from matplotlib import pyplot as plt
from PIL import Image
import pandas as pd
import matplotlib
import numpy as np
import networkx as nx
from typing import Optional, Union, Mapping # Special
from typing import Sequence, Iterable # ABCs
from typing import Tuple # Classes
from anndata import AnnData
import warnings
from stlearn.plotting.classes import ClusterPlot
from stlearn.plotting.classes_bokeh import BokehClusterPlot
from stlearn.plotting._docs import doc_spatial_base_plot, doc_cluster_plot
from stlearn.utils import _AxesSubplot, Axes, _docs_params
from bokeh.io import push_notebook, output_notebook
from bokeh.plotting import show
[docs]@_docs_params(spatial_base_plot=doc_spatial_base_plot, cluster_plot=doc_cluster_plot)
def cluster_plot(
adata: AnnData,
# plotting param
title: Optional["str"] = None,
figsize: Optional[Tuple[float, float]] = None,
cmap: Optional[str] = "default",
use_label: Optional[str] = None,
list_clusters: Optional[list] = None,
ax: Optional[matplotlib.axes._subplots.Axes] = None,
fig: Optional[matplotlib.figure.Figure] = None,
show_plot: Optional[bool] = True,
show_axis: Optional[bool] = False,
show_image: Optional[bool] = True,
show_color_bar: Optional[bool] = True,
zoom_coord: Optional[float] = None,
crop: Optional[bool] = True,
margin: Optional[bool] = 100,
size: Optional[float] = 5,
image_alpha: Optional[float] = 1.0,
cell_alpha: Optional[float] = 1.0,
fname: Optional[str] = None,
dpi: Optional[int] = 120,
# cluster plot param
show_subcluster: Optional[bool] = False,
show_cluster_labels: Optional[bool] = False,
show_trajectories: Optional[bool] = False,
reverse: Optional[bool] = False,
show_node: Optional[bool] = False,
threshold_spots: Optional[int] = 5,
text_box_size: Optional[float] = 5,
color_bar_size: Optional[float] = 10,
bbox_to_anchor: Optional[Tuple[float, float]] = (1, 1),
# trajectory
trajectory_node_size: Optional[int] = 10,
trajectory_alpha: Optional[float] = 1.0,
trajectory_width: Optional[float] = 2.5,
trajectory_edge_color: Optional[str] = "#f4efd3",
trajectory_arrowsize: Optional[int] = 17,
) -> Optional[AnnData]:
"""\
Allows the visualization of a cluster results as the discretes values
of dot points in the Spatial transcriptomics array. We also support to
visualize the spatial trajectory results
Parameters
-------------------------------------
{spatial_base_plot}
{cluster_plot}
Examples
-------------------------------------
>>> import stlearn as st
>>> adata = st.datasets.example_bcba()
>>> label = "louvain"
>>> st.pl.cluster_plot(adata, use_label = label, show_trajectories = True)
"""
assert use_label != None, "Please select `use_label` parameter"
ClusterPlot(
adata,
title=title,
figsize=figsize,
cmap=cmap,
use_label=use_label,
list_clusters=list_clusters,
ax=ax,
fig=fig,
show_plot=show_plot,
show_axis=show_axis,
show_image=show_image,
show_color_bar=show_color_bar,
zoom_coord=zoom_coord,
crop=crop,
margin=margin,
size=size,
image_alpha=image_alpha,
cell_alpha=cell_alpha,
fname=fname,
dpi=dpi,
show_subcluster=show_subcluster,
show_cluster_labels=show_cluster_labels,
show_trajectories=show_trajectories,
reverse=reverse,
show_node=show_node,
threshold_spots=threshold_spots,
text_box_size=text_box_size,
color_bar_size=color_bar_size,
bbox_to_anchor=bbox_to_anchor,
trajectory_node_size=trajectory_node_size,
trajectory_alpha=trajectory_alpha,
trajectory_width=trajectory_width,
trajectory_edge_color=trajectory_edge_color,
trajectory_arrowsize=trajectory_arrowsize,
)
[docs]def cluster_plot_interactive(
adata: AnnData,
):
bokeh_object = BokehClusterPlot(adata)
output_notebook()
show(bokeh_object.app, notebook_handle=True)