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feat: correlation parameter added to covariance again, tests extended.

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Fabian Joswig 2022-03-01 17:46:12 +00:00
parent 8d93ff95f2
commit da0c43fe9a
2 changed files with 26 additions and 15 deletions
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11
pyerrors/obs.py
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@ -1332,7 +1332,7 @@ def correlate(obs_a, obs_b):
return o
def covariance(obs, visualize=False, **kwargs):
def covariance(obs, visualize=False, correlation=False, **kwargs):
"""Calculates the covariance matrix of a set of observables.
covariance([obs, obs])[0,1] is equal to obs.dvalue ** 2
@ -1343,7 +1343,9 @@ def covariance(obs, visualize=False, **kwargs):
obs : list or numpy.ndarray
List or one dimensional array of Obs
visualize : bool
Plots the corresponding normalized correlation matrix.
If True plots the corresponding normalized correlation matrix (default False).
correlation : bool
If True the correlation instead of the covariance is returned (default False).
"""
length = len(obs)
@ -1368,7 +1370,10 @@ def covariance(obs, visualize=False, **kwargs):
plt.colorbar()
plt.draw()
return cov
if correlation is True:
return corr
else:
return cov
def _covariance_element(obs1, obs2):

30
tests/obs_test.py
View file

@ -246,17 +246,6 @@ def test_gamma_method_kwargs():
assert my_obs.N_sigma['ens'] == pe.Obs.N_sigma_global
def test_covariance_is_variance():
value = np.random.normal(5, 10)
dvalue = np.abs(np.random.normal(0, 1))
test_obs = pe.pseudo_Obs(value, dvalue, 't')
test_obs.gamma_method()
assert np.abs(test_obs.dvalue ** 2 - pe.covariance([test_obs, test_obs])[0, 1]) <= 10 * np.finfo(np.float64).eps
test_obs = test_obs + pe.pseudo_Obs(value, dvalue, 'q', 200)
test_obs.gamma_method()
assert np.abs(test_obs.dvalue ** 2 - pe.covariance([test_obs, test_obs])[0, 1]) <= 10 * np.finfo(np.float64).eps
def test_fft():
value = np.random.normal(5, 100)
dvalue = np.abs(np.random.normal(0, 5))
@ -605,6 +594,18 @@ def test_gamma_method_irregular():
ol = [a, b]
o = (ol[0] - ol[1]) / (ol[1])
def test_covariance_is_variance():
value = np.random.normal(5, 10)
dvalue = np.abs(np.random.normal(0, 1))
test_obs = pe.pseudo_Obs(value, dvalue, 't')
test_obs.gamma_method()
assert np.isclose(test_obs.dvalue ** 2, pe.covariance([test_obs, test_obs])[0, 1])
test_obs = test_obs + pe.pseudo_Obs(value, dvalue, 'q', 200)
test_obs.gamma_method()
assert np.isclose(test_obs.dvalue ** 2, pe.covariance([test_obs, test_obs])[0, 1])
def test_covariance_symmetry():
value1 = np.random.normal(5, 10)
dvalue1 = np.abs(np.random.normal(0, 1))
@ -616,7 +617,7 @@ def test_covariance_symmetry():
test_obs2.gamma_method()
cov_ab = pe.covariance([test_obs1, test_obs2])[0, 1]
cov_ba = pe.covariance([test_obs2, test_obs1])[0, 1]
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.isclose(cov_ab, cov_ba)
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
N = 100
@ -673,6 +674,11 @@ def test_covariance_factorizing():
assert np.isclose(pe.covariance([mt0, tt[1]])[0, 1], -pe.covariance(tt)[0, 1])
def test_covariance_correlation():
test_obs = pe.pseudo_Obs(-4, 0.8, 'test', samples=784)
assert np.allclose(pe.covariance([test_obs, test_obs, test_obs], correlation=True), np.ones((3, 3)))
def test_empty_obs():
o = pe.Obs([np.random.rand(100)], ['test'])
q = o + pe.Obs([], [])