fix: the covariance functions now correctly check whether the

gamma_method was run. Tests added.
This commit is contained in:
Fabian Joswig 2021-12-09 13:06:49 +00:00
parent e213b37413
commit 352b93ee2c
3 changed files with 38 additions and 2 deletions

View file

@ -1358,7 +1358,7 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], _merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
raise Exception('Shapes of ensemble', name, 'do not fit')
if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
if not hasattr(obs1, 'e_dvalue') or not hasattr(obs2, 'e_dvalue'):
raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0
@ -1452,7 +1452,7 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
if set(obs1.names).isdisjoint(set(obs2.names)):
return 0.
if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
if not hasattr(obs1, 'e_dvalue') or not hasattr(obs2, 'e_dvalue'):
raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0

View file

@ -37,6 +37,7 @@ def test_covobs():
op.gamma_method()
assert(np.isclose(oc.value, op.value, rtol=1e-14, atol=1e-14))
[o.gamma_method() for o in cl]
assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
assert(pe.covariance2(cl[0], cl[1]) == cov[1][0])

View file

@ -274,6 +274,41 @@ def test_r_value_persistence():
assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
def test_prior_fit():
def f(a, x):
return a[0] + a[1] * x
a = pe.pseudo_Obs(0.0, 0.1, 'a')
b = pe.pseudo_Obs(1.0, 0.2, 'a')
y = [a, b]
with pytest.raises(Exception):
fitp = pe.fits.least_squares([0, 1], 1 * np.array(y), f, priors=['0.0(8)', '1.0(8)'])
[o.gamma_method() for o in y]
fitp = pe.fits.least_squares([0, 1], y, f, priors=['0.0(8)', '1.0(8)'])
fitp = pe.fits.least_squares([0, 1], y, f, priors=y, resplot=True, qqplot=True)
def test_error_band():
def f(a, x):
return a[0] + a[1] * x
a = pe.pseudo_Obs(0.0, 0.1, 'a')
b = pe.pseudo_Obs(1.0, 0.2, 'a')
x = [0, 1]
y = [a, b]
fitp = pe.fits.least_squares(x, y, f)
with pytest.raises(Exception):
pe.fits.error_band(x, f, fitp.fit_parameters)
fitp.gamma_method()
pe.fits.error_band(x, f, fitp.fit_parameters)
def fit_general(x, y, func, silent=False, **kwargs):
"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.