output from comb script & electrostatic mut changes calculated

mcsm_analysis/pyrazinamide/scripts/combining_two_df.R

@@ -1,6 +1,19 @@
 #########################################################
-# TASK: To combine mcsm and meta data with af and or   
+# TASK: To combine mcsm and meta data with af and or files  
-# This script doesn't output anything, but can do if needed.
+# Input csv files:
+# 1) mcsm output formatted
+# 2) gene associated meta_data_with_AFandOR
+
+# Output: 
+# 1) muts with opposite effects on stability
+# 2) large combined df including NAs for AF, OR,etc
+# 		Dim: same no. of rows as gene associated meta_data_with_AFandOR
+# 3) small combined df including NAs for AF, OR, etc.
+# 		Dim: same as mcsm data
+# 4) large combined df excluding NAs 
+# 		Dim: dim(#1) - no. of NAs(AF|OR) + 1
+# 5) small combined df excluding NAs
+# 		Dim: dim(#2) - no. of unique NAs - 1
 # This script is sourced from other .R scripts for plotting
 #########################################################
 getwd()
@@ -10,7 +23,6 @@ getwd()
 ##########################################################
 # 				Installing and loading required packages 			 
 ##########################################################
-
 source('Header_TT.R')
 #require(data.table)
 #require(arsenal)
@@ -21,19 +33,23 @@ source('Header_TT.R')
 # Read file: normalised file
 # output of step 4 mcsm_pipeline
 #################################
-
 #%% variable assignment: input and output paths & filenames
 drug = 'pyrazinamide'
 gene = 'pncA'
 gene_match = paste0(gene,'_p.')
 cat(gene_match)
 
+#===========
+# data dir
+#===========
+datadir = paste0('~/git/Data')
+
 #===========
 # input
 #===========
 # infile1: mCSM data
 #indir = '~/git/Data/pyrazinamide/input/processed/'
-indir = paste0('~/git/Data', '/', drug, '/', 'output') # revised {TODO: change in mcsm pipeline}
+indir = paste0(datadir, '/', drug, '/', 'output') # revised {TODO: change in mcsm pipeline}
 in_filename = 'mcsm_complex1_normalised.csv'
 infile = paste0(indir, '/', in_filename)
 cat(paste0('Reading infile1: mCSM output file', ' ', infile) )
@@ -105,8 +121,9 @@ changes = mcsm_data[which(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome),]
 dl_i = which(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome)
 ld_i = which(mcsm_data$Lig_outcome != mcsm_data$DUET_outcome)
 
+cat('Identifying muts with opposite stability effects')
 if(nrow(changes) == (table(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome)[[2]]) & identical(dl_i,ld_i)) {
-  cat('PASS: muts with opposite effects on stability and affinity identified correctly'
+  cat('PASS: muts with opposite effects on stability and affinity correctly identified'
         , '\nNo. of such muts: ', nrow(changes))
 }else {
   cat('FAIL: unsuccessful in extracting muts with changed stability effects')
@@ -134,6 +151,7 @@ mcsm_data = mcsm_data[order(mcsm_data$Mutationinformation),]
 head(mcsm_data$Mutationinformation)
 
 orig_col = ncol(mcsm_data)
+
 # get freq count of positions and add to the df
 setDT(mcsm_data)[, occurrence := .N, by = .(Position)] 
 
@@ -158,6 +176,20 @@ cat('Read mcsm_data file:'
     , '\nNo.of rows: ', nrow(meta_with_afor)
     , '\nNo. of cols:', ncol(meta_with_afor))
 
+# counting NAs in AF, OR cols
+if (identical(sum(is.na(meta_with_afor$OR))
+              , sum(is.na(meta_with_afor$pvalue))
+              , sum(is.na(meta_with_afor$AF)))){
+  cat('PASS: NA count match for OR, pvalue and AF\n')
+  na_count = sum(is.na(meta_with_afor$AF))
+  cat('No. of NAs: ', sum(is.na(meta_with_afor$OR)))
+} else{
+  cat('FAIL: NA count mismatch'
+      , '\nNA in OR: ', sum(is.na(meta_with_afor$OR))
+      , '\nNA in pvalue: ', sum(is.na(meta_with_afor$pvalue))
+      , '\nNA in AF:', sum(is.na(meta_with_afor$AF)))
+}
+
 # clear variables
 rm(in_filename_comb, infile_comb)
 
