fn):
if debug_fn:
print("模板抽取服务号")
try:
x, y = find_img(img_array, template_img, reverse=False)
except:
Image.fromarray(img_array).save("err1.png")
Image.fromarray(template_img).save("err2.png")
x, y = -1, -1
if x == -1:
if debug_fn:
Image.fromarray(img_array).save(debug_fn+"-large.png")
Image.fromarray(template_img).save(debug_fn + "-small.png")
return -1, None
h, _ = template_img.shape[:2]
_, w = img_array.shape[:2]
if debug_fn:
draw_bbox(img_array, (x, y, x+w, y+h), debug_fn + "-1-1.png")
return y, img_array[y:y+h, x:x+w]
def _process_share_without_template(img_array, bottom, bg_color, debug_fn, width,
ext_template):
if debug_fn:
print("无模板抽取分享")
for r in range(bottom - 1, bottom - MAX_SEARCH_ROW, -1):
# 找到第一行非全白背景的行，此行内容是分享
if not np.all(img_array[r][LEFT_MOST:RIGHT_MOST] == bg_color):
break
if debug_fn:
draw_bbox(img_array, (0, r, width - 1, r + 1), debug_fn + "-1.png")
for r2 in range(r - 1, r - MAX_SEARCH_ROW, -1):
if np.all(img_array[r2][LEFT_MOST:RIGHT_MOST] == bg_color):
break
if debug_fn:
draw_bbox(img_array, (0, r2, width - 1, r2 + 1), debug_fn + "-2.png")
# r2-r是分享行
share_arr = img_array[r2:r, :]
share_img = Image.fromarray(share_arr)
if ext_template:
template_img = _extract_template(share_arr, debug_fn=debug_fn)
else:
template_img = None
if debug_fn and template_img is not None:
share_img.save(debug_fn + "-2-2.png")
x, y = find_img(img_array, template_img, reverse=True)
print("x={}, y={}".format(x, y))
return r2, template_img, share_img
def extract_counts(is_fuwuhao, img_array, bottom, debug_fn=None, bg_color=None,
template_img=None):
if bg_color is None:
bg_color = [255, 255, 255]
height, width = img_array.shape[:2]
if not is_fuwuhao or template_img is None:
r2, template_img, share_img = _process_share_without_template(img_array, bottom,
bg_color, debug_fn,
width, is_fuwuhao)
else:
# 服务号并且template不为空
r2, share_img = _process_share_template(img_array, template_img, bottom, debug_fn)
if r2 == -1:
if debug_fn:
print("can't find by template!!!")
r2, template_img, share_img = _process_share_without_template(img_array, bottom,
bg_color, debug_fn,
width, False)
text = ocr(share_img)
star, share = _extract_share(text)
for r3 in range(r2-1, r2-MAX_SEARCH_ROW, -1):
if not np.all(img_array[r3][LEFT_MOST:RIGHT_MOST] == bg_color):
break
if debug_fn:
draw_bbox(img_array, (0, r3, width - 1, r3 + 1), debug_fn + "-3.png")
for r4 in range(r3-1, r3-MAX_SEARCH_ROW, -1):
if | np.all(img_array[r4][LEFT_MOST:RIGHT_MOST] == bg_color) | numpy.all |
import librosa
from conv_stft import STFT
import torch
import numpy as np
from torch.nn import MSELoss
def _prepare_audio(input_audio, device):
audio = torch.FloatTensor(input_audio)
if len(audio.shape) < 2:
audio = audio.unsqueeze(0)
audio = audio.to(device)
return audio
def _prepare_network(device, win_len=1024, win_hop=512, fft_len=1024, window='hann'):
stft = STFT(
win_len=win_len,
win_hop=win_hop,
fft_len=fft_len,
win_type=window
).to(device)
return stft
def _test_stft_on_signal(input_audio, atol, device):
audio = _prepare_audio(input_audio, device)
for i in range(10):
fft_len = 2**i
for j in range(i):
win_hop = 2**j
stft = _prepare_network(device, win_len=fft_len, win_hop=win_hop, fft_len=fft_len)
output = stft(audio)
output = output.cpu().data.numpy()[..., :]
_audio = audio.cpu().data.numpy()[..., :]
assert (np.mean((output - _audio) ** 2) < atol)
def test_stft():
# White noise
test_audio = []
seed = np.random.RandomState(0)
x1 = seed.randn(2 ** 15)
test_audio.append((x1, 1e-10))
# Sin wave
x2 = np.sin(np.linspace(-np.pi, np.pi, 2 ** 15))
test_audio.append((x2, 1e-10))
# Music file
x3 = librosa.load(librosa.util.example_audio_file(), duration=1.0)[0]
test_audio.append((x3, 1e-10))
device = ['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']
for x, atol in test_audio:
for d in device:
_test_stft_on_signal(x, atol, d)
def test_against_librosa_stft():
audio = librosa.load(librosa.util.example_audio_file(), duration=10.0, offset=30)[0]
for i in range(8, 12):
filter_length = 2**i
for j in range(4, i):
hop_length = 2**j
librosa_stft = librosa.stft(audio, n_fft=filter_length, hop_length=hop_length)
_magnitude = | np.abs(librosa_stft) | numpy.abs |
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
import scipy
import scipy.stats
# BCES fitting
# ===============
def bces(y1,y1err,y2,y2err,cerr):
"""
Does the entire regression calculation for 4 slopes:
OLS(Y|X), OLS(X|Y), bisector, orthogonal.
Fitting form: Y=AX+B.
Usage:
>>> a,b,aerr,berr,covab=bces(x,xerr,y,yerr,cov)
Output:
- a,b : best-fit parameters a,b of the linear regression
- aerr,berr : the standard deviations in a,b
- covab : the covariance between a and b (e.g. for plotting confidence bands)
Arguments:
- x,y : data
- xerr,yerr: measurement errors affecting x and y
- cov : covariance between the measurement errors
(all are arrays)
v1 Mar 2012: ported from bces_regress.f. Added covariance output.
<NAME>
"""
# Arrays holding the code main results for each method:
# Elements: 0-Y|X, 1-X|Y, 2-bisector, 3-orthogonal
a,b,avar,bvar,covarxiz,covar_ba=np.zeros(4),np.zeros(4),np.zeros(4),np.zeros(4),np.zeros(4),np.zeros(4)
# Lists holding the xi and zeta arrays for each method above
xi,zeta=[],[]
# Calculate sigma's for datapoints using length of conf. intervals
sig11var = np.mean( y1err**2 )
sig22var = np.mean( y2err**2 )
sig12var = np.mean( cerr )
# Covariance of Y1 (X) and Y2 (Y)
covar_y1y2 = np.mean( (y1-y1.mean())*(y2-y2.mean()) )
# Compute the regression slopes
a[0] = (covar_y1y2 - sig12var)/(y1.var() - sig11var) # Y|X
a[1] = (y2.var() - sig22var)/(covar_y1y2 - sig12var) # X|Y
a[2] = ( a[0]*a[1] - 1.0 + np.sqrt((1.0 + a[0]**2)*(1.0 + a[1]**2)) ) / (a[0]+a[1]) # bisector
if covar_y1y2<0:
sign = -1.
else:
sign = 1.
a[3] = 0.5*((a[1]-(1./a[0])) + sign*np.sqrt(4.+(a[1]-(1./a[0]))**2)) # orthogonal
# Compute intercepts
for i in range(4):
b[i]=y2.mean()-a[i]*y1.mean()
# Set up variables to calculate standard deviations of slope/intercept
xi.append( ( (y1-y1.mean()) * (y2-a[0]*y1-b[0]) + a[0]*y1err**2 ) / (y1.var()-sig11var) ) # Y|X
xi.append( ( (y2-y2.mean()) * (y2-a[1]*y1-b[1]) - y2err**2 ) / covar_y1y2 ) # X|Y
xi.append( xi[0] * (1.+a[1]**2)*a[2] / ((a[0]+a[1])*np.sqrt((1.+a[0]**2)*(1.+a[1]**2))) + xi[1] * (1.+a[0]**2)*a[2] / ((a[0]+a[1])*np.sqrt((1.+a[0]**2)*(1.+a[1]**2))) ) # bisector
xi.append( xi[0] * a[3]/(a[0]**2*np.sqrt(4.+(a[1]-1./a[0])**2)) + xi[1]*a[3]/np.sqrt(4.+(a[1]-1./a[0])**2) ) # orthogonal
for i in range(4):
zeta.append( y2 - a[i]*y1 - y1.mean()*xi[i] )
for i in range(4):
# Calculate variance for all a and b
avar[i]=xi[i].var()/xi[i].size
bvar[i]=zeta[i].var()/zeta[i].size
# Sample covariance obtained from xi and zeta (paragraph after equation 15 in AB96)
covarxiz[i]=np.mean( (xi[i]-xi[i].mean()) * (zeta[i]-zeta[i].mean()) )
# Covariance between a and b (equation after eq. 15 in AB96)
covar_ab=covarxiz/y1.size
return a,b,np.sqrt(avar),np.sqrt(bvar),covar_ab
def bootstrap(v):
"""
Constructs Monte Carlo simulated data set using the
Bootstrap algorithm.
Usage:
>>> bootstrap(x)
where x is either an array or a list of arrays. If it is a
list, the code returns the corresponding list of bootstrapped
arrays assuming that the same position in these arrays map the
same "physical" object.
"""
if type(v)==list:
vboot=[] # list of boostrapped arrays
n=v[0].size
iran=scipy.random.randint(0,n,n) # Array of random indexes
for x in v: vboot.append(x[iran])
else: # if v is an array, not a list of arrays
n=v.size
iran=scipy.random.randint(0,n,n) # Array of random indexes
vboot=v[iran]
return vboot
def bcesboot(y1,y1err,y2,y2err,cerr,nsim=10000):
"""
Does the BCES with bootstrapping.
