From ea69739b7ed987ace7e7d89e9d3e258e109c630a Mon Sep 17 00:00:00 2001
From: Yifan Wang <yifan.wang@aei.mpg.de>
Date: Mon, 19 Jul 2021 19:21:37 +0200
Subject: [PATCH] transplant samples generation to python3
---
python3_samples/.DS_Store | Bin 0 -> 8196 bytes
python3_samples/config_files/.DS_Store | Bin 0 -> 6148 bytes
python3_samples/config_files/default.json | 12 +
.../config_files/waveform_params.ini | 116 ++++
python3_samples/generate_sample.py | 306 +++++++++
python3_samples/output/.DS_Store | Bin 0 -> 6148 bytes
python3_samples/utils/__init__.py | 0
.../utils/__pycache__/__init__.cpython-39.pyc | Bin 0 -> 198 bytes
.../__pycache__/configfiles.cpython-39.pyc | Bin 0 -> 2877 bytes
.../utils/__pycache__/hdffiles.cpython-39.pyc | Bin 0 -> 14557 bytes
.../__pycache__/samplefiles.cpython-39.pyc | Bin 0 -> 8647 bytes
.../samplegeneration.cpython-39.pyc | Bin 0 -> 4594 bytes
.../__pycache__/staticargs.cpython-39.pyc | Bin 0 -> 2397 bytes
.../__pycache__/waveforms.cpython-39.pyc | Bin 0 -> 8830 bytes
python3_samples/utils/configfiles.py | 98 +++
python3_samples/utils/hdffiles.py | 602 ++++++++++++++++++
python3_samples/utils/progressbar.py | 230 +++++++
python3_samples/utils/samplefiles.py | 368 +++++++++++
python3_samples/utils/samplegeneration.py | 288 +++++++++
python3_samples/utils/staticargs.py | 96 +++
python3_samples/utils/waveforms.py | 312 +++++++++
21 files changed, 2428 insertions(+)
create mode 100644 python3_samples/.DS_Store
create mode 100644 python3_samples/config_files/.DS_Store
create mode 100755 python3_samples/config_files/default.json
create mode 100755 python3_samples/config_files/waveform_params.ini
create mode 100644 python3_samples/generate_sample.py
create mode 100644 python3_samples/output/.DS_Store
create mode 100755 python3_samples/utils/__init__.py
create mode 100644 python3_samples/utils/__pycache__/__init__.cpython-39.pyc
create mode 100644 python3_samples/utils/__pycache__/configfiles.cpython-39.pyc
create mode 100644 python3_samples/utils/__pycache__/hdffiles.cpython-39.pyc
create mode 100644 python3_samples/utils/__pycache__/samplefiles.cpython-39.pyc
create mode 100644 python3_samples/utils/__pycache__/samplegeneration.cpython-39.pyc
create mode 100644 python3_samples/utils/__pycache__/staticargs.cpython-39.pyc
create mode 100644 python3_samples/utils/__pycache__/waveforms.cpython-39.pyc
create mode 100755 python3_samples/utils/configfiles.py
create mode 100755 python3_samples/utils/hdffiles.py
create mode 100755 python3_samples/utils/progressbar.py
create mode 100755 python3_samples/utils/samplefiles.py
create mode 100755 python3_samples/utils/samplegeneration.py
create mode 100755 python3_samples/utils/staticargs.py
create mode 100755 python3_samples/utils/waveforms.py
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diff --git a/python3_samples/config_files/default.json b/python3_samples/config_files/default.json
new file mode 100755
index 0000000..0b83109
--- /dev/null
+++ b/python3_samples/config_files/default.json
@@ -0,0 +1,12 @@
+{
+ "random_seed": 42,
+ "background_data_directory": null,
+ "dq_bits": [0, 1, 2, 3],
+ "inj_bits": [0, 1, 2, 4],
+ "waveform_params_file_name": "waveform_params.ini",
+ "max_runtime": 60,
+ "n_injection_samples": 32,
+ "n_noise_samples": 16,
+ "n_processes": 4,
+ "output_file_name": "default.hdf"
+}
diff --git a/python3_samples/config_files/waveform_params.ini b/python3_samples/config_files/waveform_params.ini
new file mode 100755
index 0000000..c6a3477
--- /dev/null
+++ b/python3_samples/config_files/waveform_params.ini
@@ -0,0 +1,116 @@
+; -----------------------------------------------------------------------------
+; DECLARE ARGUMENTS
+; -----------------------------------------------------------------------------
+
+[variable_args]
+; Waveform parameters that will vary in MCMC
+mass1 =
+mass2 =
+spin1z =
+spin2z =
+ra =
+dec =
+coa_phase =
+inclination =
+polarization =
+injection_snr =
+
+
+[static_args]
+; Waveform parameters that will not change in MCMC
+approximant = SEOBNRv4
+domain = time
+f_lower = 18
+distance = 100
+waveform_length = 128
+
+; Width of the background noise interval (in seconds) around the event_time,
+; which is used to make the injection. Should be larger than (see below):
+; sample_length = seconds_before_event + seconds_after_event
+; because we need to crop off the edges that are corrupted by the whitening.
+noise_interval_width = 16
+
+; original_sampling_rate = Sampling rate of raw HDF files (usually 4096 Hz)
+; target_sampling_rate = Desired sampling rate for sample generation output
+original_sampling_rate = 4096
+target_sampling_rate = 2048
+
+; Define parameters for the whitening procedure. See documentation of the
+; pycbc.types.TimeSeries.whiten() method for an explanation of what these
+; values exactly mean.
+whitening_segment_duration = 4
+whitening_max_filter_duration = 4
+
+; Define the lower and upper bound for the bandpass filter (in Hertz)
+bandpass_lower = 20
+bandpass_upper = 2048
+
+; Define how to align the sample around the event time. By convention, the
+; event time is the H1 time!
+; The sum of these values will be the the sample_length!
+seconds_before_event = 5.5
+seconds_after_event = 2.5
+
+; alpha for the Tukey window that is used to "fade on" the waveforms
+; It represents the fraction of the window inside the cosine tapered region.
+; To turn off the "fade on", simply choose tukey_alpha = 0.
+tukey_alpha = 0.25
+
+
+; -----------------------------------------------------------------------------
+; DEFINE DISTRIBUTIONS FOR PARAMETERS
+; -----------------------------------------------------------------------------
+
+[prior-mass1]
+; Prior for mass1
+name = uniform
+min-mass1 = 10.
+max-mass1 = 80.
+
+
+[prior-mass2]
+; Prior for mass2
+name = uniform
+min-mass2 = 10.
+max-mass2 = 80.
+
+
+[prior-spin1z]
+; Prior for spin1z
+name = uniform
+min-spin1z = 0
+max-spin1z = 0.998
+
+
+[prior-spin2z]
+; Prior for spin2z
+name = uniform
+min-spin2z = 0
+max-spin2z = 0.998
+
+
+[prior-injection_snr]
+; Prior for the injection SNR
+name = uniform
+min-injection_snr = 5
+max-injection_snr = 20
+
+
+[prior-coa_phase]
+; Coalescence phase prior
+name = uniform_angle
+
+
+[prior-inclination]
+; Inclination prior
+name = sin_angle
+
+
+[prior-ra+dec]
+; Sky position prior
+name = uniform_sky
+
+
+[prior-polarization]
+; Polarization prior
+name = uniform_angle
diff --git a/python3_samples/generate_sample.py b/python3_samples/generate_sample.py
new file mode 100644
index 0000000..d98ff98
--- /dev/null
+++ b/python3_samples/generate_sample.py
@@ -0,0 +1,306 @@
+"""
+The "main script" of this repository: Read in a configuration file and
+generate synthetic GW data according to the provided specifications.
+"""
+
+# -----------------------------------------------------------------------------
+# IMPORTS
+# -----------------------------------------------------------------------------
+
+from __future__ import print_function
+
+import argparse
+import numpy as np
+import os
+import sys
+import time
+
+from itertools import count
+from multiprocessing import Process, Queue
+from tqdm import tqdm
+
+from utils.configfiles import read_ini_config, read_json_config
+from utils.hdffiles import NoiseTimeline
+from utils.samplefiles import SampleFile
+from utils.samplegeneration import generate_sample
+from utils.waveforms import WaveformParameterGenerator
+
+# -----------------------------------------------------------------------------
+# MAIN CODE
+# -----------------------------------------------------------------------------
+
+if __name__ == '__main__':
+
+ # -------------------------------------------------------------------------
+ # Preliminaries
+ # -------------------------------------------------------------------------
+
+ # Disable output buffering ('flush' option is not available for Python 2)
+ #sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0)
+
+ # Start the stopwatch
+ script_start = time.time()
+
+ print('')
+ print('GENERATE A GW DATA SAMPLE FILE')
+ print('')
+
+ # -------------------------------------------------------------------------
+ # Parse the command line arguments
+ # -------------------------------------------------------------------------
+
+ # Set up the parser and add arguments
+ parser = argparse.ArgumentParser(description='Generate a GW data sample.')
+ parser.add_argument('--config-file',
+ help='Name of the JSON configuration file which '
+ 'controls the sample generation process.',
+ default='default.json')
+
+ # Parse the arguments that were passed when calling this script
+ print('Parsing command line arguments...', end=' ')
+ command_line_arguments = vars(parser.parse_args())
+ print('Done!')
+
+ # -------------------------------------------------------------------------
+ # Read in JSON config file specifying the sample generation process
+ # -------------------------------------------------------------------------
+
+ # Build the full path to the config file
+ json_config_name = command_line_arguments['config_file']
+ json_config_path = os.path.join('.', 'config_files', json_config_name)
+
+ # Read the JSON configuration into a dict
+ print('Reading and validating in JSON configuration file...', end=' ')
+ config = read_json_config(json_config_path)
+ print('Done!')
+
+ # -------------------------------------------------------------------------
+ # Read in INI config file specifying the static_args and variable_args
+ # -------------------------------------------------------------------------
+
+ # Build the full path to the waveform params file
+ ini_config_name = config['waveform_params_file_name']
+ ini_config_path = os.path.join('.', 'config_files', ini_config_name)
+
+ # Read in the variable_arguments and static_arguments
+ print('Reading and validating in INI configuration file...', end=' ')
+ variable_arguments, static_arguments = read_ini_config(ini_config_path)
+ print('Done!\n')
+
+ # -------------------------------------------------------------------------
+ # Shortcuts and random seed
+ # -------------------------------------------------------------------------
+
+ # Set the random seed for this script
+ np.random.seed(config['random_seed'])
+
+ # Define some useful shortcuts
+ random_seed = config['random_seed']
+ max_runtime = config['max_runtime']
+ bkg_data_dir = config['background_data_directory']
+
+ # -------------------------------------------------------------------------
+ # Construct a generator for sampling waveform parameters
+ # -------------------------------------------------------------------------
+
+ # Initialize a waveform parameter generator that can sample injection
+ # parameters from the distributions specified in the config file
+ waveform_parameter_generator = \
+ WaveformParameterGenerator(config_file=ini_config_path,
+ random_seed=random_seed)
+
+ # Wrap it in a generator expression so that we can we can easily sample
+ # from it by calling next(waveform_parameters)
+ waveform_parameters = \
+ (waveform_parameter_generator.draw() for _ in iter(int, 1))
+
+ # -------------------------------------------------------------------------
+ # Construct a generator for sampling valid noise times
+ # -------------------------------------------------------------------------
+
+ # If the 'background_data_directory' is None, we will use synthetic noise
+ if config['background_data_directory'] is None:
+
+ print('Using synthetic noise! (background_data_directory = None)\n')
+
+ # Create a iterator that returns a fake "event time", which we will
+ # use as a seed for the RNG to ensure the reproducibility of the
+ # generated synthetic noise.