@@ -172,15 +204,15 @@ head(meta_with_afor$Mutationinformation)
 # 3: merging two dfs: with NA
 ###########################
 # link col name  = 'Mutationinforamtion'
+head(mcsm_data$Mutationinformation)
+head(meta_with_afor$Mutationinformation)
+
 cat('Merging dfs with NAs: big df (1-many relationship b/w id & mut)'
     ,'\nlinking col: Mutationinforamtion'
     ,'\nfilename: merged_df2')
 
-head(mcsm_data$Mutationinformation)
-head(meta_with_afor$Mutationinformation)
-
 #########
-# merge 3a: meta data with mcsm
+# merge 3a (merged_df2): meta data with mcsm
 #########
 merged_df2 = merge(x = meta_with_afor
                   ,y = mcsm_data
@@ -192,6 +224,8 @@ cat('Dim of merged_df2: '
     , '\nNo. of cols: ', ncol(merged_df2))
 head(merged_df2$Position)
 
+# sanity check
+cat('Checking nrows in merged_df2')
 if(nrow(meta_with_afor) == nrow(merged_df2)){
   cat('nrow(merged_df2) = nrow (gene associated metadata)'
       ,'\nExpected no. of rows: ',nrow(meta_with_afor) 
@@ -229,9 +263,9 @@ table(merged_df2$Position%in%merged_df2v2$Position)
 rm(merged_df2v2)
 
 #########
-# merge 3b:remove duplicate mutation information
+# merge 3b (merged_df3):remove duplicate mutation information
 #########
-cat('Merging dfs with NAs: small df (removing duplicate muts)'
+cat('Merging dfs without NAs: small df (removing muts with no AF|OR associated)'
     ,'\nCannot trust lineage info from this'
     ,'\nlinking col: Mutationinforamtion'
     ,'\nfilename: merged_df3')
@@ -244,8 +278,8 @@ cat('Merging dfs with NAs: small df (removing duplicate muts)'
 merged_df3 = merged_df2[!duplicated(merged_df2$Mutationinformation),] 
 head(merged_df3$Position); tail(merged_df3$Position) # should be sorted
 
-# sanity checks
+# sanity check
-# nrows of merged_df3 should be the same as the nrows of mcsm_data
+cat('Checking nrows in merged_df3')
 if(nrow(mcsm_data) == nrow(merged_df3)){
   cat('PASS: No. of rows match with mcsm_data'
       ,'\nExpected no. of rows: ', nrow(mcsm_data)
@@ -256,41 +290,51 @@ if(nrow(mcsm_data) == nrow(merged_df3)){
       , '\nNo. of rows merged_df3: ', nrow(merged_df3))
 }
 
-#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+# counting NAs in AF, OR cols in merged_df3
-# uncomment as necessary
+# this is becuase mcsm has no AF, OR cols,
-# only need to run this if merged_df2v2 i.e non structural pos included
+# so you cannot count NAs
-#mcsm = mcsm_data$Mutationinformation
+if (identical(sum(is.na(merged_df3$OR))
-#my_merged = merged_df3$Mutationinformation
+              , sum(is.na(merged_df3$pvalue))
-
+              , sum(is.na(merged_df3$AF)))){
-# find the index where it differs
+  cat('PASS: NA count match for OR, pvalue and AF\n')
-#diff_n = which(!my_merged%in%mcsm)
+  na_count_df3 = sum(is.na(merged_df3$AF))
-
+  cat('No. of NAs: ', sum(is.na(merged_df3$OR)))
-#check if it is indeed pos 186
+} else{
-#merged_df3[diff_n,]
+  cat('FAIL: NA count mismatch'
-
+      , '\nNA in OR: ', sum(is.na(merged_df3$OR))
-# remove this entry
+      , '\nNA in pvalue: ', sum(is.na(merged_df3$pvalue))
-#merged_df3 = merged_df3[-diff_n,]]
+      , '\nNA in AF:', sum(is.na(merged_df3$AF)))
-#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+}
 
