spateo.tdr.interpolations.interpolation_vtk
===========================================

.. py:module:: spateo.tdr.interpolations.interpolation_vtk


Functions
---------

.. autoapisummary::

   spateo.tdr.interpolations.interpolation_vtk.vtk_interpolation


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

.. py:function:: vtk_interpolation(source_adata: anndata.AnnData, target_points: Optional[numpy.ndarray] = None, keys: Union[str, list] = None, spatial_key: str = 'spatial', layer: str = 'X', radius: Optional[float] = None, n_points: Optional[int] = None, kernel: Literal['shepard', 'gaussian', 'linear'] = 'shepard', null_strategy: Literal[0, 1, 2] = 1, null_value: Union[int, float] = 0) -> anndata.AnnData

   Learn a continuous mapping from space to gene expression pattern with the method contained in VTK.

   :param source_adata: AnnData object that contains spatial (numpy.ndarray) in the `obsm` attribute.
   :param target_points: The spatial coordinates of new data point. If target_coords is None, generate new points based on grid_num.
   :param keys: Gene list or info list in the `obs` attribute whose interpolate expression across space needs to learned.
   :param spatial_key: The key in ``.obsm`` that corresponds to the spatial coordinate of each bucket.
   :param layer: If ``'X'``, uses ``.X``, otherwise uses the representation given by ``.layers[layer]``.
   :param radius: Set the radius of the point cloud. If you are generating a Gaussian distribution, then this is the
                  standard deviation for each of x, y, and z.
   :param n_points: Specify the number of points for the source object to hold.
                    If n_points (number of the closest points to use) is set then radius value is ignored.
   :param kernel: The kernel of interpolations kernel. Available `kernels` are:
                  * `shepard`: vtkShepardKernel is an interpolations kernel that uses the method of Shepard to perform
                               interpolations. The weights are computed as 1/r^p, where r is the distance to a neighbor
                               point within the kernel radius R; and p (the power parameter) is a positive exponent
                               (typically p=2).
                  * `gaussian`: vtkGaussianKernel is an interpolations kernel that simply returns the weights for all
                                points found in the sphere defined by radius R. The weights are computed as:
                                exp(-(s*r/R)^2) where r is the distance from the point to be interpolated to a neighboring
                                point within R. The sharpness s simply affects the rate of fall off of the Gaussian.
                  * `linear`: vtkLinearKernel is an interpolations kernel that averages the contributions of all points in
                              the basis.
   :param null_strategy:
                         Specify a strategy to use when encountering a "null" point during the interpolations process.
                               Null points occur when the local neighborhood(of nearby points to interpolate from) is empty.
                         * Case 0: an output array is created that marks points as being valid (=1) or null (invalid =0), and
                                   the nullValue is set as well
                         * Case 1: the output data value(s) are set to the provided nullValue
                         * Case 2: simply use the closest point to perform the interpolations.
   :param null_value: see above.

   :returns: an anndata object that has interpolated expression.
   :rtype: interp_adata