tests.py

import unittest
import numpy as np
import os
import shutil
import pyfstat
import lalpulsar
import logging
import time

class Test(unittest.TestCase):
    outdir = "TestData"

    @classmethod
    def setUpClass(self):
        if os.path.isdir(self.outdir):
            try:
                shutil.rmtree(self.outdir)
            except OSError:
                logging.warning("{} not removed prior to tests".format(self.outdir))
        h0 = 1
        sqrtSX = 1
        F0 = 30
        F1 = -1e-10
        F2 = 0
        minStartTime = 700000000
        duration = 2 * 86400
        Alpha = 5e-3
        Delta = 1.2
        tref = minStartTime
        Writer = pyfstat.Writer(
            F0=F0,
            F1=F1,
            F2=F2,
            label="test",
            h0=h0,
            sqrtSX=sqrtSX,
            outdir=self.outdir,
            tstart=minStartTime,
            Alpha=Alpha,
            Delta=Delta,
            tref=tref,
            duration=duration,
            Band=4,
        )
        Writer.make_data()
        self.sftfilepath = Writer.sftfilepath
        self.minStartTime = minStartTime
        self.maxStartTime = minStartTime + duration
        self.duration = duration

    @classmethod
    def tearDownClass(self):
        if os.path.isdir(self.outdir):
            try:
                shutil.rmtree(self.outdir)
            except OSError:
                logging.warning("{} not removed prior to tests".format(self.outdir))


class Writer(Test):
    label = "TestWriter"

    def test_make_cff(self):
        Writer = pyfstat.Writer(self.label, outdir=self.outdir)
        Writer.make_cff()
        self.assertTrue(os.path.isfile("./{}/{}.cff".format(self.outdir, self.label)))

    def test_run_makefakedata(self):
        Writer = pyfstat.Writer(self.label, outdir=self.outdir, duration=3600)
        Writer.make_cff()
        Writer.run_makefakedata()
        self.assertTrue(
            os.path.isfile(
                "./{}/H-2_H1_1800SFT_TestWriter-700000000-3600.sft".format(self.outdir)
            )
        )

    def test_makefakedata_usecached(self):
        Writer = pyfstat.Writer(self.label, outdir=self.outdir, duration=3600)
        if os.path.isfile(Writer.sftfilepath):
            os.remove(Writer.sftfilepath)
        Writer.make_cff()
        Writer.run_makefakedata()
        time_first = os.path.getmtime(Writer.sftfilepath)
        Writer.run_makefakedata()
        time_second = os.path.getmtime(Writer.sftfilepath)
        self.assertTrue(time_first == time_second)
        time.sleep(1)  # make sure timestamp is actually different!
        os.system("touch {}".format(Writer.config_file_name))
        Writer.run_makefakedata()
        time_third = os.path.getmtime(Writer.sftfilepath)
        self.assertFalse(time_first == time_third)


class Bunch(Test):
    def test_bunch(self):
        b = pyfstat.core.Bunch(dict(x=10))
        self.assertTrue(b.x == 10)


class par(Test):
    label = "TestPar"

    def test(self):
        os.system('echo "x=100\ny=10" > {}/{}.par'.format(self.outdir, self.label))

        par = pyfstat.core.read_par(
            "{}/{}.par".format(self.outdir, self.label), return_type="Bunch"
        )
        self.assertTrue(par.x == 100)
        self.assertTrue(par.y == 10)

        par = pyfstat.core.read_par(
            outdir=self.outdir, label=self.label, return_type="dict"
        )
        self.assertTrue(par["x"] == 100)
        self.assertTrue(par["y"] == 10)
        os.system("rm -r {}".format(self.outdir))


class BaseSearchClass(Test):
    def test_shift_matrix(self):
        BSC = pyfstat.BaseSearchClass()
        dT = 10
        a = BSC._shift_matrix(4, dT)
        b = np.array(
            [
                [
                    1,
                    2 * np.pi * dT,
                    2 * np.pi * dT ** 2 / 2.0,
                    2 * np.pi * dT ** 3 / 6.0,
                ],
                [0, 1, dT, dT ** 2 / 2.0],
                [0, 0, 1, dT],
                [0, 0, 0, 1],
            ]
        )
        self.assertTrue(np.array_equal(a, b))

    def test_shift_coefficients(self):
        BSC = pyfstat.BaseSearchClass()
        thetaA = np.array([10.0, 1e2, 10.0, 1e2])
        dT = 100

