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tests.py
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tests.py
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import unittest
import numpy as np
import os
import shutil
import pyfstat
import lalpulsar
import logging
import time
from scipy.stats import chi2
class BaseForTestsWithOutdir(unittest.TestCase):
outdir = "TestData"
@classmethod
def setUpClass(self):
# ensure a clean working directory
if os.path.isdir(self.outdir):
try:
shutil.rmtree(self.outdir)
except OSError:
logging.warning("{} not removed prior to tests".format(self.outdir))
os.makedirs(self.outdir, exist_ok=True)
@classmethod
def tearDownClass(self):
if os.path.isdir(self.outdir):
try:
shutil.rmtree(self.outdir)
except OSError:
logging.warning("{} not removed after tests".format(self.outdir))
default_Writer_params = {
"label": "test",
"sqrtSX": 1,
"Tsft": 1800,
"tstart": 700000000,
"duration": 4 * 1800,
"detectors": "H1",
"SFTWindowType": "tukey",
"SFTWindowBeta": 0.001,
"randSeed": 42,
"Band": None,
}
default_signal_params = {
"F0": 30,
"F1": -1e-10,
"F2": 0,
"h0": 5.0,
"cosi": 0,
"psi": 0,
"phi": 0,
"Alpha": 5e-1,
"Delta": 1.2,
}
default_binary_params = {
"period": 45 * 24 * 3600.0,
"asini": 10.0,
"tp": default_Writer_params["tstart"] + 0.25 * default_Writer_params["duration"],
"ecc": 0.5,
"argp": 0.3,
}
class BaseForTestsWithData(BaseForTestsWithOutdir):
outdir = "TestData"
@classmethod
def setUpClass(self):
# ensure a clean working directory
if os.path.isdir(self.outdir):
try:
shutil.rmtree(self.outdir)
except OSError:
logging.warning("{} not removed prior to tests".format(self.outdir))
# skip making outdir, since Writer should do so on first call
# os.makedirs(self.outdir, exist_ok=True)
# create fake data SFTs
# if we directly set any options as self.xy = 1 here,
# then values set for derived classes may get overwritten,
# so use a default dict and only insert if no value previous set
for key, val in {**default_Writer_params, **default_signal_params}.items():
if not hasattr(self, key):
setattr(self, key, val)
self.tref = self.tstart
self.Writer = pyfstat.Writer(
label=self.label,
tstart=self.tstart,
duration=self.duration,
tref=self.tref,
F0=self.F0,
F1=self.F1,
F2=self.F2,
Alpha=self.Alpha,
Delta=self.Delta,
h0=self.h0,
cosi=self.cosi,
Tsft=self.Tsft,
outdir=self.outdir,
sqrtSX=self.sqrtSX,
Band=self.Band,
detectors=self.detectors,
SFTWindowType=self.SFTWindowType,
SFTWindowBeta=self.SFTWindowBeta,
randSeed=self.randSeed,
)
self.Writer.make_data(verbose=True)
self.search_keys = ["F0", "F1", "F2", "Alpha", "Delta"]
self.search_ranges = {key: [getattr(self, key)] for key in self.search_keys}
class TestInjectionParametersGenerator(BaseForTestsWithOutdir):
label = "TestInjectionParametersGenerator"
class_to_test = pyfstat.InjectionParametersGenerator
def test_numpy_priors(self):
numpy_priors = {
"ParameterA": {"uniform": {"low": 0.0, "high": 0.0}},
"ParameterB": {"uniform": {"low": 1.0, "high": 1.0}},
}
InjectionGenerator = self.class_to_test(numpy_priors)
parameters = InjectionGenerator.draw()
self.assertTrue(parameters["ParameterA"] == 0.0)
self.assertTrue(parameters["ParameterB"] == 1.0)
def test_callable_priors(self):
callable_priors = {"ParameterA": lambda: 0.0, "ParameterB": lambda: 1.0}
