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[Fix] Ruff rules and more precise Excpetion types (#248)

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* [Fix] Fix test for membership should be 'not in' (E713)

* [Fix] Fix module imported but unused (F401)

* [Fix] More precise Exception types in dirac, obs and correlator
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Fabian Joswig 2024-12-24 15:35:59 +01:00 • committed by GitHub
parent d908508120
commit 3eac9214b4
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11 changed files with 143 additions and 143 deletions
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16
pyerrors/__init__.py
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@ -481,12 +481,12 @@ from .obs import *
from .correlators import * from .correlators import *
from .fits import * from .fits import *
from .misc import * from .misc import *
from . import dirac from . import dirac as dirac
from . import input from . import input as input
from . import linalg from . import linalg as linalg
from . import mpm from . import mpm as mpm
from . import roots from . import roots as roots
from . import integrate from . import integrate as integrate
from . import special from . import special as special
from .version import __version__ from .version import __version__ as __version__

142
pyerrors/correlators.py
View file

@ -101,7 +101,7 @@ class Corr:
self.N = 1 self.N = 1
elif all([isinstance(item, np.ndarray) or item is None for item in data_input]) and any([isinstance(item, np.ndarray) for item in data_input]): elif all([isinstance(item, np.ndarray) or item is None for item in data_input]) and any([isinstance(item, np.ndarray) for item in data_input]):
self.content = data_input self.content = data_input
noNull = [a for a in self.content if not (a is None)] # To check if the matrices are correct for all undefined elements noNull = [a for a in self.content if a is not None] # To check if the matrices are correct for all undefined elements
self.N = noNull[0].shape[0] self.N = noNull[0].shape[0]
if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]: if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]:
raise ValueError("Smearing matrices are not NxN.") raise ValueError("Smearing matrices are not NxN.")
@ -141,7 +141,7 @@ class Corr:
def gamma_method(self, **kwargs): def gamma_method(self, **kwargs):
"""Apply the gamma method to the content of the Corr.""" """Apply the gamma method to the content of the Corr."""
for item in self.content: for item in self.content:
if not (item is None): if item is not None:
if self.N == 1: if self.N == 1:
item[0].gamma_method(**kwargs) item[0].gamma_method(**kwargs)
else: else:
@ -159,7 +159,7 @@ class Corr:
By default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to By default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to
""" """
if self.N == 1: if self.N == 1:
raise Exception("Trying to project a Corr, that already has N=1.") raise ValueError("Trying to project a Corr, that already has N=1.")
if vector_l is None: if vector_l is None:
vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.]) vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.])
@ -167,16 +167,16 @@ class Corr:
vector_r = vector_l vector_r = vector_l
if isinstance(vector_l, list) and not isinstance(vector_r, list): if isinstance(vector_l, list) and not isinstance(vector_r, list):
if len(vector_l) != self.T: if len(vector_l) != self.T:
raise Exception("Length of vector list must be equal to T") raise ValueError("Length of vector list must be equal to T")
vector_r = [vector_r] * self.T vector_r = [vector_r] * self.T
if isinstance(vector_r, list) and not isinstance(vector_l, list): if isinstance(vector_r, list) and not isinstance(vector_l, list):
if len(vector_r) != self.T: if len(vector_r) != self.T:
raise Exception("Length of vector list must be equal to T") raise ValueError("Length of vector list must be equal to T")
vector_l = [vector_l] * self.T vector_l = [vector_l] * self.T
if not isinstance(vector_l, list): if not isinstance(vector_l, list):
if not vector_l.shape == vector_r.shape == (self.N,): if not vector_l.shape == vector_r.shape == (self.N,):
raise Exception("Vectors are of wrong shape!") raise ValueError("Vectors are of wrong shape!")
if normalize: if normalize:
vector_l, vector_r = vector_l / np.sqrt((vector_l @ vector_l)), vector_r / np.sqrt(vector_r @ vector_r) vector_l, vector_r = vector_l / np.sqrt((vector_l @ vector_l)), vector_r / np.sqrt(vector_r @ vector_r)
newcontent = [None if _check_for_none(self, item) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content] newcontent = [None if _check_for_none(self, item) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
@ -201,7 +201,7 @@ class Corr:
Second index to be picked. Second index to be picked.
""" """
if self.N == 1: if self.N == 1:
raise Exception("Trying to pick item from projected Corr") raise ValueError("Trying to pick item from projected Corr")
newcontent = [None if (item is None) else item[i, j] for item in self.content] newcontent = [None if (item is None) else item[i, j] for item in self.content]
return Corr(newcontent) return Corr(newcontent)
@ -212,8 +212,8 @@ class Corr:
timeslice and the error on each timeslice. timeslice and the error on each timeslice.
""" """
if self.N != 1: if self.N != 1:
raise Exception("Can only make Corr[N=1] plottable") raise ValueError("Can only make Corr[N=1] plottable")
x_list = [x for x in range(self.T) if not self.content[x] is None] x_list = [x for x in range(self.T) if self.content[x] is not None]
y_list = [y[0].value for y in self.content if y is not None] y_list = [y[0].value for y in self.content if y is not None]
y_err_list = [y[0].dvalue for y in self.content if y is not None] y_err_list = [y[0].dvalue for y in self.content if y is not None]
@ -222,9 +222,9 @@ class Corr:
def symmetric(self): def symmetric(self):
""" Symmetrize the correlator around x0=0.""" """ Symmetrize the correlator around x0=0."""
if self.N != 1: if self.N != 1:
raise Exception('symmetric cannot be safely applied to multi-dimensional correlators.') raise ValueError('symmetric cannot be safely applied to multi-dimensional correlators.')
if self.T % 2 != 0: if self.T % 2 != 0:
raise Exception("Can not symmetrize odd T") raise ValueError("Can not symmetrize odd T")
if self.content[0] is not None: if self.content[0] is not None:
if np.argmax(np.abs([o[0].value if o is not None else 0 for o in self.content])) != 0: if np.argmax(np.abs([o[0].value if o is not None else 0 for o in self.content])) != 0:
@ -237,7 +237,7 @@ class Corr:
else: else:
newcontent.append(0.5 * (self.content[t] + self.content[self.T - t])) newcontent.append(0.5 * (self.content[t] + self.content[self.T - t]))
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception("Corr could not be symmetrized: No redundant values") raise ValueError("Corr could not be symmetrized: No redundant values")
return Corr(newcontent, prange=self.prange) return Corr(newcontent, prange=self.prange)
def anti_symmetric(self): def anti_symmetric(self):
@ -245,7 +245,7 @@ class Corr:
if self.N != 1: if self.N != 1:
raise TypeError('anti_symmetric cannot be safely applied to multi-dimensional correlators.') raise TypeError('anti_symmetric cannot be safely applied to multi-dimensional correlators.')
if self.T % 2 != 0: if self.T % 2 != 0:
raise Exception("Can not symmetrize odd T") raise ValueError("Can not symmetrize odd T")
test = 1 * self test = 1 * self
test.gamma_method() test.gamma_method()
@ -259,7 +259,7 @@ class Corr:
else: else:
newcontent.append(0.5 * (self.content[t] - self.content[self.T - t])) newcontent.append(0.5 * (self.content[t] - self.content[self.T - t]))
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception("Corr could not be symmetrized: No redundant values") raise ValueError("Corr could not be symmetrized: No redundant values")
