mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 02:30:48 +08:00
Josh Warner (Mac)
20ae0a1ca2
This PR exposes NumPy 1.8+ padding functionality to all users of scikit-image, regardless of their personal NumPy version. The improved (much better scaling with dimensionality) version introduced in NumPy 1.8 is used.
1466 lines
50 KiB
Python
1466 lines
50 KiB
Python
"""
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The arraypad module contains a group of functions to pad values onto the edges
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of an n-dimensional array.
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"""
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from __future__ import division, absolute_import, print_function
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import numpy as np
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try:
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# Available on 2.x at base, Py3 requires this compatibility import.
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# Later versions of NumPy have this for 2.x as well.
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from numpy.compat import long
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except:
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pass
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__all__ = ['pad']
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###############################################################################
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# Private utility functions.
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def _arange_ndarray(arr, shape, axis, reverse=False):
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"""
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Create an ndarray of `shape` with increments along specified `axis`
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Parameters
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----------
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arr : ndarray
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Input array of arbitrary shape.
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shape : tuple of ints
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Shape of desired array. Should be equivalent to `arr.shape` except
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`shape[axis]` which may have any positive value.
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axis : int
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Axis to increment along.
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reverse : bool
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If False, increment in a positive fashion from 1 to `shape[axis]`,
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inclusive. If True, the bounds are the same but the order reversed.
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Returns
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-------
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padarr : ndarray
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Output array sized to pad `arr` along `axis`, with linear range from
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1 to `shape[axis]` along specified `axis`.
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Notes
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-----
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The range is deliberately 1-indexed for this specific use case. Think of
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this algorithm as broadcasting `np.arange` to a single `axis` of an
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arbitrarily shaped ndarray.
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"""
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initshape = tuple(1 if i != axis else shape[axis]
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for (i, x) in enumerate(arr.shape))
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if not reverse:
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padarr = np.arange(1, shape[axis] + 1)
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else:
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padarr = np.arange(shape[axis], 0, -1)
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padarr = padarr.reshape(initshape)
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for i, dim in enumerate(shape):
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if padarr.shape[i] != dim:
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padarr = padarr.repeat(dim, axis=i)
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return padarr
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def _round_ifneeded(arr, dtype):
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"""
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Rounds arr inplace if destination dtype is integer.
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Parameters
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----------
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arr : ndarray
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Input array.
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dtype : dtype
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The dtype of the destination array.
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"""
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if np.issubdtype(dtype, np.integer):
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arr.round(out=arr)
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def _prepend_const(arr, pad_amt, val, axis=-1):
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"""
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Prepend constant `val` along `axis` of `arr`.
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Parameters
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----------
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arr : ndarray
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Input array of arbitrary shape.
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pad_amt : int
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Amount of padding to prepend.
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val : scalar
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Constant value to use. For best results should be of type `arr.dtype`;
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if not `arr.dtype` will be cast to `arr.dtype`.
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axis : int
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Axis along which to pad `arr`.
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Returns
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-------
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padarr : ndarray
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Output array, with `pad_amt` constant `val` prepended along `axis`.
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"""
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if pad_amt == 0:
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return arr
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padshape = tuple(x if i != axis else pad_amt
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for (i, x) in enumerate(arr.shape))
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if val == 0:
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return np.concatenate((np.zeros(padshape, dtype=arr.dtype), arr),
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axis=axis)
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else:
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return np.concatenate(((np.zeros(padshape) + val).astype(arr.dtype),
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arr), axis=axis)
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def _append_const(arr, pad_amt, val, axis=-1):
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"""
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Append constant `val` along `axis` of `arr`.
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Parameters
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----------
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arr : ndarray
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Input array of arbitrary shape.
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pad_amt : int
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Amount of padding to append.
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val : scalar
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Constant value to use. For best results should be of type `arr.dtype`;
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if not `arr.dtype` will be cast to `arr.dtype`.
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axis : int
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Axis along which to pad `arr`.
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Returns
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-------
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padarr : ndarray
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Output array, with `pad_amt` constant `val` appended along `axis`.
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"""
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if pad_amt == 0:
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return arr
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padshape = tuple(x if i != axis else pad_amt
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for (i, x) in enumerate(arr.shape))
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if val == 0:
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return np.concatenate((arr, np.zeros(padshape, dtype=arr.dtype)),
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axis=axis)
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else:
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return np.concatenate(
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(arr, (np.zeros(padshape) + val).astype(arr.dtype)), axis=axis)
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def _prepend_edge(arr, pad_amt, axis=-1):
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"""
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Prepend `pad_amt` to `arr` along `axis` by extending edge values.
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Parameters
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----------
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arr : ndarray
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Input array of arbitrary shape.
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pad_amt : int
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Amount of padding to prepend.
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axis : int
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Axis along which to pad `arr`.
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Returns
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-------
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padarr : ndarray
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Output array, extended by `pad_amt` edge values appended along `axis`.
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"""
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if pad_amt == 0:
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return arr
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edge_slice = tuple(slice(None) if i != axis else 0
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for (i, x) in enumerate(arr.shape))
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# Shape to restore singleton dimension after slicing
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pad_singleton = tuple(x if i != axis else 1
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for (i, x) in enumerate(arr.shape))
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edge_arr = arr[edge_slice].reshape(pad_singleton)
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return np.concatenate((edge_arr.repeat(pad_amt, axis=axis), arr),
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axis=axis)
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def _append_edge(arr, pad_amt, axis=-1):
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"""
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Append `pad_amt` to `arr` along `axis` by extending edge values.
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Parameters
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----------
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arr : ndarray
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Input array of arbitrary shape.
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pad_amt : int
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Amount of padding to append.
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axis : int
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Axis along which to pad `arr`.
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Returns
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-------
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padarr : ndarray
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Output array, extended by `pad_amt` edge values prepended along
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`axis`.
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"""
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if pad_amt == 0:
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return arr
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edge_slice = tuple(slice(None) if i != axis else arr.shape[axis] - 1
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for (i, x) in enumerate(arr.shape))
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# Shape to restore singleton dimension after slicing
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pad_singleton = tuple(x if i != axis else 1
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for (i, x) in enumerate(arr.shape))
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edge_arr = arr[edge_slice].reshape(pad_singleton)
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return np.concatenate((arr, edge_arr.repeat(pad_amt, axis=axis)),
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axis=axis)
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def _prepend_ramp(arr, pad_amt, end, axis=-1):
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"""
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Prepend linear ramp along `axis`.
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Parameters
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----------
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arr : ndarray
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Input array of arbitrary shape.
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pad_amt : int
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Amount of padding to prepend.
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end : scalar
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Constal value to use. For best results should be of type `arr.dtype`;
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if not `arr.dtype` will be cast to `arr.dtype`.
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axis : int
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Axis along which to pad `arr`.
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Returns
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-------
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padarr : ndarray
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Output array, with `pad_amt` values prepended along `axis`. The
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prepended region ramps linearly from the edge value to `end`.
