def rebin_several(self, df):
"""
TODO: Not sure how to write tests for the rebin method!
"""
ps = Powerspectrum(lc=self.lc, norm="Leahy")
bin_ps = ps.rebin(df)
assert np.isclose(bin_ps.freq[0], bin_ps.df, atol=1e-4, rtol=1e-4)
def test_classical_significances_trial_correction(self):
ps = Powerspectrum(lc=self.lc, norm="leahy")
# change the powers so that just one exceeds the threshold
ps.power = np.zeros_like(ps.power) + 2.0
index = 1
ps.power[index] = 10.0
threshold = 0.01
pval = ps.classical_significances(threshold=threshold,
trial_correction=True)
assert np.size(pval) == 0
def test_fractional_rms_in_leahy_norm(self):
"""
fractional rms should only be *approximately* equal the standard
deviation divided by the mean of the light curve. Therefore, we allow
for a larger tolerance in np.isclose()
"""
ps = Powerspectrum(lc=self.lc, norm="Leahy")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[0],
max_freq=ps.freq[-1])
rms_lc = np.std(self.lc.counts) / np.mean(self.lc.counts)
assert np.isclose(rms_ps, rms_lc, atol=0.01)
def test_rebin_makes_right_attributes(self):
ps = Powerspectrum(lc=self.lc, norm="Leahy")
# replace powers
ps.ps = np.ones_like(ps.ps) * 2.0
rebin_factor = 2.0
bin_ps = ps.rebin(rebin_factor*ps.df)
assert bin_ps.freq is not None
assert bin_ps.ps is not None
assert bin_ps.df == rebin_factor * 1.0 / self.lc.tseg
assert bin_ps.norm.lower() == "leahy"
assert bin_ps.m == 2
assert bin_ps.n == self.lc.time.shape[0]
assert bin_ps.nphots == np.sum(self.lc.counts)
def test_pvals_is_numpy_array(self):
ps = Powerspectrum(lc=self.lc, norm="leahy")
# change the powers so that just one exceeds the threshold
ps.power = np.zeros_like(ps.power) + 2.0
index = 1
ps.power[index] = 10.0
threshold = 1.0
pval = ps.classical_significances(threshold=threshold,
trial_correction=True)
assert isinstance(pval, np.ndarray)
assert pval.shape[0] == 2
def test_classical_significances_threshold(self):
ps = Powerspectrum(lc=self.lc, norm="leahy")
# change the powers so that just one exceeds the threshold
ps.ps = np.zeros(ps.ps.shape[0])+2.0
index = 1
ps.ps[index] = 10.0
threshold = 0.01
pval = ps.classical_significances(threshold=threshold,
trial_correction=False)
assert pval[0, 0] < threshold
assert pval[1, 0] == index
def test_fractional_rms_in_frac_norm_is_consistent(self):
time = np.arange(0, 100, 1) + 0.5
poisson_counts = np.random.poisson(100.0,
size=time.shape[0])
lc = Lightcurve(time, counts=poisson_counts, dt=1,
gti=[[0, 100]])
ps = Powerspectrum(lc=lc, norm="leahy")
rms_ps_l, rms_err_l = ps.compute_rms(min_freq=ps.freq[1],
max_freq=ps.freq[-1], white_noise_offset=0)
ps = Powerspectrum(lc=lc, norm="frac")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[1],
max_freq=ps.freq[-1], white_noise_offset=0)
assert np.allclose(rms_ps, rms_ps_l, atol=0.01)
assert np.allclose(rms_err, rms_err_l, atol=0.01)
def test_compute_highest_outlier_works(self):
mp_ind = 5
max_power = 1000.0
ps = Powerspectrum()
ps.freq = np.arange(10)
ps.power = np.ones_like(ps.freq)
ps.power[mp_ind] = max_power
ps.m = 1
ps.df = ps.freq[1]-ps.freq[0]
ps.norm = "leahy"
model = models.Const1D()
p_amplitude = lambda amplitude: \
scipy.stats.norm(loc=1.0, scale=1.0).pdf(
