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correlators module cleaned up, GUI_range_finder commented out for now

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fjosw 2021-01-29 11:52:36 +01:00
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examples/05_correlators.ipynb
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pyerrors/correlators.py
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@ -15,240 +15,237 @@ class Corr:
One often wants to add or multiply correlators of the same length at every timeslice and it is inconvinient One often wants to add or multiply correlators of the same length at every timeslice and it is inconvinient
to iterate over all timeslices for every operation. This is especially true, when dealing with smearing matrices. to iterate over all timeslices for every operation. This is especially true, when dealing with smearing matrices.
The correlator can have two types of content: An Obs at every timeslice OR a GEVP The correlator can have two types of content: An Obs at every timeslice OR a GEVP
smearing matrix at every timeslice. Other dependency (eg. spacial) are not supported. smearing matrix at every timeslice. Other dependency (eg. spacial) are not supported.
""" """
def __init__(self, data_input,padding_front=0,padding_back=0): def __init__(self, data_input,padding_front=0,padding_back=0):
#All data_input should be a list of things at different timeslices. This needs to be verified #All data_input should be a list of things at different timeslices. This needs to be verified
if not isinstance(data_input,list): if not isinstance(data_input,list):
raise TypeError('Corr__init__ expects a list of timeslices.') raise TypeError('Corr__init__ expects a list of timeslices.')
# data_input can have multiple shapes. The simplest one is a list of Obs. # data_input can have multiple shapes. The simplest one is a list of Obs.
#We check, if this is the case #We check, if this is the case
if all([isinstance(item,Obs) for item in data_input]): if all([isinstance(item,Obs) for item in data_input]):
self.content=[np.asarray([item]) for item in data_input] self.content=[np.asarray([item]) for item in data_input]
#Wrapping the Obs in an array ensures that the data structure is consistent with smearing matrices. #Wrapping the Obs in an array ensures that the data structure is consistent with smearing matrices.
self.N=1 # number of smearings self.N = 1 # number of smearings
#data_input in the form [np.array(Obs,NxN)] #data_input in the form [np.array(Obs,NxN)]
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]): 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]):
self.content= data_input self.content = data_input
noNull=[a for a in self.content if not (a is None)] #To check if the matrices are correct for all undefined elements noNull=[a for a in self.content if not (a is None)] #To check if the matrices are correct for all undefined elements
self.N= noNull[0].shape[0] self.N = noNull[0].shape[0]
# The checks are now identical to the case above # The checks are now identical to the case above
if self.N>1 and noNull[0].shape[0]!=noNull[0].shape[1]: if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]:
raise Exception("Smearing matrices are not NxN") raise Exception("Smearing matrices are not NxN")
if (not all([item.shape==noNull[0].shape for item in noNull])): if (not all([item.shape == noNull[0].shape for item in noNull])):
raise Exception("Items in data_input are not of identical shape."+str(noNull)) raise Exception("Items in data_input are not of identical shape." + str(noNull))
else: # In case its a list of something else. else: # In case its a list of something else.
raise Exception ("data_input contains item of wrong type") raise Exception ("data_input contains item of wrong type")
#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. #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.
#An undefined timeslice is represented by the None object #An undefined timeslice is represented by the None object
self.content=[None]*padding_front+self.content+[None]*padding_back self.content = [None] * padding_front + self.content + [None] * padding_back
self.T=len(self.content) #for convenience: will be used a lot self.T = len(self.content) #for convenience: will be used a lot
self.gamma_method() self.gamma_method()
def gamma_method(self): def gamma_method(self):
for item in self.content: for item in self.content:
if not(item is None): if not(item is None):
if self.N==1: if self.N == 1:
item[0].gamma_method() item[0].gamma_method()
else: else:
for i in range(self.N): for i in range(self.N):
for j in range(self.N): for j in range(self.N):
item[i,j].gamma_method() item[i,j].gamma_method()
#We need to project the Correlator with a Vector to get a single value at each timeslice.
#The method can use one or two vectors.
#We need to project the Correlator with a Vector to get a single value at each timeslice.
#The method can use one or two vectors.
#If two are specified it returns v1@G@v2 (the order might be very important.) #If two are specified it returns v1@G@v2 (the order might be very important.)