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"""
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if pad_amt == 0:
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return arr
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# Generate shape for final concatenated array
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padshape = tuple(x if i != axis else pad_amt
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for (i, x) in enumerate(arr.shape))
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# Generate an n-dimensional array incrementing along `axis`
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ramp_arr = _arange_ndarray(arr, padshape, axis,
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reverse=True).astype(np.float64)
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# Appropriate slicing to extract n-dimensional edge along `axis`
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edge_slice = tuple(slice(None) if i != axis else 0
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for (i, x) in enumerate(arr.shape))
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# Shape to restore singleton dimension after slicing
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pad_singleton = tuple(x if i != axis else 1
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for (i, x) in enumerate(arr.shape))
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# Extract edge, reshape to original rank, and extend along `axis`
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edge_pad = arr[edge_slice].reshape(pad_singleton).repeat(pad_amt, axis)
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# Linear ramp
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slope = (end - edge_pad) / float(pad_amt)
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ramp_arr = ramp_arr * slope
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ramp_arr += edge_pad
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_round_ifneeded(ramp_arr, arr.dtype)
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# Ramp values will most likely be float, cast them to the same type as arr
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return np.concatenate((ramp_arr.astype(arr.dtype), arr), axis=axis)
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def _append_ramp(arr, pad_amt, end, axis=-1):
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"""
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Append linear ramp along `axis`.
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Parameters
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----------
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arr : ndarray
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Input array of arbitrary shape.
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pad_amt : int
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Amount of padding to append.
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end : scalar
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Constal value to use. For best results should be of type `arr.dtype`;
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if not `arr.dtype` will be cast to `arr.dtype`.
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axis : int
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Axis along which to pad `arr`.
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Returns
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-------
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padarr : ndarray
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Output array, with `pad_amt` values appended along `axis`. The
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appended region ramps linearly from the edge value to `end`.
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"""
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if pad_amt == 0:
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return arr
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# Generate shape for final concatenated array
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padshape = tuple(x if i != axis else pad_amt
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for (i, x) in enumerate(arr.shape))
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# Generate an n-dimensional array incrementing along `axis`
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ramp_arr = _arange_ndarray(arr, padshape, axis,
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reverse=False).astype(np.float64)
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# Slice a chunk from the edge to calculate stats on
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edge_slice = tuple(slice(None) if i != axis else -1
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for (i, x) in enumerate(arr.shape))
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# Shape to restore singleton dimension after slicing
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pad_singleton = tuple(x if i != axis else 1
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for (i, x) in enumerate(arr.shape))
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# Extract edge, reshape to original rank, and extend along `axis`
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edge_pad = arr[edge_slice].reshape(pad_singleton).repeat(pad_amt, axis)
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# Linear ramp
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slope = (end - edge_pad) / float(pad_amt)
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ramp_arr = ramp_arr * slope
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ramp_arr += edge_pad
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_round_ifneeded(ramp_arr, arr.dtype)
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# Ramp values will most likely be float, cast them to the same type as arr
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return np.concatenate((arr, ramp_arr.astype(arr.dtype)), axis=axis)
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def _prepend_max(arr, pad_amt, num, axis=-1):
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"""
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Prepend `pad_amt` maximum values along `axis`.
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Parameters
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----------
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arr : ndarray
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Input array of arbitrary shape.
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pad_amt : int
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Amount of padding to prepend.
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num : int
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Depth into `arr` along `axis` to calculate maximum.
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Range: [1, `arr.shape[axis]`] or None (entire axis)
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axis : int
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Axis along which to pad `arr`.
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Returns
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-------
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padarr : ndarray
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Output array, with `pad_amt` values appended along `axis`. The
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prepended region is the maximum of the first `num` values along
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`axis`.
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"""
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if pad_amt == 0:
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return arr
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# Equivalent to edge padding for single value, so do that instead
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if num == 1:
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return _prepend_edge(arr, pad_amt, axis)
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# Use entire array if `num` is too large
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if num is not None:
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if num >= arr.shape[axis]:
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num = None
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# Slice a chunk from the edge to calculate stats on
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max_slice = tuple(slice(None) if i != axis else slice(num)
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for (i, x) in enumerate(arr.shape))
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# Shape to restore singleton dimension after slicing
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pad_singleton = tuple(x if i != axis else 1
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for (i, x) in enumerate(arr.shape))
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# Extract slice, calculate max, reshape to add singleton dimension back
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max_chunk = arr[max_slice].max(axis=axis).reshape(pad_singleton)
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# Concatenate `arr` with `max_chunk`, extended along `axis` by `pad_amt`
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return np.concatenate((max_chunk.repeat(pad_amt, axis=axis), arr),
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axis=axis)
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def _append_max(arr, pad_amt, num, axis=-1):
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"""
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Pad one `axis` of `arr` with the maximum of the last `num` elements.
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Parameters
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----------
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arr : ndarray
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Input array of arbitrary shape.
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pad_amt : int
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Amount of padding to append.
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num : int
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Depth into `arr` along `axis` to calculate maximum.
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Range: [1, `arr.shape[axis]`] or None (entire axis)
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axis : int
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Axis along which to pad `arr`.
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Returns
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-------
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padarr : ndarray
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Output array, with `pad_amt` values appended along `axis`. The
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appended region is the maximum of the final `num` values along `axis`.
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"""
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if pad_amt == 0:
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return arr
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# Equivalent to edge padding for single value, so do that instead
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if num == 1:
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return _append_edge(arr, pad_amt, axis)
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# Use entire array if `num` is too large
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if num is not None:
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if num >= arr.shape[axis]:
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num = None
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# Slice a chunk from the edge to calculate stats on
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end = arr.shape[axis] - 1
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if num is not None:
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max_slice = tuple(
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slice(None) if i != axis else slice(end, end - num, -1)
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for (i, x) in enumerate(arr.shape))
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else:
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max_slice = tuple(slice(None) for x in arr.shape)
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# Shape to restore singleton dimension after slicing
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pad_singleton = tuple(x if i != axis else 1
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for (i, x) in enumerate(arr.shape))
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# Extract slice, calculate max, reshape to add singleton dimension back
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max_chunk = arr[max_slice].max(axis=axis).reshape(pad_singleton)
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# Concatenate `arr` with `max_chunk`, extended along `axis` by `pad_amt`
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return np.concatenate((arr, max_chunk.repeat(pad_amt, axis=axis)),
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axis=axis)
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def _prepend_mean(arr, pad_amt, num, axis=-1):
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"""
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Prepend `pad_amt` mean values along `axis`.
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Parameters
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----------
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arr : ndarray
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Input array of arbitrary shape.
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pad_amt : int
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Amount of padding to prepend.
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num : int
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Depth into `arr` along `axis` to calculate mean.
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Range: [1, `arr.shape[axis]`] or None (entire axis)
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axis : int
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Axis along which to pad `arr`.
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Returns
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-------
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padarr : ndarray
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Output array, with `pad_amt` values prepended along `axis`. The
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prepended region is the mean of the first `num` values along `axis`.
