MAINT: Put exponential_weights where it's used.

`math_utils` is mostly a shim around bottleneck imports.  If we need
this somewhere else, it probably belongs in `numpy_utils`.

zipline/pipeline/factors/technical.py

@@ -14,6 +14,7 @@ from numpy import (
     dstack,
     exp,
     fmax,
+    full,
     inf,
     isnan,
     log,
@@ -35,7 +36,6 @@ from zipline.utils.math_utils import (
     nanstd,
     nansum,
     nanmin,
-    exponential_weights,
 )
 from zipline.utils.numpy_utils import rolling_window
 from .factor import CustomFactor
@@ -161,6 +161,28 @@ class AverageDollarVolume(CustomFactor):
         out[:] = nansum(close * volume, axis=0) / len(close)
 
 
+def exponential_weights(length, decay_rate):
+    """
+    Build a weight vector for an exponentially-weighted statistic.
+
+    The resulting ndarray is of the form::
+
+        [decay_rate ** length, ..., decay_rate ** 2, decay_rate]
+
+    Parameters
+    ----------
+    length : int
+        The length of the desired weight vector.
+    decay_rate : float
+        The rate at which entries in the weight vector increase or decrease.
+
+    Returns
+    -------
+    weights : ndarray[float64]
+    """
+    return full(length, decay_rate, float64_dtype) ** arange(length + 1, 1, -1)
+
+
 class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):
     """
     Base class for factors implementing exponential-weighted operations.

zipline/utils/math_utils.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 import math
 
-from numpy import isnan, full, arange
+from numpy import isnan
 
 
 def tolerant_equals(a, b, atol=10e-7, rtol=10e-7, equal_nan=False):
@@ -77,11 +77,3 @@ def round_if_near_integer(a, epsilon=1e-4):
         return round(a)
     else:
         return a
-
-
-def exponential_weights(length, decay_rate):
-    """
-    Return weighting vector for an exponential moving statistic on `length`
-    rows with a decay rate of `decay_rate`.
-    """
-    return full(length, decay_rate, float) ** arange(length + 1, 1, -1)