class TestCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = 0.0001
time = np.arange(tstart + 0.5*dt, tend + 0.5*dt, dt)
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1, gti=[[tstart, tend]], dt=dt)
self.lc2 = Lightcurve(time, counts2, gti=[[tstart, tend]], dt=dt)
self.cs = Crossspectrum(self.lc1, self.lc2)
def test_make_empty_crossspectrum(self):
cs = Crossspectrum()
assert cs.freq is None
assert cs.power is None
assert cs.df is None
assert cs.nphots1 is None
assert cs.nphots2 is None
assert cs.m == 1
assert cs.n is None
assert cs.power_err is None
def test_init_with_one_lc_none(self):
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1)
def test_init_with_multiple_gti(self):
gti = np.array([[0.0, 0.2], [0.6, 1.0]])
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1, self.lc2, gti=gti)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = Crossspectrum(norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = Crossspectrum(norm='frabs')
def test_init_with_wrong_lc1_instance(self):
lc_ = Crossspectrum()
with pytest.raises(TypeError):
cs = Crossspectrum(lc_, self.lc2)
def test_init_with_wrong_lc2_instance(self):
lc_ = Crossspectrum()
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1, lc_)
def test_make_crossspectrum_diff_lc_counts_shape(self):
counts = np.array([1]*10001)
time = np.linspace(0.0, 1.0001, 10001)
lc_ = Lightcurve(time, counts)
with pytest.raises(StingrayError):
cs = Crossspectrum(self.lc1, lc_)
def test_make_crossspectrum_diff_lc_stat(self):
lc_ = copy.copy(self.lc1)
lc_.err_dist = 'gauss'
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, lc_)
assert np.any(["different statistics" in r.message.args[0]
for r in record])
def test_make_crossspectrum_bad_lc_stat(self):
lc1 = copy.copy(self.lc1)
lc1.err_dist = 'gauss'
lc2 = copy.copy(self.lc1)
lc2.err_dist = 'gauss'
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(lc1, lc2)
assert np.any(["is not poisson" in r.message.args[0]
for r in record])
def test_make_crossspectrum_diff_dt(self):
counts = np.array([1]*10000)
time = np.linspace(0.0, 2.0, 10000)
lc_ = Lightcurve(time, counts)
with pytest.raises(StingrayError):
cs = Crossspectrum(self.lc1, lc_)
def test_rebin_smaller_resolution(self):
# Original df is between 0.9 and 1.0
with pytest.raises(ValueError):
new_cs = self.cs.rebin(df=0.1)
def test_rebin(self):
new_cs = self.cs.rebin(df=1.5)
assert new_cs.df == 1.5
new_cs.time_lag()
def test_rebin_factor(self):
new_cs = self.cs.rebin(f=1.5)
assert new_cs.df == self.cs.df * 1.5
new_cs.time_lag()
def test_rebin_log(self):
# For now, just verify that it doesn't crash
new_cs = self.cs.rebin_log(f=0.1)
assert type(new_cs) == type(self.cs)
new_cs.time_lag()
def test_norm_leahy(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
assert len(cs.power) == 4999
assert cs.norm == 'leahy'
def test_norm_frac(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='frac')
assert len(cs.power) == 4999
assert cs.norm == 'frac'
def test_norm_abs(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='abs')
assert len(cs.power) == 4999
assert cs.norm == 'abs'
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
cs = Crossspectrum(self.lc1, self.lc2, norm='wrong')
def test_coherence(self):
coh = self.cs.coherence()
assert len(coh) == 4999
assert np.abs(coh[0]) < 1
def test_timelag(self):
time_lag = self.cs.time_lag()
assert max(time_lag) <= np.pi
assert min(time_lag) >= -np.pi
def test_nonzero_err(self):
assert np.all(self.cs.power_err > 0)
def test_timelag_error(self):
class Child(Crossspectrum):
def __init__(self):
pass
obj = Child()
with pytest.raises(AttributeError):
lag = obj.time_lag()
class TestCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = 0.0001
time = np.arange(tstart + 0.5 * dt, tend + 0.5 * dt, dt)
