def test_plot_wrong_label_type(self):
cr = CrossCorrelation(self.lc1, self.lc2)
with pytest.raises(TypeError):
with warnings.catch_warnings(record=True) as w:
cr.plot(labels=123)
assert np.any([
"must be either a list or tuple" in str(wi.message)
for wi in w
])
def test_timeshift_with_no_corr_but_lc_assigned(self):
result = np.array([1.92, 2.16, 1.8, -14.44, 11.12])
lags_result = np.array([-2, -1, 0, 1, 2])
cr = CrossCorrelation()
cr.lc1 = self.lc1
cr.lc2 = self.lc2
cr.cal_timeshift(dt=1.0)
assert np.allclose(cr.lc1, self.lc1)
assert np.allclose(cr.lc2, self.lc2)
assert np.allclose(cr.corr, result)
assert np.isclose(cr.dt, self.lc1.dt)
assert cr.n == 5
assert np.allclose(cr.time_lags, lags_result)
assert np.isclose(cr.time_shift, 2.0)
assert cr.mode == 'same'
assert cr.auto is False
def test_empty_cross_correlation(self):
cr = CrossCorrelation()
assert cr.lc1 is None
assert cr.lc2 is None
assert cr.corr is None
assert cr.time_shift is None
assert cr.time_lags is None
assert cr.dt is None
assert cr.n is None
assert cr.auto is False
def test_full_mode_is_correct(self):
result = np.array([-1.76, 1.68, 1.92, 2.16, 1.8, -14.44, 11.12, -6.12, 3.64])
lags_result = np.array([-4, -3, -2, -1, 0, 1, 2, 3, 4])
cr = CrossCorrelation(self.lc1, self.lc2, mode='full')
assert np.allclose(cr.lc1, self.lc1)
assert np.allclose(cr.lc2, self.lc2)
assert cr.mode == 'full'
assert cr.n == 9
assert np.allclose(cr.corr, result)
assert np.allclose(cr.time_lags, lags_result)
assert np.isclose(cr.time_shift, 2.0)
assert cr.auto is False
def test_corr_is_correct(self):
result = np.array([1.92, 2.16, 1.8, -14.44, 11.12])
lags_result = np.array([-2, -1, 0, 1, 2])
cr = CrossCorrelation(self.lc1, self.lc2)
assert np.allclose(cr.lc1, self.lc1)
assert np.allclose(cr.lc2, self.lc2)
assert np.allclose(cr.corr, result)
assert np.isclose(cr.dt, self.lc1.dt)
assert cr.n == 5
assert np.allclose(cr.time_lags, lags_result)
assert np.isclose(cr.time_shift, 2.0)
assert cr.mode == 'same'
assert cr.auto is False
def test_cross_correlation_with_unequal_lc(self):
result = np.array([-0.66666667, -0.33333333, -1., 0.66666667, 3.13333333])
lags_result = np.array([-2, -1, 0, 1, 2])
cr = CrossCorrelation(self.lc1, self.lc_s)
assert np.allclose(cr.lc1, self.lc1)
assert np.allclose(cr.lc2, self.lc_s)
assert np.allclose(cr.corr, result)
assert np.isclose(cr.dt, self.lc1.dt)
assert cr.n == 5
assert np.allclose(cr.time_lags, lags_result)
assert np.isclose(cr.time_shift, 2.0)
assert cr.mode == 'same'
assert cr.auto is False
def test_crossparam_input(self):
# need to create new results to check against
spec = Crossspectrum(self.lc1, self.lc2, fullspec=True)
ifft = abs(scipy.fft.ifft(spec.power).real)
time = scipy.fft.fftfreq(len(ifft), spec.df)
time, resultifft = (list(t) for t in zip(*sorted(zip(time, ifft))))
cr2 = CrossCorrelation(cross=spec)
lags_result = np.array([-2, -1, 0, 1, 2])
assert np.allclose(cr2.cross.power, spec.power)
assert np.allclose(cr2.cross.freq, spec.freq)
assert np.allclose(cr2.corr, resultifft)
assert np.isclose(cr2.dt, self.lc1.dt)
assert cr2.n == 5
assert np.allclose(cr2.time_lags, lags_result)
assert cr2.mode == 'same'
assert cr2.auto is False
def test_timeshift_with_corr_and_lc_assigned(self):
result = np.array([1.92, 2.16, 1.8, -14.44, 11.12])
lags_result = np.array([-2, -1, 0, 1, 2])
cr = CrossCorrelation()
cr.lc1 = self.lc1
cr.lc2 = self.lc2
cr.corr = result
cr.cal_timeshift(dt=1.0)
assert np.allclose(cr.lc1, self.lc1)
assert np.allclose(cr.lc2, self.lc2)
assert np.allclose(cr.corr, result)
assert np.isclose(cr.dt, self.lc1.dt)
assert cr.n == 5
assert np.allclose(cr.time_lags, lags_result)
assert np.isclose(cr.time_shift, 2.0)
assert cr.mode == 'same'
assert cr.auto is False
def test_plot_axis(self):
