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refactor: maintainability issues in tests fixed.

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This commit is contained in:
Fabian Joswig 2022-06-24 12:28:49 +01:00
parent 07d947d5b1
commit 338bf8906a
3 changed files with 7 additions and 8 deletions
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4
tests/correlators_test.py
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@ -249,9 +249,9 @@ def test_matrix_corr():
def test_corr_none_entries(): def test_corr_none_entries():
a = pe.pseudo_Obs(1.0, 0.1, 'a') a = pe.pseudo_Obs(1.0, 0.1, 'a')
l = np.asarray([[a, a], [a, a]]) la = np.asarray([[a, a], [a, a]])
n = np.asarray([[None, None], [None, None]]) n = np.asarray([[None, None], [None, None]])
x = [l, n] x = [la, n]
matr = pe.Corr(x) matr = pe.Corr(x)
matr.projected(np.asarray([1.0, 0.0])) matr.projected(np.asarray([1.0, 0.0]))

7
tests/fits_test.py
View file

@ -194,7 +194,7 @@ def test_linear_fit_guesses():
lin_func = lambda a, x: a[0] + a[1] * x lin_func = lambda a, x: a[0] + a[1] * x
with pytest.raises(Exception): with pytest.raises(Exception):
pe.least_squares(xvals, yvals, lin_func) pe.least_squares(xvals, yvals, lin_func)
[o.gamma_method() for o in yvals]; [o.gamma_method() for o in yvals]
with pytest.raises(Exception): with pytest.raises(Exception):
pe.least_squares(xvals, yvals, lin_func, initial_guess=[5]) pe.least_squares(xvals, yvals, lin_func, initial_guess=[5])
@ -414,7 +414,7 @@ def test_fit_vs_jackknife():
for i, cov in enumerate([cov1, cov2, cov3]): for i, cov in enumerate([cov1, cov2, cov3]):
dat = pe.misc.gen_correlated_data(np.arange(1, 4), cov, 'test', 0.5, samples=samples) dat = pe.misc.gen_correlated_data(np.arange(1, 4), cov, 'test', 0.5, samples=samples)
[o.gamma_method(S=0) for o in dat]; [o.gamma_method(S=0) for o in dat]
func = lambda a, x: a[0] + a[1] * x func = lambda a, x: a[0] + a[1] * x
fr = pe.least_squares(np.arange(1, 4), dat, func) fr = pe.least_squares(np.arange(1, 4), dat, func)
fr.gamma_method(S=0) fr.gamma_method(S=0)
@ -448,8 +448,7 @@ def test_correlated_fit_vs_jackknife():
x_val = np.arange(1, 6, 2) x_val = np.arange(1, 6, 2)
for i, cov in enumerate([cov1, cov2, cov3]): for i, cov in enumerate([cov1, cov2, cov3]):
dat = pe.misc.gen_correlated_data(x_val + x_val ** 2 + np.random.normal(0.0, 0.1, 3), cov, 'test', 0.5, samples=samples) dat = pe.misc.gen_correlated_data(x_val + x_val ** 2 + np.random.normal(0.0, 0.1, 3), cov, 'test', 0.5, samples=samples)
[o.gamma_method(S=0) for o in dat]; [o.gamma_method(S=0) for o in dat]
dat
func = lambda a, x: a[0] * x + a[1] * x ** 2 func = lambda a, x: a[0] * x + a[1] * x ** 2
fr = pe.least_squares(x_val, dat, func, correlated_fit=True, silent=True) fr = pe.least_squares(x_val, dat, func, correlated_fit=True, silent=True)
[o.gamma_method(S=0) for o in fr] [o.gamma_method(S=0) for o in fr]

4
tests/linalg_test.py
View file

@ -258,8 +258,8 @@ def test_complex_matrix_inverse():
inverse_matrix = np.linalg.inv(matrix) inverse_matrix = np.linalg.inv(matrix)
inverse_obs_matrix = pe.linalg.inv(obs_matrix) inverse_obs_matrix = pe.linalg.inv(obs_matrix)
for (n, m), entry in np.ndenumerate(inverse_matrix): for (n, m), entry in np.ndenumerate(inverse_matrix):
assert np.isclose(inverse_matrix[n, m].real, inverse_obs_matrix[n, m].real.value) assert np.isclose(inverse_matrix[n, m].real, inverse_obs_matrix[n, m].real.value)
assert np.isclose(inverse_matrix[n, m].imag, inverse_obs_matrix[n, m].imag.value) assert np.isclose(inverse_matrix[n, m].imag, inverse_obs_matrix[n, m].imag.value)
def test_matrix_functions(): def test_matrix_functions():