#By default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to #By default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to
def projected(self,vector_l=None,vector_r=None): def projected(self,vector_l=None,vector_r=None):
if self.N==1: if self.N == 1:
raise Exception("Trying to project a Corr, that already has N=1.") raise Exception("Trying to project a Corr, that already has N=1.")
#This Exception is in no way necessary. One could just return self #This Exception is in no way necessary. One could just return self
#But there is no scenario, where a user would want that to happen and the error message might be more informative. #But there is no scenario, where a user would want that to happen and the error message might be more informative.
self.gamma_method() self.gamma_method()
if vector_l is None: if vector_l is None:
vector_l,vector_r=np.asarray( [1.]+(self.N-1)*[0.]),np.asarray( [1.]+(self.N-1)*[0.]) vector_l,vector_r=np.asarray([1.] + (self.N - 1) * [0.]),np.asarray([1.] + (self.N - 1) * [0.])
elif(vector_r is None): elif(vector_r is None):
vector_r=vector_l vector_r=vector_l
if not vector_l.shape==vector_r.shape==(self.N,):
raise Exception("Vectors are of wrong shape!")
#We always normalize before projecting! But we only raise a warning, when it is clear, they where not meant to be normalized.
if (not(0.95<vector_r@vector_r<1.05)) or (not(0.95<vector_l@vector_l<1.05)):
print("Vectors are normalized before projection!")
vector_l,vector_r=vector_l/np.sqrt((vector_l@vector_l)),vector_r/np.sqrt(vector_r@vector_r)
newcontent=[None if (item is None) else np.asarray([vector_l.T@item@vector_r]) for item in self.content] if not vector_l.shape == vector_r.shape == (self.N,):
raise Exception("Vectors are of wrong shape!")
#We always normalize before projecting! But we only raise a warning, when it is clear, they where not meant to be normalized.
if (not(0.95 < vector_r@vector_r < 1.05)) or (not(0.95 < vector_l@vector_l < 1.05)):
print("Vectors are normalized before projection!")
vector_l,vector_r = vector_l / np.sqrt((vector_l@vector_l)), vector_r / np.sqrt(vector_r@vector_r)
newcontent = [None if (item is None) else np.asarray([vector_l.T@item@vector_r]) for item in self.content]
return Corr(newcontent) return Corr(newcontent)
#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. #For purposes of debugging and verification, one might want to see a single smearing level. smearing will return a Corr at the specified i,j. where both are integers 0<=i,j<N.
def smearing(self,i,j): def smearing(self, i, j):
if self.N==1: if self.N == 1:
raise Exception("Trying to pick smearing from projected Corr") raise Exception("Trying to pick smearing from projected Corr")
newcontent=[None if(item is None) else item[i,j] for item in self.content] newcontent=[None if(item is None) else item[i, j] for item in self.content]
return Corr(newcontent) return Corr(newcontent)
#Obs and Matplotlib do not play nicely #Obs and Matplotlib do not play nicely
#We often want to retrieve x,y,y_err as lists to pass them to something like pyplot.errorbar #We often want to retrieve x,y,y_err as lists to pass them to something like pyplot.errorbar
def plottable(self): def plottable(self):
if self.N!=1: if self.N != 1:
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. 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.
x_list=[x for x in range(self.T) if (not self.content[x] is None)] x_list = [x for x in range(self.T) if (not self.content[x] is None)]
y_list=[y[0].value for y in self.content if (not y is None)] y_list = [y[0].value for y in self.content if (not y is None)]
y_err_list=[y[0].dvalue for y in self.content if (not y is None)] y_err_list = [y[0].dvalue for y in self.content if (not y is None)]
return x_list, y_list, y_err_list return x_list, y_list, y_err_list
#symmetric returns a Corr, that has been symmetrized. #symmetric returns a Corr, that has been symmetrized.
