--- a
+++ b/modules/instantiate_nwk.py
@@ -0,0 +1,120 @@
+import pandas as pd
+import argparse
+import numpy as np
+import time
+import sys
+from multiprocessing import Pool
+from sklearn.preprocessing import MinMaxScaler
+from scipy.stats import pearsonr
+
+def isNum(x):
+    try:
+        float(x)
+        return True
+    except:
+        return False
+
+def corrCut(nwk,cutoff=None):
+    '''weight cutoff, positive sorting'''
+    nwk.dropna(subset=['weight'],inplace=True)
+    nwk.sort_values(by=['weight'],inplace=True,ascending=False)
+    if cutoff!=None: 
+        return nwk.loc[lambda x:abs(x.weight)>=cutoff]
+    else:
+        return nwk
+
+def setMinExp(nwk,exp,expCut):
+    '''remove gene whose expression is lower than expCut'''
+    filtered_gene = exp[exp.max(axis=1)>1]['Hybridization REF']
+    boolean_mask = np.logical_and(nwk['protein1'].isin(filtered_gene),nwk['protein2'].isin(filtered_gene))
+    return nwk[boolean_mask]
+
+def expCut(nwk,exp,sample_list,expCut):
+    '''remove gene whose mean of group(mutated/not-mutated) expression is lower than expCut'''
+    with open(sample_list) as f:
+        mutSamples=f.read().strip().split()
+    exp['no_mut']=exp.loc[:,~exp.columns.isin(mutSamples)].mean(axis=1)
+    exp['mut']=exp.loc[:,exp.columns.isin(mutSamples)].mean(axis=1)
+    boolean_mask = np.logical_or(exp['no_mut']>=1,exp['mut']>=1)
+    gene_selected = exp[boolean_mask]['Hybridization REF']
+    boolean_mask2 = np.logical_and(nwk['protein1'].isin(gene_selected),nwk['protein2'].isin(gene_selected))
+    return nwk[boolean_mask2]
+
+def FCcut(nwk,FC_df,FCcut):
+    keys = FC_df.iloc[:,0]
+    values = FC_df.iloc[:,1]
+    dictionary = dict(zip(keys, values))
+    dictionary.pop('?','Not Found')
+    first_col = np.array([dictionary[i] for i in nwk.iloc[:,0]])
+    second_col = np.array([dictionary[i] for i in nwk.iloc[:,1]])
+    boolean_mask = np.logical_and(abs(first_col) >= FCcut, abs(second_col) >= FCcut)
+    boolean_mask2 = nwk['protein1'].apply(lambda x: dictionary[x])*nwk['protein2'].apply(lambda x: dictionary[x])>0
+    bigFC_nwk = nwk[boolean_mask & boolean_mask2]
+    return bigFC_nwk
+
+if __name__=="__main__":
+    parser = argparse.ArgumentParser(description='python 0_instantiate_nwk.py nwkFile exp -corrCut [] -nThreads [] -o []')
+    parser.add_argument('nwk',help='network')
+    parser.add_argument('exp',help='exp File')
+    parser.add_argument('-corrCut',type=float, required=False,help='corealtion cutoff')
+    parser.add_argument('-nThreads',type=int, required=False,default=1)
+    parser.add_argument('-o',required=True,help='output')
+    args=parser.parse_args()
+        
+    ####correaltion score combined with string score
+    start=time.time()
+    exp=pd.read_csv(args.exp,sep='\t',header=0,index_col=0)
+    
+    #remove duplicates
+    data=[]
+    with open(args.nwk) as f:
+        for line in f.readlines():
+            tmp=line.strip().split('\t')
+            data.append(sorted(tmp))
+    
+    df_nwk=pd.DataFrame(data[1:],columns=['node_1','node_2'],dtype=float)
+    
+    df_nwk.drop_duplicates(subset=['node_1','node_2'],inplace=True)
+    df_nwk_filt = df_nwk.loc[lambda x:np.logical_and(x.node_1.isin(exp.index),x.node_2.isin(exp.index))].loc[lambda x:x.node_1 != x.node_2]
+    
+    #make exp dictionary to calculate weight
+    lst_exps=dict() 
+    with open(args.exp) as f:
+        lines=f.readlines()
+    for line in lines:
+        s=line.strip().split('\t')
+        if not isNum(s[1]):
+            continue
+        else:
+            gene, exps = s[0], list(map(float,s[1:]))
+            lst_exps[gene]=exps
+    lst_pairs2=zip(df_nwk_filt['node_1'],df_nwk_filt['node_2'])
+    
+    def myCorr(x):
+        g1,g2=sorted(x)
+        if np.all(np.array(lst_exps[g1])==lst_exps[g1][0]) or np.all(np.array(lst_exps[g2])==lst_exps[g2][0]):
+            val,pval=(0,1)
+        else:
+            val, pval = pearsonr(lst_exps[g1],lst_exps[g2])
+        return (g1,g2,abs(val))
+    
+    p = Pool(args.nThreads)
+    res2=p.imap_unordered(myCorr, lst_pairs2)
+    p.close()
+    p.join()
+
+    corr_res2=[]
+    for g1,g2,val in res2:
+        if g1==g2:
+            continue
+        corr_res2.append([g1,g2,val])
+
+    df_nwk_corr=pd.DataFrame(corr_res2,columns=['node_1','node_2','weight'])
+    df_nwk_corrCut=corrCut(df_nwk_corr,args.corrCut)
+    
+    end=time.time()
+    time_elapsed=end-start
+    print('tr_tr',df_nwk_corrCut.shape)
+    #df_nwk_corrCut.to_csv(args.o,sep='\t',header=False,index=False)
+    df_nwk_corrCut.loc[:,['node_1','node_2']].to_csv(args.o,sep='\t',header=False,index=False)
+    #print(args.o, 'time_elapsed', time_elapsed)