test_simulateCW.py

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# Copyright (C) 2017 Karl Wette
#
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 2 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
import os
import sys
 
 
import numpy as np
from numpy.testing import assert_allclose
 
import pytest
 
import lal
import lalpulsar
from lalpulsar import simulateCW as simCW
 
# load Earth and Sun ephemerides
earth_ephem_file = os.path.join(
    os.environ["LAL_TEST_PKGDATADIR"], "earth00-40-DE405.dat.gz"
)
sun_ephem_file = os.path.join(
    os.environ["LAL_TEST_PKGDATADIR"], "sun00-40-DE405.dat.gz"
)
ephemerides = lalpulsar.InitBarycenter(earth_ephem_file, sun_ephem_file)
 
# amplitude parameters
h0 = 1e-24
assume_sqrtSh = 1e-23
cosi = 0.123
psi = 2.345
phi0 = 3.210
 
# phase parameters
freq = 10.0
fcmin = 5.0
fcmax = 15.0
f1dot = -1.35e-8
alpha = 6.12
delta = 1.02
 
# detector parameters
detector = "V1"
 
# data parameters
tref = 900043200
tstart = 900000000
Tdata = 86400
 
# SFT parameters
fmax = 32.0
Tsft = 1800
 
# F-statistic parameters
dfreq = 0.1 / Tdata
Nfs = 111
fsmin = freq - (Nfs - 1) * 0.5 * dfreq
 
 
# waveform: simple spindown model
def waveform(h0, cosi, freq, f1dot):
    def wf(dt):
        dphi = lal.TWOPI * (freq * dt + f1dot * 0.5 * dt**2)
        ap = h0 * (1.0 + cosi**2) / 2.0
        ax = h0 * cosi
        return dphi, ap, ax
 
    return wf
 
 
# compute F-statistic of reference spindown signal
def compute_Fstat_spindown_reference():
    # create fake SFT catalog
    sft_ts = lalpulsar.MakeTimestamps(tstart, Tdata, Tsft, 0)
    sft_catalog = lalpulsar.AddToFakeSFTCatalog(None, detector, sft_ts)
 
    # create default F-statistic optional arguments
    Fstat_opt_args = lalpulsar.FstatOptionalArgs(lalpulsar.FstatOptionalArgsDefaults)
    Fstat_opt_args.FstatMethod = lalpulsar.FMETHOD_DEMOD_BEST
 
    # create injection parameters
    Fstat_signal = lalpulsar.CreatePulsarParamsVector(1)
    Fstat_signal.data[0].Amp.aPlus = 0.5 * h0 * (1.0 + cosi * cosi)
    Fstat_signal.data[0].Amp.aCross = h0 * cosi
    Fstat_signal.data[0].Amp.psi = psi
    Fstat_signal.data[0].Amp.phi0 = phi0
    Fstat_signal.data[0].Doppler.refTime = tref
    Fstat_signal.data[0].Doppler.Alpha = alpha
    Fstat_signal.data[0].Doppler.Delta = delta
    Fstat_signal.data[0].Doppler.fkdot[0] = freq
    Fstat_signal.data[0].Doppler.fkdot[1] = f1dot
    Fstat_opt_args.injectSources = Fstat_signal
    Fstat_assume_noise = lalpulsar.MultiNoiseFloor()
    Fstat_assume_noise.length = 1
    Fstat_assume_noise.sqrtSn[0] = assume_sqrtSh
    Fstat_opt_args.assumeSqrtSX = Fstat_assume_noise
 
    # create F-statistic input data
    Fstat_input = lalpulsar.CreateFstatInput(
        sft_catalog, fcmin, fcmax, dfreq, ephemerides, Fstat_opt_args
    )
    Fstat_res = 0
    doppler = lalpulsar.PulsarDopplerParams(Fstat_signal.data[0].Doppler)
    doppler.fkdot[0] = fsmin
 
    # search SFTs using F-statistic
    Fstat_res = lalpulsar.ComputeFstat(
        Fstat_res, Fstat_input, doppler, Nfs, lalpulsar.FSTATQ_2F
    )
 
    return Fstat_res.twoF
 
 
# compute F-statistic of spindown signal simulated by simulateCW
def compute_Fstat_spindown_simulateCW():
    # waveform parameters
    dt_wf = 5
 
    # create simulator
    S = simCW.CWSimulator(
        tref,
        tstart,
        Tdata,
        waveform(h0, cosi, freq, f1dot),
        dt_wf,
        phi0,
        psi,
        alpha,
        delta,
        detector,
        earth_ephem_file=earth_ephem_file,
        sun_ephem_file=sun_ephem_file,
    )
 
    # write SFTs
    allpaths = []
    for path, i, N in S.write_sft_files(fmax, Tsft, "simCWTestFstatSpindown"):
        allpaths.append(path)
 
    # load SFTs from catalog
    sft_catalog = lalpulsar.SFTdataFind(
        "V-1_V1_1800SFT_simCWTestFstatSpindown-*.sft", None
    )
 
