def test_ee():
"""Image class: testing ensquared energy."""
wcs = WCS()
data = np.ones(shape=(6, 5)) * 2
image1 = Image(data=data, wcs=wcs)
image1.mask_region((2, 2), (1.5, 1.5),
inside=False,
unit_center=None,
unit_radius=None)
assert image1.ee() == 9 * 2
assert image1.ee(frac=True) == 1.0
ee = image1.ee(center=(2, 2), unit_center=None, radius=1, unit_radius=None)
assert ee == 4 * 2
r, eer = image1.eer_curve(center=(2, 2),
unit_center=None,
unit_radius=None,
cont=0)
assert r[1] == 1.0
assert eer[1] == 1.0
size = image1.ee_size(center=(2, 2),
unit_center=None,
unit_size=None,
cont=0)
assert_almost_equal(size[0], 1.775)
def test_rebin():
"""Image class: testing rebin methods."""
wcs = WCS(crval=(0, 0))
data = np.arange(30).reshape(6, 5)
image1 = Image(data=data, wcs=wcs, var=np.ones(data.shape) * 0.5)
image1.mask_region((2, 2), (1.5, 1.5),
inside=False,
unit_center=None,
unit_radius=None)
# The test data array looks as follows:
#
# ---- ---- ---- ---- ----
# ---- 6.0 7.0 8.0 ----
# ---- 11.0 12.0 13.0 ----
# ---- 16.0 17.0 18.0 ----
# ---- ---- ---- ---- ----
# ---- ---- ---- ---- ----
#
# Where ---- signifies a masked value.
#
# After reducing both dimensions by a factor of 2, we should
# get a data array of the following 6 means of 4 pixels each:
#
# ---- ---- => 6/1 ---- ---- => (7+8)/2
# ---- 6.0 7.0 8.0
#
# ---- 11.0 => (11+16)/2 12.0 13.0 => (12+13+17+18)/4
# ---- 16.0 17.0 18.0
#
# ---- ---- => ---- ---- ---- => ----
# ---- ---- ---- ----
expected = np.ma.array(data=[[6.0, 7.5], [13.5, 15], [0.0, 0.0]],
mask=[[False, False], [False, False], [True, True]])
image2 = image1.rebin(2)
assert_masked_allclose(image2.data, expected)
image2 = image1.rebin(factor=(2, 2))
assert_masked_allclose(image2.data, expected)
# The variances of the original pixels were all 0.5, so taking the
# mean of N of these should give the mean a variance of 0.5/N.
# Given the number of pixels averaged in each of the above means,
# we thus expect the variance array to look as follows.
expected = np.ma.array(data=[[0.5, 0.25], [0.25, 0.125], [0.0, 0.0]],
mask=[[False, False], [False, False], [True, True]])
assert_masked_allclose(image2.var, expected)
# Check the WCS information.
start = image2.get_start()
assert start[0] == 0.5
assert start[1] == 0.5
def test_mask():
"""Image class: testing mask functionalities"""
wcs = WCS()
data = np.ones(shape=(6, 5)) * 2
# A region of half-width=1 and half-height=1 should have a size of
# 2x2 pixels. A 2x2 region of pixels has a center at the shared
# corner of the 4 pixels, and the closest corner to the requested
# center of 2.1,1.8 is 2.5,1.5, so we expect the square of unmasked pixels
# to be pixels 2,3 along the Y axis, and pixels 1,2 along the X axis.
image1 = Image(data=data, wcs=wcs)
image1.mask_region((2.1, 1.8), (1, 1), inside=False, unit_center=None,
unit_radius=None)
expected_mask = np.array([[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 0, 0, 1, 1],
[1, 0, 0, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]], dtype=bool)
assert_array_equal(image1._mask, expected_mask)
# Test that inside=True gives the opposite result
image1.unmask()
image1.mask_region((2.1, 1.8), (1, 1), inside=True, unit_center=None,
unit_radius=None)
assert_array_equal(image1._mask, ~expected_mask)
# And test with a rotation, 90° so should give the same result
image1.unmask()
image1.mask_region((2.1, 1.8), (1, 1), inside=True, unit_center=None,
unit_radius=None, posangle=90)
assert_array_equal(image1._mask, ~expected_mask)
# Try exactly the same experiment as the above, except that the center
# and size of the region are specified in world-coordinates instead of
# pixels.
wcs = WCS(deg=True)
image1 = Image(data=data, wcs=wcs)
image1.mask_region(wcs.pix2sky([2.1, 1.8]), (3600, 3600), inside=False)
assert_array_equal(image1._mask, expected_mask)
# And same with a rotation
image1.unmask()
image1.mask_region(wcs.pix2sky([2.1, 1.8]), (3600, 3600), inside=True,
posangle=90)
assert_array_equal(image1._mask, ~expected_mask)
# Mask around a region of half-width and half-height 1.1 pixels,
# specified in arcseconds, centered close to pixel 2.4,3.8. This
# ideally corresponds to a region of 2.2x2.2 pixels. The closest
# possible size is 2x2 pixels. A region of 2x2 pixels has its
# center at the shared corner of these 4 pixels, and the nearest
# corner to the desired central index of (2.4,3.8) is (2.5,3.5).
# So all of the image should be masked, except for a 2x2 area of
# pixel indexes 2,3 along the Y axis and pixel indexes 3,4 along
# the X axis.
image1.unmask()
image1.mask_region(wcs.pix2sky([2.4, 3.8]), 1.1 * 3600.0, inside=False)
expected_mask = np.array([[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]], dtype=bool)
assert_array_equal(image1._mask, expected_mask)
# Mask outside an elliptical region centered at pixel 3.5,3.5.
# The boolean expected_mask array given below was a verified
# output of mask_ellipse() for the specified ellipse parameters.
data = np.ones(shape=(8, 8))
image1 = Image(data=data, wcs=wcs)
image1.mask_ellipse([3.5, 3.5], (2.5, 3.5), 45.0, unit_radius=None,
unit_center=None, inside=False)
expected_mask = np.array([
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 1, 1],
[1, 1, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 1, 1],
[1, 1, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1]],
dtype=bool)
assert_array_equal(image1._mask, expected_mask)
# Use np.where to select the masked pixels and check that mask_selection()
# then reproduces the same mask.
ksel = np.where(image1.data.mask)
image1.unmask()
image1.mask_selection(ksel)
assert_array_equal(image1._mask, expected_mask)
# Check inside=True
image1.unmask()
image1.mask_ellipse([3.5, 3.5], (2.5, 3.5), 45.0, unit_radius=None,
unit_center=None, inside=True)
assert_array_equal(image1._mask, ~expected_mask)