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I changed the name from range to prange (hope i did not miss something)
There is now an Eigenvalue method using the cholesky method you put in the example. I do not really use that, but it seems like a logical inclusion. I gave the standard fit an option to seht the number of fit parameters by hand, because it does not work automatically if the function is not consistent between calls.
This commit is contained in:
JanNeuendorf
parent
014c0d12ce
commit
7c03cff42f
2 changed files with 80 additions and 38 deletions
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@ -3,6 +3,7 @@ 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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@ -23,7 +24,7 @@ class Corr:
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"""
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def __init__(self, data_input, padding_front=0, padding_back=0,range=None):
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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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@ -61,7 +62,7 @@ class Corr:
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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.range=range
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self.prange=prange
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self.gamma_method()
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@ -143,7 +144,7 @@ class Corr:
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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,range= self.range if hasattr(self,"range") else None)
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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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@ -158,7 +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,range= self.range if hasattr(self,"range") else None)
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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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@ -186,6 +187,32 @@ class Corr:
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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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@ -260,6 +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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@ -275,8 +313,8 @@ class Corr:
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if fitrange is None:
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if hasattr(self,"range"):
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fitrange=self.range
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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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@ -294,8 +332,8 @@ class Corr:
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#we want to quickly get a plateau
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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,"range"):
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plateau_range=self.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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@ -316,15 +354,15 @@ class Corr:
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def set_range(self,range):
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if not len(range)==2:
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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(range[0],int)) and (isinstance(range[1],int))):
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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<=range[0]<=self.T and 0<=range[1]<=self.T and range[0]<range[1] ):
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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.range=range
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self.prange=prange
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return
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@ -380,9 +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,"range"):
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ax1.axvline(self.range[0],0,1)
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ax1.axvline(self.range[1],0,1)
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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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@ -458,7 +496,7 @@ 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,range= self.range if hasattr(self,"range") else None)
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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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@ -481,7 +519,7 @@ 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,range= self.range if hasattr(self,"range") else None)
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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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@ -519,11 +557,11 @@ 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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if hasattr(self,"range"):
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newrange=self.range
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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,range=newrange)
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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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@ -534,17 +572,17 @@ 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,range= self.range if hasattr(self,"range") else None)
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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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if hasattr(self,"range"):
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newrange=self.range
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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,range=newrange)
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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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@ -552,13 +590,13 @@ 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,range= self.range if hasattr(self,"range") else None)
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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,range= self.range if hasattr(self,"range") else None)
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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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@ -566,11 +604,11 @@ 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,range= self.range if hasattr(self,"range") else None)
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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,range= self.range if hasattr(self,"range") else None)
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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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