/usr/lib/python2.7/dist-packages/vigra/ufunc.py is in python-vigra 1.10.0+git20160211.167be93+dfsg-2+b5.
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#
# Copyright 2009-2010 by Ullrich Koethe
#
# This file is part of the VIGRA computer vision library.
# The VIGRA Website is
# http://hci.iwr.uni-heidelberg.de/vigra/
# Please direct questions, bug reports, and contributions to
# ullrich.koethe@iwr.uni-heidelberg.de or
# vigra@informatik.uni-hamburg.de
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation
# files (the "Software"), to deal in the Software without
# restriction, including without limitation the rights to use,
# copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following
# conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
# OTHER DEALINGS IN THE SOFTWARE.
#
#######################################################################
import numpy
import copy
vigraTypecastingRules = '''
Default output types are thus determined according to the following rules:
1. The output type does not depend on the order of the arguments::
a + b results in the same type as b + a
2.a With exception of logical functions and abs(), the output type
does not depend on the function to be executed.
2.b The output type of logical functions is bool.
2.c The output type of abs() follows general rules unless the
input contains complex numbers, in which case the output type
is the corresponding float number type::
a + b results in the same type as a / b
a == b => bool
abs(complex128) => float64
3. If the inputs have the same type, the type is preserved::
uint8 + uint8 => uint8
4. If (and only if) one of the inputs has at least 64 bits, the output
will also have at least 64 bits::
int64 + uint32 => int64
int64 + 1.0 => float64
5. If an array is combined with a scalar of the same kind (integer,
float, or complex), the array type is preserved. If an integer
array with at most 32 bits is combined with a float scalar, the
result is float32 (and rule 4 kicks in if the array has 64 bits)::
uint8 + 1 => uint8
uint8 + 1.0 => float32
float32 + 1.0 => float32
float64 + 1.0 => float64
6. Integer expressions with mixed types always produce signed results.
If the arguments have at most 32 bits, the result will be int32,
otherwise it will be int64 (cf. rule 4)::
int8 + uint8 => int32
int32 + uint8 => int32
int32 + uint32 => int32
int32 + int64 => int64
int64 + uint64 => int64
7. In all other cases, the output type is equal to the highest input
type::
int32 + float32 => float32
float32 + complex128 => complex128
8. All defaults can be overridden by providing an explicit output array::
ufunc.add(uint8, uint8, uint16) => uint16
In order to prevent overflow, necessary upcasting is performed before
the function is executed.
'''
class Function(object):
test_types = numpy.typecodes['AllInteger'][:-2] + numpy.typecodes['AllFloat']+'O'
len_test_types = len(test_types)
kindToNumber = {'b': 1, 'u': 2, 'i': 2, 'f': 3, 'c': 4}
boolFunctions = ['equal', 'greater', 'greater_equal', 'less', 'less_equal', 'not_equal',
'logical_and', 'logical_not', 'logical_or', 'logical_xor']
def __init__(self, function):
self.function = function
self.is_bool = function.__name__ in self.boolFunctions
self.is_abs = function.__name__ == "absolute"
self.__doc__ = function.__doc__
self.nin = function.nin
self.nout = function.nout
def __getattr__(self, name):
return getattr(self.function, name)
def __repr__(self):
return "<vigra.ufunc '%s'>" % self.__name__
def priorities(self, *args):
'''Among the inputs with largest size, find the one with highest
__array_priority__. Return this input, or None if there is no
inputs with 'size' and '__array_priority__' defined.'''
maxSize = max([getattr(x, 'size', 0) for x in args])
if maxSize == 0:
return None
priorities = [(getattr(x, '__array_priority__', -1.0), x) for x in args if getattr(x, 'size', 0) == maxSize]
priorities = sorted(priorities,key = lambda tuplepx: tuplepx[0])
if priorities[-1][0] == -1.0:
return None
else:
return priorities[-1][1]
def common_type_numpy(self, *args):
'''Find a common type for the given inputs.
This function will become obsolete when numpy.find_common_type() will be fixed.
Code taken from the partial fix in numpy changeset 7133.
