--- a
+++ b/Crawler/data_management.py
@@ -0,0 +1,118 @@
+import os
+import json
+from datetime import datetime
+from Crawler.crawler_radiomics import load_study_data
+
+
+def find_missing_filed(summary_file, log_file, out_file):
+
+    # Load summary file
+    summary = load_study_data(summary_file)
+
+    # Load log file
+    with open(log_file) as f:
+        log = json.load(f)
+
+    # Summary animals
+    sum_animals = summary['Kirsch lab iD']
+    sum_animals = ['K' + str(int(i)) for i in sum_animals]
+    first_scan = list(summary['Date of MRI image    1st'])
+    second_scan = list(summary['Date of MRI image     2nd'])
+    sum_df = {sum_animals[i]: [first_scan[i], second_scan[i]] for i in range(len(sum_animals))}
+
+    # Log animals
+    log_animals = list(log.keys())
+
+    # Get all animals names, OR
+    all_animals = sum_animals + log_animals
+    seen = set()
+    unique = []
+    for x in all_animals:
+        if x not in seen:
+            unique.append(x)
+            seen.add(x)
+
+    # Get overlap
+    overlap_animals = [i for i in sum_animals if i in log_animals]
+    overlap_animals = overlap_animals + [i for i in log_animals if i in sum_animals and not overlap_animals]
+
+    # Get unique to summary
+    sum_unique = [i for i in sum_animals if i not in log_animals]
+
+    # Get unique to MR data
+    log_unique = [i for i in log_animals if i not in sum_animals]
+
+    # Write results to file
+    n_dashes = 70
+    f = open(out_file, 'w')
+    f.write('Comparison of animals found in MR scans directories and the Summary Excel sheet.\n')
+    f.write('Shown are animal IDs and the date of the first and second listed MR scans.\n')
+    f.write('%d unique animal IDs have been found:\n' % len(unique))
+    f.write('\t%d from the Summary\n\t%d from the MR data\n' % (len(sum_animals), len(log_animals)))
+    f.write('-'*n_dashes + '\n\n')
+
+    f.write('Animals found only in MR data (missing from Summary sheets): %d animals\n' % len(log_unique))
+    f.write('\tID\t\t\tMR Data Dates\n')
+    for an in log_unique:
+        date1 = datetime.strptime(log[an]['StudyDate'][0], '%Y%m%d').date()
+        try:
+            date2 = datetime.strptime(log[an]['StudyDate'][1], '%Y%m%d').date()
+        except:
+            date2 = ''
+
+        f.write('\t%s\t\t%s\t%s\n' % (an, date1, date2))
+    f.write('\n' + '-' * n_dashes + '\n')
+
+    f.write('Animals found only in Summary sheet (missing from MR data): %d animals\n' % len(sum_unique))
+    f.write('\tID\t\t\tSummary Dates\n')
+    for an in sum_unique:
+        date1 = sum_df[an][0].date()
+        try:
+            date2 = sum_df[an][1].date()
+        except:
+            date2 = ''
+        f.write('\t%s\t\t%s\t%s\n' % (an, date1, date2))
+    f.write('\n' + '-' * n_dashes + '\n')
+
+    f.write('Animals matched in both data sources: %d animals\n' % len(overlap_animals))
+    f.write('\tID\t\t\tSummary Dates\t\t\t\tMR Data Dates\n')
+    for an in overlap_animals:
+        sdate1 = sum_df[an][0].date()
+        try:
+            sdate2 = sum_df[an][1].date()
+        except:
+            sdate2 = ''
+
+        ldate1 = datetime.strptime(log[an]['StudyDate'][0], '%Y%m%d').date()
+        try:
+            ldate2 = datetime.strptime(log[an]['StudyDate'][1], '%Y%m%d').date()
+        except:
+            ldate2 = ''
+
+        f.write('\t%s\t\t%s\t%s\t\t%s\t%s\n' % (an, sdate1, sdate2, ldate1, ldate2))
+    f.write('\n' + '-' * n_dashes + '\n')
+
+    f.write('All unique animal IDs from both sources: %d animals\n' % len(unique))
+    f.write('\tID\t\tSummary\t\tMR Data\n')
+    for an in unique:
+        s = ''
+        mr = ''
+        if an in sum_animals: s = 'X'
+        if an in log_animals: mr = 'X'
+        f.write('\t%s\t\t   %s\t\t   %s\n' % (an, s, mr))
+    f.write('\n' + '-' * n_dashes + '\n')
+
+    f.close()
+
+
+if __name__ == "__main__":
+
+    # summary_file = '/media/justify/b7TData/Results/Summary.xlsx'
+    # log_file = '/media/justify/b7TData/Results/processing_log.json'
+    # out_file = '/media/justify/b7TData/Results/animal_matching.txt'
+
+    summary_file = '/media/matt/Seagate Expansion Drive/b7TData/Results/Summary.xlsx'
+    log_file = '/media/matt/Seagate Expansion Drive/b7TData/Results/processing_log.json'
+    out_file = '/media/matt/Seagate Expansion Drive/b7TData/Results/animal_matching.txt'
+
+find_missing_filed(summary_file, log_file, out_file)
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