helper_functions.py 10.2 KB
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"""
Provides helpful functions to facilitate ease-of-use of pyfstat
"""

import os
import sys
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import subprocess
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import argparse
import logging
import inspect
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import peakutils
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from functools import wraps
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from scipy.stats.distributions import ncx2
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import numpy as np
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import lal
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import lalpulsar
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# workaround for matplotlib on X-less remote logins
if 'DISPLAY' in os.environ:
    import matplotlib.pyplot as plt
else:
    logging.info('No $DISPLAY environment variable found, so importing \
                  matplotlib.pyplot with non-interactive "Agg" backend.')
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
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def set_up_optional_tqdm():
    try:
        from tqdm import tqdm
    except ImportError:
        def tqdm(x, *args, **kwargs):
            return x
    return tqdm


def set_up_matplotlib_defaults():
    plt.switch_backend('Agg')
    plt.rcParams['text.usetex'] = True
    plt.rcParams['axes.formatter.useoffset'] = False


def set_up_command_line_arguments():
    parser = argparse.ArgumentParser()
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    parser.add_argument("-v", "--verbose", action="store_true",
                        help="Increase output verbosity [logging.DEBUG]")
    parser.add_argument("-q", "--quite", action="store_true",
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                        help="Decrease output verbosity [logging.WARNING]")
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    parser.add_argument("--no-interactive", help="Don't use interactive",
                        action="store_true")
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    parser.add_argument("-c", "--clean", action="store_true",
                        help="Force clean data, never use cached data")
    fu_parser = parser.add_argument_group(
        'follow-up options', 'Options related to MCMCFollowUpSearch')
    fu_parser.add_argument('-s', "--setup-only", action="store_true",
                           help="Only generate the setup file, don't run")
    fu_parser.add_argument(
        "--no-template-counting", action="store_true",
        help="No counting of templates, useful if the setup is predefined")
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    parser.add_argument(
        '-N', type=int, default=3, metavar='N',
        help="Number of threads to use when running in parallel")
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    parser.add_argument('unittest_args', nargs='*')
    args, unknown = parser.parse_known_args()
    sys.argv[1:] = args.unittest_args
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    if args.quite or args.no_interactive:
        def tqdm(x, *args, **kwargs):
            return x
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    else:
        tqdm = set_up_optional_tqdm()
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    logger = logging.getLogger()
    stream_handler = logging.StreamHandler()
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    stream_handler.setFormatter(logging.Formatter(
        '%(asctime)s %(levelname)-8s: %(message)s', datefmt='%H:%M'))

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    if args.quite:
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        logger.setLevel(logging.WARNING)
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        stream_handler.setLevel(logging.WARNING)
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    elif args.verbose:
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        logger.setLevel(logging.DEBUG)
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        stream_handler.setLevel(logging.DEBUG)
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    else:
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        logger.setLevel(logging.INFO)
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        stream_handler.setLevel(logging.INFO)
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    logger.addHandler(stream_handler)
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    return args, tqdm
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def get_ephemeris_files():
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    """ Returns the earth_ephem and sun_ephem """
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    config_file = os.path.expanduser('~')+'/.pyfstat.conf'
    if os.path.isfile(config_file):
        d = {}
        with open(config_file, 'r') as f:
            for line in f:
                k, v = line.split('=')
                k = k.replace(' ', '')
                for item in [' ', "'", '"', '\n']:
                    v = v.replace(item, '')
                d[k] = v
        earth_ephem = d['earth_ephem']
        sun_ephem = d['sun_ephem']
    else:
        logging.warning('No ~/.pyfstat.conf file found please provide the '
                        'paths when initialising searches')
        earth_ephem = None
        sun_ephem = None
    return earth_ephem, sun_ephem


def round_to_n(x, n):
    if not x:
        return 0
    power = -int(np.floor(np.log10(abs(x)))) + (n - 1)
    factor = (10 ** power)
    return round(x * factor) / factor


def texify_float(x, d=2):
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    if x == 0:
        return 0
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    if type(x) == str:
        return x
    x = round_to_n(x, d)
    if 0.01 < abs(x) < 100:
        return str(x)
    else:
        power = int(np.floor(np.log10(abs(x))))
        stem = np.round(x / 10**power, d)
        if d == 1:
            stem = int(stem)
        return r'${}{{\times}}10^{{{}}}$'.format(stem, power)


def initializer(func):
    """ Decorator function to automatically assign the parameters to self """
    names, varargs, keywords, defaults = inspect.getargspec(func)

    @wraps(func)
    def wrapper(self, *args, **kargs):
        for name, arg in list(zip(names[1:], args)) + list(kargs.items()):
            setattr(self, name, arg)

        for name, default in zip(reversed(names), reversed(defaults)):
            if not hasattr(self, name):
                setattr(self, name, default)

        func(self, *args, **kargs)

    return wrapper

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def get_peak_values(frequencies, twoF, threshold_2F, F0=None, F0range=None):
    if F0:
        cut_idxs = np.abs(frequencies - F0) < F0range
        frequencies = frequencies[cut_idxs]
        twoF = twoF[cut_idxs]
    idxs = peakutils.indexes(twoF, thres=1.*threshold_2F/np.max(twoF))
    F0maxs = frequencies[idxs]
    twoFmaxs = twoF[idxs]
    freq_err = frequencies[1] - frequencies[0]
    return F0maxs, twoFmaxs, freq_err*np.ones(len(idxs))


def get_comb_values(F0, frequencies, twoF, period, N=4):
    if period == 'sidereal':
        period = 23*60*60 + 56*60 + 4.0616
    elif period == 'terrestrial':
        period = 86400
    freq_err = frequencies[1] - frequencies[0]
    comb_frequencies = [n*1/period for n in range(-N, N+1)]
    comb_idxs = [np.argmin(np.abs(frequencies-F0-F)) for F in comb_frequencies]
    return comb_frequencies, twoF[comb_idxs], freq_err*np.ones(len(comb_idxs))