'''
arrayTypes = [x.dtype for x in args if hasattr(x, 'dtype')]
N = len(arrayTypes)
if N == 1:
highestArrayType = arrayTypes[0]
else:
k = 0
while k < self.len_test_types:
highestArrayType = numpy.dtype(self.test_types[k])
numcoerce = len([x for x in arrayTypes if highestArrayType >= x])
if numcoerce == N:
break
k += 1
scalarTypes = [numpy.dtype(type(x)) for x in args if numpy.isscalar(x)]
N = len(scalarTypes)
if N == 0 or highestArrayType >= scalarTypes[0]:
return (highestArrayType, highestArrayType)
else:
h, s = highestArrayType.kind, scalarTypes[0].kind
if (h in ['i', 'u'] and s in ['i', 'u']) or \
(h == 'f' and s == 'f') or \
(h == 'c' and s == 'c'):
return (highestArrayType, highestArrayType)
return (highestArrayType, scalarTypes[0])
def common_type(self, *args):
'''Find the appropriate pair (in_dtype, out_dtype) according to
vigranumpy typecasting rules. in_dtype is the type into which
the arguments will be casted before performing the operation
(to prevent possible overflow), out_type is the type the output
array will have (unless an explicit out-argument is provided).
See ufunc.vigraTypecastingRules for detailed information on coercion rules.
'''
if self.is_abs and args[0].dtype.kind == "c" and args[1] is None:
dtype = args[0].dtype
if dtype == numpy.complex64:
return dtype, numpy.float32
if dtype == numpy.complex128:
return dtype, numpy.float64
if dtype == numpy.clongdouble:
return dtype, numpy.longdouble
arrayTypes = [(self.kindToNumber[x.dtype.kind], x.dtype.itemsize, x.dtype) for x in args if hasattr(x, 'dtype')]
arrayTypes.sort()
if arrayTypes[0] != arrayTypes[-1] and arrayTypes[-1][0] == 2:
if arrayTypes[-1][1] <= 4:
highestArrayType = (2, 4, numpy.int32)
else:
highestArrayType = (2, 8, numpy.int64)
else:
highestArrayType = arrayTypes[-1]
if self.is_bool:
return (highestArrayType[-1], numpy.bool8)
scalarType = [numpy.dtype(type(x)) for x in args if numpy.isscalar(x)]
if not scalarType:
return (highestArrayType[-1], highestArrayType[-1])
scalarType = (self.kindToNumber[scalarType[0].kind], scalarType[0].itemsize, scalarType[0])
if highestArrayType[0] >= scalarType[0]:
return (highestArrayType[-1], highestArrayType[-1])
elif scalarType[0] == 3 and highestArrayType[1] <= 4:
return (highestArrayType[-1], numpy.float32)
else:
return (highestArrayType[-1], scalarType[-1])
class UnaryFunction(Function):
def __call__(self, arg, out=None):
a = arg.squeeze().transposeToNumpyOrder()
dtype, out_dtype = self.common_type(a, out)
if out is None:
out = arg.__class__(arg, dtype=out_dtype, order='A', init=False)
o = out.squeeze().transposeToNumpyOrder()
else:
o = out.squeeze().transposeToNumpyOrder()
if not a.axistags.compatible(o.axistags):
raise RuntimeError("%s(): axistag mismatch" % self.function.__name__)
a = numpy.require(a, dtype).view(numpy.ndarray) # view(ndarray) prevents infinite recursion
self.function(a, o)
return out
class UnaryFunctionOut2(Function):
def __call__(self, arg, out1=None, out2=None):
a = arg.squeeze().transposeToNumpyOrder()
dtype, out_dtype = self.common_type(a, out1, out2)
if out1 is None:
out1 = arg.__class__(arg, dtype=out_dtype, order='A', init=False)
o1 = out1.squeeze().transposeToNumpyOrder()
else:
o1 = out1.squeeze().transposeToNumpyOrder()
if not a.axistags.compatible(o1.axistags):
raise RuntimeError("%s(): axistag mismatch" % self.function.__name__)
if out2 is None:
out2 = arg.__class__(arg, dtype=out_dtype, order='A', init=False)
o2 = out2.squeeze().transposeToNumpyOrder()
else:
o2 = out2.squeeze().transposeToNumpyOrder()
if not a.axistags.compatible(o2.axistags):
raise RuntimeError("%s(): axistag mismatch" % self.function.__name__)
a = numpy.require(a, dtype).view(numpy.ndarray) # view(ndarray) prevents infinite recursion
self.function(a, o1, o2)
return out1, out2
