lattice/pyerrors
1
0
Fork 0
mirror of https://github.com/fjosw/pyerrors.git synced 2025-05-15 03:53:41 +02:00
Code Issues Projects Releases Packages Wiki Activity

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

Browse source 
* [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
This commit is contained in:
Fabian Joswig 2024-12-24 15:35:59 +01:00 • committed by GitHub
parent d908508120
commit 3eac9214b4
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
11 changed files with 143 additions and 143 deletions
Download patch file Download diff file Expand all files Collapse all files

142
pyerrors/correlators.py
View file

@ -101,7 +101,7 @@ class Corr:
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]):
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]
if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]:
raise ValueError("Smearing matrices are not NxN.")
@ -141,7 +141,7 @@ class Corr:
def gamma_method(self, **kwargs):
"""Apply the gamma method to the content of the Corr."""
for item in self.content:
if not (item is None):
if item is not None:
if self.N == 1:
item[0].gamma_method(**kwargs)
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
"""
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:
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
if isinstance(vector_l, list) and not isinstance(vector_r, list):
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
if isinstance(vector_r, list) and not isinstance(vector_l, list):
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
if not isinstance(vector_l, list):
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:
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]
@ -201,7 +201,7 @@ class Corr:
Second index to be picked.
"""
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]
return Corr(newcontent)
@ -212,8 +212,8 @@ class Corr:
timeslice and the error on each timeslice.
"""
if self.N != 1:
raise Exception("Can only make Corr[N=1] plottable")
x_list = [x for x in range(self.T) if not self.content[x] is None]
raise ValueError("Can only make Corr[N=1] plottable")
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_err_list = [y[0].dvalue for y in self.content if y is not None]
@ -222,9 +222,9 @@ class Corr:
def symmetric(self):
""" Symmetrize the correlator around x0=0."""
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:
raise Exception("Can not symmetrize odd T")
raise ValueError("Can not symmetrize odd T")
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:
@ -237,7 +237,7 @@ class Corr:
else:
newcontent.append(0.5 * (self.content[t] + self.content[self.T - t]))
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)
def anti_symmetric(self):
@ -245,7 +245,7 @@ class Corr:
if self.N != 1:
raise TypeError('anti_symmetric cannot be safely applied to multi-dimensional correlators.')
if self.T % 2 != 0:
raise Exception("Can not symmetrize odd T")
raise ValueError("Can not symmetrize odd T")
test = 1 * self
test.gamma_method()
@ -259,7 +259,7 @@ class Corr:
else:
newcontent.append(0.5 * (self.content[t] - self.content[self.T - t]))
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)
def is_matrix_symmetric(self):
@ -292,7 +292,7 @@ class Corr:
def matrix_symmetric(self):
"""Symmetrizes the correlator matrices on every timeslice."""
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():
return 1.0 * self
else:
@ -336,10 +336,10 @@ class Corr:
'''
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 <= t0):
raise Exception("ts has to be larger than t0.")
raise ValueError("ts has to be larger than t0.")
if "sorted_list" in kwargs:
warnings.warn("Argument 'sorted_list' is deprecated, use 'sort' instead.", DeprecationWarning)
@ -371,9 +371,9 @@ class Corr:
if sort 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):
raise Exception("Corr not defined at t0/ts.")
raise ValueError("Corr not defined at t0/ts.")
Gt = _get_mat_at_t(ts)
reordered_vecs = _GEVP_solver(Gt, G0, method=method, chol_inv=chol_inv)
if kwargs.get('auto_gamma', False) and vector_obs:
@ -391,14 +391,14 @@ class Corr:
all_vecs.append(None)
if sort == "Eigenvector":
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)
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:
[[[o.gm() for o in evn] for evn in ev if evn is not None] for ev in reordered_vecs]
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:
return reordered_vecs[kwargs.get("state")]
@ -435,7 +435,7 @@ class Corr:
"""
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")
new_content = []
@ -502,7 +502,7 @@ class Corr:
correlator or a Corr of same length.
"""
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 = []
for x0, t_slice in enumerate(self.content):
if _check_for_none(self, t_slice):
@ -516,7 +516,7 @@ class Corr:
elif isinstance(partner, Obs): # Should this include CObs?
new_content.append(np.array([correlate(o, partner) for o in t_slice]))
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)
@ -583,7 +583,7 @@ class Corr:
Available choice: symmetric, forward, backward, improved, log, default: symmetric
"""
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":
newcontent = []
for t in range(1, self.T - 1):
@ -592,7 +592,7 @@ class Corr:
else:
newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1]))
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])
elif variant == "forward":
newcontent = []
@ -602,7 +602,7 @@ class Corr:
else:
newcontent.append(self.content[t + 1] - self.content[t])
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])
elif variant == "backward":
newcontent = []
@ -612,7 +612,7 @@ class Corr:
else:
newcontent.append(self.content[t] - self.content[t - 1])
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])
elif variant == "improved":
newcontent = []
@ -622,7 +622,7 @@ class Corr:
else:
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])):
raise Exception('Derivative is undefined at all timeslices')
raise ValueError('Derivative is undefined at all timeslices')
return Corr(newcontent, padding=[2, 2])
elif variant == 'log':
newcontent = []
@ -632,11 +632,11 @@ class Corr:
else:
newcontent.append(np.log(self.content[t]))
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)
return self * logcorr.deriv('symmetric')
else:
raise Exception("Unknown variant.")
raise ValueError("Unknown variant.")
def second_deriv(self, variant="symmetric"):
r"""Return the second derivative of the correlator with respect to x0.
@ -656,7 +656,7 @@ class Corr:
$$f(x) = \tilde{\partial}^2_0 log(f(x_0))+(\tilde{\partial}_0 log(f(x_0)))^2$$
"""
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":