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"""
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if pad_amt == 0:
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return arr
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# Equivalent to edge padding for single value, so do that instead
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if num == 1:
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return _prepend_edge(arr, pad_amt, axis)
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# Use entire array if `num` is too large
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if num is not None:
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if num >= arr.shape[axis]:
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num = None
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# Slice a chunk from the edge to calculate stats on
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mean_slice = tuple(slice(None) if i != axis else slice(num)
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for (i, x) in enumerate(arr.shape))
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# Shape to restore singleton dimension after slicing
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pad_singleton = tuple(x if i != axis else 1
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for (i, x) in enumerate(arr.shape))
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# Extract slice, calculate mean, reshape to add singleton dimension back
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mean_chunk = arr[mean_slice].mean(axis).reshape(pad_singleton)
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_round_ifneeded(mean_chunk, arr.dtype)
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# Concatenate `arr` with `mean_chunk`, extended along `axis` by `pad_amt`
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return np.concatenate((mean_chunk.repeat(pad_amt, axis).astype(arr.dtype),
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arr), axis=axis)
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def _append_mean(arr, pad_amt, num, axis=-1):
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"""
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Append `pad_amt` mean values along `axis`.
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Parameters
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----------
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arr : ndarray
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|
Input array of arbitrary shape.
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|
pad_amt : int
|
|
Amount of padding to append.
|
|
num : int
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|
Depth into `arr` along `axis` to calculate mean.
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Range: [1, `arr.shape[axis]`] or None (entire axis)
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axis : int
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Axis along which to pad `arr`.
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|
Returns
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-------
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padarr : ndarray
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Output array, with `pad_amt` values appended along `axis`. The
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appended region is the maximum of the final `num` values along `axis`.
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"""
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if pad_amt == 0:
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return arr
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# Equivalent to edge padding for single value, so do that instead
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if num == 1:
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return _append_edge(arr, pad_amt, axis)
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# Use entire array if `num` is too large
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|
if num is not None:
|
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if num >= arr.shape[axis]:
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num = None
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# Slice a chunk from the edge to calculate stats on
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end = arr.shape[axis] - 1
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if num is not None:
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mean_slice = tuple(
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slice(None) if i != axis else slice(end, end - num, -1)
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for (i, x) in enumerate(arr.shape))
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else:
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mean_slice = tuple(slice(None) for x in arr.shape)
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# Shape to restore singleton dimension after slicing
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pad_singleton = tuple(x if i != axis else 1
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for (i, x) in enumerate(arr.shape))
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# Extract slice, calculate mean, reshape to add singleton dimension back
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mean_chunk = arr[mean_slice].mean(axis=axis).reshape(pad_singleton)
|
|
_round_ifneeded(mean_chunk, arr.dtype)
|
|
|
|
# Concatenate `arr` with `mean_chunk`, extended along `axis` by `pad_amt`
|
|
return np.concatenate(
|
|
(arr, mean_chunk.repeat(pad_amt, axis).astype(arr.dtype)), axis=axis)
|
|
|
|
|
|
def _prepend_med(arr, pad_amt, num, axis=-1):
|
|
"""
|
|
Prepend `pad_amt` median values along `axis`.
|
|
|
|
Parameters
|
|
----------
|
|
arr : ndarray
|
|
Input array of arbitrary shape.
|
|
pad_amt : int
|
|
Amount of padding to prepend.
|
|
num : int
|
|
Depth into `arr` along `axis` to calculate median.
|
|
Range: [1, `arr.shape[axis]`] or None (entire axis)
|
|
axis : int
|
|
Axis along which to pad `arr`.
|
|
|
|
Returns
|
|
-------
|
|
padarr : ndarray
|
|
Output array, with `pad_amt` values prepended along `axis`. The
|
|
prepended region is the median of the first `num` values along `axis`.
|
|
|
|
"""
|
|
if pad_amt == 0:
|
|
return arr
|
|
|
|
# Equivalent to edge padding for single value, so do that instead
|
|
if num == 1:
|
|
return _prepend_edge(arr, pad_amt, axis)
|
|
|
|
# Use entire array if `num` is too large
|
|
if num is not None:
|
|
if num >= arr.shape[axis]:
|
|
num = None
|
|
|
|
# Slice a chunk from the edge to calculate stats on
|
|
med_slice = tuple(slice(None) if i != axis else slice(num)
|
|
for (i, x) in enumerate(arr.shape))
|
|
|
|
# Shape to restore singleton dimension after slicing
|
|
pad_singleton = tuple(x if i != axis else 1
|
|
for (i, x) in enumerate(arr.shape))
|
|
|
|
# Extract slice, calculate median, reshape to add singleton dimension back
|
|
med_chunk = np.median(arr[med_slice], axis=axis).reshape(pad_singleton)
|
|
_round_ifneeded(med_chunk, arr.dtype)
|
|
|
|
# Concatenate `arr` with `med_chunk`, extended along `axis` by `pad_amt`
|
|
return np.concatenate(
|
|
(med_chunk.repeat(pad_amt, axis).astype(arr.dtype), arr), axis=axis)
|
|
|
|
|
|
def _append_med(arr, pad_amt, num, axis=-1):
|
|
"""
|
|
Append `pad_amt` median values along `axis`.
|
|
|
|
Parameters
|
|
----------
|
|
arr : ndarray
|
|
Input array of arbitrary shape.
|
|
pad_amt : int
|
|
Amount of padding to append.
|
|
num : int
|
|
Depth into `arr` along `axis` to calculate median.
|
|
Range: [1, `arr.shape[axis]`] or None (entire axis)
|
|
axis : int
|
|
Axis along which to pad `arr`.
|
|
|
|
Returns
|
|
-------
|
|
padarr : ndarray
|
|
Output array, with `pad_amt` values appended along `axis`. The
|
|
appended region is the median of the final `num` values along `axis`.
|
|
|
|
"""
|
|
if pad_amt == 0:
|
|
return arr
|
|
|
|
# Equivalent to edge padding for single value, so do that instead
|
|
if num == 1:
|
|
return _append_edge(arr, pad_amt, axis)
|
|
|
|
# Use entire array if `num` is too large
|
|
if num is not None:
|
|
if num >= arr.shape[axis]:
|
|
num = None
|
|
|
|
# Slice a chunk from the edge to calculate stats on
|
|
end = arr.shape[axis] - 1
|
|
if num is not None:
|
|
med_slice = tuple(
|
|
slice(None) if i != axis else slice(end, end - num, -1)
|
|
for (i, x) in enumerate(arr.shape))
|
|
else:
|
|
med_slice = tuple(slice(None) for x in arr.shape)
|
|
|
|
# Shape to restore singleton dimension after slicing
|
|
pad_singleton = tuple(x if i != axis else 1
|
|
for (i, x) in enumerate(arr.shape))
|
|
|
|
# Extract slice, calculate median, reshape to add singleton dimension back
|
|
med_chunk = np.median(arr[med_slice], axis=axis).reshape(pad_singleton)
|
|
_round_ifneeded(med_chunk, arr.dtype)
|
|
|
|
# Concatenate `arr` with `med_chunk`, extended along `axis` by `pad_amt`
|
|
return np.concatenate(
|
|
(arr, med_chunk.repeat(pad_amt, axis).astype(arr.dtype)), axis=axis)
|
|
|
|
|
|
def _prepend_min(arr, pad_amt, num, axis=-1):
|
|
"""
|
|
Prepend `pad_amt` minimum values along `axis`.
