def combined_sn_gal_arrays_multiprocessing(self, snTemplateLocation,
snTempFileList,
galTemplateLocation,
galTempFileList):
# TODO: Maybe do memory mapping for these arrays
self.images = []
self.labelsIndexes = []
self.filenames = []
self.typeNames = []
if galTemplateLocation is None or galTempFileList is None:
galTempList = [None]
galTemplateLocation = None
snFractions = [1.0]
else:
galTempList = temp_list(galTempFileList)
snFractions = [
0.99, 0.98, 0.95, 0.93, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2,
0.1
]
if isinstance(snTempFileList, dict):
snTempList = list(snTempFileList.keys())
ageIndexesDict = snTempFileList
else:
snTempList = temp_list(snTempFileList)
ageIndexesDict = None
galAndSnTemps = list(itertools.product(galTempList, snTempList))
argsList = []
for gal, sn in galAndSnTemps:
if ageIndexesDict is not None:
ageIdxDict = {k: ageIndexesDict[k] for k in (sn, )}
else:
ageIdxDict = {k: range(0, 1000) for k in (sn, )}
argsList.append((snTemplateLocation, [sn], galTemplateLocation,
[gal], snFractions, ageIdxDict))
pool = mp.Pool()
results = pool.map_async(self.combined_sn_gal_templates_to_arrays,
argsList)
pool.close()
pool.join()
outputs = results.get()
for i, output in enumerate(outputs):
self.collect_results(output)
print('combining results...', i, len(outputs))
self.images = np.array(self.images)
self.labelsIndexes = np.array(self.labelsIndexes)
self.filenames = np.array(self.filenames)
self.typeNames = np.array(self.typeNames)
print("Completed Creating Arrays!")
return self.images, self.labelsIndexes.astype(
np.uint16), self.filenames, self.typeNames
def train_test_split(self):
"""
Split training set before creating arrays.
Maybe should change this to include ages in train/test split instead of just SN files.
"""
snTempFileList = copy.copy(self.snidTempFileList)
fileList = temp_list(snTempFileList)
random.Random(42).shuffle(fileList)
trainSize = int(self.trainFraction * len(fileList))
dirName = os.path.dirname(self.snidTempFileList)
trainListFileName = os.path.join(dirName, 'train_templist.txt')
testListFileName = os.path.join(dirName, 'test_templist.txt')
# Save train set file list
with open(trainListFileName, 'w') as f:
for line in fileList[:trainSize]:
f.write("%s\n" % line)
# Save test set file list
with open(testListFileName, 'w') as f:
for line in fileList[trainSize:]:
f.write("%s\n" % line)
return trainListFileName, testListFileName
def template_spectra_to_list(self, tempFileList, templateDirectory):
tempList = temp_list(tempFileList)
templates = []
for filename in tempList:
spectrum = self.read_template_file(templateDirectory+filename)
templates.append(spectrum)
print(filename)
return templates
def create_sn_and_host_arrays(snTemplateDirectory, snTempFileList,
galTemplateDirectory, galTempFileList,
paramsFile):
snTemplates = {}
galTemplates = {}
snList = temp_list(snTempFileList)
galList = temp_list(galTempFileList)
with open(paramsFile, 'rb') as f:
pars = pickle.load(f)
w0, w1, nw, snTypes, galTypes, minAge, maxAge, ageBinSize = pars['w0'], pars['w1'], pars['nw'], pars['typeList'], \
pars['galTypeList'], pars['minAge'], pars['maxAge'], \
pars['ageBinSize']
ageBinning = AgeBinning(minAge, maxAge, ageBinSize)
ageLabels = ageBinning.age_labels()
# Create dictionary of dictionaries for type and age of SN
for snType in snTypes:
snTemplates[snType] = {}
for ageLabel in ageLabels:
snTemplates[snType][ageLabel] = {}
snTemplates[snType][ageLabel]['snInfo'] = []
snTemplates[snType][ageLabel]['names'] = []
for galType in galTypes:
galTemplates[galType] = {}
galTemplates[galType]['galInfo'] = []
galTemplates[galType]['names'] = []
for snFile in snList:
snBinTemplate = BinTemplate(snTemplateDirectory + snFile, 'sn', w0, w1,
nw)
nAges = snBinTemplate.nCols
ages = snBinTemplate.ages
snType = snBinTemplate.tType
filename = snBinTemplate.filename
for ageIdx in range(nAges):
age = ages[ageIdx]
if minAge < age < maxAge:
ageBin = ageBinning.age_bin(age)
ageLabel = ageLabels[ageBin]
snInfo = snBinTemplate.bin_template(ageIdx)
snTemplates[snType][ageLabel]['snInfo'].append(snInfo)
snTemplates[snType][ageLabel]['names'].append("%s_%s" %
(filename, age))
print("Reading {} {} out of {}".format(snFile, ageIdx, nAges))
for galFile in galList:
galBinTemplate = BinTemplate(galTemplateDirectory + galFile, 'gal', w0,
w1, nw)
galType = galBinTemplate.tType
filename = galBinTemplate.filename
galInfo = galBinTemplate.bin_template()
galTemplates[galType]['galInfo'].append(galInfo)
galTemplates[galType]['names'].append(filename)
print("Reading {}".format(galFile))
# Convert lists in dictionaries to numpy arrays
for snType in snTypes:
for ageLabel in ageLabels:
snTemplates[snType][ageLabel]['snInfo'] = np.array(
snTemplates[snType][ageLabel]['snInfo'])
snTemplates[snType][ageLabel]['names'] = np.array(
snTemplates[snType][ageLabel]['names'])
for galType in galTypes:
galTemplates[galType]['galInfo'] = np.array(
