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How to use test_kernel method in autotest

Best Python code snippet using autotest_python

test_convolve_nddata.py

Source:test_convolve_nddata.py

1# Licensed under a 3-clause BSD style license - see LICENSE.rst2import pytest3import numpy as np4from astropy.convolution.convolve import convolve, convolve_fft5from astropy.convolution.kernels import Gaussian2DKernel6from astropy.nddata import NDData7def test_basic_nddata():8    arr = np.zeros((11, 11))9    arr[5, 5] = 110    ndd = NDData(arr)11    test_kernel = Gaussian2DKernel(1)12    result = convolve(ndd, test_kernel)13    x, y = np.mgrid[:11, :11]14    expected = result[5, 5] * np.exp(-0.5 * ((x - 5)**2 + (y - 5)**2))15    np.testing.assert_allclose(result, expected, atol=1e-6)16    resultf = convolve_fft(ndd, test_kernel)17    np.testing.assert_allclose(resultf, expected, atol=1e-6)18@pytest.mark.parametrize('convfunc',19   [lambda *args: convolve(*args, nan_treatment='interpolate', normalize_kernel=True),20    lambda *args: convolve_fft(*args, nan_treatment='interpolate', normalize_kernel=True)])21def test_masked_nddata(convfunc):22    arr = np.zeros((11, 11))23    arr[4, 5] = arr[6, 5] = arr[5, 4] = arr[5, 6] = 0.224    arr[5, 5] = 1.525    ndd_base = NDData(arr)26    mask = arr < 0  # this is all False27    mask[5, 5] = True28    ndd_mask = NDData(arr, mask=mask)29    arrnan = arr.copy()30    arrnan[5, 5] = np.nan31    ndd_nan = NDData(arrnan)32    test_kernel = Gaussian2DKernel(1)33    result_base = convfunc(ndd_base, test_kernel)34    result_nan = convfunc(ndd_nan, test_kernel)35    result_mask = convfunc(ndd_mask, test_kernel)36    assert np.allclose(result_nan, result_mask)37    assert not np.allclose(result_base, result_mask)38    assert not np.allclose(result_base, result_nan)39    # check to make sure the mask run doesn't talk back to the initial array...

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