InjectionGenerator = self.class_to_test(callable_priors)
parameters = InjectionGenerator.draw()
self.assertTrue(parameters["ParameterA"] == 0.0)
self.assertTrue(parameters["ParameterB"] == 1.0)
def test_constant_priors(self):
constant_priors = {"ParameterA": 0.0, "ParameterB": 1.0}
InjectionGenerator = self.class_to_test(constant_priors)
parameters = InjectionGenerator.draw()
self.assertTrue(parameters["ParameterA"] == 0.0)
self.assertTrue(parameters["ParameterB"] == 1.0)
def test_rng_seed(self):
a_seed = 420
samples = []
for i in range(2):
InjectionGenerator = self.class_to_test(
priors={"ParameterA": {"normal": {"loc": 0, "scale": 1}}}, seed=a_seed
)
samples.append(InjectionGenerator.draw())
self.assertTrue(samples[0]["ParameterA"] == samples[1]["ParameterA"])
def test_rng_generation(self):
InjectionGenerator = self.class_to_test(
priors={"ParameterA": {"normal": {"loc": 0, "scale": 0.01}}}
)
samples = [InjectionGenerator.draw()["ParameterA"] for i in range(100)]
mean = np.mean(samples)
self.assertTrue(np.abs(mean) < 0.1)
class TestAllSkyInjectionParametersGenerator(TestInjectionParametersGenerator):
label = "TestAllInjectionParametersGenerator"
class_to_test = pyfstat.AllSkyInjectionParametersGenerator
def test_rng_seed(self):
a_seed = 420
samples = []
for i in range(2):
InjectionGenerator = self.class_to_test(seed=a_seed)
samples.append(InjectionGenerator.draw())
self.assertTrue(samples[0]["Alpha"] == samples[1]["Alpha"])
self.assertTrue(samples[0]["Delta"] == samples[1]["Delta"])
def test_rng_generation(self):
InjectionGenerator = self.class_to_test()
ra_samples = [
InjectionGenerator.draw()["Alpha"] / np.pi - 1.0 for i in range(500)
]
dec_samples = [
InjectionGenerator.draw()["Delta"] * 2.0 / np.pi for i in range(500)
]
self.assertTrue(np.abs(np.mean(ra_samples)) < 0.1)
self.assertTrue(np.abs(np.mean(dec_samples)) < 0.1)
class TestWriter(BaseForTestsWithData):
label = "TestWriter"
writer_class_to_test = pyfstat.Writer
signal_parameters = default_signal_params
def test_make_cff(self):
self.Writer.make_cff(verbose=True)
self.assertTrue(
os.path.isfile(os.path.join(".", self.outdir, self.label + ".cff"))
)
def test_run_makefakedata(self):
self.Writer.make_data(verbose=True)
numSFTs = int(np.ceil(self.duration / self.Tsft))
expected_outfile = os.path.join(
self.Writer.outdir,
"{:1s}-{:d}_{:2s}_{:d}SFT_{:s}-{:d}-{:d}.sft".format(
self.detectors[0],
numSFTs,
self.detectors,
self.Tsft,
self.Writer.label,
self.Writer.tstart,
numSFTs * self.Tsft,
),
)
self.assertTrue(os.path.isfile(expected_outfile))
self.assertTrue(lalpulsar.ValidateSFTFile(expected_outfile) == 0)
def test_makefakedata_usecached(self):
if os.path.isfile(self.Writer.config_file_name):
os.remove(self.Writer.config_file_name)
if os.path.isfile(self.Writer.sftfilepath):
os.remove(self.Writer.sftfilepath)
# first run: make everything from scratch
self.Writer.make_cff(verbose=True)
self.Writer.run_makefakedata()
time_first = os.path.getmtime(self.Writer.sftfilepath)
# second run: should re-use .cff and .sft
self.Writer.make_cff(verbose=True)
self.Writer.run_makefakedata()
time_second = os.path.getmtime(self.Writer.sftfilepath)
self.assertTrue(time_first == time_second)
# third run: touch the .cff to force regeneration
time.sleep(1) # make sure timestamp is actually different!