return Corr(newcontent, prange=self.prange) return Corr(newcontent, prange=self.prange)
def is_matrix_symmetric(self): def is_matrix_symmetric(self):
@ -292,7 +292,7 @@ class Corr:
def matrix_symmetric(self): def matrix_symmetric(self):
"""Symmetrizes the correlator matrices on every timeslice.""" """Symmetrizes the correlator matrices on every timeslice."""
if self.N == 1: if self.N == 1:
raise Exception("Trying to symmetrize a correlator matrix, that already has N=1.") raise ValueError("Trying to symmetrize a correlator matrix, that already has N=1.")
if self.is_matrix_symmetric(): if self.is_matrix_symmetric():
return 1.0 * self return 1.0 * self
else: else:
@ -336,10 +336,10 @@ class Corr:
''' '''
if self.N == 1: if self.N == 1:
raise Exception("GEVP methods only works on correlator matrices and not single correlators.") raise ValueError("GEVP methods only works on correlator matrices and not single correlators.")
if ts is not None: if ts is not None:
if (ts <= t0): if (ts <= t0):
raise Exception("ts has to be larger than t0.") raise ValueError("ts has to be larger than t0.")
if "sorted_list" in kwargs: if "sorted_list" in kwargs:
warnings.warn("Argument 'sorted_list' is deprecated, use 'sort' instead.", DeprecationWarning) warnings.warn("Argument 'sorted_list' is deprecated, use 'sort' instead.", DeprecationWarning)
@ -371,9 +371,9 @@ class Corr:
if sort is None: if sort is None:
if (ts is None): if (ts is None):
raise Exception("ts is required if sort=None.") raise ValueError("ts is required if sort=None.")
if (self.content[t0] is None) or (self.content[ts] is None): if (self.content[t0] is None) or (self.content[ts] is None):
raise Exception("Corr not defined at t0/ts.") raise ValueError("Corr not defined at t0/ts.")
Gt = _get_mat_at_t(ts) Gt = _get_mat_at_t(ts)
reordered_vecs = _GEVP_solver(Gt, G0, method=method, chol_inv=chol_inv) reordered_vecs = _GEVP_solver(Gt, G0, method=method, chol_inv=chol_inv)
if kwargs.get('auto_gamma', False) and vector_obs: if kwargs.get('auto_gamma', False) and vector_obs:
@ -391,14 +391,14 @@ class Corr:
all_vecs.append(None) all_vecs.append(None)
if sort == "Eigenvector": if sort == "Eigenvector":
if ts is None: if ts is None:
raise Exception("ts is required for the Eigenvector sorting method.") raise ValueError("ts is required for the Eigenvector sorting method.")
all_vecs = _sort_vectors(all_vecs, ts) all_vecs = _sort_vectors(all_vecs, ts)
reordered_vecs = [[v[s] if v is not None else None for v in all_vecs] for s in range(self.N)] reordered_vecs = [[v[s] if v is not None else None for v in all_vecs] for s in range(self.N)]
if kwargs.get('auto_gamma', False) and vector_obs: if kwargs.get('auto_gamma', False) and vector_obs:
[[[o.gm() for o in evn] for evn in ev if evn is not None] for ev in reordered_vecs] [[[o.gm() for o in evn] for evn in ev if evn is not None] for ev in reordered_vecs]
else: else:
raise Exception("Unknown value for 'sort'. Choose 'Eigenvalue', 'Eigenvector' or None.") raise ValueError("Unknown value for 'sort'. Choose 'Eigenvalue', 'Eigenvector' or None.")
if "state" in kwargs: if "state" in kwargs:
return reordered_vecs[kwargs.get("state")] return reordered_vecs[kwargs.get("state")]
@ -435,7 +435,7 @@ class Corr:
""" """
if self.N != 1: if self.N != 1:
raise Exception("Multi-operator Prony not implemented!") raise NotImplementedError("Multi-operator Prony not implemented!")
array = np.empty([N, N], dtype="object") array = np.empty([N, N], dtype="object")
new_content = [] new_content = []
@ -502,7 +502,7 @@ class Corr:
correlator or a Corr of same length. correlator or a Corr of same length.
""" """
if self.N != 1: if self.N != 1:
raise Exception("Only one-dimensional correlators can be safely correlated.") raise ValueError("Only one-dimensional correlators can be safely correlated.")
new_content = [] new_content = []
for x0, t_slice in enumerate(self.content): for x0, t_slice in enumerate(self.content):
if _check_for_none(self, t_slice): if _check_for_none(self, t_slice):
@ -516,7 +516,7 @@ class Corr:
elif isinstance(partner, Obs): # Should this include CObs? elif isinstance(partner, Obs): # Should this include CObs?
new_content.append(np.array([correlate(o, partner) for o in t_slice])) new_content.append(np.array([correlate(o, partner) for o in t_slice]))
else: else:
raise Exception("Can only correlate with an Obs or a Corr.") raise TypeError("Can only correlate with an Obs or a Corr.")
return Corr(new_content) return Corr(new_content)
@ -583,7 +583,7 @@ class Corr:
Available choice: symmetric, forward, backward, improved, log, default: symmetric Available choice: symmetric, forward, backward, improved, log, default: symmetric
""" """
if self.N != 1: if self.N != 1:
raise Exception("deriv only implemented for one-dimensional correlators.") raise ValueError("deriv only implemented for one-dimensional correlators.")
if variant == "symmetric": if variant == "symmetric":
newcontent = [] newcontent = []
for t in range(1, self.T - 1): for t in range(1, self.T - 1):
@ -592,7 +592,7 @@ class Corr:
else: else:
newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1])) newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1]))
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception('Derivative is undefined at all timeslices') raise ValueError('Derivative is undefined at all timeslices')
return Corr(newcontent, padding=[1, 1]) return Corr(newcontent, padding=[1, 1])
elif variant == "forward": elif variant == "forward":
newcontent = [] newcontent = []
@ -602,7 +602,7 @@ class Corr:
else: else:
newcontent.append(self.content[t + 1] - self.content[t]) newcontent.append(self.content[t + 1] - self.content[t])
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices") raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[0, 1]) return Corr(newcontent, padding=[0, 1])
elif variant == "backward": elif variant == "backward":
newcontent = [] newcontent = []
@ -612,7 +612,7 @@ class Corr:
else: else:
newcontent.append(self.content[t] - self.content[t - 1]) newcontent.append(self.content[t] - self.content[t - 1])
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices") raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[1, 0]) return Corr(newcontent, padding=[1, 0])
elif variant == "improved": elif variant == "improved":
newcontent = [] newcontent = []
@ -622,7 +622,7 @@ class Corr:
else: else:
newcontent.append((1 / 12) * (self.content[t - 2] - 8 * self.content[t - 1] + 8 * self.content[t + 1] - self.content[t + 2])) newcontent.append((1 / 12) * (self.content[t - 2] - 8 * self.content[t - 1] + 8 * self.content[t + 1] - self.content[t + 2]))
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception('Derivative is undefined at all timeslices') raise ValueError('Derivative is undefined at all timeslices')
return Corr(newcontent, padding=[2, 2]) return Corr(newcontent, padding=[2, 2])
elif variant == 'log': elif variant == 'log':
newcontent = [] newcontent = []
@ -632,11 +632,11 @@ class Corr:
else: else:
newcontent.append(np.log(self.content[t])) newcontent.append(np.log(self.content[t]))
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception("Log is undefined at all timeslices") raise ValueError("Log is undefined at all timeslices")
logcorr = Corr(newcontent) logcorr = Corr(newcontent)
return self * logcorr.deriv('symmetric') return self * logcorr.deriv('symmetric')
else: else:
raise Exception("Unknown variant.") raise ValueError("Unknown variant.")
def second_deriv(self, variant="symmetric"): def second_deriv(self, variant="symmetric"):
r"""Return the second derivative of the correlator with respect to x0. r"""Return the second derivative of the correlator with respect to x0.
@ -656,7 +656,7 @@ class Corr:
$$f(x) = \tilde{\partial}^2_0 log(f(x_0))+(\tilde{\partial}_0 log(f(x_0)))^2$$ $$f(x) = \tilde{\partial}^2_0 log(f(x_0))+(\tilde{\partial}_0 log(f(x_0)))^2$$
""" """
if self.N != 1: if self.N != 1:
raise Exception("second_deriv only implemented for one-dimensional correlators.") raise ValueError("second_deriv only implemented for one-dimensional correlators.")
if variant == "symmetric": if variant == "symmetric":
newcontent = [] newcontent = []
for t in range(1, self.T - 1): for t in range(1, self.T - 1):
@ -665,7 +665,7 @@ class Corr:
else: else:
newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1])) newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1]))
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices") raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[1, 1]) return Corr(newcontent, padding=[1, 1])
elif variant == "big_symmetric": elif variant == "big_symmetric":
newcontent = [] newcontent = []
@ -675,7 +675,7 @@ class Corr:
else: else:
newcontent.append((self.content[t + 2] - 2 * self.content[t] + self.content[t - 2]) / 4) newcontent.append((self.content[t + 2] - 2 * self.content[t] + self.content[t - 2]) / 4)
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices") raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[2, 2]) return Corr(newcontent, padding=[2, 2])
elif variant == "improved": elif variant == "improved":
newcontent = [] newcontent = []
@ -685,7 +685,7 @@ class Corr:
else: else:
newcontent.append((1 / 12) * (-self.content[t + 2] + 16 * self.content[t + 1] - 30 * self.content[t] + 16 * self.content[t - 1] - self.content[t - 2])) newcontent.append((1 / 12) * (-self.content[t + 2] + 16 * self.content[t + 1] - 30 * self.content[t] + 16 * self.content[t - 1] - self.content[t - 2]))
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices") raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[2, 2]) return Corr(newcontent, padding=[2, 2])
elif variant == 'log': elif variant == 'log':
newcontent = [] newcontent = []
@ -695,11 +695,11 @@ class Corr:
else: else:
newcontent.append(np.log(self.content[t])) newcontent.append(np.log(self.content[t]))
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception("Log is undefined at all timeslices") raise ValueError("Log is undefined at all timeslices")
logcorr = Corr(newcontent) logcorr = Corr(newcontent)
return self * (logcorr.second_deriv('symmetric') + (logcorr.deriv('symmetric'))**2) return self * (logcorr.second_deriv('symmetric') + (logcorr.deriv('symmetric'))**2)
else: else:
raise Exception("Unknown variant.") raise ValueError("Unknown variant.")
def m_eff(self, variant='log', guess=1.0): def m_eff(self, variant='log', guess=1.0):
"""Returns the effective mass of the correlator as correlator object """Returns the effective mass of the correlator as correlator object
@ -728,7 +728,7 @@ class Corr:
else: else:
newcontent.append(self.content[t] / self.content[t + 1]) newcontent.append(self.content[t] / self.content[t + 1])
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices') raise ValueError('m_eff is undefined at all timeslices')
return np.log(Corr(newcontent, padding=[0, 1])) return np.log(Corr(newcontent, padding=[0, 1]))
@ -742,7 +742,7 @@ class Corr:
else: else:
newcontent.append(self.content[t - 1] / self.content[t + 1]) newcontent.append(self.content[t - 1] / self.content[t + 1])
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices') raise ValueError('m_eff is undefined at all timeslices')
return np.log(Corr(newcontent, padding=[1, 1])) / 2 return np.log(Corr(newcontent, padding=[1, 1])) / 2
@ -767,7 +767,7 @@ class Corr:
else: else:
newcontent.append(np.abs(find_root(self.content[t][0] / self.content[t + 1][0], root_function, guess=guess))) newcontent.append(np.abs(find_root(self.content[t][0] / self.content[t + 1][0], root_function, guess=guess)))
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices') raise ValueError('m_eff is undefined at all timeslices')
return Corr(newcontent, padding=[0, 1]) return Corr(newcontent, padding=[0, 1])
@ -779,11 +779,11 @@ class Corr:
else: else:
newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t])) newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t]))
if (all([x is None for x in newcontent])): if (all([x is None for x in newcontent])):
raise Exception("m_eff is undefined at all timeslices") raise ValueError("m_eff is undefined at all timeslices")
return np.arccosh(Corr(newcontent, padding=[1, 1])) return np.arccosh(Corr(newcontent, padding=[1, 1]))
else: else:
raise Exception('Unknown variant.') raise ValueError('Unknown variant.')
def fit(self, function, fitrange=None, silent=False, **kwargs): def fit(self, function, fitrange=None, silent=False, **kwargs):
r'''Fits function to the data r'''Fits function to the data
@ -801,7 +801,7 @@ class Corr:
Decides whether output is printed to the standard output. Decides whether output is printed to the standard output.
''' '''
if self.N != 1: if self.N != 1:
raise Exception("Correlator must be projected before fitting") raise ValueError("Correlator must be projected before fitting")
if fitrange is None: if fitrange is None:
if self.prange: if self.prange:
@ -810,12 +810,12 @@ class Corr:
fitrange = [0, self.T - 1] fitrange = [0, self.T - 1]
else: else:
if not isinstance(fitrange, list): if not isinstance(fitrange, list):
raise Exception("fitrange has to be a list with two elements") raise TypeError("fitrange has to be a list with two elements")
if len(fitrange) != 2: if len(fitrange) != 2:
raise Exception("fitrange has to have exactly two elements [fit_start, fit_stop]") raise ValueError("fitrange has to have exactly two elements [fit_start, fit_stop]")
xs = np.array([x for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None]) xs = np.array([x for x in range(fitrange[0], fitrange[1] + 1) if self.content[x] is not None])
ys = np.array([self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None]) ys = np.array([self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if self.content[x] is not None])
result = least_squares(xs, ys, function, silent=silent, **kwargs) result = least_squares(xs, ys, function, silent=silent, **kwargs)
return result return result
@ -840,9 +840,9 @@ class Corr:
else: else:
raise Exception("no plateau range provided") raise Exception("no plateau range provided")
if self.N != 1: if self.N != 1:
raise Exception("Correlator must be projected before getting a plateau.") raise ValueError("Correlator must be projected before getting a plateau.")
if (all([self.content[t] is None for t in range(plateau_range[0], plateau_range[1] + 1)])): if (all([self.content[t] is None for t in range(plateau_range[0], plateau_range[1] + 1)])):
raise Exception("plateau is undefined at all timeslices in plateaurange.") raise ValueError("plateau is undefined at all timeslices in plateaurange.")
if auto_gamma: if auto_gamma:
self.gamma_method() self.gamma_method()
if method == "fit": if method == "fit":
@ -854,16 +854,16 @@ class Corr:
return returnvalue return returnvalue
else: else:
raise Exception("Unsupported plateau method: " + method) raise ValueError("Unsupported plateau method: " + method)
def set_prange(self, prange): def set_prange(self, prange):
"""Sets the attribute prange of the Corr object.""" """Sets the attribute prange of the Corr object."""
if not len(prange) == 2: if not len(prange) == 2:
raise Exception("prange must be a list or array with two values") raise ValueError("prange must be a list or array with two values")
if not ((isinstance(prange[0], int)) and (isinstance(prange[1], int))): if not ((isinstance(prange[0], int)) and (isinstance(prange[1], int))):
raise Exception("Start and end point must be integers") raise TypeError("Start and end point must be integers")
if not (0 <= prange[0] <= self.T and 0 <= prange[1] <= self.T and prange[0] <= prange[1]): if not (0 <= prange[0] <= self.T and 0 <= prange[1] <= self.T and prange[0] <= prange[1]):
raise Exception("Start and end point must define a range in the interval 0,T") raise ValueError("Start and end point must define a range in the interval 0,T")
self.prange = prange self.prange = prange