        # Calculate the 'long' way
        thetaB = np.zeros(len(thetaA))
        thetaB[3] = thetaA[3]
        thetaB[2] = thetaA[2] + thetaA[3] * dT
        thetaB[1] = thetaA[1] + thetaA[2] * dT + 0.5 * thetaA[3] * dT ** 2
        thetaB[0] = thetaA[0] + 2 * np.pi * (
            thetaA[1] * dT + 0.5 * thetaA[2] * dT ** 2 + thetaA[3] * dT ** 3 / 6.0
        )

        self.assertTrue(np.array_equal(thetaB, BSC._shift_coefficients(thetaA, dT)))

    def test_shift_coefficients_loop(self):
        BSC = pyfstat.BaseSearchClass()
        thetaA = np.array([10.0, 1e2, 10.0, 1e2])
        dT = 1e1
        thetaB = BSC._shift_coefficients(thetaA, dT)
        self.assertTrue(
            np.allclose(
                thetaA, BSC._shift_coefficients(thetaB, -dT), rtol=1e-9, atol=1e-9
            )
        )


class ComputeFstat(Test):
    label = "TestComputeFstat"

    def test_run_computefstatistic_single_point(self):
        Writer = pyfstat.Writer(
            self.label,
            outdir=self.outdir,
            duration=86400,
            h0=1,
            sqrtSX=1,
            detectors="H1",
        )
        Writer.make_data()
        predicted_FS = Writer.predict_fstat()

        search_H1L1 = pyfstat.ComputeFstat(
            tref=Writer.tref,
            sftfilepattern="{}/*{}*sft".format(Writer.outdir, Writer.label),
        )
        FS = search_H1L1.get_fullycoherent_twoF(
            Writer.tstart,
            Writer.tend,
            Writer.F0,
            Writer.F1,
            Writer.F2,
            Writer.Alpha,
            Writer.Delta,
        )
        self.assertTrue(np.abs(predicted_FS - FS) / FS < 0.3)

        Writer.detectors = "H1"
        predicted_FS = Writer.predict_fstat()
        search_H1 = pyfstat.ComputeFstat(
            tref=Writer.tref,
            detectors="H1",
            sftfilepattern="{}/*{}*sft".format(Writer.outdir, Writer.label),
            SSBprec=lalpulsar.SSBPREC_RELATIVISTIC,
        )
        FS = search_H1.get_fullycoherent_twoF(
            Writer.tstart,
            Writer.tend,
            Writer.F0,
            Writer.F1,
            Writer.F2,
            Writer.Alpha,
            Writer.Delta,
        )
        self.assertTrue(np.abs(predicted_FS - FS) / FS < 0.3)

    def run_computefstatistic_single_point_no_noise(self):
        Writer = pyfstat.Writer(
            self.label,
            outdir=self.outdir,
            add_noise=False,
            duration=86400,
            h0=1,
            sqrtSX=1,
        )
        Writer.make_data()
        predicted_FS = Writer.predict_fstat()

        search = pyfstat.ComputeFstat(
            tref=Writer.tref,
            assumeSqrtSX=1,
            sftfilepattern="{}/*{}*sft".format(Writer.outdir, Writer.label),
        )
        FS = search.get_fullycoherent_twoF(
            Writer.tstart,
            Writer.tend,
            Writer.F0,
            Writer.F1,
            Writer.F2,
            Writer.Alpha,
            Writer.Delta,
        )
        self.assertTrue(np.abs(predicted_FS - FS) / FS < 0.3)

    def test_injectSources(self):
        # This seems to be writing with a signal...
        Writer = pyfstat.Writer(
            self.label,
            outdir=self.outdir,
            add_noise=False,
            duration=86400,
            h0=1,
            sqrtSX=1,
        )
        Writer.make_cff()
        injectSources = Writer.config_file_name

        search = pyfstat.ComputeFstat(
            tref=Writer.tref,
            assumeSqrtSX=1,
            injectSources=injectSources,
            minCoverFreq=28,
            maxCoverFreq=32,
            minStartTime=Writer.tstart,
            maxStartTime=Writer.tstart + Writer.duration,
            detectors=Writer.detectors,
        )
        FS_from_file = search.get_fullycoherent_twoF(
            Writer.tstart,
            Writer.tend,
            Writer.F0,
            Writer.F1,
            Writer.F2,
            Writer.Alpha,
            Writer.Delta,
        )
        Writer.make_data()
        predicted_FS = Writer.predict_fstat()
        self.assertTrue(np.abs(predicted_FS - FS_from_file) / FS_from_file < 0.3)