os.system("touch {}".format(self.Writer.config_file_name))
self.Writer.run_makefakedata()
time_third = os.path.getmtime(self.Writer.sftfilepath)
self.assertFalse(time_first == time_third)
def test_noise_sfts(self):
randSeed = 69420
detectors = "H1,L1"
# create SFTs with both noise and a signal in them
noise_and_signal_writer = self.writer_class_to_test(
label="test_noiseSFTs_noise_and_signal",
outdir=self.outdir,
duration=self.duration,
Tsft=self.Tsft,
tstart=self.tstart,
detectors=detectors,
randSeed=randSeed,
SFTWindowType=self.SFTWindowType,
SFTWindowBeta=self.SFTWindowBeta,
sqrtSX=self.sqrtSX,
Band=0.5,
**self.signal_parameters,
)
noise_and_signal_writer.make_data(verbose=True)
# FIXME: here and everywhere below,
# get_sft_array() only looks at first detector
times, freqs, data = pyfstat.helper_functions.get_sft_array(
noise_and_signal_writer.sftfilepath
)
# Store the maximum SFT values for later comparison
max_values_noise_and_signal = np.max(data, axis=0)
max_freqs_noise_and_signal = freqs[np.argmax(data, axis=0)]
self.assertTrue(len(times) == int(np.ceil(self.duration / self.Tsft)))
# FIXME: CW signals don't have to peak at the same frequency, but there
# are some consistency criteria which may be useful to implement here.
# self.assertTrue(len(np.unique(max_freqs_noise_and_signal)) == 1)
# create noise-only SFTs
noise_writer = self.writer_class_to_test(
label="test_noiseSFTs_only_noise",
outdir=self.outdir,
duration=self.duration,
Tsft=self.Tsft,
tstart=self.tstart,
detectors=detectors,
randSeed=randSeed,
SFTWindowType=self.SFTWindowType,
SFTWindowBeta=self.SFTWindowBeta,
sqrtSX=self.sqrtSX,
Band=0.5,
F0=self.signal_parameters["F0"],
)
noise_writer.make_data(verbose=True)
times, freqs, data = pyfstat.helper_functions.get_sft_array(
noise_writer.sftfilepath
)
max_values_noise = np.max(data, axis=0)
max_freqs_noise = freqs[np.argmax(data, axis=0)]
self.assertEqual(len(max_freqs_noise), len(max_freqs_noise_and_signal))
# pure noise: random peak freq in each SFT, lower max values
self.assertFalse(len(np.unique(max_freqs_noise)) == 1)
self.assertTrue(np.all(max_values_noise < max_values_noise_and_signal))
# inject into noise-only SFTs without additional SFT loading constraints
add_signal_writer = self.writer_class_to_test(
label="test_noiseSFTs_add_signal",
outdir=self.outdir,
SFTWindowType=self.SFTWindowType,
SFTWindowBeta=self.SFTWindowBeta,
noiseSFTs=noise_writer.sftfilepath,
**self.signal_parameters,
)
add_signal_writer.make_data(verbose=True)
times, freqs, data = pyfstat.helper_functions.get_sft_array(
add_signal_writer.sftfilepath
)
max_values_added_signal = np.max(data, axis=0)
max_freqs_added_signal = freqs[np.argmax(data, axis=0)]
self.assertEqual(len(max_freqs_added_signal), len(max_freqs_noise_and_signal))
# peak freqs expected exactly equal to first case,
# peak values can have a bit of numerical diff
self.assertTrue(np.all(max_freqs_added_signal == max_freqs_noise_and_signal))
self.assertTrue(
np.allclose(
max_values_added_signal, max_values_noise_and_signal, rtol=1e-6, atol=0
)
)
# same again but with explicit (tstart,duration) to build constraints
add_signal_writer_constr = self.writer_class_to_test(
