MAINT: Remove unused module.

Remove module, last usage was removed during lazy access pattern
rewrite.

tests/test_munge.py

@@ -1,59 +0,0 @@
-#
-# Copyright 2015 Quantopian, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import random
-
-import pandas as pd
-import numpy as np
-from numpy.testing import assert_almost_equal
-from unittest import TestCase
-from zipline.utils.munge import bfill, ffill
-
-
-class MungeTests(TestCase):
-    def test_bfill(self):
-        # test ndim=1
-        N = 100
-        s = pd.Series(np.random.randn(N))
-        mask = random.sample(range(N), 10)
-        s.iloc[mask] = np.nan
-
-        correct = s.bfill().values
-        test = bfill(s.values)
-        assert_almost_equal(correct, test)
-
-        # test ndim=2
-        df = pd.DataFrame(np.random.randn(N, N))
-        df.iloc[mask] = np.nan
-        correct = df.bfill().values
-        test = bfill(df.values)
-        assert_almost_equal(correct, test)
-
-    def test_ffill(self):
-        # test ndim=1
-        N = 100
-        s = pd.Series(np.random.randn(N))
-        mask = random.sample(range(N), 10)
-        s.iloc[mask] = np.nan
-
-        correct = s.ffill().values
-        test = ffill(s.values)
-        assert_almost_equal(correct, test)
-
-        # test ndim=2
-        df = pd.DataFrame(np.random.randn(N, N))
-        df.iloc[mask] = np.nan
-        correct = df.ffill().values
-        test = ffill(df.values)
-        assert_almost_equal(correct, test)

zipline/utils/munge.py

@@ -1,79 +0,0 @@
-#
-# Copyright 2015 Quantopian, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from pandas.core.common import mask_missing
-try:
-    from pandas.core.common import backfill_2d, pad_2d
-except ImportError:
-    # In 0.17, pad_2d and backfill_2d werw moved from pandas.core.common to
-    # pandas.core.missing
-    from pandas.core.missing import backfill_2d, pad_2d
-
-
-def _interpolate(values, method, axis=None):
-    if values.ndim == 1:
-        axis = 0
-    elif values.ndim == 2:
-        axis = 1
-    else:
-        raise Exception("Cannot interpolate array with more than 2 dims")
-
-    values = values.copy()
-    values = interpolate_2d(values, method, axis=axis)
-    return values
-
-
-def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None):
-    """
-    Copied from the 0.15.2. This did not exist in 0.12.0.
-
-    Differences:
-        - Don't depend on pad_2d and backfill_2d to return values
-        - Removed dtype kwarg. 0.12.0 did not have this option.
-    """
-    transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
-
-    # reshape a 1 dim if needed
-    ndim = values.ndim
-    if values.ndim == 1:
-        if axis != 0:  # pragma: no cover
-            raise AssertionError("cannot interpolate on a ndim == 1 with "
-                                 "axis != 0")
-        values = values.reshape(tuple((1,) + values.shape))
-
-    if fill_value is None:
-        mask = None
-    else:  # todo create faster fill func without masking
-        mask = mask_missing(transf(values), fill_value)
-
-    # Note: pad_2d and backfill_2d work inplace in 0.12.0 and 0.15.2
-    # in 0.15.2 they also return a reference to values
-    if method == 'pad':
-        pad_2d(transf(values), limit=limit, mask=mask)
-    else:
-        backfill_2d(transf(values), limit=limit, mask=mask)
-
-    # reshape back
-    if ndim == 1:
-        values = values[0]
-
-    return values
-
-
-def ffill(values, axis=None):
-    return _interpolate(values, 'pad', axis=axis)
-
-
-def bfill(values, axis=None):
-    return _interpolate(values, 'bfill', axis=axis)