Merge branch 'JanNeuendorf-master' into develop

pyerrors/__init__.py

@@ -3,3 +3,4 @@ from . import fits
 from . import linalg
 from . import misc
 from . import mpm
+from . import correlators

pyerrors/correlators.py

@@ -0,0 +1,653 @@
+import autograd.numpy as np
+from .pyerrors import *
+from .fits import standard_fit
+from matplotlib import pyplot as plt
+from matplotlib.ticker import NullFormatter  # useful for `logit` scale
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+import PySimpleGUI as sg
+import matplotlib
+
+class Corr:
+    """The class for a correlator (time dependent sequence of pe.Obs).
+
+    Everything, this class does, can be achieved using lists or arrays of Obs. 
+    But it is simply more convenient to have a dedicated object for correlators. 
+    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.
+
+    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.
+
+    """
+    
+    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 
+     
+        if not isinstance(data_input,list):
+            raise TypeError('Corr__init__ expects a list of timeslices.')
+        # data_input can have multiple shapes. The simplest one is a list of Obs. 
+        #We check, if this is the case
+        if all([isinstance(item,Obs) 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.
+            self.N=1 # number of smearings 
+        
+        #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]):
+            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
+            self.N= noNull[0].shape[0]
+            # The checks are now identical to the case above
+            if self.N>1 and noNull[0].shape[0]!=noNull[0].shape[1]:
+                raise Exception("Smearing matrices are not NxN")
+            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))
+        
+        else: # In case its a list of something else.
+            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. 
+        #An undefined timeslice is represented by the None object
+        self.content=[None]*padding_front+self.content+[None]*padding_back
+        self.T=len(self.content) #for convenience: will be used a lot
+
+
+        self.gamma_method()
+        
+
+    def gamma_method(self):
+        for item in self.content: 
+            if not(item is None):
+                if self.N==1:
+                    item[0].gamma_method()
+                else: 
+                    for i in range(self.N):
+                        for j in range(self.N):
+                            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. 
+    #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
+    def projected(self,vector_l=None,vector_r=None):
+        if self.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
+            #But there is no scenario, where a user would want that to happen and the error message might be more informative.
+            
+        self.gamma_method()
+        
+        if vector_l is None:
+            vector_l,vector_r=np.asarray( [1.]+(self.N-1)*[0.]),np.asarray( [1.]+(self.N-1)*[0.])
+        elif(vector_r is None):
+            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]
+        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.
+    def smearing(self,i,j):
+        if self.N==1:
+            raise Exception("Trying to pick smearing from projected Corr")
+        newcontent=[None if(item is None) else item[i,j] for item in self.content]
+        return Corr(newcontent)
+
+
+    #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
+    def plottable(self):
+        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.
+        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_err_list=[y[0].dvalue for y in self.content if (not y is None)]
+
+        return x_list, y_list, y_err_list 
+
+    #symmetric returns a Corr, that has been symmetrized. 
+    #A symmetry checker is still to be implemented 
+    #The method will not delete any redundant timeslices (Bad for memory, Great for convenience)
+    def symmetric(self):
+
+        if self.T%2!=0:
+            raise Exception("Can not symmetrize odd T")
+        
+        newcontent=[self.content[0]]
+        for t in range(1,self.T):
+            if (self.content[t] is None) or (self.content[self.T-t] is None):
+                newcontent.append(None)
+            else:
+                newcontent.append(0.5*(self.content[t]+self.content[self.T-t]))
+        if(all([x is None for x in newcontent])):
+            raise Exception("Corr could not be symmetrized: No redundant values")
+        return Corr(newcontent)
+
+    def anti_symmetric(self):
+
+        if self.T%2!=0:
+            raise Exception("Can not symmetrize odd T")
+        
+        newcontent=[self.content[0]]
+        for t in range(1,self.T):
+            if (self.content[t] is None) or (self.content[self.T-t] is None):
+                newcontent.append(None)
+            else:
+                newcontent.append(0.5*(self.content[t]-self.content[self.T-t]))
+        if(all([x is None for x in newcontent])):
+            raise Exception("Corr could not be symmetrized: No redundant values")
+        return Corr(newcontent)
+        
+            
+
+    #This method will symmetrice the matrices and therefore make them positive definit.
+    def smearing_symmetric(self):
+        if self.N>1:
+            transposed=[None if (G is None) else G.T for G in self.content]
+            return 0.5*(Corr(transposed)+self)
+        if self.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 
+    def GEVP(self,t0,ts):
+        if (self.content[t0] is None) or (self.content[ts] is None):
+            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")
+        for i in range(self.N):
+            for j in range(self.N):
+                G0[i,j]=self.content[t0][i,j].value
+                Gt[i,j]=self.content[ts][i,j].value
+
+        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.sqrt(sp_vec@sp_vec)
+        return sp_vec
+
+    def deriv(self,symmetric=False): #Defaults to forward derivative f'(t)=f(t+1)-f(t) 
+        if not symmetric:
+            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:
+                    newcontent.append(self.content[t+1]-self.content[t])
+            if(all([x is None for x in newcontent])):
+                raise Exception("Derivative is undefined at all timeslices")
+            return Corr(newcontent, padding_back=1)
+        if symmetric:
+            newcontent=[]
+            for t in range(1,self.T-1):
+                if (self.content[t-1] is None) or (self.content[t+1] is None):
+                    newcontent.append(None)
+                else:
+                    newcontent.append(0.5*(self.content[t+1]-self.content[t-1]))
+            if(all([x is None for x in newcontent])):
+                raise Exception("Derivative is undefined at all timeslices")
+            return Corr(newcontent, padding_back=1,padding_front=1)
+
+    #effective mass at every timeslice
+    def m_eff(self, periodic=False):
+        if self.N!=1:
+            raise Exception("Correlator must be projected before getting m_eff")
+        if not periodic:
+            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:
+                    newcontent.append(self.content[t]/self.content[t+1])
+            if(all([x is None for x in newcontent])):
+                raise Exception("m_eff is undefined at all timeslices")
+
+            return np.log(Corr(newcontent,padding_back=1))
+
+        else: #This is usually not very stable. One could default back to periodic=False. 
+            newcontent=[]
+            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):
+                    newcontent.append(None)
+                else:
+                    newcontent.append((self.content[t+1]+self.content[t-1])/(2*self.content[t]))
+            if(all([x is None for x in newcontent])):
+                raise Exception("m_eff is undefined at all timeslices")
+            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. 
+    def fit(self,function,fitrange=None):
+        if self.N!=1:
+            raise Exception("Correlator must be projected before fitting")
+
+        if fitrange is None:
+            fitrange=[0,self.T]
+
+        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]
+        result = standard_fit(xs, ys,function,silent=(True))