amplitude)
priors = {"amplitude": p_amplitude}
lpost = PSDPosterior(ps.freq, ps.power, model, 1)
lpost.logprior = set_logprior(lpost, priors)
pe = PSDParEst(ps)
res = pe.fit(lpost, [1.0])
res.mfit = np.ones_like(ps.freq)
max_y, max_x, max_ind = pe._compute_highest_outlier(lpost, res)
assert np.isclose(max_y[0], 2*max_power)
assert np.isclose(max_x[0], ps.freq[mp_ind])
assert max_ind == mp_ind
def setup_class(cls):
m = 1
nfreq = 100000
freq = np.arange(nfreq)
noise = np.random.exponential(size=nfreq)
power = noise * 2.0
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
cls.ps = ps
cls.a_mean, cls.a_var = 2.0, 1.0
cls.model = models.Const1D()
p_amplitude = lambda amplitude: \
scipy.stats.norm(loc=cls.a_mean, scale=cls.a_var).pdf(amplitude)
cls.priors = {"amplitude": p_amplitude}
cls.lpost = PSDPosterior(cls.ps.freq, cls.ps.power, cls.model,
m=cls.ps.m)
cls.lpost.logprior = set_logprior(cls.lpost, cls.priors)
def test_calibrate_highest_outlier_works_with_mvn(self):
m = 1
nfreq = 10000
seed = 100
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(seed)
noise = rng.exponential(size=nfreq)
model = models.Const1D()
model.amplitude = 2.0
p = model(freq)
power = noise * p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
nsim = 10
loglike = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
pe = PSDParEst(ps)
pval = pe.calibrate_highest_outlier(loglike, [2.0], sample=None,
max_post=False, seed=seed,
nsim=nsim)
assert pval > 0.001
def test_generate_data_produces_correct_distribution(self):
model = models.Const1D()
model.amplitude = 2.0
p = model(self.ps.freq)
seed = 100
rng = np.random.RandomState(seed)
noise = rng.exponential(size=len(p))
power = noise*p
ps = Powerspectrum()
ps.freq = self.ps.freq
ps.power = power
ps.m = 1
ps.df = self.ps.freq[1]-self.ps.freq[0]
ps.norm = "leahy"
lpost = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
pe = PSDParEst(ps)
rng2 = np.random.RandomState(seed)
sim_data = pe._generate_data(lpost, [2.0], rng2)
assert np.allclose(ps.power, sim_data.power)
def setup_class(cls):
cls.m = 10
nfreq = 1000000
freq = np.arange(nfreq)
noise = scipy.stats.chi2(2.*cls.m).rvs(size=nfreq)/np.float(cls.m)
power = noise
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = cls.m
ps.df = freq[1]-freq[0]
ps.norm = "leahy"
cls.ps = ps
cls.a_mean, cls.a_var = 2.0, 1.0
cls.model = models.Const1D()
p_amplitude = lambda amplitude: \
scipy.stats.norm(loc=cls.a_mean, scale=cls.a_var).pdf(amplitude)
cls.priors = {"amplitude":p_amplitude}
def test_simulate_highest_outlier_works(self):
m = 1
nfreq = 100000
seed = 100
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(seed)
noise = rng.exponential(size=nfreq)
model = models.Const1D()
model.amplitude = 2.0
p = model(freq)
power = noise * p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
nsim = 10
loglike = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
s_all = np.atleast_2d(np.ones(nsim) * 2.0).T
pe = PSDParEst(ps)
res = pe.fit(loglike, [2.0], neg=True)
maxpow_sim = pe.simulate_highest_outlier(s_all, loglike, [2.0],
max_post=False, seed=seed)
assert maxpow_sim.shape[0] == nsim
assert np.all(maxpow_sim > 20.00) and np.all(maxpow_sim < 31.0)
def setup_class(cls):
