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# Image Array Manipulation and Convolution (`scipy.stsci`)

## Image Array manipulation Functions (`scipy.stsci.image`)

`average` (arrays[, output, outtype, ...]) | average() nominally computes the average pixel value for a stack of identically shaped images.
`combine` |
`median` (arrays[, output, outtype, ...]) | median() nominally computes the median pixels for a stack of identically shaped images.
`minimum` (arrays[, output, outtype, ...]) | minimum() nominally computes the minimum pixel value for a stack of identically shaped images.
`threshhold` (arrays[, low, high, outputs]) | threshhold() computes a boolean array ‘outputs’ with corresponding elements for each element of arrays. The boolean value is true where each of the arrays values is < the low or >= the high threshholds.
`translate` (a, sdx, sdy[, output, mode, cval]) | translate performs a translation of ‘a’ by (sdx, sdy) storing the result in ‘output’.

## Image Array Convolution Functions (`scipy.stsci.convolve`)

`boxcar` (data, boxshape[, output, mode, cval]) | boxcar computes a 1D or 2D boxcar filter on every 1D or 2D subarray of data.
`convolution_modes` () -> new empty dictionary. dict(mapping) -> new dictionary initialized from a mapping object’s (key, value) pairs. dict(seq) -> new dictionary initialized as if via: d , v in seq: d[k] = v dict(**kwargs) -> new dictionary initialized with the name, ...]) |
`convolve` (data, kernel[, mode]) | convolve(data, kernel, mode=FULL) Returns the discrete, linear convolution of 1-D sequences a and v; mode can be 0 (VALID), 1 (SAME), or 2 (FULL) to specify size of the resulting sequence.
`convolve2d` (data, kernel[, output, mode, cval, ...]) | convolve2d does 2d convolution of ‘data’ with ‘kernel’, storing the result in ‘output’.
`correlate` (data, kernel[, mode]) |
    >>> correlate(np.arange(8), [1, 2], mode=VALID) array([ 2,  5,  8, 11, 14, 17, 20]) >>> correlate(np.arange(8), [1, 2], mode=SAME) array([ 0,  2,  5,  8, 11, 14, 17, 20]) >>> correlate(np.arange(8), [1, 2], mode=FULL) array([ 0,  2,  5,  8, 11, 14, 17, 20,  7]) >>> correlate(np.arange(8), [1, 2, 3], mode=VALID) array([ 8, 14, 20, 26, 32, 38]) >>> correlate(np.arange(8), [1, 2, 3], mode=SAME) array([ 3,  8, 14, 20, 26, 32, 38, 20]) >>> correlate(np.arange(8), [1, 2, 3], mode=FULL) array([ 0,  3,  8, 14, 20, 26, 32, 38, 20,  7]) >>> correlate(np.arange(8), [1, 2, 3, 4, 5, 6], mode=VALID) array([ 70,  91, 112]) >>> correlate(np.arange(8), [1, 2, 3, 4, 5, 6], mode=SAME) array([ 17,  32,  50,  70,  91, 112,  85,  60]) >>> correlate(np.arange(8), [1, 2, 3, 4, 5, 6], mode=FULL) array([  0,   6,  17,  32,  50,  70,  91, 112,  85,  60,  38,  20,   7]) >>> correlate(np.arange(8), 1+1j) Traceback (most recent call last): ... TypeError: array cannot be safely cast to required type
`correlate2d` (data, kernel[, output, mode, cval, ...]) | correlate2d does 2d correlation of ‘data’ with ‘kernel’, storing the result in ‘output’.
`cross_correlate` (data, kernel[, mode]) |
    >>> correlate(np.arange(8), [1, 2], mode=VALID) array([ 2,  5,  8, 11, 14, 17, 20]) >>> correlate(np.arange(8), [1, 2], mode=SAME) array([ 0,  2,  5,  8, 11, 14, 17, 20]) >>> correlate(np.arange(8), [1, 2], mode=FULL) array([ 0,  2,  5,  8, 11, 14, 17, 20,  7]) >>> correlate(np.arange(8), [1, 2, 3], mode=VALID) array([ 8, 14, 20, 26, 32, 38]) >>> correlate(np.arange(8), [1, 2, 3], mode=SAME) array([ 3,  8, 14, 20, 26, 32, 38, 20]) >>> correlate(np.arange(8), [1, 2, 3], mode=FULL) array([ 0,  3,  8, 14, 20, 26, 32, 38, 20,  7]) >>> correlate(np.arange(8), [1, 2, 3, 4, 5, 6], mode=VALID) array([ 70,  91, 112]) >>> correlate(np.arange(8), [1, 2, 3, 4, 5, 6], mode=SAME) array([ 17,  32,  50,  70,  91, 112,  85,  60]) >>> correlate(np.arange(8), [1, 2, 3, 4, 5, 6], mode=FULL) array([  0,   6,  17,  32,  50,  70,  91, 112,  85,  60,  38,  20,   7]) >>> correlate(np.arange(8), 1+1j) Traceback (most recent call last): ... TypeError: array cannot be safely cast to required type
`dft` |
`iraf_frame` |
`pix_modes` () -> new empty dictionary. dict(mapping) -> new dictionary initialized from a mapping object’s (key, value) pairs. dict(seq) -> new dictionary initialized as if via: d , v in seq: d[k] = v dict(**kwargs) -> new dictionary initialized with the name, ...]) |

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