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def compute_P_twoFstarcheck(twoFstarcheck, twoFcheck, M0, plot=False):
    """ Returns the unnormalised pdf of twoFstarcheck given twoFcheck """
    upper = 4+twoFstarcheck + 0.5*(2*(4*M0+2*twoFcheck))
    rho2starcheck = np.linspace(1e-1, upper, 500)
    integrand = (ncx2.pdf(twoFstarcheck, 4*M0, rho2starcheck)
                 * ncx2.pdf(twoFcheck, 4, rho2starcheck))
    if plot:
        fig, ax = plt.subplots()
        ax.plot(rho2starcheck, integrand)
        fig.savefig('test')
    return np.trapz(integrand, rho2starcheck)


def compute_pstar(twoFcheck_obs, twoFstarcheck_obs, m0, plot=False):
    M0 = 2*m0 + 1
    upper = 4+twoFcheck_obs + (2*(4*M0+2*twoFcheck_obs))
    twoFstarcheck_vals = np.linspace(1e-1, upper, 500)
    P_twoFstarcheck = np.array(
        [compute_P_twoFstarcheck(twoFstarcheck, twoFcheck_obs, M0)
         for twoFstarcheck in twoFstarcheck_vals])
    C = np.trapz(P_twoFstarcheck, twoFstarcheck_vals)
    idx = np.argmin(np.abs(twoFstarcheck_vals - twoFstarcheck_obs))
    if plot:
        fig, ax = plt.subplots()
        ax.plot(twoFstarcheck_vals, P_twoFstarcheck)
        ax.fill_between(twoFstarcheck_vals[:idx+1], 0, P_twoFstarcheck[:idx+1])
        ax.axvline(twoFstarcheck_vals[idx])
        fig.savefig('test')
    pstar_l = np.trapz(P_twoFstarcheck[:idx+1]/C, twoFstarcheck_vals[:idx+1])
    return 2*np.min([pstar_l, 1-pstar_l])
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def run_commandline(cl, log_level=20, raise_error=True, return_output=True):
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    """Run a string cmd as a subprocess, check for errors and return output.
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    Parameters
    ----------
    cl: str
        Command to run
    log_level: int
        See https://docs.python.org/2/library/logging.html#logging-levels,
        default is '20' (INFO)

    """

    logging.log(log_level, 'Now executing: ' + cl)
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    if return_output:
        try:
            out = subprocess.check_output(cl,                       # what to run
                                          stderr=subprocess.STDOUT, # catch errors
                                          shell=True,               # proper environment etc
                                          universal_newlines=True,  # properly display linebreaks in error/output printing
                                         )
        except subprocess.CalledProcessError as e:
            logging.log(log_level, 'Execution failed: {}'.format(e.output))
            if raise_error:
                raise
            else:
                out = 0
        os.system('\n')
        return(out)
    else:
        process = subprocess.Popen(cl, shell=True)
        process.communicate()

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def convert_array_to_gsl_matrix(array):
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    gsl_matrix = lal.gsl_matrix(*array.shape)
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    gsl_matrix.data = array
    return gsl_matrix
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def get_sft_array(sftfilepattern, data_duration, F0, dF0):
    """ Return the raw data from a set of sfts """

    SFTCatalog = lalpulsar.SFTdataFind(
        sftfilepattern, lalpulsar.SFTConstraints())
    MultiSFTs = lalpulsar.LoadMultiSFTs(SFTCatalog, F0-dF0, F0+dF0)
    SFTs = MultiSFTs.data[0]
    data = []
    for sft in SFTs.data:
        data.append(np.abs(sft.data.data))
    data = np.array(data).T
    n, nsfts = data.shape
    freqs = np.linspace(sft.f0, sft.f0+n*sft.deltaF, n)
    times = np.linspace(0, data_duration, nsfts)

    return times, freqs, data
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def get_covering_band(tref, tstart, tend, F0, F1, F2):
    """ Get the covering band using XLALCWSignalCoveringBand

    Parameters
    ----------
    tref, tstart, tend: int
        The reference, start, and end times of interest
    F0, F1, F1:
        Frequency and spin-down of the signal

    Note: this is similar to the function
    `injection_helper_functions.get_frequency_range_of_signal`, however this
    does not use the sky position and calculates an estimate for a full year
    search over any sky position. In this sense, it is much more conservative.

    Returns
    -------
    F0min, F0max: float
        Estimates of the minimum and maximum frequencies of the signal during
        the search

    """
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    tref = lal.LIGOTimeGPS(tref)
    tstart = lal.LIGOTimeGPS(tstart)
    tend = lal.LIGOTimeGPS(tend)
    psr = lalpulsar.PulsarSpinRange()
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    psr.fkdot[0] = F0
    psr.fkdot[1] = F1
    psr.fkdot[2] = F2
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    psr.refTime = tref
    return lalpulsar.CWSignalCoveringBand(tstart, tend, psr, 0, 0, 0)


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def twoFDMoffThreshold(twoFon, knee=400, twoFDMoffthreshold_below_threshold=62,
                       prefactor=0.9, offset=0.5):
    """ Calculation of the 2F_DMoff threshold, see Eq 2 of arXiv:1707.5286 """
    if twoFon <= knee:
        return twoFDMoffthreshold_below_threshold
    else:
        return 10**(prefactor*np.log10(twoFon-offset))