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refactor: Exception corrected in Corr.GEVP, private methods renamed,

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docstrings extended
This commit is contained in:
Fabian Joswig 2022-01-18 16:50:07 +00:00
parent 7750745402
commit a63e9958c4
1 changed files with 28 additions and 29 deletions
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57
pyerrors/correlators.py
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@ -101,15 +101,15 @@ class Corr:
for j in range(self.N):
item[i, j].gamma_method(**kwargs)
# We need to project the Correlator with a Vector to get a single value at each timeslice.
# The method can use one or two vectors.
# If two are specified it returns v1@G@v2 (the order might be very important.)
# By default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to
def projected(self, vector_l=None, vector_r=None, normalize=False):
"""We need to project the Correlator with a Vector to get a single value at each timeslice.
The method can use one or two vectors.
If two are specified it returns v1@G@v2 (the order might be very important.)
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.")
# This Exception is in no way necessary. One could just return self
# But there is no scenario, where a user would want that to happen and the error message might be more informative.
self.gamma_method()
@ -155,8 +155,6 @@ class Corr:
newcontent = [None if(item is None) else item[i, j] for item in self.content]
return Corr(newcontent)
# Obs and Matplotlib do not play nicely
# We often want to retrieve x,y,y_err as lists to pass them to something like pyplot.errorbar
def plottable(self):
"""Outputs the correlator in a plotable format.
@ -171,9 +169,6 @@ class Corr:
return x_list, y_list, y_err_list
# symmetric returns a Corr, that has been symmetrized.
# A symmetry checker is still to be implemented
# The method will not delete any redundant timeslices (Bad for memory, Great for convenience)
def symmetric(self):
""" Symmetrize the correlator around x0=0."""
if self.T % 2 != 0:
@ -210,8 +205,8 @@ class Corr:
raise Exception("Corr could not be symmetrized: No redundant values")
return Corr(newcontent, prange=self.prange)
# This method will symmetrice the matrices and therefore make them positive definit.
def smearing_symmetric(self):
"""Symmetrizes the matrices and therefore make them positive definite."""
if self.N > 1:
transposed = [None if (G is None) else G.T for G in self.content]
return 0.5 * (Corr(transposed) + self)
@ -231,7 +226,7 @@ class Corr:
G0[i, j] = self.content[t0][i, j].value
Gt[i, j] = self.content[ts][i, j].value
sp_vecs = GEVP_solver(Gt, G0)
sp_vecs = _GEVP_solver(Gt, G0)
sp_vec = sp_vecs[state]
return sp_vec
if return_list:
@ -244,16 +239,16 @@ class Corr:
G0[i, j] = self.content[t0][i, j].value
Gt[i, j] = self.content[t][i, j].value
sp_vecs = GEVP_solver(Gt, G0)
sp_vecs = _GEVP_solver(Gt, G0)
if sorting == "Eigenvalue":
sp_vec = sp_vecs[state]
all_vecs.append(sp_vec)
else:
all_vecs.append(sp_vecs)
except "Failure to solve for one timeslice": # This could contain a check for real eigenvectors
except Exception:
all_vecs.append(None)
if sorting == "Eigenvector":
all_vecs = sort_vectors(all_vecs, ts)
all_vecs = _sort_vectors(all_vecs, ts)
all_vecs = [a[state] for a in all_vecs]
return all_vecs
@ -278,11 +273,20 @@ class Corr:
return Corr(newcontent)
def Hankel(self, N, periodic=False):
# Constructs an NxN Hankel matrix
# C(t) c(t+1) ... c(t+n-1)
# C(t+1) c(t+2) ... c(t+n)
# .................
# C(t+(n-1)) c(t+n) ... c(t+2(n-1))
"""Constructs an NxN Hankel matrix
C(t) c(t+1) ... c(t+n-1)
C(t+1) c(t+2) ... c(t+n)
.................
C(t+(n-1)) c(t+n) ... c(t+2(n-1))
Parameters:
-----------
N : int
Dimension of the Hankel matrix
periodic : bool, optional
determines whether the matrix is extended periodically
"""
if self.N != 1:
raise Exception("Multi-operator Prony not implemented!")
@ -519,11 +523,6 @@ class Corr:
if self.N != 1:
raise Exception("Correlator must be projected before fitting")
# The default behavior is:
# 1 use explicit fitrange
# if none is provided, use the range of the corr
# if this is also not set, use the whole length of the corr (This could come with a warning!)
if fitrange is None:
if self.prange:
fitrange = self.prange
@ -923,7 +922,8 @@ class Corr:
return self._apply_func_to_corr(return_imag)
def sort_vectors(vec_set, ts): # Helper function used to find a set of Eigenvectors consistent over all timeslices
def _sort_vectors(vec_set, ts):
"""Helper function used to find a set of Eigenvectors consistent over all timeslices"""
reference_sorting = np.array(vec_set[ts])
N = reference_sorting.shape[0]
sorted_vec_set = []
@ -942,7 +942,6 @@ def sort_vectors(vec_set, ts): # Helper function used to find a set of Eigenvec
if current_score > best_score:
best_score = current_score
best_perm = perm
# print("best perm", best_perm)
sorted_vec_set.append([vec_set[t][k] for k in best_perm])
else:
sorted_vec_set.append(vec_set[t])
@ -963,7 +962,7 @@ def permutation(lst): # Shamelessly copied
return ll
def GEVP_solver(Gt, G0): # Just so normalization an sorting does not need to be repeated. Here we could later put in some checks
def _GEVP_solver(Gt, G0): # Just so normalization an sorting does not need to be repeated. Here we could later put in some checks
sp_val, sp_vecs = scipy.linalg.eig(Gt, G0)
sp_vecs = [sp_vecs[:, np.argsort(sp_val)[-i]] for i in range(1, sp_vecs.shape[0] + 1)]
sp_vecs = [v / np.sqrt((v.T @ G0 @ v)) for v in sp_vecs]