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The GEVP method now has the parameter "state", deciding which eigenvector to return.

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Correlators get theoptional attribute "range", which is preserved under certain operations.
Fits and plateaus will default to this range.
This commit is contained in:
Jan Neuendorf 2021-10-05 12:19:54 +02:00
parent f2b8a9580b
commit 014c0d12ce
1 changed files with 75 additions and 17 deletions
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92
pyerrors/correlators.py
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@ -1,5 +1,6 @@
import numpy as np
import autograd.numpy as anp
#from scipy.special.orthogonal import _IntegerType
from .pyerrors import *
from .fits import standard_fit
from .roots import find_root
@ -22,7 +23,7 @@ class Corr:
"""
def __init__(self, data_input, padding_front=0, padding_back=0):
def __init__(self, data_input, padding_front=0, padding_back=0,range=None):
#All data_input should be a list of things at different timeslices. This needs to be verified
if not (isinstance(data_input, list)):
@ -56,6 +57,12 @@ class Corr:
self.T = len(self.content) #for convenience: will be used a lot
#The attribute "range" [start,end] marks a range of two timeslices.
#This is useful for keeping track of plateaus and fitranges.
#The range can be inherited from other Corrs, if the operation should not alter a chosen range eg. multiplication with a constant.
if not range is None:
self.range=range
self.gamma_method()
@ -98,6 +105,8 @@ class Corr:
newcontent = [None if (item is None) else np.asarray([vector_l.T@item@vector_r]) for item in self.content]
return Corr(newcontent)
def sum(self):
return np.sqrt(self.N)*self.projected(np.ones(self.N))
#For purposes of debugging and verification, one might want to see a single smearing level. smearing will return a Corr at the specified i,j. where both are integers 0<=i,j<N.
def smearing(self, i, j):
if self.N == 1:
@ -133,7 +142,8 @@ class Corr:
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")
return Corr(newcontent)
return Corr(newcontent,range= self.range if hasattr(self,"range") else None)
def anti_symmetric(self):
@ -148,7 +158,7 @@ class Corr:
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")
return Corr(newcontent)
return Corr(newcontent,range= self.range if hasattr(self,"range") else None)
#This method will symmetrice the matrices and therefore make them positive definit.
@ -161,7 +171,7 @@ class Corr:
#We also include a simple GEVP method based on Scipy.linalg
def GEVP(self, t0, ts):
def GEVP(self, t0, ts,state=1):
if (self.content[t0] is None) or (self.content[ts] is None):
raise Exception("Corr not defined at t0/ts")
G0, Gt = np.empty([self.N, self.N], dtype="double"), np.empty([self.N, self.N], dtype="double")
@ -171,7 +181,7 @@ class Corr:
Gt[i, j] = self.content[ts][i, j].value
sp_val,sp_vec = scipy.linalg.eig(Gt, G0)
sp_vec = sp_vec[:,np.argmax(sp_val)] #we only want the eigenvector belonging to the biggest eigenvalue.
