--- a
+++ b/step2.py
@@ -0,0 +1,127 @@
+"""
+Generate individual files and chr22.json
+
+Running this code need following command:
+    mkdir individual
+    mkdir promoters
+
+"""
+import sys
+import os
+import vcf
+import pandas as pd
+import numpy as np
+import csv
+import time
+import json
+
+
+
+def subfinder(mylist, pattern):
+    for i in range(len(mylist)):
+        if mylist[i] == pattern[0] and mylist[i:i + len(pattern)] == pattern:
+            return i, i + len(pattern)
+
+
+def promoter_var_idx(p_idx, promoter, var_idx):
+    pmt_var = promoter[p_idx]  # which promoter
+    p_start, p_end = subfinder(var_idx, pmt_var)
+    return p_start, p_end
+
+
+# read used ALS variants in each .vcf file
+with open('var_in_file.json') as json_data:
+    var_file_dic = json.load(json_data)
+
+# read promoter table
+with open('promoter1.csv', 'rb') as f:
+    reader = csv.reader(f)
+    promoter = list(reader)
+
+# read used variant posotions
+with open('all_ALS_var.txt','r') as f:
+    var_idx = [i.replace('\n','') for i in f]
+
+# mapping dictionary
+var_num_dict = {"0/0":'0',
+                "0/1":'1',
+                "1/0":'1',
+                "1/1":'2',
+                "./.":'-1'}
+
+# IDS:
+labels_file = 'labes.csv'
+labels_df = pd.read_csv(labels_file,index_col=0)
+ids = labels_df.FID.tolist()
+
+# create individual files
+print 'Create individual files'
+for ind in ids:
+    id_file_name = 'individual/'+str(ind)+'.txt'
+    id_file = open(id_file_name, 'w')
+    id_file.write('')
+
+## path to vcf files
+#files = os.listdir('./chr22')
+#files.sort()
+#files = files[1:]
+files = ['xaa.vcf']
+
+
+print 'Start writing....'
+num_vcf = len(files)
+num_vcf_batch = len(files)*0.05
+vcf_i = 0
+for f_str in files:
+    if vcf_i % num_vcf_batch == 0:
+        print vcf_i / num_vcf*100., '%....'
+
+    file_name = '' + f_str
+    with open(file_name, 'r') as f:
+        lines = [l.replace('\n', '').split("\t") for l in f if not l.startswith('##')]
+    info = lines[2:]
+    # create dataframe
+    v_df = pd.DataFrame.from_records(info, columns=lines[0])
+    use_less_l = ["ID", "REF", "ALT", "QUAL", "FILTER", "INFO", "FORMAT"]
+    v_df.drop(use_less_l, axis=1, inplace=True)
+    # 0/0 ==> 0 i.e.
+    samples = lines[0][9:]
+    replace_dict = {}
+    for i in samples:
+        replace_dict[i] = var_num_dict
+
+    v_df_r = v_df.replace(to_replace=replace_dict)
+    var_df = v_df_r[v_df_r['POS'].isin(var_file_dic[f_str])]
+    # write to file
+    for ind in ids:
+        # the id in vcf file is 'LP6008192-DNA_E10_LP6008192-DNA_E10' for 'LP6008192-DNA_E10'
+        new_ind = str(ind)+'_'+str(ind)
+        if new_ind in var_df.columns.tolist()[2:]:
+            info = var_df[new_ind].tolist()
+            id_file_name = 'individual/' + str(ind) + '.txt'
+            id_file = open(id_file_name, 'a')
+            for item in info:
+                id_file.write("%s\n" % item)
+            id_file.close()
+        else:
+            print ind
+    vcf_i += 1.
+
+print "100.0%... Done, generated all used ALS variants for each individual"
+
+num_pro = 0
+print "Generating No.",num_pro," promoter in chr",22
+
+p_start,p_end = promoter_var_idx(0,promoter,var_idx=var_idx)
+promoter_ind = {}
+for ind in ids:
+    indiv_file = 'individual/'+str(ind)+'.txt'
+    with open(indiv_file,'r') as f:
+        ind_v = [i.replace('\n','') for i in f]
+    promo = ind_v[p_start:p_end]
+    promoter_ind[ind] = promo
+
+with open('promoters/chr22.json', 'w') as fp:
+    json.dump(promoter_ind, fp)
+
+