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basic.py
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978 lines (893 loc) · 40.4 KB
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from __future__ import annotations
import sys
from collections import OrderedDict
from copy import deepcopy
from pathlib import Path
from typing import Any, Union
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
import spatialdata as sd
from anndata import AnnData
from dask.dataframe.core import DataFrame as DaskDataFrame
from geopandas import GeoDataFrame
from matplotlib.axes import Axes
from matplotlib.colors import Colormap, Normalize
from matplotlib.figure import Figure
from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
from spatial_image import SpatialImage
from spatialdata._core.data_extent import get_extent
from spatialdata.transformations.operations import get_transformation
from spatialdata_plot._accessor import register_spatial_data_accessor
from spatialdata_plot.pl.render import (
_render_images,
_render_labels,
_render_points,
_render_shapes,
)
from spatialdata_plot.pl.render_params import (
CmapParams,
ImageRenderParams,
LabelsRenderParams,
LegendParams,
PointsRenderParams,
ShapesRenderParams,
_FontSize,
_FontWeight,
)
from spatialdata_plot.pl.utils import (
_create_initial_element_table_mapping,
_get_cs_contents,
_get_elements_to_be_rendered,
_get_valid_cs,
_match_length_elements_groups_palette,
_maybe_set_colors,
_mpl_ax_contains_elements,
_prepare_cmap_norm,
_prepare_params_plot,
_set_outline,
_update_element_table_mapping_label_colors,
_validate_colors_element_table_mapping_points_shapes,
_validate_render_params,
_validate_show_parameters,
save_fig,
)
from spatialdata_plot.pp.utils import _verify_plotting_tree
ColorLike = Union[tuple[float, ...], str]
@register_spatial_data_accessor("pl")
class PlotAccessor:
"""
A class to provide plotting functions for `SpatialData` objects.
Parameters
----------
sdata :
The `SpatialData` object to provide plotting functions for.
Notes
-----
This class provides a number of methods that can be used to generate
plots of the data stored in a `SpatialData` object. These methods are
accessed via the `SpatialData.pl` accessor.
"""
@property
def sdata(self) -> sd.SpatialData:
"""The `SpatialData` object to provide plotting functions for."""
return self._sdata
@sdata.setter
def sdata(self, sdata: sd.SpatialData) -> None:
self._sdata = sdata
def __init__(self, sdata: sd.SpatialData) -> None:
self._sdata = sdata
def _copy(
self,
images: dict[str, SpatialImage | MultiscaleSpatialImage] | None = None,
labels: dict[str, SpatialImage | MultiscaleSpatialImage] | None = None,
points: dict[str, DaskDataFrame] | None = None,
shapes: dict[str, GeoDataFrame] | None = None,
table: dict[str, AnnData] | None = None,
) -> sd.SpatialData:
"""Copy the current `SpatialData` object, optionally modifying some of its attributes.
Parameters
----------
images : dict[str, SpatialImage | MultiscaleSpatialImage] | None, optional
A dictionary containing image data to replace the images in the
original `SpatialData` object, or `None` to keep the original
images. Defaults to `None`.
labels : dict[str, SpatialImage | MultiscaleSpatialImage] | None, optional
A dictionary containing label data to replace the labels in the
original `SpatialData` object, or `None` to keep the original
labels. Defaults to `None`.
points : dict[str, DaskDataFrame] | None, optional
A dictionary containing point data to replace the points in the
original `SpatialData` object, or `None` to keep the original
points. Defaults to `None`.
shapes : dict[str, GeoDataFrame] | None, optional
A dictionary containing shape data to replace the shapes in the
original `SpatialData` object, or `None` to keep the original
shapes. Defaults to `None`.
table : dict[str, AnnData] | None, optional
A dictionary or `AnnData` object containing table data to replace
the table in the original `SpatialData` object, or `None` to keep
the original table. Defaults to `None`.
Returns
-------
sd.SpatialData
A new `SpatialData` object that is a copy of the original
`SpatialData` object, with any specified modifications.
Notes
-----
This method creates a new `SpatialData` object with the same metadata
and similar data as the original `SpatialData` object. The new object
can be modified without affecting the original object.