label="test_noiseSFTs_add_signal_with_constraints",
outdir=self.outdir,
duration=self.duration / 2,
Tsft=self.Tsft,
tstart=self.tstart,
SFTWindowType=self.SFTWindowType,
SFTWindowBeta=self.SFTWindowBeta,
sqrtSX=0,
noiseSFTs=noise_writer.sftfilepath,
**self.signal_parameters,
)
add_signal_writer_constr.make_data(verbose=True)
times, freqs, data = pyfstat.helper_functions.get_sft_array(
add_signal_writer_constr.sftfilepath
)
max_values_added_signal_constr = np.max(data, axis=0)
max_freqs_added_signal_constr = freqs[np.argmax(data, axis=0)]
self.assertEqual(
2 * len(max_freqs_added_signal_constr), len(max_freqs_noise_and_signal)
)
# peak freqs and values expected to be exactly equal
# regardless of read-in constraints
self.assertTrue(
np.all(
max_freqs_added_signal_constr
== max_freqs_added_signal[: len(max_freqs_added_signal_constr)]
)
)
self.assertTrue(
np.all(
max_values_added_signal_constr
== max_values_added_signal[: len(max_values_added_signal_constr)]
)
)
def test_data_with_gaps(self):
duration = 10 * self.Tsft
gap_time = 4 * self.Tsft
window = "tukey"
window_beta = 0.01
Band = 0.01
first_chunk_of_data = self.writer_class_to_test(
label="first_chunk_of_data",
outdir=self.outdir,
duration=duration,
Tsft=self.Tsft,
tstart=self.tstart,
detectors=self.detectors,
SFTWindowType=window,
SFTWindowBeta=window_beta,
F0=self.F0,
Band=Band,
)
first_chunk_of_data.make_data(verbose=True)
second_chunk_of_data = self.writer_class_to_test(
label="second_chunk_of_data",
outdir=self.outdir,
duration=duration,
Tsft=self.Tsft,
tstart=self.tstart + duration + gap_time,
tref=self.tstart,
detectors=self.detectors,
SFTWindowType=window,
SFTWindowBeta=window_beta,
F0=self.F0,
Band=Band,
)
second_chunk_of_data.make_data(verbose=True)
both_chunks_of_data = self.writer_class_to_test(
label="both_chunks_of_data",
outdir=self.outdir,
noiseSFTs=first_chunk_of_data.sftfilepath
+ ";"
+ second_chunk_of_data.sftfilepath,
SFTWindowType=window,
SFTWindowBeta=window_beta,
F0=self.F0,
Band=Band,
)
both_chunks_of_data.make_data(verbose=True)
Tsft = both_chunks_of_data.Tsft
total_duration = 2 * duration + gap_time
Nsft = int((total_duration - gap_time) / Tsft)
expected_SFT_filepath = os.path.join(
self.outdir,
"{:1s}-{:d}_{:2s}_{:d}SFT_{:s}-{:d}-{:d}.sft".format(
self.detectors[0],
Nsft,
self.detectors,
Tsft,
both_chunks_of_data.label,
self.tstart,
total_duration,
),
)
self.assertTrue(os.path.isfile(expected_SFT_filepath))
class TestWriterOtherTsft(TestWriter):
label = "TestWriterOtherTsft"
writer_class_to_test = pyfstat.Writer
@classmethod
def setUpClass(self):
self.Tsft = 1024
super().setUpClass()
class TestBinaryModulatedWriter(TestWriter):
label = "TestBinaryModulatedWriter"
writer_class_to_test = pyfstat.BinaryModulatedWriter
signal_parameters = {**default_signal_params, **default_binary_params}
def test_tp_parsing(self):
this_writer = self.writer_class_to_test(
outdir=self.outdir, **default_Writer_params, **self.signal_parameters
)
this_writer.make_data()
theta_prior = {
key: value
for key, value in default_signal_params.items()
if key not in ["h0", "cosi", "psi", "phi"]
