Updates

Author: alsinmr

Data/Plotting.py

@@ -84,7 +84,9 @@ def append_data(self,data,style='plot',errorbars=True,index=None,rho_index=None,
                 data.select.sel1=data.select.sel1[index]
                 if data.select.sel2 is not None:
                     data.select.sel2=data.select.sel2[index]
-            # index=None
+            # I just uncommented the line below (26.09.2024)
+            # The reduced data from above no longer requires an index, so we set index to None
+            index=None
 
         self.data.append(data)        
         self.rho_index.append(self.calc_rho_index() if rho_index is None else np.array(rho_index,dtype=int))
@@ -239,6 +241,8 @@ def plot_data(self,i=-1,errorbars=True,split=True,**kwargs):
     def comparex(self,i=-1):
         d0,di=self.data[0],self.data[i]
 
+        if len(d0)==len(di):return np.arange(len(d0)),np.arange(len(d0))
+        
         if d0.select is not None and di.select is not None and len(d0.select) and len(di.select):
             out=d0.select.compare(di.select)
             if len(out[0]):
@@ -260,9 +264,12 @@ def xindex(self,i=-1):
         """
         i%=len(self)
         if i<len(self.index):return self.index[i] #Index already calculated
-        if i==0:
+        if i==0:  #First data entry (plot everything)
             return np.arange(self.data[0].R.shape[0])
 
+        if len(self.data[i])==len(self.index[0]): #Matching lengths (plot everything)
+            return np.arange(len(self.data[i]))
+        
         mode=self.mode
 
         in1=self.comparex(i=i)[1]

Entropy/Entropy.py

@@ -90,6 +90,7 @@ def reset(self):
         self._Scc=None
         self._CC=None
         self._CCpca=None
+        self._Chi=None
 
         return self
 
@@ -342,6 +343,19 @@ def chi(self):
             self._chi=out
         return self._chi
 
+    @property
+    def Chi(self):
+        """
+        Index-able Chi
+
+        Returns
+        -------
+        np.array
+
+        """
+        if self._Chi is None:self._Chi=Chi(self)
+        return self._Chi
+    
     @property
     def v_avg(self):
         """
@@ -728,6 +742,7 @@ def plotCCpca(self,index=None,states:list=None,ax=None,**kwargs):
 
     #%% Plotting
 
+    
     def plotCC(self,index=None,ax=None,CCsum:bool=True,**kwargs):
         """
         Make a CC plot for all residue pairs
@@ -748,10 +763,24 @@ def plotCC(self,index=None,ax=None,CCsum:bool=True,**kwargs):
 
         """
 
+        
+            
+        
+        
         if index is None:index=np.ones(self.N,dtype=bool)
         if ax is None:
             ax=plt.subplots()[1]
 
+        index=np.array(index)
+        if index.size==1 and np.issubdtype(index.dtype,np.integer):
+            ax.plot(self.CC[index],**kwargs)
+            ax.xaxis.set_major_locator(plt.MaxNLocator(30,integer=True))
+            ax.xaxis.set_major_formatter(self._axis_formatter(np.arange(len(self.resids))))
+            
+            ax.tick_params(axis='x', labelrotation=90)
+            ax.set_ylabel('C.C')
+            return ax
+            
 
         if 'cmap' not in kwargs:
             kwargs['cmap']='binary'
@@ -852,7 +881,7 @@ def format_func(value,tick_number):
         return plt.FuncFormatter(format_func)
 
 
-    def plotChi(self,index:int,ax:list=None,step:int=1):
+    def plotChi(self,index:int,ax:list=None,step:int=1,cmap='tab10'):
         """
         Creates one or more Ramachandran histogram plots, depending on the number
         of chi angles, i.e. 1D histogram for Valine, 1 Ramachandran plot for
@@ -868,6 +897,8 @@ def plotChi(self,index:int,ax:list=None,step:int=1):
         fig
 
         """
+        chi=self.Chi[index]
+        
         index=np.array(index)
         if index.dtype==bool:index=np.argmax(index)
 
@@ -878,10 +909,12 @@ def plotChi(self,index:int,ax:list=None,step:int=1):
         ax=np.atleast_1d(ax).flatten()
         assert len(ax)==nplots,f"Residue {self.resid[index].resname}{self.resid[index].resid} has {N} chi angles, and therefore requires {nplots} plots"
 
-        chi=[self.chi[k][self.index[k+3,:index].sum()][::step] for k in range(N-1,-1,-1)]
+        # chi=[self.chi[k][self.index[k+3,:index].sum()][::step] for k in range(N-1,-1,-1)]
+        
+        
 
         nstates=self.total_mult[index]
-        cmap=plt.get_cmap('turbo').resampled(nstates)
+        if isinstance(cmap,str):cmap=plt.get_cmap(cmap)
 
         for a,k in zip(ax,range(N-1)):
             for m in range(nstates):
@@ -1083,9 +1116,19 @@ def save(self,filename:str,overwrite:bool=False):
         with open(filename,'wb') as f:
             write_EntropyCC(f,self)
 
+
+class Chi():
+    def __init__(self,ECC):
+        self.ECC=ECC
 
-        
-        
+    def __getitem__(self,index):
+        ECC=self.ECC
+        index=np.array(index)
+        if index.dtype==bool:index=np.argmax(index)
+         
+        N=ECC.index[3:,index].sum()
+
+        return np.array([ECC.chi[k][ECC.index[k+3,:index].sum()] for k in range(N-1,-1,-1)])
 
 
 # Tools for selecting the correct atoms to get rotamers

PCA/CombinedPCA.py

@@ -2,7 +2,6 @@
 
 
 from . import PCA
-from ..Selection.MolSys import Trajectory
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.colors import ListedColormap
@@ -24,7 +23,6 @@ def __init__(self,*pcas):
 
         self.traj[0]
 
-        
     @property
     def traj(self):
         return self._traj
@@ -54,12 +52,14 @@ def hist_by_traj(self,nmax:int=3,cmap='Reds',cmap0='jet',**kwargs):
         colors[:,-1]=np.linspace(0,1,257)[1:]**.25
         cm=ListedColormap(colors)
 
+        maxbin=np.abs(self.PCamp[:nmax+1]).max()
+        
         for q,ax0 in enumerate(ax.T):
             for ax00,n0,n1 in zip(ax0,range(nmax),range(1,nmax+1)):
-                self.Hist.plot(n0,n1,ax=ax00,cmap=cmap0,**kwargs)
+                self.Hist.plot(n0,n1,ax=ax00,cmap=cmap0,maxbin=maxbin,**kwargs)
                 index=np.zeros(len(self.traj),dtype=bool)
                 index[self.ranges[q][0]:self.ranges[q][1]]=True
-                self.Hist.plot(n0,n1,ax=ax00,cmap=cm,index=index)
+                self.Hist.plot(n0,n1,ax=ax00,cmap=cm,maxbin=maxbin,index=index)
 
         fig.tight_layout()