#A symmetry checker is still to be implemented #A symmetry checker is still to be implemented
#The method will not delete any redundant timeslices (Bad for memory, Great for convenience) #The method will not delete any redundant timeslices (Bad for memory, Great for convenience)
def symmetric(self): def symmetric(self):
if self.T%2!=0: if self.T%2 != 0:
raise Exception("Can not symmetrize odd T") raise Exception("Can not symmetrize odd T")
newcontent=[self.content[0]] newcontent = [self.content[0]]
for t in range(1,self.T): for t in range(1, self.T):
if (self.content[t] is None) or (self.content[self.T-t] is None): if (self.content[t] is None) or (self.content[self.T - t] is None):
newcontent.append(None) newcontent.append(None)
else: else:
newcontent.append(0.5*(self.content[t]+self.content[self.T-t])) newcontent.append(0.5 * (self.content[t] + self.content[self.T - t]))
if(all([x is None for x in newcontent])): if(all([x is None for x in newcontent])):
raise Exception("Corr could not be symmetrized: No redundant values") raise Exception("Corr could not be symmetrized: No redundant values")
return Corr(newcontent) return Corr(newcontent)
def anti_symmetric(self): def anti_symmetric(self):
if self.T%2!=0: if self.T%2 != 0:
raise Exception("Can not symmetrize odd T") raise Exception("Can not symmetrize odd T")
newcontent=[self.content[0]] newcontent=[self.content[0]]
for t in range(1,self.T): for t in range(1, self.T):
if (self.content[t] is None) or (self.content[self.T-t] is None): if (self.content[t] is None) or (self.content[self.T - t] is None):
newcontent.append(None) newcontent.append(None)
else: else:
newcontent.append(0.5*(self.content[t]-self.content[self.T-t])) newcontent.append(0.5 * (self.content[t] - self.content[self.T - t]))
if(all([x is None for x in newcontent])): if(all([x is None for x in newcontent])):
raise Exception("Corr could not be symmetrized: No redundant values") raise Exception("Corr could not be symmetrized: No redundant values")
return Corr(newcontent) return Corr(newcontent)
#This method will symmetrice the matrices and therefore make them positive definit. #This method will symmetrice the matrices and therefore make them positive definit.
def smearing_symmetric(self): def smearing_symmetric(self):
if self.N>1: if self.N > 1:
transposed=[None if (G is None) else G.T for G in self.content] transposed = [None if (G is None) else G.T for G in self.content]
return 0.5*(Corr(transposed)+self) return 0.5 * (Corr(transposed)+self)
if self.N==1: if self.N == 1:
raise Exception("Trying to symmetrize a smearing matrix, that already has N=1.") raise Exception("Trying to symmetrize a smearing matrix, that already has N=1.")
#We also include a simple GEVP method based on Scipy.linalg #We also include a simple GEVP method based on Scipy.linalg
def GEVP(self,t0,ts): def GEVP(self, t0, ts):
if (self.content[t0] is None) or (self.content[ts] is None): if (self.content[t0] is None) or (self.content[ts] is None):
raise Exception("Corr not defined at t0/ts") raise Exception("Corr not defined at t0/ts")
G0, Gt=np.empty([self.N,self.N],dtype="double"),np.empty([self.N,self.N],dtype="double") G0, Gt = np.empty([self.N, self.N], dtype="double"), np.empty([self.N, self.N], dtype="double")
for i in range(self.N): for i in range(self.N):
for j in range(self.N): for j in range(self.N):
G0[i,j]=self.content[t0][i,j].value G0[i, j] = self.content[t0][i, j].value
Gt[i,j]=self.content[ts][i,j].value Gt[i, j] = self.content[ts][i, j].value
sp_val,sp_vec=scipy.linalg.eig(Gt,G0) sp_val,sp_vec = scipy.linalg.eig(Gt, G0)
sp_vec=sp_vec[:,np.argmax(sp_val)] #we only want the eigenvector belonging to the biggest eigenvalue. sp_vec = sp_vec[:,np.argmax(sp_val)] #we only want the eigenvector belonging to the biggest eigenvalue.
