Update README.md

HCP_1200/README.md

@@ -2,27 +2,28 @@
 
 The code on successful run should place three files called `hcp_data.dat`, `hcp_data.bak` and `hcp_data.dir` in the `HCP_1200` folder. You can access the content of these files as follows:
 
-
-	import zipshelve
-	from pathlib import Path
+```python
+    import zipshelve
+    from pathlib import Path
 
-	fin = Path('HCP_1200/hcp_data')
+    fin = Path('HCP_1200/hcp_data')
 
-	with zipshelve.open(fin, mode='r') as shelf:
+    with zipshelve.open(fin, mode='r') as shelf:
 
-	    print('Have subjects:')
+        print('Have subjects:')
 
-	    # Can also call shelf.ls() instead of for loop
-	    for key in shelf.keys():
-	        print(key)
+	# Can also call shelf.ls() instead of for loop
+	for key in shelf.keys():
+	    print(key)
 
-	    for key, scans in shelf.items():
-	        print('\nFor subject: ' + str(key) + '\thave:')
-	        for scan, data_dict in scans.items():
-	            if scan != 'metadata':
-	                print('Scan: \t', scan)
-	                print('With ROIs:\n', '\n'.join(list(data_dict.keys())))
-	            else:
-	                print('Subject age:\t', data_dict.loc['Age'])
+	for key, scans in shelf.items():
+	    print('\nFor subject: ' + str(key) + '\thave:')
+	    for scan, data_dict in scans.items():
+	        if scan != 'metadata':
+		    print('Scan: \t', scan)
+		    print('With ROIs:\n', '\n'.join(list(data_dict.keys())))
+	        else:
+		    print('Subject age:\t', data_dict.loc['Age'])
+```
 
 The [`shelve`](https://docs.python.org/3/library/shelve.html) module wraps around pickling, by loading only the necessary key from disk (as opposed to `pickle.load()` which would load the whole data-set into memory). We move from `pickle` to `shelve` because pickling the whole data set will likely cause memory issues. The use of disk as opposed to RAM for hold data will cause some parallelization challenges, but that is to be worked out.