def test_set_get_fill_value_for_str_column(self):
c = MaskedColumn(name='c', data=['xxxx', 'yyyy'], mask=[True, False])
# assert np.all(c.filled() == ['N/A', 'yyyy'])
c.fill_value = 'ABCDEF'
assert c.fill_value == 'ABCD' # string truncated to dtype length
assert np.all(c.filled() == ['ABCD', 'yyyy'])
assert np.all(c.filled('XY') == ['XY', 'yyyy'])
def test_set_get_fill_value_for_str_column(self):
c = MaskedColumn(name='c', data=['xxxx', 'yyyy'], mask=[True, False])
# assert np.all(c.filled() == ['N/A', 'yyyy'])
c.fill_value = 'ABCDEF'
assert c.fill_value == 'ABCD' # string truncated to dtype length
assert np.all(c.filled() == ['ABCD', 'yyyy'])
assert np.all(c.filled('XY') == ['XY', 'yyyy'])
def test_pickle_masked_column(protocol):
c = MaskedColumn(data=[1, 2], name='a', format='%05d', description='col a', unit='cm',
meta={'a': 1})
c.mask[1] = True
c.fill_value = -99
cs = pickle.dumps(c)
cp = pickle.loads(cs)
assert np.all(cp._data == c._data)
assert np.all(cp.mask == c.mask)
assert cp.attrs_equal(c)
assert cp.fill_value == -99
assert cp._parent_table is None
assert repr(c) == repr(cp)
def merge_lightcurve_fits(options):
"""Pythom program to merge the lightcurve fit results into a sigle format"""
import numpy
import astropy
import JLA_library as JLA
from astropy.table import Table, MaskedColumn, vstack
params = JLA.build_dictionary(options.config)
# ---------------- JLA ------------------------
lightCurveFits = JLA.get_full_path(params['JLAlightCurveFits'])
f=open(lightCurveFits)
header=f.readlines()
f.close()
names=header[0].strip('#').split()
# I imagine that the tables package in astropy could also be used to read the ascii input file
SNeSpec = Table(numpy.genfromtxt(lightCurveFits,
skip_header=1,
dtype='S12,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8',
names=names))
nSNeSpec=len(SNeSpec)
print 'There are %d SNe from the spectrscopically confirmed sample' % (nSNeSpec)
# Add an extra column to the table
SNeSpec['source']=['JLA']*nSNeSpec
# ---------------- Shuvo's sample ------------------------
# Photometrically identified SNe in Shuvo's sample, if the parameter exists
if params['photLightCurveFits']!='None':
lightCurveFits = JLA.get_full_path(params['photLightCurveFits'])
SNePhot=Table.read(lightCurveFits, format='fits')
nSNePhot=len(SNePhot)
print 'There are %d SNe from the photometric sample' % (nSNePhot)
# Converting from Shuvo's names to thosed used by JLA
conversion={'name':'name_adj', 'zcmb':None, 'zhel':'z', 'dz':None, 'mb':'mb', 'dmb':'emb', 'x1':'x1', 'dx1':'ex1', 'color':'c', 'dcolor':'ec', '3rdvar':'col27', 'd3rdvar':'d3rdvar', 'tmax':None, 'dtmax':None, 'cov_m_s':'cov_m_x1', 'cov_m_c':'cov_m_c', 'cov_s_c':'cov_x1_c', 'set':None, 'ra':None, 'dec':None, 'biascor':None}
# Add the uncertainty in the mass column
SNePhot['d3rdvar']=(SNePhot['col29']+SNePhot['col28'])/2. - SNePhot['col27']
# Remove columns that are not listed in conversion
for colname in SNePhot.colnames:
if colname not in conversion.values():
SNePhot.remove_column(colname)
for key in conversion.keys():
# Rename the column if it does not already exist
if conversion[key]!=None and conversion[key]!=key:
SNePhot.rename_column(conversion[key], key)
elif conversion[key]==None:
# Create it, mask it, and fill all values
SNePhot[key]=MaskedColumn(numpy.zeros(nSNePhot), numpy.ones(nSNePhot,bool))
SNePhot[key].fill_value=-99 # does not work as expected, so we set it explicitly in the next line
SNePhot[key]=-99.9
else:
# Do nothing if the column already exists
pass
# Add the source column
SNePhot['source']="Phot_Uddin"
# ---------------------- CfA4 ----------------------------------
if params['CfA4LightCurveFits']!='None':
lightCurveFits = JLA.get_full_path(params['CfA4LightCurveFits'])
f=open(lightCurveFits)
header=f.readlines()
f.close()
names=header[0].strip('#').split(',')
