U md2@sddlmZmZmZmZmZmZddlZddl Z ddl m Z ddlmZddlmZddlmZddlmZddlmZmZmZmZdd lmZdd lmZd d lmZeeeeeed eeedfdddZeeeeeed eeeedfdddZ eeeeeed ddde!e!eeeedfdddZ"d-eee#ej$feee#ej$fe#e%e#eee!e%eed dd Z&eed!d.eee#e'e'e%e!eee%e%fe!e!eee#e!feed$ d%d&Z(d/e#e#eee%e%fe!e!eee#e!fd*d+d,Z)dS)0)UnionListOptionalAnyTuple CollectionN)AnnData)Axes) _doc_params embedding)doc_adata_color_etcdoc_edges_arrowsdoc_scatter_embeddingdoc_show_save_ax)_wraps_plot_scatter)_utils)_anndata_to_wishbone)Zadata_color_etcZ edges_arrowsZ scatter_bulk show_save_ax)returncKst|df|S)a Scatter plot in PHATE basis. Parameters ---------- {adata_color_etc} {edges_arrows} {scatter_bulk} {show_save_ax} Returns ------- If `show==False`, a list of :class:`~matplotlib.axes.Axes` objects. Every second element corresponds to the 'right margin' drawing area for color bars and legends. Examples -------- >>> from anndata import AnnData >>> import scanpy.external as sce >>> import phate >>> data, branches = phate.tree.gen_dla( ... n_dim=100, ... n_branch=20, ... branch_length=100, ... ) >>> data.shape (2000, 100) >>> adata = AnnData(data) >>> adata.obs['branches'] = branches >>> sce.tl.phate(adata, k=5, a=20, t=150) >>> adata.obsm['X_phate'].shape (2000, 2) >>> sce.pl.phate( ... adata, ... color='branches', ... color_map='tab20', ... ) phater adatakwargsrK/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/scanpy/external/pl.pyrs/rcKst|df|S)z Scatter plot in TriMap basis. Parameters ---------- {adata_color_etc} {edges_arrows} {scatter_bulk} {show_save_ax} Returns ------- If `show==False` a :class:`~matplotlib.axes.Axes` or a list of it. trimapr rrrrrHsrTF)show return_fig)rr rc Ksv|jd}|j|}tdt|\}}t|D],\}} t|d|| | d||dd} | q4|rj|S|sr|SdS)aN Scatter plot in Harmony force-directed layout basis. Parameters ---------- {adata_color_etc} {edges_arrows} {scatter_bulk} {show_save_ax} Returns ------- If `return_fig` is True, a :class:`~matplotlib.figure.Figure`. If `show==False` a :class:`~matplotlib.axes.Axes` or a list of it. Zharmony_timepoint_varrZharmonyFnone)colorgroupstitleraxZ legend_locN) unsobsuniquepltsubplotslen enumerater Z set_axis_off) rrr rZtp_nameZtpsfigaxesitpprrrharmony_timeseriesas&  r2X_umap Spectral_rk$@) r projectionccmap linewidth edgecolorr.colorbarsrrc Kst|tr8z|j|} Wq<tk r4tdYqr;r<T)Z return_index)r9r:r>r;r<)r%ZticksF)r%) isinstancestrZobsmKeyError ValueErrorr)figuregcaZscatternparraylistr'Zstr_ndarrayZsamalg.utilitiesZ utilitiesZconvert_annotationsr(r=r%Zset_yticklabels)rr8r9r:r;r<r.r=r>rdtutr/ZuiZaiZcaxZcbarrrrsams   (    rK)r皙?) rmarkersno_binssmoothing_factor min_delta show_variancefigsizer rsaver%c  Cst|} |dkr*dt|} dt|} n|\} } | r>| j}ntj| | fd}t} | j||||||| d\}}} |d|jd<|d|jd <|d |jd <tjd || d |r|S|s| SdS)a} Plot marker trends along trajectory, and return trajectory branches for further analysis and visualization (heatmap, etc..) Parameters ---------- adata Annotated data matrix. markers Iterable of markers/genes to be plotted. show_variance Logical indicating if the trends should be accompanied with variance. no_bins Number of bins for calculating marker density. smoothing_factor Parameter controlling the degree of smoothing. min_delta Minimum difference in marker expression after normalization to show separate