sp_vec=sp_vec/np.sqrt(sp_vec@sp_vec) sp_vec = sp_vec/np.sqrt(sp_vec@sp_vec)
return sp_vec return sp_vec
def deriv(self,symmetric=False): #Defaults to forward derivative f'(t)=f(t+1)-f(t) def deriv(self, symmetric=False): #Defaults to forward derivative f'(t)=f(t+1)-f(t)
if not symmetric: if not symmetric:
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):
newcontent.append(None) newcontent.append(None)
else: else:
newcontent.append(self.content[t+1]-self.content[t]) newcontent.append(self.content[t + 1] - self.content[t])
if(all([x is None for x in newcontent])): if(all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices") raise Exception("Derivative is undefined at all timeslices")
return Corr(newcontent, padding_back=1) return Corr(newcontent, padding_back=1)
if symmetric: if symmetric:
newcontent=[] newcontent = []
for t in range(1,self.T-1): for t in range(1, self.T-1):
if (self.content[t-1] is None) or (self.content[t+1] is None): if (self.content[t-1] is None) or (self.content[t+1] is None):
newcontent.append(None) newcontent.append(None)
else: else:
newcontent.append(0.5*(self.content[t+1]-self.content[t-1])) newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1]))
if(all([x is None for x in newcontent])): if(all([x is None for x in newcontent])):
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)
#effective mass at every timeslice #effective mass at every timeslice
def m_eff(self, periodic=False): def m_eff(self, periodic=False):
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 not periodic:
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):
newcontent.append(None) newcontent.append(None)
else: else:
newcontent.append(self.content[t]/self.content[t+1]) newcontent.append(self.content[t] / self.content[t + 1])
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.log(Corr(newcontent,padding_back=1)) return np.log(Corr(newcontent, padding_back=1))
else: #This is usually not very stable. One could default back to periodic=False. else: #This is usually not very stable. One could default back to periodic=False.
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):
newcontent.append(None) newcontent.append(None)
else: else:
newcontent.append((self.content[t+1]+self.content[t-1])/(2*self.content[t])) newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t]))
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))
#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): def fit(self, function, fitrange=None):
if self.N!=1: if self.N != 1:
raise Exception("Correlator must be projected before fitting") raise Exception("Correlator must be projected before fitting")
if fitrange is None: if fitrange is None:
fitrange=[0,self.T] fitrange=[0, self.T]
xs = [x for x in range(fitrange[0],fitrange[1]) if not self.content[x] is None] xs = [x for x in range(fitrange[0], fitrange[1]) if not self.content[x] is None]
ys = [self.content[x][0] for x in range(fitrange[0],fitrange[1]) if not self.content[x] is None] 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=(True)) result = standard_fit(xs, ys, function, silent=True)
[item.gamma_method() for item in result if isinstance(item,Obs)] [item.gamma_method() for item in result if isinstance(item,Obs)]
return result return result
#we want to quickly get a plateau #we want to quickly get a plateau
def plateau(self,plateau_range,method="fit"): def plateau(self, plateau_range, method="fit"):
if self.N!=1: if self.N != 1:
raise Exception("Correlator must be projected before getting a plateau") 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])])): 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": if method == "fit":
def const_func(a,t): def const_func(a, t):
return a[0]+a[1]*0 # At some point pe.standard fit had an issue with single parameter fits. Being careful does not hurt return a[0] + a[1] * 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] return self.fit(const_func,plateau_range)[0]
elif method in ["avg","average","mean"]: elif method in ["avg","average","mean"]:
returnvalue= np.mean([item[0] for item in self.content if not item is None]) returnvalue= np.mean([item[0] for item in self.content if not item is None])
@ -256,57 +253,55 @@ class Corr:
return returnvalue return returnvalue
else: else:
raise Exception("Unsupported plateau method: "+method) raise Exception("Unsupported plateau method: " + method)
#quick and dirty plotting function to view Correlator inside Jupyter #quick and dirty plotting function to view Correlator inside Jupyter
#If one would not want to import pyplot, this could easily be replaced by a call to pe.plot_corrs #If one would not want to import pyplot, this could easily be replaced by a call to pe.plot_corrs
#This might be a bit more flexible later #This might be a bit more flexible later
def show(self,xrange=None,logscale=False): def show(self,xrange=None,logscale=False):
if self.N!=1: if self.N!=1:
raise Exception("Correlator must be projected before plotting") raise Exception("Correlator must be projected before plotting")
if xrange is None: if xrange is None:
xrange=[0,self.T] xrange=[0,self.T]
x,y,y_err=self.plottable() x,y,y_err=self.plottable()
plt.errorbar(x,y,y_err,fmt="o-") plt.errorbar(x,y,y_err,fmt="o-")
if logscale: if logscale:
plt.yscale("log") plt.yscale("log")
else: else:
