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correlator class cleaned up
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1 changed files with 189 additions and 264 deletions
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@ -1,15 +1,12 @@
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import numpy as np
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import autograd.numpy as anp
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#from scipy.special.orthogonal import _IntegerType
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from .pyerrors import *
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import scipy.linalg
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from .pyerrors import Obs, dump_object
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from .fits import standard_fit
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from .linalg import *
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from .linalg import eigh, mat_mat_op
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from .roots import find_root
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from matplotlib import pyplot as plt
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from matplotlib.ticker import NullFormatter # useful for `logit` scale
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from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
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#import PySimpleGUI as sg
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import matplotlib
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import matplotlib.pyplot as plt
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class Corr:
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"""The class for a correlator (time dependent sequence of pe.Obs).
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@ -25,41 +22,41 @@ class Corr:
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"""
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def __init__(self, data_input, padding_front=0, padding_back=0, prange=None):
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#All data_input should be a list of things at different timeslices. This needs to be verified
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# All data_input should be a list of things at different timeslices. This needs to be verified
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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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# 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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# We check, if this is the case
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if all([isinstance(item, Obs) 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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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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#data_input in the form [np.array(Obs,NxN)]
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elif all([isinstance(item,np.ndarray) or item==None for item in data_input]) and any([isinstance(item,np.ndarray)for item in data_input]):
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# data_input in the form [np.array(Obs,NxN)]
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elif all([isinstance(item, np.ndarray) or item is None for item in data_input]) and any([isinstance(item, np.ndarray) for item in data_input]):
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self.content = data_input
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noNull=[a for a in self.content if not (a is None)] #To check if the matrices are correct for all undefined elements
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noNull = [a for a in self.content if not (a is None)] # To check if the matrices are correct for all undefined elements
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self.N = noNull[0].shape[0]
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# The checks are now identical to the case above
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if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]:
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raise Exception("Smearing matrices are not NxN")
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if (not all([item.shape == noNull[0].shape for item in noNull])):
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if (not all([item.shape == noNull[0].shape for item in noNull])):
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raise Exception("Items in data_input are not of identical shape." + str(noNull))
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else: # In case its a list of something else.
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raise Exception ("data_input contains item of wrong type")
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else: # In case its a list of something else.
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raise Exception("data_input contains item of wrong type")
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self.tag = None
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#We now apply some padding to our list. In case that our list represents a correlator of length T but is not defined at every value.
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#An undefined timeslice is represented by the None object
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# We now apply some padding to our list. In case that our list represents a correlator of length T but is not defined at every value.
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# An undefined timeslice is represented by the None object
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self.content = [None] * padding_front + self.content + [None] * padding_back
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self.T = len(self.content) #for convenience: will be used a lot
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self.T = len(self.content) # for convenience: will be used a lot
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#The attribute "range" [start,end] marks a range of two timeslices.
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#This is useful for keeping track of plateaus and fitranges.
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#The range can be inherited from other Corrs, if the operation should not alter a chosen range eg. multiplication with a constant.
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# The attribute "range" [start,end] marks a range of two timeslices.
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# This is useful for keeping track of plateaus and fitranges.
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# The range can be inherited from other Corrs, if the operation should not alter a chosen range eg. multiplication with a constant.
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self.prange = prange
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self.gamma_method()
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@ -72,64 +69,64 @@ class Corr:
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else:
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for i in range(self.N):
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for j in range(self.N):
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item[i,j].gamma_method()
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item[i, j].gamma_method()
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#We need to project the Correlator with a Vector to get a single value at each timeslice.
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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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# We need to project the Correlator with a Vector to get a single value at each timeslice.
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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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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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#But there is no scenario, where a user would want that to happen and the error message might be more informative.
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# This Exception is in no way necessary. One could just return self
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# But there is no scenario, where a user would want that to happen and the error message might be more informative.
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self.gamma_method()
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if vector_l is None:
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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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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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vector_r = vector_l
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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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#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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# 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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vector_l,vector_r = vector_l / np.sqrt((vector_l@vector_l)), vector_r / np.sqrt(vector_r@vector_r)
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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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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 (item is None) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
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return Corr(newcontent)
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def sum(self):
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return np.sqrt(self.N)*self.projected(np.ones(self.N))
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#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.