|
|
|
|
Parameters
|
|
----------
|
|
arr : ndarray
|
|
Input array of arbitrary shape.
|
|
pad_amt : int
|
|
Amount of padding to prepend.
|
|
num : int
|
|
Depth into `arr` along `axis` to calculate minimum.
|
|
Range: [1, `arr.shape[axis]`] or None (entire axis)
|
|
axis : int
|
|
Axis along which to pad `arr`.
|
|
|
|
Returns
|
|
-------
|
|
padarr : ndarray
|
|
Output array, with `pad_amt` values prepended along `axis`. The
|
|
prepended region is the minimum of the first `num` values along
|
|
`axis`.
|
|
|
|
"""
|
|
if pad_amt == 0:
|
|
return arr
|
|
|
|
# Equivalent to edge padding for single value, so do that instead
|
|
if num == 1:
|
|
return _prepend_edge(arr, pad_amt, axis)
|
|
|
|
# Use entire array if `num` is too large
|
|
if num is not None:
|
|
if num >= arr.shape[axis]:
|
|
num = None
|
|
|
|
# Slice a chunk from the edge to calculate stats on
|
|
min_slice = tuple(slice(None) if i != axis else slice(num)
|
|
for (i, x) in enumerate(arr.shape))
|
|
|
|
# Shape to restore singleton dimension after slicing
|
|
pad_singleton = tuple(x if i != axis else 1
|
|
for (i, x) in enumerate(arr.shape))
|
|
|
|
# Extract slice, calculate min, reshape to add singleton dimension back
|
|
min_chunk = arr[min_slice].min(axis=axis).reshape(pad_singleton)
|
|
|
|
# Concatenate `arr` with `min_chunk`, extended along `axis` by `pad_amt`
|
|
return np.concatenate((min_chunk.repeat(pad_amt, axis=axis), arr),
|
|
axis=axis)
|
|
|
|
|
|
def _append_min(arr, pad_amt, num, axis=-1):
|
|
"""
|
|
Append `pad_amt` median values along `axis`.
|
|
|
|
Parameters
|
|
----------
|
|
arr : ndarray
|
|
Input array of arbitrary shape.
|
|
pad_amt : int
|
|
Amount of padding to append.
|
|
num : int
|
|
Depth into `arr` along `axis` to calculate minimum.
|
|
Range: [1, `arr.shape[axis]`] or None (entire axis)
|
|
axis : int
|
|
Axis along which to pad `arr`.
|
|
|
|
Returns
|
|
-------
|
|
padarr : ndarray
|
|
Output array, with `pad_amt` values appended along `axis`. The
|
|
appended region is the minimum of the final `num` values along `axis`.
|
|
|
|
"""
|
|
if pad_amt == 0:
|
|
return arr
|
|
|
|
# Equivalent to edge padding for single value, so do that instead
|
|
if num == 1:
|
|
return _append_edge(arr, pad_amt, axis)
|
|
|
|
# Use entire array if `num` is too large
|
|
if num is not None:
|
|
if num >= arr.shape[axis]:
|
|
num = None
|
|
|
|
# Slice a chunk from the edge to calculate stats on
|
|
end = arr.shape[axis] - 1
|
|
if num is not None:
|
|
min_slice = tuple(
|
|
slice(None) if i != axis else slice(end, end - num, -1)
|
|
for (i, x) in enumerate(arr.shape))
|
|
else:
|
|
min_slice = tuple(slice(None) for x in arr.shape)
|
|
|
|
# Shape to restore singleton dimension after slicing
|
|
pad_singleton = tuple(x if i != axis else 1
|
|
for (i, x) in enumerate(arr.shape))
|
|
|
|
# Extract slice, calculate min, reshape to add singleton dimension back
|
|
min_chunk = arr[min_slice].min(axis=axis).reshape(pad_singleton)
|
|
|
|
# Concatenate `arr` with `min_chunk`, extended along `axis` by `pad_amt`
|
|
return np.concatenate((arr, min_chunk.repeat(pad_amt, axis=axis)),
|
|
axis=axis)
|
|
|
|
|
|
def _pad_ref(arr, pad_amt, method, axis=-1):
|
|
"""
|
|
Pad `axis` of `arr` by reflection.
|
|
|
|
Parameters
|
|
----------
|
|
arr : ndarray
|
|
Input array of arbitrary shape.
|
|
pad_amt : tuple of ints, length 2
|
|
Padding to (prepend, append) along `axis`.
|
|
method : str
|
|
Controls method of reflection; options are 'even' or 'odd'.
|
|
axis : int
|
|
Axis along which to pad `arr`.
|
|
|
|
Returns
|
|
-------
|
|
padarr : ndarray
|
|
Output array, with `pad_amt[0]` values prepended and `pad_amt[1]`
|
|
values appended along `axis`. Both regions are padded with reflected
|
|
values from the original array.
|
|
|
|
Notes
|
|
-----
|
|
This algorithm does not pad with repetition, i.e. the edges are not
|
|
repeated in the reflection. For that behavior, use `method='symmetric'`.
|
|
|
|
The modes 'reflect', 'symmetric', and 'wrap' must be padded with a
|
|
single function, lest the indexing tricks in non-integer multiples of the
|
|
original shape would violate repetition in the final iteration.
|
|
|
|
"""
|
|
# Implicit booleanness to test for zero (or None) in any scalar type
|
|
if pad_amt[0] == 0 and pad_amt[1] == 0:
|
|
return arr
|
|
|
|
##########################################################################
|
|
# Prepended region
|
|
|
|
# Slice off a reverse indexed chunk from near edge to pad `arr` before
|
|
ref_slice = tuple(slice(None) if i != axis else slice(pad_amt[0], 0, -1)
|
|
for (i, x) in enumerate(arr.shape))
|
|
|
|
ref_chunk1 = arr[ref_slice]
|
|
|
|
# Shape to restore singleton dimension after slicing
|
|
pad_singleton = tuple(x if i != axis else 1
|
|
for (i, x) in enumerate(arr.shape))
|
|
if pad_amt[0] == 1:
|
|
ref_chunk1 = ref_chunk1.reshape(pad_singleton)
|
|
|
|
# Memory/computationally more expensive, only do this if `method='odd'`
|
|
if 'odd' in method and pad_amt[0] > 0:
|
|
edge_slice1 = tuple(slice(None) if i != axis else 0
|
|
for (i, x) in enumerate(arr.shape))
|
|
edge_chunk = arr[edge_slice1].reshape(pad_singleton)
|
|
ref_chunk1 = 2 * edge_chunk - ref_chunk1
|
|
del edge_chunk
|
|
|
|
##########################################################################
|
|
# Appended region
|
|
|
|
# Slice off a reverse indexed chunk from far edge to pad `arr` after
|
|
start = arr.shape[axis] - pad_amt[1] - 1
|
|
end = arr.shape[axis] - 1
|
|
ref_slice = tuple(slice(None) if i != axis else slice(start, end)
|
|
for (i, x) in enumerate(arr.shape))
|
|
rev_idx = tuple(slice(None) if i != axis else slice(None, None, -1)
|
|
for (i, x) in enumerate(arr.shape))
|
|
ref_chunk2 = arr[ref_slice][rev_idx]
|
|
|
|
if pad_amt[1] == 1:
|
|
ref_chunk2 = ref_chunk2.reshape(pad_singleton)
|
|
|
|
if 'odd' in method:
|
|
edge_slice2 = tuple(slice(None) if i != axis else -1
|
|
for (i, x) in enumerate(arr.shape))
|
|
edge_chunk = arr[edge_slice2].reshape(pad_singleton)
|
|
ref_chunk2 = 2 * edge_chunk - ref_chunk2
|
|
del edge_chunk
|
|
|
|
# Concatenate `arr` with both chunks, extending along `axis`
|
|
return np.concatenate((ref_chunk1, arr, ref_chunk2), axis=axis)
|
|
|
|
|
|
def _pad_sym(arr, pad_amt, method, axis=-1):
|
|
"""
|
|
Pad `axis` of `arr` by symmetry.