return return
@ -900,7 +900,7 @@ class Corr:
Optional title of the figure. Optional title of the figure.
""" """
if self.N != 1: if self.N != 1:
raise Exception("Correlator must be projected before plotting") raise ValueError("Correlator must be projected before plotting")
if auto_gamma: if auto_gamma:
self.gamma_method() self.gamma_method()
@ -941,7 +941,7 @@ class Corr:
hide_from = None hide_from = None
ax1.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=corr.tag, mfc=plt.rcParams['axes.facecolor']) ax1.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=corr.tag, mfc=plt.rcParams['axes.facecolor'])
else: else:
raise Exception("'comp' must be a correlator or a list of correlators.") raise TypeError("'comp' must be a correlator or a list of correlators.")
if plateau: if plateau:
if isinstance(plateau, Obs): if isinstance(plateau, Obs):
@ -950,14 +950,14 @@ class Corr:
ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau)) ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau))
ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-') ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-')
else: else:
raise Exception("'plateau' must be an Obs") raise TypeError("'plateau' must be an Obs")
if references: if references:
if isinstance(references, list): if isinstance(references, list):
for ref in references: for ref in references:
ax1.axhline(y=ref, linewidth=1, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--') ax1.axhline(y=ref, linewidth=1, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--')
else: else:
raise Exception("'references' must be a list of floating pint values.") raise TypeError("'references' must be a list of floating pint values.")
if self.prange: if self.prange:
ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',', color="black", zorder=0) ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',', color="black", zorder=0)
@ -991,7 +991,7 @@ class Corr:
if isinstance(save, str): if isinstance(save, str):
fig.savefig(save, bbox_inches='tight') fig.savefig(save, bbox_inches='tight')
else: else:
raise Exception("'save' has to be a string.") raise TypeError("'save' has to be a string.")
def spaghetti_plot(self, logscale=True): def spaghetti_plot(self, logscale=True):
"""Produces a spaghetti plot of the correlator suited to monitor exceptional configurations. """Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.
@ -1002,7 +1002,7 @@ class Corr:
Determines whether the scale of the y-axis is logarithmic or standard. Determines whether the scale of the y-axis is logarithmic or standard.
""" """
if self.N != 1: if self.N != 1:
raise Exception("Correlator needs to be projected first.") raise ValueError("Correlator needs to be projected first.")
mc_names = list(set([item for sublist in [sum(map(o[0].e_content.get, o[0].mc_names), []) for o in self.content if o is not None] for item in sublist])) mc_names = list(set([item for sublist in [sum(map(o[0].e_content.get, o[0].mc_names), []) for o in self.content if o is not None] for item in sublist]))
x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None] x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None]
@ -1044,7 +1044,7 @@ class Corr:
elif datatype == "pickle": elif datatype == "pickle":
dump_object(self, filename, **kwargs) dump_object(self, filename, **kwargs)
else: else:
raise Exception("Unknown datatype " + str(datatype)) raise ValueError("Unknown datatype " + str(datatype))
def print(self, print_range=None): def print(self, print_range=None):
print(self.__repr__(print_range)) print(self.__repr__(print_range))
@ -1094,7 +1094,7 @@ class Corr:
def __add__(self, y): def __add__(self, y):
if isinstance(y, Corr): if isinstance(y, Corr):
if ((self.N != y.N) or (self.T != y.T)): if ((self.N != y.N) or (self.T != y.T)):
raise Exception("Addition of Corrs with different shape") raise ValueError("Addition of Corrs with different shape")
newcontent = [] newcontent = []
for t in range(self.T): for t in range(self.T):
if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]): if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]):
@ -1122,7 +1122,7 @@ class Corr:
def __mul__(self, y): def __mul__(self, y):
if isinstance(y, Corr): if isinstance(y, Corr):
if not ((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T): if not ((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T):
raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T") raise ValueError("Multiplication of Corr object requires N=N or N=1 and T=T")
newcontent = [] newcontent = []
for t in range(self.T): for t in range(self.T):
if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]): if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]):
@ -1193,7 +1193,7 @@ class Corr:
def __truediv__(self, y): def __truediv__(self, y):
if isinstance(y, Corr): if isinstance(y, Corr):
if not ((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T): if not ((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T):
raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T") raise ValueError("Multiplication of Corr object requires N=N or N=1 and T=T")
newcontent = [] newcontent = []
for t in range(self.T): for t in range(self.T):
if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]): if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]):
@ -1207,16 +1207,16 @@ class Corr:
newcontent[t] = None newcontent[t] = None
if all([item is None for item in newcontent]): if all([item is None for item in newcontent]):
raise Exception("Division returns completely undefined correlator") raise ValueError("Division returns completely undefined correlator")
return Corr(newcontent) return Corr(newcontent)
elif isinstance(y, (Obs, CObs)): elif isinstance(y, (Obs, CObs)):
if isinstance(y, Obs): if isinstance(y, Obs):
if y.value == 0: if y.value == 0:
raise Exception('Division by zero will return undefined correlator') raise ValueError('Division by zero will return undefined correlator')
if isinstance(y, CObs): if isinstance(y, CObs):
if y.is_zero(): if y.is_zero():
raise Exception('Division by zero will return undefined correlator') raise ValueError('Division by zero will return undefined correlator')
newcontent = [] newcontent = []
for t in range(self.T): for t in range(self.T):
@ -1228,7 +1228,7 @@ class Corr:
elif isinstance(y, (int, float)): elif isinstance(y, (int, float)):
if y == 0: if y == 0:
raise Exception('Division by zero will return undefined correlator') raise ValueError('Division by zero will return undefined correlator')
newcontent = [] newcontent = []
for t in range(self.T): for t in range(self.T):
if _check_for_none(self, self.content[t]): if _check_for_none(self, self.content[t]):
@ -1284,7 +1284,7 @@ class Corr:
if np.isnan(tmp_sum.value): if np.isnan(tmp_sum.value):
newcontent[t] = None newcontent[t] = None
if all([item is None for item in newcontent]): if all([item is None for item in newcontent]):
raise Exception('Operation returns undefined correlator') raise ValueError('Operation returns undefined correlator')
return Corr(newcontent) return Corr(newcontent)
def sin(self): def sin(self):
@ -1392,13 +1392,13 @@ class Corr:
''' '''
if self.N == 1: if self.N == 1:
raise Exception('Method cannot be applied to one-dimensional correlators.') raise ValueError('Method cannot be applied to one-dimensional correlators.')
if basematrix is None: if basematrix is None:
basematrix = self basematrix = self
if Ntrunc >= basematrix.N: if Ntrunc >= basematrix.N:
raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N)) raise ValueError('Cannot truncate using Ntrunc <= %d' % (basematrix.N))
if basematrix.N != self.N: if basematrix.N != self.N:
raise Exception('basematrix and targetmatrix have to be of the same size.') raise ValueError('basematrix and targetmatrix have to be of the same size.')
evecs = basematrix.GEVP(t0proj, tproj, sort=None)[:Ntrunc] evecs = basematrix.GEVP(t0proj, tproj, sort=None)[:Ntrunc]

