def getParameter(parameter):
## Prepare to read in parameters
datafile = folder + data_name
fp = InferenceFile("%s" % datafile, "r")
## Take last iteration of each walker
parameter_values = np.array([])
for aa in range(num_walkers):
samples = fp.read_samples("%s" % parameter, walkers=aa)
temp = getattr(samples, parameter)
parameter_values = np.append(parameter_values, temp[-1])
return parameter_values
def results_from_cli(opts, load_samples=True, walkers=None):
"""
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 WaveformArray instance.
walkers : {None, (list of) int}
If loading samples, the walkers to load from. If None, will load from
all walkers.
Returns
-------
result_file : pycbc.io.InferenceFile
The result file as an InferenceFile.
parameters : list
List of the parameters to use, parsed from the parameters option.
labels : list
List of labels to associate with the parameters.
samples : {None, WaveformArray}
If load_samples, the samples as a WaveformArray; otherwise, None.
"""
logging.info("Reading input file")
fp = InferenceFile(opts.input_file, "r")
parameters = fp.variable_args if opts.parameters is None \
else opts.parameters
# load the labels
labels = []
for ii, p in enumerate(parameters):
if len(p.split(':')) == 2:
p, label = p.split(':')
parameters[ii] = p
else:
label = fp.read_label(p)
labels.append(label)
if load_samples:
logging.info("Loading samples")
samples = fp.read_samples(parameters,
walkers=walkers,
thin_start=opts.thin_start,
thin_interval=opts.thin_interval,
thin_end=opts.thin_end,
iteration=opts.iteration)
else:
samples = None
return fp, parameters, labels, samples
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
-------
result_file : pycbc.io.InferenceFile
The result file as an InferenceFile.
parameters : list
List of the parameters to use, parsed from the parameters option.
labels : list
List of labels to associate with the parameters.
samples : {None, FieldArray}
If load_samples, the samples as a FieldArray; otherwise, None.
"""
logging.info("Reading input file")
fp = InferenceFile(opts.input_file, "r")
parameters = fp.variable_args if opts.parameters is None \
else opts.parameters
# load the labels
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:
samples = None
return fp, parameters, labels, samples
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 = []
# loop over all input files
for input_file in opts.input_file:
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(opts.input_file) > 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
import matplotlib.pyplot as plt
import numpy as np
from pycbc.io import InferenceFile
#Pick parameter and file
parameter="penis"
folder="jobs/20170325-151901/"
walker=45
#Set up composite variables
data_name="output.hdf"
datafile=folder+data_name
#Read file and print out length
fp = InferenceFile("%s" % datafile, "r")
samples = fp.read_samples("%s" % parameter, walkers=walker)
temp=getattr(samples,parameter)
print temp