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