}
theta_prior.update({key: value for key, value in default_binary_params.items()})
theta_prior["tp"] = {
"type": "unif",
"lower": default_binary_params["tp"]
- 0.5 * default_binary_params["period"],
"upper": default_binary_params["tp"]
+ 0.5 * default_binary_params["period"],
}
theta_prior.pop
mcmc_kwargs = {
"nsteps": [50],
"nwalkers": 150,
"ntemps": 3,
}
print(theta_prior)
mcmc = pyfstat.MCMCSearch(
binary=True,
label="tp_parsing",
outdir=self.outdir,
sftfilepattern=this_writer.sftfilepath,
theta_prior=theta_prior,
tref=this_writer.tstart,
minStartTime=this_writer.tstart,
maxStartTime=this_writer.tend(),
**mcmc_kwargs,
)
mcmc.run(plot_walkers=False)
max_twoF_sample, _ = mcmc.get_max_twoF()
relative_difference = np.abs(
1.0 - max_twoF_sample["tp"] / default_binary_params["tp"]
)
self.assertTrue(relative_difference < 1e-5)
class TestGlitchWriter(TestWriter):
label = "TestGlitchWriter"
writer_class_to_test = pyfstat.GlitchWriter
def test_glitch_injection(self):
Band = 1
vanillaWriter = pyfstat.Writer(
label=self.label + "_vanilla",
outdir=self.outdir,
duration=self.duration,
tstart=self.tstart,
detectors=self.detectors,
Band=Band,
**default_signal_params,
)
vanillaWriter.make_cff(verbose=True)
vanillaWriter.run_makefakedata()
noGlitchWriter = self.writer_class_to_test(
label=self.label + "_noglitch",
outdir=self.outdir,
duration=self.duration,
tstart=self.tstart,
detectors=self.detectors,
Band=Band,
**default_signal_params,
)
noGlitchWriter.make_cff(verbose=True)
noGlitchWriter.run_makefakedata()
glitchWriter = self.writer_class_to_test(
label=self.label + "_glitch",
outdir=self.outdir,
duration=self.duration,
tstart=self.tstart,
detectors=self.detectors,
Band=Band,
**default_signal_params,
dtglitch=2 * 1800,
delta_F0=0.1,
)
glitchWriter.make_cff(verbose=True)
glitchWriter.run_makefakedata()
(
times_vanilla,
freqs_vanilla,
data_vanilla,
) = pyfstat.helper_functions.get_sft_array(vanillaWriter.sftfilepath)
(
times_noglitch,
freqs_noglitch,
data_noglitch,
) = pyfstat.helper_functions.get_sft_array(noGlitchWriter.sftfilepath)
(
times_glitch,
freqs_glitch,
data_glitch,
) = pyfstat.helper_functions.get_sft_array(glitchWriter.sftfilepath)
max_freq_vanilla = freqs_vanilla[np.argmax(data_vanilla, axis=0)]
max_freq_noglitch = freqs_noglitch[np.argmax(data_noglitch, axis=0)]
max_freq_glitch = freqs_glitch[np.argmax(data_glitch, axis=0)]
print([max_freq_vanilla, max_freq_noglitch, max_freq_glitch])
self.assertTrue(np.all(times_noglitch == times_vanilla))
self.assertTrue(np.all(times_glitch == times_vanilla))
self.assertEqual(len(np.unique(max_freq_vanilla)), 1)
self.assertEqual(len(np.unique(max_freq_noglitch)), 1)
self.assertEqual(len(np.unique(max_freq_glitch)), 2)
self.assertEqual(max_freq_noglitch[0], max_freq_vanilla[0])
self.assertEqual(max_freq_glitch[0], max_freq_noglitch[0])
self.assertTrue(max_freq_glitch[-1] > max_freq_noglitch[-1])
class TestReadParFile(BaseForTestsWithOutdir):
label = "TestReadParFile"
def test(self):
parfile = os.path.join(self.outdir, self.label + ".par")
os.system('echo "x=100\ny=10" > ' + parfile)
par = pyfstat.helper_functions.read_par(filename=parfile)
