API

Import MESA as:

import mesa as ms

Multiomics

`multiomics.multiomics_spatial.get_spatial_knn_indices`(...)	Compute the k-nearest neighbors (k-NN) of each location in a given data matrix.
`multiomics.multiomics_spatial.get_neighborhood_composition`(...)	Compute the global composition of neighbors for each sample, either in percentage or count form, based on k-nearest neighbors (k-NN) indices and specific cluster labels.
`multiomics.multiomics_spatial.get_avg_expression_neighbors`(...)	Calculate the average expression values over the neighbors of each data point.

Ecospatial

`ecospatial.generate_patches`(spatial_data, ...)	Generate a list of patches from a spatial data object, scaling them according to a given factor.
`ecospatial.calculate_shannon_entropy`(counts)	Calculate the Shannon entropy of a set of counts.
`ecospatial.calculate_diversity_index`(...[, ...])	Calculate the heterogeneity index for a set of patches using specified metrics.
`ecospatial.diversity_heatmap`(spatial_data, ...)	Visualize the diversity indices as a heatmap on the original spatial data, optionally returning the plot figure for further customization.
`ecospatial.global_spatial_stats`(grid[, ...])	Perform global spatial autocorrelation analysis on a 2D grid of diversity indices.
`ecospatial.local_spatial_stats`(grid[, mode, ...])	Compute local indicators of spatial association (LISA) for local spatial autocorrelation on a 2D grid of diversity indices, identifying significant hotspots and coldspots based on the chosen statistical method.
`ecospatial.calculate_MDI`(spatial_data, ...)	Calculate the multiscale diversity index (MDI) for spatial data.
`ecospatial.calculate_GDI`(spatial_data, ...)	Calculate a Global Diversity Index (GDI) for specified samples within spatial data, incorporating spatial statistics under chosen analysis modes.
`ecospatial.calculate_DPI`(spatial_data, ...)	Calculate the Diversity Proximity Index (DPI) for specified samples within spatial data.
`ecospatial.spot_cellfreq`(spatial_data, ...)	Analyze cell frequency and co-occurrence across different spots in spatial data.