+ # For the HDF file path that contains that time, we always yield
+ # None, so that we know that we need to generate synthetic noise.
+ noise_times = ((1000000000 + _, None) for _ in count())
+
+ # Otherwise, we set up a timeline object for the background noise, that
+ # is, we read in all HDF files in the raw_data_directory and figure out
+ # which parts of it are useable (i.e., have the right data quality and
+ # injection bits set as specified in the config file).
+ else:
+
+ print('Using real noise from LIGO recordings! '
+ '(background_data_directory = {})'.format(bkg_data_dir))
+ print('Reading in raw data. This may take several minutes...', end=' ')
+
+ # Create a timeline object by running over all HDF files once
+ noise_timeline = NoiseTimeline(background_data_directory=bkg_data_dir,
+ random_seed=random_seed)
+
+ # Create a noise time generator so that can sample valid noise times
+ # simply by calling next(noise_time_generator)
+ delta_t = int(static_arguments['noise_interval_width'] / 2)
+ noise_times = (noise_timeline.sample(delta_t=delta_t,
+ dq_bits=config['dq_bits'],
+ inj_bits=config['inj_bits'],
+ return_paths=True)
+ for _ in iter(int, 1))
+
+ print('Done!\n')
+
+ # -------------------------------------------------------------------------
+ # Define a convenience function to generate arguments for the simulation
+ # -------------------------------------------------------------------------
+
+ def generate_arguments(injection=True):
+
+ # Only sample waveform parameters if we are making an injection
+ waveform_params = next(waveform_parameters) if injection else None
+
+ # Return all necessary arguments as a dictionary
+ return dict(static_arguments=static_arguments,
+ event_tuple=next(noise_times),
+ waveform_params=waveform_params)
+
+ # -------------------------------------------------------------------------
+ # Finally: Create our samples!
+ # -------------------------------------------------------------------------
+
+ # Keep track of all the samples (and parameters) we have generated
+ samples = dict(injection_samples=[], noise_samples=[])
+ injection_parameters = dict(injection_samples=[], noise_samples=[])
+
+
+ print('Generating samples containing an injection...')
+ n_samples = config['n_injection_samples']
+ arguments_generator = \
+ (generate_arguments(injection=True) for _ in iter(int, 1))
+ print('Number of samples:',n_samples)
+
+ sample_type = 'injection_samples'
+ for i in range(n_samples):
+ print(i)
+
+ results_list = []
+ arguments = next(arguments_generator)
+ print(arguments)
+ result = generate_sample(**arguments)
+ results_list.append(result)
+
+ # ---------------------------------------------------------------------
+ # Process results in the results_list
+ # ---------------------------------------------------------------------
+
+ # Separate the samples and the injection parameters
+ samples[sample_type], injection_parameters[sample_type] = \
+ zip(*results_list)
+
+ # Sort all results by the event_time
+ idx = np.argsort([_['event_time'] for _ in list(samples[sample_type])])
+ samples[sample_type] = \
+ list([samples[sample_type][i] for i in idx])
+ injection_parameters[sample_type] = \
+ list([injection_parameters[sample_type][i] for i in idx])
+
+ print('Sample generation completed!\n')
+
+ # -------------------------------------------------------------------------
+ # Compute the normalization parameters for this file
+ # -------------------------------------------------------------------------
+
+ print('Computing normalization parameters for sample...', end=' ')
+
+ # Gather all samples (with and without injection) in one list
+ all_samples = list(samples['injection_samples'] + samples['noise_samples'])
+
+ # Group all samples by detector
+ h1_samples = [_['h1_strain'] for _ in all_samples]
+ l1_samples = [_['l1_strain'] for _ in all_samples]
+
+ # Stack recordings along first axis
+ h1_samples = np.vstack(h1_samples)
+ l1_samples = np.vstack(l1_samples)
+
+ # Compute the mean and standard deviation for both detectors as the median
+ # of the means / standard deviations for each sample. This is more robust
+ # towards outliers than computing "global" parameters by concatenating all
+ # samples and treating them as a single, long time series.
+ normalization_parameters = \
+ dict(h1_mean=np.median(np.mean(h1_samples, axis=1), axis=0),
+ l1_mean=np.median(np.mean(l1_samples, axis=1), axis=0),
+ h1_std=np.median(np.std(h1_samples, axis=1), axis=0),
+ l1_std=np.median(np.std(l1_samples, axis=1), axis=0))
+
+ print('Done!\n')
+
+ # -------------------------------------------------------------------------
+ # Create a SampleFile dict from list of samples and save it as an HDF file
+ # -------------------------------------------------------------------------
+
+ print('Saving the results to HDF file ...', end=' ')
+
+ # Initialize the dictionary that we use to create a SampleFile object
+ sample_file_dict = dict(command_line_arguments=command_line_arguments,
+ injection_parameters=dict(),
+ injection_samples=dict(),
+ noise_samples=dict(),
+ normalization_parameters=normalization_parameters,
+ static_arguments=static_arguments)
+
+ # Collect and add samples (with and without injection)
+ for sample_type in ('injection_samples', 'noise_samples'):
+ for key in ('event_time', 'h1_strain', 'l1_strain'):
+ if samples[sample_type]:
+ value = np.array([_[key] for _ in list(samples[sample_type])])
+ else:
+ value = None
+ sample_file_dict[sample_type][key] = value
+
+ # Collect and add injection_parameters (ignore noise samples here, because
+ # for those, the injection_parameters are always None)
+ other_keys = ['h1_signal', 'h1_output_signal','h1_snr', 'l1_signal','l1_output_signal', 'l1_snr', 'scale_factor']
+ for key in list(variable_arguments + other_keys):
+ if injection_parameters['injection_samples']:
+ value = np.array([_[key] for _ in
+ injection_parameters['injection_samples']])
+ else:
+ value = None
+ sample_file_dict['injection_parameters'][key] = value
+
+ # Construct the path for the output HDF file
+ output_dir = os.path.join('.', 'output')
+ if not os.path.exists(output_dir):
+ os.mkdir(output_dir)
+ sample_file_path = os.path.join(output_dir, config['output_file_name'])
+
+ # Create the SampleFile object and save it to the specified output file
+ sample_file = SampleFile(data=sample_file_dict)
+ sample_file.to_hdf(file_path=sample_file_path)
+
+ print('Done!')
+
+ # Get file size in MB and print the result
+ sample_file_size = os.path.getsize(sample_file_path) / 1024**2
+ print('Size of resulting HDF file: {:.2f}MB'.format(sample_file_size))
+ print('')
+
+ # -------------------------------------------------------------------------
+ # Postliminaries
+ # -------------------------------------------------------------------------
+
+ # PyCBC always create a copy of the waveform parameters file, which we
+ # can delete at the end of the sample generation process
+ duplicate_path = os.path.join('.', config['waveform_params_file_name'])
+ if os.path.exists(duplicate_path):
+ os.remove(duplicate_path)
+
+ # Print the total run time
+ print('Total runtime: {:.1f} seconds!'.format(time.time() - script_start))
+ print('')
diff --git a/python3_samples/output/.DS_Store b/python3_samples/output/.DS_Store
new file mode 100644
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diff --git a/python3_samples/utils/configfiles.py b/python3_samples/utils/configfiles.py
new file mode 100755
index 0000000..cf99e11
--- /dev/null
+++ b/python3_samples/utils/configfiles.py
@@ -0,0 +1,98 @@
+"""
+Provide functions for reading and parsing configuration files.
+"""
+
+# -----------------------------------------------------------------------------
+# IMPORTS
+# -----------------------------------------------------------------------------
+
+import json
+import os
+
+from pycbc.workflow import WorkflowConfigParser
+from pycbc.distributions import read_params_from_config
+
+from .staticargs import amend_static_args, typecast_static_args
+
+
+# -----------------------------------------------------------------------------
+# FUNCTION DEFINITIONS
+# -----------------------------------------------------------------------------
+
+def read_ini_config(file_path):
+ """
+ Read in a `*.ini` config file, which is used mostly to specify the
+ waveform simulation (for example, the waveform model, the parameter
+ space for the binary black holes, etc.) and return its contents.
+
+ Args:
+ file_path (str): Path to the `*.ini` config file to be read in.
+
+ Returns:
+ A tuple `(variable_arguments, static_arguments)` where
+
+ * `variable_arguments` should simply be a list of all the
+ parameters which get randomly sampled from the specified
+ distributions, usually using an instance of
+ :class:`utils.waveforms.WaveformParameterGenerator`.
+ * `static_arguments` should be a dictionary containing the keys
+ and values of the parameters that are the same for each
+ example that is generated (i.e., the non-physical parameters
+ such as the waveform model and the sampling rate).
+ """
+
+ # Make sure the config file actually exists
+ if not os.path.exists(file_path):
+ raise IOError('Specified configuration file does not exist: '
+ '{}'.format(file_path))
+
+ # Set up a parser for the PyCBC config file
+ workflow_config_parser = WorkflowConfigParser(configFiles=[file_path])
+
+ # Read the variable_arguments and static_arguments using the parser
+ variable_arguments, static_arguments = \
+ read_params_from_config(workflow_config_parser)
+
+ # Typecast and amend the static arguments
+ static_arguments = typecast_static_args(static_arguments)
+ static_arguments = amend_static_args(static_arguments)
+
+ return variable_arguments, static_arguments
+
+
+def read_json_config(file_path):
+ """
+ Read in a `*.json` config file, which is used to specify the
+ sample generation process itself (for example, the number of
+ samples to generate, the number of concurrent processes to use,
+ etc.) and return its contents.
+
+ Args:
+ file_path (str): Path to the `*.json` config file to be read in.
+
+ Returns:
+ A `dict` containing the contents of the given JSON file.
+ """
+
+ # Make sure the config file actually exists
+ if not os.path.exists(file_path):
+ raise IOError('Specified configuration file does not exist: '
+ '{}'.format(file_path))
+
+ # Open the config while and load the JSON contents as a dict
+ with open(file_path, 'r') as json_file:
+ config = json.load(json_file)
+
+ # Define the required keys for the config file in a set
+ required_keys = {'background_data_directory', 'dq_bits', 'inj_bits',
+ 'waveform_params_file_name', 'max_runtime',
+ 'n_injection_samples', 'n_noise_samples', 'n_processes',
+ 'random_seed', 'output_file_name'}
+
+ # Make sure no required keys are missing
+ missing_keys = required_keys.difference(set(config.keys()))
+ if missing_keys:
+ raise KeyError('Missing required key(s) in JSON configuration file: '
+ '{}'.format(', '.join(list(missing_keys))))
+
+ return config
diff --git a/python3_samples/utils/hdffiles.py b/python3_samples/utils/hdffiles.py
new file mode 100755
index 0000000..47310ef
--- /dev/null
+++ b/python3_samples/utils/hdffiles.py
@@ -0,0 +1,602 @@
+"""
+Provide classes and functions for reading and writing HDF files.