self.assertTrue(par["x"] == 100)
self.assertTrue(par["y"] == 10)
par = pyfstat.helper_functions.read_par(outdir=self.outdir, label=self.label)
self.assertTrue(par["x"] == 100)
self.assertTrue(par["y"] == 10)
class TestPredictFstat(BaseForTestsWithOutdir):
label = "TestPredictFstat"
# here we only test the modes WITHOUT sftfilepattern,
# which itself is tested through the Writer and Search classes
def test_PFS_noise(self):
twoF_expected, twoF_sigma = pyfstat.helper_functions.predict_fstat(
minStartTime=default_Writer_params["tstart"],
duration=default_Writer_params["duration"],
IFOs=default_Writer_params["detectors"],
assumeSqrtSX=1,
)
print(
"predict_fstat() returned: E[2F]={}+-{}".format(twoF_expected, twoF_sigma)
)
self.assertTrue(twoF_expected == 4)
self.assertAlmostEqual(twoF_sigma, chi2.std(df=4), places=5)
def test_PFS_noise_TSfiles(self):
IFOs = ["H1", "L1"]
TSfiles = [
os.path.join(self.outdir, "{:s}_{:s}.ts".format(self.label, IFO))
for IFO in IFOs
]
for f in TSfiles:
with open(f, "w") as fp:
fp.write(
"{:d} 0\n{:d} 0\n".format(
default_Writer_params["tstart"],
default_Writer_params["tstart"] + default_Writer_params["Tsft"],
)
)
twoF_expected, twoF_sigma = pyfstat.helper_functions.predict_fstat(
timestampsFiles=",".join(TSfiles),
IFOs=",".join(IFOs),
assumeSqrtSX=1,
)
print(
"predict_fstat() returned: E[2F]={}+-{}".format(twoF_expected, twoF_sigma)
)
self.assertTrue(twoF_expected == 4)
self.assertAlmostEqual(twoF_sigma, chi2.std(df=4), places=5)
def test_PFS_signal(self):
twoF_expected, twoF_sigma = pyfstat.helper_functions.predict_fstat(
h0=1,
cosi=0,
psi=0,
Alpha=0,
Delta=0,
minStartTime=default_Writer_params["tstart"],
duration=default_Writer_params["duration"],
IFOs=default_Writer_params["detectors"],
assumeSqrtSX=1,
)
print(
"predict_fstat() returned: E[2F]={}+-{}".format(twoF_expected, twoF_sigma)
)
self.assertTrue(twoF_expected > 4)
self.assertTrue(twoF_sigma > 0)
class TestBaseSearchClass(unittest.TestCase):
# TODO test the basic methods
pass
class TestComputeFstat(BaseForTestsWithData):
label = "TestComputeFstat"
def test_run_computefstatistic_single_point_injectSqrtSX(self):
# not using any SFTs
search = pyfstat.ComputeFstat(
tref=self.tref,
minStartTime=self.tstart,
maxStartTime=self.tstart + self.duration,
detectors=self.detectors,
injectSqrtSX=self.sqrtSX,
minCoverFreq=self.F0 - 0.1,
maxCoverFreq=self.F0 + 0.1,
)
FS = search.get_fullycoherent_twoF(
F0=self.F0,
F1=self.F1,
F2=self.F2,
Alpha=self.Alpha,
Delta=self.Delta,
)
self.assertTrue(FS > 0.0)
def test_run_computefstatistic_single_point_with_SFTs(self):
twoF_predicted = self.Writer.predict_fstat()
search = pyfstat.ComputeFstat(
tref=self.Writer.tref,
sftfilepattern=self.Writer.sftfilepath,
search_ranges=self.search_ranges,
)
twoF = search.get_fullycoherent_twoF(
F0=self.Writer.F0,
F1=self.Writer.F1,
F2=self.Writer.F2,
Alpha=self.Writer.Alpha,
Delta=self.Writer.Delta,
)
diff = np.abs(twoF - twoF_predicted) / twoF_predicted
print(
(
"Predicted twoF is {}"
" while recovered value is {},"
" relative difference: {}".format(twoF_predicted, twoF, diff)
)
)
self.assertTrue(diff < 0.3)