galTemplates[galType]['galInfo'])
galTemplates[galType]['names'] = np.array(
galTemplates[galType]['names'])
return snTemplates, galTemplates
def combined_sn_gal_arrays_multiprocessing(self, snTemplateLocation,
snTempFileList,
galTemplateLocation,
galTempFileList):
if galTemplateLocation is None or galTempFileList is None:
galTempList = [None]
galTemplateLocation = None
snFractions = [1.0]
else:
galTempList = temp_list(galTempFileList)
snFractions = [
0.99, 0.98, 0.95, 0.93, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2,
0.1
]
snTempList = temp_list(snTempFileList) # 514 files
galAndSnTemps = list(itertools.product(galTempList, snTempList))[0:2]
argsList = []
for gal, sn in galAndSnTemps:
argsList.append((snTemplateLocation, [sn], galTemplateLocation,
[gal], snFractions))
numSpectra = 3968 # sum nColsDict
arraySize = numSpectra * len(galTempList) * len(
snFractions) * self.numOfRedshifts
print("Arraysize is:", arraySize)
self.images = np.memmap('all_images.dat',
dtype=np.float16,
mode='w+',
shape=(arraySize, int(self.nw)))
self.labelsIndexes = np.memmap('all_labels.dat',
dtype=np.uint16,
mode='w+',
shape=arraySize)
self.filenames = np.memmap('all_filenames.dat',
dtype=object,
mode='w+',
shape=arraySize)
self.typeNames = np.memmap('all_typeNames.dat',
dtype=object,
mode='w+',
shape=arraySize)
print("images GiB:", self.images.nbytes / 2**30)
print("labels GiB:", self.labelsIndexes.nbytes / 2**30)
print("filenames GiB:", self.filenames.nbytes / 2**30)
print("typeNames GiB:", self.typeNames.nbytes / 2**30)
# # Multiprocessing with map_async (faster)
# pool = mp.Pool()
# results = pool.map_async(self.combined_sn_gal_templates_to_arrays, argsList)
# pool.close()
# pool.join()
# outputs = results.get()
# for i, output in enumerate(outputs):
# self.collect_results(output)
# print('combining results...', output[-1], i, len(outputs))
print("Begin pooling...")
# # Multiprocessing with apply_async (better when arrays are large - i.e. agnostic redshift)
results = []
pool = mp.Pool(processes=50)
print("pool")
for arg in argsList:
print("argLoop")
result = pool.apply_async(self.combined_sn_gal_templates_to_arrays,
[arg])
results.append(result)
print("close pool")
pool.close()
pool.join()
print("Finished Pooling")
wheretostartappending = 0
for i, p in enumerate(results):
output = p.get()
nRows = output[-1]
self.collect_results(output, wheretostartappending)
wheretostartappending += nRows
print('combining results...', nRows, i, len(results))
print("Completed Creating Arrays!")
# Delete temporary memory mapping files
for filename in glob.glob('images_*.dat'):
os.remove(filename)
for filename in glob.glob('labels_*.dat'):
os.remove(filename)
for filename in glob.glob('filenames_*.dat'):
os.remove(filename)
for filename in glob.glob('typeNames_*.dat'):
os.remove(filename)
return self.images, self.labelsIndexes, self.filenames, self.typeNames
def train_test_split(self):
"""
Split training set before creating arrays.
Maybe should change this to include ages in train/test split instead of just SN files.
"""
snTempFileList = copy.copy(self.snidTempFileList)
fileList = temp_list(snTempFileList)
snAndAgeIdxDict = OrderedDict()
spectraList = []
# SPLIT BY SPECTRA
# Get number of spectra per file
for i, sn in enumerate(fileList):
with open(os.path.join(self.snidTemplateLocation, sn),
'r') as FileObj:
for lineNum, line in enumerate(FileObj):
# Read Header Info
if lineNum == 0:
header = (line.strip('\n')).split(' ')
header = [x for x in header if x != '']
numAges, nwx, w0x, w1x, mostKnots, tname, dta, ttype, ittype, itstype = header
numAges, mostKnots = map(int, (numAges, mostKnots))
elif lineNum == mostKnots + 2:
ages = np.array(line.split()[1:]).astype(float)
agesIndexesInRange = np.where((ages >= self.minAge) &
(ages <= self.maxAge))[0]
snAndAgeIdxDict[sn] = agesIndexesInRange
for ageIdx in agesIndexesInRange:
spectraList.append((sn, ageIdx))
# Split train/test
random.shuffle(spectraList)
trainSize = int(self.trainFraction * len(spectraList))
trainSpectra = spectraList[:trainSize]
testSpectra = spectraList[trainSize:]
trainDict, testDict = OrderedDict(), OrderedDict()
for k, v in trainSpectra:
trainDict.setdefault(k, []).append(v)
for k, v in testSpectra:
testDict.setdefault(k, []).append(v)
# # SPLIT BY FILENAME INSTEAD OF BY SPECTRA
# random.Random(42).shuffle(fileList)
#
# trainSize = int(self.trainFraction * len(fileList))
# dirName = os.path.dirname(self.snidTempFileList)
# trainListFileName = os.path.join(dirName, 'train_templist.txt')
# testListFileName = os.path.join(dirName, 'test_templist.txt')
#
# # Save train set file list
# with open(trainListFileName, 'w') as f:
# for line in fileList[:trainSize]:
# f.write("%s\n" % line)
#
# # Save test set file list
# with open(testListFileName, 'w') as f:
# for line in fileList[trainSize:]:
# f.write("%s\n" % line)
print("trainDict", trainDict)
print("testDict", testDict)
return trainDict, testDict
