lattice/pyerrors
1
0
Fork 0
mirror of https://github.com/fjosw/pyerrors.git synced 2025-03-15 06:40:24 +01:00
Code Issues Projects Releases Packages Wiki Activity

tests: Additional tests for covariance with different idls added.

Browse source
This commit is contained in:
Fabian Joswig 2022-04-28 15:30:18 +01:00
parent 217d310ca4
commit 60f9bb6a89
1 changed files with 54 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

54
tests/obs_test.py
View file

@ -778,6 +778,60 @@ def test_correlation_intersection_of_idls():
assert np.isclose(0, pe.covariance([obs1, obs2_c])[0, 1], atol=1e-14)
def test_covariance_non_identical_objects():
obs1 = pe.Obs([np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 732)], ["ens|r1", "ens|r2", "ens2"])
obs1.gamma_method()
obs2 = obs1 + 1e-18
obs2.gamma_method()
assert obs1 == obs2
assert obs1 is not obs2
assert np.allclose(np.ones((2, 2)), pe.covariance([obs1, obs2], correlation=True), atol=1e-14)
def test_covariance_additional_non_overlapping_data():
range1 = range(1, 20, 2)
data2 = np.random.normal(0.0, 0.1, len(range1))
obs1 = pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1])
obs2_a = pe.Obs([data2], ["ens"], idl=[range1])
obs1.gamma_method()
obs2_a.gamma_method()
corr1 = pe.covariance([obs1, obs2_a], correlation=True)
added_data = np.random.normal(0.0, 0.1, len(range1))
added_data -= np.mean(added_data) - np.mean(data2)
data2_extended = np.ravel([data2, added_data], 'F')
obs2_b = pe.Obs([data2_extended], ["ens"])
obs2_b.gamma_method()
corr2 = pe.covariance([obs1, obs2_b], correlation=True)
assert np.isclose(corr1[0, 1], corr2[0, 1], atol=1e-14)
def test_coavariance_reorder_non_overlapping_data():
range1 = range(1, 20, 2)
range2 = range(1, 41, 2)
obs1 = pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1])
obs2_b = pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])
obs1.gamma_method()
obs2_b.gamma_method()
corr1 = pe.covariance([obs1, obs2_b], correlation=True)
deltas = list(obs2_b.deltas['ens'][:len(range1)]) + sorted(obs2_b.deltas['ens'][len(range1):])
obs2_a = pe.Obs([obs2_b.value + np.array(deltas)], ["ens"], idl=[range2])
obs2_a.gamma_method()
corr2 = pe.covariance([obs1, obs2_a], correlation=True)
assert np.isclose(corr1[0, 1], corr2[0, 1], atol=1e-14)
def test_empty_obs():
o = pe.Obs([np.random.rand(100)], ['test'])
q = o + pe.Obs([], [], means=[])