def injections_from_cli(opts):
"""Gets injection parameters from the inference file(s).
Parameters
----------
opts : argparser
Argparser object that has the command-line objects to parse.
Returns
-------
FieldArray
Array of the injection parameters from all of the input files given
by ``opts.input_file``.
"""
input_files = opts.input_file
if isinstance(input_files, str):
input_files = [input_files]
parameters, _ = parse_parameters_opt(opts.parameters)
if parameters is None:
with InferenceFile(input_files[0], 'r') as fp:
parameters = fp.variable_args
injections = None
# loop over all input files getting the injection files
for input_file in input_files:
# read injections from HDF input file as FieldArray
these_injs = inject.InjectionSet(
input_file,
hdf_group=opts.injection_hdf_group).table.view(FieldArray)
if injections is None:
injections = these_injs
else:
injections = injections.append(these_injs)
# check if need extra parameters than parameters stored in injection file
_, ts = transforms.get_common_cbc_transforms(parameters,
injections.fieldnames)
# add parameters not included in injection file
injections = transforms.apply_transforms(injections, ts)
return injections
from matplotlib import rcParams
rcParams['font.size'] = 16
# The data files store posteriors for the variable parameters ( `variable_args` ) in the MCMC. To check the parameter names that are stored in the posteriors file from the analysis of any of the events (say GW150914), use the appropriate file and do :
# In[2]:
fp = h5py.File("posteriors/GW150914/gw150914_posteriors_thinned.hdf", "r")
print fp.attrs['variable_args']
fp.close()
# Posteriors for the `variable_args` are stored inside the `samples` group of the hdf data files. PyCBC provides a handler class `pycbc.io.InferenceFile` that extends `h5py.File`. To read the output file and get samples for any of the `variable_args`, say `mass1`, do :
# In[3]:
fp = InferenceFile("posteriors/GW150914/gw150914_posteriors_thinned.hdf", "r")
mass1_samples = fp['samples/mass1'][:]
print mass1_samples
# Using the PyCBC software, one can map the posteriors for the `variable_args` to posteriors of other parameters. PyCBC has several functions that can be used to do the general transforms between parameters. You can look in `pycbc.conversions`, `pycbc.coordinates`, `pycbc.cosmology` for the possible transforms that you can do with the available `variable_args`. For example, to obtain the the chirp mass of the binary `mchirp` from the component masses `(mass1, mass2)` one would do :
# In[4]:
from pycbc import conversions
fp = InferenceFile("posteriors/GW150914/gw150914_posteriors_thinned.hdf", 'r')
mass1 = fp['samples/mass1'][:]
mass2 = fp['samples/mass2'][:]
fp.close()
mchirp = conversions.mchirp_from_mass1_mass2(mass1, mass2)
os.makedirs(plotDir)
n_live_points = 5000
evidence_tolerance = 0.5
filename = '../plots/fitting_gws/Ka2017_FixZPT0/u_g_r_i_z_y_J_H_K/0_14/joint/GW170817/1.00/0_640/q_lambdatilde.dat'
data = np.loadtxt(filename)
q_em = data[:, 0]
lambdatilde_em = data[:, 1]
filename = '/home/mcoughlin/gw170817-common-eos/uniform_mass_prior_common_eos_posteriors.hdf'
lambda1 = 'lambdasym*((mass2/mass1)**3)'
lambda2 = 'lambdasym*((mass1/mass2)**3)'
# read samples
params = [lambda1, lambda2]
with InferenceFile(filename, "r") as fp:
samples = fp.read_samples(params)
mass1 = samples['mass1'][:]
mass2 = samples['mass2'][:]
lambdasym = samples['lambdasym'][:]
lambda1 = lambdasym * ((mass2 / mass1)**3)
lambda2 = lambdasym * ((mass1 / mass2)**3)
lambdatilde_gw = conversions.lambda_tilde(mass1, mass2, lambda1, lambda2)
q_gw = mass1 / mass2
pts_em = np.vstack((q_em, lambdatilde_em)).T
pts_gw = np.vstack((q_gw, lambdatilde_gw)).T
kdedir_em = greedy_kde_areas_2d(pts_em)
kdedir_gw = greedy_kde_areas_2d(pts_gw)
parameters = ["q", "lambdatilde"]
def results_from_cli(opts, load_samples=True, **kwargs):
"""
Loads an inference result file along with any labels associated with it
from the command line options.