 ###########################
 # 4: merging two dfs: without NA
 ###########################
+#########
+# merge 4a (merged_df2_comp): same as merge 1 but excluding NA
+#########
 cat('Merging dfs without any NAs: big df (1-many relationship b/w id & mut)'
     ,'\nlinking col: Mutationinforamtion'
     ,'\nfilename: merged_df2_comp')
 
-#########
-# merge 4a: same as merge 1 but excluding NA
-#########
 merged_df2_comp = merged_df2[!is.na(merged_df2$AF),] 
 #merged_df2_comp = merged_df2[!duplicated(merged_df2$Mutationinformation),] 
 
-cat('Dim of merged_df2_comp: '
+# sanity check
+cat('Checking nrows in merged_df2_comp')
+if(nrow(merged_df2_comp) == (nrow(merged_df2) - na_count + 1)){
+  cat('PASS: No. of rows match'
+      ,'\nDim of merged_df2_comp: '
+      ,'\nExpected no. of rows: ', nrow(merged_df2) - na_count + 1
       , '\nNo. of rows: ', nrow(merged_df2_comp)
       , '\nNo. of cols: ', ncol(merged_df2_comp))
+}else{
+  cat('FAIL: No. of rows mismatch'
+      ,'\nExpected no. of rows: ', nrow(merged_df2) - na_count + 1
+      ,'\nGot no. of rows: ', nrow(merged_df2_comp))
+}
 
 #########
-# merge 4b: remove duplicate mutation information
+# merge 4b (merged_df3_comp): remove duplicate mutation information
 #########
 merged_df3_comp = merged_df2_comp[!duplicated(merged_df2_comp$Mutationinformation),] 
 
@@ -305,38 +349,65 @@ all.equal(foo, merged_df3)
 
 summary(comparedf(foo, merged_df3))
 
+# sanity check
+cat('Checking nrows in merged_df3_comp')
+if(nrow(merged_df3_comp) == nrow(merged_df3)){
+  cat('NO NAs detected in merged_df3 in AF|OR cols'
+      ,'\nNo. of rows are identical: ', nrow(merged_df3))
+} else{
+  if(nrow(merged_df3_comp) == nrow(merged_df3) - na_count_df3) {
+  cat('PASS: NAs detected in merged_df3 in AF|OR cols'
+      , '\nNo. of NAs: ', na_count_df3
+      , '\nExpected no. of rows in merged_df3_comp: ', nrow(merged_df3) - na_count_df3
+      , '\nGot no. of rows: ', nrow(merged_df3_comp))
+  }
+}
+
 #=============== end of combining df
 #*********************
-# write_output files
+# writing 1 file in the style of a loop: merged_df3
-# output dir
+# print(output dir)
-#outdir = '~/git/Data/pyrazinamide/output/'
+#i = 'merged_df3'
-#uncomment as necessary
+#out_filename = paste0(i, '.csv')
-#FIXME
+#outfile = paste0(outdir, '/', out_filename)
-#out_filenames = c('merged_df2'
-#             , 'merged_df3'
-#             , 'meregd_df2_comp'
-#             , 'merged_df3_comp'
-#)
 
-#cat('Writing output files: '
+#cat('Writing output file: '
-#    , '\nPath:', outdir)
+#    ,'\nFilename: ', out_filename
+#    ,'\nPath: ', outdir)
 
-#for (i in out_filenames){
+#template: write.csv(merged_df3, 'merged_df3.csv')
-#  print(i)
+#write.csv(get(i), outfile, row.names = FALSE)
-#  print(get(i))
+#cat('Finished writing: ', outfile
-#  outvar = get(i)
+#    , '\nNo. of rows: ', nrow(get(i))
-#  print(outvar)
+#    , '\nNo. of cols: ', ncol(get(i)))
-#  outfile = paste0(outdir, '/', outvar, '.csv')
-#  cat('Writing output file:'
-#      ,'\nFilename: ', outfile
-#      ,'\n')
-#  write.csv(outvar, outfile)
-#  cat('Finished writing file:'
-#      ,'\nNo. of rows:', nrow(outvar)
-#      , '\nNo. of cols:', ncol(outvar))
-#}
 