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1, gti=[[tstart, tend]], dt=dt)
self.lc2 = Lightcurve(time, counts2, gti=[[tstart, tend]], dt=dt)
self.rate1 = 100. # mean count rate (counts/sec) of light curve 1
with pytest.warns(UserWarning) as record:
self.cs = Crossspectrum(self.lc1, self.lc2)
def test_lc_keyword_deprecation(self):
cs1 = Crossspectrum(self.lc1, self.lc2)
with pytest.warns(DeprecationWarning) as record:
cs2 = Crossspectrum(lc1=self.lc1, lc2=self.lc2)
assert np.any(['lcN keywords' in r.message.args[0] for r in record])
assert np.allclose(cs1.power, cs2.power)
assert np.allclose(cs1.freq, cs2.freq)
def test_make_empty_crossspectrum(self):
cs = Crossspectrum()
assert cs.freq is None
assert cs.power is None
assert cs.df is None
assert cs.nphots1 is None
assert cs.nphots2 is None
assert cs.m == 1
assert cs.n is None
assert cs.power_err is None
def test_init_with_one_lc_none(self):
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1)
def test_init_with_multiple_gti(self):
gti = np.array([[0.0, 0.2], [0.6, 1.0]])
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1, self.lc2, gti=gti)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = Crossspectrum(norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = Crossspectrum(norm='frabs')
def test_init_with_wrong_lc1_instance(self):
lc_ = {"a": 1, "b": 2}
with pytest.raises(TypeError):
cs = Crossspectrum(lc_, self.lc2)
def test_init_with_wrong_lc2_instance(self):
lc_ = {"a": 1, "b": 2}
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1, lc_)
def test_make_crossspectrum_diff_lc_counts_shape(self):
counts = np.array([1] * 10001)
time = np.linspace(0.0, 1.0001, 10001)
lc_ = Lightcurve(time, counts)
with pytest.raises(StingrayError):
cs = Crossspectrum(self.lc1, lc_)
def test_make_crossspectrum_diff_lc_stat(self):
lc_ = copy.copy(self.lc1)
lc_.err_dist = 'gauss'
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, lc_)
assert np.any(
["different statistics" in r.message.args[0] for r in record])
def test_make_crossspectrum_bad_lc_stat(self):
lc1 = copy.copy(self.lc1)
lc1.err_dist = 'gauss'
lc2 = copy.copy(self.lc1)
lc2.err_dist = 'gauss'
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(lc1, lc2)
assert np.any(["is not poisson" in r.message.args[0] for r in record])
def test_make_crossspectrum_diff_dt(self):
counts = np.array([1] * 10000)
time = np.linspace(0.0, 2.0, 10000)
lc_ = Lightcurve(time, counts)
with pytest.raises(StingrayError):
cs = Crossspectrum(self.lc1, lc_)
def test_rebin_smaller_resolution(self):
# Original df is between 0.9 and 1.0
with pytest.raises(ValueError):
new_cs = self.cs.rebin(df=0.1)
def test_rebin(self):
new_cs = self.cs.rebin(df=1.5)
assert new_cs.df == 1.5
new_cs.time_lag()
def test_rebin_factor(self):
new_cs = self.cs.rebin(f=1.5)
assert new_cs.df == self.cs.df * 1.5
new_cs.time_lag()
def test_rebin_log(self):
# For now, just verify that it doesn't crash
new_cs = self.cs.rebin_log(f=0.1)
assert type(new_cs) == type(self.cs)
new_cs.time_lag()
def test_norm_abs(self):
# Testing for a power spectrum of lc1
cs = Crossspectrum(self.lc1, self.lc1, norm='abs')
assert len(cs.power) == 4999
assert cs.norm == 'abs'
abs_noise = 2. * self.rate1 # expected Poisson noise level
assert np.isclose(np.mean(cs.power[1:]), abs_noise)
def test_norm_leahy(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc1, norm='leahy')
assert len(cs.power) == 4999
assert cs.norm == 'leahy'
leahy_noise = 2.0 # expected Poisson noise level
assert np.isclose(np.mean(cs.power[1:]), leahy_noise, rtol=0.02)
def test_norm_frac(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc1, norm='frac')
assert len(cs.power) == 4999
assert cs.norm == 'frac'
norm = 2. / self.rate1
assert np.isclose(np.mean(cs.power[1:]), norm, rtol=0.2)