6
pyerrors/dirac.py
View file

@ -34,7 +34,7 @@ def epsilon_tensor(i, j, k):
""" """
test_set = set((i, j, k)) test_set = set((i, j, k))
if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))): if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))):
raise Exception("Unexpected input", i, j, k) raise ValueError("Unexpected input", i, j, k)
return (i - j) * (j - k) * (k - i) / 2 return (i - j) * (j - k) * (k - i) / 2
@ -52,7 +52,7 @@ def epsilon_tensor_rank4(i, j, k, o):
""" """
test_set = set((i, j, k, o)) test_set = set((i, j, k, o))
if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))): if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))):
raise Exception("Unexpected input", i, j, k, o) raise ValueError("Unexpected input", i, j, k, o)
return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12 return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12
@ -92,5 +92,5 @@ def Grid_gamma(gamma_tag):
elif gamma_tag == 'SigmaZT': elif gamma_tag == 'SigmaZT':
g = 0.5 * (gamma[2] @ gamma[3] - gamma[3] @ gamma[2]) g = 0.5 * (gamma[2] @ gamma[3] - gamma[3] @ gamma[2])
else: else:
raise Exception('Unkown gamma structure', gamma_tag) raise ValueError('Unkown gamma structure', gamma_tag)
return g return g

16
pyerrors/input/__init__.py
View file

@ -5,11 +5,11 @@ r'''
For comparison with other analysis workflows `pyerrors` can also generate jackknife samples from an `Obs` object or import jackknife samples into an `Obs` object. For comparison with other analysis workflows `pyerrors` can also generate jackknife samples from an `Obs` object or import jackknife samples into an `Obs` object.
See `pyerrors.obs.Obs.export_jackknife` and `pyerrors.obs.import_jackknife` for details. See `pyerrors.obs.Obs.export_jackknife` and `pyerrors.obs.import_jackknife` for details.
''' '''
from . import bdio from . import bdio as bdio
from . import dobs from . import dobs as dobs
from . import hadrons from . import hadrons as hadrons
from . import json from . import json as json
from . import misc from . import misc as misc
from . import openQCD from . import openQCD as openQCD
from . import pandas from . import pandas as pandas
from . import sfcf from . import sfcf as sfcf

4
pyerrors/input/dobs.py
View file

@ -79,7 +79,7 @@ def _dict_to_xmlstring_spaces(d, space=' '):
o += space o += space
o += li + '\n' o += li + '\n'
if li.startswith('<') and not cm: if li.startswith('<') and not cm:
if not '<%s' % ('/') in li: if '<%s' % ('/') not in li:
c += 1 c += 1
cm = False cm = False
return o return o
@ -671,7 +671,7 @@ def _dobsdict_to_xmlstring_spaces(d, space=' '):
o += space o += space
o += li + '\n' o += li + '\n'
if li.startswith('<') and not cm: if li.startswith('<') and not cm:
if not '<%s' % ('/') in li: if '<%s' % ('/') not in li:
c += 1 c += 1
cm = False cm = False
return o return o

4
pyerrors/input/hadrons.py
View file

@ -113,7 +113,7 @@ def read_hd5(filestem, ens_id, group, attrs=None, idl=None, part="real"):
infos = [] infos = []
for hd5_file in files: for hd5_file in files:
h5file = h5py.File(path + '/' + hd5_file, "r") h5file = h5py.File(path + '/' + hd5_file, "r")
if not group + '/' + entry in h5file: if group + '/' + entry not in h5file:
raise Exception("Entry '" + entry + "' not contained in the files.") raise Exception("Entry '" + entry + "' not contained in the files.")
raw_data = h5file[group + '/' + entry + '/corr'] raw_data = h5file[group + '/' + entry + '/corr']
real_data = raw_data[:].view("complex") real_data = raw_data[:].view("complex")
@ -186,7 +186,7 @@ def _extract_real_arrays(path, files, tree, keys):
for hd5_file in files: for hd5_file in files:
h5file = h5py.File(path + '/' + hd5_file, "r") h5file = h5py.File(path + '/' + hd5_file, "r")
for key in keys: for key in keys:
if not tree + '/' + key in h5file: if tree + '/' + key not in h5file:
raise Exception("Entry '" + key + "' not contained in the files.") raise Exception("Entry '" + key + "' not contained in the files.")
raw_data = h5file[tree + '/' + key + '/data'] raw_data = h5file[tree + '/' + key + '/data']
real_data = raw_data[:].astype(np.double) real_data = raw_data[:].astype(np.double)

2
pyerrors/input/openQCD.py
View file

@ -47,7 +47,7 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
Reweighting factors read Reweighting factors read
""" """
known_oqcd_versions = ['1.4', '1.6', '2.0'] known_oqcd_versions = ['1.4', '1.6', '2.0']
if not (version in known_oqcd_versions): if version not in known_oqcd_versions:
raise Exception('Unknown openQCD version defined!') raise Exception('Unknown openQCD version defined!')
print("Working with openQCD version " + version) print("Working with openQCD version " + version)
if 'postfix' in kwargs: if 'postfix' in kwargs:

