def test_inverse_y():
data_shape = (10, 10)
data = np.arange(100).reshape(data_shape)
test_subject = CutoutND(data)
cutout_regions = [(1, 2), (8, 4)]
cutout = test_subject.extract(cutout_regions)
expected_data = np.array([[70, 71], [60, 61], [50, 51], [40, 41], [30,
31]])
np.testing.assert_array_equal(expected_data, cutout.data,
'Arrays do not match.')
def test_extract_invalid():
data_shape = (10, 10)
data = np.arange(100).reshape(data_shape)
test_subject = CutoutND(data)
cutout_regions = [('')]
with pytest.raises(ValueError,
match=r"Should have at least two values "
r"\(lower, upper\)\."):
test_subject.extract(cutout_regions)
def test_extract():
data_shape = (9, 4)
data = np.arange(36).reshape(data_shape)
test_subject = CutoutND(data)
cutout_region = PixelCutoutHDU([(4, 18)])
cutout = test_subject.extract(cutout_region.get_ranges())
expected_data = np.array([[3], [7], [11], [15], [19], [23], [27], [31],
[35]])
np.testing.assert_array_equal(expected_data, cutout.data,
'Arrays do not match.')
def test_extract_striding_wildcard():
data_shape = (10, 10)
data = np.arange(100).reshape(data_shape)
test_subject = CutoutND(data)
cutout_regions = [('*', 7)]
cutout = test_subject.extract(cutout_regions)
expected_data = np.array([[0, 7], [10, 17], [20, 27], [30, 37], [40, 47],
[50, 57], [60, 67], [70, 77], [80, 87], [90,
97]])
np.testing.assert_array_equal(expected_data, cutout.data,
'Arrays do not match.')
def test_extract_striding():
data_shape = (10, 10)
data = np.arange(100).reshape(data_shape)
test_subject = CutoutND(data)
cutout_regions = [(4, 18, 5)]
cutout = test_subject.extract(cutout_regions)
expected_data = np.array([[3, 8], [13, 18], [23, 28], [33, 38], [43, 48],
[53, 58], [63, 68], [73, 78], [83, 88], [93,
98]])
np.testing.assert_array_equal(expected_data, cutout.data,
'Arrays do not match.')
def do_cutout(self, data, cutout_dimension, wcs):
"""
Perform a Cutout of the given data at the given position and size.
:param data: The data to cutout from
:param cutout_dimension: `PixelCutoutHDU` Cutout object.
:param wcs: The WCS object to use with the cutout to return a copy
of the WCS object.
:return: CutoutND instance
"""
c = CutoutND(data=data, wcs=wcs)
return c.extract(cutout_dimension.get_ranges())
def test_with_wcs():
data = np.arange(100).reshape(10, 10)
header = Header()
wcs = WCS(fix=False)
wcs.wcs.cd = [[0.9, 0.8], [0.7, 0.6]]
header.set('REMAIN1', 'VALUE1')
header.set('DQ1', 'dqvalue1')
header.set('NAXIS', 2)
test_subject = CutoutND(data, wcs=wcs)
cutout_result = test_subject.extract([(1, 6, 2), (4, 10, 2)])
result_wcs = cutout_result.wcs
np.testing.assert_array_equal([[1.8, 1.6], [1.4, 1.2]], result_wcs.wcs.cd,
'Wrong CD output.')
def do_cutout(self, data, cutout_dimension, wcs):
"""
Perform a Cutout of the given data at the given position and size.
:param data: The data to cutout from
:param cutout_dimension: `PixelCutoutHDU` Cutout object.
:param wcs: The WCS object to use with the cutout to return a copy
of the WCS object.
:return: CutoutND instance
"""
# Sanitize the array by removing the single-dimensional entries.
sanitized_data = np.squeeze(data)
c = CutoutND(data=sanitized_data, wcs=wcs)
return c.extract(cutout_dimension)
def test_create():
with pytest.raises(ValueError):
CutoutND(data=None)