Usage:
>>> a,b,aerr,berr,covab=bcesboot(x,xerr,y,yerr,cov,nsim)
:param x,y: data
:param xerr,yerr: measurement errors affecting x and y
:param cov: covariance between the measurement errors (all are arrays)
:param nsim: number of Monte Carlo simulations (bootstraps)
:returns: a,b -- best-fit parameters a,b of the linear regression
:returns: aerr,berr -- the standard deviations in a,b
:returns: covab -- the covariance between a and b (e.g. for plotting confidence bands)
.. note:: this method is definitely not nearly as fast as bces_regress.f. Needs to be optimized. Maybe adapt the fortran routine using f2python?
"""
import tqdm
print("Bootstrapping progress:")
"""
My convention for storing the results of the bces code below as
matrixes for processing later are as follow:
simulation-method y|x x|y bisector orthogonal
sim0 ...
Am = sim1 ...
sim2 ...
sim3 ...
"""
for i in tqdm.tqdm(range(nsim)):
[y1sim,y1errsim,y2sim,y2errsim,cerrsim]=bootstrap([y1,y1err,y2,y2err,cerr])
asim,bsim,errasim,errbsim,covabsim=bces(y1sim,y1errsim,y2sim,y2errsim,cerrsim)
if i==0:
# Initialize the matrixes
am,bm=asim.copy(),bsim.copy()
else:
am=np.vstack((am,asim))
bm=np.vstack((bm,bsim))
if True in np.isnan(am):
am,bm=checkNan(am,bm)
# Bootstrapping results
a=np.array([ am[:,0].mean(),am[:,1].mean(),am[:,2].mean(),am[:,3].mean() ])
b=np.array([ bm[:,0].mean(),bm[:,1].mean(),bm[:,2].mean(),bm[:,3].mean() ])
# Error from unbiased sample variances
erra,errb,covab=np.zeros(4),np.zeros(4),np.zeros(4)
for i in range(4):
erra[i]=np.sqrt( 1./(nsim-1) * ( np.sum(am[:,i]**2)-nsim*(am[:,i].mean())**2 ))
errb[i]=np.sqrt( 1./(nsim-1) * ( np.sum(bm[:,i]**2)-nsim*(bm[:,i].mean())**2 ))
covab[i]=1./(nsim-1) * ( np.sum(am[:,i]*bm[:,i])-nsim*am[:,i].mean()*bm[:,i].mean() )
return a,b,erra,errb,covab
def checkNan(am,bm):
"""
Sometimes, if the dataset is very small, the regression parameters in
some instances of the bootstrapped sample may have NaNs i.e. failed
regression (I need to investigate this in more details).
This method checks to see if there are NaNs in the bootstrapped
fits and remove them from the final sample.
"""
import nmmn.lsd
idel=nmmn.lsd.findnan(am[:,2])
print("Bootstrapping error: regression failed in",np.size(idel),"instances. They were removed.")
return np.delete(am,idel,0),np.delete(bm,idel,0)
# Methods which make use of parallelization
# ===========================================
def ab(x):
"""
This method is the big bottleneck of the parallel BCES code. That's the
reason why I put these calculations in a separate method, in order to
distribute this among the cores. In the original BCES method, this is
inside the main routine.
Argument:
[y1,y1err,y2,y2err,cerr,nsim]
where nsim is the number of bootstrapping trials sent to each core.
:returns: am,bm : the matrixes with slope and intercept where each line corresponds to a bootrap trial and each column maps a different BCES method (ort, y|x etc).
Be very careful and do not use lambda functions when calling this
method and passing it to multiprocessing or ipython.parallel!
I spent >2 hours figuring out why the code was not working until I
realized the reason was the use of lambda functions.
"""
y1,y1err,y2,y2err,cerr,nsim=x[0],x[1],x[2],x[3],x[4],x[5]
for i in range(int(nsim)):
[y1sim,y1errsim,y2sim,y2errsim,cerrsim]=bootstrap([y1,y1err,y2,y2err,cerr])
asim,bsim,errasim,errbsim,covabsim=bces(y1sim,y1errsim,y2sim,y2errsim,cerrsim)
if i==0:
# Initialize the matrixes
am,bm=asim.copy(),bsim.copy()
else:
am=np.vstack((am,asim))
bm=np.vstack((bm,bsim))
return am,bm
def bcesp(y1,y1err,y2,y2err,cerr,nsim=10000):
"""
Parallel implementation of the BCES with bootstrapping.
Divide the bootstraps equally among the threads (cores) of
the machine. It will automatically detect the number of
cores available.
Usage:
>>> a,b,aerr,berr,covab=bcesp(x,xerr,y,yerr,cov,nsim)
:param x,y: data
:param xerr,yerr: measurement errors affecting x and y
:param cov: covariance between the measurement errors (all are arrays)
:param nsim: number of Monte Carlo simulations (bootstraps)
:returns: a,b - best-fit parameters a,b of the linear regression
:returns: aerr,berr - the standard deviations in a,b
:returns: covab - the covariance between a and b (e.g. for plotting confidence bands)
.. seealso:: Check out ~/work/projects/playground/parallel python/bcesp.py for the original, testing, code. I deleted some line from there to make the "production" version.
* v1 Mar 2012: serial version ported from bces_regress.f. Added covariance output.
* v2 May 3rd 2012: parallel version ported from nemmen.bcesboot.
.. codeauthor: <NAME>
"""
import time # for benchmarking
import multiprocessing
print("BCES,", nsim,"trials... ")
tic=time.time()
# Find out number of cores available
ncores=multiprocessing.cpu_count()
# We will divide the processing into how many parts?
n=2*ncores
"""
Must create lists that will be distributed among the many
cores with structure
core1 <- [y1,y1err,y2,y2err,cerr,nsim/n]
core2 <- [y1,y1err,y2,y2err,cerr,nsim/n]
etc...
"""
pargs=[] # this is a list of lists!
for i in range(n):
pargs.append([y1,y1err,y2,y2err,cerr,nsim/n])
# Initializes the parallel engine
pool = multiprocessing.Pool(processes=ncores) # multiprocessing package
"""
Each core processes ab(input)
return matrixes Am,Bm with the results of nsim/n
presult[i][0] = Am with nsim/n lines
presult[i][1] = Bm with nsim/n lines
"""
presult=pool.map(ab, pargs) # multiprocessing
pool.close() # close the parallel engine
# vstack the matrixes processed from all cores
i=0
for m in presult:
if i==0:
# Initialize the matrixes
am,bm=m[0].copy(),m[1].copy()
else:
am= | np.vstack((am,m[0])) | numpy.vstack |
from __future__ import division
import pytest
import datetime
import numpy as np
from numpy import pi, cos, sin
from numpy.testing import assert_array_equal, assert_allclose
import pyamps
from pyamps.amps import AMPS, get_B_space, get_B_ground
@pytest.fixture()
def amps_model(model_coeff):
model_args = [
0.4, # v
100.6, # By
200.7, # Bz
0.11, # tilt
75.2 # F107
]
model_kwargs = dict(
minlat=71.2,
maxlat=85.1,
height=90.1,
dr=4,
M0=8,
resolution=21
)
try:
model = AMPS(*model_args, **model_kwargs)
except Exception:
# allow test_init to fail instead
model = None
return model, model_args, model_kwargs
class Test_AMPS(object):
def test_init(self, amps_model):
model, m_args, m_kwargs = amps_model
model = AMPS(*m_args, **m_kwargs)
model_vectors = pyamps.model_utils.get_model_vectors(*m_args)
assert_allclose(model.tor_s, model_vectors[1])
assert_allclose(model.pol_c, model_vectors[2])
assert_array_equal(model.tor_keys, model_vectors[5])
assert model.N == 2
assert model.M == 2
assert model.plotgrid_scalar[0].shape == (m_kwargs['resolution'], m_kwargs['resolution'])
def test_update_model(self, amps_model):
model, m_args, m_kwargs = amps_model
old_tor_c = model.tor_c.copy()
m_args[0] += 1
model.update_model(*m_args)
new_tor_c = model.tor_c
with pytest.raises(AssertionError):
assert_allclose(old_tor_c, new_tor_c, atol=1e-5)
def test__get_vectorgrid(self, amps_model):
model, _, _ = amps_model
mlat, mlt = model._get_vectorgrid()
mlat_, mlt_, mlt_res = pyamps.plot_utils.equal_area_grid(dr=model.dr, M0=model.M0)
assert (np.abs(mlat) >= model.minlat).all()
assert (-model.maxlat <= mlat).all() and (mlat <= model.maxlat).all()
assert (0 <= mlt).all() and (mlt <= 24).all()
assert mlat.shape == mlt.shape
def test__get_scalargrid(self, amps_model):
model, _, m_kwargs = amps_model
resolution = m_kwargs['resolution'] + 1
mlat, mlt = model._get_scalargrid(resolution)
assert model.scalar_resolution == resolution
assert mlat.shape == mlt.shape
assert mlat.shape == (2 * resolution**2, 1)
assert (np.abs(mlat) >= model.minlat).all()
assert (-model.maxlat <= mlat).all() and (mlat <= model.maxlat).all()
assert (0 <= mlt).all() and (mlt <= 24).all()
def test_calculate_matrices(self, amps_model):
model, _, m_kwargs = amps_model
assert model.tor_sinmphi_scalar.shape == (882, 5)
assert model.pol_dP_vector.shape == (160, 5)
assert_allclose(model.tor_sinmphi_vector[2],
[0.000000, 0.471397, 0.000000, 0.471397, 0.831470],atol=1e-6)
assert_allclose(model.pol_cosmphi_vector[0],
[1.000000, 0.995185, 1.000000, 0.995185, 0.980785], atol=1e-6)
assert_allclose(model.pol_P_scalar[1],
[0.950489, 0.310759, 0.855143, 0.511601, 0.083633], atol=1e-6)
assert_allclose(model.tor_dP_vector[5],
[0.309017, -0.951057, 0.881678, -1.401259, -0.509037], atol=1e-6)
@pytest.mark.parametrize("mlat, mlt", [(np.array([60.]), np.array([0.])),
(np.array([71.]), np.array([6.]))])