"""
sdata = sd.SpatialData(
images=self._sdata.images if images is None else images,
labels=self._sdata.labels if labels is None else labels,
points=self._sdata.points if points is None else points,
shapes=self._sdata.shapes if shapes is None else shapes,
table=self._sdata.tables if table is None else table,
)
sdata.plotting_tree = self._sdata.plotting_tree if hasattr(self._sdata, "plotting_tree") else OrderedDict()
return sdata
def render_shapes(
self,
elements: list[str] | str | None = None,
color: str | None = None,
fill_alpha: float | int = 1.0,
groups: list[list[str | None]] | list[str] | str | None = None,
palette: list[list[str | None]] | list[str] | str | None = None,
na_color: ColorLike | None = "lightgrey",
outline: bool = False,
outline_width: float | int = 1.5,
outline_color: str | list[float] = "#000000ff",
outline_alpha: float | int = 1.0,
cmap: Colormap | str | None = None,
norm: bool | Normalize = False,
scale: float | int = 1.0,
table_name: list[str] | str | None = None,
**kwargs: Any,
) -> sd.SpatialData:
"""
Render shapes elements in SpatialData.
Parameters
----------
elements : list[str] | str | None, optional
The name(s) of the shapes element(s) to render. If `None`, all shapes
elements in the `SpatialData` object will be used.
color : Colorlike | str | None, optional
Can either be a color-like or a key in :attr:`sdata.table.obs`. The latter
can be used to color by categorical or continuous variables.
fill_alpha : float | int, default 1.0
Alpha value for the fill of shapes.
groups : list[str] | str | None, optional
When using `color` and the key represents discrete labels, `groups`
can be used to show only a subset of them. Other values are set to NA.
palette : list[str] | str | None, optional
Palette for discrete annotations. List of valid color names that should be
used for the categories. Must match the number of groups.
na_color : str | list[float] | None, default "lightgrey"
Color to be used for NAs values, if present. Can either be a named color
("red"), a hex representation ("#000000ff") or a list of floats that
represent RGB/RGBA values (1.0, 0.0, 0.0, 1.0). When None, the values won't
be shown.
outline : bool, default False
If `True`, a border around the shape elements is plotted.
outline_width : float | int, default 1.5
Width of the border.
outline_color : str | list[float], default "#000000ff"
Color of the border. Can either be a named color ("red"), a hex
representation ("#000000ff") or a list of floats that represent RGB/RGBA
values (1.0, 0.0, 0.0, 1.0).
outline_alpha : float | int, default 1.0
Alpha value for the outline of shapes.
cmap : Colormap | str | None, optional
Colormap for discrete or continuous annotations using 'color', see :class:`matplotlib.colors.Colormap`.
norm : bool | Normalize, default False
Colormap normalization for continuous annotations.
scale : float | int, default 1.0
Value to scale circles, if present.
table_name:
Name of the table(s) containing the color(s) columns. If one name is given than the table is used for each
spatial element to be plotted if the table annotates it. If multiple names are given in a list than the
length must be equal to the number of spatial elements being plotted.
**kwargs : Any
Additional arguments to be passed to cmap and norm.
Notes
-----
- Empty geometries will be removed at the time of plotting.
- An `outline_width` of 0.0 leads to no border being plotted.
- When passing a color-like to 'color', this has precendence over the potential existence as a column name.
Returns
-------
sd.SpatialData
The modified SpatialData object with the rendered shapes.
"""
params_dict = _validate_render_params(
"shapes",
self._sdata,
elements=elements,
fill_alpha=fill_alpha,
groups=groups,
palette=palette,
color=color,
na_color=na_color,
outline=outline,
outline_alpha=outline_alpha,
outline_color=outline_color,
outline_width=outline_width,
cmap=cmap,
norm=norm,
scale=scale,
)
sdata = self._copy()
sdata = _verify_plotting_tree(sdata)
n_steps = len(sdata.plotting_tree.keys())
cmap_params = _prepare_cmap_norm(
cmap=cmap,
norm=norm,
na_color=na_color, # type: ignore[arg-type]
**kwargs,
)
outline_params = _set_outline(outline, outline_width, outline_color)
sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
elements=params_dict["elements"],
color=params_dict["color"],
col_for_color=params_dict["col_for_color"],
groups=params_dict["groups"],
scale=scale,
outline_params=outline_params,
cmap_params=cmap_params,
palette=params_dict["palette"],
outline_alpha=outline_alpha,
fill_alpha=fill_alpha,
transfunc=kwargs.get("transfunc", None),
element_table_mapping=table_name,
)
return sdata
def render_points(
self,
elements: list[str] | str | None = None,
color: list[str] | str | None = None,
alpha: float | int = 1.0,
groups: list[list[str | None]] | list[str] | str | None = None,
palette: list[list[str | None]] | list[str] | str | None = None,
na_color: ColorLike | None = "lightgrey",
cmap: Colormap | str | None = None,
norm: None | Normalize = None,
size: float | int = 1.0,
table_name: list[str] | str | None = None,
**kwargs: Any,
) -> sd.SpatialData:
"""
Render points elements in SpatialData.
