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refactor: calculation of permutations in correlator module simplified.

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Fabian Joswig 2022-02-17 09:32:34 +00:00
commit 0e073da5c6
1 changed files with 2 additions and 14 deletions
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pyerrors/correlators.py
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@ -1,4 +1,5 @@
import warnings import warnings
from itertools import permutations
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
@ -1057,7 +1058,7 @@ def _sort_vectors(vec_set, ts):
if vec_set[t] is None: if vec_set[t] is None:
sorted_vec_set.append(None) sorted_vec_set.append(None)
elif not t == ts: elif not t == ts:
perms = permutation([i for i in range(N)]) perms = [list(o) for o in permutations([i for i in range(N)], N)]
best_score = 0 best_score = 0
for perm in perms: for perm in perms:
current_score = 1 current_score = 1
@ -1075,19 +1076,6 @@ def _sort_vectors(vec_set, ts):
return sorted_vec_set return sorted_vec_set
def permutation(lst): # Shamelessly copied
if len(lst) == 1:
return [lst]
ll = []
for i in range(len(lst)):
m = lst[i]
remLst = lst[:i] + lst[i + 1:]
# Generating all permutations where m is first
for p in permutation(remLst):
ll.append([m] + p)
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_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 = [sp_vecs[:, np.argsort(sp_val)[-i]] for i in range(1, sp_vecs.shape[0] + 1)]