        injectSourcesdict = pyfstat.core.read_par(Writer.config_file_name)
        injectSourcesdict["F0"] = injectSourcesdict["Freq"]
        injectSourcesdict["F1"] = injectSourcesdict["f1dot"]
        injectSourcesdict["F2"] = injectSourcesdict["f2dot"]
        search = pyfstat.ComputeFstat(
            tref=Writer.tref,
            assumeSqrtSX=1,
            injectSources=injectSourcesdict,
            minCoverFreq=28,
            maxCoverFreq=32,
            minStartTime=Writer.tstart,
            maxStartTime=Writer.tstart + Writer.duration,
            detectors=Writer.detectors,
        )
        FS_from_dict = search.get_fullycoherent_twoF(
            Writer.tstart,
            Writer.tend,
            Writer.F0,
            Writer.F1,
            Writer.F2,
            Writer.Alpha,
            Writer.Delta,
        )
        self.assertTrue(FS_from_dict == FS_from_file)


class SemiCoherentSearch(Test):
    label = "TestSemiCoherentSearch"

    def test_get_semicoherent_twoF(self):
        duration = 10 * 86400
        Writer = pyfstat.Writer(
            self.label, outdir=self.outdir, duration=duration, h0=1, sqrtSX=1
        )
        Writer.make_data()

        search = pyfstat.SemiCoherentSearch(
            label=self.label,
            outdir=self.outdir,
            nsegs=2,
            sftfilepattern="{}/*{}*sft".format(Writer.outdir, Writer.label),
            tref=Writer.tref,
            minStartTime=Writer.tstart,
            maxStartTime=Writer.tend,
        )

        search.get_semicoherent_twoF(
            Writer.F0,
            Writer.F1,
            Writer.F2,
            Writer.Alpha,
            Writer.Delta,
            record_segments=True,
        )

        # Compute the predicted semi-coherent Fstat
        minStartTime = Writer.tstart
        maxStartTime = Writer.tend

        Writer.maxStartTime = minStartTime + duration / 2.0
        FSA = Writer.predict_fstat()

        Writer.tstart = minStartTime + duration / 2.0
        Writer.tend = maxStartTime
        FSB = Writer.predict_fstat()

        FSs = np.array([FSA, FSB])
        diffs = (np.array(search.detStat_per_segment) - FSs) / FSs
        self.assertTrue(np.all(diffs < 0.3))

    def test_get_semicoherent_BSGL(self):
        duration = 10 * 86400
        Writer = pyfstat.Writer(
            self.label, outdir=self.outdir, duration=duration, detectors="H1,L1"
        )
        Writer.make_data()

        search = pyfstat.SemiCoherentSearch(
            label=self.label,
            outdir=self.outdir,
            nsegs=2,
            sftfilepattern="{}/*{}*sft".format(Writer.outdir, Writer.label),
            tref=Writer.tref,
            minStartTime=Writer.tstart,
            maxStartTime=Writer.tend,
            BSGL=True,
        )

        BSGL = search.get_semicoherent_twoF(
            Writer.F0,
            Writer.F1,
            Writer.F2,
            Writer.Alpha,
            Writer.Delta,
            record_segments=True,
        )
        self.assertTrue(BSGL > 0)


class SemiCoherentGlitchSearch(Test):
    label = "TestSemiCoherentGlitchSearch"

    def test_get_semicoherent_nglitch_twoF(self):
        duration = 10 * 86400
        dtglitch = 0.5 * duration
        delta_F0 = 0
        h0 = 1
        sqrtSX = 1
        Writer = pyfstat.GlitchWriter(
            self.label,
            outdir=self.outdir,
            duration=duration,
            dtglitch=dtglitch,
            delta_F0=delta_F0,
            sqrtSX=sqrtSX,
            h0=h0,
        )

        Writer.make_data()

        search = pyfstat.SemiCoherentGlitchSearch(
            label=self.label,
            outdir=self.outdir,
            sftfilepattern="{}/*{}*sft".format(Writer.outdir, Writer.label),
            tref=Writer.tref,
            minStartTime=Writer.tstart,
            maxStartTime=Writer.tend,
            nglitch=1,
        )

        FS = search.get_semicoherent_nglitch_twoF(
            Writer.F0,
            Writer.F1,
            Writer.F2,
            Writer.Alpha,
            Writer.Delta,
            Writer.delta_F0,
            Writer.delta_F1,
            search.minStartTime + dtglitch,
        )

        # Compute the predicted semi-coherent glitch Fstat
        minStartTime = Writer.tstart
        maxStartTime = Writer.tend

        Writer.maxStartTime = minStartTime + dtglitch
        FSA = Writer.predict_fstat()