-#sapply(out_filenames, function(x) write.csv(x, 'x.csv'))
+#%% write_output files;  all 4 files: 
+outvars = c('merged_df2'
+             , 'merged_df3'
+             , 'merged_df2_comp'
+             , 'merged_df3_comp')
+
+cat('Writing output files: '
+    , '\nPath:', outdir)
+
+for (i in outvars){
+#  cat(i, '\n')
+  out_filename = paste0(i, '.csv')
+#  cat(out_filename, '\n')
+#  cat('getting value of variable: ', get(i))
+  outfile = paste0(outdir, '/', out_filename)
+#  cat('Full output path: ', outfile, '\n')
+  cat('Writing output file:'
+      ,'\nFilename: ', out_filename,'\n')
+  write.csv(get(i), outfile, row.names = FALSE)
+  cat('Finished writing: ', outfile
+      , '\nNo. of rows: ', nrow(get(i))
+      , '\nNo. of cols: ', ncol(get(i)), '\n')
+}
+
+# alternate way to replace with implicit loop 
+# FIXME
+#sapply(outvars, function(x, y) write.csv(get(outvars), paste0(outdir, '/', outvars, '.csv')))
 #*************************
 # clear variables
 rm(mcsm_data, meta_with_afor, foo, drug, gene, gene_match, indir, merged_muts_u, meta_muts_u, na_count, orig_col, outdir)

mcsm_analysis/pyrazinamide/scripts/combining_two_df_lig.R

@@ -1,8 +1,8 @@
 #########################################################
 # TASK: To combine mcsm and meta data with af and or
 # by filtering for distance to ligand (<10Ang).
-# This script doesn't output anything, but can do if needed.
+# This script doesn't output anything.
-# This script is sourced from other .R scripts for plotting
+# This script is sourced from other .R scripts for plotting ligand plots
 #########################################################
 getwd()
 setwd('~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/')

meta_data_analysis/data_extraction.py

@@ -9,9 +9,9 @@ Created on Tue Aug  6 12:56:03 2019
 # FIXME: include error checking to enure you only
 # concentrate on positions that have structural info?
 
+# FIXME: import dirs.py to get the basic dir paths available
+
 #%% load libraries
-###################
-# load libraries
 import os, sys
 import pandas as pd
 #import numpy as np
@@ -52,19 +52,19 @@ from reference_dict import my_aa_dict #CHECK DIR STRUC THERE!
 #========================================================
 
 #%% variable assignment: input and output paths & filenames
-
 drug = 'pyrazinamide'
 gene = 'pncA'
 gene_match = gene + '_p.'
 
-#=======
+#==========
 # input dir
-#=======
+#==========
 #indir = 'git/Data/pyrazinamide/input/original'
 indir = homedir + '/' + 'git/Data'
-#=========
+
+#===========
 # output dir
-#=========
+#===========
 # several output files
 # output filenames in respective sections at the time of outputting files
 #outdir = 'git/Data/pyrazinamide/output'

meta_data_analysis/mut_electrostatic_changes.py

@@ -1,13 +1,12 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-"""
+'''
 Created on Tue Aug  6 12:56:03 2019
 
 @author: tanu
-"""
+'''
 
-# FIXME: include error checking to enure you only
+# FIXME: import dirs.py to get the basic dir paths available
-# concentrate on positions that have structural info
 
 #%% load libraries
 ###################
@@ -16,126 +15,111 @@ import os, sys
 import pandas as pd
 #import numpy as np
 
-#from pandas.api.types import is_string_dtype
-#from pandas.api.types import is_numeric_dtype
-
 #====================================================
 # TASK: calculate how many mutations result in 
 # electrostatic changes wrt wt
 # Input: mcsm and AF_OR file
-# output: mut_elec_changes_results.txt 
+# Output: mut_elec_changes_results.txt 
 #========================================================
-#%%
+#%% specify homedir as python doesn't recognise tilde
-####################
+homedir = os.path.expanduser('~') 
+
 # my working dir
 os.getcwd()
-homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
 os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
 os.getcwd()
-#%%
-from reference_dict import my_aa_dict #CHECK DIR STRUC THERE!
-#%%
-############# specify variables for input and output paths and filenames
-drug = "pyrazinamide"
-gene = "pnca"
 
-datadir = homedir + "/git/Data"
+#========================================================
-basedir =  datadir + "/" + drug + "/input"
+#%% variable assignment: input and output paths & filenames
+drug = 'pyrazinamide'
+gene = 'pncA'
+gene_match = gene + '_p.'
 