m = 1
nfreq = 100000
freq = np.arange(nfreq)
noise = np.random.exponential(size=nfreq)
power = noise * 2.0
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
cls.ps = ps
cls.a_mean, cls.a_var = 2.0, 1.0
cls.model = models.Const1D()
p_amplitude = lambda amplitude: \
scipy.stats.norm(loc=cls.a_mean, scale=cls.a_var).pdf(amplitude)
cls.priors = {"amplitude": p_amplitude}
cls.lpost = PSDPosterior(cls.ps.freq, cls.ps.power,
cls.model, m=cls.ps.m)
cls.lpost.logprior = set_logprior(cls.lpost, cls.priors)
cls.fitmethod = "BFGS"
cls.max_post = True
cls.t0 = [2.0]
cls.neg = True
cls.opt = scipy.optimize.minimize(cls.lpost, cls.t0,
method=cls.fitmethod,
args=cls.neg, tol=1.e-10)
def test_calibrate_lrt_works_with_mvn(self):
m = 1
nfreq = 10000
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(100)
noise = rng.exponential(size=nfreq)
model = models.Const1D()
model.amplitude = 2.0
p = model(freq)
power = noise * p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
loglike = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
model2 = models.PowerLaw1D() + models.Const1D()
model2.x_0_0.fixed = True
loglike2 = PSDLogLikelihood(ps.freq, ps.power, model2, 1)
pe = PSDParEst(ps)
pval = pe.calibrate_lrt(loglike, [2.0], loglike2,
[2.0, 1.0, 2.0], sample=None,
max_post=False, nsim=10,
seed=100)
assert pval > 0.001
def test_calibrate_lrt_works_with_sampling(self):
m = 1
nfreq = 10000
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(100)
noise = rng.exponential(size=nfreq)
model = models.Const1D()
model.amplitude = 2.0
p = model(freq)
power = noise * p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
lpost = PSDPosterior(ps.freq, ps.power, model, m=1)
p_amplitude_1 = lambda amplitude: \
scipy.stats.norm(loc=2.0, scale=1.0).pdf(amplitude)
p_alpha_0 = lambda alpha: \
scipy.stats.uniform(0.0, 5.0).pdf(alpha)
p_amplitude_0 = lambda amplitude: \
scipy.stats.norm(loc=self.a2_mean, scale=self.a2_var).pdf(
amplitude)
priors = {"amplitude": p_amplitude_1}
priors2 = {"amplitude_1": p_amplitude_1,
"amplitude_0": p_amplitude_0,
"alpha_0": p_alpha_0}
lpost.logprior = set_logprior(lpost, priors)
model2 = models.PowerLaw1D() + models.Const1D()
model2.x_0_0.fixed = True
lpost2 = PSDPosterior(ps.freq, ps.power, model2, 1)
lpost2.logprior = set_logprior(lpost2, priors2)
pe = PSDParEst(ps)
pval = pe.calibrate_lrt(lpost, [2.0], lpost2,
[2.0, 1.0, 2.0], sample=None,
max_post=True, nsim=10, nwalkers=100,
burnin=100, niter=20,
seed=100)
assert pval > 0.001
def setup_class(cls):
m = 1
nfreq = 100000
freq = np.arange(nfreq)
noise = np.random.exponential(size=nfreq)
power = noise * 2.0
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
cls.ps = ps
cls.a_mean, cls.a_var = 2.0, 1.0
cls.model = models.Const1D()
p_amplitude = lambda amplitude: \
scipy.stats.norm(loc=cls.a_mean, scale=cls.a_var).pdf(
amplitude)
cls.priors = {"amplitude": p_amplitude}
cls.lpost = PSDPosterior(cls.ps.freq, cls.ps.power,
cls.model, m=cls.ps.m)
cls.lpost.logprior = set_logprior(cls.lpost, cls.priors)
cls.fitmethod = "BFGS"
cls.max_post = True
cls.t0 = [2.0]
cls.neg = True
pe = ParameterEstimation()
res = pe.fit(cls.lpost, cls.t0)
cls.nwalkers = 100
cls.niter = 200
np.random.seed(200)
