diff --git a/scripts/combine_afs_ors.py b/scripts/combine_afs_ors.py
new file mode 100755
index 0000000..253260a
--- /dev/null
+++ b/scripts/combine_afs_ors.py
@@ -0,0 +1,341 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+'''
+Created on Tue Aug  6 12:56:03 2019
+
+@author: tanu
+'''
+# FIXME: change filename 4 (mcsm normalised data)
+# to be consistent like (pnca_complex_mcsm_norm.csv) : changed manually, but ensure this is done in the mcsm pipeline
+#=======================================================================
+# Task: combine 2 dfs with aa position as linking column
+# This is done in 2 steps:
+# merge 1: 
+
+# useful link
+# https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
+#=======================================================================
+#%% load packages
+import sys, os
+import pandas as pd
+import numpy as np
+import argparse
+#=======================================================================
+#%% specify input and curr dir
+homedir = os.path.expanduser('~')
+
+# set working dir
+os.getcwd()
+os.chdir(homedir + '/git/LSHTM_analysis/scripts')
+os.getcwd()
+
+# local import
+from reference_dict import my_aa_dict # CHECK DIR STRUC THERE!
+#=======================================================================
+#%% command line args
+arg_parser = argparse.ArgumentParser()
+arg_parser.add_argument('-d', '--drug', help = 'drug name', default = None)
+arg_parser.add_argument('-g', '--gene', help = 'gene name', default = None) # case sensitive
+args = arg_parser.parse_args()
+#=======================================================================
+#%% variable assignment: input and output 
+drug = 'pyrazinamide'
+gene = 'pncA'
+gene_match = gene + '_p.'
+
+# cmd variables
+#drug = args.drug
+#gene = args.gene
+
+#==========
+# dir
+#==========
+datadir = homedir + '/' + 'git/Data'
+outdir = datadir + '/' + drug + '/' + 'output'
+
+#=======
+# input
+#=======
+in_filename_afor = gene.lower() + '_af_or.csv'
+# FIXME
+in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
+# needs to contain OR. it only has beta!
+
+infile1 = outdir + '/' + in_filename_afor
+infile2 = outdir + '/' + in_filename_afor_kin
+
+
+print('Input file1:', infile1
+      , '\nInput file2:', infile2
+      , '\n===================================================================')
+
+#=======
+# output 
+#=======
+out_filename = gene.lower() + '_metadata_afs_ors.csv'
+outfile =  outdir + '/' + out_filename
+print('Output file:', outfile
+      , '\n===================================================================')
+
+
+del(in_filename_afor, in_filename_afor_kin, outfile, datadir, outdir)
+#%% end of variable assignment for input and output files
+#=======================================================================
+#%% format mutations
+# mut_format: gene.abc1cde | 1A>1B
+
+#========================
+# read input csv files to combine
+#========================
+afor_df = pd.read_csv(infile1, sep = ',')
+afor_df_ncols = len(afor_df.columns) 
+afor_df_nrows = len(afor_df) 
+print('No. of rows in', infile1, ':', afor_df_nrows
+      , '\nNo. of cols in', infile1, ':', afor_df_ncols)
+
+afor_kin_df = pd.read_csv(infile2, sep = ',')
+afor_kin_df_nrows = len(afor_kin_df)
+afor_kin_df_ncols = len(afor_kin_df.columns)
+print('No. of rows in', infile2, ':', afor_kin_df_nrows
+      , '\nNo. of cols in', infile2, ':', afor_kin_df_ncols)
+
+#=======
+# Iterate through the dict, create a lookup dict i.e
+# lookup_dict = {three_letter_code: one_letter_code}.
+# lookup dict should be the key and the value (you want to create a column for)
+# Then use this to perform the mapping separetly for wild type and mutant cols.