|
|
|
|
Parameters
|
|
----------
|
|
arr : ndarray
|
|
Input array of arbitrary shape.
|
|
pad_amt : tuple of ints, length 2
|
|
Padding to (prepend, append) along `axis`.
|
|
method : str
|
|
Controls method of symmetry; options are 'even' or 'odd'.
|
|
axis : int
|
|
Axis along which to pad `arr`.
|
|
|
|
Returns
|
|
-------
|
|
padarr : ndarray
|
|
Output array, with `pad_amt[0]` values prepended and `pad_amt[1]`
|
|
values appended along `axis`. Both regions are padded with symmetric
|
|
values from the original array.
|
|
|
|
Notes
|
|
-----
|
|
This algorithm DOES pad with repetition, i.e. the edges are repeated.
|
|
For a method that does not repeat edges, use `method='reflect'`.
|
|
|
|
The modes 'reflect', 'symmetric', and 'wrap' must be padded with a
|
|
single function, lest the indexing tricks in non-integer multiples of the
|
|
original shape would violate repetition in the final iteration.
|
|
|
|
"""
|
|
# Implicit booleanness to test for zero (or None) in any scalar type
|
|
if pad_amt[0] == 0 and pad_amt[1] == 0:
|
|
return arr
|
|
|
|
##########################################################################
|
|
# Prepended region
|
|
|
|
# Slice off a reverse indexed chunk from near edge to pad `arr` before
|
|
sym_slice = tuple(slice(None) if i != axis else slice(0, pad_amt[0])
|
|
for (i, x) in enumerate(arr.shape))
|
|
rev_idx = tuple(slice(None) if i != axis else slice(None, None, -1)
|
|
for (i, x) in enumerate(arr.shape))
|
|
sym_chunk1 = arr[sym_slice][rev_idx]
|
|
|
|
# Shape to restore singleton dimension after slicing
|
|
pad_singleton = tuple(x if i != axis else 1
|
|
for (i, x) in enumerate(arr.shape))
|
|
if pad_amt[0] == 1:
|
|
sym_chunk1 = sym_chunk1.reshape(pad_singleton)
|
|
|
|
# Memory/computationally more expensive, only do this if `method='odd'`
|
|
if 'odd' in method and pad_amt[0] > 0:
|
|
edge_slice1 = tuple(slice(None) if i != axis else 0
|
|
for (i, x) in enumerate(arr.shape))
|
|
edge_chunk = arr[edge_slice1].reshape(pad_singleton)
|
|
sym_chunk1 = 2 * edge_chunk - sym_chunk1
|
|
del edge_chunk
|
|
|
|
##########################################################################
|
|
# Appended region
|
|
|
|
# Slice off a reverse indexed chunk from far edge to pad `arr` after
|
|
start = arr.shape[axis] - pad_amt[1]
|
|
end = arr.shape[axis]
|
|
sym_slice = tuple(slice(None) if i != axis else slice(start, end)
|
|
for (i, x) in enumerate(arr.shape))
|
|
sym_chunk2 = arr[sym_slice][rev_idx]
|
|
|
|
if pad_amt[1] == 1:
|
|
sym_chunk2 = sym_chunk2.reshape(pad_singleton)
|
|
|
|
if 'odd' in method:
|
|
edge_slice2 = tuple(slice(None) if i != axis else -1
|
|
for (i, x) in enumerate(arr.shape))
|
|
edge_chunk = arr[edge_slice2].reshape(pad_singleton)
|
|
sym_chunk2 = 2 * edge_chunk - sym_chunk2
|
|
del edge_chunk
|
|
|
|
# Concatenate `arr` with both chunks, extending along `axis`
|
|
return np.concatenate((sym_chunk1, arr, sym_chunk2), axis=axis)
|
|
|
|
|
|
def _pad_wrap(arr, pad_amt, axis=-1):
|
|
"""
|
|
Pad `axis` of `arr` via wrapping.
|
|
|
|
Parameters
|
|
----------
|
|
arr : ndarray
|
|
Input array of arbitrary shape.
|
|
pad_amt : tuple of ints, length 2
|
|
Padding to (prepend, append) along `axis`.
|
|
axis : int
|
|
Axis along which to pad `arr`.
|
|
|
|
Returns
|
|
-------
|
|
padarr : ndarray
|
|
Output array, with `pad_amt[0]` values prepended and `pad_amt[1]`
|
|
values appended along `axis`. Both regions are padded wrapped values
|
|
from the opposite end of `axis`.
|
|
|
|
Notes
|
|
-----
|
|
This method of padding is also known as 'tile' or 'tiling'.
|
|
|
|
The modes 'reflect', 'symmetric', and 'wrap' must be padded with a
|
|
single function, lest the indexing tricks in non-integer multiples of the
|
|
original shape would violate repetition in the final iteration.
|
|
|
|
"""
|
|
# Implicit booleanness to test for zero (or None) in any scalar type
|
|
if pad_amt[0] == 0 and pad_amt[1] == 0:
|
|
return arr
|
|
|
|
##########################################################################
|
|
# Prepended region
|
|
|
|
# Slice off a reverse indexed chunk from near edge to pad `arr` before
|
|
start = arr.shape[axis] - pad_amt[0]
|
|
end = arr.shape[axis]
|
|
wrap_slice = tuple(slice(None) if i != axis else slice(start, end)
|
|
for (i, x) in enumerate(arr.shape))
|
|
wrap_chunk1 = arr[wrap_slice]
|
|
|
|
# Shape to restore singleton dimension after slicing
|
|
pad_singleton = tuple(x if i != axis else 1
|
|
for (i, x) in enumerate(arr.shape))
|
|
if pad_amt[0] == 1:
|
|
wrap_chunk1 = wrap_chunk1.reshape(pad_singleton)
|
|
|
|
##########################################################################
|
|
# Appended region
|
|
|
|
# Slice off a reverse indexed chunk from far edge to pad `arr` after
|
|
wrap_slice = tuple(slice(None) if i != axis else slice(0, pad_amt[1])
|
|
for (i, x) in enumerate(arr.shape))
|
|
wrap_chunk2 = arr[wrap_slice]
|
|
|
|
if pad_amt[1] == 1:
|
|
wrap_chunk2 = wrap_chunk2.reshape(pad_singleton)
|
|
|
|
# Concatenate `arr` with both chunks, extending along `axis`
|
|
return np.concatenate((wrap_chunk1, arr, wrap_chunk2), axis=axis)
|
|
|
|
|
|
def _normalize_shape(narray, shape):
|
|
"""
|
|
Private function which does some checks and normalizes the possibly
|
|
much simpler representations of 'pad_width', 'stat_length',
|
|
'constant_values', 'end_values'.
