U mdf @sdZddlZddlmZddlmZmZmZddl m Z ddl m Z ejejdd d Zdeeeeeeeeeefdd d dZdS)z)Calculate density of cells in embeddings N)AnnData)UnionOptionalSequence)logging)sanitize_anndata)xycCsNddlm}t||g}|||}t|}t|}||||}|S)z: Calculates the density of points in 2 dimensions. r) gaussian_kde)Z scipy.statsr npZvstackminmax)r r r ZxyzZmin_zZmax_zZscaled_zrX/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/scanpy/tools/_embedding_density.py _calc_density s    rumap)adatabasisgroupby key_added componentsreturnc CsHt|td||}|dkr,d}d||krNtd|d|dkrZd}t|trn|d }t | t d }t |d krtd |d kr|d 7}|dk r||jkrtd|d|j|jjdkrt|d|dk r|}n$|dk r|d|}n |d}|dk r|j|jj}t |j}|D]^}|j||k} |jd|| |df} |jd|| |d f} t| | } | || <qB||j|<nP|jd|dd|df} |jd|dd|d f} t| | |j|<|d kr|d 7}t||d|j|d<td|d|ddS)a Calculate the density of cells in an embedding (per condition). Gaussian kernel density estimation is used to calculate the density of cells in an embedded space. This can be performed per category over a categorical cell annotation. The cell density can be plotted using the `pl.embedding_density` function. Note that density values are scaled to be between 0 and 1. Thus, the density value at each cell is only comparable to densities in the same category. Beware that the KDE estimate used (`scipy.stats.gaussian_kde`) becomes unreliable if you don't have enough cells in a category. This function was written by Sophie Tritschler and implemented into Scanpy by Malte Luecken. Parameters ---------- adata The annotated data matrix. basis The embedding over which the density will be calculated. This embedded representation should be found in `adata.obsm['X_[basis]']``. groupby Key for categorical observation/cell annotation for which densities are calculated per category. key_added Name of the `.obs` covariate that will be added with the density estimates. components The embedding dimensions over which the density should be calculated. This is limited to two components. Returns ------- Updates `adata.obs` with an additional field specified by the `key_added` parameter. This parameter defaults to `[basis]_density_[groupby]`, where `[basis]` is one of `umap`, `diffmap`, `pca`, `tsne`, or `draw_graph_fa` and `[groupby]` denotes the parameter input. Updates `adata.uns` with an additional field `[key_added]_params`. Examples -------- .. plot:: :context: close-figs import scanpy as sc adata = sc.datasets.pbmc68k_reduced() sc.tl.umap(adata) sc.tl.embedding_density(adata, basis='umap', groupby='phase') sc.pl.embedding_density( adata, basis='umap', key='umap_density_phase', group='G1' ) .. plot:: :context: close-figs sc.pl.embedding_density( adata, basis='umap', key='umap_density_phase', group='S' ) .. currentmodule:: scanpy See also -------- pl.embedding_density zcomputing density on faZ draw_graph_faZX_z7Cannot find the embedded representation `adata.obsm['X_z!']`. Compute the embedding first.Nz1,2,rz/Please specify exactly 2 components, or `None`.ZdiffmapzCould not find z `.obs` column.categoryz) column does not contain categorical dataZ _density_Z_densityr)Z covariater_paramsz added 'z', densities (adata.obs) 'z_params', parameter (adata.uns))rlogginfolowerZ obsm_keys ValueError isinstancestrsplitr arrayZastypeintlenZobsZdtypenamecat categoriesZzerosZn_obsZobsmrdicttolistZunshint) rrrrrZdensity_covariater+Zdensity_valuesr*Zcat_maskZembed_xZembed_yZ dens_embedrrrembedding_density sdP               r/)rNNN)__doc__numpyr Zanndatartypingrrrrr_utilsrZndarrayrr$r/rrrrs$