-# input
+#==========
-inpath = "/processed"
+# data dir
+#==========
+#indir = 'git/Data/pyrazinamide/input/original'
+datadir = homedir + '/' + 'git/Data'
 
-# uncomment as necessary
+#==========
-in_filename  = "/meta_data_with_AFandOR.csv"
+# input dir
-#in_filename  = "/mcsm_complex1_normalised.csv" # probably simpler
+#==========
+indir = datadir + '/' + drug + '/' + 'input'
 
-infile = basedir + inpath + in_filename
+#============
-#print(infile)
+# output dir
+#============
+# several output files
+outdir = datadir + '/' + drug + '/' + 'output'
 
-# output file 
+# specify output file 
-outpath = "/output"
+out_filename = 'mut_elec_changes.txt'
-outdir =   datadir + "/" + drug + outpath
+outfile =  outdir + '/' + out_filename
-out_filename = "/mut_elec_changes_results.txt"
+print('Output path: ', outdir)
-outfile =  outdir + out_filename
 
-#print(outdir)
+#%% end of variable assignment for input and output files
+#=============================================================
+#%% Read input files
+#in_filename  = gene.lower() + '_meta_data_with_AFandOR.csv'
+in_filename  = 'merged_df3.csv'
+infile = outdir + '/' + in_filename
+print('Reading input file (merged file):', infile)
 
-if not os.path.exists(datadir):
+comb_df = pd.read_csv(infile, sep = ',')  
-    print('Error!', datadir, 'does not exist. Please ensure it exists. Dir struc specified in README.md')
-    os.makedirs(datadir)
-    exit()
 
-if not os.path.exists(outdir):
+print('Input filename: ', in_filename,
-    print('Error!', outdir, 'does not exist.Please ensure it exists. Dir struc specified in README.md')
+      '\nPath :', outdir,
-    exit()
+      '\nNo. of rows: ', len(comb_df),
-    
+      '\nNo. of cols: ', infile)
-else:
-    print('Dir exists: Carrying on')
-
-################## end of variable assignment for input and output files
-#%%
-#==============================================================================
-############
-# STEP 1: Read file
-############
-meta_pnca = pd.read_csv(infile, sep = ',')  
 
 # column names
-list(meta_pnca.columns)
+list(comb_df.columns)
-
-#========
-# Step 2: iterate through the dict, create a lookup dict that i.e
-# lookup_dict =  {three_letter_code: aa_prop_polarity} 
-# Do this for both wild_type and mutant as above.
-#=========
-# initialise a sub dict that is lookup dict for three letter code to aa prop
-lookup_dict = dict()
-
-for k, v in my_aa_dict.items():
-    lookup_dict[k] = v['aa_calcprop']
-    #print(lookup_dict)
-    wt = meta_pnca['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
-    meta_pnca['wt_calcprop'] = wt.map(lookup_dict)   
-    mut = meta_pnca['mutation'].str.extract(r'\d+(\w{3})$').squeeze()
-    meta_pnca['mut_calcprop'] = mut.map(lookup_dict)
-    
-# added two more cols
 
 # clear variables
-del(k, v, wt, mut, lookup_dict)
+del(in_filename, infile)
-del(in_filename, infile, inpath)
 
-#%%
+#%% subset unique mutations
-###########
+df = comb_df.drop_duplicates(['Mutationinformation'], keep = 'first')
-# Step 3: subset unique mutations
-###########
-meta_pnca_muts = meta_pnca.drop_duplicates(['Mutationinformation'], keep = 'first')
-non_struc = meta_pnca_muts[meta_pnca_muts.position == 186]
 
-# remove pos non_struc 186 : (in case you used file with AF and OR)
-df = meta_pnca_muts[meta_pnca_muts.position != 186]
 total_muts = df.Mutationinformation.nunique()
 #df.Mutationinformation.count()
+print('Total mutations associated with structure: ', total_muts)
 
-###########
+#%% combine aa_calcprop cols so that you can count the changes as value_counts
-# Step 4: combine cols
+# check if all muts have been categorised
-###########
+print('Checking if all muts have been categorised: ')
+if df['wt_calcprop'].isna().sum() == 0 & df['mut_calcprop'].isna().sum():
+    print('PASS: No. NA detected i.e all muts have aa prop associated')
+else:
+    print('FAIL: NAs detected i.e some muts remain unclassified')
 
