Changed zipline -> catalyst import paths

* Updated cython build scripts
 * Updated setup.py to to install catalyst package
 * Updated momentum example to use catalyst package
 * catalyst executable now supports loading pipelines from multiple bundles

catalyst/lib/normalize.py

@@ -0,0 +1,51 @@
+import numpy as np
+
+
+def naive_grouped_rowwise_apply(data,
+                                group_labels,
+                                func,
+                                func_args=(),
+                                out=None):
+    """
+    Simple implementation of grouped row-wise function application.
+
+    Parameters
+    ----------
+    data : ndarray[ndim=2]
+        Input array over which to apply a grouped function.
+    group_labels : ndarray[ndim=2, dtype=int64]
+        Labels to use to bucket inputs from array.
+        Should be the same shape as array.
+    func : function[ndarray[ndim=1]] -> function[ndarray[ndim=1]]
+        Function to apply to pieces of each row in array.
+    func_args : tuple
+        Additional positional arguments to provide to each row in array.
+    out : ndarray, optional
+        Array into which to write output.  If not supplied, a new array of the
+        same shape as ``data`` is allocated and returned.
+
+    Examples
+    --------
+    >>> data = np.array([[1., 2., 3.],
+    ...                  [2., 3., 4.],
+    ...                  [5., 6., 7.]])
+    >>> labels = np.array([[0, 0, 1],
+    ...                    [0, 1, 0],
+    ...                    [1, 0, 2]])
+    >>> naive_grouped_rowwise_apply(data, labels, lambda row: row - row.min())
+    array([[ 0.,  1.,  0.],
+           [ 0.,  0.,  2.],
+           [ 0.,  0.,  0.]])
+    >>> naive_grouped_rowwise_apply(data, labels, lambda row: row / row.sum())
+    array([[ 0.33333333,  0.66666667,  1.        ],
+           [ 0.33333333,  1.        ,  0.66666667],
+           [ 1.        ,  1.        ,  1.        ]])
+    """
+    if out is None:
+        out = np.empty_like(data)
+
+    for (row, label_row, out_row) in zip(data, group_labels, out):
+        for label in np.unique(label_row):
+            locs = (label_row == label)
+            out_row[locs] = func(row[locs], *func_args)
+    return out