spateo.tools.utils#

Module Contents#

Functions#

rescaling(→ Union[numpy.ndarray, scipy.sparse.spmatrix])

This function rescale the resolution of the input matrix that represents a spatial domain. For example, if you

get_mapper([smoothed])

update_dict(dict1, dict2)

flatten(arr)

calc_1nd_moment(X, W[, normalize_W])

affine_transform(X, A, b)

gen_rotation_2d(degree)

compute_smallest_distance(→ float)

Compute and return smallest distance. A wrapper for sklearn API

polyhull(→ pyvista.PolyData)

Create a PolyData object from the convex hull constructed with the input data points.

in_hull(→ numpy.ndarray)

Test if points in p are in hull
spateo.tools.utils.rescaling(mat: Union[numpy.ndarray, scipy.sparse.spmatrix], new_shape: Union[List, Tuple]) → Union[numpy.ndarray, scipy.sparse.spmatrix][source]#

This function rescale the resolution of the input matrix that represents a spatial domain. For example, if you want to decrease the resolution of a matrix by a factor of 2, the new_shape will be mat.shape / 2.

Parameters
mat

The input matrix of the spatial domain (or an image).

new_shape

The rescaled shape of the spatial domain, each dimension must be an factorial of the original dimension.

Returns

the spatial resolution rescaled matrix.

Return type

res

spateo.tools.utils.get_mapper(smoothed=True)[source]#
spateo.tools.utils.update_dict(dict1, dict2)[source]#
spateo.tools.utils.flatten(arr)[source]#
spateo.tools.utils.calc_1nd_moment(X, W, normalize_W=True)[source]#
spateo.tools.utils.affine_transform(X, A, b)[source]#
spateo.tools.utils.gen_rotation_2d(degree: float)[source]#
spateo.tools.utils.compute_smallest_distance(coords: numpy.ndarray, leaf_size: int = 40, sample_num=None, use_unique_coords=True) → float[source]#

Compute and return smallest distance. A wrapper for sklearn API :param coords: NxM matrix. N is the number of data points and M is the dimension of each point’s feature. :param leaf_size: Leaf size parameter for building Kd-tree, by default 40. :type leaf_size: int, optional :param sample_num: The number of cells to be sampled. :param use_unique_coords: Whether to remove duplicate coordinates

Returns

min_dist: float the minimum distance between points

spateo.tools.utils.polyhull(x: numpy.ndarray, y: numpy.ndarray, z: numpy.ndarray) → pyvista.PolyData[source]#

Create a PolyData object from the convex hull constructed with the input data points.

scipy’s ConvexHull to be 500X faster than using vtkDelaunay3D and vtkDataSetSurfaceFilter because you skip the expensive 3D tesselation of the volume.

Parameters
x

x coordinates of the data points.

y

y coordinates of the data points.

z

z coordinates of the data points.

Returns

a PolyData object generated with the convex hull constructed based on the input data points.

Return type

poly

spateo.tools.utils.in_hull(p: numpy.ndarray, hull: Tuple[scipy.spatial.Delaunay, numpy.ndarray]) → numpy.ndarray[source]#

Test if points in p are in hull

Parameters
p

a N x K coordinates of N points in K dimensions

hull

either a scipy.spatial.Delaunay object or the MxK array of the coordinates of M points in K

computed. : dimensions for which Delaunay triangulation will be

Returns

A numpy array with boolean values indicating whether the input points is in the convex hull.

Return type

res