spateo.digitization.borderline
==============================

.. py:module:: spateo.digitization.borderline

.. autoapi-nested-parse::

   Written by @Jinerhal, adapted by @Xiaojieqiu.



Functions
---------

.. autoapisummary::

   spateo.digitization.borderline.get_borderline
   spateo.digitization.borderline.grid_borderline


Module Contents
---------------

.. py:function:: get_borderline(adata: spateo.digitization.utils.AnnData, cluster_key: str, source_clusters: int, target_clusters: int, bin_size: int = 1, spatial_key: str = 'spatial', borderline_key: str = 'borderline', k_size: int = 8, min_area: int = 30, dilate_k_size: int = 3) -> spateo.digitization.utils.np.ndarray

   Identify the borderline at the interface of the source and target cell clusters.

   The borderline will be identified by first retrieving the outline/contour formed by the source clusters, which will
   then be cleaned up to retrieve the borderline by masking with the expanded contours formed by the target clusters.

   :param adata: The adata object to be used for identifying the borderline.
   :param cluster_key: The key name of the spatial cluster in `adata.obs`
   :param source_clusters: The source cluster(s) that will interface with the target clusters.
   :param target_clusters: The target cluster(s) that will interface with the source clusters.
   :param bin_size: The size of the binning.
   :param spatial_key: The key name of the spatial coordinates in `adata.obs`
   :param borderline_key: The key name in `adata.obs` that will be used to store the borderline.
   :param k_size: Kernel size of the elliptic structuring element.
   :param min_area: Minimal area threshold corresponding to the resulting contour(s).
   :param dilate_k_size: Kernel size of the cv2.dilate function.

   :returns:

             The matrix that stores the image information of the borderline between the source and target
                 cluster(s). Note that the adata object will also be updated with the `boundary_line` key that stores the
                 information about whether the bucket is on the borderline.
   :rtype: borderline_img


.. py:function:: grid_borderline(adata: spateo.digitization.utils.AnnData, borderline_img: spateo.digitization.utils.np.ndarray, borderline_list: spateo.digitization.utils.List, layer_num: int = 3, column_num: int = 25, layer_width: int = 10, spatial_key: str = 'spatial', init: bool = False) -> None

   Extend the borderline to either interior or exterior side to each create `layer_num` layers, and segment such
       layers to `column_num` columns.

   :param adata: The adata object to be used for identifying the interior/exterior layers and columns.
   :param borderline_img: The matrix that stores the image information of the borderline between the source and target
                          cluster(s).
   :param borderline_list: An order list of np.arrays of coordinates of the borderlines.
   :param layer_num: Number of layers to extend on either interior or exterior side.
   :param column_num: Number of columns to segment for each layer.
   :param layer_width: Layer/column boundary width. This only affects grid_label.
   :param spatial_key: The key name in `adata.obsm` of the spatial coordinates. Default to "spatial". Passed to
                       `fill_grid_label` function.
   :param init: Whether to generate (and potentially overwrite) the `layer_label_key` and `column_label_key` in
                `fill_grid_label` function.

   :returns:

             1. layer_label_key: this key points to layer labels.
             2. column_label_key: this key points to column labels.
   :rtype: Nothing but update the adata object with following keys in `.obs`