PyAutoArray/autoarray/mask/derive/mask_1d.py at 9c0044bf4c01900fc5f5ba764a551291912f569f · PyAutoLabs/PyAutoArray · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
from __future__ import annotations
import logging
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from autoarray.mask.mask_2d import Mask1D
    from autoarray.mask.mask_2d import Mask2D

logging.basicConfig()
logger = logging.getLogger(__name__)


class DeriveMask1D:
    def __init__(self, mask: Mask1D):
        """
        Derives ``Mask1D`` objects from a ``Mask1D``.

        A ``Mask1D`` masks values which are associated with a 1D uniform grid of pixels, where unmasked
        entries (which are ``False``) are used in subsequent calculations and masked values (which are ``True``) are
        omitted (for a full description see
        the :meth:`Mask1D class API documentation <autoarray.mask.mask_1d.Mask1D.__new__>`).

        From a ``Mask1D`` other ``Mask1D``s can be derived, which represent a subset of pixels with significance.
        For example:

        - An ``all_false`` ``Mask1D``: the same shape as the original ``Mask1D`` but has unmasked ``False`` values
          everywhere.

        Parameters
        ----------
        mask
            The ``Mask1D`` from which new ``Mask1D`` objects are derived.

        Examples
        --------

        .. code-block:: python

            import autoarray as aa

            mask_1d = aa.Mask1D(
                mask=[True, False, False, False, True],
                pixel_scales=1.0,
            )

            derive_mask_1d = aa.DeriveMask1D(mask=mask_1d)

            print(derive_mask_1d.edge)
        """
        self.mask = mask

    @property
    def all_false(self) -> Mask1D:
        """
        Returns a ``Mask1D`` which has the same
        geometry (``shape_native`` / ``pixel_scales`` / ``origin``) as this ``Mask1D`` but all
        entries are unmasked (given by``False``).

        For example, for the following ``Mask1D``:

        ::
           [True, False, False, False, True]

        The unmasked mask (given via ``mask_1d.derive_mask.all_false``) is:

        ::
           [False, False, False, False, False]

        Examples
        --------

        .. code-block:: python

            import autoarray as aa

            mask_1d = aa.Mask1D(
                mask[True, False, False, False, True],
                pixel_scales=1.0,
            )

            derive_mask_1d = aa.DeriveMask1D(mask=mask_1d)

            print(derive_mask_1d.all_false)
        """
        from autoarray.mask.mask_1d import Mask1D

        return Mask1D.all_false(
            shape_slim=self.mask.shape_slim,
            pixel_scales=self.mask.pixel_scales,
            origin=self.mask.origin,
        )

    @property
    def to_mask_2d(self) -> Mask2D:
        """
        Map the ``Mask1D`` to a Mask2D of shape [total_mask_1d_pixel, 1].

        The change in shape and dimensions of the mask is necessary for mapping results from 1D to 2D data
        structures (e.g. an ``Array1D`` to ``Array2D``).

        For example, for the following ``Mask1D``:

        ::
           [True, False, False, False, True]

        The corresponding ``Mask2D`` (given via ``mask_1d.derive_mask.to_mask_2d``) is:

        ::
           [[False, False, False, False, False]]

        Examples
        --------

        .. code-block:: python

            import autoarray as aa

            mask_1d = aa.Mask1D(
                mask[True, False, False, False, True],
                pixel_scales=1.0,
            )

            derive_mask_1d = aa.DeriveMask1D(mask=mask_1d)

            print(derive_mask_1d.to_mask_2d)
        """

        from autoarray.mask.mask_2d import Mask2D

        return Mask2D(
            [self.mask],
            pixel_scales=(self.mask.pixel_scale, self.mask.pixel_scale),
            origin=(0.0, 0.0),
        )