SNeCfA4=Table(numpy.genfromtxt(lightCurveFits,
skip_header=1,
dtype='S12,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8',
names=names,delimiter=','))
nSNeCfA4=len(SNeCfA4)
conversion={'name':'name', 'zcmb':None, 'zhel':'z', 'dz':None, 'mb':'mb', 'dmb':'emb', 'x1':'x1', 'dx1':'ex1', 'color':'c', 'dcolor':'ec', '3rdvar':None, 'd3rdvar':None, 'tmax':None, 'dtmax':None, 'cov_m_s':'cov_m_x1', 'cov_m_c':'cov_m_c', 'cov_s_c':'cov_x1_c', 'set':None, 'ra':None, 'dec':None, 'biascor':None}
# Remove columns that are not listed in conversion
for colname in SNeCfA4.colnames:
if colname not in conversion.values():
SNeCfA4.remove_column(colname)
for key in conversion.keys():
# Rename the column if it does not already exist
if conversion[key]!=None and conversion[key]!=key:
SNeCfA4.rename_column(conversion[key], key)
elif conversion[key]==None:
# Create it, mask it, and fill all values
SNeCfA4[key]=MaskedColumn(numpy.zeros(nSNeCfA4), numpy.ones(nSNeCfA4,bool))
SNeCfA4[key].fill_value=-99 # does not work as expected, so we set it explicitly in the next line
SNeCfA4[key]=-99.9
else:
# Do nothing if the column already exists
pass
# Add the source column
SNeCfA4['source']="CfA4"
try:
SNe=vstack([SNeSpec,SNePhot,SNeCfA4])
except:
SNe=SNeSpec
# Write out the result as a FITS table
date = JLA.get_date()
SNe.write('%s_%s.fits' % (options.output, date), format='fits')
return
def merge_lightcurve_fits(options):
"""Pythom program to merge the lightcurve fit results into a sigle format"""
import numpy
import astropy
import JLA_library as JLA
from astropy.table import Table, MaskedColumn, vstack
params = JLA.build_dictionary(options.config)
# ---------------- JLA ------------------------
lightCurveFits = JLA.get_full_path(params['JLAlightCurveFits'])
f = open(lightCurveFits)
header = f.readlines()
f.close()
names = header[0].strip('#').split()
# I imagine that the tables package in astropy could also be used to read the ascii input file
SNeSpec = Table(
numpy.genfromtxt(
lightCurveFits,
skip_header=1,
dtype=
'S12,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8',
names=names))
nSNeSpec = len(SNeSpec)
print 'There are %d SNe from the spectrscopically confirmed sample' % (
nSNeSpec)
# Add an extra column to the table
SNeSpec['source'] = ['JLA'] * nSNeSpec
# ---------------- Shuvo's sample ------------------------
# Photometrically identified SNe in Shuvo's sample, if the parameter exists
if params['photLightCurveFits'] != 'None':
lightCurveFits = JLA.get_full_path(params['photLightCurveFits'])
SNePhot = Table.read(lightCurveFits, format='fits')
nSNePhot = len(SNePhot)
print 'There are %d SNe from the photometric sample' % (nSNePhot)
# Converting from Shuvo's names to thosed used by JLA
conversion = {
'name': 'name_adj',
'zcmb': None,
'zhel': 'z',
'dz': None,
'mb': 'mb',
'dmb': 'emb',
'x1': 'x1',
'dx1': 'ex1',
'color': 'c',
'dcolor': 'ec',
'3rdvar': 'col27',
'd3rdvar': 'd3rdvar',
'tmax': None,
'dtmax': None,
'cov_m_s': 'cov_m_x1',
'cov_m_c': 'cov_m_c',
'cov_s_c': 'cov_x1_c',
'set': None,
'ra': None,
'dec': None,
'biascor': None
}
# Add the uncertainty in the mass column
SNePhot['d3rdvar'] = (SNePhot['col29'] +
SNePhot['col28']) / 2. - SNePhot['col27']
# Remove columns that are not listed in conversion
for colname in SNePhot.colnames:
if colname not in conversion.values():
SNePhot.remove_column(colname)
for key in conversion.keys():
# Rename the column if it does not already exist
if conversion[key] != None and conversion[key] != key:
SNePhot.rename_column(conversion[key], key)
elif conversion[key] == None:
# Create it, mask it, and fill all values
SNePhot[key] = MaskedColumn(numpy.zeros(nSNePhot),
numpy.ones(nSNePhot, bool))
SNePhot[
key].fill_value = -99 # does not work as expected, so we set it explicitly in the next line
SNePhot[key] = -99.9
else:
# Do nothing if the column already exists
pass
# Add the source column
SNePhot['source'] = "Phot_Uddin"
# ---------------------- CfA4 ----------------------------------
if params['CfA4LightCurveFits'] != 'None':
lightCurveFits = JLA.get_full_path(params['CfA4LightCurveFits'])
f = open(lightCurveFits)
header = f.readlines()
f.close()
names = header[0].strip('#').split(',')
SNeCfA4 = Table(
numpy.genfromtxt(lightCurveFits,
skip_header=1,
dtype='S12,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8',
names=names,
delimiter=','))
nSNeCfA4 = len(SNeCfA4)
conversion = {
'name': 'name',
'zcmb': None,
'zhel': 'z',
'dz': None,
'mb': 'mb',
'dmb': 'emb',
'x1': 'x1',
'dx1': 'ex1',
'color': 'c',
'dcolor': 'ec',
'3rdvar': None,
'd3rdvar': None,
'tmax': None,
'dtmax': None,
'cov_m_s': 'cov_m_x1',
'cov_m_c': 'cov_m_c',
'cov_s_c': 'cov_x1_c',
'set': None,
'ra': None,
'dec': None,
'biascor': None
}
# Remove columns that are not listed in conversion
for colname in SNeCfA4.colnames:
if colname not in conversion.values():
SNeCfA4.remove_column(colname)
for key in conversion.keys():
# Rename the column if it does not already exist
if conversion[key] != None and conversion[key] != key:
SNeCfA4.rename_column(conversion[key], key)
elif conversion[key] == None:
# Create it, mask it, and fill all values
SNeCfA4[key] = MaskedColumn(numpy.zeros(nSNeCfA4),
numpy.ones(nSNeCfA4, bool))
SNeCfA4[
key].fill_value = -99 # does not work as expected, so we set it explicitly in the next line
SNeCfA4[key] = -99.9
else:
# Do nothing if the column already exists
pass
# Add the source column
SNeCfA4['source'] = "CfA4"
try:
SNe = vstack([SNeSpec, SNePhot, SNeCfA4])
except:
SNe = SNeSpec
# Write out the result as a FITS table
date = JLA.get_date()
SNe.write('%s_%s.fits' % (options.output, date), format='fits')
return
def merge_lightcurve_fits(options):
"""Pythom program to merge the lightcurve fit results into a sigle format"""
import numpy
import astropy
import os
import JLA_library as JLA
from astropy.table import Table, MaskedColumn, vstack
from scipy.optimize import leastsq
params = JLA.build_dictionary(options.config)
# ---------------- JLA ------------------------
lightCurveFits = JLA.get_full_path(params['JLAlightCurveFits'])
f=open(lightCurveFits)
header=f.readlines()
f.close()
names=header[0].strip('#').split()
# I imagine that the tables package in astropy could also be used to read the ascii input file
SNeSpec = Table(numpy.genfromtxt(lightCurveFits,
skip_header=1,
dtype='S12,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8',
names=names))
nSNeSpec=len(SNeSpec)
print 'There are %d SNe from the spectrscopically confirmed sample' % (nSNeSpec)
# Add an extra column to the table
SNeSpec['source']=['JLA']*nSNeSpec
# -------------- Malmquist bias fits
malmBias={}
if options.bias:
# Compute the bias correction
bias = numpy.genfromtxt(JLA.get_full_path(params['biasPolynomial']),
skip_header=3,
usecols=(0, 1, 2, 3),
dtype='S10,f8,f8,f8',
names=['sample', 'redshift', 'bias', 'e_bias'])
for sample in numpy.unique(bias['sample']):
selection=(bias['sample']==sample)
guess=[0,0,0]
plsq=leastsq(residuals, guess, args=(bias[selection]['bias'],
bias[selection]['redshift'],
bias[selection]['e_bias'],
'poly'), full_output=1)
if plsq[4] in [1,2,3,4]:
print 'Solution for %s found' % (sample)
malmBias[sample]=plsq[0]
# ---------------- Shuvo's sample aka JLA++ ------------------------
# Photometrically identified SNe in Shuvo's sample, if the parameter exists
if params['photLightCurveFits']!='None':
lightCurveFits = JLA.get_full_path(params['photLightCurveFits'])
SNePhot=Table.read(lightCurveFits, format='fits')
nSNePhot=len(SNePhot)
print 'There are %d SNe from the photometric sample' % (nSNePhot)