46
pyerrors/obs.py
View file

@ -222,7 +222,7 @@ class Obs:
tmp = kwargs.get(kwarg_name) tmp = kwargs.get(kwarg_name)
if isinstance(tmp, (int, float)): if isinstance(tmp, (int, float)):
if tmp < 0: if tmp < 0:
raise Exception(kwarg_name + ' has to be larger or equal to 0.') raise ValueError(kwarg_name + ' has to be larger or equal to 0.')
for e, e_name in enumerate(self.e_names): for e, e_name in enumerate(self.e_names):
getattr(self, kwarg_name)[e_name] = tmp getattr(self, kwarg_name)[e_name] = tmp
else: else:
@ -291,7 +291,7 @@ class Obs:
texp = self.tau_exp[e_name] texp = self.tau_exp[e_name]
# Critical slowing down analysis # Critical slowing down analysis
if w_max // 2 <= 1: if w_max // 2 <= 1:
raise Exception("Need at least 8 samples for tau_exp error analysis") raise ValueError("Need at least 8 samples for tau_exp error analysis")
for n in range(1, w_max // 2): for n in range(1, w_max // 2):
_compute_drho(n + 1) _compute_drho(n + 1)
if (self.e_rho[e_name][n] - self.N_sigma[e_name] * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2: if (self.e_rho[e_name][n] - self.N_sigma[e_name] * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2:
@ -620,7 +620,7 @@ class Obs:
if not hasattr(self, 'e_dvalue'): if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.') raise Exception('Run the gamma method first.')
if np.isclose(0.0, self._dvalue, atol=1e-15): if np.isclose(0.0, self._dvalue, atol=1e-15):
raise Exception('Error is 0.0') raise ValueError('Error is 0.0')
labels = self.e_names labels = self.e_names
sizes = [self.e_dvalue[name] ** 2 for name in labels] / self._dvalue ** 2 sizes = [self.e_dvalue[name] ** 2 for name in labels] / self._dvalue ** 2
fig1, ax1 = plt.subplots() fig1, ax1 = plt.subplots()
@ -659,7 +659,7 @@ class Obs:
with open(file_name + '.p', 'wb') as fb: with open(file_name + '.p', 'wb') as fb:
pickle.dump(self, fb) pickle.dump(self, fb)
else: else:
raise Exception("Unknown datatype " + str(datatype)) raise TypeError("Unknown datatype " + str(datatype))
def export_jackknife(self): def export_jackknife(self):
"""Export jackknife samples from the Obs """Export jackknife samples from the Obs
@ -676,7 +676,7 @@ class Obs:
""" """
if len(self.names) != 1: if len(self.names) != 1:
raise Exception("'export_jackknife' is only implemented for Obs defined on one ensemble and replicum.") raise ValueError("'export_jackknife' is only implemented for Obs defined on one ensemble and replicum.")
name = self.names[0] name = self.names[0]
full_data = self.deltas[name] + self.r_values[name] full_data = self.deltas[name] + self.r_values[name]
@ -711,7 +711,7 @@ class Obs:
should agree with samples from a full bootstrap analysis up to O(1/N). should agree with samples from a full bootstrap analysis up to O(1/N).
""" """
if len(self.names) != 1: if len(self.names) != 1:
raise Exception("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.") raise ValueError("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
name = self.names[0] name = self.names[0]
length = self.N length = self.N
@ -1267,7 +1267,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
if 'man_grad' in kwargs: if 'man_grad' in kwargs:
deriv = np.asarray(kwargs.get('man_grad')) deriv = np.asarray(kwargs.get('man_grad'))
if new_values.shape + data.shape != deriv.shape: if new_values.shape + data.shape != deriv.shape:
raise Exception('Manual derivative does not have correct shape.') raise ValueError('Manual derivative does not have correct shape.')
elif kwargs.get('num_grad') is True: elif kwargs.get('num_grad') is True:
if multi > 0: if multi > 0:
raise Exception('Multi mode currently not supported for numerical derivative') raise Exception('Multi mode currently not supported for numerical derivative')
@ -1333,7 +1333,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
new_covobs = {name: Covobs(0, allcov[name], name, grad=new_grad[name]) for name in new_grad} new_covobs = {name: Covobs(0, allcov[name], name, grad=new_grad[name]) for name in new_grad}
if not set(new_covobs.keys()).isdisjoint(new_deltas.keys()): if not set(new_covobs.keys()).isdisjoint(new_deltas.keys()):
raise Exception('The same name has been used for deltas and covobs!') raise ValueError('The same name has been used for deltas and covobs!')
new_samples = [] new_samples = []
new_means = [] new_means = []
new_idl = [] new_idl = []
@ -1374,7 +1374,7 @@ def _reduce_deltas(deltas, idx_old, idx_new):
Has to be a subset of idx_old. Has to be a subset of idx_old.
""" """
if not len(deltas) == len(idx_old): if not len(deltas) == len(idx_old):
raise Exception('Length of deltas and idx_old have to be the same: %d != %d' % (len(deltas), len(idx_old))) raise ValueError('Length of deltas and idx_old have to be the same: %d != %d' % (len(deltas), len(idx_old)))
if type(idx_old) is range and type(idx_new) is range: if type(idx_old) is range and type(idx_new) is range:
if idx_old == idx_new: if idx_old == idx_new:
return deltas return deltas
@ -1382,7 +1382,7 @@ def _reduce_deltas(deltas, idx_old, idx_new):
return deltas return deltas
indices = np.intersect1d(idx_old, idx_new, assume_unique=True, return_indices=True)[1] indices = np.intersect1d(idx_old, idx_new, assume_unique=True, return_indices=True)[1]
if len(indices) < len(idx_new): if len(indices) < len(idx_new):
raise Exception('Error in _reduce_deltas: Config of idx_new not in idx_old') raise ValueError('Error in _reduce_deltas: Config of idx_new not in idx_old')
return np.array(deltas)[indices] return np.array(deltas)[indices]
@ -1404,12 +1404,12 @@ def reweight(weight, obs, **kwargs):
result = [] result = []
for i in range(len(obs)): for i in range(len(obs)):
if len(obs[i].cov_names): if len(obs[i].cov_names):
raise Exception('Error: Not possible to reweight an Obs that contains covobs!') raise ValueError('Error: Not possible to reweight an Obs that contains covobs!')
if not set(obs[i].names).issubset(weight.names): if not set(obs[i].names).issubset(weight.names):
raise Exception('Error: Ensembles do not fit') raise ValueError('Error: Ensembles do not fit')
for name in obs[i].names: for name in obs[i].names:
if not set(obs[i].idl[name]).issubset(weight.idl[name]): if not set(obs[i].idl[name]).issubset(weight.idl[name]):
raise Exception('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name)) raise ValueError('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name))
new_samples = [] new_samples = []
w_deltas = {} w_deltas = {}
for name in sorted(obs[i].names): for name in sorted(obs[i].names):
@ -1446,14 +1446,14 @@ def correlate(obs_a, obs_b):
""" """
if sorted(obs_a.names) != sorted(obs_b.names): if sorted(obs_a.names) != sorted(obs_b.names):
raise Exception(f"Ensembles do not fit {set(sorted(obs_a.names)) ^ set(sorted(obs_b.names))}") raise ValueError(f"Ensembles do not fit {set(sorted(obs_a.names)) ^ set(sorted(obs_b.names))}")
if len(obs_a.cov_names) or len(obs_b.cov_names): if len(obs_a.cov_names) or len(obs_b.cov_names):
raise Exception('Error: Not possible to correlate Obs that contain covobs!') raise ValueError('Error: Not possible to correlate Obs that contain covobs!')
for name in obs_a.names: for name in obs_a.names:
if obs_a.shape[name] != obs_b.shape[name]: if obs_a.shape[name] != obs_b.shape[name]:
raise Exception('Shapes of ensemble', name, 'do not fit') raise ValueError('Shapes of ensemble', name, 'do not fit')
if obs_a.idl[name] != obs_b.idl[name]: if obs_a.idl[name] != obs_b.idl[name]:
raise Exception('idl of ensemble', name, 'do not fit') raise ValueError('idl of ensemble', name, 'do not fit')
if obs_a.reweighted is True: if obs_a.reweighted is True:
warnings.warn("The first observable is already reweighted.", RuntimeWarning) warnings.warn("The first observable is already reweighted.", RuntimeWarning)
@ -1555,7 +1555,7 @@ def invert_corr_cov_cholesky(corr, inverrdiag):
condn = np.linalg.cond(corr) condn = np.linalg.cond(corr)
if condn > 0.1 / np.finfo(float).eps: if condn > 0.1 / np.finfo(float).eps:
raise Exception(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})") raise ValueError(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
if condn > 1e13: if condn > 1e13:
warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning) warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
chol = np.linalg.cholesky(corr) chol = np.linalg.cholesky(corr)
@ -1636,7 +1636,7 @@ def _smooth_eigenvalues(corr, E):
Number of eigenvalues to be left substantially unchanged Number of eigenvalues to be left substantially unchanged
""" """
if not (2 < E < corr.shape[0] - 1): if not (2 < E < corr.shape[0] - 1):
raise Exception(f"'E' has to be between 2 and the dimension of the correlation matrix minus 1 ({corr.shape[0] - 1}).") raise ValueError(f"'E' has to be between 2 and the dimension of the correlation matrix minus 1 ({corr.shape[0] - 1}).")
vals, vec = np.linalg.eigh(corr) vals, vec = np.linalg.eigh(corr)
lambda_min = np.mean(vals[:-E]) lambda_min = np.mean(vals[:-E])
vals[vals < lambda_min] = lambda_min vals[vals < lambda_min] = lambda_min
@ -1768,9 +1768,9 @@ def merge_obs(list_of_obs):
""" """
replist = [item for obs in list_of_obs for item in obs.names] replist = [item for obs in list_of_obs for item in obs.names]
if (len(replist) == len(set(replist))) is False: if (len(replist) == len(set(replist))) is False:
raise Exception('list_of_obs contains duplicate replica: %s' % (str(replist))) raise ValueError('list_of_obs contains duplicate replica: %s' % (str(replist)))
if any([len(o.cov_names) for o in list_of_obs]): if any([len(o.cov_names) for o in list_of_obs]):
raise Exception('Not possible to merge data that contains covobs!') raise ValueError('Not possible to merge data that contains covobs!')
new_dict = {} new_dict = {}
idl_dict = {} idl_dict = {}
for o in list_of_obs: for o in list_of_obs:
@ -1821,7 +1821,7 @@ def cov_Obs(means, cov, name, grad=None):
for i in range(len(means)): for i in range(len(means)):
ol.append(covobs_to_obs(Covobs(means[i], cov, name, pos=i, grad=grad))) ol.append(covobs_to_obs(Covobs(means[i], cov, name, pos=i, grad=grad)))
if ol[0].covobs[name].N != len(means): if ol[0].covobs[name].N != len(means):
raise Exception('You have to provide %d mean values!' % (ol[0].N)) raise ValueError('You have to provide %d mean values!' % (ol[0].N))
if len(ol) == 1: if len(ol) == 1:
return ol[0] return ol[0]
return ol return ol
@ -1837,7 +1837,7 @@ def _determine_gap(o, e_content, e_name):
gap = min(gaps) gap = min(gaps)
if not np.all([gi % gap == 0 for gi in gaps]): if not np.all([gi % gap == 0 for gi in gaps]):
raise Exception(f"Replica for ensemble {e_name} do not have a common spacing.", gaps) raise ValueError(f"Replica for ensemble {e_name} do not have a common spacing.", gaps)
return gap return gap

