mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-03-15 06:40:24 +01:00
commit
7750745402
1 changed files with 196 additions and 37 deletions
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@ -3,7 +3,7 @@ import numpy as np
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import autograd.numpy as anp
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import matplotlib.pyplot as plt
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import scipy.linalg
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from .obs import Obs, reweight, correlate
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from .obs import Obs, reweight, correlate, CObs
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from .misc import dump_object
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from .fits import least_squares
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from .linalg import eigh, inv, cholesky
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@ -41,7 +41,11 @@ class Corr:
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if not isinstance(data_input, list):
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raise TypeError('Corr__init__ expects a list of timeslices.')
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if all([isinstance(item, Obs) for item in data_input]):
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# data_input can have multiple shapes. The simplest one is a list of Obs.
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# We check, if this is the case
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if all([(isinstance(item, Obs) or isinstance(item, CObs)) for item in data_input]):
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self.content = [np.asarray([item]) for item in data_input]
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self.N = 1
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@ -101,7 +105,7 @@ class Corr:
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# The method can use one or two vectors.
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# If two are specified it returns v1@G@v2 (the order might be very important.)
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# By default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to
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def projected(self, vector_l=None, vector_r=None):
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def projected(self, vector_l=None, vector_r=None, normalize=False):
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if self.N == 1:
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raise Exception("Trying to project a Corr, that already has N=1.")
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# This Exception is in no way necessary. One could just return self
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@ -113,17 +117,32 @@ class Corr:
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vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.])
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elif(vector_r is None):
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vector_r = vector_l
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if isinstance(vector_l, list) and not isinstance(vector_r, list):
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if len(vector_l) != self.T:
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raise Exception("Length of vector list must be equal to T")
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vector_r = [vector_r] * self.T
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if isinstance(vector_r, list) and not isinstance(vector_l, list):
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if len(vector_r) != self.T:
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raise Exception("Length of vector list must be equal to T")
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vector_l = [vector_l] * self.T
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if not vector_l.shape == vector_r.shape == (self.N,):
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raise Exception("Vectors are of wrong shape!")
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if not isinstance(vector_l, list):
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if not vector_l.shape == vector_r.shape == (self.N,):
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raise Exception("Vectors are of wrong shape!")
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if normalize:
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vector_l, vector_r = vector_l / np.sqrt((vector_l @ vector_l)), vector_r / np.sqrt(vector_r @ vector_r)
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# if (not (0.95 < vector_r @ vector_r < 1.05)) or (not (0.95 < vector_l @ vector_l < 1.05)):
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# print("Vectors are normalized before projection!")
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# We always normalize before projecting! But we only raise a warning, when it is clear, they where not meant to be normalized.
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if (not (0.95 < vector_r @ vector_r < 1.05)) or (not (0.95 < vector_l @ vector_l < 1.05)):
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print("Vectors are normalized before projection!")
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newcontent = [None if (item is None) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
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vector_l, vector_r = vector_l / np.sqrt((vector_l @ vector_l)), vector_r / np.sqrt(vector_r @ vector_r)
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else:
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# There are no checks here yet. There are so many possible scenarios, where this can go wrong.
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if normalize:
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for t in range(self.T):
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vector_l[t], vector_r[t] = vector_l[t] / np.sqrt((vector_l[t] @ vector_l[t])), vector_r[t] / np.sqrt(vector_r[t] @ vector_r[t])
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newcontent = [None if (item is None) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
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newcontent = [None if (self.content[t] is None or vector_l[t] is None or vector_r[t] is None) else np.asarray([vector_l[t].T @ self.content[t] @ vector_r[t]]) for t in range(self.T)]
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return Corr(newcontent)
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def sum(self):
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@ -199,20 +218,45 @@ class Corr:
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if self.N == 1:
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raise Exception("Trying to symmetrize a smearing matrix, that already has N=1.")
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# We also include a simple GEVP method based on Scipy.linalg
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def GEVP(self, t0, ts, state=1):
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if (self.content[t0] is None) or (self.content[ts] is None):
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raise Exception("Corr not defined at t0/ts")
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G0, Gt = np.empty([self.N, self.N], dtype="double"), np.empty([self.N, self.N], dtype="double")
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for i in range(self.N):
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for j in range(self.N):
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G0[i, j] = self.content[t0][i, j].value
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Gt[i, j] = self.content[ts][i, j].value
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# There are two ways, the GEVP metod can be called.
