def test__setup_4x3_array__five_now_in_mask(self):
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
mask = np.array([[True, False, True], [False, False, False],
[True, False, True], [True, True, True]])
array_1d = mapping_util.map_2d_array_to_masked_1d_array_from_array_2d_and_mask(
mask, array_2d)
assert (array_1d == np.array([2, 4, 5, 6, 8])).all()
def test__setup_3x3_data(self):
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
mask = np.array([[True, True, True], [True, False, True],
[True, True, True]])
array_1d = mapping_util.map_2d_array_to_masked_1d_array_from_array_2d_and_mask(
mask, array_2d)
assert (array_1d == np.array([5])).all()
def test__setup_3x4_array(self):
array_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
mask = np.array([[True, False, True,
True], [False, False, False, True],
[True, False, True, False]])
array_1d = mapping_util.map_2d_array_to_masked_1d_array_from_array_2d_and_mask(
mask, array_2d)
assert (array_1d == np.array([2, 5, 6, 7, 10, 12])).all()
def map_2d_array_to_masked_1d_array(self, array_2d):
"""For a 2D array (e.g. an image, noise_map, etc.) map it to a masked 1D array of valuees using this mask.
Parameters
----------
array_2d : ndarray | None | float
The 2D array to be mapped to a masked 1D array.
"""
if array_2d is None or isinstance(array_2d, float):
return array_2d
return mapping_util.map_2d_array_to_masked_1d_array_from_array_2d_and_mask(
self, array_2d)
def test__map_2d_array_to_masked_1d_array__compare_to_array_util(self):
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
mask = np.array([[True, False, True], [False, False, False],
[True, False, True], [True, True, True]])
array_1d_util = mapping_util.map_2d_array_to_masked_1d_array_from_array_2d_and_mask(
mask, array_2d)
mask = msk.Mask(mask, pixel_scale=3.0)
array_1d = mask.map_2d_array_to_masked_1d_array(array_2d)
assert (array_1d == array_1d_util).all()
def convolve_array_1d_with_psf(self, padded_array_1d, psf):
"""Convolve a 1d padded array of values (e.g. intensities before PSF blurring) with a PSF, and then trim \
the convolved array to its original 2D shape.
Parameters
-----------
padded_array_1d: ndarray
A 1D array of values which were computed using the *PaddedRegularGrid*.
psf : ndarray
An array describing the PSF kernel of the image.
"""
padded_array_2d = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=padded_array_1d, shape=self.mask.shape)
blurred_padded_array_2d = psf.convolve(array=padded_array_2d)
return mapping_util.map_2d_array_to_masked_1d_array_from_array_2d_and_mask(
array_2d=blurred_padded_array_2d,
mask=np.full(self.mask.shape, False))