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SciPy Reference Guide, Release 0.8.dev See Also: Returns return_value : ndarray The result of transforming the input. The shape of the output is derived from that of coordinates by dropping the first axis. spline_filter, geometric_transform, scipy.interpolate Examples >>> import scipy.ndimage >>> a = np.arange(12.).reshape((4,3)) >>> print a array([[ 0., 1., 2.], [ 3., 4., 5.], [ 6., 7., 8.], [ 9., 10., 11.]]) >>> sp.ndimage.map_coordinates(a, [[0.5, 2], [0.5, 1]], order=1) [ 2. 7.] Above, the interpolated value of a[0.5, 0.5] gives output[0], while a[2, 1] is output[1]. >>> inds = np.array([[0.5, 2], [0.5, 4]]) >>> sp.ndimage.map_coordinates(a, inds, order=1, cval=-33.3) array([ 2. , -33.3]) >>> sp.ndimage.map_coordinates(a, inds, order=1, mode=’nearest’) array([ 2., 8.]) >>> sp.ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool) array([ True, False], dtype=bool rotate(input, angle, axes=(1, 0), reshape=True, output_type=None, output=None, order=3, mode=’constant’, cval=0.0, prefilter=True) Rotate an array. The array is rotated in the plane defined by the two axes given by the axes parameter using spline interpolation of the requested order. The angle is given in degrees. Points outside the boundaries of the input are filled according to the given mode. If reshape is true, the output shape is adapted so that the input array is contained completely in the output. The parameter prefilter determines if the input is pre- filtered before interpolation, if False it is assumed that the input is already filtered. shift(input, shift, output_type=None, output=None, order=3, mode=’constant’, cval=0.0, prefilter=True) Shift an array. The array is shifted using spline interpolation of the requested order. Points outside the boundaries of the input are filled according to the given mode. The parameter prefilter determines if the input is pre-filtered before interpolation, if False it is assumed that the input is already filtered. spline_filter(input, order=3, output=, output_type=None) Multi-dimensional spline filter. Note: The multi-dimensional filter is implemented as a sequence of one-dimensional spline filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision. spline_filter1d(input, order=3, axis=-1, output=, output_type=None) Calculates a one-dimensional spline filter along the given axis. The lines of the array along the given axis are filtered by a spline filter. The order of the spline must be >= 2 and
SciPy Reference Guide, Release 0.8.dev zoom(input, zoom, output_type=None, output=None, order=3, mode=’constant’, cval=0.0, prefilter=True) Zoom an array. The array is zoomed using spline interpolation of the requested order. Points outside the boundaries of the input are filled according to the given mode. The parameter prefilter determines if the input is pre- filtered before interpolation, if False it is assumed that the input is already filtered. 3.10.4 Measurements scipy.ndimage.measurements center_of_mass(input[, labels, index]) Calculate the center of mass of of the array. extrema(input[, labels, index]) Calculate the minimum, the maximum and their positions of the values of the array. find_objects(input[, max_label]) Find objects in a labeled array. histogram(input, min, max, bins[, labels, index]) Calculate a histogram of of the array. label(input[, structure, output]) Label features in an array. maximum(input[, labels, index]) Return the maximum input value. maximum_position(input[, labels, index]) Find the position of the maximum of the values of the array. mean(input[, labels, index]) Calculate the mean of the values of the array. minimum(input[, labels, index]) Calculate the minimum of the values of the array. minimum_position(input[, labels, index]) Find the position of the minimum of the values of the array. standard_deviation(input[, labels, index]) Calculate the standard deviation of the values of the array. sum(input[, labels, index]) Calculate the sum of the values of the array. variance(input[, labels, index]) Calculate the variance of the values of the array. watershed_ift(input, markers[, Apply watershed from markers using a iterative forest transform structure, ...]) algorithm. center_of_mass(input, labels=None, index=None) 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. 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. find_objects(input, max_label=0) 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. 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. 3.10. Multi-dimensional image processing (scipy.ndimage) 283
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