diff --git a/ann_benchmarks/datasets.py b/ann_benchmarks/datasets.py
index 2ee8551..ad80eca 100644
--- a/ann_benchmarks/datasets.py
+++ b/ann_benchmarks/datasets.py
@@ -69,50 +69,50 @@ def write_output(train, test, fn, distance, point_type='float', count=1000, SMIL
         f.close()
 
     print('Write Dataset %s' % fn)
-        f = h5sparse.File(fn, 'w')
-        f.attrs['distance'] = distance
-        f.attrs['point_type'] = point_type
-        print('train size: %9d * %4d' % train.shape)
-        print('test size:  %9d * %4d' % test.shape)
-        if issparse(train):
-            f.create_dataset('train',data=train)
-        else:
-            f.create_dataset('train', train.shape, dtype=train.dtype)[:] = train
-        if issparse(test):
-            f.create_dataset('test',data=test)
-        else:
-            f.create_dataset('test', test.shape, dtype=test.dtype)[:] = test
-        neighbors = f.create_dataset('neighbors', (test.shape[0], count), dtype='i')
-        distances = f.create_dataset('distances', (test.shape[0], count), dtype='f')
-
-        # use which method to compute the groundtruth
-        if issparse(train):
-            train = train.toarray()
-        method = 'bruteforce'
+    f = h5sparse.File(fn, 'w')
+    f.attrs['distance'] = distance
+    f.attrs['point_type'] = point_type
+    print('train size: %9d * %4d' % train.shape)
+    print('test size:  %9d * %4d' % test.shape)
+    if issparse(train):
+        f.create_dataset('train',data=train)
+    else:
+        f.create_dataset('train', train.shape, dtype=train.dtype)[:] = train
+    if issparse(test):
+        f.create_dataset('test',data=test)
+    else:
+        f.create_dataset('test', test.shape, dtype=test.dtype)[:] = test
+    neighbors = f.create_dataset('neighbors', (test.shape[0], count), dtype='i')
+    distances = f.create_dataset('distances', (test.shape[0], count), dtype='f')
+
+    # use which method to compute the groundtruth
+    if issparse(train):
+        train = train.toarray()
+    method = 'bruteforce'
+    if method == 'balltree':
+        tree = sklearn.neighbors.BallTree(train, leaf_size=1000000, metric=distance)
+    else:
+        bf = BruteForceBLAS(metric=distance, precision=train.dtype)
+        bf.fit(train)
+
+    print(test)
+    for i, x in enumerate(test):
+        if i % 1 == 0:
+            print('%d/%d...' % (i, test.shape[0]))
         if method == 'balltree':
-            tree = sklearn.neighbors.BallTree(train, leaf_size=1000000, metric=distance)
+            dist, ind = tree.query([x], k=count)
+            neighbors[i] = ind[0]
+            distances[i] = dist[0]
         else:
-            bf = BruteForceBLAS(metric=distance, precision=train.dtype)
-            bf.fit(train)
-
-        print(test)
-        for i, x in enumerate(test):
-            if i % 1 == 0:
-                print('%d/%d...' % (i, test.shape[0]))
-            if method == 'balltree':
-                dist, ind = tree.query([x], k=count)
-                neighbors[i] = ind[0]
-                distances[i] = dist[0]
-            else:
-                res = list(bf.query_with_distances(x, count))
-                print(len(res))
-                res.sort(key=lambda t: t[-1])
-                neighbors[i] = [j for j, _ in res]
-                distances[i] = [d for _, d in res]
-            print(neighbors[i])
-            print(distances[i])
-        f.close()
-        print('Finish.')
+            res = list(bf.query_with_distances(x, count))
+            print(len(res))
+            res.sort(key=lambda t: t[-1])
+            neighbors[i] = [j for j, _ in res]
+            distances[i] = [d for _, d in res]
+        print(neighbors[i])
+        print(distances[i])
+    f.close()
+    print('Finish.')
 
 
 def train_test_split(X, test_size=10000):