# we generate ylim instead of using autoscaling. # we generate ylim instead of using autoscaling.
y_min=min([ (x[0].value-x[0].dvalue) for x in self.content[xrange[0]:xrange[1]] if(not x is None)]) y_min=min([ (x[0].value-x[0].dvalue) for x in self.content[xrange[0]:xrange[1]] if(not x is None)])
y_max=max([ (x[0].value+x[0].dvalue) for x in self.content[xrange[0]:xrange[1]] if(not x is None)]) y_max=max([ (x[0].value+x[0].dvalue) for x in self.content[xrange[0]:xrange[1]] if(not x is None)])
plt.ylim([y_min-0.1*(y_max-y_min),y_max+0.1*(y_max-y_min)]) plt.ylim([y_min-0.1*(y_max-y_min),y_max+0.1*(y_max-y_min)])
plt.xlabel(r"$n_t$ [a]") plt.xlabel(r"$n_t$ [a]")
plt.xlim(xrange) plt.xlim(xrange)
plt.title("Quickplot") plt.title("Quickplot")
plt.grid() plt.grid()
plt.show() plt.show()
plt.clf() plt.clf()
return return
def dump(self,filename): def dump(self,filename):
dump_object(self,filename) dump_object(self,filename)
return return
def __repr__(self): def __repr__(self):
return("Corr[T="+str(self.T)+" , N="+str(self.N)+" , content="+str(self.content)+"]") return("Corr[T="+str(self.T)+" , N="+str(self.N)+" , content="+str(self.content)+"]")
def __str__(self): def __str__(self):
return ("Corr[T="+str(self.T)+" , N="+str(self.N)+" , content="+str(self.content)+"]") return ("Corr[T="+str(self.T)+" , N="+str(self.N)+" , content="+str(self.content)+"]")
#We define the basic operations, that can be performed with correlators. #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. #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. #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 #One could try and tell Obs to check if the y in __mul__ is a Corr and
def __add__(self, y): def __add__(self, y):
if isinstance(y, Corr): 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") raise Exception("Addition of Corrs with different shape")
newcontent=[] newcontent=[]
for t in range(self.T): for t in range(self.T):
if (self.content[t] is None) or (y.content[t] is None): if (self.content[t] is None) or (y.content[t] is None):
@ -325,7 +320,7 @@ class Corr:
return Corr(newcontent) return Corr(newcontent)
else: else:
raise TypeError("Corr + wrong type") raise TypeError("Corr + wrong type")
def __mul__(self,y): def __mul__(self,y):
if isinstance(y,Corr): if isinstance(y,Corr):
if not((self.N==1 or y.N==1 or self.N==y.N) and self.T==y.T): if not((self.N==1 or y.N==1 or self.N==y.N) and self.T==y.T):
@ -371,7 +366,7 @@ class Corr:
raise Exception("Division returns completely undefined correlator") raise Exception("Division returns completely undefined correlator")
return Corr(newcontent) return Corr(newcontent)
elif isinstance(y, Obs): elif isinstance(y, Obs):
@ -401,7 +396,7 @@ class Corr:
def __neg__(self): def __neg__(self):
newcontent=[None if (item is None) else -1.*item for item in self.content] newcontent=[None if (item is None) else -1.*item for item in self.content]
return Corr(newcontent) return Corr(newcontent)
def __sub__(self,y): def __sub__(self,y):
return self +(-y) return self +(-y)
@ -411,10 +406,10 @@ class Corr:
return Corr(newcontent) return Corr(newcontent)
else: else:
raise TypeError("type of exponent not supported") raise TypeError("type of exponent not supported")
#The numpy functions: #The numpy functions:
def sqrt(self): def sqrt(self):
return self**0.5 return self**0.5
def log(self): def log(self):
newcontent=[None if (item is None) else np.log(item) for item in self.content] newcontent=[None if (item is None) else np.log(item) for item in self.content]
@ -423,7 +418,7 @@ class Corr:
def exp(self): def exp(self):
newcontent=[None if (item is None) else np.exp(item) for item in self.content] newcontent=[None if (item is None) else np.exp(item) for item in self.content]
return Corr(newcontent) return Corr(newcontent)
def sin(self): def sin(self):
newcontent=[None if (item is None) else np.sin(item) for item in self.content] newcontent=[None if (item is None) else np.sin(item) for item in self.content]
return Corr(newcontent) return Corr(newcontent)
@ -463,7 +458,7 @@ class Corr:
if all([item is None for item in newcontent]): if all([item is None for item in newcontent]):
raise Exception("Operation returns completely undefined correlator") raise Exception("Operation returns completely undefined correlator")
return Corr(newcontent) return Corr(newcontent)
def arcsin(self): def arcsin(self):
newcontent=[None if (item is None) else np.arcsin(item) for item in self.content] newcontent=[None if (item is None) else np.arcsin(item) for item in self.content]
for t in range(self.T): for t in range(self.T):
@ -537,117 +532,117 @@ class Corr:
def __rmul__(self, y): def __rmul__(self, y):
return self * y return self * y
def __radd__(self,y): def __radd__(self,y):
return self + y return self + y
#One of the most common tasks is to select a range for a plateau or a fit. This is best done visually. ##One of the most common tasks is to select a range for a plateau or a fit. This is best done visually.