p0 = np.array(
[np.random.multivariate_normal(res.p_opt, res.cov) for
i in range(cls.nwalkers)])
cls.sampler = emcee.EnsembleSampler(cls.nwalkers,
len(res.p_opt), cls.lpost,
args=[False], threads=1)
_, _, _ = cls.sampler.run_mcmc(p0, cls.niter)
def test_find_highest_outlier_works_as_expected(self):
mp_ind = 5
max_power = 1000.0
ps = Powerspectrum()
ps.freq = np.arange(10)
ps.power = np.ones_like(ps.freq)
ps.power[mp_ind] = max_power
ps.m = 1
ps.df = ps.freq[1]-ps.freq[0]
ps.norm = "leahy"
pe = PSDParEst(ps)
max_x, max_ind = pe._find_outlier(ps.freq, ps.power, max_power)
assert np.isclose(max_x, ps.freq[mp_ind])
assert max_ind == mp_ind
def test_plotfits_log_pow(self):
ps = Powerspectrum()
ps.freq = self.ps.freq
ps.power = self.ps.power
ps.m = self.ps.m
ps.df = self.ps.df
ps.norm = "none"
pe = PSDParEst(ps)
t0 = [2.0, 1, 1, 1]
res = pe.fit(self.lpost, t0)
pe.plotfits(res, res2=res, save_plot=True, log=True)
assert os.path.exists("test_ps_fit.png")
os.unlink("test_ps_fit.png")
def test_fitting_with_ties_and_bounds(self, capsys):
double_f = lambda model : model.x_0_0 * 2
model = self.model.copy()
model += models.Lorentz1D(amplitude=model.amplitude_0,
x_0 = model.x_0_0 * 2,
fwhm = model.fwhm_0)
model.x_0_0 = self.model.x_0_0
model.amplitude_0 = self.model.amplitude_0
model.amplitude_1 = self.model.amplitude_1
model.fwhm_0 = self.model.fwhm_0
model.x_0_2.tied = double_f
model.fwhm_0.bounds = [0, 10]
model.amplitude_0.fixed = True
p = model(self.ps.freq)
noise = np.random.exponential(size=len(p))
power = noise*p
ps = Powerspectrum()
ps.freq = self.ps.freq
ps.power = power
ps.m = self.ps.m
ps.df = self.ps.df
ps.norm = "leahy"
pe = PSDParEst(ps, fitmethod="TNC")
llike = PSDLogLikelihood(ps.freq, ps.power, model)
true_pars = [self.x_0_0, self.fwhm_0,
self.amplitude_1,
model.amplitude_2.value,
model.fwhm_2.value]
res = pe.fit(llike, true_pars, neg=True)
compare_pars = [self.x_0_0, self.fwhm_0,
self.amplitude_1,
model.amplitude_2.value,
model.fwhm_2.value]
assert np.allclose(compare_pars, res.p_opt, rtol=0.5)
def test_calibrate_highest_outlier_works_with_sampling(self):
m = 1
nfreq = 100000
seed = 100
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(seed)
noise = rng.exponential(size=nfreq)
model = models.Const1D()
model.amplitude = 2.0
p = model(freq)
power = noise * p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
nsim = 10
lpost = PSDPosterior(ps.freq, ps.power, model, m=1)
p_amplitude = lambda amplitude: \
scipy.stats.norm(loc=1.0, scale=1.0).pdf(
amplitude)
priors = {"amplitude": p_amplitude}
lpost.logprior = set_logprior(lpost, priors)
pe = PSDParEst(ps)
pval = pe.calibrate_highest_outlier(lpost, [2.0], sample=None,
max_post=True, seed=seed,
nsim=nsim, niter=20, nwalkers=100,
burnin=100)
assert pval > 0.001
def test_compute_lrt_works(self):
m = 1
nfreq = 100000
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(100)
noise = rng.exponential(size=nfreq)
model = models.Const1D()
model.amplitude = 2.0
p = model(freq)
power = noise * p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
loglike = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
s_all = np.atleast_2d(np.ones(10) * 2.0).T
model2 = models.PowerLaw1D() + models.Const1D()
