def _broadcast_arguments(args, shape):
"""If there are no arrays/sequences in the args tuple, return args
and shape unchanged. Otherwise: if shape is empty, broadcast all
arrays/sequences in the arguments to a common shape, and return
the result together with the broadcasted shape. If shape is not
empty, broadcast all arrays/sequences in the arguments to the
given shape and return these together with the unchanged
shape. Scalar arguments are never changed.
"""
if isinstance(shape, _types.IntType):
shape = [shape]
array_args = []
for arg in args:
if (isinstance(arg, num.ArrayType) or isinstance(arg, _types.ListType)
or isinstance(arg, _types.TupleType)):
array_args.append(num.array(arg))
if len(array_args) > 0:
if len(shape) == 0:
array_args = _gen._nWayBroadcast(array_args)
shape = array_args[0].shape
else:
for ii in range(len(array_args)):
array_args[ii] = _gen._broadcast(num.array(array_args[ii]),
shape)
new_args = []
for arg in args:
if (isinstance(arg, num.ArrayType)
or isinstance(arg, _types.ListType)
or isinstance(arg, _types.TupleType)):
new_args.append(array_args.pop(0))
else:
new_args.append(arg)
args = tuple(new_args)
return args, shape
def _broadcast_arguments(args, shape):
"""If there are no arrays/sequences in the args tuple, return args
and shape unchanged. Otherwise: if shape is empty, broadcast all
arrays/sequences in the arguments to a common shape, and return
the result together with the broadcasted shape. If shape is not
empty, broadcast all arrays/sequences in the arguments to the
given shape and return these together with the unchanged
shape. Scalar arguments are never changed.
"""
if isinstance(shape, _types.IntType):
shape = [shape]
array_args = []
for arg in args:
if (isinstance(arg, num.ArrayType) or
isinstance(arg, _types.ListType) or
isinstance(arg, _types.TupleType)):
array_args.append(num.array(arg))
if len(array_args) > 0:
if len(shape) == 0:
array_args = _gen._nWayBroadcast(array_args)
shape = array_args[0].shape
else:
for ii in range(len(array_args)):
array_args[ii] = _gen._broadcast(num.array(array_args[ii]), shape)
new_args = []
for arg in args:
if (isinstance(arg, num.ArrayType) or
isinstance(arg, _types.ListType) or
isinstance(arg, _types.TupleType)):
new_args.append(array_args.pop(0))
else:
new_args.append(arg)
args = tuple(new_args)
return args, shape
def sum(input, labels=None, index=None):
"""Calculate the sum of the values of the array.
The index parameter is a single label number or a sequence of
label numbers of the objects to be measured. If index is None, all
values are used where labels is larger than zero.
"""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
if labels != None:
labels = numarray.asarray(labels)
labels = _broadcast(labels, input.shape)
if labels.shape != input.shape:
raise RuntimeError, 'input and labels shape are not equal'
return _nd_image.statistics(input, labels, index, 0)
def sum(input, labels = None, index = None):
"""Calculate the sum of the values of the array.
The index parameter is a single label number or a sequence of
label numbers of the objects to be measured. If index is None, all
values are used where labels is larger than zero.
"""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
if labels != None:
labels = numarray.asarray(labels)
labels = _broadcast(labels, input.shape)
if labels.shape != input.shape:
raise RuntimeError, 'input and labels shape are not equal'
return _nd_image.statistics(input, labels, index, 0)
def maximum_position(input, labels=None, index=None):
"""Find the position of the maximum of the values of the array.
The index parameter is a single label number or a sequence of
label numbers of the objects to be measured. If index is None, all
values are used where labels is larger than zero.
