def getdata(self, *fargs, **features):
pfd = None
def extract(key, value, pfd):
feature = '%s:%s' % (key, value)
if feature in self.extracted_feature:
#print 'use extracted feature %s' % feature
newdata = self.extracted_feature[feature]
else:
#print 'extracting new feature %' % feature
newdata = pfd.getdata(**{key:value})
self.extracted_feature.update({feature:newdata})
return newdata
data = np.array([])
#process the args (a list of single-item dictionaries)
for i in fargs:
key, value = i.items()[0]
feature = '%s:%s' % (key, value)
if (feature not in self.extracted_feature) and (pfd is None):
pfd = pfddata(self.pfdfile, align=True)
data = np.append(data, extract(key, value, pfd))
#process the kwargs
for key, value in features.iteritems():
feature = '%s:%s' % (key, value)
if (feature not in self.extracted_feature) and (pfd is None):
pfd = pfddata(self.pfdfile, align=True)
data = np.append(data, extract(key, value, pfd))
del(pfd)
return data
def getdata(self, *fargs, **features):
pfd = None
def extract(key, value, pfd):
feature = '%s:%s' % (key, value)
if feature in self.extracted_feature:
#print 'use extracted feature %s' % feature
newdata = self.extracted_feature[feature]
else:
#print 'extracting new feature %' % feature
newdata = pfd.getdata(**{key: value})
self.extracted_feature.update({feature: newdata})
return newdata
data = np.array([])
#process the args (a list of single-item dictionaries)
for i in fargs:
key, value = i.items()[0]
feature = '%s:%s' % (key, value)
if (feature not in self.extracted_feature) and (pfd is None):
if not type(self.pfdfile
) is str and self.pfdfile.__class__ == singlepulse:
pfd = self.pfdfile
elif os.path.splitext(self.pfdfile)[1] == '.pfd':
pfd = pfddata(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.ar2':
pfd = ar2data(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.ar':
pfd = ar2data(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.spd':
pfd = SPdata(self.pfdfile, align=True)
else:
print "unrecognized file format ", self.pfdfile
raise Error
data = np.append(data, extract(key, value, pfd))
#process the kwargs
for key, value in features.iteritems():
feature = '%s:%s' % (key, value)
if (feature not in self.extracted_feature) and (pfd is None):
if not type(self.pfdfile
) is str and self.pfdfile.__class__ == singlepulse:
pfd = self.pfdfile
elif os.path.splitext(self.pfdfile)[1] == '.pfd':
pfd = pfddata(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.ar2':
pfd = ar2data(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.ar':
pfd = ar2data(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.spd':
pfd = SPdata(self.pfdfile, align=True)
else:
print "unrecognized file format ", self.pfdfile
raise Error
data = np.append(data, extract(key, value, pfd))
del (pfd)
return data
def getdata(self, *fargs, **features):
pfd = None
def extract(key, value, pfd):
feature = '%s:%s' % (key, value)
if feature in self.extracted_feature:
#print 'use extracted feature %s' % feature
newdata = self.extracted_feature[feature]
else:
#print 'extracting new feature %' % feature
newdata = pfd.getdata(**{key:value})
self.extracted_feature.update({feature:newdata})
return newdata
data = np.array([])
#process the args (a list of single-item dictionaries)
for i in fargs:
key, value = i.items()[0]
feature = '%s:%s' % (key, value)
if (feature not in self.extracted_feature) and (pfd is None):
if not type(self.pfdfile) is str and self.pfdfile.__class__ == singlepulse:
pfd = self.pfdfile
elif os.path.splitext(self.pfdfile)[1] == '.pfd':
pfd = pfddata(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.ar2':
pfd = ar2data(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.ar':
pfd = ar2data(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.spd':
pfd = SPdata(self.pfdfile, align=True)
else:
print "unrecognized file format ", self.pfdfile
raise Error
data = np.append(data, extract(key, value, pfd))
#process the kwargs
for key, value in features.iteritems():
feature = '%s:%s' % (key, value)
if (feature not in self.extracted_feature) and (pfd is None):
if not type(self.pfdfile) is str and self.pfdfile.__class__ == singlepulse:
pfd = self.pfdfile
elif os.path.splitext(self.pfdfile)[1] == '.pfd':
pfd = pfddata(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.ar2':
pfd = ar2data(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.ar':
pfd = ar2data(self.pfdfile, align=True)
