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The changes i tried to push before
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1 changed files with 41 additions and 58 deletions
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@ -30,7 +30,7 @@ class Corr:
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raise TypeError('Corr__init__ expects a list of timeslices.')
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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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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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# Wrapping the Obs in an array ensures that the data structure is consistent with smearing matrices.
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self.N = 1 # number of smearings
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@ -97,7 +97,7 @@ 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,normalize=False):
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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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@ -109,18 +109,16 @@ 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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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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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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vector_l = [vector_l] * self.T
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if not isinstance(vector_l,list):
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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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@ -215,9 +213,7 @@ class Corr:
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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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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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@ -227,11 +223,11 @@ class Corr:
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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_vecs=GEVP_solver(Gt,G0)
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sp_vec=sp_vecs[state]
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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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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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@ -240,24 +236,19 @@ class Corr:
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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_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: #This could contain a check for real eigenvectors
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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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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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@ -278,27 +269,23 @@ class Corr:
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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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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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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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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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if i >= self.T:
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return i-self.T
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return i
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@ -306,18 +293,14 @@ class Corr:
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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][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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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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@ -701,8 +684,8 @@ class Corr:
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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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# 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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@ -929,7 +912,7 @@ class Corr:
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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 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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@ -948,7 +931,7 @@ class Corr:
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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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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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@ -963,7 +946,7 @@ def sort_vectors(vec_set, ts): #Helper function used to find a set of Eigenvecto
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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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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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@ -980,7 +963,7 @@ def sort_vectors(vec_set, ts): #Helper function used to find a set of Eigenvecto
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def permutation(lst): #Shamelessly copied
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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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l = []
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@ -993,8 +976,8 @@ def permutation(lst): #Shamelessly copied
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return l
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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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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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return sp_vecs
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