+"""
+
+# -----------------------------------------------------------------------------
+# IMPORTS
+# -----------------------------------------------------------------------------
+
+from __future__ import print_function
+
+import numpy as np
+import h5py
+import os
+import sys
+
+from pycbc.catalog import Catalog
+from pycbc.types.timeseries import TimeSeries
+from lal import LIGOTimeGPS
+
+
+# -----------------------------------------------------------------------------
+# FUNCTION DEFINITIONS
+# -----------------------------------------------------------------------------
+
+def get_file_paths(directory, extensions=None):
+ """
+ Take a directory and return the paths to all files in this
+ directory and its subdirectories. Optionally filter out only
+ files specific extensions.
+
+ Args:
+ directory (str): Path to a directory.
+ extensions (list): List of allowed file extensions,
+ for example: `['hdf', 'h5']`.
+
+ Returns:
+ List of paths of all files matching the above descriptions.
+ """
+
+ file_paths = []
+
+ # Walk over the directory and find all files
+ for path, dirs, files in os.walk(directory):
+ for f in files:
+ file_paths.append(os.path.join(path, f))
+
+ # If a list of extensions is provided, only keep the corresponding files
+ if extensions is not None:
+ file_paths = [_ for _ in file_paths if any([_.endswith(ext) for
+ ext in extensions])]
+
+ return file_paths
+
+
+# -----------------------------------------------------------------------------
+
+
+def get_strain_from_hdf_file(hdf_file_paths,
+ gps_time,
+ interval_width,
+ original_sampling_rate=4096,
+ target_sampling_rate=4096,
+ as_pycbc_timeseries=False):
+ """
+ For a given `gps_time`, select the interval of length
+ `interval_width` (centered around `gps_time`) from the HDF files
+ specified in `hdf_file_paths`, and resample them to the given
+ `target_sampling_rate`.
+
+ Args:
+ hdf_file_paths (dict): A dictionary with keys `{'H1', 'L1'}`,
+ which holds the paths to the HDF files containing the
+ interval around `gps_time`.
+ gps_time (int): A (valid) background noise time (GPS timestamp).
+ interval_width (int): The length of the strain sample (in
+ seconds) to be selected from the HDF files.
+ original_sampling_rate (int): The original sampling rate (in
+ Hertz) of the HDF files sample. Default is 4096.
+ target_sampling_rate (int): The sampling rate (in Hertz) to
+ which the strain should be down-sampled (if desired). Must
+ be a divisor of the `original_sampling_rate`.
+ as_pycbc_timeseries (bool): Whether to return the strain as a
+ dict of numpy arrays or as a dict of objects of type
+ `pycbc.types.timeseries.TimeSeries`.
+
+ Returns:
+ A dictionary with keys `{'H1', 'L1'}`. For each key, the
+ dictionary contains a strain sample (as a numpy array) of the
+ given length, centered around `gps_time`, (down)-sampled to
+ the desired `target_sampling_rate`.
+ """
+
+ # -------------------------------------------------------------------------
+ # Perform some basic sanity checks on the arguments
+ # -------------------------------------------------------------------------
+
+ assert isinstance(gps_time, int), \
+ 'time is not an integer!'
+ assert isinstance(interval_width, int), \
+ 'interval_width is not an integer'
+ assert isinstance(original_sampling_rate, int), \
+ 'original_sampling_rate is not an integer'
+ assert isinstance(target_sampling_rate, int), \
+ 'target_sampling_rate is not an integer'
+ assert original_sampling_rate % target_sampling_rate == 0, \
+ 'Invalid target_sampling_rate: Not a divisor of ' \
+ 'original_sampling_rate!'
+
+ # -------------------------------------------------------------------------
+ # Read out the strain from the HDF files
+ # -------------------------------------------------------------------------
+
+ # Compute the offset = half the interval width (intervals are centered
+ # around the given gps_time)
+ offset = int(interval_width / 2)
+
+ # Compute the resampling factor
+ sampling_factor = int(original_sampling_rate / target_sampling_rate)
+
+ # Store the sample we have selected from the HDF files
+ sample = dict()
+
+ # Loop over both detectors
+ for detector in ('H1', 'L1'):
+
+ # Extract the path to the HDF file
+ file_path = hdf_file_paths[detector]
+
+ # Read in the HDF file and select the noise sample
+ with h5py.File(file_path, 'r') as hdf_file:
+
+ # Get the start_time and compute array indices
+ start_time = int(hdf_file['meta']['GPSstart'][()])
+ start_idx = \
+ (gps_time - start_time - offset) * original_sampling_rate
+ end_idx = \
+ (gps_time - start_time + offset) * original_sampling_rate
+
+ # Select the sample from the strain
+ strain = np.array(hdf_file['strain']['Strain'])
+ sample[detector] = strain[start_idx:end_idx]
+
+ # Down-sample the selected sample to the target_sampling_rate
+ sample[detector] = sample[detector][::sampling_factor]
+
+ # -------------------------------------------------------------------------
+ # Convert to PyCBC time series, if necessary
+ # -------------------------------------------------------------------------
+
+ # If we just want a plain numpy array, we can return it right away
+ if not as_pycbc_timeseries:
+ return sample
+
+ # Otherwise we need to convert the numpy array to a time series first
+ else:
+
+ # Initialize an empty dict for the time series results
+ timeseries = dict()
+
+ # Convert strain of both detectors to a TimeSeries object
+ for detector in ('H1', 'L1'):
+
+ timeseries[detector] = \
+ TimeSeries(initial_array=sample[detector],
+ delta_t=1.0/target_sampling_rate,
+ epoch=LIGOTimeGPS(gps_time - offset))
+
+ return timeseries
+
+
+# -----------------------------------------------------------------------------
+# CLASS DEFINITIONS
+# -----------------------------------------------------------------------------
+
+class NoiseTimeline:
+ """
+ A ``NoiseTimeline`` object stores information about the data
+ quality and hardware injection flags of the files in the given
+ `background_data_directory`. This is information is read in only
+ once at the beginning of the sample generation and can then be
+ utilized to quickly sample "valid" noise times, that is, GPS times
+ where the files in `background_data_directory` provide data which
+ pass certain desired quality criteria.
+
+ Args:
+ background_data_directory (str): Path to the directory which
+ contains the raw data (HDF files). These files may also be
+ distributed over several subdirectories.
+ random_seed (int): Seed for the random number generator which
+ is used for sampling valid noise times.
+ verbose (bool): Whether or not this instance should print
+ logging information to the command line.
+ """
+
+ def __init__(self,
+ background_data_directory,
+ random_seed=42,
+ verbose=False):
+
+ # Store the directory and sampling rate of the raw HDF files
+ self.background_data_directory = background_data_directory
+
+ # Print debug messages or not?
+ self.verbose = verbose
+
+ # Create a new random number generator with the given seed to
+ # decouple this from the global numpy RNG (for reproducibility)
+ self.rng = np.random.RandomState(seed=random_seed)
+
+ # Get the list of all HDF files in the specified directory
+ self.vprint('Getting HDF file paths...', end=' ')
+ self.hdf_file_paths = get_file_paths(self.background_data_directory,
+ extensions=['hdf', 'h5'])
+ self.vprint('Done!')
+
+ # Read in the meta information and masks from HDF files
+ self.vprint('Reading information from HDF files', end=' ')
+ self.hdf_files = self._get_hdf_files()
+ self.vprint('Done!')
+
+ # Build the timeline for these HDF files
+ self.vprint('Building timeline object...', end=' ')
+ self.timeline = self._build_timeline()
+ self.vprint('Done!')
+
+ # -------------------------------------------------------------------------
+
+ def vprint(self, string, *args, **kwargs):
+ """
+ Verbose printing: Wrapper around `print()` to only call it if
+ `self.verbose` is set to true.
+
+ Args:
+ string (str): String to be printed if `self.verbose`
+ is `True`.
+ *args: Arguments passed to `print()`.
+ **kwargs: Keyword arguments passed to `print()`.
+ """
+
+ if self.verbose:
+ print(string, *args, **kwargs)
+ sys.stdout.flush()
+
+ # -------------------------------------------------------------------------
+
+ def _get_hdf_files(self):
+
+ # Keep track of all the files whose information we need to store
+ hdf_files = []
+
+ # Open every HDF file once to read in the meta information as well
+ # as the injection and data quality (DQ) masks
+ n_files = len(self.hdf_file_paths)
+ for i, hdf_file_path in enumerate(self.hdf_file_paths):
+ with h5py.File(hdf_file_path, 'r') as f:
+
+ self.vprint('({:>4}/{:>4})...'.format(i, n_files), end=' ')
+
+ # Select necessary information from the HDF file
+ start_time = f['meta']['GPSstart'][()]
+ detector = f['meta']['Detector'][()].decode('utf-8')
+ duration = f['meta']['Duration'][()]
+ inj_mask = np.array(f['quality']['injections']['Injmask'],
+ dtype=np.int32)
+ dq_mask = np.array(f['quality']['simple']['DQmask'],
+ dtype=np.int32)
+
+ # Perform some basic sanity checks
+ assert detector in ['H1', 'L1'], \
+ 'Invalid detector {}!'.format(detector)
+ assert duration == len(inj_mask) == len(dq_mask), \
+ 'Length of InjMask or DQMask does not match the duration!'
+
+ # Collect this information in a dict
+ hdf_files.append(dict(file_path=hdf_file_path,
+ start_time=start_time,
+ detector=detector,
+ duration=duration,
+ inj_mask=inj_mask,
+ dq_mask=dq_mask))
+
+ self.vprint('\033[15D\033[K', end='')
+
+ # Sort the read in HDF files by start time and return them
+ self.vprint('({:>4}/{:>4})...'.format(n_files, n_files), end=' ')
+ return sorted(hdf_files, key=lambda _: _['start_time'])
+
+ # -------------------------------------------------------------------------
+
+ def _build_timeline(self):
+
+ # Get the size of the arrays that we need to initialize
+ n_entries = self.gps_end_time - self.gps_start_time
+
+ # Initialize the empty timeline
+ timeline = dict(h1_inj_mask=np.zeros(n_entries, dtype=np.int32),
+ l1_inj_mask=np.zeros(n_entries, dtype=np.int32),
+ h1_dq_mask=np.zeros(n_entries, dtype=np.int32),
+ l1_dq_mask=np.zeros(n_entries, dtype=np.int32))
+
+ # Add information from HDF files to timeline
+ for hdf_file in self.hdf_files:
+
+ # Define some shortcuts
+ detector = hdf_file['detector']
+ dq_mask = hdf_file['dq_mask']
+ inj_mask = hdf_file['inj_mask']
+
+ # Map start/end from GPS time to array indices
+ idx_start = hdf_file['start_time'] - self.gps_start_time
+ idx_end = idx_start + hdf_file['duration']
+
+ # Add the mask information to the correct detector
+ if detector == 'H1':
+ timeline['h1_inj_mask'][idx_start:idx_end] = inj_mask
+ timeline['h1_dq_mask'][idx_start:idx_end] = dq_mask
+ else:
+ timeline['l1_inj_mask'][idx_start:idx_end] = inj_mask
+ timeline['l1_dq_mask'][idx_start:idx_end] = dq_mask
+
+ # Return the completed timeline
+ return timeline
+
+ # -------------------------------------------------------------------------
+
+ def is_valid(self,
+ gps_time,
+ delta_t=16,
+ dq_bits=(0, 1, 2, 3),
+ inj_bits=(0, 1, 2, 4)):
+ """
+ For a given `gps_time`, check if is a valid time to sample
+ noise from by checking if all data points in the interval
+ `[gps_time - delta_t, gps_time + delta_t]` have the specified
+ `dq_bits` and `inj_bits` set.