def test_toroidal_scalar(self, amps_model, mlat, mlt):
model, _, m_kwargs = amps_model
mlt2r = np.pi / 12
P, dP = pyamps.sh_utils.legendre(model.N, model.M, 90 - mlat)
T = 0
for i, (n, m) in enumerate(model.keys_T):
T += P[n, m] * (model.tor_c[i] * cos(m * mlt * mlt2r) +
model.tor_s[i] * sin(m * mlt * mlt2r))
assert_allclose(T, model.get_toroidal_scalar(mlat, mlt))
assert_allclose(np.split(model.get_toroidal_scalar(), 2)[0],
model.get_toroidal_scalar(*model.plotgrid_scalar))
pass
@pytest.mark.parametrize("mlat, mlt", [(np.array([60.]), np.array([0.])),
(np.array([71.]), np.array([6.]))])
def test_poloidal_scalar(self, amps_model, mlat, mlt):
model, _, m_kwargs = amps_model
mlt2r = pi / 12
REFRE = 6371.2
P, dP = pyamps.sh_utils.legendre(model.N, model.M, 90 - mlat)
V = 0
for i, (n, m) in enumerate(model.keys_P):
V += (REFRE / (REFRE + m_kwargs['height']))**(n + 1) * P[n, m] * (
model.pol_c[i] * cos(m * mlt * mlt2r) +
model.pol_s[i] * sin(m * mlt * mlt2r))
V *= REFRE
assert_allclose(V, model.get_poloidal_scalar(mlat, mlt))
assert_allclose(np.split(model.get_poloidal_scalar(), 2)[0],
model.get_poloidal_scalar(*model.plotgrid_scalar))
pass
@pytest.mark.parametrize("mlat, mlt", [(np.array([[60.]]), np.array([[0.]])),
(np.array([[71.]]), np.array([[6.]]))])
def test_get_divergence_free_current_function(self, amps_model, mlat, mlt):
model, _, m_kwargs = amps_model
mlt2r = pi / 12
REFRE = 6371.2
MU0 = pi * 4e-7
P, dP = pyamps.sh_utils.legendre(model.N, model.M, 90 - mlat)
Psi = 0
for i, (n, m) in enumerate(model.keys_P):
Psi += (REFRE / (REFRE + m_kwargs['height']))**(n + 1) \
* (2 * n + 1) / n * P[n, m] * (
model.pol_c[i] * cos(m * mlt * mlt2r) +
model.pol_s[i] * sin(m * mlt * mlt2r))
Psi *= -REFRE / MU0 * 1e-9
assert_allclose(Psi, model.get_divergence_free_current_function(mlat, mlt))
assert_allclose(np.split(model.get_divergence_free_current_function(), 2)[0].reshape(
m_kwargs['resolution'],m_kwargs['resolution']),
model.get_divergence_free_current_function(*model.plotgrid_scalar))
pass
@pytest.mark.parametrize("mlat, mlt", [( | np.array([[60.]]) | numpy.array |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
import faiss
from transformers.configuration_bart import BartConfig
from transformers.configuration_dpr import DPRConfig
from transformers.configuration_rag import RagConfig
from transformers.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.testing_utils import (
require_datasets,
require_faiss,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from transformers.tokenization_bart import BartTokenizer
from transformers.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.tokenization_dpr import DPRQuestionEncoderTokenizer
from transformers.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
@require_faiss
@require_datasets
class RagRetrieverTest(TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
self.retrieval_vector_size = 8
# DPR tok
vocab_tokens = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
dpr_tokenizer_path = os.path.join(self.tmpdirname, "dpr_tokenizer")
os.makedirs(dpr_tokenizer_path, exist_ok=True)
self.vocab_file = os.path.join(dpr_tokenizer_path, DPR_VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
# BART tok
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
self.special_tokens_map = {"unk_token": "<unk>"}
bart_tokenizer_path = os.path.join(self.tmpdirname, "bart_tokenizer")
os.makedirs(bart_tokenizer_path, exist_ok=True)
self.vocab_file = os.path.join(bart_tokenizer_path, BART_VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(bart_tokenizer_path, BART_VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_dpr_tokenizer(self) -> DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname, "dpr_tokenizer"))
def get_bart_tokenizer(self) -> BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname, "bart_tokenizer"))
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def get_dummy_dataset(self):
dataset = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size), 2 * np.ones(self.retrieval_vector_size)],
}
)
dataset.add_faiss_index("embeddings", string_factory="Flat", metric_type=faiss.METRIC_INNER_PRODUCT)
return dataset
def get_dummy_canonical_hf_index_retriever(self):
dataset = self.get_dummy_dataset()
config = RagConfig(
retrieval_vector_size=self.retrieval_vector_size,
question_encoder=DPRConfig().to_dict(),
generator=BartConfig().to_dict(),
)
with patch("transformers.retrieval_rag.load_dataset") as mock_load_dataset:
mock_load_dataset.return_value = dataset
retriever = RagRetriever(
config,
question_encoder_tokenizer=self.get_dpr_tokenizer(),
generator_tokenizer=self.get_bart_tokenizer(),
)
return retriever
def get_dummy_custom_hf_index_retriever(self, from_disk: bool):
dataset = self.get_dummy_dataset()
config = RagConfig(
retrieval_vector_size=self.retrieval_vector_size,
question_encoder=DPRConfig().to_dict(),
generator=BartConfig().to_dict(),
index_name="custom",
)
if from_disk:
config.passages_path = os.path.join(self.tmpdirname, "dataset")
config.index_path = os.path.join(self.tmpdirname, "index.faiss")
dataset.get_index("embeddings").save(os.path.join(self.tmpdirname, "index.faiss"))
dataset.drop_index("embeddings")
dataset.save_to_disk(os.path.join(self.tmpdirname, "dataset"))
del dataset
retriever = RagRetriever(
config,
question_encoder_tokenizer=self.get_dpr_tokenizer(),
generator_tokenizer=self.get_bart_tokenizer(),
)
else:
retriever = RagRetriever(
config,
question_encoder_tokenizer=self.get_dpr_tokenizer(),
generator_tokenizer=self.get_bart_tokenizer(),
index=CustomHFIndex(config.retrieval_vector_size, dataset),
)
return retriever
def get_dummy_legacy_index_retriever(self):
dataset = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size + 1), 2 * np.ones(self.retrieval_vector_size + 1)],
}
)
dataset.add_faiss_index("embeddings", string_factory="Flat", metric_type=faiss.METRIC_INNER_PRODUCT)
index_file_name = os.path.join(self.tmpdirname, "hf_bert_base.hnswSQ8_correct_phi_128.c_index")
dataset.save_faiss_index("embeddings", index_file_name + ".index.dpr")
pickle.dump(dataset["id"], open(index_file_name + ".index_meta.dpr", "wb"))
passages_file_name = os.path.join(self.tmpdirname, "psgs_w100.tsv.pkl")
passages = {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
pickle.dump(passages, open(passages_file_name, "wb"))
config = RagConfig(
retrieval_vector_size=self.retrieval_vector_size,
question_encoder=DPRConfig().to_dict(),
generator=BartConfig().to_dict(),
index_name="legacy",
index_path=self.tmpdirname,
)
retriever = RagRetriever(
config, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer()
)
return retriever
def test_canonical_hf_index_retriever_retrieve(self):
n_docs = 1
retriever = self.get_dummy_canonical_hf_index_retriever()
hidden_states = np.array(
[np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
)
retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs)
self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
self.assertEqual(len(doc_dicts), 2)
self.assertEqual(sorted(doc_dicts[0]), ["embeddings", "id", "text", "title"])
self.assertEqual(len(doc_dicts[0]["id"]), n_docs)
self.assertEqual(doc_dicts[0]["id"][0], "1") # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0], "0") # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]])
def test_canonical_hf_index_retriever_save_and_from_pretrained(self):
retriever = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("transformers.retrieval_rag.load_dataset") as mock_load_dataset:
mock_load_dataset.return_value = self.get_dummy_dataset()
retriever.save_pretrained(tmp_dirname)
retriever = RagRetriever.from_pretrained(tmp_dirname)
self.assertIsInstance(retriever, RagRetriever)
hidden_states = np.array(
[np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
)
out = retriever.retrieve(hidden_states, n_docs=1)
self.assertTrue(out is not None)
def test_custom_hf_index_retriever_retrieve(self):
n_docs = 1
retriever = self.get_dummy_custom_hf_index_retriever(from_disk=False)
hidden_states = np.array(
[np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
)
retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs)
self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
self.assertEqual(len(doc_dicts), 2)
self.assertEqual(sorted(doc_dicts[0]), ["embeddings", "id", "text", "title"])
self.assertEqual(len(doc_dicts[0]["id"]), n_docs)
self.assertEqual(doc_dicts[0]["id"][0], "1") # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0], "0") # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]])
def test_custom_hf_index_retriever_save_and_from_pretrained(self):
retriever = self.get_dummy_custom_hf_index_retriever(from_disk=False)
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(tmp_dirname)
retriever = RagRetriever.from_pretrained(tmp_dirname)
self.assertIsInstance(retriever, RagRetriever)
hidden_states = np.array(
[np.ones(self.retrieval_vector_size), - | np.ones(self.retrieval_vector_size) | numpy.ones |
import dask.array as da
import numpy as np
import pytest
from skimage import set_backend
from skimage import filters
from uskimage_demo import dask_backend
modes = ['wrap', 'reflect', 'nearest', 'mirror', 'constant']
@pytest.mark.parametrize('channel_axis', [None, 0, -1])
@pytest.mark.parametrize('coerce_input', [False, True])
@pytest.mark.parametrize('dask_input', [False, True])
@pytest.mark.parametrize('mode', modes)
def test_gaussian(channel_axis, coerce_input, dask_input, mode):
image = np.random.randn(1024, 512)
chunks = (256, 256)
if channel_axis is not None:
n_channels = 3
image = np.stack((image,) * n_channels, axis=channel_axis)
chunks = list(chunks)
chunks.insert(channel_axis % image.ndim, n_channels)
chunks = tuple(chunks)
sigma = (1.5, 1.0)
expected_output = filters.gaussian(
image, sigma=sigma, mode=mode, channel_axis=channel_axis)
if dask_input:
image = da.asarray(image, chunks=chunks)
only = (coerce_input == True or dask_input == True)
with set_backend(dask_backend, coerce=coerce_input, only=only):
out = filters.gaussian(image, mode=mode, sigma=sigma,
channel_axis=channel_axis)
if dask_input or coerce_input:
assert isinstance(out, da.Array)
if dask_input:
assert out.chunksize == chunks
out = out.compute()
assert isinstance(out, np.ndarray)
np.testing.assert_allclose(expected_output, out)
@pytest.mark.parametrize('channel_axis', [None, 0, -1])
@pytest.mark.parametrize('coerce_input', [False, ])
@pytest.mark.parametrize('dask_input', [False, True])
@pytest.mark.parametrize('mode', ['reflect'])
def test_difference_of_gaussians(channel_axis, coerce_input, dask_input, mode):
image = | np.random.randn(1024, 512) | numpy.random.randn |
"""
Function and classes used to identify barcodes
"""
from typing import *
import pandas as pd
import numpy as np
import pickle
import logging
from sklearn.neighbors import NearestNeighbors
# from pynndescent import NNDescent
from pathlib import Path
from itertools import groupby
from pysmFISH.logger_utils import selected_logger
from pysmFISH.data_models import Output_models
from pysmFISH.errors import Registration_errors
class simplify_barcodes_reference():
"""Utility Class use to convert excels files with codebook info
in smaller size pandas dataframe/parquet files to pass to dask
workers during the processing. This utility function must be
run before running the experiment analysis. The pipeline
require the output of this function.