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return np.sqrt(self.N) * self.projected(np.ones(self.N))
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# 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.
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def smearing(self, i, j):
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if self.N == 1:
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raise Exception("Trying to pick smearing from projected Corr")
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newcontent=[None if(item is None) else item[i, j] for item in self.content]
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newcontent = [None if(item is None) else item[i, j] for item in self.content]
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return Corr(newcontent)
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#Obs and Matplotlib do not play nicely
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#We often want to retrieve x,y,y_err as lists to pass them to something like pyplot.errorbar
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# Obs and Matplotlib do not play nicely
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# We often want to retrieve x,y,y_err as lists to pass them to something like pyplot.errorbar
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def plottable(self):
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if self.N != 1:
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raise Exception("Can only make Corr[N=1] plottable") #We could also autoproject to the groundstate or expect vectors, but this is supposed to be a super simple function.
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raise Exception("Can only make Corr[N=1] plottable") # We could also autoproject to the groundstate or expect vectors, but this is supposed to be a super simple function.
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x_list = [x for x in range(self.T) if (not self.content[x] is None)]
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y_list = [y[0].value for y in self.content if (not y is None)]
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y_err_list = [y[0].dvalue for y in self.content if (not y is None)]
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y_list = [y[0].value for y in self.content if (y is not None)]
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y_err_list = [y[0].dvalue for y in self.content if (y is not None)]
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return x_list, y_list, y_err_list
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#symmetric returns a Corr, that has been symmetrized.
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#A symmetry checker is still to be implemented
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#The method will not delete any redundant timeslices (Bad for memory, Great for convenience)
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# symmetric returns a Corr, that has been symmetrized.
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# A symmetry checker is still to be implemented
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# The method will not delete any redundant timeslices (Bad for memory, Great for convenience)
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def symmetric(self):
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if self.T%2 != 0:
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if self.T % 2 != 0:
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raise Exception("Can not symmetrize odd T")
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newcontent = [self.content[0]]
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@ -142,13 +139,12 @@ class Corr:
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raise Exception("Corr could not be symmetrized: No redundant values")
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return Corr(newcontent, prange=self.prange)
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def anti_symmetric(self):
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if self.T%2 != 0:
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if self.T % 2 != 0:
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raise Exception("Can not symmetrize odd T")
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newcontent=[self.content[0]]
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newcontent = [self.content[0]]
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for t in range(1, self.T):
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if (self.content[t] is None) or (self.content[self.T - t] is None):
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newcontent.append(None)
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@ -158,18 +154,16 @@ class Corr:
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raise Exception("Corr could not be symmetrized: No redundant values")
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return Corr(newcontent, prange=self.prange)
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#This method will symmetrice the matrices and therefore make them positive definit.
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# This method will symmetrice the matrices and therefore make them positive definit.
