Source code for stlearn.plotting.gene_plot

from matplotlib import pyplot as plt
from PIL import Image
import pandas as pd
import matplotlib
import numpy as np

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 GenePlot
from stlearn.plotting.classes_bokeh import BokehGenePlot
from stlearn.plotting._docs import doc_spatial_base_plot, doc_gene_plot
from stlearn.utils import Empty, _empty, _AxesSubplot, _docs_params

from import push_notebook, output_notebook
from bokeh.plotting import show

[docs]@_docs_params(spatial_base_plot=doc_spatial_base_plot, gene_plot=doc_gene_plot) def gene_plot( adata: AnnData, gene_symbols: Union[str, list] = None, threshold: Optional[float] = None, method: str = "CumSum", contour: bool = False, step_size: Optional[int] = None, title: Optional["str"] = None, figsize: Optional[Tuple[float, float]] = None, cmap: Optional[str] = "Spectral_r", 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, color_bar_label: Optional[str] = "", zoom_coord: Optional[float] = None, crop: Optional[bool] = True, margin: Optional[bool] = 100, size: Optional[float] = 7, image_alpha: Optional[float] = 1.0, cell_alpha: Optional[float] = 0.7, use_raw: Optional[bool] = False, fname: Optional[str] = None, dpi: Optional[int] = 120, vmin: Optional[float] = None, vmax: Optional[float] = None, ) -> Optional[AnnData]: """\ Allows the visualization of a single gene or multiple genes as the values of dot points or contour in the Spatial transcriptomics array. Parameters ------------------------------------- {spatial_base_plot} {gene_plot} Examples ------------------------------------- >>> import stlearn as st >>> adata = st.datasets.example_bcba() >>> genes = ["BRCA1","BRCA2"] >>>, gene_symbols = genes) """ GenePlot( adata, gene_symbols=gene_symbols, threshold=threshold, method=method, contour=contour, step_size=step_size, 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, color_bar_label=color_bar_label, zoom_coord=zoom_coord, crop=crop, margin=margin, size=size, image_alpha=image_alpha, cell_alpha=cell_alpha, use_raw=use_raw, fname=fname, dpi=dpi, vmin=vmin, vmax=vmax, )
[docs]def gene_plot_interactive(adata: AnnData): bokeh_object = BokehGenePlot(adata) output_notebook() show(, notebook_handle=True)