output from comb script & electrostatic mut changes calculated
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4 changed files with 250 additions and 167 deletions
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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'''
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Created on Tue Aug 6 12:56:03 2019
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@author: tanu
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"""
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'''
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# FIXME: include error checking to enure you only
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# concentrate on positions that have structural info
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# FIXME: import dirs.py to get the basic dir paths available
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#%% load libraries
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###################
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@ -16,147 +15,160 @@ import os, sys
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import pandas as pd
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#import numpy as np
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#from pandas.api.types import is_string_dtype
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#from pandas.api.types import is_numeric_dtype
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#====================================================
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# TASK: calculate how many mutations result in
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# electrostatic changes wrt wt
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# Input: mcsm and AF_OR file
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# output: mut_elec_changes_results.txt
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# Output: mut_elec_changes_results.txt
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#========================================================
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#%%
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####################
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#%% specify homedir as python doesn't recognise tilde
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homedir = os.path.expanduser('~')
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# my working dir
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os.getcwd()
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homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
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os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
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os.getcwd()
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#%%
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from reference_dict import my_aa_dict #CHECK DIR STRUC THERE!
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#%%
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############# specify variables for input and output paths and filenames
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drug = "pyrazinamide"
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gene = "pnca"
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datadir = homedir + "/git/Data"
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basedir = datadir + "/" + drug + "/input"
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#========================================================
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#%% variable assignment: input and output paths & filenames
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = gene + '_p.'
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# input
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inpath = "/processed"
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#==========
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# data dir
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#==========
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#indir = 'git/Data/pyrazinamide/input/original'
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datadir = homedir + '/' + 'git/Data'
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# uncomment as necessary
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in_filename = "/meta_data_with_AFandOR.csv"
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#in_filename = "/mcsm_complex1_normalised.csv" # probably simpler
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#==========
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# input dir
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#==========
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indir = datadir + '/' + drug + '/' + 'input'
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infile = basedir + inpath + in_filename
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#print(infile)
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#============
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# output dir
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#============
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# several output files
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outdir = datadir + '/' + drug + '/' + 'output'
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# output file
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outpath = "/output"
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outdir = datadir + "/" + drug + outpath
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out_filename = "/mut_elec_changes_results.txt"
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outfile = outdir + out_filename
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# specify output file
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out_filename = 'mut_elec_changes.txt'
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outfile = outdir + '/' + out_filename
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print('Output path: ', outdir)
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#print(outdir)
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#%% end of variable assignment for input and output files
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#=============================================================
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#%% Read input files
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#in_filename = gene.lower() + '_meta_data_with_AFandOR.csv'
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in_filename = 'merged_df3.csv'
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infile = outdir + '/' + in_filename
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print('Reading input file (merged file):', infile)
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if not os.path.exists(datadir):
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print('Error!', datadir, 'does not exist. Please ensure it exists. Dir struc specified in README.md')
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os.makedirs(datadir)
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exit()
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comb_df = pd.read_csv(infile, sep = ',')
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if not os.path.exists(outdir):
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print('Error!', outdir, 'does not exist.Please ensure it exists. Dir struc specified in README.md')
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exit()
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else:
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print('Dir exists: Carrying on')
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################## end of variable assignment for input and output files
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#%%
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#==============================================================================
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############
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# STEP 1: Read file
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############
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meta_pnca = pd.read_csv(infile, sep = ',')
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print('Input filename: ', in_filename,
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'\nPath :', outdir,
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'\nNo. of rows: ', len(comb_df),
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'\nNo. of cols: ', infile)
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# column names
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list(meta_pnca.columns)
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list(comb_df.columns)
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#========
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# Step 2: iterate through the dict, create a lookup dict that i.e
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# lookup_dict = {three_letter_code: aa_prop_polarity}
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# Do this for both wild_type and mutant as above.