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def smearing_symmetric(self):
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if self.N > 1:
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transposed = [None if (G is None) else G.T for G in self.content]
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return 0.5 * (Corr(transposed)+self)
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return 0.5 * (Corr(transposed) + self)
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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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# 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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@ -178,39 +172,35 @@ 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_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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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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def Eigenvalue(self,t0,state=1):
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G=self.smearing_symmetric()
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G0=G.content[t0]
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def Eigenvalue(self, t0, state=1):
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G = self.smearing_symmetric()
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G0 = G.content[t0]
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L = mat_mat_op(anp.linalg.cholesky, G0)
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Li = mat_mat_op(anp.linalg.inv, L)
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LT=L.T
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LTi=mat_mat_op(anp.linalg.inv, LT)
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newcontent=[]
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LT = L.T
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LTi = mat_mat_op(anp.linalg.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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Gt = G.content[t]
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M = Li @ Gt @ LTi
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eigenvalues = eigh(M)[0]
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#print(eigenvalues)
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eigenvalue=eigenvalues[-state]
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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 roll(self, dt):
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return Corr(list(np.roll(np.array(self.content, dtype=object), dt)))
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def deriv(self, symmetric=True): #Defaults to symmetric derivative
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def deriv(self, symmetric=True): # Defaults to symmetric derivative
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if not symmetric:
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newcontent = []
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for t in range(self.T - 1):
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if (self.content[t] is None) or (self.content[t+1] is None):
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if (self.content[t] is None) or (self.content[t + 1] is None):
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newcontent.append(None)
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else:
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newcontent.append(self.content[t + 1] - self.content[t])
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@ -219,8 +209,8 @@ class Corr:
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return Corr(newcontent, padding_back=1)
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if symmetric:
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newcontent = []
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for t in range(1, self.T-1):
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if (self.content[t-1] is None) or (self.content[t+1] is None):
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for t in range(1, self.T - 1):
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if (self.content[t - 1] is None) or (self.content[t + 1] is None):
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newcontent.append(None)
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else:
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newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1]))
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raise Exception('Derivative is undefined at all timeslices')
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return Corr(newcontent, padding_back=1, padding_front=1)
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def second_deriv(self):
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newcontent = []
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for t in range(1, self.T-1):
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if (self.content[t-1] is None) or (self.content[t+1] is None):
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for t in range(1, self.T - 1):
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if (self.content[t - 1] is None) or (self.content[t + 1] is None):
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newcontent.append(None)
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else:
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newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1]))
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@ -240,7 +229,6 @@ class Corr:
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raise Exception("Derivative is undefined at all timeslices")
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return Corr(newcontent, padding_back=1, padding_front=1)
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def m_eff(self, variant='log', guess=1.0):
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"""Returns the effective mass of the correlator as correlator object
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@ -252,7 +240,7 @@ class Corr:
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"""
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if self.N != 1:
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raise Exception('Correlator must be projected before getting m_eff')
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if variant is 'log':
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if variant == 'log':
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newcontent = []
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for t in range(self.T - 1):
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if (self.content[t] is None) or (self.content[t + 1] is None):
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@ -264,42 +252,42 @@ class Corr:
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return np.log(Corr(newcontent, padding_back=1))
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elif variant is 'periodic':
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elif variant == 'periodic':
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newcontent = []
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for t in range(self.T - 1):
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if (self.content[t] is None) or (self.content[t + 1] is None):
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newcontent.append(None)
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else:
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func = lambda x, d : anp.cosh(x * (t - self.T / 2)) / anp.cosh(x * (t + 1 - self.T / 2)) - d
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func = lambda x, d: anp.cosh(x * (t - self.T / 2)) / anp.cosh(x * (t + 1 - self.T / 2)) - d
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newcontent.append(np.abs(find_root(self.content[t][0] / self.content[t + 1][0], func, guess=guess)))
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if(all([x is None for x in newcontent])):
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raise Exception('m_eff is undefined at all timeslices')
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return Corr(newcontent, padding_back=1)
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elif variant is 'arccosh':
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newcontent=[]
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for t in range(1,self.T-1):
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if (self.content[t] is None) or (self.content[t+1] is None)or (self.content[t-1] is None):
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elif variant == 'arccosh':
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newcontent = []
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for t in range(1, self.T - 1):
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if (self.content[t] is None) or (self.content[t + 1] is None) or (self.content[t - 1] is None):
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newcontent.append(None)
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else:
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newcontent.append((self.content[t+1]+self.content[t-1])/(2*self.content[t]))
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newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t]))
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if(all([x is None for x in newcontent])):
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raise Exception("m_eff is undefined at all timeslices")
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return np.arccosh(Corr(newcontent,padding_back=1,padding_front=1))
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return np.arccosh(Corr(newcontent, padding_back=1, padding_front=1))
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else:
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raise Exception('Unkown variant.')
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#We want to apply a pe.standard_fit directly to the Corr using an arbitrary function and range.
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# We want to apply a pe.standard_fit directly to the Corr using an arbitrary function and range.