"""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
if labels != None:
labels = numarray.asarray(labels)
labels = _broadcast(labels, input.shape)
if labels.shape != input.shape:
raise RuntimeError, 'input and labels shape are not equal'
pos = _nd_image.statistics(input, labels, index, 6)
if (isinstance(pos, types.ListType)):
return [_index_to_position(x, input.shape) for x in pos]
else:
return _index_to_position(pos, input.shape)
def maximum_position(input, labels = None, index = None):
"""Find the position of the maximum of the values of the array.
The index parameter is a single label number or a sequence of
label numbers of the objects to be measured. If index is None, all
values are used where labels is larger than zero.
"""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
if labels != None:
labels = numarray.asarray(labels)
labels = _broadcast(labels, input.shape)
if labels.shape != input.shape:
raise RuntimeError, 'input and labels shape are not equal'
pos = _nd_image.statistics(input, labels, index, 6)
if (isinstance(pos, types.ListType)):
return [_index_to_position(x, input.shape) for x in pos]
else:
return _index_to_position(pos, input.shape)
def histogram(input, min, max, bins, labels=None, index=None):
"""Calculate a histogram of of the array.
The histogram is defined by its minimum and maximum value and the
number of bins.
The index parameter is a single label number or a sequence of
label numbers of the objects to be measured. If index is None, all
values are used where labels is larger than zero.
"""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
if labels != None:
labels = numarray.asarray(labels)
labels = _broadcast(labels, input.shape)
if labels.shape != input.shape:
raise RuntimeError, 'input and labels shape are not equal'
if bins < 1:
raise RuntimeError, 'number of bins must be >= 1'
if min >= max:
raise RuntimeError, 'min must be < max'
return _nd_image.histogram(input, min, max, bins, labels, index)
def histogram(input, min, max, bins, labels = None, index = None):
"""Calculate a histogram of of the array.
The histogram is defined by its minimum and maximum value and the
number of bins.
The index parameter is a single label number or a sequence of
label numbers of the objects to be measured. If index is None, all
values are used where labels is larger than zero.
"""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
if labels != None:
labels = numarray.asarray(labels)
labels = _broadcast(labels, input.shape)
if labels.shape != input.shape:
raise RuntimeError, 'input and labels shape are not equal'
if bins < 1:
raise RuntimeError, 'number of bins must be >= 1'
if min >= max:
raise RuntimeError, 'min must be < max'
return _nd_image.histogram(input, min, max, bins, labels, index)
def extrema(input, labels=None, index=None):
"""Calculate the minimum, the maximum and their positions of the
values of the array.
The index parameter is a single label number or a sequence of
label numbers of the objects to be measured. If index is None, all
values are used where labels is larger than zero.
"""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
if labels != None:
labels = numarray.asarray(labels)
labels = _broadcast(labels, input.shape)
if labels.shape != input.shape:
raise RuntimeError, 'input and labels shape are not equal'
min, max, minp, maxp = _nd_image.statistics(input, labels, index, 7)
if (isinstance(minp, types.ListType)):
minp = [_index_to_position(x, input.shape) for x in minp]
maxp = [_index_to_position(x, input.shape) for x in maxp]
else:
minp = _index_to_position(minp, input.shape)
maxp = _index_to_position(maxp, input.shape)
return min, max, minp, maxp
def extrema(input, labels = None, index = None):
"""Calculate the minimum, the maximum and their positions of the
values of the array.
The index parameter is a single label number or a sequence of
label numbers of the objects to be measured. If index is None, all
values are used where labels is larger than zero.
"""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
if labels != None:
labels = numarray.asarray(labels)
labels = _broadcast(labels, input.shape)
if labels.shape != input.shape:
raise RuntimeError, 'input and labels shape are not equal'
min, max, minp, maxp = _nd_image.statistics(input, labels, index, 7)
if (isinstance(minp, types.ListType)):
minp = [_index_to_position(x, input.shape) for x in minp]
maxp = [_index_to_position(x, input.shape) for x in maxp]
else:
minp = _index_to_position(minp, input.shape)
maxp = _index_to_position(maxp, input.shape)
return min, max, minp, maxp