+
+ .. seealso:: For more information about the `dq_bits` and
+ `inj_bits`, check out the website of the GW Open Science
+ Center, which explains these for the case of O1:
+
+ https://www.gw-openscience.org/archive/dataset/O1
+
+ Args:
+ gps_time (int): The GPS time whose validity we are checking.
+ delta_t (int): The number of seconds around `gps_time`
+ which we also want to be valid (because the sample will
+ be an interval).
+ dq_bits (tuple): The Data Quality Bits which one would like
+ to require (see note above).
+ *For example:* `dq_bits=(0, 1, 2, 3)` means that the
+ data quality needs to pass all tests up to `CAT3`.
+ inj_bits (tuple): The Injection Bits which one would like
+ to require (see note above).
+ *For example:* `inj_bits=(0, 1, 2, 4)` means that only
+ continuous wave (CW) injections are permitted; all
+ recordings containing any of other type of injection
+ will be invalid for sampling.
+
+ Returns:
+ `True` if `gps_time` is valid, otherwise `False`.
+ """
+
+ # ---------------------------------------------------------------------
+ # Perform some basic sanity checks
+ # ---------------------------------------------------------------------
+
+ assert isinstance(gps_time, int), \
+ 'Received GPS time that is not an integer!'
+ assert delta_t >= 0, \
+ 'Received an invalid value for delta_t!'
+ assert set(dq_bits).issubset(set(range(7))), \
+ 'Invalid Data Quality bit specification passed to is_valid()!'
+ assert set(inj_bits).issubset(set(range(5))), \
+ 'Invalid Injection bit specification passed to is_valid()!'
+
+ # ---------------------------------------------------------------------
+ # Check if given time is too close to a real event
+ # ---------------------------------------------------------------------
+
+ # Get GPS times of all confirmed mergers
+ catalog = Catalog()
+ real_event_times = [catalog.mergers[_].time for _ in catalog.names]
+
+ # Check if gps_time is too close to any of these times
+ if any(abs(gps_time - _) <= delta_t for _ in real_event_times):
+ return False
+
+ # ---------------------------------------------------------------------
+ # Check if the given time is too close to the edge within its HDF file
+ # ---------------------------------------------------------------------
+
+ # Loop over all HDF files to find the one that contains the given
+ # gps_time. Here, we do not distinguish between H1 and L1, because
+ # we assume that the files for the detectors are aligned on a grid.
+ for hdf_file in self.hdf_files:
+
+ # Get the start and end time for the current HDF file
+ start_time = hdf_file['start_time']
+ end_time = start_time + hdf_file['duration']
+
+ # Find the file that contains the given gps_time
+ if start_time < gps_time < end_time:
+
+ # Check if it is far away enough from the edges: If not, it
+ # is not a valid time; otherwise we can still stop searching
+ if not start_time + delta_t < gps_time < end_time - delta_t:
+ return False
+ else:
+ break
+
+ # ---------------------------------------------------------------------
+ # Select the environment around the specified time
+ # ---------------------------------------------------------------------
+
+ # Map time to indices
+ idx_start = self.gps2idx(gps_time) - delta_t
+ idx_end = self.gps2idx(gps_time) + delta_t
+
+ # Select the mask intervals
+ environment = \
+ dict(h1_inj_mask=self.timeline['h1_inj_mask'][idx_start:idx_end],
+ l1_inj_mask=self.timeline['l1_inj_mask'][idx_start:idx_end],
+ h1_dq_mask=self.timeline['h1_dq_mask'][idx_start:idx_end],
+ l1_dq_mask=self.timeline['l1_dq_mask'][idx_start:idx_end])
+
+ # ---------------------------------------------------------------------
+ # Data Quality Check
+ # ---------------------------------------------------------------------
+
+ # Compute the minimum data quality
+ min_dq = sum([2**i for i in dq_bits])
+
+ # Perform the DQ check for H1
+ environment['h1_dq_mask'] = environment['h1_dq_mask'] > min_dq
+ if not np.all(environment['h1_dq_mask']):
+ return False
+
+ # Perform the DQ check for L1
+ environment['l1_dq_mask'] = environment['l1_dq_mask'] > min_dq
+ if not np.all(environment['l1_dq_mask']):
+ return False
+
+ # ---------------------------------------------------------------------
+ # Injection Check
+ # ---------------------------------------------------------------------
+
+ # Define an array of ones that matches the length of the environment.
+ # This is needed because for a given number N, we can check if the
+ # K-th bit is set by evaluating the expression: N & (1 << K)
+ ones = np.ones(2 * delta_t, dtype=np.int32)
+
+ # For each requested injection bit, check if it is set for the whole
+ # environment (for both H1 and L1)
+ for i in inj_bits:
+
+ # Perform the injection check for H1
+ if not np.all(np.bitwise_and(environment['h1_inj_mask'],
+ np.left_shift(ones, i))):
+ return False
+
+ # Perform the injection check for L1
+ if not np.all(np.bitwise_and(environment['l1_inj_mask'],
+ np.left_shift(ones, i))):
+ return False
+
+ # If we have not returned False yet, the time must be valid!
+ return True
+
+ # -------------------------------------------------------------------------
+
+ def sample(self,
+ delta_t=16,
+ dq_bits=(0, 1, 2, 3),
+ inj_bits=(0, 1, 2, 4),
+ return_paths=False):
+
+ """
+ Randomly sample a time from `[gps_start_time, gps_end_time]`
+ which passes the :func:`NoiseTimeline.is_valid()` test.
+
+ Args:
+ delta_t (int): For an explanation, see
+ :func:`NoiseTimeline.is_valid()`.
+ dq_bits (tuple): For an explanation, see
+ :func:`NoiseTimeline.is_valid()`.
+ inj_bits (tuple): For an explanation, see
+ :func:`NoiseTimeline.is_valid()`.
+ return_paths (bool): Whether or not to return the paths to
+ the HDF files containing the `gps_time`.
+
+ Returns:
+ A valid GPS time and optionally a `dict` with the file
+ paths to the HDF files containing that GPS time (keys will
+ correspond to the different detectors).
+ """
+
+ # Keep sampling random times until we find a valid one...
+ while True:
+
+ # Randomly choose a GPS time between the start and end
+ gps_time = self.rng.randint(self.gps_start_time + delta_t,
+ self.gps_end_time - delta_t)
+
+ # If it is a valid time, return it
+ if self.is_valid(gps_time=gps_time, delta_t=delta_t,
+ dq_bits=dq_bits, inj_bits=inj_bits):
+ if return_paths:
+ return gps_time, self.get_file_paths_for_time(gps_time)
+ else:
+ return gps_time
+
+ # -------------------------------------------------------------------------
+
+ def get_file_paths_for_time(self, gps_time):
+ """
+ For a given (valid) GPS time, find the two HDF files (for the
+ two detectors H1 and L1) which contain the corresponding strain.
+
+ Args:
+ gps_time (int): A valid GPS time stamp.
+
+ Returns:
+ A dictionary with keys `{'H1', 'L1'}` containing the paths
+ to the HDF files, or None if no such files could be found.
+ """
+
+ # Keep track of the results, i.e., the paths to the HDF files
+ result = dict()
+
+ # Loop over all HDF files to find the ones containing the given time
+ for hdf_file in self.hdf_files:
+
+ # Get the start and end time for the current HDF file
+ start_time = hdf_file['start_time']
+ end_time = start_time + hdf_file['duration']
+
+ # Check if the given GPS time falls into the interval of the
+ # current HDF file, and if so, store the file path for it
+ if start_time < gps_time < end_time:
+ result[hdf_file['detector']] = hdf_file['file_path']
+
+ # If both files were found, we are done!
+ if 'H1' in result.keys() and 'L1' in result.keys():
+ return result
+
+ # If we didn't both files, return None
+ return None
+
+ # -------------------------------------------------------------------------
+
+ def idx2gps(self, idx):
+ """
+ Map an index to a GPS time by correcting for the start time of
+ the observation run, as determined from the HDF files.
+
+ Args:
+ idx (int): An index of a time series array (covering an
+ observation run).
+
+ Returns:
+ The corresponding GPS time.
+ """
+
+ return idx + self.gps_start_time
+
+ # -------------------------------------------------------------------------
+
+ def gps2idx(self, gps):
+ """
+ Map an GPS time to an index by correcting for the start time of
+ the observation run, as determined from the HDF files.
+
+ Args:
+ gps (int): A GPS time belonging to a point in time between
+ the start and end of an observation run.
+
+ Returns:
+ The corresponding time series index.
+ """
+
+ return gps - self.gps_start_time
+
+ # -------------------------------------------------------------------------
+
+ @property
+ def gps_start_time(self):
+ """
+ The GPS start time of the observation run.
+ """
+
+ return self.hdf_files[0]['start_time']
+
+ # -------------------------------------------------------------------------
+
+ @property
+ def gps_end_time(self):
+ """
+ The GPS end time of the observation run.
+ """
+
+ return self.hdf_files[-1]['start_time'] + \
+ self.hdf_files[-1]['duration']
diff --git a/python3_samples/utils/progressbar.py b/python3_samples/utils/progressbar.py
new file mode 100755
index 0000000..e7c644a
--- /dev/null
+++ b/python3_samples/utils/progressbar.py
@@ -0,0 +1,230 @@
+"""
+Provide a custom ProgressBar class, which provides a wrapper around
+an iterable that automatically produces a progressbar when iterating
+over it.
+"""
+
+# -----------------------------------------------------------------------------
+# IMPORTS
+# -----------------------------------------------------------------------------
+
+import sys
+import time
+from threading import Event, Thread
+
+
+# -----------------------------------------------------------------------------
+# CLASS DEFINITIONS
+# -----------------------------------------------------------------------------
+
+class RepeatedTimer:
+ """
+ Wrapper class to repeat the given `func` every `interval` seconds
+ (asynchronously in the background).
+ Source: https://stackoverflow.com/a/33054922/4100721.