        Writer.tstart = minStartTime + dtglitch
        Writer.tend = maxStartTime
        FSB = Writer.predict_fstat()

        print(FSA, FSB)
        predicted_FS = FSA + FSB

        print((predicted_FS, FS))
        self.assertTrue(np.abs((FS - predicted_FS)) / predicted_FS < 0.3)


class MCMCSearch(Test):
    label = "TestMCMCSearch"

    def test_fully_coherent(self):
        h0 = 1
        sqrtSX = 1
        F0 = 30
        F1 = -1e-10
        F2 = 0
        minStartTime = 700000000
        duration = 1 * 86400
        maxStartTime = minStartTime + duration
        Alpha = 5e-3
        Delta = 1.2
        tref = minStartTime
        Writer = pyfstat.Writer(
            F0=F0,
            F1=F1,
            F2=F2,
            label=self.label,
            h0=h0,
            sqrtSX=sqrtSX,
            outdir=self.outdir,
            tstart=minStartTime,
            Alpha=Alpha,
            Delta=Delta,
            tref=tref,
            duration=duration,
            Band=4,
        )

        Writer.make_data()
        predicted_FS = Writer.predict_fstat()

        theta = {
            "F0": {"type": "norm", "loc": F0, "scale": np.abs(1e-10 * F0)},
            "F1": {"type": "norm", "loc": F1, "scale": np.abs(1e-10 * F1)},
            "F2": F2,
            "Alpha": Alpha,
            "Delta": Delta,
        }

        search = pyfstat.MCMCSearch(
            label=self.label,
            outdir=self.outdir,
            theta_prior=theta,
            tref=tref,
            sftfilepattern="{}/*{}*sft".format(Writer.outdir, Writer.label),
            minStartTime=minStartTime,
            maxStartTime=maxStartTime,
            nsteps=[100, 100],
            nwalkers=100,
            ntemps=2,
            log10beta_min=-1,
        )
        search.run(create_plots=False)
        _, FS = search.get_max_twoF()

        print(("Predicted twoF is {} while recovered is {}".format(predicted_FS, FS)))
        self.assertTrue(
            FS > predicted_FS or np.abs((FS - predicted_FS)) / predicted_FS < 0.3
        )


class GridSearch(Test):
    F0s = [29, 31, 0.1]
    F1s = [-1e-10, 0, 1e-11]
    tref = 700000000

    def test_grid_search(self):
        search = pyfstat.GridSearch(
            "grid_search",
            self.outdir,
            self.sftfilepath,
            F0s=self.F0s,
            F1s=[0],
            F2s=[0],
            Alphas=[0],
            Deltas=[0],
            tref=self.tref,
        )
        search.run()
        self.assertTrue(os.path.isfile(search.out_file))

    def test_semicoherent_grid_search(self):
        search = pyfstat.GridSearch(
            "sc_grid_search",
            self.outdir,
            self.sftfilepath,
            F0s=self.F0s,
            F1s=[0],
            F2s=[0],
            Alphas=[0],
            Deltas=[0],
            tref=self.tref,
            nsegs=2,
        )
        search.run()
        self.assertTrue(os.path.isfile(search.out_file))

    def test_slice_grid_search(self):
        search = pyfstat.SliceGridSearch(
            "slice_grid_search",
            self.outdir,
            self.sftfilepath,
            F0s=self.F0s,
            F1s=self.F1s,
            F2s=[0],
            Alphas=[0],
            Deltas=[0],
            tref=self.tref,
            Lambda0=[30, 0, 0, 0],
        )
        fig, axes = search.run(save=False)
        self.assertTrue(fig is not None)

    def test_glitch_grid_search(self):
        search = pyfstat.GridGlitchSearch(
            "grid_grid_search",
            self.outdir,
            self.sftfilepath,
            F0s=self.F0s,
            F1s=self.F1s,
            F2s=[0],
            Alphas=[0],
            Deltas=[0],
            tref=self.tref,
            tglitchs=[self.tref],
        )
        search.run()
        self.assertTrue(os.path.isfile(search.out_file))

    def test_sliding_window(self):
        search = pyfstat.FrequencySlidingWindow(
            "grid_grid_search",
            self.outdir,
            self.sftfilepath,
            F0s=self.F0s,
            F1=0,
            F2=0,
            Alpha=0,
            Delta=0,
            tref=self.tref,
            minStartTime=self.minStartTime,
            maxStartTime=self.maxStartTime,
        )
        search.run()
        self.assertTrue(os.path.isfile(search.out_file))


if __name__ == "__main__":
    unittest.main()