|
|
|
|
Parameters
|
|
----------
|
|
narray : ndarray
|
|
Input ndarray
|
|
shape : {sequence, int}, optional
|
|
The width of padding (pad_width) or the number of elements on the
|
|
edge of the narray used for statistics (stat_length).
|
|
((before_1, after_1), ... (before_N, after_N)) unique number of
|
|
elements for each axis where `N` is rank of `narray`.
|
|
((before, after),) yields same before and after constants for each
|
|
axis.
|
|
(constant,) or int is a shortcut for before = after = constant for
|
|
all axes.
|
|
|
|
Returns
|
|
-------
|
|
_normalize_shape : tuple of tuples
|
|
int => ((int, int), (int, int), ...)
|
|
[[int1, int2], [int3, int4], ...] => ((int1, int2), (int3, int4), ...)
|
|
((int1, int2), (int3, int4), ...) => no change
|
|
[[int1, int2], ] => ((int1, int2), (int1, int2), ...)
|
|
((int1, int2), ) => ((int1, int2), (int1, int2), ...)
|
|
[[int , ], ] => ((int, int), (int, int), ...)
|
|
((int , ), ) => ((int, int), (int, int), ...)
|
|
|
|
"""
|
|
normshp = None
|
|
shapelen = len(np.shape(narray))
|
|
if (isinstance(shape, int)) or shape is None:
|
|
normshp = ((shape, shape), ) * shapelen
|
|
elif (isinstance(shape, (tuple, list))
|
|
and isinstance(shape[0], (tuple, list))
|
|
and len(shape) == shapelen):
|
|
normshp = shape
|
|
for i in normshp:
|
|
if len(i) != 2:
|
|
fmt = "Unable to create correctly shaped tuple from %s"
|
|
raise ValueError(fmt % (normshp,))
|
|
elif (isinstance(shape, (tuple, list))
|
|
and isinstance(shape[0], (int, float, long))
|
|
and len(shape) == 1):
|
|
normshp = ((shape[0], shape[0]), ) * shapelen
|
|
elif (isinstance(shape, (tuple, list))
|
|
and isinstance(shape[0], (int, float, long))
|
|
and len(shape) == 2):
|
|
normshp = (shape, ) * shapelen
|
|
if normshp is None:
|
|
fmt = "Unable to create correctly shaped tuple from %s"
|
|
raise ValueError(fmt % (shape,))
|
|
return normshp
|
|
|
|
|
|
def _validate_lengths(narray, number_elements):
|
|
"""
|
|
Private function which does some checks and reformats pad_width and
|
|
stat_length using _normalize_shape.
|
|
|
|
Parameters
|
|
----------
|
|
narray : ndarray
|
|
Input ndarray
|
|
number_elements : {sequence, int}, optional
|
|
The width of padding (pad_width) or the number of elements on the edge
|
|
of the narray used for statistics (stat_length).
|
|
((before_1, after_1), ... (before_N, after_N)) unique number of
|
|
elements for each axis.
|
|
((before, after),) yields same before and after constants for each
|
|
axis.
|
|
(constant,) or int is a shortcut for before = after = constant for all
|
|
axes.
|
|
|
|
Returns
|
|
-------
|
|
_validate_lengths : tuple of tuples
|
|
int => ((int, int), (int, int), ...)
|
|
[[int1, int2], [int3, int4], ...] => ((int1, int2), (int3, int4), ...)
|
|
((int1, int2), (int3, int4), ...) => no change
|
|
[[int1, int2], ] => ((int1, int2), (int1, int2), ...)
|
|
((int1, int2), ) => ((int1, int2), (int1, int2), ...)
|
|
[[int , ], ] => ((int, int), (int, int), ...)
|
|
((int , ), ) => ((int, int), (int, int), ...)
|
|
|
|
"""
|
|
normshp = _normalize_shape(narray, number_elements)
|
|
for i in normshp:
|
|
chk = [1 if x is None else x for x in i]
|
|
chk = [1 if x > 0 else -1 for x in chk]
|
|
if (chk[0] < 0) or (chk[1] < 0):
|
|
fmt = "%s cannot contain negative values."
|
|
raise ValueError(fmt % (number_elements,))
|
|
return normshp
|
|
|
|
|
|
###############################################################################
|
|
# Public functions
|
|
|
|
|
|
def pad(array, pad_width, mode=None, **kwargs):
|
|
"""
|
|
Pads an array.
|
|
|
|
Parameters
|
|
----------
|
|
array : array_like of rank N
|
|
Input array
|
|
pad_width : {sequence, int}
|
|
Number of values padded to the edges of each axis.
|
|
((before_1, after_1), ... (before_N, after_N)) unique pad widths
|
|
for each axis.
|
|
((before, after),) yields same before and after pad for each axis.
|
|
(pad,) or int is a shortcut for before = after = pad width for all
|
|
axes.
|
|
mode : {str, function}
|
|
One of the following string values or a user supplied function.
|
|
|
|
'constant' Pads with a constant value.
|
|
'edge' Pads with the edge values of array.
|
|
'linear_ramp' Pads with the linear ramp between end_value and the
|
|
array edge value.
|
|
'maximum' Pads with the maximum value of all or part of the
|
|
vector along each axis.
|
|
'mean' Pads with the mean value of all or part of the
|
|
vector along each axis.
|
|
'median' Pads with the median value of all or part of the
|
|
vector along each axis.
|
|
'minimum' Pads with the minimum value of all or part of the
|
|
vector along each axis.
|
|
'reflect' Pads with the reflection of the vector mirrored on
|
|
the first and last values of the vector along each
|
|
axis.
|
|
'symmetric' Pads with the reflection of the vector mirrored
|
|
along the edge of the array.
|
|
'wrap' Pads with the wrap of the vector along the axis.
|
|
The first values are used to pad the end and the
|
|
end values are used to pad the beginning.
|
|
<function> Padding function, see Notes.
|
|
stat_length : {sequence, int}, optional
|
|
Used in 'maximum', 'mean', 'median', and 'minimum'. Number of
|
|
values at edge of each axis used to calculate the statistic value.
|
|
|
|
((before_1, after_1), ... (before_N, after_N)) unique statistic
|
|
lengths for each axis.
|
|
|
|
((before, after),) yields same before and after statistic lengths
|
|
for each axis.
|
|
|
|
(stat_length,) or int is a shortcut for before = after = statistic
|
|
length for all axes.
|
|
|
|
Default is ``None``, to use the entire axis.
|
|
constant_values : {sequence, int}, optional
|
|
Used in 'constant'. The values to set the padded values for each
|
|
axis.