"""
def __init__(self, barcode_fpath: str):
"""Class initialization
Args:
barcode_fpath (str): Path to the xlsx file with the codebook
"""
self.barcode_fpath = Path(barcode_fpath)
self.barcode_fname = self.barcode_fpath.stem
@staticmethod
def format_codeword(codeword: str):
"""[summary]
Args:
codeword (str): codeword representing a gene
Returns:
byte: codeword converted in byte representation
"""
str_num = codeword.split('[')[-1].split(']')[0]
converted_codeword = np.array([int(el) for el in list(str_num)]).astype(np.int8)
converted_codeword = converted_codeword.tobytes()
return converted_codeword
def convert_codebook(self):
used_gene_codebook_df = pd.read_excel(self.barcode_fpath)
# used_gene_codebook_df = pd.read_parquet(self.barcode_fpath)
self.codebook_df = used_gene_codebook_df.loc[:,['Barcode','Gene']]
self.codebook_df.rename(columns = {'Barcode':'Code'}, inplace = True)
self.codebook_df.Code = self.codebook_df.Code.apply(lambda x: self.format_codeword(x))
self.codebook_df.to_parquet(self.barcode_fpath.parent / (self.barcode_fname + '.parquet'))
def dots_hoods(coords: np.ndarray,pxl: int)->np.ndarray:
"""Function that calculate the coords of the peaks searching
neighborhood for identifying the barcodes.
Args:
coords (np.ndarray): coords of the identified peaks
pxl (int): size of the neighborhood in pixel
Returns:
np.ndarray: coords that define the neighborhood (r_tl,r_br,c_tl,c_tr)
"""
r_tl = coords[:,0]-pxl
r_br = coords[:,0]+pxl
c_tl = coords[:,1]-pxl
c_tr = coords[:,1]+pxl
r_tl = r_tl[:,np.newaxis]
r_br = r_br[:,np.newaxis]
c_tl = c_tl[:,np.newaxis]
c_tr = c_tr[:,np.newaxis]
chunks_coords = np.hstack((r_tl,r_br,c_tl,c_tr))
chunks_coords = chunks_coords.astype(int)
return chunks_coords
def extract_dots_images(barcoded_df: pd.DataFrame,registered_img_stack: np.ndarray,
experiment_fpath: str, metadata: dict):
"""Function used to extract the images corresponding to a barcode
after running the decoding identification. It can save the images
but to avoid increasing too much the space occupied by a processed
experiment an array with the maximum intensity value of the pxl in
each round is calculated and saved
Args:
barcoded_df (pd.DataFrame): Dataframe with decoded barcodes
for a specific field of view.
registered_img_stack (np.ndarray): Preprocessed image of a single field of view
the imaging round correspond to the z-stack position
experiment_fpath (str): Path to the folder of the experiment to process
metadata (dict): Overall experiment info
"""
round_intensity_labels = ['bit_' + str(el) +'_intensity' for el in np.arange(1,int(metadata['total_rounds'])+1)]
if isinstance(registered_img_stack, np.ndarray) and (barcoded_df.shape[0] >1):
experiment_fpath = Path(experiment_fpath)
barcodes_names = barcoded_df['barcode_reference_dot_id'].values
coords = barcoded_df.loc[:, ['r_px_registered', 'c_px_registered']].to_numpy()
barcodes_extraction_resolution = barcoded_df['barcodes_extraction_resolution'].values[0]
chunks_coords = dots_hoods(coords,barcodes_extraction_resolution)
chunks_coords[chunks_coords<0]=0
chunks_coords[chunks_coords>registered_img_stack.shape[1]]= registered_img_stack.shape[1]
for idx in np.arange(chunks_coords.shape[0]):
selected_region = registered_img_stack[:,chunks_coords[idx,0]:chunks_coords[idx,1]+1,chunks_coords[idx,2]:chunks_coords[idx,3]+1]
if selected_region.size >0:
max_array = selected_region.max(axis=(1,2))
barcoded_df.loc[barcoded_df.dot_id == barcodes_names[idx],round_intensity_labels] = max_array
# for channel in channels:
# all_regions[channel] = {}
# all_max[channel] = {}
# img_stack = registered_img_stack[channel]
# trimmed_df_channel = trimmed_df.loc[trimmed_df.channel == channel]
# if trimmed_df_channel.shape[0] >0:
# barcodes_names = trimmed_df_channel['barcode_reference_dot_id'].values
# coords = trimmed_df_channel.loc[:, ['r_px_registered', 'c_px_registered']].to_numpy()
# barcodes_extraction_resolution = trimmed_df_channel['barcodes_extraction_resolution'].values[0]
# chunks_coords = dots_hoods(coords,barcodes_extraction_resolution)
# chunks_coords[chunks_coords<0]=0
# chunks_coords[chunks_coords>img_stack.shape[1]]= img_stack.shape[1]
# for idx in np.arange(chunks_coords.shape[0]):
# selected_region = img_stack[:,chunks_coords[idx,0]:chunks_coords[idx,1]+1,chunks_coords[idx,2]:chunks_coords[idx,3]+1]
# if selected_region.size >0:
# max_array = selected_region.max(axis=(1,2))
# # all_regions[channel][barcodes_names[idx]]= selected_region
# all_max[channel][barcodes_names[idx]]= max_array
# barcoded_df.loc[barcoded_df.dot_id == barcodes_names[idx],round_intensity_labels] = max_array
# fpath = experiment_fpath / 'tmp' / 'combined_rounds_images' / (experiment_name + '_' + channel + '_img_dict_fov_' + str(fov) + '.pkl')
# pickle.dump(all_regions,open(fpath,'wb'))
# fpath = experiment_fpath / 'results' / (experiment_name + '_barcodes_max_array_dict_fov_' + str(fov) + '.pkl')
# pickle.dump(all_max,open(fpath,'wb'))
else:
barcoded_df.loc[:,round_intensity_labels] = np.nan
return barcoded_df
def identify_flipped_bits(codebook: pd.DataFrame, gene: str,
raw_barcode: ByteString)-> Tuple[ByteString, ByteString]:
"""Utility function used to identify the position of the bits that are
flipped after the nearest neighbors and the definition of the
acceptable hamming distance for a single dot.
Args:
codebook (pd.DataFrame): Codebook used for the decoding
gene (str): Name of the gene identified
raw_barcode (ByteString): identifide barcode from the images
Returns:
Tuple[ByteString, ByteString]: (flipped_position, flipping_direction)
"""
gene_barcode_str =codebook.loc[codebook.Gene == gene, 'Code'].values[0]
gene_barcode = np.frombuffer(gene_barcode_str, np.int8)
raw_barcode = np.frombuffer(raw_barcode, np.int8)
flipped_positions = np.where(raw_barcode != gene_barcode)[0].astype(np.int8)
flipping_directions = (gene_barcode[flipped_positions] - raw_barcode[flipped_positions]).astype(np.int8)
# flipped_positions = flipped_positions.tobytes()
# flipping_directions = flipping_directions.tobytes()
return flipped_positions,flipping_directions
def define_flip_direction(codebook_dict: dict,experiment_fpath: str,
output_df: pd.DataFrame):
"""Function used to determinethe the position of the bits that are
flipped after the nearest neighbors and the definition of the
acceptable hamming distance for fov.
Args:
codebook (dict): Codebooks used for the decoding
experiment_fpath (str): Path to the folder of the experiment to process
output_df (pd.DataFrame): Dataframe with the decoded results for
the specific fov.