model2.x_0_0.fixed = True
loglike2 = PSDLogLikelihood(ps.freq, ps.power, model2, 1)
pe = PSDParEst(ps)
lrt_obs, res1, res2 = pe.compute_lrt(loglike, [2.0], loglike2,
[2.0, 1.0, 2.0], neg=True)
lrt_sim = pe.simulate_lrts(s_all, loglike, [2.0], loglike2,
[2.0, 1.0, 2.0],
seed=100)
assert (lrt_obs > 0.4) and (lrt_obs < 0.6)
assert np.all(lrt_sim < 10.0) and np.all(lrt_sim > 0.01)
def test_init_with_nonsense_norm(self):
nonsense_norm = "bla"
with pytest.raises(ValueError):
assert Powerspectrum(self.lc, norm=nonsense_norm)
def test_init_with_wrong_norm_type(self):
nonsense_norm = 1.0
with pytest.raises(TypeError):
assert Powerspectrum(self.lc, norm=nonsense_norm)
def test_classical_significances_fails_in_rms(self):
ps = Powerspectrum(lc=self.lc, norm="rms")
ps.classical_significances()
def createPspec(lc):
Powerspectrum(lc)
def test_periodogram_types(self):
ps = Powerspectrum(lc=self.lc)
assert isinstance(ps.freq, np.ndarray)
assert isinstance(ps.power, np.ndarray)
def test_init_without_lightcurve(self):
with pytest.raises(TypeError):
assert Powerspectrum(self.lc.counts)
def test_classical_significances_runs(self):
ps = Powerspectrum(lc=self.lc, norm="Leahy")
ps.classical_significances()
def setup_class(cls):
m = 1
nfreq = 100000
freq = np.linspace(1, 10.0, nfreq)
rng = np.random.RandomState(
100) # set the seed for the random number generator
noise = rng.exponential(size=nfreq)
cls.model = models.Lorentz1D() + models.Const1D()
cls.x_0_0 = 2.0
cls.fwhm_0 = 0.1
cls.amplitude_0 = 100.0
cls.amplitude_1 = 2.0
cls.model.x_0_0 = cls.x_0_0
cls.model.fwhm_0 = cls.fwhm_0
cls.model.amplitude_0 = cls.amplitude_0
cls.model.amplitude_1 = cls.amplitude_1
p = cls.model(freq)
np.random.seed(400)
power = noise * p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
cls.ps = ps
cls.a_mean, cls.a_var = 2.0, 1.0
cls.a2_mean, cls.a2_var = 100.0, 10.0
p_amplitude_1 = lambda amplitude: \
scipy.stats.norm(loc=cls.a_mean, scale=cls.a_var).pdf(amplitude)
p_x_0_0 = lambda alpha: \
scipy.stats.uniform(0.0, 5.0).pdf(alpha)
p_fwhm_0 = lambda alpha: \
scipy.stats.uniform(0.0, 0.5).pdf(alpha)
p_amplitude_0 = lambda amplitude: \
scipy.stats.norm(loc=cls.a2_mean, scale=cls.a2_var).pdf(amplitude)
cls.priors = {
"amplitude_1": p_amplitude_1,
"amplitude_0": p_amplitude_0,
"x_0_0": p_x_0_0,
"fwhm_0": p_fwhm_0
}
cls.lpost = PSDPosterior(cls.ps.freq,
cls.ps.power,
cls.model,
m=cls.ps.m)
cls.lpost.logprior = set_logprior(cls.lpost, cls.priors)
cls.fitmethod = "BFGS"
cls.max_post = True
cls.t0 = [cls.x_0_0, cls.fwhm_0, cls.amplitude_0, cls.amplitude_1]
cls.neg = True
def test_fractional_rms_in_frac_norm(self):
ps = Powerspectrum(lc=self.lc, norm="frac")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[1],
max_freq=ps.freq[-1])
rms_lc = np.std(self.lc.counts) / np.mean(self.lc.counts)
assert np.isclose(rms_ps, rms_lc, atol=0.01)
def test_fractional_rms_fails_when_rms_not_leahy(self):
with pytest.raises(Exception):
ps = Powerspectrum(lc=self.lc, norm="rms")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[0],
max_freq=ps.freq[-1])
def setup_class(cls):
m = 1
nfreq = 100000
freq = np.linspace(1, 10.0, nfreq)
