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# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
from ...tests.helper import pytest
from ..convolve import convolve, convolve_fft
from ..kernels import Gaussian2DKernel
from ...nddata import NDData
def test_basic_nddata():
arr = np.zeros((11, 11))
arr[5, 5] = 1
ndd = NDData(arr)
test_kernel = Gaussian2DKernel(1)
result = convolve(ndd, test_kernel)
x, y = np.mgrid[:11, :11]
expected = result[5, 5] * np.exp(-0.5 * ((x - 5)**2 + (y - 5)**2))
np.testing.assert_allclose(result, expected, atol=1e-6)
resultf = convolve_fft(ndd, test_kernel)
np.testing.assert_allclose(resultf, expected, atol=1e-6)
@pytest.mark.parametrize('convfunc', [convolve,
lambda *args: convolve_fft(*args, interpolate_nan=True, normalize_kernel=True)])
def test_masked_nddata(convfunc):
arr = np.zeros((11, 11))
arr[4, 5] = arr[6, 5] = arr[5, 4] = arr[5, 6] = 0.2
ndd_base = NDData(arr)
mask = arr < 0
mask[5, 5] = True
ndd_mask = NDData(arr, mask=mask)
arrnan = arr.copy()
arrnan[5, 5] = np.nan
ndd_nan = NDData(arrnan)
test_kernel = Gaussian2DKernel(1)
result_base = convfunc(ndd_base, test_kernel)
result_nan = convfunc(ndd_nan, test_kernel)
result_mask = convfunc(ndd_mask, test_kernel)
assert np.allclose(result_nan, result_mask)
assert not np.allclose(result_base, result_mask)
assert not np.allclose(result_base, result_nan)
# check to make sure the mask run doesn't talk back to the initial array
assert np.sum(np.isnan(ndd_base.data)) != np.sum(np.isnan(ndd_nan.data))
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