# the following seems to be a leftover from when this test case was
# doing separate H1 vs H1,L1 searches, but now only really tests the
# SSBprec. But well, it still does add a tiny bit of coverage, can still
# be replaced by something more systematic later.
search = pyfstat.ComputeFstat(
tref=self.Writer.tref,
detectors=self.Writer.detectors,
sftfilepattern=self.Writer.sftfilepath,
SSBprec=lalpulsar.SSBPREC_RELATIVISTIC,
search_ranges=self.search_ranges,
)
twoF2 = search.get_fullycoherent_twoF(
F0=self.Writer.F0,
F1=self.Writer.F1,
F2=self.Writer.F2,
Alpha=self.Writer.Alpha,
Delta=self.Writer.Delta,
)
diff = np.abs(twoF2 - twoF_predicted) / twoF_predicted
print(
(
"Predicted twoF is {}"
" while recovered value is {},"
" relative difference: {}".format(twoF_predicted, twoF2, diff)
)
)
self.assertTrue(diff < 0.3)
diff = np.abs(twoF2 - twoF) / twoF
self.assertTrue(diff < 0.001)
def test_run_computefstatistic_single_point_injectSources(self):
predicted_FS = self.Writer.predict_fstat()
injectSources = self.Writer.config_file_name
search = pyfstat.ComputeFstat(
tref=self.Writer.tref,
assumeSqrtSX=1,
injectSources=injectSources,
minCoverFreq=28,
maxCoverFreq=32,
minStartTime=self.Writer.tstart,
maxStartTime=self.Writer.tend(),
detectors=self.Writer.detectors,
)
FS_from_file = search.get_fullycoherent_twoF(
F0=self.Writer.F0,
F1=self.Writer.F1,
F2=self.Writer.F2,
Alpha=self.Writer.Alpha,
Delta=self.Writer.Delta,
)
self.assertTrue(np.abs(predicted_FS - FS_from_file) / FS_from_file < 0.3)
injectSourcesdict = search.read_par(filename=injectSources)
injectSourcesdict["F0"] = injectSourcesdict.pop("Freq")
injectSourcesdict["F1"] = injectSourcesdict.pop("f1dot")
injectSourcesdict["F2"] = injectSourcesdict.pop("f2dot")
injectSourcesdict["phi"] = injectSourcesdict.pop("phi0")
search = pyfstat.ComputeFstat(
tref=self.Writer.tref,
assumeSqrtSX=1,
injectSources=injectSourcesdict,
minCoverFreq=28,
maxCoverFreq=32,
minStartTime=self.Writer.tstart,
maxStartTime=self.Writer.tend(),
detectors=self.Writer.detectors,
)
FS_from_dict = search.get_fullycoherent_twoF(
F0=self.Writer.F0,
F1=self.Writer.F1,
F2=self.Writer.F2,
Alpha=self.Writer.Alpha,
Delta=self.Writer.Delta,
)
self.assertTrue(FS_from_dict == FS_from_file)
def test_get_fully_coherent_BSGL(self):
# first pure noise, expect log10BSGL<0
search_H1L1 = pyfstat.ComputeFstat(
tref=self.tref,
minStartTime=self.tstart,
maxStartTime=self.tstart + self.duration,
detectors="H1,L1",
injectSqrtSX=np.repeat(self.sqrtSX, 2),
minCoverFreq=self.F0 - 0.1,
maxCoverFreq=self.F0 + 0.1,
BSGL=True,
)
log10BSGL = search_H1L1.get_fullycoherent_detstat(
F0=self.F0,
F1=self.F1,
F2=self.F2,
Alpha=self.Alpha,
Delta=self.Delta,
)
self.assertTrue(log10BSGL < 0)
# now with an added signal, expect log10BSGL>0
search_H1L1 = pyfstat.ComputeFstat(
tref=self.tref,
minStartTime=self.tstart,
maxStartTime=self.tstart + self.duration,
detectors="H1,L1",
injectSqrtSX=np.repeat(self.sqrtSX, 2),
injectSources="{{Alpha={:g}; Delta={:g}; h0={:g}; cosi={:g}; Freq={:g}; f1dot={:g}; f2dot={:g}; refTime={:d};}}".format(
self.Alpha,
self.Delta,
self.h0,
self.cosi,
self.F0,
self.F1,
self.F2,
self.tref,
),
minCoverFreq=self.F0 - 0.1,
maxCoverFreq=self.F0 + 0.1,
BSGL=True,
)
log10BSGL = search_H1L1.get_fullycoherent_detstat(
F0=self.F0,
F1=self.F1,
F2=self.F2,
Alpha=self.Alpha,
Delta=self.Delta,
)
self.assertTrue(log10BSGL > 0)
def test_cumulative_twoF(self):
Nsft = 100
# not using any SFTs on disk
search = pyfstat.ComputeFstat(
tref=self.tref,
minStartTime=self.tstart,
maxStartTime=self.tstart + Nsft * self.Tsft,
detectors=self.detectors,
injectSqrtSX=self.sqrtSX,
injectSources=default_signal_params,
