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feat: eigenvalue calculation in pe.covariance is not done if only the

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correlation matrix is requested.
This commit is contained in:
Fabian Joswig 2022-05-27 14:17:05 +01:00
parent 328c564fdd
commit 658c2b30de
1 changed files with 9 additions and 9 deletions
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18
pyerrors/obs.py
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@ -1452,13 +1452,6 @@ def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
if isinstance(smooth, int):
corr = _smooth_eigenvalues(corr, smooth)
errors = [o.dvalue for o in obs]
cov = np.diag(errors) @ corr @ np.diag(errors)
eigenvalues = np.linalg.eigh(cov)[0]
if not np.all(eigenvalues >= 0):
warnings.warn("Covariance matrix is not positive semi-definite (Eigenvalues: " + str(eigenvalues) + ")", RuntimeWarning)
if visualize:
plt.matshow(corr, vmin=-1, vmax=1)
plt.set_cmap('RdBu')
@ -1467,8 +1460,15 @@ def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
if correlation is True:
return corr
else:
return cov
errors = [o.dvalue for o in obs]
cov = np.diag(errors) @ corr @ np.diag(errors)
eigenvalues = np.linalg.eigh(cov)[0]
if not np.all(eigenvalues >= 0):
warnings.warn("Covariance matrix is not positive semi-definite (Eigenvalues: " + str(eigenvalues) + ")", RuntimeWarning)
return cov
def _smooth_eigenvalues(corr, E):