"""
if output_df.shape[0] > 1:
correct_hamming_distance = 0
selected_hamming_distance = 3 / output_df.iloc[0].barcode_length
experiment_fpath = Path(experiment_fpath)
experiment_name = experiment_fpath.stem
channels = codebook_dict.keys()
all_evaluated = []
for channel in channels:
codebook = codebook_dict[channel]
fov = output_df.fov_num.values[0]
trimmed_df = output_df.loc[(output_df.dot_id == output_df.barcode_reference_dot_id) &
(output_df.channel == channel) &
(output_df['hamming_distance'] > correct_hamming_distance) &
(output_df['hamming_distance'] < selected_hamming_distance),
['barcode_reference_dot_id', 'decoded_genes', 'raw_barcodes','hamming_distance']]
trimmed_df = trimmed_df.dropna(subset=['decoded_genes'])
trimmed_df.loc[:,('flip_and_direction')] = trimmed_df.apply(lambda x: identify_flipped_bits(codebook,x.decoded_genes,x.raw_barcodes),axis=1)
trimmed_df['flip_position'] = trimmed_df['flip_and_direction'].apply(lambda x: x[0])
trimmed_df['flip_direction'] = trimmed_df['flip_and_direction'].apply(lambda x: x[1])
trimmed_df.drop(columns=['flip_and_direction'],inplace=True)
all_evaluated.append(trimmed_df)
all_evaluated = pd.concat(all_evaluated,axis=0,ignore_index=True,inplace=True)
fpath = experiment_fpath / 'results' / (experiment_name + '_' + channel + '_df_flip_direction_fov' + str(fov) + '.parquet')
all_evaluated.to_parquet(fpath)
# return trimmed_df
def chunk_dfs(dataframes_list: list, chunk_size: int):
"""
Functions modified from
https://stackoverflow.com/questions/45217120/how-to-efficiently-join-merge-concatenate-large-data-frame-in-pandas
yields n dataframes at a time where n == chunksize
"""
dfs = []
for f in dataframes_list:
dfs.append(f)
if len(dfs) == chunk_size:
yield dfs
dfs = []
if dfs:
yield dfs
def merge_with_concat(dfs: list)->pd.DataFrame:
"""Utility function used to merge dataframes
Args:
dsf (list): List with the dataframe to merge
Returns:
pd.DataFrame: Merged dataframe
"""
# dfs = (df.set_index(col, drop=True) for df in dfs)
merged = pd.concat(dfs, axis=0, join='outer', copy=False)
return merged
"""
Class used to extract the barcodes from the registered
counts using nearest neighbour
Parameters:
-----------
counts: pandas.DataFrame
pandas file with the fov counts after
registration
analysis_parameters: dict
parameters for data processing
codebook_df: pandas.DataFrame
pandas file with the codebook used to
deconvolve the barcode
NB: if there is a problem with the registration the barcode assigned
will be 0*barcode_length
"""
def extract_barcodes_NN_fast_multicolor(registered_counts_df: pd.DataFrame, analysis_parameters: Dict,
codebook_df: pd.DataFrame, metadata:dict)-> Tuple[pd.DataFrame,pd.DataFrame]:
"""Function used to extract the barcodes from the registered
counts using nearest neighbour. if there is a problem with the registration the barcode assigned
will be 0*barcode_length
Args:
registered_counts_df (pd.Dataframe): Fov counts after registration
analysis_parameters (Dict): Parameters for data processing
codebook_df (pd.DataFrame): codebook used to deconvolve the barcode
Returns:
Tuple[pd.DataFrame,pd.DataFrame]: (barcoded_round, all_decoded_dots_df)
"""
logger = selected_logger()
barcodes_extraction_resolution = analysis_parameters['BarcodesExtractionResolution']
RegistrationMinMatchingBeads = analysis_parameters['RegistrationMinMatchingBeads']
barcode_length = metadata['barcode_length']
registration_errors = Registration_errors()
stitching_channel = metadata['stitching_channel']
registered_counts_df.dropna(subset=['dot_id'],inplace=True)
# Starting level for selection of dots
dropping_counts = registered_counts_df.copy(deep=True)
all_decoded_dots_list = []
barcoded_round = []
if registered_counts_df['r_px_registered'].isnull().values.any():
all_decoded_dots_df = pd.DataFrame(columns = registered_counts_df.columns)
all_decoded_dots_df['decoded_genes'] = np.nan
all_decoded_dots_df['hamming_distance'] = np.nan
all_decoded_dots_df['number_positive_bits'] = np.nan
all_decoded_dots_df['barcode_reference_dot_id'] = np.nan
all_decoded_dots_df['raw_barcodes'] = np.nan
all_decoded_dots_df['barcodes_extraction_resolution'] = barcodes_extraction_resolution
# Save barcoded_round and all_decoded_dots_df
return registered_counts_df, all_decoded_dots_df
else:
for ref_round_number in np.arange(1,barcode_length+1):
#ref_round_number = 1
reference_round_df = dropping_counts.loc[dropping_counts.round_num == ref_round_number,:]
# Step one (all dots not in round 1)
compare_df = dropping_counts.loc[dropping_counts.round_num!=ref_round_number,:]
if (not reference_round_df.empty):
if not compare_df.empty:
nn = NearestNeighbors(n_neighbors=1, metric="euclidean")
nn.fit(reference_round_df[['r_px_registered','c_px_registered']])
dists, indices = nn.kneighbors(compare_df[['r_px_registered','c_px_registered']], return_distance=True)
# select only the nn that are below barcodes_extraction_resolution distance
idx_distances_below_resolution = np.where(dists <= barcodes_extraction_resolution)[0]
comp_idx = idx_distances_below_resolution
ref_idx = indices[comp_idx].flatten()
# Subset the dataframe according to the selected points
# The reference selected will have repeated points
comp_selected_df = compare_df.iloc[comp_idx]
ref_selected_df = reference_round_df.iloc[ref_idx]
# The size of ref_selected_df w/o duplicates may be smaller of reference_round_df if
# some of the dots in reference_round_df have no neighbours
# Test approach where we get rid of the single dots
comp_selected_df.loc[:,'barcode_reference_dot_id'] = ref_selected_df['dot_id'].values
ref_selected_df_no_duplicates = ref_selected_df.drop_duplicates()
ref_selected_df_no_duplicates.loc[:,'barcode_reference_dot_id'] = ref_selected_df_no_duplicates['dot_id'].values
# Collect singletons
# Remeber that this method works only because there are no duplicates inside the dataframes
# https://stackoverflow.com/questions/48647534/python-pandas-find-difference-between-two-data-frames
if reference_round_df.shape[0] > ref_selected_df_no_duplicates.shape[0]:
singletons_df = pd.concat([reference_round_df,ref_selected_df_no_duplicates]).drop_duplicates(keep=False)
singletons_df.loc[:,'barcode_reference_dot_id'] = singletons_df['dot_id'].values
barcoded_round = pd.concat([comp_selected_df, ref_selected_df_no_duplicates,singletons_df], axis=0,ignore_index=False)
else:
barcoded_round = pd.concat([comp_selected_df, ref_selected_df_no_duplicates], axis=0,ignore_index=False)
# barcoded_round = pd.concat([comp_selected_df, ref_selected_df_no_duplicates,singletons_df], axis=0,ignore_index=False)
barcoded_round_grouped = barcoded_round.groupby('barcode_reference_dot_id')
compare_df = compare_df.drop(comp_selected_df.index)
dropping_counts = compare_df
else:
# Collecting singleton of last bit
reference_round_df.loc[:,'barcode_reference_dot_id'] = reference_round_df['dot_id'].values
barcoded_round_grouped = reference_round_df.groupby('barcode_reference_dot_id')
ref_selected_df_no_duplicates = reference_round_df
for brdi, grp in barcoded_round_grouped:
barcode = np.zeros([barcode_length],dtype=np.int8)
barcode[grp.round_num.values.astype(np.int8)-1] = 1
#hamming_dist, index_gene = nn_sklearn.kneighbors(barcode.reshape(1, -1), return_distance=True)
#gene= codebook_df.loc[index_gene.reshape(index_gene.shape[0]),'Gene'].tolist()
barcode = barcode.tostring()
if len(ref_selected_df_no_duplicates) != 0:
ref_selected_df_no_duplicates.loc[ref_selected_df_no_duplicates.barcode_reference_dot_id == brdi,'raw_barcodes'] = barcode
#ref_selected_df_no_duplicates.loc[ref_selected_df_no_duplicates.barcode_reference_dot_id == brdi,'decoded_gene_name'] = gene
#ref_selected_df_no_duplicates.loc[ref_selected_df_no_duplicates.barcode_reference_dot_id == brdi,'hamming_distance'] = hamming_dist.flatten()[0]
#fish_counts.loc[grp.index,'barcode_reference_dot_id'] = brdi
#fish_counts.loc[grp.index,'raw_barcodes'] = barcode
#dists, index = nn_sklearn.kneighbors(all_barcodes, return_distance=True)
all_decoded_dots_list.append(ref_selected_df_no_duplicates)
if all_decoded_dots_list:
all_decoded_dots_df = pd.concat(all_decoded_dots_list,ignore_index=False)
codebook_df = convert_str_codebook(codebook_df,'Code')
codebook_array = make_codebook_array(codebook_df,'Code')
nn_sklearn = NearestNeighbors(n_neighbors=1, metric="hamming")
nn_sklearn.fit(codebook_array)
all_barcodes = np.vstack(all_decoded_dots_df.raw_barcodes.map(lambda x: np.frombuffer(x, np.int8)).values)
dists_arr, index_arr = nn_sklearn.kneighbors(all_barcodes, return_distance=True)
genes=codebook_df.loc[index_arr.reshape(index_arr.shape[0]),'Gene'].tolist()
all_decoded_dots_df.loc[:,'decoded_genes'] = genes
all_decoded_dots_df.loc[:,'hamming_distance'] = dists_arr
all_decoded_dots_df.loc[:,'number_positive_bits'] = all_barcodes.sum(axis=1)
all_decoded_dots_df['barcodes_extraction_resolution'] = barcodes_extraction_resolution
else:
all_decoded_dots_df = pd.DataFrame(columns = registered_counts_df.columns)
all_decoded_dots_df['decoded_genes'] = np.nan
all_decoded_dots_df['hamming_distance'] = np.nan
all_decoded_dots_df['number_positive_bits'] = np.nan
all_decoded_dots_df['barcode_reference_dot_id'] = np.nan
all_decoded_dots_df['raw_barcodes'] = np.nan
all_decoded_dots_df['barcodes_extraction_resolution'] = barcodes_extraction_resolution
# Save barcoded_round and all_decoded_dots_df
return barcoded_round, all_decoded_dots_df
# TODO Remove all the functions below
######## -------------------------------------------------------------------
class extract_barcodes_NN():
"""
Class used to extract the barcodes from the registered
counts using nearest neighbour
Parameters:
-----------
counts: pandas.DataFrame
pandas file with the fov counts after
registration
analysis_parameters: dict
parameters for data processing
experiment_config: Dict
dictionary with the experimental data
codebook_df: pandas.DataFrame
pandas file with the codebook used to
deconvolve the barcode
NB: if there is a problem with the registration the barcode assigned
will be 0*barcode_length
"""