16
tests/correlators_test.py
View file

@ -129,7 +129,7 @@ def test_m_eff():
with pytest.warns(RuntimeWarning): with pytest.warns(RuntimeWarning):
my_corr.m_eff('sinh') my_corr.m_eff('sinh')
with pytest.raises(Exception): with pytest.raises(ValueError):
my_corr.m_eff('unkown_variant') my_corr.m_eff('unkown_variant')
@ -140,7 +140,7 @@ def test_m_eff_negative_values():
assert m_eff_log[padding + 1] is None assert m_eff_log[padding + 1] is None
m_eff_cosh = my_corr.m_eff('cosh') m_eff_cosh = my_corr.m_eff('cosh')
assert m_eff_cosh[padding + 1] is None assert m_eff_cosh[padding + 1] is None
with pytest.raises(Exception): with pytest.raises(ValueError):
my_corr.m_eff('logsym') my_corr.m_eff('logsym')
@ -155,7 +155,7 @@ def test_correlate():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')]) my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
corr1 = my_corr.correlate(my_corr) corr1 = my_corr.correlate(my_corr)
corr2 = my_corr.correlate(my_corr[0]) corr2 = my_corr.correlate(my_corr[0])
with pytest.raises(Exception): with pytest.raises(TypeError):
corr3 = my_corr.correlate(7.3) corr3 = my_corr.correlate(7.3)
@ -176,9 +176,9 @@ def test_fit_correlator():
assert fit_res[0] == my_corr[0] assert fit_res[0] == my_corr[0]
assert fit_res[1] == my_corr[1] - my_corr[0] assert fit_res[1] == my_corr[1] - my_corr[0]
with pytest.raises(Exception): with pytest.raises(TypeError):
my_corr.fit(f, "from 0 to 3") my_corr.fit(f, "from 0 to 3")
with pytest.raises(Exception): with pytest.raises(ValueError):
my_corr.fit(f, [0, 2, 3]) my_corr.fit(f, [0, 2, 3])
@ -256,11 +256,11 @@ def test_prange():
corr = pe.correlators.Corr(corr_content) corr = pe.correlators.Corr(corr_content)
corr.set_prange([2, 4]) corr.set_prange([2, 4])
with pytest.raises(Exception): with pytest.raises(ValueError):
corr.set_prange([2]) corr.set_prange([2])
with pytest.raises(Exception): with pytest.raises(TypeError):
corr.set_prange([2, 2.3]) corr.set_prange([2, 2.3])
with pytest.raises(Exception): with pytest.raises(ValueError):
corr.set_prange([4, 1]) corr.set_prange([4, 1])