# Converting from Shuvo's names to thosed used by JLA
conversion={'name':'name_adj', 'zcmb':None, 'zhel':'z', 'dz':None, 'mb':'mb', 'dmb':'emb', 'x1':'x1', 'dx1':'ex1', 'color':'c', 'dcolor':'ec', '3rdvar':'col27', 'd3rdvar':'d3rdvar', 'tmax':None, 'dtmax':None, 'cov_m_s':'cov_m_x1', 'cov_m_c':'cov_m_c', 'cov_s_c':'cov_x1_c', 'set':None, 'ra':'col4', 'dec':'col5', 'biascor':None}
# Add the uncertainty in the mass column
SNePhot['d3rdvar']=(SNePhot['col29']+SNePhot['col28'])/2. - SNePhot['col27']
# Remove columns that are not listed in conversion
for colname in SNePhot.colnames:
if colname not in conversion.values():
SNePhot.remove_column(colname)
for key in conversion.keys():
# Rename the column if it does not already exist
if conversion[key]!=None and conversion[key]!=key:
SNePhot.rename_column(conversion[key], key)
elif conversion[key]==None:
# Create it, mask it, and fill all values
SNePhot[key]=MaskedColumn(numpy.zeros(nSNePhot), numpy.ones(nSNePhot,bool))
SNePhot[key].fill_value=-99 # does not work as expected, so we set it explicitly in the next line
SNePhot[key]=-99.9
else:
# Do nothing if the column already exists
pass
# Compute the bias correction
for i,SN in enumerate(SNePhot):
if 'SDSS' in SN['name']:
SNePhot['biascor'][i]=poly(SN['zhel'],malmBias['SDSS'])
else:
SNePhot['biascor'][i]=poly(SN['zhel'],malmBias['SNLS'])
# Add the source column
SNePhot['source']="Phot_Uddin"
# ---------------------- CfA4 ----------------------------------
if params['CfA4LightCurveFits']!='None':
lightCurveFits = JLA.get_full_path(params['CfA4LightCurveFits'])
f=open(lightCurveFits)
header=f.readlines()
f.close()
names=header[0].strip('#').split(',')
SNeCfA4=Table(numpy.genfromtxt(lightCurveFits,
skip_header=1,
dtype='S12,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8',
names=names,delimiter=','))
nSNeCfA4=len(SNeCfA4)
conversion={'name':'name', 'zcmb':None, 'zhel':'z', 'dz':None, 'mb':'mb', 'dmb':'emb', 'x1':'x1', 'dx1':'ex1', 'color':'c', 'dcolor':'ec', '3rdvar':None, 'd3rdvar':None, 'tmax':None, 'dtmax':None, 'cov_m_s':'cov_m_x1', 'cov_m_c':'cov_m_c', 'cov_s_c':'cov_x1_c', 'set':None, 'ra':None, 'dec':None, 'biascor':None}
# Remove columns that are not listed in conversion
for colname in SNeCfA4.colnames:
if colname not in conversion.values():
SNeCfA4.remove_column(colname)
for key in conversion.keys():
# Rename the column if it does not already exist
if conversion[key]!=None and conversion[key]!=key:
SNeCfA4.rename_column(conversion[key], key)
elif conversion[key]==None:
# Create it, mask it, and fill all values
SNeCfA4[key]=MaskedColumn(numpy.zeros(nSNeCfA4), numpy.ones(nSNeCfA4,bool))
SNeCfA4[key].fill_value=-99 # does not work as expected, so we set it explicitly in the next line
SNeCfA4[key]=-99.9
else:
# Do nothing if the column already exists
pass
# Add the source column
SNeCfA4['source']="CfA4"
# We also need to gather information on the host mass, the host mass uncertainty, CMB redshift and Malmquist bias
CfA4lightcurves=[]
CfA4_lcDirectories=params['CfA4MassesAndCMBz'].split(',')
for lcDir in CfA4_lcDirectories:
listing=os.listdir(JLA.get_full_path(lcDir))
for lc in listing:
CfA4lightcurves.append(JLA.get_full_path(lcDir)+lc)
for i,SN in enumerate(SNeCfA4):
for lc in CfA4lightcurves:
if SN['name'][2:] in lc:
keywords=getKeywords(lc)
SNeCfA4[i]['zcmb']=keywords['REDSHIFT_CMB']
SNeCfA4[i]['3rdvar']=keywords['HOSTGAL_LOGMASS']
SNeCfA4[i]['d3rdvar']=keywords['e_HOSTGAL_LOGMASS']
SNeCfA4[i]['ra']=keywords['RA']
SNeCfA4[i]['dec']=keywords['DECL']
if SNeCfA4[i]['3rdvar'] < 0:
SNeCfA4[i]['3rdvar']=-99.9
SNeCfA4[i]['d3rdvar']=-99.9
# Compute the bias correction
SNeCfA4['biascor']=poly(SNeCfA4['zcmb'],malmBias['nearby'])
try:
SNe=vstack([SNeSpec,SNePhot,SNeCfA4])
except:
SNe=SNeSpec
# print len(SNe),len(numpy.unique(SNe['name']))
# Write out the result as a FITS table
date = JLA.get_date()
SNe.write('%s_%s.fits' % (options.output, date), format='fits', overwrite=True)
return