def __init__(self, counts, analysis_parameters:Dict,experiment_config:Dict,codebook_df,file_tags,status:str):
self.barcodes_extraction_resolution = analysis_parameters['BarcodesExtractionResolution']
self.RegistrationMinMatchingBeads = analysis_parameters['RegistrationMinMatchingBeads']
self.barcode_length = experiment_config['Barcode_length']
self.counts = counts
self.logger = selected_logger()
self.codebook_df = codebook_df
self.file_tags = file_tags
self.status = status
self.registration_errors = Registration_errors()
@staticmethod
def barcode_nn(counts_df, ref_round_number, barcodes_extraction_resolution):
column_names = list(counts_df.columns.values)
column_names = column_names.append('barcode_reference_dot_id')
barcoded_df = pd.DataFrame(columns=column_names)
reference_array = counts_df.loc[counts_df.round_num == ref_round_number, ['r_px_registered','c_px_registered']].to_numpy()
reference_round_df = counts_df.loc[counts_df.round_num == ref_round_number,:].reset_index(drop=True)
# Step one (all dots not in round 1)
coords_compare = counts_df.loc[counts_df.round_num != ref_round_number, ['r_px_registered','c_px_registered']].to_numpy()
compare_df = counts_df.loc[counts_df.round_num != ref_round_number,:].reset_index(drop=True)
if (reference_array.shape[0] >0) and (coords_compare.shape[0] >0):
# initialize network
nn = NearestNeighbors(n_neighbors=1, metric="euclidean")
nn.fit(reference_array)
# Get the nn
dists, indices = nn.kneighbors(coords_compare, return_distance=True)
# select only the nn that are below barcodes_extraction_resolution distance
idx_selected_coords_compare = np.where(dists <= barcodes_extraction_resolution)[0]
compare_selected_df = compare_df.loc[idx_selected_coords_compare,:]
compare_selected_df['barcode_reference_dot_id'] = np.nan
# ref_idx = indices[idx_selected_coords_compare]
# compare_selected_df.loc[compare_selected_df.index.isin(idx_selected_coords_compare),'barcode_reference_dot_id'] = reference_round_df.loc[ref_idx,'dot_id'].values[0]
for idx in idx_selected_coords_compare:
ref_idx = indices[idx]
compare_selected_df.loc[idx,'barcode_reference_dot_id'] = reference_round_df.loc[ref_idx,'dot_id'].values[0]
reference_round_df['barcode_reference_dot_id'] = reference_round_df.dot_id
barcoded_df = barcoded_df.append([compare_selected_df, reference_round_df], ignore_index=True)
compare_df = compare_df.drop(compare_selected_df.index)
compare_df = compare_df.reset_index(drop=True)
return compare_df, barcoded_df
@staticmethod
def convert_str_codebook(codebook_df,column_name):
codebook_df[column_name] = codebook_df[column_name].map(lambda x: | np.frombuffer(x, np.int8) | numpy.frombuffer |
from __future__ import print_function, division
import unittest, numpy as np
from pyscf import gto, tddft, scf
from pyscf.nao import bse_iter
from pyscf.nao import polariz_freq_osc_strength
from pyscf.data.nist import HARTREE2EV
class KnowValues(unittest.TestCase):
def test_0147_bse_h2o_rks_pz(self):
""" Interacting case """
mol=gto.M(verbose=0,atom='O 0 0 0;H 0 0.489 1.074;H 0 0.489 -1.074',basis='cc-pvdz',)
gto_hf = scf.RKS(mol)
gto_hf.kernel()
gto_td = tddft.TDDFT(gto_hf)
gto_td.nstates = 95
gto_td.kernel()
omegas = | np.arange(0.0, 2.0, 0.01) | numpy.arange |
#!/usr/bin/python
########################################################################################################################
#
# Copyright (c) 2014, Regents of the University of California
# All rights reserved.
#
# 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 following
# disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the
# following disclaimer in the documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
########################################################################################################################
"""ADC library
"""
import laygo
import numpy as np
import os
#import logging;logging.basicConfig(level=logging.DEBUG)
def generate_tap(laygen, objectname_pfix, placement_grid, routing_grid_m1m2_thick, devname_tap_boundary, devname_tap_body,
m=1, origin=np.array([0,0]), transform='R0'):
"""generate a tap primitive"""
pg = placement_grid
rg_m1m2_thick = routing_grid_m1m2_thick
# placement
itapbl0 = laygen.place("I" + objectname_pfix + 'BL0', devname_tap_boundary, pg, xy=origin, transform=transform)
itap0 = laygen.relplace(name = "I" + objectname_pfix + '0', templatename = devname_tap_body, gridname = pg, refinstname = itapbl0.name, shape=np.array([m, 1]), transform=transform)
itapbr0 = laygen.relplace(name = "I" + objectname_pfix + 'BR0', templatename = devname_tap_boundary, gridname = pg, refinstname = itap0.name, transform=transform)
#power route
laygen.route(None, laygen.layers['metal'][2], xy0=np.array([0, 0]), xy1=np.array([0, 0]), gridname0=rg_m1m2_thick,
refinstname0=itap0.name, refpinname0='TAP0', refinstindex0=np.array([0, 0]),
refinstname1=itap0.name, refpinname1='TAP1', refinstindex1=np.array([m-1, 0])
)
for i in range(1-1, int(m/2)+0):
laygen.via(None, np.array([0, 0]), refinstname=itap0.name, refpinname='TAP0', refinstindex=np.array([2*i, 0]),
gridname=rg_m1m2_thick)
return [itapbl0, itap0, itapbr0]
def generate_boundary(laygen, objectname_pfix, placement_grid,
devname_bottom, devname_top, devname_left, devname_right,
shape_bottom=None, shape_top=None, shape_left=None, shape_right=None,
transform_bottom=None, transform_top=None, transform_left=None, transform_right=None,
origin=np.array([0, 0])):
#generate a boundary structure to resolve boundary design rules
pg = placement_grid
#parameters
if shape_bottom == None:
shape_bottom = [np.array([1, 1]) for d in devname_bottom]
if shape_top == None:
shape_top = [np.array([1, 1]) for d in devname_top]
if shape_left == None:
shape_left = [np.array([1, 1]) for d in devname_left]
if shape_right == None:
shape_right = [np.array([1, 1]) for d in devname_right]
if transform_bottom == None:
transform_bottom = ['R0' for d in devname_bottom]
if transform_top == None:
transform_top = ['R0' for d in devname_top]
if transform_left == None:
transform_left = ['R0' for d in devname_left]
if transform_right == None:
transform_right = ['R0' for d in devname_right]
#bottom
dev_bottom=[]
dev_bottom.append(laygen.place("I" + objectname_pfix + 'BNDBTM0', devname_bottom[0], pg, xy=origin,
shape=shape_bottom[0], transform=transform_bottom[0]))
for i, d in enumerate(devname_bottom[1:]):
dev_bottom.append(laygen.relplace(name = "I" + objectname_pfix + 'BNDBTM'+str(i+1), templatename = d, gridname = pg, refinstname = dev_bottom[-1].name,
shape=shape_bottom[i+1], transform=transform_bottom[i+1]))
dev_left=[]
dev_left.append(laygen.relplace(name = "I" + objectname_pfix + 'BNDLFT0', templatename = devname_left[0], gridname = pg, refinstname = dev_bottom[0].name, direction='top',
shape=shape_left[0], transform=transform_left[0]))
for i, d in enumerate(devname_left[1:]):
dev_left.append(laygen.relplace(name = "I" + objectname_pfix + 'BNDLFT'+str(i+1), templatename = d, gridname = pg, refinstname = dev_left[-1].name, direction='top',
shape=shape_left[i+1], transform=transform_left[i+1]))
dev_right=[]
dev_right.append(laygen.relplace(name = "I" + objectname_pfix + 'BNDRHT0', templatename = devname_right[0], gridname = pg, refinstname = dev_bottom[-1].name, direction='top',
shape=shape_right[0], transform=transform_right[0]))
for i, d in enumerate(devname_right[1:]):
dev_right.append(laygen.relplace(name = "I" + objectname_pfix + 'BNDRHT'+str(i+1), templatename = d, gridname = pg, refinstname = dev_right[-1].name, direction='top',
shape=shape_right[i+1], transform=transform_right[i+1]))
dev_top=[]
dev_top.append(laygen.relplace(name = "I" + objectname_pfix + 'BNDTOP0', templatename = devname_top[0], gridname = pg, refinstname = dev_left[-1].name, direction='top',
shape=shape_top[0], transform=transform_top[0]))
for i, d in enumerate(devname_top[1:]):
dev_top.append(laygen.relplace(name = "I" + objectname_pfix + 'BNDTOP'+str(i+1), templatename = d, gridname = pg, refinstname = dev_top[-1].name,
shape=shape_top[i+1], transform=transform_top[i+1]))
#dev_right=[]
return [dev_bottom, dev_top, dev_left, dev_right]
def generate_clkdis_cell(laygen, objectname_pfix, logictemp_lib, working_lib, grid, origin=np.array([0, 0]), num_bits=5, phy_width=20.16, num_capsw_dmy=10, num_dff_dmy=90,
len_cal=30, len_capsw=10, m_clki=2, y1_clki=5, y2_clki=10, m_clko=2, y1_clko=25, y2_clko=10, num_vss_vleft=2, num_vdd_vleft=2, num_vss_vright=3, num_vdd_vright=3,
num_vss_h=4, num_vdd_h=4, m_tgate=4, m_dff=2, m_inv1=2, m_inv2=4):
"""generate cap driver """
pg = grid['pg']
rg_m1m2 = grid['rg_m1m2']
rg_m1m2_thick = grid['rg_m1m2_thick']
rg_m2m3 = grid['rg_m2m3']
rg_m2m3_thick = grid['rg_m2m3_thick']
rg_m2m3_thick2 = grid['rg_m2m3_thick2']
rg_m3m4 = grid['rg_m3m4']
rg_m3m4_dense = grid['rg_m3m4_dense']
rg_m3m4_thick2 = grid['rg_m3m4_thick2']
rg_m4m5 = grid['rg_m4m5']
rg_m5m6 = grid['rg_m5m6']
rg_m6m7 = grid['rg_m6m7']
'''
phy_width = 20.16 #in um
num_capsw_dmy = 10 #capsw left dummy number
num_dff_dmy = 90 #dff left dummy number
len_cal = 30 #calibration input length
len_capsw = 10 #cap control output length
#clock input
m_clki = 2
y1_clki = 5
y2_clki = 10
#clock output
m_clko = 2
y1_clko = 25
y2_clko = 10
#virtically vss and vdd metals
num_vss_vleft = 2
num_vdd_vleft = 2
num_vss_vright = 3
num_vdd_vright = 3
'''
m_in=3
m_out=3
#Get width for pg grid
width =laygen.grids.get_absgrid_coord_x(gridname=pg, x=phy_width)
#Half width, using for clock put output at the center of the cell
half_width=width/2
#####Place Boundary
#Calculate size of boundary cell
bnd_left_size_x = laygen.get_xy(obj=laygen.get_template(name='nmos4_fast_left', libname=tech + '_microtemplates_dense'), gridname=pg)[0]
bnd_right_size_x = laygen.get_xy(obj=laygen.get_template(name='nmos4_fast_right', libname=tech + '_microtemplates_dense'), gridname=pg)[0]
tap4_size_x = laygen.get_xy(obj=laygen.get_template(name='ptap_fast_space_nf4', libname=tech + '_microtemplates_dense'), gridname=pg)[0]
#Caluclate number of top and bottom cells
bnd_m = width - bnd_left_size_x - bnd_right_size_x ##This is all the numbe of the cells, using a lot in code!!