def GUI_range_finder(corr, current_range=None): #def GUI_range_finder(corr, current_range=None):
T=corr.T # T=corr.T
if corr.N!=1: # if corr.N!=1:
raise Exception("The Corr needs to be projected to select a range.") # raise Exception("The Corr needs to be projected to select a range.")
#We need to define few helper functions for the Gui # #We need to define few helper functions for the Gui
def get_figure(corr,values): # def get_figure(corr,values):
fig = matplotlib.figure.Figure(figsize=(7, 4), dpi=100) # fig = matplotlib.figure.Figure(figsize=(7, 4), dpi=100)
fig.clf() # fig.clf()
x,y,err=corr.plottable() # x,y,err=corr.plottable()
ax=fig.add_subplot(111,label="main")#.plot(t, 2 * np.sin(2 * np.pi * t)) # ax=fig.add_subplot(111,label="main")#.plot(t, 2 * np.sin(2 * np.pi * t))
end=int(max(values["range_start"],values["range_end"])) # end=int(max(values["range_start"],values["range_end"]))
start=int(min(values["range_start"],values["range_end"])) # start=int(min(values["range_start"],values["range_end"]))
db=[0.1,0.2,0.8] # db=[0.1,0.2,0.8]
ax.errorbar(x,y,err, fmt="-o",color=[0.4,0.6,0.8]) # ax.errorbar(x,y,err, fmt="-o",color=[0.4,0.6,0.8])
ax.errorbar(x[start:end],y[start:end],err[start:end], fmt="-o",color=db) # ax.errorbar(x[start:end],y[start:end],err[start:end], fmt="-o",color=db)
offset=int(0.3*(end-start)) # offset=int(0.3*(end-start))
xrange=[max(min(start-1,int(start-offset)),0),min(max(int(end+offset),end+1),T-1)] # xrange=[max(min(start-1,int(start-offset)),0),min(max(int(end+offset),end+1),T-1)]
ax.grid() # ax.grid()
if values["Plateau"]: # if values["Plateau"]:
plateau=corr.plateau([start,end]) # plateau=corr.plateau([start,end])
ax.hlines(plateau.value,0,T+1,lw=plateau.dvalue,color="red",alpha=0.5) # ax.hlines(plateau.value,0,T+1,lw=plateau.dvalue,color="red",alpha=0.5)
ax.hlines(plateau.value,0,T+1,lw=1,color="red") # ax.hlines(plateau.value,0,T+1,lw=1,color="red")
ax.set_title(r"Current Plateau="+str(plateau)[4:-1]) # ax.set_title(r"Current Plateau="+str(plateau)[4:-1])
if(values["Crop X"]): # if(values["Crop X"]):
ax.set_xlim(xrange) # ax.set_xlim(xrange)
ax.set_xticks([x for x in ax.get_xticks() if (x-int(x)==0) and (0<=x<T)]) # ax.set_xticks([x for x in ax.get_xticks() if (x-int(x)==0) and (0<=x<T)])
if(values["Crop Y"]): # if(values["Crop Y"]):
y_min=min([ (x[0].value-x[0].dvalue) for x in corr.content[xrange[0]:xrange[1]] if(not x is None)]) # y_min=min([ (x[0].value-x[0].dvalue) for x in corr.content[xrange[0]:xrange[1]] if(not x is None)])
y_max=max([ (x[0].value+x[0].dvalue) for x in corr.content[xrange[0]:xrange[1]] if(not x is None)]) # y_max=max([ (x[0].value+x[0].dvalue) for x in corr.content[xrange[0]:xrange[1]] if(not x is None)])
ax.set_ylim([y_min-0.1*(y_max-y_min),y_max+0.1*(y_max-y_min)]) # ax.set_ylim([y_min-0.1*(y_max-y_min),y_max+0.1*(y_max-y_min)])
else: # else:
y_min=min([ (x[0].value-x[0].dvalue) for x in corr.content if(not x is None)]) # y_min=min([ (x[0].value-x[0].dvalue) for x in corr.content if(not x is None)])
y_max=max([ (x[0].value+x[0].dvalue) for x in corr.content if(not x is None)]) # y_max=max([ (x[0].value+x[0].dvalue) for x in corr.content if(not x is None)])
ax.set_ylim([y_min-0.1*(y_max-y_min),y_max+0.1*(y_max-y_min)]) # ax.set_ylim([y_min-0.1*(y_max-y_min),y_max+0.1*(y_max-y_min)])
