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Implemented new 'root' variant for the m_eff of correlator class

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Fabian Joswig 2021-09-27 15:42:21 +01:00
parent 0339912fae
commit 12469e304b
1 changed files with 20 additions and 4 deletions
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24
pyerrors/correlators.py
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@ -2,6 +2,7 @@ import numpy as np
import autograd.numpy as anp import autograd.numpy as anp
from .pyerrors import * from .pyerrors import *
from .fits import standard_fit from .fits import standard_fit
from .roots import find_root
from matplotlib import pyplot as plt from matplotlib import pyplot as plt
from matplotlib.ticker import NullFormatter # useful for `logit` scale from matplotlib.ticker import NullFormatter # useful for `logit` scale
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
@ -202,16 +203,18 @@ class Corr:
raise Exception('Derivative is undefined at all timeslices') raise Exception('Derivative is undefined at all timeslices')
return Corr(newcontent, padding_back=1, padding_front=1) return Corr(newcontent, padding_back=1, padding_front=1)
def m_eff(self, periodic=False): def m_eff(self, variant='log'):
"""Returns the effective mass of the correlator as correlator object """Returns the effective mass of the correlator as correlator object
Parameters Parameters
---------- ----------
x_range -- if true the function uses arccosh( (C(t+1)+C(t-1)) / (2C(t) ) instead of the standard expression for the effective mass variant -- log: uses the standard effective mass log(C(t) / C(t+1))
periodic : uses arccosh((C(t+1)+C(t-1)) / (2C(t))
root : Solves C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m
""" """
if self.N != 1: if self.N != 1:
raise Exception('Correlator must be projected before getting m_eff') raise Exception('Correlator must be projected before getting m_eff')
if not periodic: if variant is 'log':
newcontent = [] newcontent = []
for t in range(self.T - 1): for t in range(self.T - 1):
if (self.content[t] is None) or (self.content[t + 1] is None): if (self.content[t] is None) or (self.content[t + 1] is None):
@ -223,7 +226,7 @@ class Corr:
return np.log(Corr(newcontent, padding_back=1)) return np.log(Corr(newcontent, padding_back=1))
else: # This is usually not very stable. elif variant is 'periodic': # This is usually not very stable.
newcontent = [] newcontent = []
for t in range(1, self.T - 1): for t in range(1, self.T - 1):
if (self.content[t] is None) or (self.content[t + 1] is None)or (self.content[t - 1] is None): if (self.content[t] is None) or (self.content[t + 1] is None)or (self.content[t - 1] is None):
@ -233,7 +236,20 @@ class Corr:
if(all([x is None for x in newcontent])): if(all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices') raise Exception('m_eff is undefined at all timeslices')
return np.arccosh(Corr(newcontent, padding_back=1, padding_front=1)) return np.arccosh(Corr(newcontent, padding_back=1, padding_front=1))
elif variant is 'root':
newcontent = []
for t in range(self.T - 1):
if (self.content[t] is None) or (self.content[t + 1] is None):
newcontent.append(None)
else:
func = lambda x, d : anp.cosh(x * (t - self.T / 2)) / anp.cosh(x * (t + 1 - self.T / 2)) - d
newcontent.append(np.abs(find_root(self.content[t][0] / self.content[t + 1][0], func)))
if(all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices')
return Corr(newcontent, padding_back=1)
else:
raise Exception('Unkown variant.')
#We want to apply a pe.standard_fit directly to the Corr using an arbitrary function and range. #We want to apply a pe.standard_fit directly to the Corr using an arbitrary function and range.
def fit(self, function, fitrange=None, silent=False): def fit(self, function, fitrange=None, silent=False):