#print(bnd_m)
[bnd_bottom, bnd_top, bnd_left, bnd_right]=generate_boundary(laygen, objectname_pfix='BND0',
placement_grid=pg,
devname_bottom = ['boundary_bottomleft', 'boundary_bottom', 'boundary_bottomright'],
shape_bottom = [np.array([1, 1]), np.array([bnd_m, 1]), np.array([1, 1])],
devname_top = ['boundary_topleft', 'boundary_top', 'boundary_topright'],
shape_top = [np.array([1, 1]), np.array([bnd_m, 1]), np.array([1, 1])],
devname_left = ['ptap_fast_left', 'nmos4_fast_left', 'pmos4_fast_left',
'ntap_fast_left', 'pmos4_fast_left', 'nmos4_fast_left',
'ptap_fast_left', ],
transform_left=['R0', 'R0', 'R0', 'MX', 'MX', 'MX', 'MX', ],
devname_right=['ptap_fast_right', 'nmos4_fast_right', 'pmos4_fast_right',
'ntap_fast_right', 'pmos4_fast_right', 'nmos4_fast_right',
'ptap_fast_right',],
transform_right = ['R0', 'R0', 'R0', 'MX', 'MX', 'MX', 'MX',],
origin=np.array([0, 0]))
#####Placing all the rows
##Bottom ptap row
ptap0_0 = laygen.relplace(name='I'+objectname_pfix+'PTAP0_0', templatename='ptap_fast_space_nf4',
gridname=pg, refinstname=bnd_left[0].name, template_libname=tech+'_microtemplates_dense')
ptap0_1= laygen.relplace(name='I'+objectname_pfix+'PTAP0_1', templatename='ptap_fast_center_nf1',
gridname=pg, refinstname=ptap0_0.name, template_libname=tech+'_microtemplates_dense',
shape=np.array([bnd_m-2*tap4_size_x, 1]))
ptap0_2 = laygen.relplace(name='I'+objectname_pfix+'PTAP0_2', templatename='ptap_fast_space_nf4',
gridname=pg, refinstname=ptap0_1.name, template_libname=tech+'_microtemplates_dense')
##CAP switch row
#Calculate coodinate of sw_dmy0
bnd_left_1_y=laygen.get_xy(obj =bnd_left[1], gridname=pg)[1] #y coodinate
sw_dmy_xy=np.array([bnd_left_size_x, bnd_left_1_y]) #xy coodinate
#Place sw_dmy0 and capsw0
sw_dmy0= laygen.place(name='I'+objectname_pfix+'SWDM0', templatename='space_1x', gridname=pg, xy=sw_dmy_xy,
template_libname=logictemp_lib, shape=np.array([num_capsw_dmy, 1]))
capsw0=laygen.relplace(name='I'+objectname_pfix+'SW0', templatename='cap_sw_array', gridname=pg,
refinstname=sw_dmy0.name, template_libname='clk_dis_generated')
#Calculate number of sw_dmy1
capsw0_size_x = laygen.get_xy(obj=laygen.get_template(name='cap_sw_array', libname='clk_dis_generated'), gridname=pg)[0]
sw_dmy1_m = bnd_m-num_capsw_dmy-capsw0_size_x
#Place sw_dmy1
sw_dmy1= laygen.relplace(name='I'+objectname_pfix+'SWDM1', templatename='space_1x', gridname=pg,
refinstname=capsw0.name, template_libname=logictemp_lib, shape=np.array([sw_dmy1_m, 1]))
##Mitddle ntap row
ntap0_0= laygen.relplace(name='I'+objectname_pfix+'NTAP0_0', templatename='ntap_fast_space_nf4', gridname=pg,
refinstname=bnd_left[3].name, template_libname=tech+'_microtemplates_dense', shape=np.array([1, 1]), transform='MX')
ntap0_1= laygen.relplace(name='I'+objectname_pfix+'NTAP0_1', templatename='ntap_fast_center_nf1', gridname=pg,
refinstname=ntap0_0.name, template_libname=tech+'_microtemplates_dense', shape=np.array([bnd_m-2*tap4_size_x, 1]), transform='MX')
ntap0_2= laygen.relplace(name='I'+objectname_pfix+'NTAP0_2', templatename='ntap_fast_space_nf4', gridname=pg,
refinstname=ntap0_1.name, template_libname=tech+'_microtemplates_dense', shape=np.array([1, 1]), transform='MX')
##DFF row
#Calculate coodinate of dff_dmy0
bnd_left_5_y = laygen.get_xy(obj =bnd_left[5], gridname=pg)[1] #y coodinate
dff_dmy0_xy = np.array([bnd_left_size_x, bnd_left_5_y]) #xy coodinate
#Place dff_dmy0, tgated0, dff0, inv0, and inv1
dff_dmy0 = laygen.place(name='I'+objectname_pfix+'DFFDM0', templatename='space_1x', gridname=pg, xy=dff_dmy0_xy,
template_libname=logictemp_lib, shape=np.array([num_dff_dmy, 1]), transform='MX')
tgated0=laygen.relplace(name='I'+objectname_pfix+'TGD0', templatename='tgate_dn_'+str(m_tgate)+'x', gridname=pg,
refinstname=dff_dmy0.name, template_libname=logictemp_lib, transform='R180')
dff0=laygen.relplace(name='I'+objectname_pfix+'DFF0', templatename='dff_strsth_ckb_'+str(m_dff)+'x', gridname=pg,
refinstname=tgated0.name, template_libname=tech+'_logic_templates', transform='MX')
inv0=laygen.relplace(name='I'+objectname_pfix+'INV0', templatename='inv_'+str(m_inv1)+'x', gridname=pg,
refinstname=dff0.name, template_libname=tech+'_logic_templates', transform='MX')
inv1=laygen.relplace(name='I'+objectname_pfix+'INV1', templatename='inv_'+str(m_inv2)+'x', gridname=pg,
refinstname=inv0.name, template_libname=tech+'_logic_templates', transform='MX')
#Calculate number of dff_dmy1
inv1_x = laygen.get_xy(obj =inv1, gridname=pg)[0]
m_inv1_x = laygen.get_xy(obj=laygen.get_template(name='inv_' + str(m_inv2) + 'x', libname=tech + '_logic_templates'), gridname=pg)[0]
bnd_right_5_x = laygen.get_xy(obj =bnd_right[5], gridname=pg)[0] #y coodinate
dff_dmy1_m = bnd_right_5_x-(inv1_x+m_inv1_x)
##Calculate coodinate of dff_dmy1
dff_dmy1_x = inv1_x+m_inv1_x
dff_dmy1_xy = np.array([dff_dmy1_x, bnd_left_5_y])
dff_dmy1= laygen.place(name='I'+objectname_pfix+'DFFDM1', templatename='space_1x', gridname=pg, xy=dff_dmy1_xy,
template_libname=logictemp_lib, shape=np.array([dff_dmy1_m, 1]), transform='MX')
##Top ptap row
ptap1_0 = laygen.relplace(name='I'+objectname_pfix+'PTAP1_0', templatename='ptap_fast_space_nf4',
gridname=pg, refinstname=bnd_left[6].name, template_libname=tech+'_microtemplates_dense',
transform='MX')
ptap1_1= laygen.relplace(name='I'+objectname_pfix+'PTAP1_1', templatename='ptap_fast_center_nf1',
gridname=pg, refinstname=ptap1_0.name, template_libname=tech+'_microtemplates_dense',
shape=np.array([bnd_m-2*tap4_size_x, 1]), transform='MX')
ptap1_2 = laygen.relplace(name='I'+objectname_pfix+'PTAP1_2', templatename='ptap_fast_space_nf4',
gridname=pg, refinstname=ptap1_1.name, template_libname=tech+'_microtemplates_dense',
transform='MX')
#####Route and Pin
#Connection between DFFs, tage_up and inverts
#route from dff_O to inv0_I
dff0_O_xy = laygen.get_inst_pin_xy(dff0.name, 'O', rg_m3m4)[0]
dff0_O_y = dff0_O_xy[1]
inv0_I_xy = laygen.get_inst_pin_xy(inv0.name, 'I', rg_m3m4)[0]
laygen.route_vhv(laygen.layers['metal'][3], laygen.layers['metal'][4], dff0_O_xy, inv0_I_xy, dff0_O_y, rg_m3m4)
#route from inv0_O to inv1_I
inv0_O_xy = laygen.get_inst_pin_xy(inv0.name, 'O', rg_m3m4)[0]
inv1_I_xy = laygen.get_inst_pin_xy(inv1.name, 'I', rg_m3m4)[0]
laygen.route_vhv(laygen.layers['metal'][3], laygen.layers['metal'][4], inv0_O_xy, inv1_I_xy, dff0_O_y-1, rg_m3m4)
#route from inv1_O to tgated_EN
inv1_O_xy = laygen.get_inst_pin_xy(inv1.name, 'O', rg_m3m4)[0]
tgated_EN_xy = laygen.get_inst_pin_xy(tgated0.name, 'EN', rg_m3m4)[0]
