/usr/lib/python3/dist-packages/photutils/psf/tests/test_imagemodel.py is in python3-photutils 0.4-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import division
import numpy as np
from astropy.modeling.models import Gaussian2D
import pytest
from .. import FittableImageModel
try:
import scipy # noqa
HAS_SCIPY = True
except ImportError:
HAS_SCIPY = False
@pytest.mark.skipif('not HAS_SCIPY')
def test_image_model():
gm = Gaussian2D(x_stddev=3, y_stddev=3)
xg, yg = np.mgrid[-2:3, -2:3]
imod_nonorm = FittableImageModel(gm(xg, yg))
assert np.allclose(imod_nonorm(0, 0), gm(0, 0))
assert np.allclose(imod_nonorm(1, 1), gm(1, 1))
assert np.allclose(imod_nonorm(-2, 1), gm(-2, 1))
# now sub-pixel should *not* match, but be reasonably close
assert not np.allclose(imod_nonorm(0.5, 0.5), gm(0.5, 0.5))
# in this case good to ~0.1% seems to be fine
assert np.allclose(imod_nonorm(0.5, 0.5), gm(0.5, 0.5), rtol=.001)
assert np.allclose(imod_nonorm(-0.5, 1.75), gm(-0.5, 1.75), rtol=.001)
imod_norm = FittableImageModel(gm(xg, yg), normalize=True)
assert not np.allclose(imod_norm(0, 0), gm(0, 0))
assert np.allclose(np.sum(imod_norm(xg, yg)), 1)
imod_norm2 = FittableImageModel(gm(xg, yg), normalize=True,
normalization_correction=2)
assert not np.allclose(imod_norm2(0, 0), gm(0, 0))
assert np.allclose(imod_norm(0, 0), imod_norm2(0, 0)*2)
assert np.allclose(np.sum(imod_norm2(xg, yg)), 0.5)
@pytest.mark.skipif('not HAS_SCIPY')
def test_image_model_oversampling():
gm = Gaussian2D(x_stddev=3, y_stddev=3)
osa = 3 # oversampling factor
xg, yg = np.mgrid[-3:3.00001:(1/osa), -3:3.00001:(1/osa)]
im = gm(xg, yg)
# should be obvious, but at least ensures the test is right:
assert im.shape[0] > 7
imod_oversampled = FittableImageModel(im, oversampling=osa)
assert np.allclose(imod_oversampled(0, 0), gm(0, 0))
assert np.allclose(imod_oversampled(1, 1), gm(1, 1))
assert np.allclose(imod_oversampled(-2, 1), gm(-2, 1))
assert np.allclose(imod_oversampled(0.5, 0.5), gm(0.5, 0.5), rtol=.001)
assert np.allclose(imod_oversampled(-0.5, 1.75), gm(-0.5, 1.75), rtol=.001)
imod_wrongsampled = FittableImageModel(im)
# now make sure that all *fails* without the oversampling
# except for at the origin
assert np.allclose(imod_wrongsampled(0, 0), gm(0, 0))
assert not np.allclose(imod_wrongsampled(1, 1), gm(1, 1))
assert not np.allclose(imod_wrongsampled(-2, 1), gm(-2, 1))
assert not np.allclose(imod_wrongsampled(0.5, 0.5), gm(0.5, 0.5),
rtol=.001)
assert not np.allclose(imod_wrongsampled(-0.5, 1.75), gm(-0.5, 1.75),
rtol=.001)
@pytest.mark.skipif('not HAS_SCIPY')
def test_centering_oversampled():
gm = Gaussian2D(x_stddev=2, y_stddev=3)
osa = 3 # oversampling factor
xg, yg = np.mgrid[-3:3.00001:(1/osa), -3:3.00001:(1/osa)]
imod_oversampled = FittableImageModel(gm(xg, yg), oversampling=osa)
valcen = gm(0, 0)
val36 = gm(0.66, 0.66)
assert np.allclose(valcen, imod_oversampled(0, 0))
assert np.allclose(val36, imod_oversampled(0.66, 0.66))
imod_oversampled.x_0 = 2.5
imod_oversampled.y_0 = -3.5
assert np.allclose(valcen, imod_oversampled(2.5, -3.5))
assert np.allclose(val36, imod_oversampled(2.5 + 0.66, -3.5 + 0.66))
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