+ """
+
+ def __init__(self, interval, func, *args, **kwargs):
+ self.interval = interval
+ self.func = func
+ self.args = args
+ self.kwargs = kwargs
+ self.start = time.time()
+ self.event = Event()
+ self.thread = Thread(target=self._target)
+ self.thread.start()
+
+ def _target(self):
+ while not self.event.wait(self._time):
+ self.func(*self.args, **self.kwargs)
+
+ @property
+ def _time(self):
+ return self.interval - ((time.time() - self.start) % self.interval)
+
+ def stop(self):
+ self.event.set()
+ self.thread.join()
+
+
+# -----------------------------------------------------------------------------
+
+
+class ProgressBar:
+ """
+ :class:`ProgressBar` objects are a custom way to "decorate"
+ a given iterable to produce a progress bar when looping over it.
+ This class allows to also produce some output with the progress
+ bar, such as information about the element of the iterable that is
+ currently being processed.
+
+ Args:
+ iterable (iterable): The iterable to be "decorated" with
+ a progressbar.
+ bar_length (int): Length of the bar itself (in characters).
+ auto_update (bool): Whether or not to automatically write
+ the updated progressbar to the command line.
+ """
+
+ def __init__(self,
+ iterable,
+ bar_length=50,
+ auto_update=False):
+
+ self.iterable = iterable
+ self.max_value = len(iterable)
+ self.bar_length = bar_length
+ self.auto_update = auto_update
+
+ self.start_time = None
+ self.last_timediff = None
+
+ self.progressbar = self.get_progressbar(-1)
+
+ self.extras_ = []
+ self.scheduler = None
+
+ # -------------------------------------------------------------------------
+
+ def __iter__(self):
+
+ # Start the stop watch as soon as we start iterating
+ self.start_time = time.time()
+
+ # Initialize index to 0 to ensure it is always defined
+ index = 0
+
+ # Start the scheduler that will update the elapsed time every second
+ def update():
+ self.progressbar = self.get_progressbar(index)
+ self.write(extras=self.extras_)
+ self.scheduler = RepeatedTimer(1, update)
+
+ # Actually loop over the iterable
+ for index, value in enumerate(self.iterable):
+
+ # Update the last_timediff, which is used to estimate when we
+ # will be done
+ self.last_timediff = self.get_timediff()
+
+ # Update the progressbar string
+ self.progressbar = self.get_progressbar(index)
+
+ # If are doing auto-updates (i.e. no extras), we can already
+ # write the progress bar to stdout
+ if self.auto_update:
+ self.write()
+
+ # Finally, actually yield the current value of the iterable
+ yield value
+
+ # Update our progress bar string one last time to indicate we have
+ # made it to 100%
+ self.progressbar = self.get_progressbar(self.max_value)
+
+ # Stop our background scheduler
+ self.scheduler.stop()
+
+ # -------------------------------------------------------------------------
+
+ def get_timediff(self):
+ """
+ Returns: Time elapsed since progress bar was instantiated.
+ """
+
+ if self.start_time is not None:
+ return time.time() - self.start_time
+ else:
+ return None
+
+ # -------------------------------------------------------------------------
+
+ def get_eta(self,
+ percent):
+ """
+ Get the estimated time of arrival (ETA) by linear interpolation.
+
+ Args:
+ percent (float): Current progress in percent.
+
+ Returns:
+ Estimated time of arrival in seconds.
+ """
+
+ if self.last_timediff is not None and percent != 0:
+ return max(0, self.last_timediff / percent - self.get_timediff())
+ else:
+ return None
+
+ # -------------------------------------------------------------------------
+
+ def get_progressbar(self,
+ index):
+ """
+ Construct the progressbar itself (bar, ETA, etc.).
+
+ Args:
+ index (int): Current index of the iterable; used to compute
+ the current progress percentage.
+
+ Returns:
+ A string containing the basic progress bar.
+ """
+
+ # Construct the actual progress bar
+ percent = float(index) / self.max_value
+ bar = '=' * int(round(percent * self.bar_length))
+ spaces = '-' * (self.bar_length - len(bar))
+
+ # Get the elapsed time as a proper string
+ elapsed_time = self.get_timediff()
+ if elapsed_time is None:
+ elapsed_time = 0
+ elapsed_time = '{:.2f}'.format(elapsed_time)
+
+ # Get the expected time of arrival (ETA) as a proper string
+ eta = self.get_eta(percent)
+ if eta is None:
+ eta = '?'
+ else:
+ eta = '{:.2f}'.format(eta)
+
+ # Construct the actual progress bar string
+ out = "[{0}] {1:>3}% ({2:>{3}}/{4:>{3}}) | Elapsed: {5} | ETA: {6}"
+ progressbar = out.format(bar + spaces, round(percent * 100),
+ index, len(str(self.max_value)),
+ self.max_value, elapsed_time, eta)
+
+ return progressbar
+
+ # -------------------------------------------------------------------------
+
+ def write(self,
+ clear_line=False,
+ extras=()):
+ """
+ Construct the progress bar and write it to the command line.
+
+ Args:
+ clear_line (bool): Whether or not to clear the last line.
+ extras (list): List of additional outputs (e.g., the file
+ that is currently being downloaded).
+ """
+
+ self.extras_ = extras
+
+ if extras:
+ for _ in range(len(extras)):
+ sys.stdout.write('\r\033[K\033[F')
+ if clear_line:
+ sys.stdout.write('\r\033[K\033[F')
+
+ # Actually write the finished progress bar to the command line
+ sys.stdout.write('\r\033[K\033[F')
+ sys.stdout.write('\r\033[K\033[K')
+ sys.stdout.write(self.progressbar)
+ if extras:
+ sys.stdout.write('\n' + '\n'.join(extras))
+ if not clear_line:
+ sys.stdout.write('\n')
+ sys.stdout.flush()
diff --git a/python3_samples/utils/samplefiles.py b/python3_samples/utils/samplefiles.py
new file mode 100755
index 0000000..4a6de14
--- /dev/null
+++ b/python3_samples/utils/samplefiles.py
@@ -0,0 +1,368 @@
+"""
+Provide tools for writing and reading the sample HDF files produced by
+the sample generation.
+"""
+
+# -----------------------------------------------------------------------------
+# IMPORTS
+# -----------------------------------------------------------------------------
+
+import numpy as np
+import pandas as pd
+import h5py
+
+from six import iteritems
+from pprint import pformat
+from warnings import warn
+
+
+# -----------------------------------------------------------------------------
+# CLASS DEFINITIONS
+# -----------------------------------------------------------------------------
+
+class SampleFile:
+ """
+ :class:`SampleFile` objects serve as an abstraction for the result
+ files of the sample generation.
+
+ Args:
+ data (dict): A dictionary containing the following keys:
+
+ .. code-block:: python
+
+ {'command_line_arguments', 'static_arguments',
+ 'injection_samples', 'noise_samples',
+ 'injection_parameters', 'normalization_parameters'}
+
+ The value for every key must again be a dictionary relating
+ the names of sample parameters (e.g., 'h1_snr') to a numpy
+ array containing the values for that parameter.
+ """
+
+ def __init__(self,
+ data=None):
+
+ # Perform sanity checks on data
+ self.__check_data(data)
+
+ # If we have received data, store it; else initialize an empty dict
+ if data is not None:
+ self.data = data
+ else:
+ self.data = dict(command_line_arguments=dict(),
+ static_arguments=dict(),
+ injection_samples=dict(),
+ noise_samples=dict(),
+ injection_parameters=dict(),
+ normalization_parameters=dict())
+
+ # -------------------------------------------------------------------------
+
+ @staticmethod
+ def __check_data(data):
+ """
+ Run some sanity checks on `data`. Raises an assertion error if
+ the data fail any of these sanity checks.
+
+ Args:
+ data (dict): A dictionary as specified in the ``__init__``
+ of this class, that is, a dictionary containing the
+ following keys:
+
+ .. code-block:: python
+
+ {'command_line_arguments', 'static_arguments',
+ 'injection_samples', 'noise_samples',
+ 'injection_parameters', 'normalization_parameters'}
+ """
+
+ assert isinstance(data, dict) or data is None, \
+ 'data must be either dict or None!'
+
+ if data is not None:
+
+ assert 'command_line_arguments' in data.keys(), \
+ 'data must provide key "command_line_arguments"!'
+ assert 'static_arguments' in data.keys(), \
+ 'data must provide key "static_arguments"!'
+ assert 'injection_samples' in data.keys(), \
+ 'data must provide key "injection_samples"!'
+ assert 'noise_samples' in data.keys(), \
+ 'data must provide key "noise_samples"!'
+ assert 'injection_parameters' in data.keys(), \
+ 'data must provide key "injection_parameters"!'
+ assert 'normalization_parameters' in data.keys(), \
+ 'data must provide key "normalization_parameters"!'
+
+ # -------------------------------------------------------------------------
+
+ def __repr__(self):
+
+ return pformat(self.data, indent=4)
+
+ # -------------------------------------------------------------------------
+
+ def __str__(self):
+
+ return pformat(self.data, indent=4)
+
+ # -------------------------------------------------------------------------
+
+ def __getitem__(self, item):
+
+ return self.data[item]
+
+ # -------------------------------------------------------------------------
+
+ def __setitem__(self, key, value):
+
+ self.data[key] = value
+
+ # -------------------------------------------------------------------------
+
+ def read_hdf(self, file_path):
+ """
+ Read in an existing HDF sample file (e.g., to use an instance
+ of :class:`SampleFile` as a convenience wrapper for accessing
+ the contents of an HDF samples file).
+
+ Args:
+ file_path (str): The path to the HDF file to be read into
+ the :class:`SampleFile` object.
