コード例 #1
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ファイル: RandomArray2.py プロジェクト: joshfermin/AI
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
コード例 #2
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ファイル: RandomArray2.py プロジェクト: fxia22/ASM_xf
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
コード例 #3
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ファイル: measurements.py プロジェクト: joshfermin/AI
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)
コード例 #4
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ファイル: measurements.py プロジェクト: fxia22/ASM_xf
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)
コード例 #5
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ファイル: measurements.py プロジェクト: joshfermin/AI
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)
コード例 #6
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ファイル: measurements.py プロジェクト: fxia22/ASM_xf
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)
コード例 #7
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ファイル: measurements.py プロジェクト: joshfermin/AI
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)
コード例 #8
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ファイル: measurements.py プロジェクト: fxia22/ASM_xf
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)
コード例 #9
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ファイル: measurements.py プロジェクト: joshfermin/AI
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
コード例 #10
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ファイル: measurements.py プロジェクト: fxia22/ASM_xf
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