trends for the two branches. figsize width, height return_fig Return the matplotlib figure. {show_save_ax} Returns ------- Updates `adata` with the following fields: `trunk_wishbone` : :class:`pandas.DataFrame` (`adata.uns`) Computed values before branching `branch1_wishbone` : :class:`pandas.DataFrame` (`adata.uns`) Computed values for the first branch `branch2_wishbone` : :class:`pandas.DataFrame` (`adata.uns`) Computed values for the second branch. Nr g?rS)rNrRrOrPrQr-r%ZTrunkZtrunk_wishboneZBranch1Zbranch1_wishboneZBranch2Zbranch2_wishboneZwishbone_trajectoryrrT) rr+rCr)rDZplot_marker_trajectoryr&rsavefig_or_show)rrNrOrPrQrRrSr rrTr%wbwidthheightr-Z ret_valuesrrrwishbone_marker_trajectorys43  r[loglinear)scale_hist_obsscale_hist_simrSr rrTcCsdtjtjttddd}d|jkr,tdd|jdkr~|jdd}|j|jdd d } t | j j } |d |d | f}n t j td |jd|jd } d } tj| d|d\} } t|j| D]\} \}}d|jdkr0|jdd|dd}|jdd|d}d|}| | d }| | d }n4|jddd}|jdd}d}| d }| d }|||d|d||d||||d||dq| tjd||d|r| S|s| SdS)a Plot histogram of doublet scores for observed transcriptomes and simulated doublets. The histogram for simulated doublets is useful for determining the correct doublet score threshold. Scrublet must have been run previously with the input object. Parameters ---------- adata An annData object resulting from func:`~scanpy.external.scrublet`. scale_hist_obs Set y axis scale transformation in matplotlib for the plot of observed transcriptomes (e.g. "linear", "log", "symlog", "logit") scale_hist_sim Set y axis scale transformation in matplotlib for the plot of simulated doublets (e.g. "linear", "log", "symlog", "logit") figsize width, height show Show the plot, do not return axis. save If `True` or a `str`, save the figure. A string is appended to the default filename. Infer the filetype if ending on {`'.pdf'`, `'.png'`, `'.svg'`}. Returns ------- If `return_fig` is True, a :class:`~matplotlib.figure.Figure`. If `show==False` a list of :class:`~matplotlib.axes.Axes`. See also -------- :func:`~scanpy.external.pp.scrublet`: Main way of running Scrublet, runs preprocessing, doublet simulation and calling. :func:`~scanpy.external.pp.scrublet_simulate_doublets`: Run Scrublet's doublet simulation separately for advanced usage. N)r%scoresscaler$cSs|j|tddddddd|||}|||dk r^|j|td|ddd ||| d | d dS) Nrr2grayT)r"r;Zdensityr black)r9r;z Doublet scorez Prob. density) histrEZlinspaceZ set_yscaleZget_ylimZset_ylimZplotZones set_titleZ set_xlabelZ set_ylabel)r%rcrdr$ thresholdZylrrr _plot_scoreszs     z1scrublet_score_distribution.._plot_scoresZscrubletz@Please run scrublet before trying to generate the scrublet plot. batched_bycategoryF)copyrr)Zdtypeindexr rUbatchesrjdoublet_scores_simz (%s)Z doublet_scorezObserved transcriptomes )rdr$rjzSimulated doublets scrublet_score_distributionrV)N)r)r rErHr@r&rBr'Zastyper+cat categoriespdZSeriesZ broadcast_toZn_obsZ obs_namesr*r,groupbygetZ tight_layoutrrW)rrarbrSr rrTrkrlrpZ n_batchesr-ZaxsidxZ batch_keyZsub_obsrjrqZaxis_lab_suffixZobs_axZsim_axrrrrsIsx2     rs)r3Nr4r5r6NTr7) rLrrMFNFTNN)r\r]r^FTN)*typingrrrrrrnumpyrEZpandasrvZmatplotlib.pyplotZpyplotr)ZanndatarZmatplotlib.axesr rr Zplottingr Zplotting._docsrrrrZplotting._tools.scatterplotsrZ tl._wishbonerrrboolr2r@rHfloatrKintr[rsrrrrs         + +  aX