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tests.py 14.65 KiB
import unittest
import numpy as np
import os
import shutil
import pyfstat
import lalpulsar
class Test(unittest.TestCase):
outdir = 'TestData'
@classmethod
def setUpClass(self):
if os.path.isdir(self.outdir):
shutil.rmtree(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):
shutil.rmtree(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)
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('mkdir {}'.format(self.outdir))
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., 1e2, 10., 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 + .5*thetaA[3]*dT**2
thetaB[0] = thetaA[0] + 2*np.pi*(thetaA[1]*dT + .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., 1e2, 10., 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,L1')
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 = .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])
search.run()
self.assertTrue(os.path.isfile('{}/{}_slice_projection.png'
.format(search.outdir, search.label)))
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()