def test_norm_abs(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm='abs')
assert len(cs.power) == 4999
assert cs.norm == 'abs'
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
cs = Crossspectrum(self.lc1, self.lc2, norm='wrong')
def test_coherence(self):
coh = self.cs.coherence()
assert len(coh) == 4999
assert np.abs(coh[0]) < 1
def test_timelag(self):
time_lag = self.cs.time_lag()
assert np.max(time_lag) <= np.pi
assert np.min(time_lag) >= -np.pi
def test_nonzero_err(self):
assert np.all(self.cs.power_err > 0)
def test_timelag_error(self):
class Child(Crossspectrum):
def __init__(self):
pass
obj = Child()
with pytest.raises(AttributeError):
lag = obj.time_lag()
def test_plot_simple(self):
self.cs.plot()
assert plt.fignum_exists('crossspectrum')
def test_rebin_error(self):
cs = Crossspectrum()
with pytest.raises(ValueError):
cs.rebin()
def test_classical_significances_runs(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
cs.classical_significances()
def test_classical_significances_fails_in_rms(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm='frac')
with pytest.raises(ValueError):
cs.classical_significances()
def test_classical_significances_threshold(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
# change the powers so that just one exceeds the threshold
cs.power = np.zeros_like(cs.power) + 2.0
index = 1
cs.power[index] = 10.0
threshold = 0.01
pval = cs.classical_significances(threshold=threshold,
trial_correction=False)
assert pval[0, 0] < threshold
assert pval[1, 0] == index
def test_classical_significances_trial_correction(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
# change the powers so that just one exceeds the threshold
cs.power = np.zeros_like(cs.power) + 2.0
index = 1
cs.power[index] = 10.0
threshold = 0.01
pval = cs.classical_significances(threshold=threshold,
trial_correction=True)
assert np.size(pval) == 0
def test_classical_significances_with_logbinned_psd(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
cs_log = cs.rebin_log()
pval = cs_log.classical_significances(threshold=1.1,
trial_correction=False)
assert len(pval[0]) == len(cs_log.power)
def test_pvals_is_numpy_array(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
# change the powers so that just one exceeds the threshold
cs.power = np.zeros_like(cs.power) + 2.0
index = 1
cs.power[index] = 10.0
threshold = 1.0
pval = cs.classical_significances(threshold=threshold,
trial_correction=True)
assert isinstance(pval, np.ndarray)
assert pval.shape[0] == 2
class TestCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = 0.0001
time = np.linspace(tstart, tend, int((tend - tstart)/dt))
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1)
self.lc2 = Lightcurve(time, counts2)
self.cs = Crossspectrum(self.lc1, self.lc2)
def test_make_empty_crossspectrum(self):
cs = Crossspectrum()
assert cs.freq is None
assert cs.power is None
assert cs.df is None
assert cs.nphots1 is None
assert cs.nphots2 is None
assert cs.m == 1
assert cs.n is None
def test_init_with_one_lc_none(self):
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = Crossspectrum(norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = Crossspectrum(norm='frabs')
def test_init_with_wrong_lc1_instance(self):
lc_ = Crossspectrum()
with pytest.raises(TypeError):
cs = Crossspectrum(lc_, self.lc2)
def test_init_with_wrong_lc2_instance(self):
lc_ = Crossspectrum()
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1, lc_)
def test_make_crossspectrum_diff_lc_counts_shape(self):
counts = np.array([1]*10001)
time = np.linspace(0.0, 1.0001, 10001)
lc_ = Lightcurve(time, counts)
with pytest.raises(StingrayError):
cs = Crossspectrum(self.lc1, lc_)
def test_make_crossspectrum_diff_dt(self):
counts = np.array([1]*10000)