|
|
|
|
((before_1, after_1), ... (before_N, after_N)) unique pad constants
|
|
for each axis.
|
|
|
|
((before, after),) yields same before and after constants for each
|
|
axis.
|
|
|
|
(constant,) or int is a shortcut for before = after = constant for
|
|
all axes.
|
|
|
|
Default is 0.
|
|
end_values : {sequence, int}, optional
|
|
Used in 'linear_ramp'. The values used for the ending value of the
|
|
linear_ramp and that will form the edge of the padded array.
|
|
|
|
((before_1, after_1), ... (before_N, after_N)) unique end values
|
|
for each axis.
|
|
|
|
((before, after),) yields same before and after end values for each
|
|
axis.
|
|
|
|
(constant,) or int is a shortcut for before = after = end value for
|
|
all axes.
|
|
|
|
Default is 0.
|
|
reflect_type : str {'even', 'odd'}, optional
|
|
Used in 'reflect', and 'symmetric'. The 'even' style is the
|
|
default with an unaltered reflection around the edge value. For
|
|
the 'odd' style, the extented part of the array is created by
|
|
subtracting the reflected values from two times the edge value.
|
|
|
|
Returns
|
|
-------
|
|
pad : ndarray
|
|
Padded array of rank equal to `array` with shape increased
|
|
according to `pad_width`.
|
|
|
|
Notes
|
|
-----
|
|
For an array with rank greater than 1, some of the padding of later
|
|
axes is calculated from padding of previous axes. This is easiest to
|
|
think about with a rank 2 array where the corners of the padded array
|
|
are calculated by using padded values from the first axis.
|
|
|
|
The padding function, if used, should return a rank 1 array equal in
|
|
length to the vector argument with padded values replaced. It has the
|
|
following signature:
|
|
|
|
padding_func(vector, iaxis_pad_width, iaxis, **kwargs)
|
|
|
|
where
|
|
|
|
vector : ndarray
|
|
A rank 1 array already padded with zeros. Padded values are
|
|
vector[:pad_tuple[0]] and vector[-pad_tuple[1]:].
|
|
iaxis_pad_width : tuple
|
|
A 2-tuple of ints, iaxis_pad_width[0] represents the number of
|
|
values padded at the beginning of vector where
|
|
iaxis_pad_width[1] represents the number of values padded at
|
|
the end of vector.
|
|
iaxis : int
|
|
The axis currently being calculated.
|
|
kwargs : misc
|
|
Any keyword arguments the function requires.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = [1, 2, 3, 4, 5]
|
|
>>> np.lib.pad(a, (2,3), 'constant', constant_values=(4,6))
|
|
array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])
|
|
|
|
>>> np.lib.pad(a, (2,3), 'edge')
|
|
array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5])
|
|
|
|
>>> np.lib.pad(a, (2,3), 'linear_ramp', end_values=(5,-4))
|
|
array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4])
|
|
|
|
>>> np.lib.pad(a, (2,), 'maximum')
|
|
array([5, 5, 1, 2, 3, 4, 5, 5, 5])
|
|
|
|
>>> np.lib.pad(a, (2,), 'mean')
|
|
array([3, 3, 1, 2, 3, 4, 5, 3, 3])
|
|
|
|
>>> np.lib.pad(a, (2,), 'median')
|
|
array([3, 3, 1, 2, 3, 4, 5, 3, 3])
|
|
|
|
>>> a = [[1,2], [3,4]]
|
|
>>> np.lib.pad(a, ((3, 2), (2, 3)), 'minimum')
|
|
array([[1, 1, 1, 2, 1, 1, 1],
|
|
[1, 1, 1, 2, 1, 1, 1],
|
|
[1, 1, 1, 2, 1, 1, 1],
|
|
[1, 1, 1, 2, 1, 1, 1],
|
|
[3, 3, 3, 4, 3, 3, 3],
|
|
[1, 1, 1, 2, 1, 1, 1],
|
|
[1, 1, 1, 2, 1, 1, 1]])
|
|
|
|
>>> a = [1, 2, 3, 4, 5]
|
|
>>> np.lib.pad(a, (2,3), 'reflect')
|
|
array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
|
|
|
|
>>> np.lib.pad(a, (2,3), 'reflect', reflect_type='odd')
|
|
array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8])
|
|
|
|
>>> np.lib.pad(a, (2,3), 'symmetric')
|
|
array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
|
|
|
|
>>> np.lib.pad(a, (2,3), 'symmetric', reflect_type='odd')
|
|
array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])
|
|
|
|
>>> np.lib.pad(a, (2,3), 'wrap')
|
|
array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
|
|
|
|
>>> def padwithtens(vector, pad_width, iaxis, kwargs):
|
|
... vector[:pad_width[0]] = 10
|
|
... vector[-pad_width[1]:] = 10
|
|
... return vector
|
|
|
|
>>> a = np.arange(6)
|
|
>>> a = a.reshape((2,3))
|
|
|
|
>>> np.lib.pad(a, 2, padwithtens)
|
|
array([[10, 10, 10, 10, 10, 10, 10],
|
|
[10, 10, 10, 10, 10, 10, 10],
|
|
[10, 10, 0, 1, 2, 10, 10],
|
|
[10, 10, 3, 4, 5, 10, 10],
|
|
[10, 10, 10, 10, 10, 10, 10],
|
|
[10, 10, 10, 10, 10, 10, 10]])
|
|
|
|
"""
|
|
|
|
narray = np.array(array)
|
|
pad_width = _validate_lengths(narray, pad_width)
|
|
|
|
allowedkwargs = {
|
|
'constant': ['constant_values'],
|
|
'edge': [],
|
|
'linear_ramp': ['end_values'],
|
|
'maximum': ['stat_length'],
|
|
'mean': ['stat_length'],
|
|
'median': ['stat_length'],
|
|
'minimum': ['stat_length'],
|
|
'reflect': ['reflect_type'],
|
|
'symmetric': ['reflect_type'],
|
|
'wrap': []}
|
|
|
|
kwdefaults = {
|
|
'stat_length': None,
|
|
'constant_values': 0,
|
|
'end_values': 0,
|
|
'reflect_type': 'even'}
|
|
|
|
if isinstance(mode, str):
|
|
# Make sure have allowed kwargs appropriate for mode
|
|
for key in kwargs:
|
|
if key not in allowedkwargs[mode]:
|
|
raise ValueError('%s keyword not in allowed keywords %s' %
|
|
(key, allowedkwargs[mode]))
|
|
|
|
# Set kwarg defaults
|
|
for kw in allowedkwargs[mode]:
|
|
kwargs.setdefault(kw, kwdefaults[kw])