ax.vlines(values["range_start"]-0.5,-2*abs(y_min),2*y_max,color=db) # ax.vlines(values["range_start"]-0.5,-2*abs(y_min),2*y_max,color=db)
ax.vlines(values["range_end"]-0.5,-2*abs(y_min),2*y_max,color=db) # ax.vlines(values["range_end"]-0.5,-2*abs(y_min),2*y_max,color=db)
return fig # return fig
#
def draw_figure(canvas, figure): # def draw_figure(canvas, figure):
#matplotlib.use('TkAgg') # #matplotlib.use('TkAgg')
figure_canvas_agg = FigureCanvasTkAgg(figure, canvas) # figure_canvas_agg = FigureCanvasTkAgg(figure, canvas)
figure_canvas_agg.draw() # figure_canvas_agg.draw()
figure_canvas_agg.get_tk_widget().pack(side='top', fill='both', expand=1) # figure_canvas_agg.get_tk_widget().pack(side='top', fill='both', expand=1)
return figure_canvas_agg # return figure_canvas_agg
#
def delete_figure_agg(figure_agg): # def delete_figure_agg(figure_agg):
figure_agg.get_tk_widget().forget() # figure_agg.get_tk_widget().forget()
plt.close('all') # plt.close('all')
#
#We change settings for mpl only inside the function # #We change settings for mpl only inside the function
#matplotlib.use('TkAgg') # #matplotlib.use('TkAgg')
#
#now we can call our gui # #now we can call our gui
# define window layout # # define window layout
default_values={} # default_values={}
default_values["Crop X"]=False # default_values["Crop X"]=False
default_values["Crop Y"]=False # default_values["Crop Y"]=False
default_values["Plateau"]=False # default_values["Plateau"]=False
if current_range is None: # if current_range is None:
default_values["range_start"]=1 # default_values["range_start"]=1
default_values["range_end"]=int(T/2) # default_values["range_end"]=int(T/2)
else: # else:
default_values["range_start"]=current_range[0] # default_values["range_start"]=current_range[0]
default_values["range_end"]=current_range[1] # default_values["range_end"]=current_range[1]
#
#
layout = [ # layout = [
[sg.Canvas(key='-CANVAS-')], # [sg.Canvas(key='-CANVAS-')],
[sg.Slider(range=(0,T),default_value=default_values["range_start"],size=(40,15),orientation='horizontal',key="range_start",enable_events = True)], # [sg.Slider(range=(0,T),default_value=default_values["range_start"],size=(40,15),orientation='horizontal',key="range_start",enable_events = True)],
[sg.Slider(range=(0,T),default_value=default_values["range_end"],size=(40,15),orientation='horizontal',key="range_end",enable_events = True)], # [sg.Slider(range=(0,T),default_value=default_values["range_end"],size=(40,15),orientation='horizontal',key="range_end",enable_events = True)],
[sg.Checkbox('Crop X',key="Crop X",default=default_values["Crop X"],enable_events = True),sg.Checkbox('Crop Y',key="Crop Y",default=default_values["Crop Y"],enable_events = True),sg.Checkbox('Plateau', key="Plateau",default=default_values["Plateau"],enable_events = True),sg.Button('Return')]] # [sg.Checkbox('Crop X',key="Crop X",default=default_values["Crop X"],enable_events = True),sg.Checkbox('Crop Y',key="Crop Y",default=default_values["Crop Y"],enable_events = True),sg.Checkbox('Plateau', key="Plateau",default=default_values["Plateau"],enable_events = True),sg.Button('Return')]]
