projektAI/venv/Lib/site-packages/scipy/ndimage/_ni_support.py

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2021-06-06 22:13:05 +02:00
# Copyright (C) 2003-2005 Peter J. Verveer
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of the author may not be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from collections.abc import Iterable
import warnings
import numpy
def _extend_mode_to_code(mode):
"""Convert an extension mode to the corresponding integer code.
"""
if mode == 'nearest':
return 0
elif mode == 'wrap':
return 1
elif mode in ['reflect', 'grid-mirror']:
return 2
elif mode == 'mirror':
return 3
elif mode == 'constant':
return 4
elif mode == 'grid-wrap':
return 5
elif mode == 'grid-constant':
return 6
else:
raise RuntimeError('boundary mode not supported')
def _normalize_sequence(input, rank):
"""If input is a scalar, create a sequence of length equal to the
rank by duplicating the input. If input is a sequence,
check if its length is equal to the length of array.
"""
is_str = isinstance(input, str)
if not is_str and isinstance(input, Iterable):
normalized = list(input)
if len(normalized) != rank:
err = "sequence argument must have length equal to input rank"
raise RuntimeError(err)
else:
normalized = [input] * rank
return normalized
def _get_output(output, input, shape=None, complex_output=False):
if shape is None:
shape = input.shape
if output is None:
if not complex_output:
output = numpy.zeros(shape, dtype=input.dtype.name)
else:
complex_type = numpy.promote_types(input.dtype, numpy.complex64)
output = numpy.zeros(shape, dtype=complex_type)
elif isinstance(output, (type, numpy.dtype)):
# Classes (like `np.float32`) and dtypes are interpreted as dtype
if complex_output and numpy.dtype(output).kind != 'c':
warnings.warn("promoting specified output dtype to complex")
output = numpy.promote_types(output, numpy.complex64)
output = numpy.zeros(shape, dtype=output)
elif isinstance(output, str):
output = numpy.sctypeDict[output]
if complex_output and numpy.dtype(output).kind != 'c':
raise RuntimeError("output must have complex dtype")
output = numpy.zeros(shape, dtype=output)
elif output.shape != shape:
raise RuntimeError("output shape not correct")
elif complex_output and output.dtype.kind != 'c':
raise RuntimeError("output must have complex dtype")
return output