Better implementations of preprocess functions.

setup.py

@@ -16,7 +16,7 @@
         install_requires.append(line)
 
 setup(name="weaver-core",
-      version='0.4.9',
+      version='0.4.10',
       description="A streamlined deep-learning framework for high energy physics",
       long_description_content_type="text/markdown",
       author="H. Qu, C. Li",

weaver/utils/data/tools.py

@@ -83,26 +83,17 @@ def _batch_knn(supports, queries, k, maxlen_s, maxlen_q=None, n_jobs=1):
     return batch_knn_idx
 
 
-def _batch_permute_indices(array, maxlen):
-    batch_permute_idx = np.tile(np.arange(maxlen), (len(array), 1))
-    for i, a in enumerate(array):
-        batch_permute_idx[i, :len(a)] = np.random.permutation(len(a[:maxlen]))
-    return batch_permute_idx
+def _batch_permute_indices(array):
+    random_array = ak.unflatten(np.random.rand(ak.count(array)), ak.num(array))
+    return ak.argsort(random_array)
 
 
-def _batch_argsort(array, maxlen):
-    batch_argsort_idx = np.tile(np.arange(maxlen), (len(array), 1))
-    for i, a in enumerate(array):
-        batch_argsort_idx[i, :len(a)] = np.argsort(a[:maxlen])
-    return batch_argsort_idx
+def _batch_argsort(array):
+    return ak.argsort(array)
 
 
 def _batch_gather(array, indices):
-    out = array.zeros_like()
-    for i, (a, idx) in enumerate(zip(array, indices)):
-        maxlen = min(len(a), len(idx))
-        out[i][:maxlen] = a[idx[:maxlen]]
-    return out
+    return array[indices]
 
 
 def _p4_from_pxpypze(px, py, pz, energy):