rng = np.random.RandomState(100) # set the seed for the random number generator
noise = rng.exponential(size=nfreq)
cls.model = models.Lorentz1D() + models.Const1D()
cls.x_0_0 = 2.0
cls.fwhm_0 = 0.1
cls.amplitude_0 = 100.0
cls.amplitude_1 = 2.0
cls.model.x_0_0 = cls.x_0_0
cls.model.fwhm_0 = cls.fwhm_0
cls.model.amplitude_0 = cls.amplitude_0
cls.model.amplitude_1 = cls.amplitude_1
p = cls.model(freq)
np.random.seed(400)
power = noise*p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1]-freq[0]
ps.norm = "leahy"
cls.ps = ps
cls.a_mean, cls.a_var = 2.0, 1.0
cls.a2_mean, cls.a2_var = 100.0, 10.0
p_amplitude_1 = lambda amplitude: \
scipy.stats.norm(loc=cls.a_mean, scale=cls.a_var).pdf(amplitude)
p_x_0_0 = lambda alpha: \
scipy.stats.uniform(0.0, 5.0).pdf(alpha)
p_fwhm_0 = lambda alpha: \
scipy.stats.uniform(0.0, 0.5).pdf(alpha)
p_amplitude_0 = lambda amplitude: \
scipy.stats.norm(loc=cls.a2_mean, scale=cls.a2_var).pdf(amplitude)
cls.priors = {"amplitude_1": p_amplitude_1,
"amplitude_0": p_amplitude_0,
"x_0_0": p_x_0_0,
"fwhm_0": p_fwhm_0}
cls.lpost = PSDPosterior(cls.ps.freq, cls.ps.power,
cls.model, m=cls.ps.m)
cls.lpost.logprior = set_logprior(cls.lpost, cls.priors)
cls.fitmethod = "BFGS"
cls.max_post = True
cls.t0 = [cls.x_0_0, cls.fwhm_0, cls.amplitude_0, cls.amplitude_1]
cls.neg = True
def test_classical_significances_runs(self):
ps = Powerspectrum(lc=self.lc, norm="Leahy")
ps.classical_significances()
def test_classical_significances_fails_in_rms(self):
ps = Powerspectrum(lc=self.lc, norm="frac")
with pytest.raises(ValueError):
ps.classical_significances()
def test_init_with_nonsense_data(self):
nonsense_data = [None for i in range(100)]
with pytest.raises(TypeError):
assert Powerspectrum(nonsense_data)
def test_fractional_rms_fails_when_rms_not_leahy(self):
with pytest.raises(Exception):
ps = Powerspectrum(lc=self.lc, norm="rms")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[0],
max_freq=ps.freq[-1])
def test_fractional_rms_in_rms_norm(self):
ps = Powerspectrum(lc=self.lc, norm="rms")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[1],
max_freq=ps.freq[-1])
rms_lc = np.std(self.lc.counts) / np.mean(self.lc.counts)
assert np.isclose(rms_ps, rms_lc, atol=0.01)
def test_classical_significances_fails_in_rms(self):
ps = Powerspectrum(lc=self.lc, norm="frac")
with pytest.raises(ValueError):
ps.classical_significances()
def setup_class(cls):
m = 1
nfreq = 100000
freq = np.linspace(1, 1000, nfreq)
np.random.seed(100) # set the seed for the random number generator
noise = np.random.exponential(size=nfreq)
cls.model = models.PowerLaw1D() + models.Const1D()
cls.model.x_0_0.fixed = True
cls.alpha_0 = 2.0
cls.amplitude_0 = 100.0
cls.amplitude_1 = 2.0
cls.model.alpha_0 = cls.alpha_0
cls.model.amplitude_0 = cls.amplitude_0
cls.model.amplitude_1 = cls.amplitude_1
p = cls.model(freq)
power = noise * p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
cls.ps = ps
cls.a_mean, cls.a_var = 2.0, 1.0
cls.a2_mean, cls.a2_var = 100.0, 10.0
p_amplitude_1 = lambda amplitude: \
scipy.stats.norm(loc=cls.a_mean, scale=cls.a_var).pdf(amplitude)
p_alpha_0 = lambda alpha: \
scipy.stats.uniform(0.0, 5.0).pdf(alpha)
p_amplitude_0 = lambda amplitude: \