+        [item.gamma_method() for item in result if isinstance(item,Obs)]
+        return result 
+
+    #we want to quickly get a plateau
+    def plateau(self,plateau_range,method="fit"):
+        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")
+        if method=="fit":
+            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 self.fit(const_func,plateau_range)[0]
+        elif method in ["avg","average","mean"]:
+            returnvalue= np.mean([item[0] for item in self.content if not item is None])
+            returnvalue.gamma_method()
+            return returnvalue
+
+        else:
+            raise Exception("Unsupported plateau method: "+method)
+    
+    #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 
+    #This might be a bit more flexible later 
+    def show(self,xrange=None,logscale=False):
+        if self.N!=1:
+            raise Exception("Correlator must be projected before plotting")
+        if  xrange is None:
+            xrange=[0,self.T]
+        
+        x,y,y_err=self.plottable()
+        plt.errorbar(x,y,y_err,fmt="o-")
+        if logscale:
+            plt.yscale("log")
+        else:
+        # 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_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.xlabel(r"$n_t$ [a]")
+        plt.xlim(xrange)
+        plt.title("Quickplot")
+        plt.grid()
+    
+        plt.show()
+        plt.clf()
+        return 
+    
+    def dump(self,filename):
+        dump_object(self,filename)
+        return
+
+    
+
+    
+    def __repr__(self):
+        return("Corr[T="+str(self.T)+" , N="+str(self.N)+" , content="+str(self.content)+"]")
+    def __str__(self):
+        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. 
+    #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) ):
+                raise Exception("Addition of Corrs with different shape") 
+            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])
+            return Corr(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)
+            return Corr(newcontent)
+        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):
+                raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T")
+            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])
+            return Corr(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)
+            return Corr(newcontent)
+        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):
+                raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T")
+            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
+            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("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=[]
+            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)
+
+        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)
+        else:
+            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)
+    
+    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)
+        else:
+            raise TypeError("type of exponent not supported")
+    
+    #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)
+
+    def exp(self):
+        newcontent=[None if (item is None) else np.exp(item) for item in self.content]
+        return Corr(newcontent)
+    
+    def sin(self):
+        newcontent=[None if (item is None) else np.sin(item) for item in self.content]
+        return Corr(newcontent)
+
+    def cos(self):
+        newcontent=[None if (item is None) else np.cos(item) for item in self.content]
+        return Corr(newcontent)
+
+    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)
+
+    def sinh(self):
+        newcontent=[None if (item is None) else np.sinh(item) for item in self.content]
+        return Corr(newcontent)
+
+    def cosh(self):
+        newcontent=[None if (item is None) else np.cosh(item) for item in self.content]
+        return Corr(newcontent)
+
+    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)
+    
+    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)
+
+    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)
+
+    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)
+
+    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)
+
+    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)
+
+    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)
+
+
+    #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):
+        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. 
+def GUI_range_finder(corr, current_range=None):
+    T=corr.T 
+    if corr.N!=1:
+        raise Exception("The Corr needs to be projected to select a range.")
+    #We need to define few helper functions for the Gui
+    def get_figure(corr,values):
+        fig = matplotlib.figure.Figure(figsize=(7, 4), dpi=100)
+        fig.clf()
+        x,y,err=corr.plottable()
+        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"]))
+        start=int(min(values["range_start"],values["range_end"]))
+        db=[0.1,0.2,0.8]
+        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)
+        offset=int(0.3*(end-start))
+        xrange=[max(min(start-1,int(start-offset)),0),min(max(int(end+offset),end+1),T-1)]
+        ax.grid()
+        if values["Plateau"]:
+            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=1,color="red")
+            ax.set_title(r"Current Plateau="+str(plateau)[4:-1])
+        if(values["Crop X"]):
+            ax.set_xlim(xrange)
+        ax.set_xticks([x for x in ax.get_xticks() if (x-int(x)==0) and (0<=x<T)])
+        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_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)])
+        else:
+            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)])
+            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_end"]-0.5,-2*abs(y_min),2*y_max,color=db)
+        return fig
+
+    def draw_figure(canvas, figure):
+        #matplotlib.use('TkAgg')
+        figure_canvas_agg = FigureCanvasTkAgg(figure, canvas)
+        figure_canvas_agg.draw()
+        figure_canvas_agg.get_tk_widget().pack(side='top', fill='both', expand=1)
+        return figure_canvas_agg
+
+    def delete_figure_agg(figure_agg):
+        figure_agg.get_tk_widget().forget()
+        plt.close('all')
+
+    #We change settings for mpl only inside the function
+    #matplotlib.use('TkAgg')
+
+    #now we can call our gui 
+    # define window layout
+    default_values={}
+    default_values["Crop X"]=False
+    default_values["Crop Y"]=False
+    default_values["Plateau"]=False
+    if current_range is None:
+        default_values["range_start"]=1
+        default_values["range_end"]=int(T/2)
+    else:
+        default_values["range_start"]=current_range[0]
+        default_values["range_end"]=current_range[1]
+
+    
+    layout = [
+            [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_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')]]
+
+    #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)
+
+    # add the plot to the window
+    fig = get_figure(corr,default_values)
+    fig_canvas_agg =draw_figure(window['-CANVAS-'].TKCanvas, fig)
+    while True:
+        event, values = window.read()
+        if event is None or event=="Return" or event=="\r":
+            break 
+        else:
+            if values["range_end"]<=values["range_start"]+2:
+                if values["range_start"]+3<T:
+                    window["range_end"].update(values["range_start"]+3)
+                else:
+                    window["range_start"].update(T-3)
+                    window["range_end"].update(values["range_start"]+3)
+            # we need a distance of 2 fo a plateau
+            if values["range_end"]<=values["range_start"]+1:
+                values["Plateau"]=False
+                window["Plateau"].update(False)
+            if fig_canvas_agg:
+                delete_figure_agg(fig_canvas_agg)
+            fig = get_figure(corr,values)
+            fig_canvas_agg =draw_figure(window['-CANVAS-'].TKCanvas, fig)
+
+             
+    window.close()
+    #It is easier to read the last event, that occurred
+    if event=="Return" or event=="\r":
+        end=int(max(values["range_start"],values["range_end"]))
+        start=int(min(values["range_start"],values["range_end"]))
+        window.close()
+        return [start,end]
+    else:
+        return 