+ """
+
+ # Clear the existing data
+ self.data = {}
+
+ with h5py.File(file_path, 'r') as hdf_file:
+
+ # Read in dict with command_line_arguments
+ self.data['command_line_arguments'] = \
+ dict(hdf_file['command_line_arguments'].attrs)
+ self.data['command_line_arguments'] = \
+ {key: value.decode('ascii') for key, value in
+ iteritems(self.data['command_line_arguments'])}
+
+ # Read in dict with static_arguments
+ self.data['static_arguments'] = \
+ dict(hdf_file['static_arguments'].attrs)
+ self.data['static_arguments'] = \
+ {key: value.decode('ascii') for key, value in
+ iteritems(self.data['static_arguments'])}
+
+ # Read in group containing injection samples
+ self.data['injection_samples'] = dict()
+ for key in ('event_time', 'h1_strain', 'l1_strain'):
+ try:
+ self.data['injection_samples'][key] = \
+ np.array(hdf_file['injection_samples'][key])
+ except TypeError:
+ self.data['injection_samples'][key] = np.array(None)
+
+ # Read in group containing noise samples
+ self.data['noise_samples'] = dict()
+ for key in ('event_time', 'h1_strain', 'l1_strain'):
+ try:
+ self.data['noise_samples'][key] = \
+ np.array(hdf_file['noise_samples'][key])
+ except TypeError:
+ self.data['noise_samples'][key] = np.array(None)
+
+ # Read in injection parameters
+ self.data['injection_parameters'] = dict()
+ for key in hdf_file['/injection_parameters'].keys():
+ try:
+ self.data['injection_parameters'][key] = \
+ np.array(hdf_file['injection_parameters'][key])
+ except TypeError:
+ self.data['injection_parameters'][key] = np.array(None)
+
+ # Read in dict with normalization parameters
+ self.data['normalization_parameters'] = \
+ dict(hdf_file['normalization_parameters'].attrs)
+ self.data['normalization_parameters'] = \
+ {key: float(value) for key, value in
+ iteritems(self.data['normalization_parameters'])}
+
+ # -------------------------------------------------------------------------
+
+ def to_hdf(self, file_path):
+
+ with h5py.File(file_path, 'w') as hdf_file:
+
+ # Create group for command_line_arguments and save the values of
+ # the dict as attributes of the group
+ group = hdf_file.create_group('command_line_arguments')
+ for key, value in iteritems(self.data['command_line_arguments']):
+ group.attrs[key] = str(value)
+
+ # Create group for static_arguments and save the values of
+ # the dict as attributes of the group
+ group = hdf_file.create_group('static_arguments')
+ for key, value in iteritems(self.data['static_arguments']):
+ group.attrs[key] = str(value)
+
+ # Create group for injection_samples and save every item of the
+ # dict as a new dataset
+ group = hdf_file.create_group('injection_samples')
+ for key, value in iteritems(self.data['injection_samples']):
+ dtype = 'float64' if key == 'event_time' else 'float32'
+ if value is not None:
+ group.create_dataset(name=key,
+ shape=value.shape,
+ dtype=dtype,
+ data=value)
+ else:
+ group.create_dataset(name=key,
+ shape=None,
+ dtype=dtype)
+
+ # Create group for noise_samples and save every item of the
+ # dict as a new dataset
+ group = hdf_file.create_group('noise_samples')
+ for key, value in iteritems(self.data['noise_samples']):
+ dtype = 'float64' if key == 'event_time' else 'float32'
+ if value is not None:
+ group.create_dataset(name=key,
+ shape=value.shape,
+ dtype=dtype,
+ data=value)
+ else:
+ group.create_dataset(name=key,
+ shape=None,
+ dtype=dtype)
+
+ # Create group for injection_parameters and save every item of the
+ # dict as a new dataset
+ group = hdf_file.create_group('injection_parameters')
+ for key, value in iteritems(self.data['injection_parameters']):
+ if value is not None:
+ group.create_dataset(name=key,
+ shape=value.shape,
+ dtype='float64',
+ data=value)
+ else:
+ group.create_dataset(name=key,
+ shape=None,
+ dtype='float64')
+
+ # Create group for normalization_parameters and save every item
+ # of the dict as a new attribute
+ group = hdf_file.create_group('normalization_parameters')
+ for key, value in iteritems(self.data['normalization_parameters']):
+ group.attrs[key] = float(value)
+
+ # -------------------------------------------------------------------------
+
+ def as_dataframe(self,
+ injection_parameters=False,
+ static_arguments=False,
+ command_line_arguments=False,
+ split_injections_noise=False):
+ """
+ Return the contents of the :class:`SampleFile` as a ``pandas``
+ data frame.
+
+ Args:
+ injection_parameters (bool): Whether or not to return
+ the `injection parameters` for every sample.
+ static_arguments (bool): Whether or not to return
+ the `static_arguments` for every sample.
+ command_line_arguments (bool): Whether or not to return
+ the `command_line_arguments` for every sample.
+ split_injections_noise (bool): If this is set to True, a
+ separate data frame will be returned for both the
+ samples with and without an injection.
+
+ Returns:
+ One (or two, if `split_injections_noise` is set to `True`)
+ pandas data frame containing the sample stored in the
+ :class:`SampleFile` object.
+ """
+
+ # Create a data frame for the samples containing an injection
+ injection_samples = []
+ if self.data['injection_samples']['event_time'].shape != ():
+ for i in range(len(self.data['injection_samples']['event_time'])):
+ _ = {k: v[i] for k, v in
+ iteritems(self.data['injection_samples'])}
+ injection_samples.append(_)
+ df_injection_samples = pd.DataFrame().append(injection_samples,
+ ignore_index=True,
+ sort=True)
+ else:
+ df_injection_samples = pd.DataFrame()
+
+ # Create a data frame for the samples not containing an injection
+ noise_samples = []
+ if self.data['noise_samples']['event_time'].shape != ():
+ for i in range(len(self.data['noise_samples']['event_time'])):
+ _ = {k: v[i] for k, v in
+ iteritems(self.data['noise_samples'])}
+ noise_samples.append(_)
+ df_noise_samples = pd.DataFrame().append(noise_samples,
+ ignore_index=True,
+ sort=True)
+ else:
+ df_noise_samples = pd.DataFrame()
+
+ # If requested, create a data frame for the injection parameters and
+ # merge it with the data frame containing the injection samples
+ if injection_parameters:
+ injection_params = []
+
+ # Check if we even have any injection parameters
+ if self.data['injection_parameters']['mass1'].shape != ():
+ for i in range(len(df_injection_samples)):
+ _ = {k: v[i] for k, v in
+ iteritems(self.data['injection_parameters'])}
+ injection_params.append(_)
+ df_injection_params = pd.DataFrame().append(injection_params,
+ ignore_index=True,
+ sort=True)
+ else:
+ df_injection_params = pd.DataFrame()
+
+ df = pd.concat([df_injection_samples, df_injection_params],
+ axis=1, sort=True)
+
+ else:
+ df = df_injection_samples
+
+ # If requested, add the static_arguments to the data frame
+ # containing the injections, and a smaller subset of the
+ # static_arguments also to the data frame containing the noise
+ # samples (only those arguments that make sense there)
+ if static_arguments:
+ for key, value in iteritems(self.data['static_arguments']):
+ df[key] = value
+ if key in ('random_seed', 'target_sampling_rate',
+ 'bandpass_lower', 'bandpass_upper',
+ 'seconds_before_event', 'seconds_after_event',
+ 'sample_length'):
+ df_noise_samples[key] = value
+
+ # Merge the data frames for the samples with and without injections
+ df = df.append(df_noise_samples, ignore_index=True, sort=True)
+
+ # If requested, add the command line arguments that were used in the
+ # creation of the sample file to the combined data frame
+ if command_line_arguments:
+ for key, value in iteritems(self.data['command_line_arguments']):
+ df[key] = value
+
+ # Ensure the `event_time` variable is an integer
+ try:
+ df['event_time'] = df['event_time'].astype(int)
+ except KeyError:
+ warn('\nNo key "event_time": Data frame is probably empty!')
+
+ # Either split into two data frames for injection and noise samples
+ if split_injections_noise:
+ df_injections = df[df.h1_signal.notnull()]
+ df_noise = df[~df.h1_signal.notnull()]
+ return df_injections, df_noise
+
+ # Or just return a single data frame containing both types of samples
+ else:
+ return df
diff --git a/python3_samples/utils/samplegeneration.py b/python3_samples/utils/samplegeneration.py
new file mode 100755
index 0000000..76be58a
--- /dev/null
+++ b/python3_samples/utils/samplegeneration.py
@@ -0,0 +1,288 @@
+"""
+Provide the :func:`generate_sample()` method, which is at the heart of
+the sample generation process.
+"""
+
+# -----------------------------------------------------------------------------
+# IMPORTS
+# -----------------------------------------------------------------------------
+
+from __future__ import print_function
+
+import numpy as np
+
+from lal import LIGOTimeGPS
+from pycbc.psd import interpolate
+from pycbc.psd.analytical import aLIGOZeroDetHighPower
+from pycbc.noise import noise_from_psd
+from pycbc.filter import sigma
+
+from .hdffiles import get_strain_from_hdf_file
+from .waveforms import get_detector_signals, get_waveform
+
+
+# -----------------------------------------------------------------------------
+# FUNCTION DEFINITIONS
+# -----------------------------------------------------------------------------
+
+def generate_sample(static_arguments,
+ event_tuple,
+ waveform_params=None):
+ """
+ Generate a single sample (or example) by taking a piece of LIGO
+ background noise (real or synthetic, depending on `event_tuple`),
+ optionally injecting a simulated waveform (depending on
+ `waveform_params`) and post-processing the result (whitening,
+ band-passing).
+
+ Args:
+ static_arguments (dict): A dictionary containing global
+ technical parameters for the sample generation, for example
+ the target_sampling_rate of the output.
+ event_tuple (tuple): A tuple `(event_time, file_path)`, which
+ specifies the GPS time at which to make an injection and
+ the path of the HDF file which contains said GPS time.
+ If `file_path` is `None`, synthetic noise will be used
+ instead and the `event_time` only serves as a seed for
+ the corresponding (random) noise generator.
+ waveform_params (dict): A dictionary containing the randomly
+ sampled parameters that are passed as inputs to the
+ waveform model (e.g., the masses, spins, position, ...).
+
+ Returns:
+ A tuple `(sample, injection_parameters)`, which contains the
+ generated `sample` itself (a dict with keys `{'event_time',
+ 'h1_strain', 'l1_strain'}`), and the `injection_parameters`,
+ which are either `None` (in case no injection was made), or a
+ dict containing the `waveform_params` and some additional
+ parameters (e.g., single detector SNRs).
+ """
+
+ # -------------------------------------------------------------------------
+ # Define shortcuts for some elements of self.static_arguments
+ # -------------------------------------------------------------------------
+
+ # Read out frequency-related arguments
+ original_sampling_rate = static_arguments['original_sampling_rate']
+ target_sampling_rate = static_arguments['target_sampling_rate']
+ f_lower = static_arguments['f_lower']
+ delta_f = static_arguments['delta_f']
+ fd_length = static_arguments['fd_length']
+
+ # Get the width of the noise sample that we either select from the raw
+ # HDF files, or generate synthetically
+ noise_interval_width = static_arguments['noise_interval_width']
+
+ # Get how many seconds before and after the event time to use
+ seconds_before_event = static_arguments['seconds_before_event']
+ seconds_after_event = static_arguments['seconds_after_event']
+
+ # Get the event time and the dict containing the HDF file path
+ event_time, hdf_file_paths = event_tuple
+
+ # -------------------------------------------------------------------------
+ # Get the background noise (either from data or synthetically)
+ # -------------------------------------------------------------------------
+
+ # If the event_time is None, we generate synthetic noise
+ if hdf_file_paths is None:
+
+ # Create an artificial PSD for the noise
+ # TODO: Is this the best choice for this task?
+ psd = aLIGOZeroDetHighPower(length=fd_length,
+ delta_f=delta_f,
+ low_freq_cutoff=f_lower)
+
+ # Actually generate the noise using the PSD and LALSimulation
+ noise = dict()
+ for i, det in enumerate(('H1', 'L1')):
+
+ # Compute the length of the noise sample in time steps
+ noise_length = noise_interval_width * target_sampling_rate
+
+ # Generate the noise for this detector
+ noise[det] = noise_from_psd(length=noise_length,
+ delta_t=(1.0 / target_sampling_rate),
+ psd=psd,
+ seed=(2 * event_time + i))
+
+ # Manually fix the noise start time to match the fake event time.