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def fit(self, function, fitrange=None, silent=False, **kwargs):
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if self.N != 1:
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raise Exception("Correlator must be projected before fitting")
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#The default behaviour is:
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#1 use explicit fitrange
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# if none is provided, use the range of the corr
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# if this is also not set, use the whole length of the corr (This could come with a warning!)
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# The default behaviour is:
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# 1 use explicit fitrange
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# if none is provided, use the range of the corr
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# if this is also not set, use the whole length of the corr (This could come with a warning!)
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if fitrange is None:
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if self.prange:
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||||
|
@ -311,15 +299,13 @@ class Corr:
|
|||
ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1]) if not self.content[x] is None]
|
||||
result = standard_fit(xs, ys, function, silent=silent, **kwargs)
|
||||
if isinstance(result, list):
|
||||
[item.gamma_method() for item in result if isinstance(item,Obs)]
|
||||
[item.gamma_method() for item in result if isinstance(item, Obs)]
|
||||
elif isinstance(result, dict):
|
||||
[item.gamma_method() for item in result['fit_parameters'] if isinstance(item,Obs)]
|
||||
[item.gamma_method() for item in result['fit_parameters'] if isinstance(item, Obs)]
|
||||
else:
|
||||
raise Exception('Unexpected fit result.')
|
||||
return result
|
||||
|
||||
|
||||
#we want to quickly get a plateau
|
||||
def plateau(self, plateau_range=None, method="fit"):
|
||||
if not plateau_range:
|
||||
if self.prange:
|
||||
|
@ -329,13 +315,13 @@ class Corr:
|
|||
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])])):
|
||||
raise Exception("plateau is undefined at all timeslices in plateaurange.")
|
||||
raise Exception("plateau is undefined at all timeslices in plateaurange.")
|
||||
if method == "fit":
|
||||
def const_func(a, t):
|
||||
return a[0] # At some point pe.standard fit had an issue with single parameter fits. Being careful does not hurt
|
||||
return self.fit(const_func,plateau_range)[0]
|
||||
elif method in ["avg","average","mean"]:
|
||||
returnvalue= np.mean([item[0] for item in self.content[plateau_range[0]:plateau_range[1]+1] if not item is None])
|
||||
return a[0] # At some point pe.standard fit had an issue with single parameter fits. Being careful does not hurt
|
||||
return self.fit(const_func, plateau_range)[0]
|
||||
elif method in ["avg", "average", "mean"]:
|
||||
returnvalue = np.mean([item[0] for item in self.content[plateau_range[0]:plateau_range[1] + 1] if item is not None])
|
||||
returnvalue.gamma_method()
|
||||
return returnvalue
|
||||
|
||||
|
@ -343,11 +329,11 @@ class Corr:
|
|||
raise Exception("Unsupported plateau method: " + method)
|
||||
|
||||
def set_prange(self, prange):
|
||||
if not len(prange)==2:
|
||||
if not len(prange) == 2:
|
||||
raise Exception("prange must be a list or array with two values")
|
||||
if not ((isinstance(prange[0],int)) and (isinstance(prange[1],int))):
|
||||
if not ((isinstance(prange[0], int)) and (isinstance(prange[1], int))):
|
||||
raise Exception("Start and end point must be integers")
|
||||
if not (0<=prange[0]<=self.T and 0<=prange[1]<=self.T and prange[0]<prange[1] ):
|
||||
if not (0 <= prange[0] <= self.T and 0 <= prange[1] <= self.T and prange[0] < prange[1]):
|
||||
raise Exception("Start and end point must define a range in the interval 0,T")
|
||||
|
||||
self.prange = prange
|
||||
|
@ -364,15 +350,15 @@ class Corr:
|
|||
logscale -- Sets y-axis to logscale
|
||||
save -- path to file in which the figure should be saved
|
||||
"""
|
||||
if self.N!=1:
|
||||
if self.N != 1:
|
||||
raise Exception("Correlator must be projected before plotting")
|
||||
if x_range is None:
|
||||
if x_range is None:
|
||||
x_range = [0, self.T]
|
||||
|
||||
fig = plt.figure()
|
||||
ax1 = fig.add_subplot(111)
|
||||
|
||||
x,y,y_err=self.plottable()
|
||||
x, y, y_err = self.plottable()
|
||||
ax1.errorbar(x, y, y_err, label=self.tag)
|
||||
if logscale:
|
||||
ax1.set_yscale('log')
|
||||
|
@ -380,8 +366,8 @@ class Corr:
|
|||
# we generate ylim instead of using autoscaling.
|
||||
if y_range is None:
|
||||
try:
|
||||
y_min=min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
|
||||
y_max=max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
|
||||
y_min = min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]: x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
|
||||
y_max = max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]: x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
|
||||
ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)])
|
||||
except:
|
||||
pass
|
||||
|
@ -390,7 +376,7 @@ class Corr:
|
|||
if comp:
|
||||
if isinstance(comp, Corr) or isinstance(comp, list):
|
||||
for corr in comp if isinstance(comp, list) else [comp]:
|
||||
x,y,y_err=corr.plottable()
|
||||
x, y, y_err = corr.plottable()
|
||||
plt.errorbar(x, y, y_err, label=corr.tag, mfc=plt.rcParams['axes.facecolor'])
|
||||
else:
|
||||
raise Exception('comp must be a correlator or a list of correlators.')
|
||||
|
@ -408,8 +394,8 @@ class Corr:
|
|||
if fit_res:
|
||||
x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05)
|
||||
ax1.plot(x_samples,
|
||||
fit_res['fit_function']([o.value for o in fit_res['fit_parameters']], x_samples)
|
||||
, ls='-', marker=',', lw=2)
|
||||
fit_res['fit_function']([o.value for o in fit_res['fit_parameters']], x_samples),
|
||||
ls='-', marker=',', lw=2)
|
||||
|
||||
ax1.set_xlabel(r'$x_0 / a$')
|
||||
if ylabel:
|
||||
|
@ -418,7 +404,7 @@ class Corr:
|
|||
|
||||
handles, labels = ax1.get_legend_handles_labels()
|
||||
if labels:
|
||||
legend = ax1.legend()
|
||||
ax1.legend()
|
||||
plt.draw()
|
||||
|
||||
if save:
|
||||
|
@ -429,8 +415,8 @@ class Corr:
|
|||
|
||||
return
|
||||
|
||||
def dump(self,filename):
|
||||
dump_object(self,filename)
|
||||
def dump(self, filename):
|
||||
dump_object(self, filename)
|
||||
return
|
||||
|
||||
def print(self, range=[0, None]):
|
||||
|
@ -449,251 +435,190 @@ class Corr:
|
|||
content_string += '\t' + element.__repr__()[4:-1]
|
||||
content_string += '\n'
|
||||
return content_string
|
||||
|
||||
def __str__(self):
|
||||
return self.__repr__()
|
||||
#return ("Corr[T="+str(self.T)+" , N="+str(self.N)+" , content="+str([o[0] for o in [o for o in self.content]])+"]")
|
||||
|
||||
#We define the basic operations, that can be performed with correlators.
|
||||
#While */+- get defined here, they only work for Corr*Obs and not Obs*Corr.
|
||||
#This is because Obs*Corr checks Obs.__mul__ first and does not catch an exception.
