Package ndimage :: Module measurements

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Module measurements

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Functions

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label(input, structure=None, output=None)
Label an array of objects.

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find_objects(input, max_label=0)
Find objects in a labeled array.

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sum(input, labels=None, index=None)
Calculate the sum of the values of the array.

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mean(input, labels=None, index=None)
Calculate the mean of the values of the array.

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variance(input, labels=None, index=None)
Calculate the variance of the values of the array.

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standard_deviation(input, labels=None, index=None)
Calculate the standard deviation of the values of the array.

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minimum(input, labels=None, index=None)
Calculate the minimum of the values of the array.

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maximum(input, labels=None, index=None)
Return the maximum input value.

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_index_to_position(index, shape)
Convert a linear index to a position

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minimum_position(input, labels=None, index=None)
Find the position of the minimum of the values of the array.

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maximum_position(input, labels=None, index=None)
Find the position of the maximum of the values of the array.

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extrema(input, labels=None, index=None)
Calculate the minimum, the maximum and their positions of the values of the array.

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center_of_mass(input, labels=None, index=None)
Calculate the center of mass of of the array.

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histogram(input, min, max, bins, labels=None, index=None)
Calculate a histogram of of the array.

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watershed_ift(input, markers, structure=None, output=None)
Apply watershed from markers using a iterative forest transform algorithm.

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_broadcast(arr, sshape)
Return broadcast view of arr, else return None.

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Variables

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__package__ = 'ndimage'

Imports: types, math, numpy, _ni_support, _nd_image, morphology

Function Details

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label(input, structure=None, output=None)

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Label an array of objects.

The structure that defines the object connections must be symmetric. If no structuring element is provided an element is generated with a squared connectivity equal to one. This function returns a tuple consisting of the array of labels and the number of objects found. If an output array is provided only the number of objects found is returned.

find_objects(input, max_label=0)

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Find objects in a labeled array.

The input must be an array with labeled objects. A list of slices into the array is returned that contain the objects. The list represents a sequence of the numbered objects. If a number is missing, None is returned instead of a slice. If max_label > 0, it gives the largest object number that is searched for, otherwise all are returned.

sum(input, labels=None, index=None)

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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]

mean(input, labels=None, index=None)

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Calculate the mean 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.

variance(input, labels=None, index=None)

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Calculate the variance 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.

standard_deviation(input, labels=None, index=None)

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Calculate the standard deviation 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.

minimum(input, labels=None, index=None)

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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.

maximum(input, labels=None, index=None)

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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.

minimum_position(input, labels=None, index=None)

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Find the position of 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.

maximum_position(input, labels=None, index=None)

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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.

extrema(input, labels=None, index=None)

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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.

center_of_mass(input, labels=None, index=None)

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Calculate the center of mass of 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.

histogram(input, min, max, bins, labels=None, index=None)

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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.

watershed_ift(input, markers, structure=None, output=None)

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Apply watershed from markers using a iterative forest transform algorithm.

Negative markers are considered background markers which are processed after the other markers. A structuring element defining the connectivity of the object can be provided. If none is provided an element is generated iwth a squared connecitiviy equal to one. An output array can optionally be provided.