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# 1. return_list=False will return a single eigenvector, normalized according to V*C(t_0)*V=1
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# 2. return_list=True will return a new eigenvector for every timeslice. The time t_s is used to order the vectors according to. arXiv:2004.10472 [hep-lat]
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def GEVP(self, t0, ts, state=0, sorting="Eigenvalue", return_list=False):
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if not return_list:
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if (self.content[t0] is None) or (self.content[ts] is None):
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raise Exception("Corr not defined at t0/ts")
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G0, Gt = np.empty([self.N, self.N], dtype="double"), np.empty([self.N, self.N], dtype="double")
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for i in range(self.N):
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for j in range(self.N):
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G0[i, j] = self.content[t0][i, j].value
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Gt[i, j] = self.content[ts][i, j].value
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sp_val, sp_vec = scipy.linalg.eig(Gt, G0)
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sp_vec = sp_vec[:, np.argsort(sp_val)[-state]] # We only want the eigenvector belonging to the selected state
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sp_vec = sp_vec / np.sqrt(sp_vec @ sp_vec)
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return sp_vec
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sp_vecs = GEVP_solver(Gt, G0)
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sp_vec = sp_vecs[state]
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return sp_vec
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if return_list:
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all_vecs = []
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for t in range(self.T):
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try:
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G0, Gt = np.empty([self.N, self.N], dtype="double"), np.empty([self.N, self.N], dtype="double")
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for i in range(self.N):
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for j in range(self.N):
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G0[i, j] = self.content[t0][i, j].value
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Gt[i, j] = self.content[t][i, j].value
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sp_vecs = GEVP_solver(Gt, G0)
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if sorting == "Eigenvalue":
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sp_vec = sp_vecs[state]
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all_vecs.append(sp_vec)
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else:
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all_vecs.append(sp_vecs)
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except "Failure to solve for one timeslice": # This could contain a check for real eigenvectors
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all_vecs.append(None)
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if sorting == "Eigenvector":
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all_vecs = sort_vectors(all_vecs, ts)
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all_vecs = [a[state] for a in all_vecs]
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return all_vecs
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def Eigenvalue(self, t0, state=1):
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G = self.smearing_symmetric()
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@ -223,13 +267,48 @@ class Corr:
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LTi = inv(LT)
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newcontent = []
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for t in range(self.T):
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Gt = G.content[t]
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M = Li @ Gt @ LTi
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eigenvalues = eigh(M)[0]
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eigenvalue = eigenvalues[-state]
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newcontent.append(eigenvalue)
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if self.content[t] is None:
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newcontent.append(None)
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else:
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Gt = G.content[t]
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M = Li @ Gt @ LTi
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eigenvalues = eigh(M)[0]
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eigenvalue = eigenvalues[-state]
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newcontent.append(eigenvalue)
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return Corr(newcontent)
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def Hankel(self, N, periodic=False):
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# Constructs an NxN Hankel matrix
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# C(t) c(t+1) ... c(t+n-1)
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# C(t+1) c(t+2) ... c(t+n)
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# .................
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# C(t+(n-1)) c(t+n) ... c(t+2(n-1))
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if self.N != 1:
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raise Exception("Multi-operator Prony not implemented!")
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array = np.empty([N, N], dtype="object")
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new_content = []
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for t in range(self.T):
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new_content.append(array.copy())
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def wrap(i):
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if i >= self.T:
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return i - self.T
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return i
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for t in range(self.T):
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for i in range(N):
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for j in range(N):
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if periodic:
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new_content[t][i, j] = self.content[wrap(t + i + j)][0]
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elif (t + i + j) >= self.T:
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new_content[t] = None
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else:
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new_content[t][i, j] = self.content[t + i + j][0]
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return Corr(new_content)
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def roll(self, dt):
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"""Periodically shift the correlator by dt timeslices
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@ -242,7 +321,7 @@ class Corr:
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def reverse(self):
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"""Reverse the time ordering of the Corr"""
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return Corr(self.content[::-1])
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return Corr(self.content[:: -1])
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def correlate(self, partner):
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"""Correlate the correlator with another correlator or Obs
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@ -264,7 +343,7 @@ class Corr:
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new_content.append(None)
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else:
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new_content.append(np.array([correlate(o, partner.content[x0][0]) for o in t_slice]))
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elif isinstance(partner, Obs):
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elif isinstance(partner, Obs): # Should this include CObs?
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new_content.append(np.array([correlate(o, partner) for o in t_slice]))
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else:
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raise Exception("Can only correlate with an Obs or a Corr.")
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@ -590,7 +669,6 @@ class Corr:
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def dump(self, filename, **kwargs):
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"""Dumps the Corr into a pickle file
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Parameters
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----------
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filename : str
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@ -599,14 +677,23 @@ class Corr:
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specifies a custom path for the file (default '.')