scipy.stats.norm(loc=cls.a2_mean, scale=cls.a2_var).pdf(
amplitude)
cls.priors = {
"amplitude_1": p_amplitude_1,
"amplitude_0": p_amplitude_0,
"alpha_0": p_alpha_0
}
cls.lpost = PSDPosterior(cls.ps.freq,
cls.ps.power,
cls.model,
m=cls.ps.m)
cls.lpost.logprior = set_logprior(cls.lpost, cls.priors)
cls.fitmethod = "BFGS"
cls.max_post = True
cls.t0 = [cls.amplitude_0, cls.alpha_0, cls.amplitude_1]
cls.neg = True
cls.opt = scipy.optimize.minimize(cls.lpost,
cls.t0,
method=cls.fitmethod,
args=cls.neg,
tol=1.e-5)
cls.optres = OptimizationResultsSubclassDummy(cls.lpost,
cls.opt,
neg=True)
def test_classical_significances_with_logbinned_psd(self):
ps = Powerspectrum(lc=self.lc, norm="leahy")
ps_log = ps.rebin_log()
pval = ps_log.classical_significances(threshold=1.1, trial_correction=False)
assert len(pval[0]) == len(ps_log.power)
def test_init_with_lightcurve(self):
assert Powerspectrum(self.lc)
def test_ccf(self):
# to make testing faster, fitting is not done.
ref_ps = Powerspectrum(self.ref_lc, norm='abs')
ci_counts_0 = self.ci_counts[0]
ci_times = np.arange(0, self.n_seconds * self.n_seg, self.dt)
ci_lc = Lightcurve(ci_times, ci_counts_0, dt=self.dt)
# rebinning factor used in `rebin_log`
rebin_log_factor = 0.4
acs = AveragedCrossspectrum(lc1=ci_lc, lc2=self.ref_lc,
segment_size=self.n_seconds, norm='leahy',
power_type="absolute")
acs = acs.rebin_log(rebin_log_factor)
# parest, res = fit_crossspectrum(acs, self.model, fitmethod="CG")
acs_result_model = self.model
# using optimal filter
optimal_filter = Optimal1D(acs_result_model)
optimal_filter_freq = optimal_filter(acs.freq)
filtered_acs_power = optimal_filter_freq * np.abs(acs.power)
# rebinning power spectrum
new_df = spec.get_new_df(ref_ps, self.n_bins)
ref_ps_rebinned = ref_ps.rebin(df=new_df)
# parest, res = fit_powerspectrum(ref_ps_rebinned, self.model)
ref_ps_rebinned_result_model = self.model
# calculating rms from power spectrum
ref_ps_rebinned_rms = spec.compute_rms(ref_ps_rebinned,
ref_ps_rebinned_result_model,
criteria="optimal")
# calculating normalized ccf
ccf_norm = spec.ccf(filtered_acs_power, ref_ps_rebinned_rms,
self.n_bins)
# calculating ccf error
meta = {'N_SEG': self.n_seg, 'NSECONDS': self.n_seconds, 'DT': self.dt,
'N_BINS': self.n_bins}
error_ccf, avg_seg_ccf = spec.ccf_error(self.ref_counts, ci_counts_0,
acs_result_model,
rebin_log_factor,
meta, ref_ps_rebinned_rms,
filter_type="optimal")
assert np.all(np.isclose(ccf_norm, avg_seg_ccf, atol=0.01))
assert np.all(np.isclose(error_ccf, np.zeros(shape=error_ccf.shape),
atol=0.01))
# using window function
tophat_filter = Window1D(acs_result_model)
tophat_filter_freq = tophat_filter(acs.freq)
filtered_acs_power = tophat_filter_freq * np.abs(acs.power)
ref_ps_rebinned_rms = spec.compute_rms(ref_ps_rebinned,
ref_ps_rebinned_result_model,
criteria="window")
ccf_norm = spec.ccf(filtered_acs_power, ref_ps_rebinned_rms,
self.n_bins)
error_ccf, avg_seg_ccf = spec.ccf_error(self.ref_counts, ci_counts_0,
acs_result_model,
rebin_log_factor,
meta, ref_ps_rebinned_rms,
filter_type="window")
assert np.all(np.isclose(ccf_norm, avg_seg_ccf, atol=0.01))
assert np.all(np.isclose(error_ccf, np.zeros(shape=error_ccf.shape),
atol=0.01))
def test_ccf(self):