#
#Calling a theme after the layout is set, preserves default sliders and Buttons # #Calling a theme after the layout is set, preserves default sliders and Buttons
#
window = sg.Window('Range Finder', layout, finalize=True, element_justification='center', font='Helvetica 18',return_keyboard_events=True) # window = sg.Window('Range Finder', layout, finalize=True, element_justification='center', font='Helvetica 18',return_keyboard_events=True)
#
# add the plot to the window # # add the plot to the window
fig = get_figure(corr,default_values) # fig = get_figure(corr,default_values)
fig_canvas_agg =draw_figure(window['-CANVAS-'].TKCanvas, fig) # fig_canvas_agg =draw_figure(window['-CANVAS-'].TKCanvas, fig)
while True: # while True:
event, values = window.read() # event, values = window.read()
if event is None or event=="Return" or event=="\r": # if event is None or event=="Return" or event=="\r":
break # break
else: # else:
if values["range_end"]<=values["range_start"]+2: # if values["range_end"]<=values["range_start"]+2:
if values["range_start"]+3<T: # if values["range_start"]+3<T:
window["range_end"].update(values["range_start"]+3) # window["range_end"].update(values["range_start"]+3)
else: # else:
window["range_start"].update(T-3) # window["range_start"].update(T-3)
window["range_end"].update(values["range_start"]+3) # window["range_end"].update(values["range_start"]+3)
# we need a distance of 2 fo a plateau # # we need a distance of 2 fo a plateau
if values["range_end"]<=values["range_start"]+1: # if values["range_end"]<=values["range_start"]+1:
values["Plateau"]=False # values["Plateau"]=False
window["Plateau"].update(False) # window["Plateau"].update(False)
if fig_canvas_agg: # if fig_canvas_agg:
delete_figure_agg(fig_canvas_agg) # delete_figure_agg(fig_canvas_agg)
fig = get_figure(corr,values) # fig = get_figure(corr,values)
fig_canvas_agg =draw_figure(window['-CANVAS-'].TKCanvas, fig) # fig_canvas_agg =draw_figure(window['-CANVAS-'].TKCanvas, fig)
#
#
window.close() # window.close()
#It is easier to read the last event, that occurred # #It is easier to read the last event, that occurred
if event=="Return" or event=="\r": # if event=="Return" or event=="\r":
end=int(max(values["range_start"],values["range_end"])) # end=int(max(values["range_start"],values["range_end"]))
start=int(min(values["range_start"],values["range_end"])) # start=int(min(values["range_start"],values["range_end"]))
window.close() # window.close()
return [start,end] # return [start,end]
else: # else:
return # return

4
setup.py
View file

@ -3,11 +3,11 @@
from setuptools import setup, find_packages from setuptools import setup, find_packages
setup(name='pyerrors', setup(name='pyerrors',
version='1.0.1_forked_JN', version='1.1.0',
description='Error analysis for lattice QCD', description='Error analysis for lattice QCD',
author='Fabian Joswig', author='Fabian Joswig',
author_email='fabian.joswig@wwu.de', author_email='fabian.joswig@wwu.de',
packages=find_packages(), packages=find_packages(),
python_requires='>=3.5.0', python_requires='>=3.5.0',
install_requires=['numpy>=1.16', 'autograd>=1.2', 'numdifftools', 'matplotlib', 'scipy', 'iminuit','PySimpleGUI'] install_requires=['numpy>=1.16', 'autograd>=1.2', 'numdifftools', 'matplotlib', 'scipy', 'iminuit']
) )