minCoverFreq=self.F0 - 0.1,
maxCoverFreq=self.F0 + 0.1,
)
start_time, taus, twoF_cumulative = search.calculate_twoF_cumulative(
self.Writer.F0,
self.Writer.F1,
self.Writer.F2,
self.Writer.Alpha,
self.Writer.Delta,
num_segments=Nsft + 1,
)
twoF = search.get_fullycoherent_detstat(
F0=self.Writer.F0,
F1=self.Writer.F1,
F2=self.Writer.F2,
Alpha=self.Writer.Alpha,
Delta=self.Writer.Delta,
tstart=self.Writer.tstart,
tend=self.Writer.tstart + taus[-1],
)
reldiff = np.abs(twoF_cumulative[-1] - twoF) / twoF
print(
"2F from get_fullycoherent_detstat() is {:.4f}"
" while last value from calculate_twoF_cumulative() is {:.4f};"
" relative difference: {:g}".format(twoF, twoF_cumulative[-1], reldiff)
)
self.assertTrue(reldiff < 0.1)
idx = int(Nsft / 2)
partial_2F_expected = (taus[idx] / taus[-1]) * twoF
reldiff = (
np.abs(twoF_cumulative[idx] - partial_2F_expected) / partial_2F_expected
)
print(
"Middle 2F value from calculate_twoF_cumulative() is {:.4f}"
" while from duration ratio we'd expect {:.4f}*{:.4f}={:.4f};"
" relative difference: {:g}".format(
twoF_cumulative[idx],
taus[idx] / taus[-1],
twoF,
partial_2F_expected,
reldiff,
)
)
self.assertTrue(reldiff < 0.1)
class TestComputeFstatNoNoise(BaseForTestsWithData):
# FIXME: should be possible to merge into TestComputeFstat with smart
# defaults handlingf
label = "TestComputeFstatSinglePointNoNoise"
sqrtSX = 0
def test_run_computefstatistic_single_point_no_noise(self):
predicted_FS = self.Writer.predict_fstat(assumeSqrtSX=1)
search = pyfstat.ComputeFstat(
tref=self.Writer.tref,
assumeSqrtSX=1,
sftfilepattern=self.Writer.sftfilepath,
search_ranges=self.search_ranges,
)
FS = search.get_fullycoherent_twoF(
F0=self.Writer.F0,
F1=self.Writer.F1,
F2=self.Writer.F2,
Alpha=self.Writer.Alpha,
Delta=self.Writer.Delta,
)
self.assertTrue(np.abs(predicted_FS - FS) / FS < 0.3)
def test_run_computefstatistic_single_point_no_noise_manual_ephem(self):
predicted_FS = self.Writer.predict_fstat(assumeSqrtSX=1)
# let's get the default ephemeris files (to be sure their paths exist)
# and then pretend we pass them manually, to test those class options
(
earth_ephem_default,
sun_ephem_default,
) = pyfstat.helper_functions.get_ephemeris_files()
search = pyfstat.ComputeFstat(
tref=self.Writer.tref,
assumeSqrtSX=1,
sftfilepattern=self.Writer.sftfilepath,
earth_ephem=earth_ephem_default,
sun_ephem=sun_ephem_default,
search_ranges=self.search_ranges,
)
FS = search.get_fullycoherent_twoF(
F0=self.Writer.F0,
F1=self.Writer.F1,
F2=self.Writer.F2,
Alpha=self.Writer.Alpha,
Delta=self.Writer.Delta,
)
self.assertTrue(np.abs(predicted_FS - FS) / FS < 0.3)
class TestSearchForSignalWithJumps(TestBaseSearchClass):
def test_shift_matrix(self):
search = pyfstat.SearchForSignalWithJumps()
dT = 10
a = search._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):
search = pyfstat.SearchForSignalWithJumps()
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, search._shift_coefficients(thetaA, dT)))
def test_shift_coefficients_loop(self):
search = pyfstat.SearchForSignalWithJumps()
thetaA = np.array([10.0, 1e2, 10.0, 1e2])
dT = 1e1
thetaB = search._shift_coefficients(thetaA, dT)
self.assertTrue(
np.allclose(
thetaA, search._shift_coefficients(thetaB, -dT), rtol=1e-9, atol=1e-9
)
)
class TestSemiCoherentSearch(BaseForTestsWithData):
label = "TestSemiCoherentSearch"
detectors = "H1,L1"
nsegs = 2
def test_get_semicoherent_twoF(self):
search = pyfstat.SemiCoherentSearch(
label=self.label,
outdir=self.outdir,
nsegs=self.nsegs,
sftfilepattern=self.Writer.sftfilepath,
tref=self.Writer.tref,
search_ranges=self.search_ranges,
BSGL=False,
)
twoF_sc = search.get_semicoherent_det_stat(
self.Writer.F0,