laygen.route_vhv(laygen.layers['metal'][3], laygen.layers['metal'][4], inv1_O_xy, tgated_EN_xy, dff0_O_y-5, rg_m3m4)
#route from inv0_O to tgated_ENB
inv0_O_xy = laygen.get_inst_pin_xy(inv0.name, 'O', rg_m3m4)[0]
tgated_ENB_xy = laygen.get_inst_pin_xy(tgated0.name, 'ENB', rg_m3m4)[0]
laygen.route_vhv(laygen.layers['metal'][3], laygen.layers['metal'][4], inv0_O_xy, tgated_ENB_xy, dff0_O_y-6, rg_m3m4)
#route from dff0_CLKB to tgated_IN
dff0_CLKB_xy = laygen.get_inst_pin_xy(dff0.name, 'CLKB', rg_m3m4)[0]
tgated_I_xy = laygen.get_inst_pin_xy(tgated0.name, 'I_' + str(m_in - 1), rg_m3m4)[0]
laygen.route_vhv(laygen.layers['metal'][3], laygen.layers['metal'][4], dff0_CLKB_xy, tgated_I_xy, dff0_O_y-1, rg_m3m4)
for i in range(m_in):
clkiv=laygen.via(None, np.array([tgated_I_xy[0]+2*i, dff0_O_y-1]), gridname=rg_m3m4)
#I/O and Pin
#I Pin
i_xy=laygen.get_inst_pin_xy(dff0.name, 'I', rg_m3m4)
ipp=laygen.route(None, laygen.layers['metal'][3], xy0=np.array([0,0]), xy1=np.array([0,1]), gridname0=rg_m3m4,
refinstname0=dff0.name, refpinname0='I', refinstindex0=np.array([0, 0]),
refinstname1=dff0.name, refpinname1='I', refinstindex1=np.array([0, 0])
)
laygen.boundary_pin_from_rect(ipp, gridname=rg_m3m4, name='I', layer=laygen.layers['pin'][3], size=1,
direction='top')
#O Pin
o_xy=laygen.get_inst_pin_xy(dff0.name, 'O', rg_m3m4)
opp=laygen.route(None, laygen.layers['metal'][3], xy0=np.array([0,0]), xy1=np.array([0,1]), gridname0=rg_m3m4,
refinstname0=inv1.name, refpinname0='O', refinstindex0=np.array([0, 0]),
refinstname1=inv1.name, refpinname1='O', refinstindex1=np.array([0, 0])
)
laygen.boundary_pin_from_rect(opp, gridname=rg_m3m4, name='O', layer=laygen.layers['pin'][3], size=1,
direction='top')
#CAL signal and pin
for i in range(num_bits):
capswp0=laygen.route(None, laygen.layers['metal'][3], xy0=np.array([0,0]), xy1=np.array([0, len_cal]), gridname0=rg_m3m4,
refinstname0=capsw0.name, refpinname0='EN<'+str(i)+'>', refinstindex0=np.array([0, 0]),
refinstname1=capsw0.name, refpinname1='EN<'+str(i)+'>', refinstindex1=np.array([0, 0])
)
laygen.boundary_pin_from_rect(capswp0, gridname=rg_m3m4, name='CAL<' + str(i) + '>',
layer=laygen.layers['pin'][3], size=1, direction='top')
#CAPSW signal and pin
for i in range(num_bits):
ctrlp0=laygen.route(None, laygen.layers['metal'][3], xy0=np.array([0,0]), xy1=np.array([0,-1*len_capsw]), gridname0=rg_m3m4,
refinstname0=capsw0.name, refpinname0='VO<'+str(i)+'>', refinstindex0=np.array([0, 0]),
refinstname1=capsw0.name, refpinname1='VO<'+str(i)+'>', refinstindex1=np.array([0, 0])
)
laygen.boundary_pin_from_rect(ctrlp0, gridname=rg_m3m4, name='CAPSW<' + str(i) + '>',
layer=laygen.layers['pin'][3], size=1, direction='bottom')
clki_x = laygen.get_inst_pin_xy(tgated0.name, 'I_0', rg_m3m4)[0]
clkp_x = laygen.grids.get_absgrid_coord_x(gridname=rg_m4m5, x=phy_width/2)
##Create muti tracks to clki and create pin
for i in range(m_clki):
for j in range(m_in):
clkiv=laygen.via(None, np.array([clki_x[0]-2*j, clki_x[1]+y1_clki+2*i]), gridname=rg_m3m4)
laygen.route(None, laygen.layers['metal'][3], xy0=np.array([clki_x[0]-2*j, clki_x[1]]), xy1=np.array([clki_x[0]-2*j, clki_x[1]+y1_clki+2*(m_clki-1)]), gridname0=rg_m3m4)
if i==0 and j==m_in-1:
v_xy=laygen.get_xy(obj = clkiv, gridname = rg_m4m5)
clki_d=clkp_x-v_xy[0]
for j in range(m_clki):
[clkh, clkv]=laygen.route_hv(laygen.layers['metal'][4], laygen.layers['metal'][5], np.array([v_xy[0]-1, v_xy[1]+2*i]),
np.array([v_xy[0]+clki_d+m_clki/2-2*j+1,v_xy[1]+y2_clki]), rg_m4m5)
if (i==0):
laygen.boundary_pin_from_rect(clkv, gridname=rg_m4m5, name='CLKI_' + str(j),
layer=laygen.layers['pin'][5], size=1, direction='top',
netname='CLKI')
#laygen.boundary_pin_from_rect(clkv, gridname=rg_m4m5, pinname='CLKI', layer=laygen.layers['pin'][5], size=1, direction='top')
clko_x = laygen.get_inst_pin_xy(tgated0.name, 'O_0', rg_m3m4)[0]
##Create muti tracks to clko and create pin
for i in range(m_clko):
for j in range(m_out):
clkov=laygen.via(None, np.array([clko_x[0]+2*j, clko_x[1]-y1_clko-2*i]), gridname=rg_m3m4)
laygen.route(None, laygen.layers['metal'][3], xy0=np.array([clko_x[0]+2*j, clko_x[1]]), xy1=np.array([clko_x[0]+2*j, clko_x[1]-y1_clko-2*(m_clko-1)]), gridname0=rg_m3m4)
if i==0 and j==0:
v_xy=laygen.get_xy(obj = clkov, gridname = rg_m4m5)
clko_d=clkp_x-v_xy[0]
for j in range(m_clko):
[clkh, clkv]=laygen.route_hv(laygen.layers['metal'][4], laygen.layers['metal'][5], np.array([v_xy[0]-1, v_xy[1]-2*i]),
np.array([v_xy[0]+clko_d-m_clko/2+2*j,v_xy[1]-y2_clko]), rg_m4m5)
if (i==0):
laygen.boundary_pin_from_rect(clkv, gridname=rg_m4m5, name='CLKO_' + str(j),
layer=laygen.layers['pin'][5], size=1, direction='bottom',
netname='CLKO')
#####VSS and VDD
##Bottom ptap row
#Generate horizental metal
vss0_y = laygen.get_inst_pin_xy(ptap0_1.name, 'TAP0', rg_m1m2_thick)[0][1]
rvss0 = laygen.route(None, laygen.layers['metal'][2], xy0=np.array([0, vss0_y]), xy1=np.array([width, vss0_y]), gridname0=rg_m1m2_thick)
vss0_1_y = laygen.get_inst_pin_xy(sw_dmy0.name, 'VSS', rg_m1m2)[0][1]
rvss0_1 = laygen.route(None, laygen.layers['metal'][2], xy0=np.array([0, vss0_1_y]), xy1=np.array([width, vss0_1_y]), gridname0=rg_m1m2)
#Generate thick viaes
for i in range(0, bnd_m-2, 2):
laygen.via(None, np.array([0, 0]), refinstname=ptap0_1.name, refpinname='TAP0', refinstindex=np.array([i-tap4_size_x+1, 0]), gridname=rg_m1m2_thick)
#Generate left cotacts and metals
for i in range(0, num_capsw_dmy, 2):
laygen.via(None, np.array([0, 3]), refinstname=ptap0_1.name, refpinname='TAP0', refinstindex=np.array([i-tap4_size_x+1, 0]), gridname=rg_m1m2)
laygen.route(None, laygen.layers['metal'][1], xy0= | np.array([0, 0]) | numpy.array |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import pickle
import gzip
np.set_printoptions(threshold=np.inf)
f = open('data/ACML_Movies.csv', 'r')
movie_strngs = f.read()
movie_strngs = movie_strngs.split('\n')
movie_strngs = movie_strngs[1:]
movie_strngs = movie_strngs[:-1]
ratings = []
for strng in movie_strngs:
split_strng = strng.split(',')
rate = np.array([int(d) for d in split_strng])
ratings.append(rate)
ratings = np.array(ratings)
ratings = ratings[:, 1:]
test_ratings = np.copy(ratings[-11:])
ratings = ratings[:-11]
weights = np.random.uniform(-0.3, 0.3, (20,35*5))
learn_rate = 0.01
epochs = 400
def sigmoid(x):
out = np.zeros(x.shape)
for i in range(out.shape[0]):
if x[i] >= 0:
out[i] = 1/(1+np.exp(-x[i]))
else:
out[i] = np.exp(x[i])/(1+ | np.exp(x[i]) | numpy.exp |
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