hats.pixel_tree.pixel_alignment =============================== .. py:module:: hats.pixel_tree.pixel_alignment Attributes ---------- .. autoapisummary:: hats.pixel_tree.pixel_alignment.LEFT_INCLUDE_ALIGNMENT_TYPES hats.pixel_tree.pixel_alignment.RIGHT_INCLUDE_ALIGNMENT_TYPES hats.pixel_tree.pixel_alignment.NONE_PIX hats.pixel_tree.pixel_alignment.LEFT_SIDE hats.pixel_tree.pixel_alignment.RIGHT_SIDE Classes ------- .. autoapisummary:: hats.pixel_tree.pixel_alignment.PixelAlignment Functions --------- .. autoapisummary:: hats.pixel_tree.pixel_alignment.align_trees hats.pixel_tree.pixel_alignment.get_pixel_mapping_df hats.pixel_tree.pixel_alignment.perform_inner_align_trees hats.pixel_tree.pixel_alignment.perform_align_trees hats.pixel_tree.pixel_alignment.filter_alignment_by_moc hats.pixel_tree.pixel_alignment.align_with_mocs Module Contents --------------- .. py:data:: LEFT_INCLUDE_ALIGNMENT_TYPES .. py:data:: RIGHT_INCLUDE_ALIGNMENT_TYPES .. py:data:: NONE_PIX .. py:data:: LEFT_SIDE :value: True .. py:data:: RIGHT_SIDE :value: False .. py:class:: PixelAlignment(aligned_tree: hats.pixel_tree.pixel_tree.PixelTree, pixel_mapping: pandas.DataFrame, alignment_type: hats.pixel_tree.pixel_alignment_types.PixelAlignmentType, moc: mocpy.MOC = None) Represents how two pixel trees align with each other, meaning which pixels match or overlap between the catalogs, and a new tree with the smallest pixels from each tree For more information on the pixel alignment algorithm, view this document: https://docs.google.com/document/d/1gqb8qb3HiEhLGNav55LKKFlNjuusBIsDW7FdTkc5mJU/edit?usp=sharing :Attributes: **pixel_tree** : PixelTree The aligned tree generated by using the smallest pixels in each tree. For example, a tree with pixels at order 0, pixel 1, and a tree with order 1, pixel 4,5,6, and 7, would result in the smaller order 1 pixels in the aligned tree. **pixel_mapping** : pd.DataFrame A dataframe where each row contains a pixel from each tree that match, and which pixel in the aligned tree they match with **alignment_type** : PixelAlignmentType The type of alignment describing how to handle nodes which exist in one tree but not the other. Options are: - inner - only use pixels that appear in both catalogs - left - use all pixels that appear in the left catalog and any overlapping from the right - right - use all pixels that appear in the right catalog and any overlapping from the left - outer - use all pixels from both catalogs .. !! processed by numpydoc !! .. py:attribute:: PRIMARY_ORDER_COLUMN_NAME :value: 'primary_Norder' .. py:attribute:: PRIMARY_PIXEL_COLUMN_NAME :value: 'primary_Npix' .. py:attribute:: JOIN_ORDER_COLUMN_NAME :value: 'join_Norder' .. py:attribute:: JOIN_PIXEL_COLUMN_NAME :value: 'join_Npix' .. py:attribute:: ALIGNED_ORDER_COLUMN_NAME :value: 'aligned_Norder' .. py:attribute:: ALIGNED_PIXEL_COLUMN_NAME :value: 'aligned_Npix' .. py:attribute:: pixel_tree .. py:attribute:: pixel_mapping .. py:attribute:: alignment_type .. py:attribute:: moc :value: None .. py:function:: align_trees(left: hats.pixel_tree.pixel_tree.PixelTree, right: hats.pixel_tree.pixel_tree.PixelTree, alignment_type: hats.pixel_tree.pixel_alignment_types.PixelAlignmentType = PixelAlignmentType.INNER) -> PixelAlignment Generate a `PixelAlignment` object from two pixel trees A `PixelAlignment` represents how two pixel trees align with each other, meaning which pixels match or overlap between the catalogs, and includes a new tree with the smallest pixels from each tree For more information on the pixel alignment algorithm, view this document: https://docs.google.com/document/d/1gqb8qb3HiEhLGNav55LKKFlNjuusBIsDW7FdTkc5mJU/edit?usp=sharing :Parameters: **left** : PixelTree The left tree to align **right** : PixelTree The right tree to align **alignment_type** : PixelAlignmentType (Default value = PixelAlignmentType.INNER) The type of alignment describing how to handle nodes which exist in one tree but not the other. Options are: - inner - only use pixels that appear in both catalogs - left - use all pixels