cr = CrossCorrelation(self.lc1, self.lc2)
cr.plot(axis=[0, 1, 0, 100])
assert plt.fignum_exists(1)
def test_plot_labels_index_error(self):
cr = CrossCorrelation(self.lc1, self.lc2)
with warnings.catch_warnings(record=True) as w:
cr.plot(labels='x')
assert np.any(
["must have two labels" in str(wi.message) for wi in w])
def test_plot_custom_filename(self):
cr = CrossCorrelation(self.lc1, self.lc2)
cr.plot(save=True, filename='cr.png')
assert os.path.isfile('cr.png')
os.unlink('cr.png')
def test_simple_plot(self):
cr = CrossCorrelation(self.lc1, self.lc2)
cr.plot()
assert plt.fignum_exists(1)
def test_init_with_invalid_lc2(self):
data = np.array([[2, 3, 2, 4, 1]])
with pytest.raises(TypeError):
CrossCorrelation(self.lc1, data)
def test_empty_cross_correlation_with_dt(self):
cr = CrossCorrelation()
with pytest.raises(StingrayError):
cr.cal_timeshift(dt=2.0)
assert np.isclose(cr.dt, 2.0)
def test_plot_custom_filename(self):
cr = CrossCorrelation(self.lc1, self.lc2)
cr.plot(save=True, filename='cr.png')
assert os.path.isfile('cr.png')
os.unlink('cr.png')
def test_crosscorr_norm(self):
cr = CrossCorrelation(self.lc1, self.lc2, norm="variance")
assert np.isclose(cr.time_shift, -20, atol=0.1)
assert np.isclose(np.max(cr.corr), 1, atol=0.01)
assert np.isclose(np.min(cr.corr), -1, atol=0.01)
def test_crosscorr(self):
cr = CrossCorrelation(self.lc1, self.lc2)
assert np.isclose(cr.time_shift, -20, atol=0.1)
def test_plot_axis(self):
cr = CrossCorrelation(self.lc1, self.lc2)
cr.plot(axis=[0, 1, 0, 100])
assert plt.fignum_exists(1)
def test_plot_title(self):
cr = CrossCorrelation(self.lc1, self.lc2)
cr.plot(title="Test for Cross Correlation")
assert plt.fignum_exists(1)
def test_plot_matplotlib_not_installed(self):
cr = CrossCorrelation(self.lc1, self.lc2)
with pytest.raises(ImportError) as excinfo:
cr.plot()
message = str(excinfo.value)
assert "Matplotlib required for plot()" in message
def test_empty_cross_correlation_with_dt(self):
cr = CrossCorrelation()
with pytest.raises(StingrayError):
cr.cal_timeshift(dt=2.0)
assert np.isclose(cr.dt, 2.0)
def test_plot_title(self):
cr = CrossCorrelation(self.lc1, self.lc2)
cr.plot(title="Test for Cross Correlation")
assert plt.fignum_exists(1)
def test_plot_default_filename(self):
cr = CrossCorrelation(self.lc1, self.lc2)
cr.plot(save=True, title="Correlation")
assert os.path.isfile('corr.pdf')
os.unlink('corr.pdf')
def test_mode_with_bad_input(self):
with pytest.raises(TypeError):
CrossCorrelation(self.lc1, self.lc2, mode=123)
def test_plot_wrong_label_type(self):
cr = CrossCorrelation(self.lc1, self.lc2)
with pytest.raises(TypeError):
with warnings.catch_warnings(record=True) as w:
cr.plot(labels=123)
assert "must be either a list or tuple" in str(w[0].message)
def test_plot_matplotlib_not_installed(self):
cr = CrossCorrelation(self.lc1, self.lc2)
with pytest.raises(ImportError) as excinfo:
cr.plot()
message = str(excinfo.value)
assert "Matplotlib required for plot()" in message
def test_init_with_only_one_lc(self):
with pytest.raises(TypeError):
CrossCorrelation(self.lc1)
def test_simple_plot(self):
cr = CrossCorrelation(self.lc1, self.lc2)
cr.plot()
assert plt.fignum_exists(1)
def test_init_with_diff_time_bin(self):
with pytest.raises(StingrayError):
CrossCorrelation(self.lc_u, self.lc2)
def test_plot_labels_index_error(self):
cr = CrossCorrelation(self.lc1, self.lc2)
with warnings.catch_warnings(record=True) as w:
cr.plot(labels='x')
assert "must have two labels" in str(w[0].message)
def test_mode_with_wrong_input(self):
with pytest.raises(ValueError):
CrossCorrelation(self.lc1, self.lc2, mode='default')
def test_plot_default_filename(self):
cr = CrossCorrelation(self.lc1, self.lc2)
cr.plot(save=True)
assert os.path.isfile('corr.png')
os.unlink('corr.png')
