/usr/lib/python3/dist-packages/photutils/background/tests/test_background_2d.py is in python3-photutils 0.3-3.
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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.tests.helper import pytest
from ..core import MeanBackground
from ..background_2d import (BkgZoomInterpolator, BkgIDWInterpolator,
Background2D)
try:
import scipy
HAS_SCIPY = True
except ImportError:
HAS_SCIPY = False
DATA = np.ones((100, 100))
BKG_RMS = np.zeros((100, 100))
BKG_MESH = np.ones((4, 4))
BKG_RMS_MESH = np.zeros((4, 4))
PADBKG_MESH = np.ones((5, 5))
PADBKG_RMS_MESH = np.zeros((5, 5))
FILTER_SIZES = [(1, 1), (3, 3)]
EDGE_METHODS = ['pad', 'crop']
INTERPOLATORS = [BkgZoomInterpolator(), BkgIDWInterpolator()]
@pytest.mark.skipif('not HAS_SCIPY')
class TestBackground2D(object):
@pytest.mark.parametrize(('filter_size', 'interpolator'),
list(itertools.product(FILTER_SIZES,
INTERPOLATORS)))
def test_background(self, filter_size, interpolator):
b = Background2D(DATA, (25, 25), filter_size=filter_size,
interpolator=interpolator)
assert_allclose(b.background, DATA)
assert_allclose(b.background_rms, BKG_RMS)
assert_allclose(b.background_mesh, BKG_MESH)
assert_allclose(b.background_rms_mesh, BKG_RMS_MESH)
assert b.background_median == 1.0
assert b.background_rms_median == 0.0
@pytest.mark.parametrize('interpolator', INTERPOLATORS)
def test_background_nonconstant(self, interpolator):
data = np.copy(DATA)
data[25:50, 50:75] = 10.
bkg_low_res = np.copy(BKG_MESH)
bkg_low_res[1, 2] = 10.
b1 = Background2D(data, (25, 25), filter_size=(1, 1),
interpolator=interpolator)
assert_allclose(b1.background_mesh, bkg_low_res)
assert b1.background.shape == data.shape
b2 = Background2D(data, (25, 25), filter_size=(1, 1),
edge_method='pad', interpolator=interpolator)
assert_allclose(b2.background_mesh, bkg_low_res)
assert b2.background.shape == data.shape
def test_no_sigma_clipping(self):
data = np.copy(DATA)
data[10, 10] = 100.
b1 = Background2D(data, (25, 25), filter_size=(1, 1),
bkg_estimator=MeanBackground())
b2 = Background2D(data, (25, 25), filter_size=(1, 1), sigma_clip=None,
bkg_estimator=MeanBackground())
assert b2.background_mesh[0, 0] > b1.background_mesh[0, 0]
@pytest.mark.parametrize('filter_size', FILTER_SIZES)
def test_resizing(self, filter_size):
b1 = Background2D(DATA, (22, 22), filter_size=filter_size,
bkg_estimator=MeanBackground(), edge_method='crop')
b2 = Background2D(DATA, (22, 22), filter_size=filter_size,
bkg_estimator=MeanBackground(), edge_method='pad')
assert_allclose(b1.background, b2.background)
assert_allclose(b1.background_rms, b2.background_rms)
@pytest.mark.parametrize('box_size', ([(25, 25), (22, 22)]))
def test_background_mask(self, box_size):
"""
Test with an input mask. Note that box_size=(22, 22) tests the
resizing of the image and mask.
"""
data = np.copy(DATA)
data[25:50, 25:50] = 100.
mask = np.zeros_like(DATA, dtype=np.bool)
mask[25:50, 25:50] = True
b = Background2D(data, box_size, filter_size=(1, 1), mask=mask,
bkg_estimator=MeanBackground())
assert_allclose(b.background, DATA)
assert_allclose(b.background_rms, BKG_RMS)
# test mask padding
b2 = Background2D(data, box_size, filter_size=(1, 1), mask=mask,
bkg_estimator=MeanBackground(), edge_method='pad')
assert_allclose(b2.background, DATA)
@pytest.mark.parametrize('exclude_mesh_method',
(['any', 'all', 'threshold']))
def test_exclude_mesh(self, exclude_mesh_method):
b = Background2D(DATA, (25, 25),
exclude_mesh_method=exclude_mesh_method)
assert_allclose(b.background, DATA)
# test if data is completely masked
with pytest.raises(ValueError):
mask = np.ones_like(DATA, dtype=np.bool)
Background2D(DATA, (25, 25), mask=mask,
exclude_mesh_method=exclude_mesh_method)
def test_filter_threshold(self):
"""Only meshes greater than filter_threshold are filtered."""
data = np.copy(DATA)
data[25:50, 50:75] = 10.
b = Background2D(data, (25, 25), filter_size=(3, 3),
filter_threshold=9.)
assert_allclose(b.background, DATA)
assert_allclose(b.background_mesh, BKG_MESH)
b2 = Background2D(data, (25, 25), filter_size=(3, 3),
filter_threshold=11.) # no filtering
assert b2.background_mesh[1, 2] == 10
def test_filter_threshold_high(self):
"""No filtering because filter_threshold is too large."""
data = np.copy(DATA)
data[25:50, 50:75] = 10.
ref_data = np.copy(BKG_MESH)
ref_data[1, 2] = 10.
b = Background2D(data, (25, 25), filter_size=(3, 3),
filter_threshold=100.)
assert_allclose(b.background_mesh, ref_data)
def test_filter_threshold_nofilter(self):
"""No filtering because filter_size is (1, 1)."""
data = np.copy(DATA)
data[25:50, 50:75] = 10.
ref_data = np.copy(BKG_MESH)
ref_data[1, 2] = 10.
b = Background2D(data, (25, 25), filter_size=(1, 1),
filter_threshold=1.)
assert_allclose(b.background_mesh, ref_data)
def test_scalar_sizes(self):
b1 = Background2D(DATA, (25, 25), filter_size=(3, 3))
b2 = Background2D(DATA, 25, filter_size=3)
assert_allclose(b1.background, b2.background)
assert_allclose(b1.background_rms, b2.background_rms)
def test_exclude_mesh_percentile(self):
with pytest.raises(ValueError):
Background2D(DATA, (5, 5), exclude_mesh_method='threshold',
exclude_mesh_percentile=101)
def test_mask_badshape(self):
with pytest.raises(ValueError):
Background2D(DATA, (25, 25), filter_size=(1, 1),
mask=np.zeros((2, 2)))
def test_invalid_edge_method(self):
with pytest.raises(ValueError):
Background2D(DATA, (22, 22), filter_size=(1, 1),
edge_method='not_valid')
def test_invalid_exclude_mesh_method(self):
with pytest.raises(ValueError):
Background2D(DATA, (22, 22), filter_size=(1, 1),
exclude_mesh_method='not_valid')
def test_invalid_mesh_idx_len(self):
with pytest.raises(ValueError):
bkg = Background2D(DATA, (25, 25), filter_size=(1, 1))
bkg._make_2d_array(np.arange(3))
def test_plot_meshes(self):
"""
This test should run without any errors, but there is no return
value.
"""
b = Background2D(DATA, (25, 25))
b.plot_meshes(outlines=True)
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