Parameters
----------
elements : list[str] | str | None, optional
The name(s) of the points element(s) to render. If `None`, all points
elements in the `SpatialData` object will be used.
color : Colorlike | str | None, optional
Can either be a color-like or a key in :attr:`sdata.table.obs`. The latter
can be used to color by categorical or continuous variables.
alpha : float | int, default 1.0
Alpha value for the points.
groups : list[str] | str | None, optional
When using `color` and the key represents discrete labels, `groups`
can be used to show only a subset of them. Other values are set to NA.
palette : list[str] | str | None, optional
Palette for discrete annotations. List of valid color names that should be
used for the categories. Must match the number of groups.
na_color : str | list[float] | None, default "lightgrey"
Color to be used for NAs values, if present. Can either be a named color
("red"), a hex representation ("#000000ff") or a list of floats that
represent RGB/RGBA values (1.0, 0.0, 0.0, 1.0). When None, the values won't
be shown.
cmap : Colormap | str | None, optional
Colormap for discrete or continuous annotations using 'color', see
:class:`matplotlib.colors.Colormap`. If no palette is given and `color`
refers to a categorical, the colors are sampled from this colormap.
norm : bool | Normalize, default False
Colormap normalization for continuous annotations.
size : float | int, default 1.0
Size of the points
table_name:
Name of the table(s) containing the color(s) columns. If one name is given than the table is used for each
spatial element to be plotted if the table annotates it. If multiple names are given in a list than the
length must be equal to the number of spatial elements being plotted.
kwargs
Additional arguments to be passed to cmap and norm.
Returns
-------
sd.SpatialData
The modified SpatialData object with the rendered shapes.
"""
params_dict = _validate_render_params(
"points",
self._sdata,
elements=elements,
alpha=alpha,
color=color,
groups=groups,
palette=palette,
na_color=na_color,
cmap=cmap,
norm=norm,
size=size,
)
sdata = self._copy()
sdata = _verify_plotting_tree(sdata)
n_steps = len(sdata.plotting_tree.keys())
cmap_params = _prepare_cmap_norm(
cmap=cmap,
norm=norm,
na_color=na_color, # type: ignore[arg-type]
**kwargs,
)
sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
elements=params_dict["elements"],
color=params_dict["color"],
col_for_color=params_dict["col_for_color"],
groups=params_dict["groups"],
cmap_params=cmap_params,
palette=params_dict["palette"],
alpha=alpha,
transfunc=kwargs.get("transfunc", None),
size=size,
element_table_mapping=table_name,
)
return sdata
def render_images(
self,
elements: list[str] | str | None = None,
channel: list[str] | list[int] | str | int | None = None,
cmap: list[Colormap] | Colormap | str | None = None,
norm: Normalize | None = None,
na_color: ColorLike | None = (0.0, 0.0, 0.0, 0.0),
palette: list[str] | str | None = None,
alpha: float | int = 1.0,
quantiles_for_norm: tuple[float | None, float | None] | None = None,
scale: list[str] | str | None = None,
**kwargs: Any,
) -> sd.SpatialData:
"""
Render image elements in SpatialData.
Parameters
----------
elements : list[str] | str | None, optional
The name(s) of the image element(s) to render. If `None`, all image
elements in the `SpatialData` object will be used. If a string is provided,
it is converted into a single-element list.
channel : list[str] | list[int] | str | int | None, optional
To select specific channels to plot. Can be a single channel name/int or a
list of channel names/ints. If `None`, all channels will be used.
cmap : list[Colormap] | Colormap | str | None, optional
Colormap or list of colormaps for continuous annotations, see :class:`matplotlib.colors.Colormap`.
Each colormap applies to a corresponding channel.
norm : Normalize | None, optional
Colormap normalization for continuous annotations, see :class:`matplotlib.colors.Normalize`.