+# The three letter code is extracted using a string match match from the dataframe and then converted
+# to 'pandas series'since map only works in pandas series
+#=======
+gene_regex = gene_match.lower()+'(\w{3})'
+print('gene regex being used:', gene_regex)
+
+# initialise a sub dict that is lookup dict for three letter code to 1-letter code
+# adding three more cols
+lookup_dict = dict()
+for k, v in my_aa_dict.items():
+    lookup_dict[k] = v['one_letter_code']
+#    wt = gene_LF1['mutation'].str.extract('gene_p.(\w{3})').squeeze() # converts to a series that map works on
+    wt = afor_df['mutation'].str.extract(gene_regex).squeeze()
+    afor_df['wild_type'] = wt.map(lookup_dict)   
+    mut = afor_df['mutation'].str.extract('\d+(\w{3})$').squeeze()
+    afor_df['mutant_type'] = mut.map(lookup_dict)
+
+# extract position info from mutation column separetly using string match
+afor_df['position'] = afor_df['mutation'].str.extract(r'(\d+)') 
+
+# combine the wild_type+poistion+mutant_type columns to generate 
+# mutationinformation (matches mCSM output field)
+# Remember to use .map(str) for int col types to allow string concatenation
+
+afor_df['mutationinformation'] = afor_df['wild_type'] + afor_df['position'].map(str) + afor_df['mutant_type']
+print('Created column: mutationinformation'
+	, '\n====================================================================='
+    ,  afor_df['mutationinformation'].head(10))
+
+# sanity check
+ncols_add = 4 # beware of hardcoding (3 cols for mcsm style mut + 1 for concatenating them all)
+if len(afor_df.columns) == afor_df_ncols + ncols_add:
+    afor_df_ncols = len(afor_df.columns) # update afor_df_ncols after adding cols
+    print('PASS: successfully added', ncols_add, 'cols'
+          , '\nold length:', afor_df_ncols
+          , '\nnew length:', len(afor_df.columns))
+else:
+    print('FAIL: failed to add cols:'
+          , '\nExpected cols:', afor_df_ncols + ncols_add
+          , '\nGot:', len(afor_df.columns))
+    sys.exit()
+#%% Detect mutation format to see if you apply this func
+# FIXME
+#afor_df.iloc[[0]].str.match('pnca_')
+#afor_df.dtypes
+
+#foo = afor_df.loc[:, afor_df.dtypes == object]
+
+genomic_mut_regex = gene_match.lower()+'\w{3}\d+\w{3}'
+print('gene regex being used:', genomic_mut_regex)
+afor_df[(afor_df == genomic_mut_regex).any(axis = 1)]
+
+#%% Finding common col to merge on
+# Define merging column: multiple cols have been used for merge else the common cols 
+# get suffixes '_x'  and '_y' attached
+# also, couldn't include 'position' in merging_cols since data types don't match
+merging_cols = ['wild_type', 'mutant_type', 'mutationinformation']
+ncommon_cols= len(merging_cols)
+
+# checking cross-over of mutations in the two dfs to merge
+ndiff1 = afor_kin_df_nrows - afor_df['mutationinformation'].isin(afor_kin_df['mutationinformation']).sum()
+print(ndiff1)
+ndiff2 = afor_kin_df_nrows - afor_kin_df['mutationinformation'].isin(afor_df['mutationinformation']).sum()
+print(ndiff2)
+
+# Define join type
+#my_join = 'inner'
+#my_join = 'right'
+##my_join = 'left'
+my_join = 'outer'
+
+# sanity check: how many muts from afor_kin_df are in afor_df. should be a complete subset
+if ndiff2 == 0:
+    print('PASS: all muts in afor_kin_df are present in afor_df'
+          , '\nProceeding with combining the dfs...')
+    
+    combined_df = pd.merge(afor_df, afor_kin_df, on = merging_cols, how = my_join)
+    
+    if my_join == 'outer':
+        expected_rows = afor_df_nrows + ndiff1
+        expected_cols = (afor_df_ncols + afor_kin_df_ncols) - ncommon_cols
+        if len(combined_df) == expected_rows and len(combined_df.columns) == expected_cols:
+            print('PASS: successfully combined dfs with:', my_join, 'join')
+        else:
+            print('FAIL: ', my_join, 'join')
+        
+        print('\nExpected no. of rows:', expected_rows
+              , '\nGot:', len(combined_df)
+              , '\nExpected no. of cols:', expected_cols
+              , '\nGot:', len(combined_df.columns))
+    
+    elif my_join == 'inner':
+        expected_rows = afor_kin_df_nrows
+        expected_cols = (afor_df_ncols + afor_kin_df_ncols) - ncommon_cols
+        if len(combined_df) == expected_rows and len(combined_df.columns) == expected_cols:
+            print('PASS: successfully combined dfs with:', my_join, 'join')
+        else:
+            print('FAIL: ', my_join, 'join')
+        
+        print('\nExpected no. of rows:', expected_rows
+              , '\nGot:', len(combined_df)
+              , '\nExpected no. of cols:', expected_cols
+              , '\nGot:', len(combined_df.columns))
+    
+    elif my_join == 'left':
+        expected_rows = afor_df_nrows + ndiff1
+        expected_cols = (afor_df_ncols + afor_kin_df_ncols) - ncommon_cols
+        if len(combined_df) == expected_rows and len(combined_df.columns) == expected_cols:
+            print('PASS: successfully combined dfs with:', my_join, 'join')
+        else:
+            print('FAIL: ', my_join, 'join')
+        
+        print('\nExpected no. of rows:', expected_rows
+              , '\nGot:', len(combined_df)
+              , '\nExpected no. of cols:', expected_cols
+              , '\nGot:', len(combined_df.columns))
+    
+    
+    elif my_join == 'right':
+        expected_rows = afor_kin_df_nrows
+        expected_cols = (afor_df_ncols + afor_kin_df_ncols) - ncommon_cols
+        if len(combined_df) == expected_rows and len(combined_df.columns) == expected_cols:
+            print('PASS: successfully combined dfs with:', my_join, 'join')
+        else:
+            print('FAIL: ', my_join, 'join')
+        
+        print('\nExpected no. of rows:', expected_rows
+              , '\nGot:', len(combined_df)
+              , '\nExpected no. of cols:', expected_cols
+              , '\nGot:', len(combined_df.columns))
+    
+    else:
+        print('FAIL: failed to combine dfs, expected rows and cols not matched')
+else:
+    print('FAIL: numbers mismatch, mutations present in afor_kin_df but not in afor_df')
+
+    
+#%% check duplicate cols: ones containing suffix '_x' or '_y'
+# should only be position
+foo = combined_df.filter(regex = r'.*_x|_y', axis = 1)
+print(foo.columns) # should only be position
+
+# drop position col containing suffix '_y' and then rename col without suffix
+combined_or_df = combined_df.drop(combined_df.filter(regex = r'.*_y').columns, axis = 1)
+combined_or_df['position_x'].head()
+
+# renaming columns
+combined_or_df.rename(columns = {'position_x': 'position'}, inplace = True)
+combined_or_df['position'].head()
+
+# recheck
+foo = combined_or_df.filter(regex = r'.*_x|_y', axis = 1)
+print(foo.columns) # should only be position
+
+combined_or_df['af'].head()
+combined_or_df.rename(columns = {'af': 'af_kin'}, inplace = True)
+combined_or_df['af_kin']
+#%% calculate OR for kinship
+combined_or_df['or_kin'] = np.exp(combined_or_df['beta'])
+
+# drop duplicate columns
+#if combined_or_df['alternate_allele'].equals(combined_or_df['alt_allele0']):
+#    combined_or_df.drop('alternate_allele', axis = 1, inplace = True)
+   
+combined_or_df2 = combined_or_df.T.drop_duplicates().T# changes dtypes in cols
+dup_cols = set(combined_or_df.columns).difference(combined_or_df2.columns)
+#tot_diff is equal to n_diff 
+
+# drop some not required cols
+combined_or_df.drop(list(dup_cols), axis = 1, inplace = True)
+print(combined_or_df.columns)
+combined_or_df.drop(['chromosome_text', 'chr', 'symbol', '_merge', ], axis = 1, inplace = True)
+ 
+combined_or_df.rename(columns = {'ref_allele1': 'reference_allele'}, inplace = True)
+   
+print(combined_or_df.columns)
+#%% reorder columns
+#https://stackoverflow.com/questions/13148429/how-to-change-the-order-of-dataframe-columns
+
+# check af: curiosity
+
+# setting column's order
+output_df = combined_or_df[['mutation', 'wild_type', 'position', 'mutant_type', 'mutationinformation'
+                            , 'chr_num_allele', 'ref_allele', 'alt_allele'
+                            , 'mut_info', 'mut_type', 'gene_id', 'gene_number', 'mut_region'
+                            , 'reference_allele', 'alternate_allele', 'chromosome_number'
+                            , 'afs', 'af_kin', 'ors_logistic', 'ors_chi_cus', 'or_kin', 'ors_fisher'
+                            , 'pvals_logistic', 'pvals_fisher', 'p_wald', 'ci_lb_fisher', 'ci_ub_fisher' 
+                            , 'beta', 'se', 'logl_H1', 'l_remle','stat_chi', 'pvals_chi', 'n_diff' , 'n_miss']]
+
+#%% output combined or df
+#===============
+# writing file
+#===============
+print('Writing file...')
