imgProcessor.interpolate package

Submodules

imgProcessor.interpolate.interpolate2dStructuredIDW module

imgProcessor.interpolate.interpolate2dStructuredIDW.interpolate2dStructuredIDW(grid, mask, kernel=15, power=2, fx=1, fy=1)

replace all values in [grid] indicated by [mask] with the inverse distance weighted interpolation of all values within px+-kernel [power] -> distance weighting factor: 1/distance**[power]

imgProcessor.interpolate.interpolate2dStructuredPointSpreadIDW module

imgProcessor.interpolate.interpolate2dStructuredPointSpreadIDW.interpolate2dStructuredPointSpreadIDW(grid, mask, kernel=15, power=2, maxIter=100000.0, copy=True)

same as interpolate2dStructuredIDW but using the point spread method this is faster if there are bigger connected masked areas and the border length is smaller

replace all values in [grid] indicated by [mask] with the inverse distance weighted interpolation of all values within px+-kernel

[power] -> distance weighting factor: 1/distance**[power] [copy] -> False: a bit faster, but modifies ‘grid’ and ‘mask’

imgProcessor.interpolate.interpolate2dUnstructuredIDW module

imgProcessor.interpolate.interpolate2dUnstructuredIDW.interpolate2dUnstructuredIDW

x,y,v –> 1d numpy.array grid –> 2d numpy.array

fast if number of given values is small relative to grid resolution

imgProcessor.interpolate.interpolateCircular2dStructuredIDW module

imgProcessor.interpolate.interpolateCircular2dStructuredIDW.interpolateCircular2dStructuredIDW

same as interpolate2dStructuredIDW but calculation distance to neighbour using polar coordinates fr, fphi –> weight factors for radian and radius differences cx,cy -> polar center of the array e.g. middle->(sx//2+1,sy//2+1)

imgProcessor.interpolate.polyfit2d module

imgProcessor.interpolate.polyfit2d.polyfit2d(x, y, z, order=3)

fit unstructured data

imgProcessor.interpolate.polyfit2d.polyfit2dGrid(arr, mask, order=3, copy=False)

replace all masked values with polynomal fitted ones

imgProcessor.interpolate.polyfit2d.polyval2d(x, y, m, dtype=None)