|
||||
#One could try and tell Obs to check if the y in __mul__ is a Corr and
|
||||
# We define the basic operations, that can be performed with correlators.
|
||||
# While */+- get defined here, they only work for Corr*Obs and not Obs*Corr.
|
||||
# This is because Obs*Corr checks Obs.__mul__ first and does not catch an exception.
|
||||
# One could try and tell Obs to check if the y in __mul__ is a Corr and
|
||||
|
||||
def __add__(self, y):
|
||||
if isinstance(y, Corr):
|
||||
if ((self.N!=y.N) or (self.T!=y.T) ):
|
||||
if ((self.N != y.N) or (self.T != y.T)):
|
||||
raise Exception("Addition of Corrs with different shape")
|
||||
newcontent=[]
|
||||
newcontent = []
|
||||
for t in range(self.T):
|
||||
if (self.content[t] is None) or (y.content[t] is None):
|
||||
newcontent.append(None)
|
||||
else:
|
||||
newcontent.append(self.content[t]+y.content[t])
|
||||
newcontent.append(self.content[t] + y.content[t])
|
||||
return Corr(newcontent)
|
||||
|
||||
elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y,float):
|
||||
newcontent=[]
|
||||
elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float):
|
||||
newcontent = []
|
||||
for t in range(self.T):
|
||||
if (self.content[t] is None):
|
||||
newcontent.append(None)
|
||||
else:
|
||||
newcontent.append(self.content[t]+y)
|
||||
newcontent.append(self.content[t] + y)
|
||||
return Corr(newcontent, prange=self.prange)
|
||||
else:
|
||||
raise TypeError("Corr + wrong type")
|
||||
|
||||
def __mul__(self,y):
|
||||
if isinstance(y,Corr):
|
||||
if not((self.N==1 or y.N==1 or self.N==y.N) and self.T==y.T):
|
||||
def __mul__(self, y):
|
||||
if isinstance(y, Corr):
|
||||
if not((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T):
|
||||
raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T")
|
||||
newcontent=[]
|
||||
newcontent = []
|
||||
for t in range(self.T):
|
||||
if (self.content[t] is None) or (y.content[t] is None):
|
||||
newcontent.append(None)
|
||||
else:
|
||||
newcontent.append(self.content[t]*y.content[t])
|
||||
newcontent.append(self.content[t] * y.content[t])
|
||||
return Corr(newcontent)
|
||||
|
||||
elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y,float):
|
||||
newcontent=[]
|
||||
elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float):
|
||||
newcontent = []
|
||||
for t in range(self.T):
|
||||
if (self.content[t] is None):
|
||||
newcontent.append(None)
|
||||
else:
|
||||
newcontent.append(self.content[t]*y)
|
||||
newcontent.append(self.content[t] * y)
|
||||
return Corr(newcontent, prange=self.prange)
|
||||
else:
|
||||
raise TypeError("Corr * wrong type")
|
||||
|
||||
def __truediv__(self,y):
|
||||
if isinstance(y,Corr):
|
||||
if not((self.N==1 or y.N==1 or self.N==y.N) and self.T==y.T):
|
||||
def __truediv__(self, y):
|
||||
if isinstance(y, Corr):
|
||||
if not((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T):
|
||||
raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T")
|
||||
newcontent=[]
|
||||
newcontent = []
|
||||
for t in range(self.T):
|
||||
if (self.content[t] is None) or (y.content[t] is None):
|
||||
newcontent.append(None)
|
||||
else:
|
||||
newcontent.append(self.content[t]/y.content[t])
|
||||
#Here we set the entire timeslice to undefined, if one of the smearings has encountered an division by zero.
|
||||
#While this might throw away perfectly good values in other smearings, we will never have to check, if all values in our matrix are defined
|
||||
newcontent.append(self.content[t] / y.content[t])
