spateo.io.utils#
IO utility functions.
Module Contents#
Functions#
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Take a DNB coordinate, the mimimum coordinate and the binsize, calculate the index of bins for the current |
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Take a bin index, the mimimum coordinate and the binsize, calculate the centroid of the current bin. |
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Transfer contours to shapely.geometry |
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Calculate properties of labeled coordinates. |
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Measure properties of labeled cell regions. |
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Simulate properties of bin regions. |
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Test if points in p are in concave_hull using scipy.spatial Delaunay's find_simplex. |
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Test if points in p are in convex_hull using scipy.spatial Delaunay's find_simplex. |
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Bin a matrix. |
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Convert labels into sparse-format dataframe. |
- spateo.io.utils.bin_indices(coords: numpy.ndarray, coord_min: float, binsize: int = 50) int[source]#
Take a DNB coordinate, the mimimum coordinate and the binsize, calculate the index of bins for the current coordinate.
- Parameters:
- coord float
Current x or y coordinate.
- coord_min float
Minimal value for the current x or y coordinate on the entire tissue slide measured by the spatial transcriptomics.
- binsize float
Size of the bins to aggregate data.
- Returns:
num – The bin index for the current coordinate.
- Return type:
int
- spateo.io.utils.centroids(bin_indices: numpy.ndarray, coord_min: float = 0, binsize: int = 50) float[source]#
Take a bin index, the mimimum coordinate and the binsize, calculate the centroid of the current bin.
- Parameters:
- bin_ind float
The bin index for the current coordinate.
- coord_min float
Minimal value for the current x or y coordinate on the entire tissue slide measured by the spatial transcriptomics.
- binsize int
Size of the bins to aggregate data.
- Returns:
num – The bin index for the current coordinate.
- Return type:
int
- spateo.io.utils.get_points_props(data: pandas.DataFrame) pandas.DataFrame[source]#
Calculate properties of labeled coordinates.
- Parameters:
- data
Pandas Dataframe containing x, y, label columns.
- Returns:
A dataframe with properties and contours indexed by label
- spateo.io.utils.get_label_props(labels: numpy.ndarray) pandas.DataFrame[source]#
Measure properties of labeled cell regions.
- Parameters:
- labels
cell segmentation label matrix
- Returns:
A dataframe with properties and contours indexed by label
- spateo.io.utils.get_bin_props(data: pandas.DataFrame, binsize: int) pandas.DataFrame[source]#
Simulate properties of bin regions.
- Parameters:
- data
Pandas dataframe containing binned x, y, and cell labels. There should not be any duplicate cell labels.
- binsize
Bin size used
- Returns:
A dataframe with properties and contours indexed by cell label
- spateo.io.utils.in_concave_hull(p: numpy.ndarray, concave_hull: shapely.geometry.Polygon | shapely.geometry.MultiPolygon) numpy.ndarray[source]#
Test if points in p are in concave_hull using scipy.spatial Delaunay’s find_simplex.
- Parameters:
- p
a Nx2 coordinates of N points in K dimensions
- concave_hull
A polygon returned from the concave_hull function (the first value).
Returns:
- spateo.io.utils.in_convex_hull(p: numpy.ndarray, convex_hull: scipy.spatial.Delaunay | numpy.ndarray) numpy.ndarray[source]#
Test if points in p are in convex_hull using scipy.spatial Delaunay’s find_simplex.
- Parameters:
- p
a NxK coordinates of N points in K dimensions
- convex_hull
either a scipy.spatial.Delaunay object or the MxK array of the coordinates of M points in K dimensions for which Delaunay triangulation will be computed.
Returns:
- spateo.io.utils.bin_matrix(X: numpy.ndarray | scipy.sparse.spmatrix, binsize: int) numpy.ndarray | scipy.sparse.csr_matrix[source]#
Bin a matrix.
- Parameters:
- X
Dense or sparse matrix.
- binsize
Bin size
- Returns:
Dense or spares matrix, depending on what the input was.
- spateo.io.utils.get_coords_labels(labels: numpy.ndarray) pandas.DataFrame[source]#
Convert labels into sparse-format dataframe.
- Parameters:
- labels
cell segmentation labels matrix.
- Returns:
A DataFrame of columns “x”, “y”, and “label”. The coordinates are relative to the labels matrix.