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# 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.
import types
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 == 'reflect':
return 2
elif mode == 'mirror':
return 3
elif mode == 'constant':
return 4
else:
raise RuntimeError, 'boundary mode not supported'
def _normalize_sequence(input, rank, array_type = None):
"""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.
"""
if (isinstance(input, (types.IntType, types.LongType,
types.FloatType))):
normalized = [input] * rank
else:
normalized = list(input)
if len(normalized) != rank:
err = "sequence argument must have length equal to input rank"
raise RuntimeError, err
return normalized
import warnings
def _get_output(output, input, output_type = None, shape = None):
if output_type is not None:
msg = "'output_type' argument is deprecated."
msg += " Assign type to 'output' instead."
raise RuntimeError, msg
warnings.warn(msg, DeprecationWarning)
if output is None:
output = output_type
elif ((type(output) is not type(types.TypeType)) or
output.dtype != output_type):
raise RuntimeError, "'output' type and 'output_type' not equal"
if shape is None:
shape = input.shape
if output is None:
output = numpy.zeros(shape, dtype = input.dtype.name)
return_value = output
elif type(output) in [type(types.TypeType), type(numpy.zeros((4,)).dtype)]:
output = numpy.zeros(shape, dtype = output)
return_value = output
elif type(output) is types.StringType:
output = numpy.typeDict[output]
output = numpy.zeros(shape, dtype = output)
return_value = output
else:
if output.shape != shape:
raise RuntimeError, "output shape not correct"
return_value = None
return output, return_value
def _check_axis(axis, rank):
if axis < 0:
axis += rank
if axis < 0 or axis >= rank:
raise ValueError, 'invalid axis'
return axis
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