+ # However, for some reason, the correct setter method seems broken?
+ start_time = event_time - noise_interval_width / 2
+ # noinspection PyProtectedMember
+ noise[det]._epoch = LIGOTimeGPS(start_time)
+
+ # Otherwise we select the noise from the corresponding HDF file
+ else:
+
+ kwargs = dict(hdf_file_paths=hdf_file_paths,
+ gps_time=event_time,
+ interval_width=noise_interval_width,
+ original_sampling_rate=original_sampling_rate,
+ target_sampling_rate=target_sampling_rate,
+ as_pycbc_timeseries=True)
+ noise = get_strain_from_hdf_file(**kwargs)
+
+ # -------------------------------------------------------------------------
+ # If applicable, make an injection
+ # -------------------------------------------------------------------------
+
+ # If no waveform parameters are given, we are not making an injection.
+ # In this case, there are no detector signals and no injection
+ # parameters, and the strain is simply equal to the noise
+ if waveform_params is None:
+ detector_signals = None
+ injection_parameters = None
+ strain = noise
+
+ # Otherwise, we need to simulate a waveform for the given waveform_params
+ # and add it into the noise to create the strain
+ else:
+
+ # ---------------------------------------------------------------------
+ # Simulate the waveform with the given injection parameters
+ # ---------------------------------------------------------------------
+
+ # Actually simulate the waveform with these parameters
+ waveform = get_waveform(static_arguments=static_arguments,
+ waveform_params=waveform_params)
+
+ # Get the detector signals by projecting on the antenna patterns
+ detector_signals = \
+ get_detector_signals(static_arguments=static_arguments,
+ waveform_params=waveform_params,
+ event_time=event_time,
+ waveform=waveform)
+
+ # ---------------------------------------------------------------------
+ # Add the waveform into the noise as is to calculate the NOMF-SNR
+ # ---------------------------------------------------------------------
+
+ # Store the dummy strain, the PSDs and the SNRs for the two detectors
+ strain_ = {}
+ psds = {}
+ snrs = {}
+
+ # Calculate these quantities for both detectors
+ for det in ('H1', 'L1'):
+
+ # Add the simulated waveform into the noise to get the dummy strain
+ strain_[det] = noise[det].add_into(detector_signals[det])
+
+ # Estimate the Power Spectral Density from the dummy strain
+ psds[det] = strain_[det].psd(4)
+ psds[det] = interpolate(psds[det], delta_f=delta_f)
+
+ # Use the PSD estimate to calculate the optimal matched
+ # filtering SNR for this injection and this detector
+ snrs[det] = sigma(htilde=detector_signals[det],
+ psd=psds[det],
+ low_frequency_cutoff=f_lower)
+
+ # Calculate the network optimal matched filtering SNR for this
+ # injection (which we need for scaling to the chosen injection SNR)
+ nomf_snr = np.sqrt(snrs['H1']**2 + snrs['L1']**2)
+
+ # ---------------------------------------------------------------------
+ # Add the waveform into the noise with the chosen injection SNR
+ # ---------------------------------------------------------------------
+
+ # Compute the rescaling factor
+ injection_snr = waveform_params['injection_snr']
+ scale_factor = 1.0 * injection_snr / nomf_snr
+
+ strain = {}
+ for det in ('H1', 'L1'):
+
+ # Add the simulated waveform into the noise, using a scaling
+ # factor to ensure that the resulting NOMF-SNR equals the chosen
+ # injection SNR
+ strain[det] = noise[det].add_into(scale_factor *
+ detector_signals[det])
+
+ # ---------------------------------------------------------------------
+ # Store some information about the injection we just made
+ # ---------------------------------------------------------------------
+
+ # Store the information we have computed ourselves
+ injection_parameters = {'scale_factor': scale_factor,
+ 'h1_snr': snrs['H1'],
+ 'l1_snr': snrs['L1']}
+
+ # Also add the waveform parameters we have sampled
+ for key, value in waveform_params.items():
+ injection_parameters[key] = value
+
+ # -------------------------------------------------------------------------
+ # Whiten and bandpass the strain (also for noise-only samples)
+ # -------------------------------------------------------------------------
+
+ for det in ('H1', 'L1'):
+
+ # Get the whitening parameters
+ segment_duration = static_arguments['whitening_segment_duration']
+ max_filter_duration = static_arguments['whitening_max_filter_duration']
+
+ # Whiten the strain (using the built-in whitening of PyCBC)
+ # We don't need to remove the corrupted samples here, because we
+ # crop the strain later on
+ strain[det] = \
+ strain[det].whiten(segment_duration=segment_duration,
+ max_filter_duration=max_filter_duration,
+ remove_corrupted=False)
+
+ # Get the limits for the bandpass
+ bandpass_lower = static_arguments['bandpass_lower']
+ bandpass_upper = static_arguments['bandpass_upper']
+
+ # Apply a high-pass to remove everything below `bandpass_lower`;
+ # If bandpass_lower = 0, do not apply any high-pass filter.
+ if bandpass_lower != 0:
+ strain[det] = strain[det].highpass_fir(frequency=bandpass_lower,
+ remove_corrupted=False,
+ order=512)
+
+ # Apply a low-pass filter to remove everything above `bandpass_upper`.
+ # If bandpass_upper = sampling rate, do not apply any low-pass filter.
+ if bandpass_upper != target_sampling_rate:
+ strain[det] = strain[det].lowpass_fir(frequency=bandpass_upper,
+ remove_corrupted=False,
+ order=512)
+
+ # -------------------------------------------------------------------------
+ # Cut strain (and signal) time series to the pre-specified length
+ # -------------------------------------------------------------------------
+
+ for det in ('H1', 'L1'):
+
+ # Define some shortcuts for slicing
+ a = event_time - seconds_before_event
+ b = event_time + seconds_after_event
+
+ # Cut the strain to the desired length
+ strain[det] = strain[det].time_slice(a, b)
+
+ # If we've made an injection, also cut the simulated signal
+ if waveform_params is not None:
+
+ # Cut the detector signals to the specified length
+ detector_signals[det] = detector_signals[det].time_slice(a, b)
+
+ # Also add the detector signals to the injection parameters
+ injection_parameters['h1_signal'] = \
+ np.array(detector_signals['H1'])
+ injection_parameters['l1_signal'] = \
+ np.array(detector_signals['L1'])
+
+ # -------------------------------------------------------------------------
+ # Collect all available information about this sample and return results
+ # -------------------------------------------------------------------------
+
+ # The whitened strain is numerically on the order of O(1), so we can save
+ # it as a 32-bit float (unlike the original signal, which is down to
+ # O(10^-{30}) and thus requires 64-bit floats).
+ sample = {'event_time': event_time,
+ 'h1_strain': np.array(strain['H1']).astype(np.float32),
+ 'l1_strain': np.array(strain['L1']).astype(np.float32)}
+
+ return sample, injection_parameters
diff --git a/python3_samples/utils/staticargs.py b/python3_samples/utils/staticargs.py
new file mode 100755
index 0000000..b77e55c
--- /dev/null
+++ b/python3_samples/utils/staticargs.py
@@ -0,0 +1,96 @@
+"""
+Provide tools that are needed for amending and typecasting the static
+arguments from an `*.ini` configuration file, which controls the
+waveform simulation process.
+"""
+
+# -----------------------------------------------------------------------------
+# IMPORTS
+# -----------------------------------------------------------------------------
+
+import copy
+
+
+# -----------------------------------------------------------------------------
+# FUNCTION DEFINITIONS
+# -----------------------------------------------------------------------------
+
+def amend_static_args(static_args):
+ """
+ Amend the static_args from the `*.ini` configuration file by adding
+ the parameters that can be computed directly from others (more
+ intuitive ones). Note that the static_args should have been
+ properly typecast first; see :func:`typecast_static_args()`.
+
+ Args:
+ static_args (dict): The static_args dict after it has been
+ typecast by :func:`typecast_static_args()`.
+
+ Returns:
+ The amended `static_args`, where implicitly defined variables
+ have been added.
+ """
+
+ # Create a copy of the original static_args
+ args = copy.deepcopy(static_args)
+
+ # If necessary, compute the sample length
+ if 'sample_length' not in args.keys():
+ args['sample_length'] = \
+ args['seconds_before_event'] + args['seconds_after_event']
+
+ # If necessary, add delta_t = 1 / target_sampling_rate
+ if 'delta_t' not in args.keys():
+ args['delta_t'] = 1.0 / args['target_sampling_rate']
+
+ # If necessary, add delta_f = 1 / waveform_length
+ if 'delta_f' not in args.keys():
+ args['delta_f'] = 1.0 / args['waveform_length']
+
+ # If necessary, add td_length = waveform_length * target_sampling_rate
+ if 'td_length' not in args.keys():
+ args['td_length'] = \
+ int(args['waveform_length'] * args['target_sampling_rate'])
+
+ # If necessary, add fd_length = td_length / 2 + 1
+ if 'fd_length' not in args.keys():
+ args['fd_length'] = int(args['td_length'] / 2.0 + 1)
+
+ return args
+
+
+def typecast_static_args(static_args):
+ """
+ Take the `static_args` dictionary as it is read in from the PyCBC
+ configuration file (i.e., all values are strings) and cast the
+ values to the correct types (`float` or `int`).
+
+ Args:
+ static_args (dict): The raw `static_args` dictionary as it is
+ read from the `*.ini` configuration file.
+
+ Returns:
+ The `static_args` dictionary with proper types for all values.
+ """
+
+ args = copy.deepcopy(static_args)
+
+ # Cast variables to integer that need to be integers
+ args['bandpass_lower'] = int(args['bandpass_lower'])
+ args['bandpass_upper'] = int(args['bandpass_upper'])
+ args['waveform_length'] = int(args['waveform_length'])
+ args['noise_interval_width'] = int(args['noise_interval_width'])
+ args['original_sampling_rate'] = int(args['original_sampling_rate'])
+ args['target_sampling_rate'] = int(args['target_sampling_rate'])
+ args['whitening_segment_duration'] = \
+ float(args['whitening_segment_duration'])
+ args['whitening_max_filter_duration'] = \
+ int(args['whitening_max_filter_duration'])
+
+ # Cast variables to float that need to be floats
+ args['distance'] = float(args['distance'])
+ args['f_lower'] = float(args['f_lower'])
+ args['seconds_before_event'] = float(args['seconds_before_event'])
+ args['seconds_after_event'] = float(args['seconds_after_event'])
+
+ return args
diff --git a/python3_samples/utils/waveforms.py b/python3_samples/utils/waveforms.py
new file mode 100755
index 0000000..9c82fd4
--- /dev/null
+++ b/python3_samples/utils/waveforms.py
@@ -0,0 +1,312 @@
+"""
+Provide methods for generating and processing simulated
+gravitational-wave waveforms.