time = np.linspace(0.0, 2.0, 10000)
lc_ = Lightcurve(time, counts)
with pytest.raises(StingrayError):
cs = Crossspectrum(self.lc1, lc_)
def test_rebin_smaller_resolution(self):
# Original df is between 0.9 and 1.0
with pytest.raises(ValueError):
new_cs = self.cs.rebin(df=0.1)
def test_rebin(self):
new_cs = self.cs.rebin(df=1.5)
assert new_cs.df == 1.5
def test_norm_leahy(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
assert len(cs.power) == 4999
assert cs.norm == 'leahy'
def test_norm_frac(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='frac')
assert len(cs.power) == 4999
assert cs.norm == 'frac'
def test_norm_abs(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='abs')
assert len(cs.power) == 4999
assert cs.norm == 'abs'
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
cs = Crossspectrum(self.lc1, self.lc2, norm='wrong')
def test_coherence(self):
coh = self.cs.coherence()
assert len(coh) == 4999
assert np.abs(coh[0]) < 1
def test_timelag(self):
time_lag = self.cs.time_lag()
assert max(time_lag) <= np.pi
assert min(time_lag) >= -np.pi
def test_timelag_error(self):
class Child(Crossspectrum):
def __init__(self):
pass
obj = Child()
with pytest.raises(AttributeError):
lag = obj.time_lag()
class TestCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = 0.0001
time = np.linspace(tstart, tend, int((tend - tstart)/dt))
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1)
self.lc2 = Lightcurve(time, counts2)
self.cs = Crossspectrum(self.lc1, self.lc2)
def test_make_empty_crossspectrum(self):
cs = Crossspectrum()
assert cs.freq is None
assert cs.power is None
assert cs.df is None
assert cs.nphots1 is None
assert cs.nphots2 is None
assert cs.m == 1
assert cs.n is None
def test_init_with_one_lc_none(self):
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = Crossspectrum(norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = Crossspectrum(norm='frabs')
def test_init_with_wrong_lc1_instance(self):
lc_ = Crossspectrum()
with pytest.raises(AssertionError):
cs = Crossspectrum(lc_, self.lc2)
def test_init_with_wrong_lc2_instance(self):
lc_ = Crossspectrum()
with pytest.raises(AssertionError):
cs = Crossspectrum(self.lc1, lc_)
def test_make_crossspectrum_diff_lc_counts_shape(self):
counts = np.array([1]*10001)
time = np.linspace(0.0, 1.0001, 10001)
lc_ = Lightcurve(time, counts)
with pytest.raises(AssertionError):
cs = Crossspectrum(self.lc1, lc_)
def test_make_crossspectrum_diff_dt(self):
counts = np.array([1]*10000)
time = np.linspace(0.0, 2.0, 10000)
lc_ = Lightcurve(time, counts)
with pytest.raises(AssertionError):
cs = Crossspectrum(self.lc1, lc_)
def test_rebin_smaller_resolution(self):
# Original df is between 0.9 and 1.0
with pytest.raises(AssertionError):
new_cs = self.cs.rebin(df=0.1)
def test_rebin(self):
new_cs = self.cs.rebin(df=1.5)
assert new_cs.df == 1.5
def test_norm_leahy(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
assert len(cs.power) == 4999
assert cs.norm == 'leahy'
def test_norm_frac(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='frac')
assert len(cs.power) == 4999
assert cs.norm == 'frac'
def test_norm_abs(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='abs')
assert len(cs.power) == 4999
assert cs.norm == 'abs'
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
cs = Crossspectrum(self.lc1, self.lc2, norm='wrong')
def test_coherence(self):
coh = self.cs.coherence()
assert len(coh) == 4999
assert np.abs(coh[0]) < 1
def test_timelag(self):
time_lag = self.cs.time_lag()
assert max(time_lag) <= np.pi
assert min(time_lag) >= -np.pi
def test_timelag_error(self):
class Child(Crossspectrum):
def __init__(self):
pass
obj = Child()
with pytest.raises(AttributeError):
lag = obj.time_lag()
class TestCrossspectrum(object):