+
+#combined_or_df.to_csv(outfile, header = True, index = False)
+output_df.to_csv(outfile, header = True, index = False)
+
+print('Finished writing file:', outfile
+      , '\nNo. of rows:', len(combined_or_df)
+      , '\nNo. of cols:', len(combined_or_df.columns)
+      , '\n=========================================================')
+
+
+
+
+
+
+
+#%%
+df = pd.DataFrame()
+column_names = ['x','y','z','mean']
+for col in column_names: 
+    df[col] = np.random.randint(0,100, size=10000)
+df.head()
+
+
+# drop duplicate col with dup values not necessarily colnames
+df['xdup'] = df['x']
+df
+
+df.T.drop_duplicates().T
+
+import math
+math.exp(0)
+
+df['expX'] = np.exp(df['x']) # math doesn't understand series dtype
+df
+
+
+
+
+
+
diff --git a/scripts/find_missense.py b/scripts/find_missense.py
new file mode 100755
index 0000000..51339cb
--- /dev/null
+++ b/scripts/find_missense.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+
+import pandas as pd
+
+DEBUG = False
+#%%
+#def find_missense(test_df, ref_allele1, alt_allele0):
+def find_missense(test_df, ref_allele_column, alt_allele_column, n_diff_colname = 'n_diff', tot_diff_colname = 'tot_diff', ref_a_colname = 'ref_allele', alt_a_colname = 'alt_allele'):
+
+
+    """Find mismatches in pairwise comparison of strings b/w col_a and col_b
+    
+    Case insensitive, converts strings to uppercase before comparison
+    
+    @test_df: df containing columns to compare
+    @type: pandas df
+    
+    @ref_allele_column: column containing ref allele str
+    @type: str (converts to uppercase)
+    
+    @alt_allele_column: column containing alt_allele str
+    @type: str (converts to uppercase)
+    
+    @n_diff_colname: user defined colname for no. of char diff b/w ref_allele_str and alt_allele_str
+    @type: str
+
+    @tot_diff_colname: user defined colname abs diff to indicate if strings are of equal length
+    @type: str
+
+    @ref_a_colname: user defined colname containing extracted referece allele
+    @type: str
+
+    @alt_a_colname: user defined colname containing extracted alt allele
+    @type: str
+    
+    returns df: with 4 columns. If column names clash, the function column 
+                name will override original column
+    @rtype: pandas df
+    """
+    for ind, val in test_df.iterrows():
+        if DEBUG:
+            print('index:', ind, 'value:', val
+                  , '\n============================================================')
+        ref_a = val[ref_allele_column].upper()
+        alt_a = val[alt_allele_column].upper()
+        if DEBUG:
+            print('ref_allele_string:', ref_a, 'alt_allele_string:', alt_a)
+        difference = sum(1 for e in zip(ref_a, alt_a) if e[0] != e[1])
+        test_df.at[ind, n_diff_colname] = difference # adding column
+        tot_difference = difference + abs(len(ref_a) - len(alt_a))
+        test_df.at[ind, tot_diff_colname] = tot_difference # adding column
+        if difference != tot_difference:
+            print('WARNING: lengths of ref_allele and alt_allele differ at index:', ind
+                  , '\nNon-missense muts detected')
+                  
+        # Now finding the mismatched char
+        ref_aln = ''
+        alt_aln = ''
+        if ref_a == alt_a:
+            ##test_df.at[ind, 'ref_allele'] = 'no_change' # adding column
+            ##test_df.at[ind, 'alt_allele'] = 'no_change' # adding column
+            test_df.at[ind, ref_a_colname] = 'no_change' # adding column
+            test_df.at[ind, alt_a_colname] = 'no_change' # adding column
+        elif len(ref_a) == len(alt_a) and len(ref_a) > 0:
+            print('ref:', ref_a, 'alt:', alt_a)
+            for n in range(len(ref_a)):
+                if ref_a[n] != alt_a[n]:
+                    ref_aln += ref_a[n]
+                    alt_aln += alt_a[n]
+            ##test_df.at[ind, 'ref_allele'] = ref_aln
+            ##test_df.at[ind, 'alt_allele'] = alt_aln
+            test_df.at[ind, ref_a_colname] = ref_aln
+            test_df.at[ind, alt_a_colname] = alt_aln
+            print('ref:', ref_aln)
+            print('alt:', alt_aln)
+        else:
+            ##test_df.at[ind, 'ref_allele'] = 'ERROR_Not_nsSNP'
+            ##test_df.at[ind, 'alt_allele'] = 'ERROR_Not_nsSNP'
+            test_df.at[ind, ref_a_colname] = 'ERROR_Not_nsSNP'
+            test_df.at[ind, alt_a_colname] = 'ERROR_Not_nsSNP'
+
+    return test_df
+#========================================
+# a representative example
+eg_df = {'chromosome_number': [2288719, 2288766, 2288775, 2288779, 2288827, 1111111, 2222222],
+           'ref_allele1': ['Tc', 'AG', 'AGCACCCTG', 'CCCTGGTGGCC', 'CACA', 'AA', 'CAT'],
+           'alt_allele0': ['CC', 'CA', 'GGCACCCTGZ','TCCTGGTGGCCAAD', 'TACA', 'AA', 'TCZ']}
+
+# snippet of actual data
+#eg_df = pd.read_csv('pnca_assoc.txt', sep = '\t', nrows = 10, header = 0, index_col = False)
+eg_df = pd.DataFrame(eg_df)
+    
+def main():
+    #find_missense(eg_df, ref_allele1 = 'ref_allele', alt_allele0 = 'alt_allele')
+    find_missense(test_df = eg_df, ref_allele_column = 'ref_allele1', alt_allele_column = 'alt_allele0')
+    print(eg_df)
+
+if __name__ == '__main__':
+    main()