def test_nanstd_with_ddof(self):
x = np.random.uniform(0, 10, (20, 100))
for axis in [None, 0, 1]:
np.testing.assert_almost_equal(
np.nanstd(x, axis=axis, ddof=10),
nanstd(csr_matrix(x), axis=axis, ddof=10),
)
def test_nanstd_with_ddof(self):
x = np.random.uniform(0, 10, (20, 100))
for axis in [None, 0, 1]:
np.testing.assert_almost_equal(
np.nanstd(x, axis=axis, ddof=10),
nanstd(csr_matrix(x), axis=axis, ddof=10),
)
def _get_error_bar(self):
std = nanstd(self.y_data, axis=0)
return pg.ErrorBarItem(x=self.x_data,
y=self.__mean,
bottom=std,
top=std,
beam=0.01)
def test_nanstd(self, array):
for X in self.data:
X_sparse = array(X)
np.testing.assert_array_equal(nanstd(X_sparse), np.nanstd(X))
def test_nanstd(self, array):
for X in self.data:
X_sparse = array(X)
np.testing.assert_array_equal(
nanstd(X_sparse),
np.nanstd(X))
def _get_error_bar(self):
std = nanstd(self.y_data, axis=0)
return pg.ErrorBarItem(x=self.x_data, y=self.__mean,
bottom=std, top=std, beam=0.01)