    # create default F-statistic optional arguments
    Fstat_opt_args = lalpulsar.FstatOptionalArgs(lalpulsar.FstatOptionalArgsDefaults)
    Fstat_opt_args.FstatMethod = lalpulsar.FMETHOD_DEMOD_BEST
    Fstat_assume_noise = lalpulsar.MultiNoiseFloor()
    Fstat_assume_noise.length = 1
    Fstat_assume_noise.sqrtSn[0] = assume_sqrtSh
    Fstat_opt_args.assumeSqrtSX = Fstat_assume_noise
 
    # create F-statistic input data
    Fstat_input = lalpulsar.CreateFstatInput(
        sft_catalog, fcmin, fcmax, dfreq, ephemerides, Fstat_opt_args
    )
    Fstat_res = 0
    doppler = lalpulsar.PulsarDopplerParams()
    doppler.refTime = tref
    doppler.Alpha = alpha
    doppler.Delta = delta
    doppler.fkdot[0] = fsmin
    doppler.fkdot[1] = f1dot
 
    # search SFTs using F-statistic
    Fstat_res = lalpulsar.ComputeFstat(
        Fstat_res, Fstat_input, doppler, Nfs, lalpulsar.FSTATQ_2F
    )
 
    # remove SFTs
    for path in allpaths:
        os.remove(path)
 
    return Fstat_res.twoF
 
 
def test_fstatistic():
    # compute F-statistic of reference spindown signal
    Fstat_ref = compute_Fstat_spindown_reference()
    print(
        "F-statistic of reference spindown signal:\n   %s\n"
        % (", ".join(["%0.4f"] * len(Fstat_ref)) % tuple(Fstat_ref))
    )
 
    # compute F-statistic of spindown signal from simulateCW
    Fstat_simCW = compute_Fstat_spindown_simulateCW()
    print(
        "F-statistic of spindown signal from simulateCW:\n   %s\n"
        % ("  ".join(["%0.4f"] * len(Fstat_simCW)) % tuple(Fstat_simCW))
    )
 
    # compute root-mean-square error between F-statistics
    Fstat_rmserr = np.sqrt(np.mean((1 - (Fstat_simCW / Fstat_ref)) ** 2))
    assert_allclose(
        Fstat_rmserr,
        0.0,
        atol=1e-3,
        err_msg="Root-mean-square error between F-statistics: %0.2e" % Fstat_rmserr,
    )
 
 
def test_random_seeds():
    # simulation parameters
    dt_wf = 5
    Tdata = 3600
    asd = 1.0
 
    # create noise-only simulator
    alpha = 1
    S = simCW.CWSimulator(
        tref,
        tstart,
        Tdata,
        waveform(h0=0, cosi=0, freq=0, f1dot=0),
        dt_wf,
        phi0,
        psi,
        alpha,
        delta,
        detector,
        earth_ephem_file=earth_ephem_file,
        sun_ephem_file=sun_ephem_file,
    )
 
    # write sets of 2 SFTs each with different noise seed choices, two rounds each
    allpaths = []
    paths = {}
    paths["None"] = [[], []]
    paths["1"] = [[], []]
    for path, i, N in S.write_sft_files(
        fmax, Tsft, noise_sqrt_Sh=asd, comment="simCWTestSeedNoneRound1"
    ):
        allpaths.append(path)
        paths["None"][0].append(path)
        pass
    for path, i, N in S.write_sft_files(
        fmax, Tsft, noise_sqrt_Sh=asd, comment="simCWTestSeedNoneRound2"
    ):
        allpaths.append(path)
        paths["None"][1].append(path)
        pass
    for path, i, N in S.write_sft_files(
        fmax, Tsft, noise_sqrt_Sh=asd, comment="simCWTestSeed1Round1", noise_seed=1
    ):
        allpaths.append(path)
        paths["1"][0].append(path)
        pass
    for path, i, N in S.write_sft_files(
        fmax, Tsft, noise_sqrt_Sh=asd, comment="simCWTestSeed1Round2", noise_seed=1
    ):
        allpaths.append(path)
        paths["1"][1].append(path)
        pass
    return_codes = {}
    return_codes["None"] = np.zeros(2)
    return_codes["1"] = np.zeros(2)
    for seed in ["None", "1"]:
        for i in range(N):
            catalog0 = lalpulsar.SFTdataFind(paths[seed][0][i], None)
            sft0 = lalpulsar.LoadSFTs(catalog0, -1, -1)
            catalog1 = lalpulsar.SFTdataFind(paths[seed][1][i], None)
            sft1 = lalpulsar.LoadSFTs(catalog1, -1, -1)
            rms = np.sqrt(
                np.mean(abs(sft0.data[0].data.data - sft1.data[0].data.data) ** 2)
            )
            return_codes[seed][i] = rms > 0
    for code in return_codes["None"]:
        # if randoms seeds are redrawn internally, we expect the comparison of rounds 1 and 2 to fail
        assert code > 0
    for code in return_codes["1"]:
        # if seeds are fixed, we expect identical outputs from both rounds
        assert code == 0
 
    # remove SFTs
    for path in allpaths:
        os.remove(path)
 
 
if __name__ == "__main__":
    args = sys.argv[1:] or ["-v", "-rs", "--junit-xml=junit-simulateCW.xml"]
    sys.exit(pytest.main(args=[__file__] + args))