pyerrors/pyerrors.py

@@ -501,10 +501,10 @@ class Obs:
         else:
             if isinstance(y, np.ndarray):
                 return np.array([self + o for o in y])
+            elif(y.__class__.__name__=="Corr"):
+                return NotImplemented 
             else:
                 return derived_observable(lambda x, **kwargs: x[0] + y, [self], man_grad=[1])
-
-
     def __radd__(self, y):
         return self + y
 
@@ -515,10 +515,12 @@ class Obs:
         else:
             if isinstance(y, np.ndarray):
                 return np.array([self * o for o in y])
+            elif(y.__class__.__name__=="Corr"):
+                return NotImplemented 
+                
             else:
                 return derived_observable(lambda x, **kwargs: x[0] * y, [self], man_grad=[y])
 
-
     def __rmul__(self, y):
         return self * y
 
@@ -529,6 +531,10 @@ class Obs:
         else:
             if isinstance(y, np.ndarray):
                 return np.array([self - o for o in y])
+            
+            elif(y.__class__.__name__=="Corr"):
+                return NotImplemented 
+
             else:
                 return derived_observable(lambda x, **kwargs: x[0] - y, [self], man_grad=[1])
 
@@ -547,6 +553,10 @@ class Obs:
         else:
             if isinstance(y, np.ndarray):
                 return np.array([self / o for o in y])
+
+            elif(y.__class__.__name__=="Corr"):
+                return NotImplemented 
+                
             else:
                 return derived_observable(lambda x, **kwargs: x[0] / y, [self], man_grad=[1 / y])
 

setup.py

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