Parameters
----------
opts : ArgumentParser options
The options from the command line.
load_samples : {True, bool}
Load samples from the results file using the parameters, thin_start,
and thin_interval specified in the options. The samples are returned
as a FieldArray instance.
\**kwargs :
All other keyword arguments are passed to the InferenceFile's
read_samples function.
Returns
-------
fp_all : pycbc.io.InferenceFile
The result file as an InferenceFile. If more than one input file,
then it returns a list.
parameters_all : list
List of the parameters to use, parsed from the parameters option.
If more than one input file, then it returns a list.
labels_all : list
List of labels to associate with the parameters. If more than one
input file, then it returns a list.
samples_all : {None, FieldArray}
If load_samples, the samples as a FieldArray; otherwise, None.
If more than one input file, then it returns a list.
"""
# lists for files and samples from all input files
fp_all = []
parameters_all = []
labels_all = []
samples_all = []
input_files = opts.input_file
if isinstance(input_files, str):
input_files = [input_files]
# loop over all input files
for input_file in input_files:
logging.info("Reading input file %s", input_file)
# read input file
fp = InferenceFile(input_file, "r")
# get parameters and a dict of labels for each parameter
parameters = fp.variable_args if opts.parameters is None \
else opts.parameters
parameters, ldict = parse_parameters_opt(parameters)
# convert labels dict to list
labels = []
for p in parameters:
try:
label = ldict[p]
except KeyError:
label = fp.read_label(p)
labels.append(label)
# load the samples
if load_samples:
logging.info("Loading samples")
# check if need extra parameters for a non-sampling parameter
file_parameters, ts = transforms.get_common_cbc_transforms(
parameters, fp.variable_args)
# read samples from file
samples = fp.read_samples(
file_parameters, thin_start=opts.thin_start,
thin_interval=opts.thin_interval, thin_end=opts.thin_end,
iteration=opts.iteration,
samples_group=opts.parameters_group, **kwargs)
# add parameters not included in file
samples = transforms.apply_transforms(samples, ts)
# else do not read samples
else:
samples = None
# add results to lists from all input files
if len(input_files) > 1:
fp_all.append(fp)
parameters_all.append(parameters)
labels_all.append(labels)
samples_all.append(samples)
# else only one input file then do not return lists
else:
fp_all = fp
parameters_all = parameters
labels_all = labels
samples_all = samples
return fp_all, parameters_all, labels_all, samples_all
def results_from_cli(opts, load_samples=True, **kwargs):
"""
Loads an inference result file along with any labels associated with it
from the command line options.
Parameters
----------
opts : ArgumentParser options
The options from the command line.
load_samples : {True, bool}
Load samples from the results file using the parameters, thin_start,
and thin_interval specified in the options. The samples are returned
as a FieldArray instance.
\**kwargs :
All other keyword arguments are passed to the InferenceFile's
read_samples function.
Returns
-------
fp_all : pycbc.io.InferenceFile
The result file as an InferenceFile. If more than one input file,
then it returns a list.
parameters_all : list
List of the parameters to use, parsed from the parameters option.
If more than one input file, then it returns a list.
labels_all : list
List of labels to associate with the parameters. If more than one
input file, then it returns a list.
samples_all : {None, FieldArray}
If load_samples, the samples as a FieldArray; otherwise, None.
If more than one input file, then it returns a list.
