From c09a56b9c03a1525a55384fc9a6718725fed85d8 Mon Sep 17 00:00:00 2001
From: fjosw <f.j@wwu.de>
Date: Fri, 29 Jan 2021 11:52:36 +0100
Subject: [PATCH] correlators module cleaned up, GUI_range_finder commented out
 for now

---
 examples/05_correlators.ipynb | 220 +++++++--------
 pyerrors/correlators.py       | 495 +++++++++++++++++-----------------
 setup.py                      |   4 +-
 3 files changed, 348 insertions(+), 371 deletions(-)

diff --git a/examples/05_correlators.ipynb b/examples/05_correlators.ipynb
index 616968e9..eb62eef7 100644
--- a/examples/05_correlators.ipynb
+++ b/examples/05_correlators.ipynb
@@ -1,50 +1,28 @@
 {
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
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- "nbformat_minor": 2,
  "cells": [
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Correlator Example"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We are often dealing with lists of observables defined at every time slice. For a more convenient analysis, those can be represented using the \"correlators.Corr\" class.\n",
     "This is especially useful, if there is not one Obs per time slice, but a whole smearing matrix. We will load an example of such an object."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "64 4\n"
      ]
@@ -58,40 +36,43 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "What we just printed out, are the only parameters a Corr has. T represents the number of time slices and N the rank of the NxN smearing matrix. \n",
     "The content is accessible with P5P5.content and gives a list of np.arrays of obs. There is no formal difference between correlators, which contain a single observable per time slice \n",
     "and those, which hold a smearing matrix. \n",
     "To initialize a Corr, we only need to pass the content or in the case of N=1, we might pass a list of obs.\n",
     "Lets run some code!"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
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\n"
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 432x288 with 0 Axes>"
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -99,6 +80,8 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We will go over this line the way it is executed, left to right. \n",
     "The correlator has a method called **symmetric()**, which returns a time symmetrized version of itself. \n",
@@ -107,20 +90,18 @@
     "This methods *projects* the smearing matrices $G(t)$ with a set of vectors, returning a single value at every $t$. $$v_l^T G(t) v_r$$ Since we did not pass an argument, it defaulted to $v_l=v_r=(1,0,...,0)$, giving us $G(t)[0,0]$ . The method returns another Corr, but this time with N=1.   \n",
     "          The last method **.show()** just allows us to quickly inspect a correlator.     \n",
     "Let us now look at the GEVP. \n"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "[-6.26306805e-04  1.64198098e-01 -5.99926044e-01  7.83024479e-01]\n"
      ]
@@ -132,6 +113,8 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "**.GEVP()** Needs two time indices as arguments. It then solves:\n",
     "\n",
@@ -139,45 +122,47 @@
     "\n",
     "and returns the vector $v$ for the largest eigenvalue. It uses a *Scipy* method, which itself is based on a *LAPACK* method. \n",
     "To really see the difference this makes, we can visualize the effective mass, which we also have a method for."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 4,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
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\n"
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\n"
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
-      "text/plain": "<Figure size 432x288 with 0 Axes>"
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -186,65 +171,51 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "The plateau is definetely larger now. We can use it to extract the pseudoscalar mass. All we need is a plateau range. \n",
     "Finding those can be time consuming, if we are dealing with many correlators and we need to zoom in to the plot, to see the range properly.\n",
-    "We can call a GUI method to help us to visualize the range. Play around with the checkboxes at the bottom of the window to make the program adjust plot to your selected range."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "The pseudoscalar mass is:  Obs[0.64457(66)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "m_eff=P5P5.symmetric().projected(eigenvector).m_eff() # Our lines were getting a little long, so we just assign a new Corr. \n",
-    "plateau_range=pe.correlators.GUI_range_finder(m_eff)\n",
-    "m_p=m_eff.plateau(plateau_range)\n",
-    "print(\"The pseudoscalar mass is: \",m_p)"
+    "~We can call a GUI method to help us to visualize the range. Play around with the checkboxes at the bottom of the window to make the program adjust plot to your selected range.~"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
-    "For fun you can compare your result with https://arxiv.org/abs/1912.09937v1 Table XV (the first value for $am_{H_{is}}$).    \n",
+    "# m_eff=P5P5.symmetric().projected(eigenvector).m_eff() # Our lines were getting a little long, so we just assign a new Corr. \n",
+    "# plateau_range=pe.correlators.GUI_range_finder(m_eff)\n",
+    "# m_p=m_eff.plateau(plateau_range)\n",
+    "# print(\"The pseudoscalar mass is: \",m_p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "~For fun you can compare your result with https://arxiv.org/abs/1912.09937v1 Table XV (the first value for $am_{H_{is}}$).~\n",
     "Before we wrap up, we should look at Corrs and math operations. They can be multiplied by and added to other Corrs (of same N,T), or scaled by an Obs or float. \n",
     "Usually the operation is just done for every value at the same time and smearing. \n",
     "\n"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "64 4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "new_correlator=0.5*P5P5+np.sqrt(P5P5)/np.sin(P5P5**2)+np.arcsinh(P5P5)\n",
     "print(new_correlator.T, new_correlator.N)"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "This is a senseless but valid expression, which does exactly, what one would expect. It returns a Corr of the same shape as P5P5. \n",
     " It is really important, that there is never any confusion about the appropriate time slices. Lets look at *m_eff* once again. \n",
@@ -254,25 +225,15 @@
     "Another reason for a Corr being **None** at one time slice, is a division by zero or other undefined operation. \n",
     "Even if a Corr is partially undefined, math operations still work, as long as T and N are identical.\n",
     " "
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "64\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print((m_eff+P5P5.projected()).T)"
+    "# print((m_eff+P5P5.projected()).T)"
    ]
   },
   {
@@ -282,5 +243,26 @@
    "outputs": [],
    "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyerrors/correlators.py b/pyerrors/correlators.py
index 60dd857a..994043da 100644
--- a/pyerrors/correlators.py
+++ b/pyerrors/correlators.py
@@ -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 
     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.
 