elif os.path.splitext(self.pfdfile)[1] == '.spd':
pfd = SPdata(self.pfdfile, align=True)
else:
print "unrecognized file format ", self.pfdfile
raise Error
data = np.append(data, extract(key, value, pfd))
del(pfd)
return data
import time
t0 = time.time()
#pfd_files_pulsars = glob.glob('/beegfs/vishnu/scripts/neural_network/test/pulsars/*.pfd')
pfd_files_nonpulsars = sorted(
glob.glob('/beegfs/vishnu/scripts/neural_network/test/nonpulsars/*.pfd'))
fraction = 4
current_segment = 2
max_value = int(math.ceil(len(pfd_files_nonpulsars) / fraction))
print(max_value)
double_max_value = 2 * max_value
print(double_max_value)
# Initialise data objects from getdata class
#data_object_pulsars = [pfddata(f) for f in pfd_files_pulsars]
data_object_nonpulsars = [
pfddata(f) for f in pfd_files_nonpulsars[max_value:double_max_value]
]
print('loaded data into memory')
# Extract 4 features based on Zhu et.al 2014
#1 time vs phase plot
#time_phase_plots_pulsars = [f.getdata(intervals=48) for f in data_object_pulsars]
time_phase_plots_nonpulsars = [
f.getdata(intervals=48) for f in data_object_nonpulsars
]
print('time phase done')
#2 freq vs phase plot
#freq_phase_plots_pulsars = [f.getdata(subbands=48) for f in data_object_pulsars]
freq_phase_plots_nonpulsars = [
f.getdata(subbands=48) for f in data_object_nonpulsars
]
import sys, os, glob
sys.path.append(
'/home/psr/software/psrchive/install/lib/python2.7/site-packages')
sys.path.append('/home/psr')
import numpy as np
from ubc_AI.training import pfddata
#pfd_files_pulsars = glob.glob('/beegfs/vishnu/scripts/neural_network/train/pulsars/*.pfd')
pfd_files_nonpulsars = glob.glob(
'/beegfs/vishnu/scripts/neural_network/train/nonpulsars/*.pfd')
# Initialise data objects from getdata class
#data_object_pulsars = [pfddata(f) for f in pfd_files_pulsars]
data_object_nonpulsars = [pfddata(f) for f in pfd_files_nonpulsars]
# Extract 4 features based on Zhu et.al 2014
#1 time vs phase plot
#time_phase_plots_pulsars = [f.getdata(intervals=48) for f in data_object_pulsars]
time_phase_plots_nonpulsars = [
f.getdata(intervals=48) for f in data_object_nonpulsars
]
#2 freq vs phase plot
#freq_phase_plots_pulsars = [f.getdata(subbands=48) for f in data_object_pulsars]
freq_phase_plots_nonpulsars = [
f.getdata(subbands=48) for f in data_object_nonpulsars
]
#3 Pulse Profile
#pulse_profile_pulsars = [f.getdata(phasebins=64) for f in data_object_pulsars]
t0 = time.time()
#pfd_files_pulsars = glob.glob('/beegfs/vishnu/scripts/neural_network/test/pulsars/*.pfd')
pfd_files_nonpulsars = sorted(
glob.glob('/beegfs/vishnu/scripts/neural_network/test/nonpulsars/*.pfd'))
fraction = 4
current_segment = 2
max_value = int(math.ceil(len(pfd_files_nonpulsars) / fraction))
double_max_value = 2 * max_value
triple_max_value = 3 * max_value
quadruple_max_value = 4 * max_value
print(quadruple_max_value)
# Initialise data objects from getdata class
#data_object_pulsars = [pfddata(f) for f in pfd_files_pulsars]
data_object_nonpulsars = [
pfddata(f)
for f in pfd_files_nonpulsars[triple_max_value:quadruple_max_value]
]
print('loaded data into memory')
# Extract 4 features based on Zhu et.al 2014
#1 time vs phase plot
#time_phase_plots_pulsars = [f.getdata(intervals=48) for f in data_object_pulsars]
time_phase_plots_nonpulsars = [
f.getdata(intervals=48) for f in data_object_nonpulsars
]
print('time phase done')
#2 freq vs phase plot
#freq_phase_plots_pulsars = [f.getdata(subbands=48) for f in data_object_pulsars]
freq_phase_plots_nonpulsars = [
f.getdata(subbands=48) for f in data_object_nonpulsars
# for filename in fnmatch.filter(filenames, '*.pfd'):
# matches.append(os.path.join(root, filename))
def chunks(pfd_files, n):
"""Yield n-sized chunks from list of pfd files."""
for i in range(0, len(pfd_files), n):
yield pfd_files[i:i + n]
# Extract 4 features based on Zhu et.al 2014
if batch_size > 1:
batch_number = 0
for value in chunks(pfd_files, batch_size):
batch_number += 1
# Initialise data objects from getdata class
data_object = [pfddata(filename) for filename in value]
time_phase_data = [
filename.getdata(intervals=48) for filename in data_object
]
freq_phase_data = [
filename.getdata(subbands=48) for filename in data_object
]
pulse_profile_data = [
filename.getdata(phasebins=64) for filename in data_object
]
dm_curve_data = [
filename.getdata(DMbins=60) for filename in data_object
]
###Save all features as numpy array files