def setup_class(self):
tstart = 0.0
tend = 1.0
dt = 0.0001
time = np.arange(tstart + 0.5*dt, tend + 0.5*dt, dt)
counts1 = np.random.poisson(0.01, size=time.shape[0])
counts2 = np.random.negative_binomial(1, 0.09, size=time.shape[0])
self.lc1 = Lightcurve(time, counts1, gti=[[tstart, tend]], dt=dt)
self.lc2 = Lightcurve(time, counts2, gti=[[tstart, tend]], dt=dt)
self.cs = Crossspectrum(self.lc1, self.lc2)
def test_make_empty_crossspectrum(self):
cs = Crossspectrum()
assert cs.freq is None
assert cs.power is None
assert cs.df is None
assert cs.nphots1 is None
assert cs.nphots2 is None
assert cs.m == 1
assert cs.n is None
assert cs.power_err is None
def test_init_with_one_lc_none(self):
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1)
def test_init_with_multiple_gti(self):
gti = np.array([[0.0, 0.2], [0.6, 1.0]])
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1, self.lc2, gti=gti)
def test_init_with_norm_not_str(self):
with pytest.raises(TypeError):
cs = Crossspectrum(norm=1)
def test_init_with_invalid_norm(self):
with pytest.raises(ValueError):
cs = Crossspectrum(norm='frabs')
def test_init_with_wrong_lc1_instance(self):
lc_ = Crossspectrum()
with pytest.raises(TypeError):
cs = Crossspectrum(lc_, self.lc2)
def test_init_with_wrong_lc2_instance(self):
lc_ = Crossspectrum()
with pytest.raises(TypeError):
cs = Crossspectrum(self.lc1, lc_)
def test_make_crossspectrum_diff_lc_counts_shape(self):
counts = np.array([1]*10001)
time = np.linspace(0.0, 1.0001, 10001)
lc_ = Lightcurve(time, counts)
with pytest.raises(StingrayError):
cs = Crossspectrum(self.lc1, lc_)
def test_make_crossspectrum_diff_lc_stat(self):
lc_ = copy.copy(self.lc1)
lc_.err_dist = 'gauss'
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, lc_)
assert np.any(["different statistics" in r.message.args[0]
for r in record])
def test_make_crossspectrum_bad_lc_stat(self):
lc1 = copy.copy(self.lc1)
lc1.err_dist = 'gauss'
lc2 = copy.copy(self.lc1)
lc2.err_dist = 'gauss'
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(lc1, lc2)
assert np.any(["is not poisson" in r.message.args[0]
for r in record])
def test_make_crossspectrum_diff_dt(self):
counts = np.array([1]*10000)
time = np.linspace(0.0, 2.0, 10000)
lc_ = Lightcurve(time, counts)
with pytest.raises(StingrayError):
cs = Crossspectrum(self.lc1, lc_)
def test_rebin_smaller_resolution(self):
# Original df is between 0.9 and 1.0
with pytest.raises(ValueError):
new_cs = self.cs.rebin(df=0.1)
def test_rebin(self):
new_cs = self.cs.rebin(df=1.5)
assert new_cs.df == 1.5
new_cs.time_lag()
def test_rebin_factor(self):
new_cs = self.cs.rebin(f=1.5)
assert new_cs.df == self.cs.df * 1.5
new_cs.time_lag()
def test_rebin_log(self):
# For now, just verify that it doesn't crash
new_cs = self.cs.rebin_log(f=0.1)
assert type(new_cs) == type(self.cs)
new_cs.time_lag()
def test_norm_leahy(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
assert len(cs.power) == 4999
assert cs.norm == 'leahy'
def test_norm_frac(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='frac')
assert len(cs.power) == 4999
assert cs.norm == 'frac'
def test_norm_abs(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='abs')
assert len(cs.power) == 4999
assert cs.norm == 'abs'
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
cs = Crossspectrum(self.lc1, self.lc2, norm='wrong')
def test_coherence(self):
coh = self.cs.coherence()
assert len(coh) == 4999
assert np.abs(coh[0]) < 1
def test_timelag(self):
time_lag = self.cs.time_lag()
assert max(time_lag) <= np.pi
assert min(time_lag) >= -np.pi
def test_nonzero_err(self):
assert np.all(self.cs.power_err > 0)
def test_timelag_error(self):
class Child(Crossspectrum):
def __init__(self):
pass
obj = Child()
with pytest.raises(AttributeError):
lag = obj.time_lag()