sfs plotting

sfs_tools.py

         #     else:
         #         sfs_val.append(ksi)
         if transformed:
+            ylab = r'$ \phi_i $'
             if folded:
                 val = ((k*(2*n - k)) / (2*n))*(ksi)
             else:
         else:
             val = ksi
         sfs_val.append(val)
+
+        if not transformed and not normalized:
+            ylab = r'$ \eta_i $'
             
     #terminal case, same for folded or unfolded
     if transformed:
         last_bin = list(sfs.values())[n-1]
     sfs_val[-1] = last_bin
     if normalized:
-        ylab = "Fraction of SNPs"
+        #ylab = "Fraction of SNPs "
+        ylab = r'$ \phi_i $'
         sum_val = sum(sfs_val)
         for k, sfs_bin in enumerate(sfs_val):
             sfs_val[k] = sfs_bin / sum_val
+        
         #print(sum(sfs_val))
     #build the plot
-    title = title+" (n="+str(len(sfs_val)+1)+") [folded="+str(folded)+"]"+" [transformed="+str(transformed)+"]"
+    title = title+" (n="+str(len(sfs_val))+") [folded="+str(folded)+"]"+" [transformed="+str(transformed)+"]"
     print("SFS =", sfs)
     if folded:
         xlab = "Minor allele frequency"
     if transformed:
-        print("Transformed SFS ( n =",len(sfs_val)+1, ") :", sfs_val)
-        plt.axhline(y=1/n, color='r', linestyle='-')
+        print("Transformed SFS ( n =",len(sfs_val), ") :", sfs_val)
+        #plt.axhline(y=1/n, color='r', linestyle='-')
     else:
         if normalized:
             # then plot a theoritical distribution as 1/i