6
tests/dirac_test.py
View file

@ -30,7 +30,7 @@ def test_grid_dirac():
'SigmaYZ', 'SigmaYZ',
'SigmaZT']: 'SigmaZT']:
pe.dirac.Grid_gamma(gamma) pe.dirac.Grid_gamma(gamma)
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.dirac.Grid_gamma('Not a gamma matrix') pe.dirac.Grid_gamma('Not a gamma matrix')
@ -44,7 +44,7 @@ def test_epsilon_tensor():
(1, 1, 3) : 0.0} (1, 1, 3) : 0.0}
for key, value in check.items(): for key, value in check.items():
assert pe.dirac.epsilon_tensor(*key) == value assert pe.dirac.epsilon_tensor(*key) == value
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.dirac.epsilon_tensor(0, 1, 3) pe.dirac.epsilon_tensor(0, 1, 3)
@ -59,5 +59,5 @@ def test_epsilon_tensor_rank4():
(1, 2, 3, 1) : 0.0} (1, 2, 3, 1) : 0.0}
for key, value in check.items(): for key, value in check.items():
assert pe.dirac.epsilon_tensor_rank4(*key) == value assert pe.dirac.epsilon_tensor_rank4(*key) == value
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.dirac.epsilon_tensor_rank4(0, 1, 3, 4) pe.dirac.epsilon_tensor_rank4(0, 1, 3, 4)

28
tests/obs_test.py
View file

@ -61,9 +61,9 @@ def test_Obs_exceptions():
my_obs.plot_rep_dist() my_obs.plot_rep_dist()
with pytest.raises(Exception): with pytest.raises(Exception):
my_obs.plot_piechart() my_obs.plot_piechart()
with pytest.raises(Exception): with pytest.raises(TypeError):
my_obs.gamma_method(S='2.3') my_obs.gamma_method(S='2.3')
with pytest.raises(Exception): with pytest.raises(ValueError):
my_obs.gamma_method(tau_exp=2.3) my_obs.gamma_method(tau_exp=2.3)
my_obs.gamma_method() my_obs.gamma_method()
my_obs.details() my_obs.details()
@ -199,7 +199,7 @@ def test_gamma_method_no_windowing():
assert np.isclose(np.sqrt(np.var(obs.deltas['ens'], ddof=1) / obs.shape['ens']), obs.dvalue) assert np.isclose(np.sqrt(np.var(obs.deltas['ens'], ddof=1) / obs.shape['ens']), obs.dvalue)
obs.gamma_method(S=1.1) obs.gamma_method(S=1.1)
assert obs.e_tauint['ens'] > 0.5 assert obs.e_tauint['ens'] > 0.5
with pytest.raises(Exception): with pytest.raises(ValueError):
obs.gamma_method(S=-0.2) obs.gamma_method(S=-0.2)
@ -490,12 +490,12 @@ def test_reweighting():
r_obs2 = r_obs[0] * my_obs r_obs2 = r_obs[0] * my_obs
assert r_obs2.reweighted assert r_obs2.reweighted
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov') my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.reweight(my_obs, [my_covobs]) pe.reweight(my_obs, [my_covobs])
my_obs2 = pe.Obs([np.random.rand(1000)], ['t2']) my_obs2 = pe.Obs([np.random.rand(1000)], ['t2'])
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.reweight(my_obs, [my_obs + my_obs2]) pe.reweight(my_obs, [my_obs + my_obs2])
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.reweight(my_irregular_obs, [my_obs]) pe.reweight(my_irregular_obs, [my_obs])
@ -505,10 +505,10 @@ def test_merge_obs():
merged = pe.merge_obs([my_obs1, my_obs2]) merged = pe.merge_obs([my_obs1, my_obs2])
diff = merged - my_obs2 - my_obs1 diff = merged - my_obs2 - my_obs1
assert diff == -(my_obs1.value + my_obs2.value) / 2 assert diff == -(my_obs1.value + my_obs2.value) / 2
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.merge_obs([my_obs1, my_obs1]) pe.merge_obs([my_obs1, my_obs1])
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov') my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.merge_obs([my_obs1, my_covobs]) pe.merge_obs([my_obs1, my_covobs])
@ -531,11 +531,11 @@ def test_correlate():
assert corr1 == corr2 assert corr1 == corr2
my_obs3 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(2, 102)]) my_obs3 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(2, 102)])
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.correlate(my_obs1, my_obs3) pe.correlate(my_obs1, my_obs3)
my_obs4 = pe.Obs([np.random.rand(99)], ['t']) my_obs4 = pe.Obs([np.random.rand(99)], ['t'])
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.correlate(my_obs1, my_obs4) pe.correlate(my_obs1, my_obs4)
my_obs5 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(5, 505, 5)]) my_obs5 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(5, 505, 5)])
@ -544,10 +544,10 @@ def test_correlate():
assert my_obs5.idl == corr3.idl assert my_obs5.idl == corr3.idl
my_new_obs = pe.Obs([np.random.rand(100)], ['q3']) my_new_obs = pe.Obs([np.random.rand(100)], ['q3'])
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.correlate(my_obs1, my_new_obs) pe.correlate(my_obs1, my_new_obs)
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov') my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception): with pytest.raises(ValueError):
pe.correlate(my_covobs, my_covobs) pe.correlate(my_covobs, my_covobs)
r_obs = pe.reweight(my_obs1, [my_obs1])[0] r_obs = pe.reweight(my_obs1, [my_obs1])[0]
with pytest.warns(RuntimeWarning): with pytest.warns(RuntimeWarning):
@ -774,7 +774,7 @@ def test_gamma_method_irregular():
my_obs.gm() my_obs.gm()
idl += [range(1, 400, 4)] idl += [range(1, 400, 4)]
my_obs = pe.Obs([dat for i in range(len(idl))], ['%s|%d' % ('A', i) for i in range(len(idl))], idl=idl) my_obs = pe.Obs([dat for i in range(len(idl))], ['%s|%d' % ('A', i) for i in range(len(idl))], idl=idl)
with pytest.raises(Exception): with pytest.raises(ValueError):
my_obs.gm() my_obs.gm()
# check cases where tau is large compared to the chain length # check cases where tau is large compared to the chain length
@ -1122,7 +1122,7 @@ def test_jackknife():
assert np.allclose(tmp_jacks, my_obs.export_jackknife()) assert np.allclose(tmp_jacks, my_obs.export_jackknife())
my_new_obs = my_obs + pe.Obs([full_data], ['test2']) my_new_obs = my_obs + pe.Obs([full_data], ['test2'])
with pytest.raises(Exception): with pytest.raises(ValueError):
my_new_obs.export_jackknife() my_new_obs.export_jackknife()