"""
# lists for files and samples from all input files
fp_all = []
parameters_all = []
labels_all = []
samples_all = []
input_files = opts.input_file
if isinstance(input_files, str):
input_files = [input_files]
# loop over all input files
input_files = [opts.input_file] if isinstance(opts.input_file, str) \
else opts.input_file
for input_file in input_files:
logging.info("Reading input file %s", input_file)
# read input file
fp = InferenceFile(input_file, "r")
# get parameters and a dict of labels for each parameter
parameters = fp.variable_args if opts.parameters is None \
else opts.parameters
parameters, ldict = parse_parameters_opt(parameters)
# convert labels dict to list
labels = []
for p in parameters:
try:
label = ldict[p]
except KeyError:
label = fp.read_label(p)
labels.append(label)
# load the samples
if load_samples:
logging.info("Loading samples")
# check if need extra parameters for a non-sampling parameter
file_parameters, ts = transforms.get_common_cbc_transforms(
parameters, fp.variable_args)
# read samples from file
samples = fp.read_samples(
file_parameters, thin_start=opts.thin_start,
thin_interval=opts.thin_interval, thin_end=opts.thin_end,
iteration=opts.iteration,
samples_group=opts.parameters_group, **kwargs)
# add parameters not included in file
samples = transforms.apply_transforms(samples, ts)
# else do not read samples
else:
samples = None
# add results to lists from all input files
if len(input_files) > 1:
fp_all.append(fp)
parameters_all.append(parameters)
labels_all.append(labels)
samples_all.append(samples)
# else only one input file then do not return lists
else:
fp_all = fp
parameters_all = parameters
labels_all = labels
samples_all = samples
return fp_all, parameters_all, labels_all, samples_all
def validate_checkpoint_files(checkpoint_file, backup_file):
"""Checks if the given checkpoint and/or backup files are valid.
The checkpoint file is considered valid if:
* it passes all tests run by ``InferenceFile.check_integrity``;
* it has at least one sample written to it (indicating at least one
checkpoint has happened).
The same applies to the backup file. The backup file must also have the
same number of samples as the checkpoint file, otherwise, the backup is
considered invalid.
If the checkpoint (backup) file is found to be valid, but the backup
(checkpoint) file is not valid, then the checkpoint (backup) is copied to
the backup (checkpoint). Thus, this function ensures that checkpoint and
backup files are either both valid or both invalid.
Parameters
----------
checkpoint_file : string
Name of the checkpoint file.
backup_file : string
Name of the backup file.
Returns
-------
checkpoint_valid : bool
Whether or not the checkpoint (and backup) file may be used for loading
samples.
"""
# check if checkpoint file exists and is valid
logging.info("Validating checkpoint and backup files")
try:
check_integrity(checkpoint_file)
checkpoint_valid = True
except (ValueError, KeyError, IOError):
checkpoint_valid = False
# backup file
try:
check_integrity(backup_file)
backup_valid = True
except (ValueError, KeyError, IOError):
backup_valid = False
# check if there are any samples in the file; if not, we'll just start from
# scratch
if checkpoint_valid:
with InferenceFile(checkpoint_file, 'r') as fp:
try:
group = '{}/{}'.format(fp.samples_group, fp.variable_args[0])
nsamples = fp[group].size
checkpoint_valid = nsamples != 0
except KeyError:
checkpoint_valid = False
# check if there are any samples in the backup file
if backup_valid:
with InferenceFile(backup_file, 'r') as fp:
try:
group = '{}/{}'.format(fp.samples_group, fp.variable_args[0])
backup_nsamples = fp[group].size
backup_valid = backup_nsamples != 0
except KeyError:
backup_valid = False
# check that the checkpoint and backup have the same number of samples;
# if not, assume the checkpoint has the correct number
if checkpoint_valid and backup_valid:
backup_valid = nsamples == backup_nsamples
# decide what to do based on the files' statuses
if checkpoint_valid and not backup_valid:
# copy the checkpoint to the backup
logging.info("Backup invalid; copying checkpoint file")
shutil.copy(checkpoint_file, backup_file)
backup_valid = True
elif backup_valid and not checkpoint_valid:
logging.info("Checkpoint invalid; copying backup file")
# copy the backup to the checkpoint
shutil.copy(backup_file, checkpoint_file)
checkpoint_valid = True
return checkpoint_valid