-df['aa_calcprop_combined'] = df['wt_calcprop']+ '->' + df['mut_calcprop']
+df['wt_calcprop'].head()
-df['aa_calcprop_combined']
+df['mut_calcprop'].head()
+
+print('Combining wt_calcprop and mut_calcprop...')
+#df['aa_calcprop_combined'] = df['wt_calcprop']+ '->' + df['mut_calcprop']
+df['aa_calcprop_combined'] = df.wt_calcprop.str.cat(df.mut_calcprop, sep = '->')
+df['aa_calcprop_combined'].head()
+
+mut_categ = df["aa_calcprop_combined"].unique()
+print('Total no. of aa_calc properties: ', len(mut_categ))
+print('Categories are: ', mut_categ)
+
+# counting no. of muts in each mut categ
 
 # way1: count values within each combinaton
 df.groupby('aa_calcprop_combined').size()
 #df.groupby('aa_calcprop_combined').count()
 
 # way2: count values within each combinaton
-#df['aa_calcprop_combined'].value_counts()
+df['aa_calcprop_combined'].value_counts()
 
 # comment: the two ways should be identical
-# groupby result order is similar to pivot table order
+# groupby result order is similar to pivot table order,
+# I prefer the value_counts look
 
-#assign to variable: count values within each combinaton
+# assign to variable: count values within each combinaton
-all_prop = df.groupby('aa_calcprop_combined').size()
+all_prop = df['aa_calcprop_combined'].value_counts()
 
 # convert to a df from Series
 ap_df = pd.DataFrame({'aa_calcprop': all_prop.index, 'mut_count': all_prop.values})
@@ -144,11 +128,18 @@ ap_df = pd.DataFrame({'aa_calcprop': all_prop.index, 'mut_count': all_prop.value
 all_prop_change = ap_df[ap_df['aa_calcprop'].isin(['neg->neg','non-polar->non-polar','polar->polar', 'pos->pos']) == False]
                       
 elec_count = all_prop_change.mut_count.sum()
+print('Total no.of muts with elec changes: ', elec_count)
 
 # calculate percentage of electrostatic changes
 elec_changes = (elec_count/total_muts) * 100
            
-print("Total number of electrostatic changes resulting from Mutation is (%):", elec_changes)
+print('Total number of electrostatic changes resulting from Mutation is (%):', elec_changes)
+ 
+# check no change muts
+no_change_muts = ap_df[ap_df['aa_calcprop'].isin(['neg->neg','non-polar->non-polar','polar->polar', 'pos->pos']) == True]
+  
+no_change_muts.mut_count.sum()
+ 
                      
 ###########
 # Step 5: output from console
@@ -156,7 +147,28 @@ print("Total number of electrostatic changes resulting from Mutation is (%):", e
 #sys.stdout = open(file, 'w')
 sys.stdout = open(outfile, 'w')
 
-print(df.groupby('aa_calcprop_combined').size()  )
+#print(no_change_muts, '\n', 
-print("=======================================================================================")                
+#      all_prop_change)
-print("Total number of electrostatic changes resulting from Mutation is (%):", elec_changes)
+      
-print("=======================================================================================")    
+print('======================\n'
+      ,'Unchanged muts'
+      ,'\n=====================\n'
+      , no_change_muts
+      ,'\n=============================\n'      
+      , 'Muts with changed prop:'
+      , '\n============================\n'
+      , all_prop_change)
+
+#print('======================================================================')                
+#print('Total number of electrostatic changes resulting from Mutation is (%):', elec_changes)
+#print('Total no. of muts: ', total_muts)
+#print('Total no. of changed muts: ', all_prop_change.mut_count.sum())
+#print('Total no. of unchanged muts: ', no_change_muts.mut_count.sum() )
+#print('=======================================================================')    
+
+print('========================================================================'                
+, '\nTotal number of electrostatic changes resulting from Mtation is (%):', elec_changes
+, '\nTotal no. of muts: ', total_muts
+, '\nTotal no. of changed muts: ', all_prop_change.mut_count.sum()
+, '\nTotal no. of unchanged muts: ', no_change_muts.mut_count.sum() 
+, '\n=========================================================================')