Resample 2d Numpy Array To Arbitrary Dimensions

I am looking for a way to rescale a numpy 2D array to arbitrary dimensions in such a way that each cell in the rescaled array contains a weighted mean of all the cells that it (par

Solution 1:

Here you go:#

It uses the Interval package to easily calculate the overlaps of the cells of the different grids, so you'll need to grab that.

from matplotlib import pyplot
import numpy
from interval import Interval, IntervalSet

def overlap(rect1, rect2):
  """Calculate the overlap between two rectangles"""
  xInterval = Interval(rect1[0][0], rect1[1][0]) & Interval(rect2[0][0], rect2[1][0])
  yInterval = Interval(rect1[0][1], rect1[1][1]) & Interval(rect2[0][1], rect2[1][1])
  area = (xInterval.upper_bound - xInterval.lower_bound) * (yInterval.upper_bound - yInterval.lower_bound)
  return area


def meanInterp(data, m, n):

  newData = numpy.zeros((m,n))
  mOrig, nOrig = data.shape

  hBoundariesOrig, vBoundariesOrig = numpy.linspace(0,1,mOrig+1), numpy.linspace(0,1,nOrig+1)
  hBoundaries, vBoundaries = numpy.linspace(0,1,m+1), numpy.linspace(0,1,n+1)

  for iOrig in range(mOrig):
    for jOrig in range(nOrig):
      for i in range(m):
        if hBoundaries[i+1] <= hBoundariesOrig[iOrig]: continue
        if hBoundaries[i] >= hBoundariesOrig[iOrig+1]: break
        for j in range(n):
          if vBoundaries[j+1] <= vBoundariesOrig[jOrig]: continue
          if vBoundaries[j] >= vBoundariesOrig[jOrig+1]: break

          boxCoords = ((hBoundaries[i], vBoundaries[j]),(hBoundaries[i+1], vBoundaries[j+1]))
          origBoxCoords = ((hBoundariesOrig[iOrig], vBoundariesOrig[jOrig]),(hBoundariesOrig[iOrig+1], vBoundariesOrig[jOrig+1]))

          newData[i][j] += overlap(boxCoords, origBoxCoords) * data[iOrig][jOrig] / (hBoundaries[1] * vBoundaries[1])

  return newData



fig = pyplot.figure()
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)

m1, n1 = 37,59
m2, n2 = 10,13

dataGrid1 = numpy.random.rand(m1, n1)
dataGrid2 = meanInterp(dataGrid1, m2, n2)

mat1 = ax1.matshow(dataGrid1, cmap="YlOrRd")
mat2 = ax2.matshow(dataGrid2, cmap="YlOrRd")

#make both plots square
ax1.set_aspect(float(n1)/float(m1))
ax2.set_aspect(float(n2)/float(m2))



pyplot.show()

Here are a couple of examples with differing grids:

Down sampling is possible too.

After having done this, i'm pretty sure all i've done is some form of image sampling. If you're looking to do this on large lists, then you're going to need to make things a bit more efficient, as it will be pretty slow.