transplant samples generation to python3

python3_samples/config_files/default.json

+{
+  "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"
+}

python3_samples/config_files/waveform_params.ini

+; -----------------------------------------------------------------------------
+; 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

python3_samples/generate_sample.py

+"""
+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('')

python3_samples/utils/configfiles.py

+"""
+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

python3_samples/utils/progressbar.py

+"""
+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()
+
+
+# -----------------------------------------------------------------------------
+
+