persist_spatial.smoothed_expression
Functions
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Computes the dtm at a single point. |
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Computes the distance to measure at each point. |
Module Contents
- persist_spatial.smoothed_expression.distance_to_measure_point(weights: numpy.ndarray, distances: numpy.ndarray, m: float) float
Computes the dtm at a single point.
Given a sorted vector of probability masses and squared distances to the neighbours of a single point, computes the distance to measure at that point. To be called on by smoothed_expression.distance_to_measure_weighted().
- Parameters:
weights (np.ndarray) – Probability masses at neighbour points, pre sorted by increasing distance from central vertex.
distances (np.ndarray) – Squared distances to neighbour vertices, in increasing order.
m (float) – Threshold probability mass to compute distance to. Should lie in (0,1).
- Returns:
Distance to measure at input point.
- Return type:
float
- persist_spatial.smoothed_expression.distance_to_measure_weighted(weights: numpy.ndarray, dmat: numpy.ndarray, m: float) numpy.ndarray
Computes the distance to measure at each point.
Given a vector of expression weights, a well-well distance matrix, and an ordered list of distances from the central vertex in a radial network, computes the distance to measure at each vertex. Designed to be called upon by compute_persistence.run_persistence()
- Parameters:
weights (np.ndarray) – Expression in each well, should be normalised to sum to 1. Dimensions n_wells x 1
dmat (np.ndarray) – Well-well distance matrix where dmat[i,j] = d(well_i, well_j). Dimensions n_wells x n_wells
network_distances (np.ndarray) – Sorted array of distances from a central well to its nearest neighbour wells, distances[i] = d(central vertex, ith nearest other vertex). Dimensions n_wells x 1.
m (float) – Probability mass threshold to use in distance to measure calculations. Should lie in (0,1).
- Returns:
Distance to measure at each well. Dimensions n_wells x 1.
- Return type:
np.ndarray