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Warning messages promoted to RuntimeWarnings

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This commit is contained in:
Fabian Joswig 2021-10-15 12:11:06 +01:00
parent 8488df00fd
commit 4ca6f8ea49
3 changed files with 9 additions and 8 deletions
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5
pyerrors/correlators.py
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@ -1,3 +1,4 @@
import warnings
import numpy as np import numpy as np
import autograd.numpy as anp import autograd.numpy as anp
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -130,7 +131,7 @@ class Corr:
raise Exception("Can not symmetrize odd T") raise Exception("Can not symmetrize odd T")
if np.argmax(np.abs(self.content)) != 0: if np.argmax(np.abs(self.content)) != 0:
print('Warning: correlator does not seem to be symmetric around x0=0.') warnings.warn("Correlator does not seem to be symmetric around x0=0.", RuntimeWarning)
newcontent = [self.content[0]] newcontent = [self.content[0]]
for t in range(1, self.T): for t in range(1, self.T):
@ -148,7 +149,7 @@ class Corr:
raise Exception("Can not symmetrize odd T") raise Exception("Can not symmetrize odd T")
if not all([o.zero_within_error() for o in self.content[0]]): if not all([o.zero_within_error() for o in self.content[0]]):
print('Warning: correlator does not seem to be anti-symmetric around x0=0.') warnings.warn("Correlator does not seem to be anti-symmetric around x0=0.", RuntimeWarning)
newcontent = [self.content[0]] newcontent = [self.content[0]]
for t in range(1, self.T): for t in range(1, self.T):

7
pyerrors/fits.py
View file

@ -1,6 +1,7 @@
#!/usr/bin/env python #!/usr/bin/env python
# coding: utf-8 # coding: utf-8
import warnings
import numpy as np import numpy as np
import autograd.numpy as anp import autograd.numpy as anp
import scipy.optimize import scipy.optimize
@ -300,7 +301,7 @@ def odr_fit(x, y, func, silent=False, **kwargs):
P_phi = A @ np.linalg.inv(A.T @ A) @ A.T P_phi = A @ np.linalg.inv(A.T @ A) @ A.T
expected_chisquare = np.trace((np.identity(P_phi.shape[0]) - P_phi) @ W @ cov @ W) expected_chisquare = np.trace((np.identity(P_phi.shape[0]) - P_phi) @ W @ cov @ W)
if expected_chisquare <= 0.0: if expected_chisquare <= 0.0:
print('Warning, negative expected_chisquare.') warnings.warn("Negative expected_chisquare.", RuntimeWarning)
expected_chisquare = np.abs(expected_chisquare) expected_chisquare = np.abs(expected_chisquare)
result_dict['chisquare/expected_chisquare'] = odr_chisquare(np.concatenate((output.beta, output.xplus.ravel()))) / expected_chisquare result_dict['chisquare/expected_chisquare'] = odr_chisquare(np.concatenate((output.beta, output.xplus.ravel()))) / expected_chisquare
if not silent: if not silent:
@ -611,9 +612,7 @@ def error_band(x, func, beta):
"""Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta.""" """Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
cov = covariance_matrix(beta) cov = covariance_matrix(beta)
if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps): if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
print('Warning, Covariance matrix is not symmetric within floating point precision') warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
print('cov - cov.T:')
print(cov - cov.T)
deriv = [] deriv = []
for i, item in enumerate(x): for i, item in enumerate(x):

5
pyerrors/pyerrors.py
View file

@ -1,6 +1,7 @@
#!/usr/bin/env python #!/usr/bin/env python
# coding: utf-8 # coding: utf-8
import warnings
import pickle import pickle
import numpy as np import numpy as np
import autograd.numpy as anp # Thinly-wrapped numpy import autograd.numpy as anp # Thinly-wrapped numpy
@ -784,9 +785,9 @@ def correlate(obs_a, obs_b):
raise Exception('Shapes of ensemble', name, 'do not fit') raise Exception('Shapes of ensemble', name, 'do not fit')
if obs_a.reweighted == 1: if obs_a.reweighted == 1:
print('Warning: The first observable is already reweighted.') warnings.warn("The first observable is already reweighted.", RuntimeWarning)
if obs_b.reweighted == 1: if obs_b.reweighted == 1:
print('Warning: The second observable is already reweighted.') warnings.warn("The second observable is already reweighted.", RuntimeWarning)
new_samples = [] new_samples = []
for name in sorted(obs_a.names): for name in sorted(obs_a.names):