|
||||
# Here we set the entire timeslice to undefined, if one of the smearings has encountered an division by zero.
|
||||
# While this might throw away perfectly good values in other smearings, we will never have to check, if all values in our matrix are defined
|
||||
for t in range(self.T):
|
||||
if newcontent[t] is None:
|
||||
continue
|
||||
if np.isnan(np.sum(newcontent[t]).value):
|
||||
newcontent[t]=None
|
||||
newcontent[t] = None
|
||||
|
||||
if all([item is None for item in newcontent]):
|
||||
raise Exception("Division returns completely undefined correlator")
|
||||
|
||||
|
||||
|
||||
return Corr(newcontent)
|
||||
|
||||
elif isinstance(y, Obs):
|
||||
if y.value==0:
|
||||
raise Exception("Division by Zero will return undefined correlator")
|
||||
newcontent=[]
|
||||
if y.value == 0:
|
||||
raise Exception('Division by zero will return undefined correlator')
|
||||
newcontent = []
|
||||
for t in range(self.T):
|
||||
if (self.content[t] is None):
|
||||
newcontent.append(None)
|
||||
else:
|
||||
newcontent.append(self.content[t]/y)
|
||||
newcontent.append(self.content[t] / y)
|
||||
return Corr(newcontent, prange=self.prange)
|
||||
|
||||
elif isinstance(y, int) or isinstance(y,float):
|
||||
if y==0:
|
||||
raise Exception("Division by Zero will return undefined correlator")
|
||||
newcontent=[]
|
||||
elif isinstance(y, int) or isinstance(y, float):
|
||||
if y == 0:
|
||||
raise Exception('Division by zero will return undefined correlator')
|
||||
newcontent = []
|
||||
for t in range(self.T):
|
||||
if (self.content[t] is None):
|
||||
newcontent.append(None)
|
||||
else:
|
||||
newcontent.append(self.content[t]/y)
|
||||
return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
|
||||
newcontent.append(self.content[t] / y)
|
||||
return Corr(newcontent, prange=self.prange)
|
||||
else:
|
||||
raise TypeError("Corr / wrong type")
|
||||
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,prange=self.prange)
|
||||
newcontent = [None if (item is None) else -1. * item for item in self.content]
|
||||
return Corr(newcontent, prange=self.prange)
|
||||
|
||||
def __sub__(self,y):
|
||||
return self +(-y)
|
||||
def __sub__(self, y):
|
||||
return self + (-y)
|
||||
|
||||
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,prange=self.prange)
|
||||
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, prange=self.prange)
|
||||
else:
|
||||
raise TypeError("type of exponent not supported")
|
||||
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,prange= self.prange if hasattr(self,"prange") else None)
|
||||
newcontent = [None if (item is None) else np.abs(item) for item in self.content]
|
||||
return Corr(newcontent, prange=self.prange)
|
||||
|
||||
#The numpy functions:
|
||||
# The numpy functions:
|
||||
def sqrt(self):
|
||||
return self**0.5
|
||||
|
||||
def log(self):
|
||||
newcontent=[None if (item is None) else np.log(item) for item in self.content]
|
||||
return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
|
||||
newcontent = [None if (item is None) else np.log(item) for item in self.content]
|
||||
return Corr(newcontent, prange=self.prange)
|
||||
|
||||
def exp(self):
|
||||
newcontent=[None if (item is None) else np.exp(item) for item in self.content]
|
||||
return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
|
||||
newcontent = [None if (item is None) else np.exp(item) for item in self.content]
|
||||
return Corr(newcontent, prange=self.prange)
|
||||
|
||||
def _apply_func_to_corr(self, func):
|
||||
newcontent = [None if (item is None) else func(item) for item in self.content]
|
||||
for t in range(self.T):
|
||||
if newcontent[t] is None:
|
||||
continue
|
||||
if np.isnan(np.sum(newcontent[t]).value):
|
||||
newcontent[t] = None
|
||||
if all([item is None for item in newcontent]):
|
||||
raise Exception('Operation returns undefined correlator')
|
||||
return Corr(newcontent)
|
||||
|
||||
def sin(self):
|
||||
newcontent=[None if (item is None) else np.sin(item) for item in self.content]
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.sin)
|
||||
|
||||
def cos(self):
|
||||
newcontent=[None if (item is None) else np.cos(item) for item in self.content]
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.cos)
|
||||
|
||||
def tan(self):
|
||||
newcontent=[None if (item is None) else np.tan(item) for item in self.content]
|
||||
|
||||
for t in range(self.T):
|
||||
