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 = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, "Complex type not supported"
if labels is not None:
labels = numpy.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 = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
if labels is not None:
labels = numpy.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 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 minimum(input, labels=None, index=None):
"""Calculate the minimum 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 = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, "Complex type not supported"
if labels is not None:
labels = numpy.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, 3)
def maximum(input, labels=None, index=None):
"""Return the maximum input value.
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 = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
if labels is not None:
labels = numpy.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, 4)
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 sum(input, labels=None, index=None):
"""Calculate the sum of the values of the array.
:Parameters:
labels : array of integers, same shape as input
Assign labels to the values of the array.
index : scalar or array
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.
Examples
--------
>>> input = [0,1,2,3]
>>> labels = [1,1,2,2]
>>> sum(input, labels, index=[1,2])
[1.0, 5.0]
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
if labels is not None:
labels = numpy.asarray(labels)
labels = _broadcast(labels, input.shape)
if labels.shape != input.shape:
raise RuntimeError, 'input and labels shape are not equal'
if index is not None:
T = getattr(index, 'dtype', numpy.int32)
if T not in [
numpy.int8, numpy.int16, numpy.int32, numpy.uint8,
numpy.uint16, numpy.bool
]:
raise ValueError("Invalid index type")
index = numpy.asarray(index, dtype=T)
return _nd_image.statistics(input, labels, index, 0)
def sum(input, labels=None, index=None):
"""Calculate the sum of the values of the array.
:Parameters:
labels : array of integers, same shape as input
Assign labels to the values of the array.
index : scalar or array
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.
Examples
--------
>>> input = [0,1,2,3]
>>> labels = [1,1,2,2]
>>> sum(input, labels, index=[1,2])
[1.0, 5.0]
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
if labels is not None:
labels = numpy.asarray(labels)
labels = _broadcast(labels, input.shape)
if labels.shape != input.shape:
raise RuntimeError, 'input and labels shape are not equal'
if index is not None:
T = getattr(index,'dtype',numpy.int32)
if T not in [numpy.int8, numpy.int16, numpy.int32,
numpy.uint8, numpy.uint16, numpy.bool]:
raise ValueError("Invalid index type")
index = numpy.asarray(index,dtype=T)
return _nd_image.statistics(input, labels, index, 0)