that appear in the left catalog and any overlapping from the right - right - use all pixels that appear in the right catalog and any overlapping from the left - outer - use all pixels from both catalogs :Returns: PixelAlignment The `PixelAlignment` object with the alignment from the two trees .. !! processed by numpydoc !! .. py:function:: get_pixel_mapping_df(mapping: numpy.ndarray, map_order: int) -> pandas.DataFrame Construct a DataFrame with HEALPix orders and pixels mapping left right and aligned pixels :Parameters: **mapping** : np.ndarray array of shape (6, len(aligned_pixels)) where the first two rows are the intervals for the left pixels, the next two for right pixels, and the last two for aligned pixels **map_order** : int The HEALPix order of the intervals in the mapping array :Returns: pd.DataFrame A DataFrame with the orders and pixels of the aligned left and right pixels, .. !! processed by numpydoc !! .. py:function:: perform_inner_align_trees(left: numpy.ndarray, right: numpy.ndarray) -> numpy.ndarray Performs an inner alignment on arrays of pixel intervals Pixel interval lists must be of to the same order :Parameters: **left** : np.ndarray the left array of intervals **right** : np.ndarray the right array of intervals :Returns: np.ndarray The pixel mapping of the matching left, right, and aligned pixels with each row containing an array of [left_order, left_pixel, right_order, right_pixel, aligned_order, aligned_pixel] .. !! processed by numpydoc !! .. py:function:: perform_align_trees(left: numpy.ndarray, right: numpy.ndarray, include_all_left: bool, include_all_right: bool) -> list[numpy.ndarray] Performs an alignment on arrays of pixel intervals Pixel interval lists must be of to the same order :Parameters: **left** : np.ndarray the left array of intervals **right** : np.ndarray the right array of intervals **include_all_left** : bool if all pixels from the left tree should be covered in the final alignment **include_all_right** : bool if all pixels from the right tree should be covered in the final alignment :Returns: list[np.ndarray] The pixel mapping of the matching left, right, and aligned pixels with each row containing an array of [left_order, left_pixel, right_order, right_pixel, aligned_order, aligned_pixel] .. !! processed by numpydoc !! .. py:function:: filter_alignment_by_moc(alignment: PixelAlignment, moc: mocpy.MOC) -> PixelAlignment Filters an alignment by a moc to only include pixels in the aligned_tree that overlap with the moc, and the corresponding rows in the mapping. :Parameters: **alignment** : PixelAlignment The pixel alignment to filter **moc** : mocpy.MOC The moc to filter by :Returns: PixelAlignment PixelAlignment object with the filtered mapping and tree .. !! processed by numpydoc !! .. py:function:: align_with_mocs(left_tree: hats.pixel_tree.pixel_tree.PixelTree, right_tree: hats.pixel_tree.pixel_tree.PixelTree, left_moc: mocpy.MOC | None, right_moc: mocpy.MOC | None, alignment_type: hats.pixel_tree.pixel_alignment_types.PixelAlignmentType = PixelAlignmentType.INNER) -> PixelAlignment Aligns two pixel trees and mocs together, resulting in a pixel alignment with only aligned pixels that have coverage in the mocs. :Parameters: **left_tree** : PixelTree The left tree to align **right_tree** : PixelTree The right tree to align **left_moc** : mocpy.MOC the moc with the coverage of the left catalog **right_moc** : mocpy.MOC the moc with the coverage of the right catalog **alignment_type** : PixelAlignmentType The type of alignment describing how to handle nodes which exist in one tree but not the other. Options are: - inner - only use pixels that appear in both catalogs - left - use all pixels that appear in the left catalog and any overlapping from the right - right - use all pixels that appear in the right catalog and any overlapping from the left - outer - use all pixels from both catalogs :Returns: PixelAlignment The PixelAlignment object with the aligned trees filtered by the coverage in the catalogs. .. !! processed by numpydoc !!