Select Git revision
Forked from
finesse / pykat
Source project has a limited visibility.
-
Daniel Brown authoredDaniel Brown authored
tests.py 6.73 KiB
import unittest
import numpy as np
import os
import shutil
import pyfstat
class Test(unittest.TestCase):
@classmethod
def setUpClass(cls):
pass
@classmethod
def tearDownClass(cls):
pass
class TestWriter(Test):
label = "Test"
def test_make_cff(self):
Writer = pyfstat.Writer(self.label, outdir=outdir)
Writer.make_cff()
self.assertTrue(os.path.isfile('./TestData/Test.cff'))
def test_run_makefakedata(self):
Writer = pyfstat.Writer(self.label, outdir=outdir)
Writer.make_cff()
Writer.run_makefakedata()
self.assertTrue(os.path.isfile(
'./TestData/H-4800_H1_1800SFT_Test-700000000-8640000.sft'))
def test_makefakedata_usecached(self):
Writer = pyfstat.Writer(self.label, outdir=outdir)
if os.path.isfile(Writer.sftfilepath):
os.remove(Writer.sftfilepath)
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 TestBaseSearchClass(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 TestComputeFstat(Test):
label = "Test"
def test_run_computefstatistic_single_point(self):
Writer = pyfstat.Writer(self.label, outdir=outdir)
Writer.make_data()
predicted_FS = Writer.predict_fstat()
search = pyfstat.ComputeFstat(
tref=Writer.tref,
sftfilepath='{}/*{}*sft'.format(Writer.outdir, Writer.label))
FS = search.run_computefstatistic_single_point(Writer.tstart,
Writer.tend,
Writer.F0,
Writer.F1,
Writer.F2,
Writer.Alpha,
Writer.Delta)
print predicted_FS, FS
self.assertTrue(np.abs(predicted_FS-FS)/FS < 0.1)
class TestSemiCoherentGlitchSearch(Test):
label = "Test"
def test_compute_nglitch_fstat(self):
duration = 100*86400
dtglitch = 100*43200
delta_F0 = 0
Writer = pyfstat.Writer(self.label, outdir=outdir,
duration=duration, dtglitch=dtglitch,
delta_F0=delta_F0)
Writer.make_data()
search = pyfstat.SemiCoherentGlitchSearch(
label=self.label, outdir=outdir,
sftfilepath='{}/*{}*sft'.format(Writer.outdir, Writer.label),
tref=Writer.tref, tstart=Writer.tstart, tend=Writer.tend,
nglitch=1)
FS = search.compute_nglitch_fstat(Writer.F0, Writer.F1, Writer.F2,
Writer.Alpha, Writer.Delta,
Writer.delta_F0, Writer.delta_F1,
search.tstart+dtglitch)
# Compute the predicted semi-coherent glitch Fstat
tstart = Writer.tstart
tend = Writer.tend
Writer.tend = tstart + dtglitch
FSA = Writer.predict_fstat()
Writer.tstart = tstart + dtglitch
Writer.tend = tend
FSB = Writer.predict_fstat()
predicted_FS = (FSA + FSB)
print(predicted_FS, FS)
self.assertTrue(np.abs((FS - predicted_FS))/predicted_FS < 0.3)
class TestMCMCSearch(Test):
label = "Test"
def test_fully_coherent(self):
h0 = 1e-24
sqrtSX = 1e-22
F0 = 30
F1 = -1e-10
F2 = 0
tstart = 700000000
duration = 100 * 86400
tend = tstart + duration
Alpha = 5e-3
Delta = 1.2
tref = tstart
dtglitch = duration
delta_F0 = 0
Writer = pyfstat.Writer(F0=F0, F1=F1, F2=F2, label=self.label,
h0=h0, sqrtSX=sqrtSX,
outdir=outdir, tstart=tstart,
Alpha=Alpha, Delta=Delta, tref=tref,
duration=duration, dtglitch=dtglitch,
delta_F0=delta_F0, Band=4)
Writer.make_data()
predicted_FS = Writer.predict_fstat()
theta = {'F0': {'type': 'norm', 'loc': F0, 'scale': np.abs(1e-9*F0)},
'F1': {'type': 'norm', 'loc': F1, 'scale': np.abs(1e-9*F1)},
'F2': F2, 'Alpha': Alpha, 'Delta': Delta}
search = pyfstat.MCMCSearch(
label=self.label, outdir=outdir, theta_prior=theta, tref=tref,
sftfilepath='{}/*{}*sft'.format(Writer.outdir, Writer.label),
tstart=tstart, tend=tend, nsteps=[100, 100], nwalkers=100,
ntemps=1)
search.run()
search.plot_corner(add_prior=True)
_, 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)
if __name__ == '__main__':
outdir = 'TestData'
if os.path.isdir(outdir):
shutil.rmtree(outdir)
unittest.main()
if os.path.isdir(outdir):
shutil.rmtree(outdir)