sp_vec=sp_vec[:,np.argsort(sp_val)[-state]] #we only want the eigenvector belonging to the selected state
sp_vec = sp_vec/np.sqrt(sp_vec@sp_vec)
return sp_vec
@ -258,8 +268,17 @@ class Corr:
if self.N != 1:
raise Exception("Correlator must be projected before fitting")
#The default behaviour 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:
fitrange=[0, self.T]
if hasattr(self,"range"):
fitrange=self.range
else:
fitrange=[0, self.T]
xs = [x for x in range(fitrange[0], fitrange[1]) if not self.content[x] is None]
ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1]) if not self.content[x] is None]
@ -273,7 +292,12 @@ class Corr:
return result
#we want to quickly get a plateau
def plateau(self, plateau_range, method="fit"):
def plateau(self, plateau_range=None, method="fit"):
if not plateau_range:
if hasattr(self,"range"):
plateau_range=self.range
else:
raise Exception("no plateau range provided")
if self.N != 1:
raise Exception("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])])):
@ -288,7 +312,26 @@ class Corr:
return returnvalue
else:
raise Exception("Unsupported plateau method: " + method)
raise Exception("Unsupported plateau method: " + method)
def set_range(self,range):
if not len(range)==2:
raise Exception("range must be a list or array with two values")
if not ((isinstance(range[0],int)) and (isinstance(range[1],int))):
raise Exception("start and end point must be integers")
if not (0<=range[0]<=self.T and 0<=range[1]<=self.T and range[0]<range[1] ):
raise Exception("start and end point must define a range in the interval 0,T")
self.range=range
return
#quick and dirty plotting function to view Correlator inside Jupyter
#If one would not want to import pyplot, this could easily be replaced by a call to pe.plot_corrs
@ -337,6 +380,9 @@ class Corr:
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')
if hasattr(self,"range"):
ax1.axvline(self.range[0],0,1)
ax1.axvline(self.range[1],0,1)
if fit_res:
x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05)
@ -367,6 +413,10 @@ class Corr:
def print(self, range=[0, None]):
print(self.__repr__(range))
def __repr__(self, range=[0, None]):
if range[1]:
range[1] += 1
@ -408,7 +458,7 @@ class Corr:
newcontent.append(None)
else:
newcontent.append(self.content[t]+y)
return Corr(newcontent)
return Corr(newcontent,range= self.range if hasattr(self,"range") else None)
else:
raise TypeError("Corr + wrong type")
@ -431,7 +481,7 @@ class Corr:
newcontent.append(None)
else:
newcontent.append(self.content[t]*y)
return Corr(newcontent)
return Corr(newcontent,range= self.range if hasattr(self,"range") else None)
else:
raise TypeError("Corr * wrong type")
@ -469,7 +519,11 @@ class Corr:
newcontent.append(None)
else:
newcontent.append(self.content[t]/y)
return Corr(newcontent)
if hasattr(self,"range"):
newrange=self.range
else:
newrange=None
return Corr(newcontent,range=newrange)
elif isinstance(y, int) or isinstance(y,float):
if y==0:
@ -480,13 +534,17 @@ class Corr:
newcontent.append(None)
else:
newcontent.append(self.content[t]/y)
return Corr(newcontent)
return Corr(newcontent,range= self.range if hasattr(self,"range") else None)
else:
raise TypeError("Corr / wrong type")
def __neg__(self):
newcontent=[None if (item is None) else -1.*item for item in self.content]
return Corr(newcontent)
if hasattr(self,"range"):
newrange=self.range
else:
newrange=None
return Corr(newcontent,range=newrange)
def __sub__(self,y):
return self +(-y)
@ -494,13 +552,13 @@ class Corr:
def __pow__(self, y):
if isinstance(y, Obs) or isinstance(y,int) or isinstance(y,float):
newcontent=[None if (item is None) else item**y for item in self.content]
return Corr(newcontent)
return Corr(newcontent,range= self.range if hasattr(self,"range") else None)
else:
raise TypeError("type of exponent not supported")
def __abs__(self):
newcontent=[None if (item is None) else np.abs(item) for item in self.content]
return Corr(newcontent)
return Corr(newcontent,range= self.range if hasattr(self,"range") else None)
#The numpy functions:
def sqrt(self):
@ -508,11 +566,11 @@ class Corr:
def log(self):
newcontent=[None if (item is None) else np.log(item) for item in self.content]
return Corr(newcontent)
return Corr(newcontent,range= self.range if hasattr(self,"range") else None)
def exp(self):
newcontent=[None if (item is None) else np.exp(item) for item in self.content]
return Corr(newcontent)
return Corr(newcontent,range= self.range if hasattr(self,"range") else None)
def sin(self):
newcontent=[None if (item is None) else np.sin(item) for item in self.content]