class BinaryFunction(Function):
def __call__(self, arg1, arg2, out=None):
if arg1.__class__ is numpy.ndarray or arg2.__class__ is numpy.ndarray:
return self.function(arg1, arg2, out)
dtype, out_dtype = self.common_type(arg1, arg2, out)
if isinstance(arg1, numpy.ndarray):
a1 = arg1.transposeToNumpyOrder()
if isinstance(arg2, numpy.ndarray):
a2 = arg2.transposeToNumpyOrder()
if arg1.__array_priority__ == arg2.__array_priority__:
priorityArg = arg2 if arg1.ndim < arg2.ndim else arg1
else:
priorityArg = arg2 if arg1.__array_priority__ < arg2.__array_priority__ else arg1
if a1.ndim < a2.ndim:
a1 = a1.insertChannelAxis(order='C')
elif a1.ndim > a2.ndim:
a2 = a2.insertChannelAxis(order='C')
axistags = a1.axistags
if not axistags.compatible(a2.axistags):
raise RuntimeError("%s(): input axistag mismatch %r vs. %r" %
(self.function.__name__, axistags, a2.axistags))
shape = tuple(max(k) for k in zip(a1.shape, a2.shape))
a2 = numpy.require(a2, dtype).view(numpy.ndarray)
else:
priorityArg = arg1
axistags = a1.axistags
shape = a1.shape
a2 = arg2
a1 = numpy.require(a1, dtype).view(numpy.ndarray) # view(ndarray) prevents infinite recursion
else:
a1 = arg1
a2 = arg2.transposeToNumpyOrder()
axistags = a2.axistags
shape = a2.shape
priorityArg = arg2
a2 = numpy.require(a2, dtype).view(numpy.ndarray)
if out is None:
outClass = priorityArg.__class__
inversePermutation = priorityArg.permutationFromNumpyOrder()
o = outClass(shape, dtype=out_dtype, order='C', axistags=axistags, init=False)
if priorityArg.ndim < o.ndim:
out = o.dropChannelAxis().transpose(inversePermutation)
else:
out = o.transpose(inversePermutation)
else:
o = out.transposeToNumpyOrder()
if o.ndim < len(shape):
o = o.insertChannelAxis(order='C')
if not axistags.compatible(o.axistags):
raise RuntimeError("%s(): output axistag mismatch %r vs. %r" %
(self.function.__name__, axistags, o.axistags))
self.function(a1, a2, o)
return out
__all__ = []
for _k in numpy.__dict__.values():
if type(_k) == numpy.ufunc:
if _k.nin == 1 and _k.nout == 1:
exec(_k.__name__ + " = UnaryFunction(_k)")
if _k.nin == 1 and _k.nout == 2:
exec(_k.__name__ + " = UnaryFunctionOut2(_k)")
if _k.nin == 2:
exec(_k.__name__ + " = BinaryFunction(_k)")
__all__.append(_k.__name__)
__all__ = sorted(__all__)
def _prepareDoc():
doc = '''
The following mathematical functions are available in this module
(refer to numpy for detailed documentation)::
'''
k = 0
while k < len(__all__):
t = 8
while True:
d = ' ' + ' '.join(__all__[k:k+t]) + '\n'
if len(d) <= 80:
break
t -= 1
doc += d
k += t
return doc + '''
Some of these functions are also provided as member functions of
VigraArray::
__abs__ __add__ __and__ __div__ __divmod__ __eq__
__floordiv__ __ge__ __gt__ __invert__ __le__ __lshift__
__lt__ __mod__ __mul__ __ne__ __neg__ __or__ __pos__
__pow__ __radd__ __radd__ __rand__ __rdiv__ __rdivmod__
__rfloordiv__ __rlshift__ __rmod__ __rmul__ __ror__ __rpow__
__rrshift__ __rshift__ __rsub__ __rtruediv__ __rxor__ __sub__
__truediv__ __xor__
As usual, these functions are applied independently at each pixel.
Vigranumpy overloads the numpy-versions of these functions in order to make their
behavior more suitable for image analysis. In particular, we changed two aspects:
* Axistag consistency is checked, and the order of axes and strides is
preserved in the result array. (In contrast, plain numpy functions
always create C-order arrays, disregarding the stride order of the
inputs.)
* Typecasting rules are changed such that (i) data are represented with
at most 32 bits, when possible, (ii) the number of types that occur as
results of mixed expressions is reduced, and (iii) the chance of bad
surprises is minimized.
''' + vigraTypecastingRules
__doc__ = _prepareDoc()
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