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"""
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dump_object(self, filename, **kwargs)
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return
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def print(self, range=[0, None]):
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print(self.__repr__(range))
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def __repr__(self, range=[0, None]):
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content_string = ""
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content_string += "Corr T=" + str(self.T) + " N=" + str(self.N) + "\n" # +" filled with"+ str(type(self.content[0][0])) there should be a good solution here
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if self.tag is not None:
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content_string += "Description: " + self.tag + "\n"
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if self.N != 1:
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return content_string
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# This avoids a crash for N>1. I do not know, what else to do here. I like the list representation for N==1. We could print only one "smearing" or one matrix. Printing everything will just
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# be a wall of numbers.
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if range[1]:
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range[1] += 1
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content_string += 'x0/a\tCorr(x0/a)\n------------------\n'
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@ -640,7 +727,7 @@ class Corr:
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newcontent.append(self.content[t] + y.content[t])
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return Corr(newcontent)
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elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float):
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elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float) or isinstance(y, CObs):
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newcontent = []
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for t in range(self.T):
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if (self.content[t] is None):
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@ -663,7 +750,7 @@ class Corr:
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newcontent.append(self.content[t] * y.content[t])
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return Corr(newcontent)
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elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float):
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elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float) or isinstance(y, CObs):
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newcontent = []
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for t in range(self.T):
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if (self.content[t] is None):
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@ -696,9 +783,14 @@ class Corr:
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raise Exception("Division returns completely undefined correlator")
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return Corr(newcontent)
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elif isinstance(y, Obs):
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if y.value == 0:
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raise Exception('Division by zero will return undefined correlator')
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elif isinstance(y, Obs) or isinstance(y, CObs):
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if isinstance(y, Obs):
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if y.value == 0:
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raise Exception('Division by zero will return undefined correlator')
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if isinstance(y, CObs):
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if y.is_zero():
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raise Exception('Division by zero will return undefined correlator')
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newcontent = []
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for t in range(self.T):
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if (self.content[t] is None):
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@ -728,7 +820,7 @@ class Corr:
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return self + (-y)
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def __pow__(self, y):
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if isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float):
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if isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float) or isinstance(y, CObs):
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newcontent = [None if (item is None) else item**y for item in self.content]
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return Corr(newcontent, prange=self.prange)
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else:
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@ -809,3 +901,70 @@ class Corr:
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def __rtruediv__(self, y):
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return (self / y) ** (-1)
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@property
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def real(self):
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def return_real(obs_OR_cobs):
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if isinstance(obs_OR_cobs, CObs):
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return obs_OR_cobs.real
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else:
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return obs_OR_cobs
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return self._apply_func_to_corr(return_real)
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@property
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def imag(self):
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def return_imag(obs_OR_cobs):
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if isinstance(obs_OR_cobs, CObs):
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return obs_OR_cobs.imag
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else:
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return obs_OR_cobs * 0 # So it stays the right type
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return self._apply_func_to_corr(return_imag)
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def sort_vectors(vec_set, ts): # Helper function used to find a set of Eigenvectors consistent over all timeslices
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reference_sorting = np.array(vec_set[ts])
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N = reference_sorting.shape[0]
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sorted_vec_set = []
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for t in range(len(vec_set)):
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if vec_set[t] is None:
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sorted_vec_set.append(None)
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elif not t == ts:
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perms = permutation([i for i in range(N)])
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best_score = 0
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for perm in perms:
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current_score = 1
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for k in range(N):
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new_sorting = reference_sorting.copy()
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new_sorting[perm[k], :] = vec_set[t][k]
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current_score *= abs(np.linalg.det(new_sorting))
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if current_score > best_score:
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best_score = current_score
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best_perm = perm
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# print("best perm", best_perm)
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sorted_vec_set.append([vec_set[t][k] for k in best_perm])
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else:
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sorted_vec_set.append(vec_set[t])
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return sorted_vec_set
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def permutation(lst): # Shamelessly copied
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if len(lst) == 1:
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return [lst]
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ll = []
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for i in range(len(lst)):
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m = lst[i]
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remLst = lst[:i] + lst[i + 1:]
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# Generating all permutations where m is first
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for p in permutation(remLst):
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ll.append([m] + p)
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return ll
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def GEVP_solver(Gt, G0): # Just so normalization an sorting does not need to be repeated. Here we could later put in some checks
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sp_val, sp_vecs = scipy.linalg.eig(Gt, G0)
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sp_vecs = [sp_vecs[:, np.argsort(sp_val)[-i]] for i in range(1, sp_vecs.shape[0] + 1)]
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sp_vecs = [v / np.sqrt((v.T @ G0 @ v)) for v in sp_vecs]
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return sp_vecs
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