mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-03-15 06:40:24 +01:00
Fabian Joswig
GitHub
commit
090a26919b
2 changed files with 126 additions and 26 deletions
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@ -1,7 +1,9 @@
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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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from .fits import standard_fit
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from .linalg import *
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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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@ -22,7 +24,7 @@ class Corr:
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"""
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def __init__(self, data_input, padding_front=0, padding_back=0):
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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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if not (isinstance(data_input, list)):
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@ -56,6 +58,12 @@ class Corr:
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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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if not range is None:
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self.prange=prange
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self.gamma_method()
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@ -98,6 +106,8 @@ class Corr:
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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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def smearing(self, i, j):
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if self.N == 1:
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@ -133,7 +143,8 @@ class Corr:
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newcontent.append(0.5 * (self.content[t] + self.content[self.T - t]))
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if(all([x is None for x in newcontent])):
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raise Exception("Corr could not be symmetrized: No redundant values")
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return Corr(newcontent)
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return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
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def anti_symmetric(self):
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@ -148,7 +159,7 @@ class Corr:
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newcontent.append(0.5 * (self.content[t] - self.content[self.T - t]))
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if(all([x is None for x in newcontent])):
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raise Exception("Corr could not be symmetrized: No redundant values")
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return Corr(newcontent)
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return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
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#This method will symmetrice the matrices and therefore make them positive definit.
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@ -161,7 +172,7 @@ class Corr:
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#We also include a simple GEVP method based on Scipy.linalg
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def GEVP(self, t0, ts):
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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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@ -171,11 +182,37 @@ class Corr:
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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.argmax(sp_val)] #we only want the eigenvector belonging to the biggest eigenvalue.
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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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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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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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#print(eigenvalues)
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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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@ -250,6 +287,17 @@ class Corr:
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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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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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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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else:
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raise Exception('Unkown variant.')
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@ -258,8 +306,17 @@ class Corr:
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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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if fitrange is None:
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fitrange=[0, self.T]
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if hasattr(self,"prange"):
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fitrange=self.prange
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else:
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fitrange=[0, self.T]
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xs = [x for x in range(fitrange[0], fitrange[1]) if not self.content[x] is None]
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ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1]) if not self.content[x] is None]
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@ -273,7 +330,12 @@ class Corr:
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return result
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#we want to quickly get a plateau
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def plateau(self, plateau_range, method="fit"):
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def plateau(self, plateau_range=None, method="fit"):
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if not plateau_range:
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if hasattr(self,"prange"):
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plateau_range=self.prange
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else:
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raise Exception("no plateau range provided")
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if self.N != 1:
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raise Exception("Correlator must be projected before getting a plateau.")
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if(all([self.content[t] is None for t in range(plateau_range[0], plateau_range[1])])):
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@ -288,7 +350,26 @@ class Corr:
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return returnvalue
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else:
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raise Exception("Unsupported plateau method: " + method)
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raise Exception("Unsupported plateau method: " + method)
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def set_prange(self,prange):
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if not len(prange)==2:
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raise Exception("range must be a list or array with two values")
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if not ((isinstance(prange[0],int)) and (isinstance(prange[1],int))):
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raise Exception("start and end point must be integers")
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if not (0<=prange[0]<=self.T and 0<=prange[1]<=self.T and prange[0]<prange[1] ):
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raise Exception("start and end point must define a range in the interval 0,T")
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self.prange=prange
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return
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#quick and dirty plotting function to view Correlator inside Jupyter
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#If one would not want to import pyplot, this could easily be replaced by a call to pe.plot_corrs
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@ -337,6 +418,9 @@ class Corr:
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ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-')
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else:
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raise Exception('plateau must be an Obs')
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if hasattr(self,"prange"):
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ax1.axvline(self.prange[0],0,1)
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ax1.axvline(self.prange[1],0,1)
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if fit_res:
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x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05)
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@ -369,6 +453,10 @@ class Corr:
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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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if range[1]:
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range[1] += 1
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@ -410,7 +498,7 @@ class Corr:
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newcontent.append(None)
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else:
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newcontent.append(self.content[t]+y)
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return Corr(newcontent)
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return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
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else:
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raise TypeError("Corr + wrong type")
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@ -433,7 +521,7 @@ class Corr:
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newcontent.append(None)
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else:
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newcontent.append(self.content[t]*y)
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return Corr(newcontent)
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return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
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else:
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raise TypeError("Corr * wrong type")
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@ -471,7 +559,11 @@ class Corr:
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newcontent.append(None)
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else:
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newcontent.append(self.content[t]/y)
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return Corr(newcontent)
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if hasattr(self,"prange"):
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newrange=self.prange
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else:
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newrange=None
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return Corr(newcontent,prange=newrange)
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elif isinstance(y, int) or isinstance(y,float):
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if y==0:
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@ -482,13 +574,17 @@ class Corr:
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newcontent.append(None)
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else:
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newcontent.append(self.content[t]/y)
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return Corr(newcontent)
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return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
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else:
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raise TypeError("Corr / wrong type")
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def __neg__(self):
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newcontent=[None if (item is None) else -1.*item for item in self.content]
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return Corr(newcontent)
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if hasattr(self,"prange"):
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newrange=self.prange
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else:
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newrange=None
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return Corr(newcontent,prange=newrange)
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def __sub__(self,y):
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return self +(-y)
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@ -496,13 +592,13 @@ class Corr:
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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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newcontent=[None if (item is None) else item**y for item in self.content]
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return Corr(newcontent)
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return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
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else:
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raise TypeError("type of exponent not supported")
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def __abs__(self):
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newcontent=[None if (item is None) else np.abs(item) for item in self.content]
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return Corr(newcontent)
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return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
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#The numpy functions:
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def sqrt(self):
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@ -510,11 +606,11 @@ class Corr:
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def log(self):
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newcontent=[None if (item is None) else np.log(item) for item in self.content]
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return Corr(newcontent)
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return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
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def exp(self):
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newcontent=[None if (item is None) else np.exp(item) for item in self.content]
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return Corr(newcontent)
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return Corr(newcontent,prange= self.prange if hasattr(self,"prange") else None)
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def sin(self):
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newcontent=[None if (item is None) else np.sin(item) for item in self.content]
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@ -14,7 +14,7 @@ from autograd import elementwise_grad as egrad
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from .pyerrors import Obs, derived_observable, covariance, pseudo_Obs
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def standard_fit(x, y, func, silent=False, **kwargs):
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def standard_fit(x, y, func,n_parms="auto", silent=False, **kwargs):
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"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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x has to be a list of floats.
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@ -68,15 +68,19 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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if not callable(func):
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raise TypeError('func has to be a function.')
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for i in range(25):
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try:
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func(np.arange(i), x.T[0])
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except:
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pass
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else:
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break
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if n_parms=="auto":
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for i in range(25):
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try:
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func(np.arange(i), x.T[0])
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except:
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pass
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else:
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break
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n_parms = i
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n_parms = i
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if not silent:
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print('Fit with', n_parms, 'parameters')
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