     """
-    
+
     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):
             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
         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)] 
+            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
+            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]
+            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]:
+            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))
-        
+            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. 
+
+        #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.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: 
+        for item in self.content:
             if not(item is None):
-                if self.N==1:
+                if self.N == 1:
                     item[0].gamma_method()
-                else: 
+                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. 
+    #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:
+        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.])
+            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]
+        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:
+    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]
+        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 
+    #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:
+        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)]
+        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 
+        return x_list, y_list, y_err_list
 
-    #symmetric returns a Corr, that has been symmetrized. 
-    #A symmetry checker is still to be implemented 
+    #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:
+        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 = [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]))
+                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:
+        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):
+        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]))
+                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:
+        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):
+
+    #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")
+        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
+                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)
+        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) 
+    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):
+            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])
+                    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):
+            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]))
+                    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)
+            return Corr(newcontent, padding_back=1, padding_front=1)
 
     #effective mass at every timeslice
     def m_eff(self, periodic=False):
-        if self.N!=1:
+        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 = []
+            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])
+                    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))
+            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):
+        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]))
+                    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))
+            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:
+    #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]
+            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))
+        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 
+        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
+    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])
@@ -256,57 +253,55 @@ class Corr:
             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 
+            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. 
+        # 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 
-    
+        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. 
+    #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 
-    
+    #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") 
+                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):
@@ -325,7 +320,7 @@ class Corr:
             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):
@@ -371,7 +366,7 @@ class Corr:
                 raise Exception("Division returns completely undefined correlator")
 
 
-                
+
             return Corr(newcontent)
 
         elif isinstance(y, Obs):
@@ -401,7 +396,7 @@ class Corr:
     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)
 
@@ -411,10 +406,10 @@ class Corr:
             return Corr(newcontent)
         else:
             raise TypeError("type of exponent not supported")
-    
+
     #The numpy functions:
     def sqrt(self):
-        return self**0.5       
+        return self**0.5
 
     def log(self):
         newcontent=[None if (item is None) else np.log(item) for item in self.content]
@@ -423,7 +418,7 @@ class Corr:
     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)
@@ -463,7 +458,7 @@ class Corr:
         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):
@@ -537,117 +532,117 @@ class Corr:
     def __rmul__(self, y):
         return 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. 
-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 
\ No newline at end of file
+##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
diff --git a/setup.py b/setup.py
index 56cc6ab7..0d0132b9 100644
--- a/setup.py
+++ b/setup.py
@@ -3,11 +3,11 @@
 from setuptools import setup, find_packages
 
 setup(name='pyerrors',
-      version='1.0.1_forked_JN',
+      version='1.1.0',
       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','PySimpleGUI']
+      install_requires=['numpy>=1.16', 'autograd>=1.2', 'numdifftools', 'matplotlib', 'scipy', 'iminuit']
      )