# to make testing faster, fitting is not done.
ref_ps = Powerspectrum(self.ref_lc, norm='abs')
ci_counts_0 = self.ci_counts[0]
ci_times = np.arange(0, self.n_seconds * self.n_seg, self.dt)
ci_lc = Lightcurve(ci_times, ci_counts_0, dt=self.dt)
# rebinning factor used in `rebin_log`
rebin_log_factor = 0.4
acs = AveragedCrossspectrum(lc1=ci_lc,
lc2=self.ref_lc,
segment_size=self.n_seconds,
norm='leahy',
power_type="absolute")
acs = acs.rebin_log(rebin_log_factor)
# parest, res = fit_crossspectrum(acs, self.model, fitmethod="CG")
acs_result_model = self.model
# using optimal filter
optimal_filter = Optimal1D(acs_result_model)
optimal_filter_freq = optimal_filter(acs.freq)
filtered_acs_power = optimal_filter_freq * np.abs(acs.power)
# rebinning power spectrum
new_df = spec.get_new_df(ref_ps, self.n_bins)
ref_ps_rebinned = ref_ps.rebin(df=new_df)
# parest, res = fit_powerspectrum(ref_ps_rebinned, self.model)
ref_ps_rebinned_result_model = self.model
# calculating rms from power spectrum
ref_ps_rebinned_rms = spec.compute_rms(ref_ps_rebinned,
ref_ps_rebinned_result_model,
criteria="optimal")
# calculating normalized ccf
ccf_norm = spec.ccf(filtered_acs_power, ref_ps_rebinned_rms,
self.n_bins)
# calculating ccf error
meta = {
'N_SEG': self.n_seg,
'NSECONDS': self.n_seconds,
'DT': self.dt,
'N_BINS': self.n_bins
}
error_ccf, avg_seg_ccf = spec.ccf_error(self.ref_counts,
ci_counts_0,
acs_result_model,
rebin_log_factor,
meta,
ref_ps_rebinned_rms,
filter_type="optimal")
assert np.all(np.isclose(ccf_norm, avg_seg_ccf, atol=0.01))
assert np.all(
np.isclose(error_ccf, np.zeros(shape=error_ccf.shape), atol=0.01))
# using window function
tophat_filter = Window1D(acs_result_model)
tophat_filter_freq = tophat_filter(acs.freq)
filtered_acs_power = tophat_filter_freq * np.abs(acs.power)
ref_ps_rebinned_rms = spec.compute_rms(ref_ps_rebinned,
ref_ps_rebinned_result_model,
criteria="window")
ccf_norm = spec.ccf(filtered_acs_power, ref_ps_rebinned_rms,
self.n_bins)
error_ccf, avg_seg_ccf = spec.ccf_error(self.ref_counts,
ci_counts_0,
acs_result_model,
rebin_log_factor,
meta,
ref_ps_rebinned_rms,
filter_type="window")
assert np.all(np.isclose(ccf_norm, avg_seg_ccf, atol=0.01))
assert np.all(
np.isclose(error_ccf, np.zeros(shape=error_ccf.shape), atol=0.01))