if newcontent[t] is None:
|
||||
continue
|
||||
if np.isnan(np.sum(newcontent[t]).value):
|
||||
newcontent[t]=None
|
||||
if all([item is None for item in newcontent]):
|
||||
raise Exception("Operation returns completely undefined correlator")
|
||||
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.tan)
|
||||
|
||||
def sinh(self):
|
||||
newcontent=[None if (item is None) else np.sinh(item) for item in self.content]
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.sinh)
|
||||
|
||||
def cosh(self):
|
||||
newcontent=[None if (item is None) else np.cosh(item) for item in self.content]
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.cosh)
|
||||
|
||||
def tanh(self):
|
||||
newcontent=[None if (item is None) else np.tanh(item) for item in self.content]
|
||||
for t in range(self.T):
|
||||
if newcontent[t] is None:
|
||||
continue
|
||||
if np.isnan(np.sum(newcontent[t]).value):
|
||||
newcontent[t]=None
|
||||
if all([item is None for item in newcontent]):
|
||||
raise Exception("Operation returns completely undefined correlator")
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.tanh)
|
||||
|
||||
def arcsin(self):
|
||||
newcontent=[None if (item is None) else np.arcsin(item) for item in self.content]
|
||||
for t in range(self.T):
|
||||
if newcontent[t] is None:
|
||||
continue
|
||||
if np.isnan(np.sum(newcontent[t]).value):
|
||||
newcontent[t]=None
|
||||
if all([item is None for item in newcontent]):
|
||||
raise Exception("Operation returns completely undefined correlator")
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.arcsin)
|
||||
|
||||
def arccos(self):
|
||||
newcontent=[None if (item is None) else np.arccos(item) for item in self.content]
|
||||
for t in range(self.T):
|
||||
if newcontent[t] is None:
|
||||
continue
|
||||
if np.isnan(np.sum(newcontent[t]).value):
|
||||
newcontent[t]=None
|
||||
if all([item is None for item in newcontent]):
|
||||
raise Exception("Operation returns completely undefined correlator")
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.arccos)
|
||||
|
||||
def arctan(self):
|
||||
newcontent=[None if (item is None) else np.arctan(item) for item in self.content]
|
||||
for t in range(self.T):
|
||||
if newcontent[t] is None:
|
||||
continue
|
||||
if np.isnan(np.sum(newcontent[t]).value):
|
||||
newcontent[t]=None
|
||||
if all([item is None for item in newcontent]):
|
||||
raise Exception("Operation returns completely undefined correlator")
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.arctan)
|
||||
|
||||
def arcsinh(self):
|
||||
newcontent=[None if (item is None) else np.arcsinh(item) for item in self.content]
|
||||
for t in range(self.T):
|
||||
if newcontent[t] is None:
|
||||
continue
|
||||
if np.isnan(np.sum(newcontent[t]).value):
|
||||
newcontent[t]=None
|
||||
if all([item is None for item in newcontent]):
|
||||
raise Exception("Operation returns completely undefined correlator")
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.arcsinh)
|
||||
|
||||
def arccosh(self):
|
||||
newcontent=[None if (item is None) else np.arccosh(item) for item in self.content]
|
||||
for t in range(self.T):
|
||||
if newcontent[t] is None:
|
||||
continue
|
||||
if np.isnan(np.sum(newcontent[t]).value):
|
||||
newcontent[t]=None
|
||||
if all([item is None for item in newcontent]):
|
||||
raise Exception("Operation returns completely undefined correlator")
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.arccosh)
|
||||
|
||||
def arctanh(self):
|
||||
newcontent=[None if (item is None) else np.arctanh(item) for item in self.content]
|
||||
for t in range(self.T):
|
||||
if newcontent[t] is None:
|
||||
continue
|
||||
if np.isnan(np.sum(newcontent[t]).value):
|
||||
newcontent[t]=None
|
||||
if all([item is None for item in newcontent]):
|
||||
raise Exception("Operation returns completely undefined correlator")
|
||||
return Corr(newcontent)
|
||||
return self._apply_func_to_corr(np.arctanh)
|
||||
|
||||
# Right hand side operations (require tweak in main module to work)
|
||||
def __rsub__(self, y):
|
||||
return -self + y
|
||||
|
||||
#right hand side operations (require tweak in main module to work)
|
||||
def __rsub__(self,y):
|
||||
return -self+y
|
||||
def __rmul__(self, y):
|
||||
return self * y
|
||||
def __radd__(self,y):
|
||||
|
||||
def __radd__(self, y):
|
||||
return self + y
|
||||
|
|
Loading…
Add table
Reference in a new issue