[Python scipy] Upscale / downscale 2D data

Vocabulary Book. Reprinting a lot because it is troublesome to search every time. There is an original URL.

Sometimes it gets confusing:

--Upscale: Coarse resolution --Downscale: Finer resolution

Value interpolation is summarized here: [SciPy.org: Interpolation (scipy.interpolate)] (https://docs.scipy.org/doc/scipy-0.14.0/reference/interpolate.html)

Of these, the one that is often used for upscale / downscale

Example using RegularGridInterpolator

[stackoverflow: Scipy interpolation with masked data?] (https://stackoverflow.com/questions/35807321/scipy-interpolation-with-masked-data) Is easy to understand. In the case of MaskedArray, it is faster to convert the mask itself (rather than filling the masked part with np.nan).

import numpy as np
from scipy import interpolate

def conv_resol(arr, newshape, *args, **kwargs):
        arr [2d ndarray or MaskedArray]
        newshape [tuple of int]
        *args, **kwargs: for interpolate.RegularGridInterpolator
    nx0, ny0 = arr.shape
    nx1, ny1 = newshape
    x0 = np.linspace(0, 1, nx0)
    y0 = np.linspace(0, 1, ny0)
    x1 = np.linspace(0, 1, nx1)
    y1 = np.linspace(0, 1, ny1)
    x1, y1 = np.meshgrid(x1, y1) # x1 [ny1,nx1], y1 [ny1, nx1]
    xy1 = np.array((x1, y1)).T   # xy1 [nx1, ny1, 2]
    arr1 = interpolate.RegularGridInterpolator((x0, y0), arr, *args, **kwargs)(xy1)
    if isinstance(arr, np.ma.MaskedArray):
        mask1 = interpolate.RegularGridInterpolator((x0, y0), arr.mask.astype('float'), *args, **kwargs)(xy1) > 0.0
        return np.ma.masked_array(arr1, mask=mask1)
    return arr1


import matplotlib.pyplot as plt

nx0, ny0 = 10, 20
arr0 = np.arange(nx0 * ny0).reshape(nx0, ny0) * (1.0 / (nx0 * ny0))

nx1, ny1 = 5, 10
arr1 = conv_resol(arr0, (nx1, ny1))

nx2, ny2 = 20, 40
arr2 = conv_resol(arr0, (nx2, ny2))

plt.imshow(arr0, vmin=0.0, vmax=1.0)
plt.imshow(arr1, vmin=0.0, vmax=1.0)
plt.imshow(arr2, vmin=0.0, vmax=1.0)


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