+"""
+
+# -----------------------------------------------------------------------------
+# IMPORTS
+# -----------------------------------------------------------------------------
+
+from __future__ import print_function
+
+import numpy as np
+from scipy.signal.windows import tukey
+
+from pycbc.distributions import JointDistribution, read_params_from_config, \
+ read_constraints_from_config, read_distributions_from_config
+from pycbc.transforms import apply_transforms, read_transforms_from_config
+from pycbc.workflow import WorkflowConfigParser
+from pycbc.waveform import get_td_waveform, get_fd_waveform
+from pycbc.detector import Detector
+from pycbc.types.timeseries import TimeSeries
+
+
+# -----------------------------------------------------------------------------
+# CLASS DEFINITIONS
+# -----------------------------------------------------------------------------
+
+class WaveformParameterGenerator(object):
+ """
+ :class:`WaveformParameterGenerator` objects are essentially just a
+ simple convenience wrapper to construct the joint probability
+ distribution (and provide a method to draw samples from it) of the
+ parameters specified by the `[variable_args]` section of an
+ `*.ini` configuration file and their distributions as defined in
+ the corresponding `[prior-*]` sections.
+
+ Args:
+ config_file (str): Path to the `*.ini` configuration file,
+ which contains the information about the parameters to be
+ generated and their distributions.
+ random_seed (int): Seed for the random number generator.
+ Caveat: We can only set the seed of the global numpy RNG.
+ """
+
+ def __init__(self,
+ config_file,
+ random_seed):
+
+ # Fix the seed for the random number generator
+ np.random.seed(random_seed)
+
+ # Read in the configuration file using a WorkflowConfigParser.
+ # Note that the argument `configFiles` has to be a list here,
+ # so we need to wrap the `config_file` argument accordingly...
+ config_file = WorkflowConfigParser(configFiles=[config_file])
+
+ # Extract variable arguments and constraints
+ # We don't need the static_args here, hence they do not get amended.
+ self.var_args, _ = read_params_from_config(config_file)
+ self.constraints = read_constraints_from_config(config_file)
+
+ # Extract distributions
+ dist = read_distributions_from_config(config_file)
+
+ # Extract transformations
+ self.trans = read_transforms_from_config(config_file)
+
+ # Set up a joint distribution to sample from
+ self.pval = JointDistribution(self.var_args,
+ *dist,
+ **{'constraints': self.constraints})
+
+ # -------------------------------------------------------------------------
+
+ def draw(self):
+ """
+ Draw a sample from the joint distribution and construct a
+ dictionary that maps the parameter names to the values
+ generated for them.
+
+ Returns:
+ A `dict` containing a the names and values of a set of
+ randomly sampled waveform parameters (e.g., masses, spins,
+ position in the sky, ...).
+ """
+ values = apply_transforms(self.pval.rvs(), self.trans)[0]
+ result = dict(zip(self.var_args, values))
+
+ return result
+
+
+# -----------------------------------------------------------------------------
+# FUNCTION DEFINITIONS
+# -----------------------------------------------------------------------------
+
+def fade_on(timeseries,
+ alpha=0.25):
+ """
+ Take a PyCBC time series and use a one-sided Tukey window to "fade
+ on" the waveform (to reduce discontinuities in the amplitude).
+
+ Args:
+ timeseries (pycbc.types.timeseries.TimeSeries): The PyCBC
+ TimeSeries object to be faded on.
+ alpha (float): The alpha parameter for the Tukey window.
+
+ Returns:
+ The `timeseries` which has been faded on.
+ """
+
+ # Save the parameters from the time series we are about to fade on
+ delta_t = timeseries.delta_t
+ epoch = timeseries.start_time
+ duration = timeseries.duration
+ sample_rate = timeseries.sample_rate
+
+ # Create a one-sided Tukey window for the turn on
+ window = tukey(M=int(duration * sample_rate), alpha=alpha)
+ window[int(0.5*len(window)):] = 1
+
+ # Apply the one-sided Tukey window for the fade-on
+ ts = window * np.array(timeseries)
+
+ # Create and return a TimeSeries object again from the resulting array
+ # using the original parameters (delta_t and epoch) of the time series
+ return TimeSeries(initial_array=ts,
+ delta_t=delta_t,
+ epoch=epoch)
+
+
+def get_waveform(static_arguments,
+ waveform_params):
+ """
+ Simulate a waveform (using methods provided by PyCBC / LALSuite)
+ based on the `static_arguments` (which define, e.g., the waveform
+ model to be used) and the `waveform_params`, which specify the
+ physical parameters of the waveform (e.g., the masses and spins).
+
+ .. note::
+ The actual simulation of the waveform is, depending on your
+ choice of the `domain`, performed by the PyCBC methods
+ :func:`get_td_waveform()` and :func:`get_fd_waveform()`,
+ respectively.
+ These take as arguments a combination of the `static_arguments`
+ and the `waveform_params.` A (more) comprehensive explanation of
+ the parameters that are supported by these methods can be found
+ in the `PyCBC documentation <https://pycbc.org/pycbc/latest/html/
+ pycbc.waveform.html#pycbc.waveform.waveform.get_td_waveform>`_.
+ Currently, however, only the following keys are actually passed
+ to the simulation routines:
+
+ .. code-block:: python
+
+ {'approximant', 'coa_phase', 'delta_f', 'delta_t',
+ 'distance', 'f_lower', 'inclination', 'mass1', 'mass2',
+ 'spin1z', 'spin2z'}
+
+ .. warning::
+ If you want to use a different waveform model or a different
+ parameter space, you may need to edit this function according
+ to your exact needs!
+
+
+ Args:
+ static_arguments (dict): The static arguments (e.g., the
+ waveform approximant and the sampling rate) defined in the
+ `*.ini` configuration file, which specify technical aspects
+ of the simulation process.
+ waveform_params (dict): The physical parameters of the
+ waveform to be simulated, such as the masses or the
+ position in the sky. Usually, these values are sampled
+ using a :class:`WaveformParameterGenerator` instance,
+ which is based in the variable arguments section in the
+ `*.ini` configuration file.
+
+ Returns:
+ A tuple `(h_plus, h_cross)` with the two polarization modes of
+ the simulated waveform, resized to the desired length.
+ """
+
+ # Check if we are using a time domain (TD) or frequency domain (FD)
+ # approximant and retrieve the required parameters for the simulation
+ if static_arguments['domain'] == 'time':
+ simulate_waveform = get_td_waveform
+ length = int(static_arguments['td_length'])
+ elif static_arguments['domain'] == 'frequency':
+ simulate_waveform = get_fd_waveform
+ length = int(static_arguments['fd_length'])
+ else:
+ raise ValueError('Invalid domain! Must be "time" or "frequency"!')
+
+ # Collect all the required parameters for the simulation from the given
+ # static and variable parameters
+ simulation_parameters = dict(approximant=static_arguments['approximant'],
+ coa_phase=waveform_params['coa_phase'],
+ delta_f=static_arguments['delta_f'],
+ delta_t=static_arguments['delta_t'],
+ distance=static_arguments['distance'],
+ f_lower=static_arguments['f_lower'],
+ inclination=waveform_params['inclination'],
+ mass1=waveform_params['mass1'],
+ mass2=waveform_params['mass2'],
+ spin1z=waveform_params['spin1z'],
+ spin2z=waveform_params['spin2z'])
+
+ # Perform the actual simulation with the given parameters
+ h_plus, h_cross = simulate_waveform(**simulation_parameters)
+
+ # Apply the fade-on filter to them
+ h_plus = fade_on(h_plus, alpha=static_arguments['tukey_alpha'])
+ h_cross = fade_on(h_cross, alpha=static_arguments['tukey_alpha'])
+
+ # Resize the simulated waveform to the specified length
+ h_plus.resize(length)
+ h_cross.resize(length)
+
+ return h_plus, h_cross
+
+
+# -----------------------------------------------------------------------------
+
+
+def get_detector_signals(static_arguments,
+ waveform_params,
+ event_time,
+ waveform):
+ """
+ Project the raw `waveform` (i.e., the tuple `(h_plus, h_cross)`
+ returned by :func:`get_waveform()`) onto the antenna patterns of
+ the detectors in Hanford and Livingston. This requires the position
+ of the source in the sky, which is contained in `waveform_params`.
+
+ Args:
+ static_arguments (dict): The static arguments (e.g., the
+ waveform approximant and the sampling rate) defined in the
+ `*.ini` configuration file.
+ waveform_params (dict): The parameters that were used as inputs
+ for the waveform simulation, although this method will only
+ require the following parameters to be present:
+
+ - ``ra`` = Right ascension of the source
+ - ``dec`` = Declination of the source
+ - ``polarization`` = Polarization angle of the source
+
+ event_time (int): The GPS time for the event, which, by
+ convention, is the time at which the simulated signal
+ reaches its maximum amplitude in the `H1` channel.
+ waveform (tuple): The pure simulated wavefrom, represented by
+ a tuple `(h_plus, h_cross)`, which is usually generated
+ by :func:`get_waveform()`.
+
+ Returns:
+ A dictionary with keys `{'H1', 'L1'}` that contains the pure
+ signal as it would be observed at Hanford and Livingston.
+ """
+
+ # Retrieve the two polarization modes from the waveform tuple
+ h_plus, h_cross = waveform
+
+ # Extract the parameters we will need later for the projection
+ right_ascension = waveform_params['ra']
+ declination = waveform_params['dec']
+ polarization = waveform_params['polarization']
+
+ # Store the detector signals we will get through projection
+ detector_signals = {}
+
+ # Set up detectors
+ detectors = {'H1': Detector('H1'), 'L1': Detector('L1')}
+
+ # Loop over both detectors and calculate the signal we would see there
+ for detector_name in ('H1', 'L1'):
+
+ # Set up the detector based on its name
+ detector = detectors[detector_name]
+
+ # Calculate the antenna pattern for this detector
+ f_plus, f_cross = \
+ detector.antenna_pattern(right_ascension=right_ascension,
+ declination=declination,
+ polarization=polarization,
+ t_gps=100)
+
+ # Calculate the time offset from H1 for this detector
+ delta_t_h1 = \
+ detector.time_delay_from_detector(other_detector=detectors['H1'],
+ right_ascension=right_ascension,
+ declination=declination,
+ t_gps=100)
+
+ # Project the waveform onto the antenna pattern
+ detector_signal = f_plus * h_plus + f_cross * h_cross
+
+ # Map the signal from geocentric coordinates to the specific
+ # reference frame of the detector. This depends on whether we have
+ # simulated the waveform in the time or frequency domain:
+ if static_arguments['domain'] == 'time':
+ offset = 100 + delta_t_h1 + detector_signal.start_time
+ detector_signal = detector_signal.cyclic_time_shift(offset)
+ detector_signal.start_time = event_time - 100
+ elif static_arguments['domain'] == 'frequency':
+ offset = 100 + delta_t_h1
+ detector_signal = detector_signal.cyclic_time_shift(offset)
+ detector_signal.start_time = event_time - 100
+ detector_signal = detector_signal.to_timeseries()
+ else:
+ raise ValueError('Invalid domain! Must be "time" or "frequency"!')
+
+ # Store the result
+ detector_signals[detector_name] = detector_signal
+
+ return detector_signals
--
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