newcontent = []
for t in range(1, self.T - 1):
@ -665,7 +665,7 @@ class Corr:
else:
newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1]))
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])
elif variant == "big_symmetric":
newcontent = []
@ -675,7 +675,7 @@ class Corr:
else:
newcontent.append((self.content[t + 2] - 2 * self.content[t] + self.content[t - 2]) / 4)
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])
elif variant == "improved":
newcontent = []
@ -685,7 +685,7 @@ class Corr:
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]))
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])
elif variant == 'log':
newcontent = []
@ -695,11 +695,11 @@ class Corr:
else:
newcontent.append(np.log(self.content[t]))
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)
return self * (logcorr.second_deriv('symmetric') + (logcorr.deriv('symmetric'))**2)
else:
raise Exception("Unknown variant.")
raise ValueError("Unknown variant.")
def m_eff(self, variant='log', guess=1.0):
"""Returns the effective mass of the correlator as correlator object
@ -728,7 +728,7 @@ class Corr:
else:
newcontent.append(self.content[t] / self.content[t + 1])
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]))
@ -742,7 +742,7 @@ class Corr:
else:
newcontent.append(self.content[t - 1] / self.content[t + 1])
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
@ -767,7 +767,7 @@ class Corr:
else:
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])):
raise Exception('m_eff is undefined at all timeslices')
raise ValueError('m_eff is undefined at all timeslices')
return Corr(newcontent, padding=[0, 1])
@ -779,11 +779,11 @@ class Corr:
else:
newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t]))
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]))
else:
raise Exception('Unknown variant.')
raise ValueError('Unknown variant.')
def fit(self, function, fitrange=None, silent=False, **kwargs):
r'''Fits function to the data
@ -801,7 +801,7 @@ class Corr:
Decides whether output is printed to the standard output.
'''
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 self.prange:
@ -810,12 +810,12 @@ class Corr:
fitrange = [0, self.T - 1]
else:
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:
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])
ys = np.array([self.content[x][0] 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 self.content[x] is not None])
result = least_squares(xs, ys, function, silent=silent, **kwargs)
return result
@ -840,9 +840,9 @@ class Corr:
else:
raise Exception("no plateau range provided")
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)])):
raise Exception("plateau is undefined at all timeslices in plateaurange.")
raise ValueError("plateau is undefined at all timeslices in plateaurange.")
if auto_gamma:
self.gamma_method()
if method == "fit":
@ -854,16 +854,16 @@ class Corr:
return returnvalue
else:
raise Exception("Unsupported plateau method: " + method)
raise ValueError("Unsupported plateau method: " + method)
def set_prange(self, prange):
"""Sets the attribute prange of the Corr object."""
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))):
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]):
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
return
@ -900,7 +900,7 @@ class Corr:
Optional title of the figure.
"""
if self.N != 1:
raise Exception("Correlator must be projected before plotting")
raise ValueError("Correlator must be projected before plotting")
if auto_gamma:
self.gamma_method()
@ -941,7 +941,7 @@ class Corr:
hide_from = None
ax1.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=corr.tag, mfc=plt.rcParams['axes.facecolor'])
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 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.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-')
else:
raise Exception("'plateau' must be an Obs")
raise TypeError("'plateau' must be an Obs")
if references:
if isinstance(references, list):
for ref in references:
ax1.axhline(y=ref, linewidth=1, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--')
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:
ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',', color="black", zorder=0)
@ -991,7 +991,7 @@ class Corr:
if isinstance(save, str):
fig.savefig(save, bbox_inches='tight')
else:
raise Exception("'save' has to be a string.")
raise TypeError("'save' has to be a string.")
def spaghetti_plot(self, logscale=True):
"""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.
"""
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]))
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":
dump_object(self, filename, **kwargs)
else:
raise Exception("Unknown datatype " + str(datatype))
raise ValueError("Unknown datatype " + str(datatype))
def print(self, print_range=None):
print(self.__repr__(print_range))
@ -1094,7 +1094,7 @@ class Corr:
def __add__(self, y):
if isinstance(y, Corr):
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 = []
for t in range(self.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):
if isinstance(y, Corr):
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 = []
for t in range(self.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):
if isinstance(y, Corr):
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 = []
for t in range(self.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
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)
elif isinstance(y, (Obs, CObs)):
if isinstance(y, Obs):
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 y.is_zero():
raise Exception('Division by zero will return undefined correlator')
raise ValueError('Division by zero will return undefined correlator')
newcontent = []
for t in range(self.T):
@ -1228,7 +1228,7 @@ class Corr:
elif isinstance(y, (int, float)):
if y == 0:
raise Exception('Division by zero will return undefined correlator')
raise ValueError('Division by zero will return undefined correlator')
newcontent = []
for t in range(self.T):
if _check_for_none(self, self.content[t]):
@ -1284,7 +1284,7 @@ class Corr:
if np.isnan(tmp_sum.value):
newcontent[t] = None
if all([item is None for item in newcontent]):
raise Exception('Operation returns undefined correlator')
raise ValueError('Operation returns undefined correlator')
return Corr(newcontent)
def sin(self):
@ -1392,13 +1392,13 @@ class Corr:
'''
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:
basematrix = self
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:
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]