def __init__(self, path, data, command_line):
# Unusual construction, since the data files are not distributed
# alongside JLA (size problems)
try:
Likelihood_sn.__init__(self, path, data, command_line)
except IOError:
raise io_mp.LikelihoodError(
"The JLA data files were not found. Please download the "
"following link "
"http://supernovae.in2p3.fr/sdss_snls_jla/jla_likelihood_v4.tgz"
", extract it, and copy all files present in "
"`jla_likelihood_v4/data` to `montepython/data/JLA`")
# Load matrices from text files, whose names were read in the
# configuration file
self.C00 = self.read_matrix(self.mag_covmat_file)
self.C11 = self.read_matrix(self.stretch_covmat_file)
self.C22 = self.read_matrix(self.colour_covmat_file)
self.C01 = self.read_matrix(self.mag_stretch_covmat_file)
self.C02 = self.read_matrix(self.mag_colour_covmat_file)
self.C12 = self.read_matrix(self.stretch_colour_covmat_file)
# Reading light-curve parameters from self.data_file (jla_lcparams.txt)
self.light_curve_params = self.read_light_curve_parameters()
def __init__(self, path, data, command_line):
# Unusual construction, since the data files are not distributed
# alongside JLA (size problems)
try:
Likelihood_sn.__init__(self, path, data, command_line)
except IOError:
raise io_mp.LikelihoodError(
"The JLA data files were not found. Please download the "
"following link "
"http://supernovae.in2p3.fr/sdss_snls_jla/jla_likelihood_v4.tgz"
", extract it, and copy all files present in "
"`jla_likelihood_v4/data` to `your_montepython/data/JLA`")
# Load matrices from text files, whose names were read in the
# configuration file
self.C00 = self.read_matrix(self.mag_covmat_file)
self.C11 = self.read_matrix(self.stretch_covmat_file)
self.C22 = self.read_matrix(self.colour_covmat_file)
self.C01 = self.read_matrix(self.mag_stretch_covmat_file)
self.C02 = self.read_matrix(self.mag_colour_covmat_file)
self.C12 = self.read_matrix(self.stretch_colour_covmat_file)
# Reading light-curve parameters from self.data_file (jla_lcparams.txt)
self.light_curve_params = self.read_light_curve_parameters()
def __init__(self, path, data, command_line):
# Unusual construction, since the data files are not distributed
# alongside Pantheon (size problems)
try:
Likelihood_sn.__init__(self, path, data, command_line)
except IOError:
raise io_mp.LikelihoodError(
"The Pantheon data files were not found. Please check if "
"the following files are in the data/Pantheon directory: "
"\n-> pantheon.dataset"
"\n-> lcparam_full_long.txt"
"\n-> sys_full_long.dat")
# Load matrices from text files, whose names were read in the
# configuration file
self.C00 = self.read_matrix(self.mag_covmat_file)
# Reading light-curve parameters from self.data_file (lcparam_full_long.txt)
self.light_curve_params = self.read_light_curve_parameters()
# Reordering by J. Renk. The following steps can be computed in the
# initialisation step as they do not depend on the point in parameter-space
# -> likelihood evaluation is 30% faster
# Compute the covariance matrix
# The module numexpr is used for doing quickly the long multiplication
# of arrays (factor of 3 improvements over numpy). It is used as a
# replacement of blas routines cblas_dcopy and cblas_daxpy
# For numexpr to work, we need (seems like a bug, but anyway) to create
# local variables holding the arrays. This cost no time (it is a simple
# pointer assignment)
C00 = self.C00
covm = ne.evaluate("C00")
sn = self.light_curve_params
# Update the diagonal terms of the covariance matrix with the
# statistical error
covm += np.diag(sn.dmb**2)
# Whiten the residuals, in two steps.
# Step 1) Compute the Cholesky decomposition of the covariance matrix, in
# place. This is a time expensive (0.015 seconds) part, which is why it is
# now done in init. Note that this is different to JLA, where it needed to
# be done inside the loglkl function.
self.cov = la.cholesky(covm, lower=True, overwrite_a=True)
def __init__(self, path, data, command_line):
# Unusual construction, since the data files are not distributed
# alongside Pantheon (size problems)
try:
Likelihood_sn.__init__(self, path, data, command_line)
except IOError:
raise io_mp.LikelihoodError(
"The Pantheon data files were not found. Please check if "
"the following files are in the data/Pantheon directory: "
"\n-> pantheon.dataset"
"\n-> lcparam_full_long.txt"
"\n-> sys_full_long.dat")
# Load matrices from text files, whose names were read in the
# configuration file
self.C00 = self.read_matrix(self.mag_covmat_file)
# Reading light-curve parameters from self.data_file (lcparam_full_long.txt)
self.light_curve_params = self.read_light_curve_parameters()
def __init__(self, path, data, command_line):
# This reads the configuration file as well
try:
Likelihood_sn.__init__(self, path, data, command_line)
except IOError:
raise io_mp.LikelihoodError(
"The JLA data files were not found. Please download the "
"following link "
"http://supernovae.in2p3.fr/sdss_snls_jla/jla_likelihood_v4.tgz"
", extract it, and copy all files present in "
"`jla_likelihood_v4/data` to `your_montepython/data/JLA`")
# read the only matrix
self.C00 = self.read_matrix(self.mu_covmat_file)
# Read the simplified light-curve self.data_file
self.light_curve_params = self.read_light_curve_parameters()
# The covariance matrix can be already inverted, once and for all
# (cholesky)
self.C00 = la.cholesky(self.C00, lower=True, overwrite_a=True)
def __init__(self, path, data, command_line):
# This reads the configuration file as well
try:
Likelihood_sn.__init__(self, path, data, command_line)
except IOError:
raise io_mp.LikelihoodError(
"The JLA data files were not found. Please download the "
"following link "
"http://supernovae.in2p3.fr/sdss_snls_jla/jla_likelihood_v4.tgz"
", extract it, and copy all files present in "
"`jla_likelihood_v4/data` to `your_montepython/data/JLA`")
# read the only matrix
self.C00 = self.read_matrix(self.mu_covmat_file)
# Read the simplified light-curve self.data_file
self.light_curve_params = self.read_light_curve_parameters()
# The covariance matrix can be already inverted, once and for all
# (cholesky)
self.C00 = la.cholesky(self.C00, lower=True, overwrite_a=True)