|
|
|
|
# Need to only normalize particular keywords.
|
|
for i in kwargs:
|
|
if i == 'stat_length':
|
|
kwargs[i] = _validate_lengths(narray, kwargs[i])
|
|
if i in ['end_values', 'constant_values']:
|
|
kwargs[i] = _normalize_shape(narray, kwargs[i])
|
|
elif mode is None:
|
|
raise ValueError('Keyword "mode" must be a function or one of %s.' %
|
|
(list(allowedkwargs.keys()),))
|
|
else:
|
|
# Drop back to old, slower np.apply_along_axis mode for user-supplied
|
|
# vector function
|
|
function = mode
|
|
|
|
# Create a new padded array
|
|
rank = list(range(len(narray.shape)))
|
|
total_dim_increase = [np.sum(pad_width[i]) for i in rank]
|
|
offset_slices = [slice(pad_width[i][0],
|
|
pad_width[i][0] + narray.shape[i])
|
|
for i in rank]
|
|
new_shape = np.array(narray.shape) + total_dim_increase
|
|
newmat = np.zeros(new_shape).astype(narray.dtype)
|
|
|
|
# Insert the original array into the padded array
|
|
newmat[offset_slices] = narray
|
|
|
|
# This is the core of pad ...
|
|
for iaxis in rank:
|
|
np.apply_along_axis(function,
|
|
iaxis,
|
|
newmat,
|
|
pad_width[iaxis],
|
|
iaxis,
|
|
kwargs)
|
|
return newmat
|
|
|
|
# If we get here, use new padding method
|
|
newmat = narray.copy()
|
|
|
|
# API preserved, but completely new algorithm which pads by building the
|
|
# entire block to pad before/after `arr` with in one step, for each axis.
|
|
if mode == 'constant':
|
|
for axis, ((pad_before, pad_after), (before_val, after_val)) \
|
|
in enumerate(zip(pad_width, kwargs['constant_values'])):
|
|
newmat = _prepend_const(newmat, pad_before, before_val, axis)
|
|
newmat = _append_const(newmat, pad_after, after_val, axis)
|
|
|
|
elif mode == 'edge':
|
|
for axis, (pad_before, pad_after) in enumerate(pad_width):
|
|
newmat = _prepend_edge(newmat, pad_before, axis)
|
|
newmat = _append_edge(newmat, pad_after, axis)
|
|
|
|
elif mode == 'linear_ramp':
|
|
for axis, ((pad_before, pad_after), (before_val, after_val)) \
|
|
in enumerate(zip(pad_width, kwargs['end_values'])):
|
|
newmat = _prepend_ramp(newmat, pad_before, before_val, axis)
|
|
newmat = _append_ramp(newmat, pad_after, after_val, axis)
|
|
|
|
elif mode == 'maximum':
|
|
for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \
|
|
in enumerate(zip(pad_width, kwargs['stat_length'])):
|
|
newmat = _prepend_max(newmat, pad_before, chunk_before, axis)
|
|
newmat = _append_max(newmat, pad_after, chunk_after, axis)
|
|
|
|
elif mode == 'mean':
|
|
for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \
|
|
in enumerate(zip(pad_width, kwargs['stat_length'])):
|
|
newmat = _prepend_mean(newmat, pad_before, chunk_before, axis)
|
|
newmat = _append_mean(newmat, pad_after, chunk_after, axis)
|
|
|
|
elif mode == 'median':
|
|
for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \
|
|
in enumerate(zip(pad_width, kwargs['stat_length'])):
|
|
newmat = _prepend_med(newmat, pad_before, chunk_before, axis)
|
|
newmat = _append_med(newmat, pad_after, chunk_after, axis)
|
|
|
|
elif mode == 'minimum':
|
|
for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \
|
|
in enumerate(zip(pad_width, kwargs['stat_length'])):
|
|
newmat = _prepend_min(newmat, pad_before, chunk_before, axis)
|
|
newmat = _append_min(newmat, pad_after, chunk_after, axis)
|
|
|
|
elif mode == 'reflect':
|
|
for axis, (pad_before, pad_after) in enumerate(pad_width):
|
|
# Recursive padding along any axis where `pad_amt` is too large
|
|
# for indexing tricks. We can only safely pad the original axis
|
|
# length, to keep the period of the reflections consistent.
|
|
if ((pad_before > 0) or
|
|
(pad_after > 0)) and newmat.shape[axis] == 1:
|
|
# Extending singleton dimension for 'reflect' is legacy
|
|
# behavior; it really should raise an error.
|
|
newmat = _prepend_edge(newmat, pad_before, axis)
|
|
newmat = _append_edge(newmat, pad_after, axis)
|
|
continue
|
|
|
|
method = kwargs['reflect_type']
|
|
safe_pad = newmat.shape[axis] - 1
|
|
while ((pad_before > safe_pad) or (pad_after > safe_pad)):
|
|
offset = 0
|
|
pad_iter_b = min(safe_pad,
|
|
safe_pad * (pad_before // safe_pad))
|
|
pad_iter_a = min(safe_pad, safe_pad * (pad_after // safe_pad))
|
|
newmat = _pad_ref(newmat, (pad_iter_b,
|
|
pad_iter_a), method, axis)
|
|
pad_before -= pad_iter_b
|
|
pad_after -= pad_iter_a
|
|
if pad_iter_b > 0:
|
|
offset += 1
|
|
if pad_iter_a > 0:
|
|
offset += 1
|
|
safe_pad += pad_iter_b + pad_iter_a
|
|
newmat = _pad_ref(newmat, (pad_before, pad_after), method, axis)
|
|
|
|
elif mode == 'symmetric':
|
|
for axis, (pad_before, pad_after) in enumerate(pad_width):
|
|
# Recursive padding along any axis where `pad_amt` is too large
|
|
# for indexing tricks. We can only safely pad the original axis
|
|
# length, to keep the period of the reflections consistent.
|
|
method = kwargs['reflect_type']
|
|
safe_pad = newmat.shape[axis]
|
|
while ((pad_before > safe_pad) or
|
|
(pad_after > safe_pad)):
|
|
pad_iter_b = min(safe_pad,
|
|
safe_pad * (pad_before // safe_pad))
|
|
pad_iter_a = min(safe_pad, safe_pad * (pad_after // safe_pad))
|
|
newmat = _pad_sym(newmat, (pad_iter_b,
|
|
pad_iter_a), method, axis)
|
|
pad_before -= pad_iter_b
|
|
pad_after -= pad_iter_a
|
|
safe_pad += pad_iter_b + pad_iter_a
|
|
newmat = _pad_sym(newmat, (pad_before, pad_after), method, axis)
|
|
|
|
elif mode == 'wrap':
|
|
for axis, (pad_before, pad_after) in enumerate(pad_width):
|
|
# Recursive padding along any axis where `pad_amt` is too large
|
|
# for indexing tricks. We can only safely pad the original axis
|
|
# length, to keep the period of the reflections consistent.
|
|
safe_pad = newmat.shape[axis]
|
|
while ((pad_before > safe_pad) or
|
|
(pad_after > safe_pad)):
|
|
pad_iter_b = min(safe_pad,
|
|
safe_pad * (pad_before // safe_pad))
|
|
pad_iter_a = min(safe_pad, safe_pad * (pad_after // safe_pad))
|
|
newmat = _pad_wrap(newmat, (pad_iter_b, pad_iter_a), axis)
|
|
|
|
pad_before -= pad_iter_b
|
|
pad_after -= pad_iter_a
|
|
safe_pad += pad_iter_b + pad_iter_a
|
|
newmat = _pad_wrap(newmat, (pad_before, pad_after), axis)
|
|
|
|
return newmat
|