U mdq@s"ddlmZmZmZmZddlZddlZddl m Z ddl m Z ddl mZddlmZddlmZd d lmZmZzdd lmZWn(ek rGd d d Zde_YnXddddddddddddd e eeeeeefejeeejeeeeeeeeeeeee dddZ dS))OptionalTupleSequenceTypeN) natsorted)AnnData)sparse)_utils)logging) rename_groupsrestrict_adjacency)MutableVertexPartitionc@s eZdZdS)rN)__name__ __module__ __qualname__rrM/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/scanpy/tools/_leiden.pyrsrzleidenalg.VertexPartitionleidenTF) restrict_to random_state key_added adjacencydirected use_weights n_iterationspartition_type neighbors_keyobspcopy)adata resolutionrrrrrrrrrr r!returnc Ksz ddl}Wntk r(tdYnXt| } td}| rH|n|}|dkrbt|| | }|dk r|\}}t||||\}}tj ||d}| dkr|j } |rt |j dt j| d<|| d<|| d <|dk r|| d <|j|| f| }t |j}|dk r*|d kr|d 7}t||||||}tj|d tttt |d|j|<i|jd <t|||d|jd d<tjd|dtt |d|dd| r|SdS)u Cluster cells into subgroups [Traag18]_. Cluster cells using the Leiden algorithm [Traag18]_, an improved version of the Louvain algorithm [Blondel08]_. It has been proposed for single-cell analysis by [Levine15]_. This requires having ran :func:`~scanpy.pp.neighbors` or :func:`~scanpy.external.pp.bbknn` first. Parameters ---------- adata The annotated data matrix. resolution A parameter value controlling the coarseness of the clustering. Higher values lead to more clusters. Set to `None` if overriding `partition_type` to one that doesn’t accept a `resolution_parameter`. random_state Change the initialization of the optimization. restrict_to Restrict the clustering to the categories within the key for sample annotation, tuple needs to contain `(obs_key, list_of_categories)`. key_added `adata.obs` key under which to add the cluster labels. adjacency Sparse adjacency matrix of the graph, defaults to neighbors connectivities. directed Whether to treat the graph as directed or undirected. use_weights If `True`, edge weights from the graph are used in the computation (placing more emphasis on stronger edges). n_iterations How many iterations of the Leiden clustering algorithm to perform. Positive values above 2 define the total number of iterations to perform, -1 has the algorithm run until it reaches its optimal clustering. partition_type Type of partition to use. Defaults to :class:`~leidenalg.RBConfigurationVertexPartition`. For the available options, consult the documentation for :func:`~leidenalg.find_partition`. neighbors_key Use neighbors connectivities as adjacency. If not specified, leiden looks .obsp['connectivities'] for connectivities (default storage place for pp.neighbors). If specified, leiden looks .obsp[.uns[neighbors_key]['connectivities_key']] for connectivities. obsp Use .obsp[obsp] as adjacency. You can't specify both `obsp` and `neighbors_key` at the same time. copy Whether to copy `adata` or modify it inplace. **partition_kwargs Any further arguments to pass to `~leidenalg.find_partition` (which in turn passes arguments to the `partition_type`). Returns ------- `adata.obs[key_added]` Array of dim (number of samples) that stores the subgroup id (`'0'`, `'1'`, ...) for each cell. `adata.uns['leiden']['params']` A dict with the values for the parameters `resolution`, `random_state`, and `n_iterations`. rNzjPlease install the leiden algorithm: `conda install -c conda-forge leidenalg` or `pip3 install leidenalg`.zrunning Leiden clustering)rweightweightsrseedZresolution_parameterrZ_RU)values categories)r#rrparamsz finishedzfound z clusters and added z-, the cluster labels (adata.obs, categorical))timedeep) leidenalg ImportErrordictlogginfor!r Z _choose_graphrZget_igraph_from_adjacencyZRBConfigurationVertexPartitionnparrayesZastypeZfloat64Zfind_partitionZ membershipr pdZ CategoricalrmapstruniqueZobsZunslen)r"r#rrrrrrrrrr r!Zpartition_kwargsr.startZ restrict_keyZrestrict_categoriesZrestrict_indicesgpartgroupsrrrrsrS        )r )!typingrrrrnumpyr3Zpandasr6ZnatsortrZanndatarZscipyrr r r1Z_utils_clusteringr rZleidenalg.VertexPartitionrr/rfloatr8Z AnyRandomZspmatrixboolintrrrrrsT