/usr/lib/python3/dist-packages/photutils/centroids/tests/test_core.py is in python3-photutils 0.4-1.
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from __future__ import (absolute_import, division, print_function,
unicode_literals)
import itertools
import numpy as np
from numpy.testing import assert_allclose
from astropy.modeling.models import Gaussian1D, Gaussian2D
import pytest
from ..core import (centroid_com, centroid_1dg, centroid_2dg,
gaussian1d_moments, fit_2dgaussian)
try:
import skimage # noqa
HAS_SKIMAGE = True
except ImportError:
HAS_SKIMAGE = False
XCS = [25.7]
YCS = [26.2]
XSTDDEVS = [3.2, 4.0]
YSTDDEVS = [5.7, 4.1]
THETAS = np.array([30., 45.]) * np.pi / 180.
DATA = np.zeros((3, 3))
DATA[0:2, 1] = 1.
DATA[1, 0:2] = 1.
DATA[1, 1] = 2.
@pytest.mark.parametrize(
('xc_ref', 'yc_ref', 'x_stddev', 'y_stddev', 'theta'),
list(itertools.product(XCS, YCS, XSTDDEVS, YSTDDEVS, THETAS)))
@pytest.mark.skipif('not HAS_SKIMAGE')
def test_centroids(xc_ref, yc_ref, x_stddev, y_stddev, theta):
model = Gaussian2D(2.4, xc_ref, yc_ref, x_stddev=x_stddev,
y_stddev=y_stddev, theta=theta)
y, x = np.mgrid[0:50, 0:47]
data = model(x, y)
xc, yc = centroid_com(data)
assert_allclose([xc_ref, yc_ref], [xc, yc], rtol=0, atol=1.e-3)
xc2, yc2 = centroid_1dg(data)
assert_allclose([xc_ref, yc_ref], [xc2, yc2], rtol=0, atol=1.e-3)
xc3, yc3 = centroid_2dg(data)
assert_allclose([xc_ref, yc_ref], [xc3, yc3], rtol=0, atol=1.e-3)
@pytest.mark.parametrize(
('xc_ref', 'yc_ref', 'x_stddev', 'y_stddev', 'theta'),
list(itertools.product(XCS, YCS, XSTDDEVS, YSTDDEVS, THETAS)))
@pytest.mark.skipif('not HAS_SKIMAGE')
def test_centroids_witherror(xc_ref, yc_ref, x_stddev, y_stddev, theta):
model = Gaussian2D(2.4, xc_ref, yc_ref, x_stddev=x_stddev,
y_stddev=y_stddev, theta=theta)
y, x = np.mgrid[0:50, 0:50]
data = model(x, y)
error = np.sqrt(data)
xc2, yc2 = centroid_1dg(data, error=error)
assert_allclose([xc_ref, yc_ref], [xc2, yc2], rtol=0, atol=1.e-3)
xc3, yc3 = centroid_2dg(data, error=error)
assert_allclose([xc_ref, yc_ref], [xc3, yc3], rtol=0, atol=1.e-3)
@pytest.mark.skipif('not HAS_SKIMAGE')
def test_centroids_withmask():
xc_ref, yc_ref = 24.7, 25.2
model = Gaussian2D(2.4, xc_ref, yc_ref, x_stddev=5.0, y_stddev=5.0)
y, x = np.mgrid[0:50, 0:50]
data = model(x, y)
mask = np.zeros_like(data, dtype=bool)
data[10, 10] = 1.e5
mask[10, 10] = True
xc, yc = centroid_com(data, mask=mask)
assert_allclose([xc, yc], [xc_ref, yc_ref], rtol=0, atol=1.e-3)
xc2, yc2 = centroid_1dg(data, mask=mask)
assert_allclose([xc2, yc2], [xc_ref, yc_ref], rtol=0, atol=1.e-3)
xc3, yc3 = centroid_2dg(data, mask=mask)
assert_allclose([xc3, yc3], [xc_ref, yc_ref], rtol=0, atol=1.e-3)
@pytest.mark.skipif('not HAS_SKIMAGE')
@pytest.mark.parametrize('use_mask', [True, False])
def test_centroids_nan_withmask(use_mask):
xc_ref, yc_ref = 24.7, 25.2
model = Gaussian2D(2.4, xc_ref, yc_ref, x_stddev=5.0, y_stddev=5.0)
y, x = np.mgrid[0:50, 0:50]
data = model(x, y)
data[20, :] = np.nan
if use_mask:
mask = np.zeros_like(data, dtype=bool)
mask[20, :] = True
else:
mask = None
xc, yc = centroid_com(data, mask=mask)
assert_allclose(xc, xc_ref, rtol=0, atol=1.e-3)
assert yc > yc_ref
xc2, yc2 = centroid_1dg(data, mask=mask)
assert_allclose([xc2, yc2], [xc_ref, yc_ref], rtol=0, atol=1.e-3)
xc3, yc3 = centroid_2dg(data, mask=mask)
assert_allclose([xc3, yc3], [xc_ref, yc_ref], rtol=0, atol=1.e-3)
@pytest.mark.skipif('not HAS_SKIMAGE')
def test_centroid_com_mask():
"""Test centroid_com with and without an image_mask."""
data = np.ones((2, 2)).astype(np.float)
mask = [[False, False], [True, True]]
centroid = centroid_com(data, mask=None)
centroid_mask = centroid_com(data, mask=mask)
assert_allclose([0.5, 0.5], centroid, rtol=0, atol=1.e-6)
assert_allclose([0.5, 0.0], centroid_mask, rtol=0, atol=1.e-6)
@pytest.mark.skipif('not HAS_SKIMAGE')
def test_invalid_mask_shape():
"""
Test if ValueError raises if mask shape doesn't match data
shape.
"""
data = np.zeros((4, 4))
mask = np.zeros((2, 2), dtype=bool)
with pytest.raises(ValueError):
centroid_com(data, mask=mask)
with pytest.raises(ValueError):
centroid_1dg(data, mask=mask)
with pytest.raises(ValueError):
centroid_2dg(data, mask=mask)
with pytest.raises(ValueError):
gaussian1d_moments(data, mask=mask)
@pytest.mark.skipif('not HAS_SKIMAGE')
def test_invalid_error_shape():
"""
Test if ValueError raises if error shape doesn't match data
shape.
"""
error = np.zeros((2, 2), dtype=bool)
with pytest.raises(ValueError):
centroid_1dg(np.zeros((4, 4)), error=error)
with pytest.raises(ValueError):
centroid_2dg(np.zeros((4, 4)), error=error)
def test_gaussian1d_moments():
x = np.arange(100)
desired = (75, 50, 5)
g = Gaussian1D(*desired)
data = g(x)
result = gaussian1d_moments(data)
assert_allclose(result, desired, rtol=0, atol=1.e-6)
data[0] = 1.e5
mask = np.zeros_like(data).astype(bool)
mask[0] = True
result = gaussian1d_moments(data, mask=mask)
assert_allclose(result, desired, rtol=0, atol=1.e-6)
data[0] = np.nan
mask = np.zeros_like(data).astype(bool)
mask[0] = True
result = gaussian1d_moments(data, mask=mask)
assert_allclose(result, desired, rtol=0, atol=1.e-6)
def test_fit2dgaussian_dof():
data = np.ones((2, 2))
with pytest.raises(ValueError):
fit_2dgaussian(data)
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