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#=========
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# initialise a sub dict that is lookup dict for three letter code to aa prop
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lookup_dict = dict()
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for k, v in my_aa_dict.items():
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lookup_dict[k] = v['aa_calcprop']
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#print(lookup_dict)
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wt = meta_pnca['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
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meta_pnca['wt_calcprop'] = wt.map(lookup_dict)
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mut = meta_pnca['mutation'].str.extract(r'\d+(\w{3})$').squeeze()
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meta_pnca['mut_calcprop'] = mut.map(lookup_dict)
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# added two more cols
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# clear variables
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del(k, v, wt, mut, lookup_dict)
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del(in_filename, infile, inpath)
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del(in_filename, infile)
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#%%
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###########
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# Step 3: subset unique mutations
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###########
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meta_pnca_muts = meta_pnca.drop_duplicates(['Mutationinformation'], keep = 'first')
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non_struc = meta_pnca_muts[meta_pnca_muts.position == 186]
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#%% subset unique mutations
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df = comb_df.drop_duplicates(['Mutationinformation'], keep = 'first')
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# remove pos non_struc 186 : (in case you used file with AF and OR)
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df = meta_pnca_muts[meta_pnca_muts.position != 186]
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total_muts = df.Mutationinformation.nunique()
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#df.Mutationinformation.count()
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print('Total mutations associated with structure: ', total_muts)
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###########
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# Step 4: combine cols
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###########
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#%% combine aa_calcprop cols so that you can count the changes as value_counts
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# check if all muts have been categorised
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print('Checking if all muts have been categorised: ')
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if df['wt_calcprop'].isna().sum() == 0 & df['mut_calcprop'].isna().sum():
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print('PASS: No. NA detected i.e all muts have aa prop associated')
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else:
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print('FAIL: NAs detected i.e some muts remain unclassified')
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df['aa_calcprop_combined'] = df['wt_calcprop']+ '->' + df['mut_calcprop']
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df['aa_calcprop_combined']
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df['wt_calcprop'].head()
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df['mut_calcprop'].head()
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print('Combining wt_calcprop and mut_calcprop...')
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#df['aa_calcprop_combined'] = df['wt_calcprop']+ '->' + df['mut_calcprop']
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df['aa_calcprop_combined'] = df.wt_calcprop.str.cat(df.mut_calcprop, sep = '->')
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df['aa_calcprop_combined'].head()
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mut_categ = df["aa_calcprop_combined"].unique()
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print('Total no. of aa_calc properties: ', len(mut_categ))
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print('Categories are: ', mut_categ)
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# counting no. of muts in each mut categ
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# way1: count values within each combinaton
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df.groupby('aa_calcprop_combined').size()
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#df.groupby('aa_calcprop_combined').count()
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# way2: count values within each combinaton
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#df['aa_calcprop_combined'].value_counts()
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df['aa_calcprop_combined'].value_counts()
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# comment: the two ways should be identical
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# groupby result order is similar to pivot table order
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# groupby result order is similar to pivot table order,
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# I prefer the value_counts look
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#assign to variable: count values within each combinaton
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all_prop = df.groupby('aa_calcprop_combined').size()
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# assign to variable: count values within each combinaton
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all_prop = df['aa_calcprop_combined'].value_counts()
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# convert to a df from Series
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ap_df = pd.DataFrame({'aa_calcprop': all_prop.index, 'mut_count': all_prop.values})
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# subset df to contain only the changes in prop
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all_prop_change = ap_df[ap_df['aa_calcprop'].isin(['neg->neg','non-polar->non-polar','polar->polar', 'pos->pos']) == False]
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elec_count = all_prop_change.mut_count.sum()
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print('Total no.of muts with elec changes: ', elec_count)
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# calculate percentage of electrostatic changes
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elec_changes = (elec_count/total_muts) * 100
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print("Total number of electrostatic changes resulting from Mutation is (%):", elec_changes)
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print('Total number of electrostatic changes resulting from Mutation is (%):', elec_changes)
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# check no change muts
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no_change_muts = ap_df[ap_df['aa_calcprop'].isin(['neg->neg','non-polar->non-polar','polar->polar', 'pos->pos']) == True]
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no_change_muts.mut_count.sum()
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###########
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# Step 5: output from console
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###########
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#sys.stdout = open(file, 'w')
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sys.stdout = open(outfile, 'w')
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print(df.groupby('aa_calcprop_combined').size() )
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print("=======================================================================================")
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print("Total number of electrostatic changes resulting from Mutation is (%):", elec_changes)
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print("=======================================================================================")
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#print(no_change_muts, '\n',
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# all_prop_change)
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print('======================\n'
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,'Unchanged muts'
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,'\n=====================\n'
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, no_change_muts
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,'\n=============================\n'
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, 'Muts with changed prop:'
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, '\n============================\n'
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, all_prop_change)
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#print('======================================================================')
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#print('Total number of electrostatic changes resulting from Mutation is (%):', elec_changes)
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#print('Total no. of muts: ', total_muts)
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#print('Total no. of changed muts: ', all_prop_change.mut_count.sum())
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#print('Total no. of unchanged muts: ', no_change_muts.mut_count.sum() )
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#print('=======================================================================')
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print('========================================================================'
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, '\nTotal number of electrostatic changes resulting from Mtation is (%):', elec_changes
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, '\nTotal no. of muts: ', total_muts
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, '\nTotal no. of changed muts: ', all_prop_change.mut_count.sum()
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, '\nTotal no. of unchanged muts: ', no_change_muts.mut_count.sum()
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, '\n=========================================================================')
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