Applies to all channels if set.
na_color : ColorLike | None, default (0.0, 0.0, 0.0, 0.0)
Color to be used for NA values. Accepts color-like values (string, hex, RGB(A)).
alpha : float | int, default 1.0
Alpha value for the images. Must be a numeric between 0 and 1.
quantiles_for_norm : tuple[float | None, float | None] | None, optional
Optional pair of floats (pmin < pmax, 0-100) which will be used for quantile normalization.
scale : list[str] | str | None, optional
Influences the resolution of the rendering. Possibilities include:
1) `None` (default): The image is rasterized to fit the canvas size. For
multiscale images, the best scale is selected before rasterization.
2) A scale name: Renders the specified scale as-is (with adjustments for dpi
in `show()`).
3) "full": Renders the full image without rasterization. In the case of
multiscale images, the highest resolution scale is selected. Note that
this may result in long computing times for large images.
4) A list matching the list of elements. Can contain `None`, scale names, or
"full". Each scale applies to the corresponding element.
kwargs
Additional arguments to be passed to cmap, norm, and other rendering functions.
Returns
-------
sd.SpatialData
The SpatialData object with the rendered images.
"""
params_dict = _validate_render_params(
"images",
self._sdata,
elements=elements,
channel=channel,
alpha=alpha,
palette=palette,
na_color=na_color,
cmap=cmap,
norm=norm,
scale=scale,
quantiles_for_norm=quantiles_for_norm,
)
sdata = self._copy()
sdata = _verify_plotting_tree(sdata)
n_steps = len(sdata.plotting_tree.keys())
cmap_params: list[CmapParams] | CmapParams
if isinstance(cmap, list):
cmap_params = [
_prepare_cmap_norm(
cmap=c,
norm=norm,
na_color=na_color, # type: ignore[arg-type]
**kwargs,
)
for c in cmap
]
else:
cmap_params = _prepare_cmap_norm(
cmap=cmap,
norm=norm,
na_color=na_color, # type: ignore[arg-type]
**kwargs,
)
sdata.plotting_tree[f"{n_steps+1}_render_images"] = ImageRenderParams(
elements=params_dict["elements"],
channel=channel,
cmap_params=cmap_params,
palette=params_dict["palette"],
alpha=alpha,
quantiles_for_norm=params_dict["quantiles_for_norm"],
scale=params_dict["scale"],
)
return sdata
def render_labels(
self,
elements: list[str] | str | None = None,
color: list[str] | str | None = None,
groups: list[list[str | None]] | list[str] | str | None = None,
contour_px: int = 3,
outline: bool = False,
palette: list[list[str | None]] | list[str] | str | None = None,
cmap: Colormap | str | None = None,
norm: Normalize | None = None,
na_color: ColorLike | None = (0.0, 0.0, 0.0, 0.0),
outline_alpha: float | int = 1.0,
fill_alpha: float | int = 0.3,
scale: list[str] | str | None = None,
table_name: list[str] | str | None = None,
**kwargs: Any,
) -> sd.SpatialData:
"""
Render labels elements in SpatialData.
Parameters
----------
elements : list[str] | str | None, optional
The name(s) of the label element(s) to render. If `None`, all label
elements in the `SpatialData` object will be used.
color : str | None, optional
Key for annotations in :attr:`anndata.AnnData.obs` or variables/genes.
groups : list[str] | str | None, optional
When using `color` and the key represents discrete labels, `groups`
can be used to show only a subset of them. Other values are set to NA.
contour_px : int, default 3
Draw contour of specified width for each segment. If `None`, fills
entire segment, see :func:`skimage.morphology.erosion`.
outline : bool, default False
Whether to plot boundaries around segmentation masks.
palette : list[str] | str | None, optional
Palette for discrete annotations. List of valid color names that should be
used for the categories. Must match the number of groups.
cmap : Colormap | str | None, optional
Colormap for continuous annotations, see :class:`matplotlib.colors.Colormap`.
norm : Normalize | None, optional
Colormap normalization for continuous annotations, see :class:`matplotlib.colors.Normalize`.
na_color : ColorLike | None, optional
Color to be used for NAs values, if present.
outline_alpha : float | int, default 1.0
Alpha value for the outline of the labels.
fill_alpha : float | int, default 0.3
Alpha value for the fill of the labels.
scale : list[str] | str | None, optional
Influences the resolution of the rendering. Possibilities for setting this parameter:
1) None (default). The image is rasterized to fit the canvas size. For multiscale images, the best scale
is selected before the rasterization step.
2) Name of one of the scales in the multiscale image to be rendered. This scale is rendered as it is
(exception: a dpi is specified in `show()`. Then the image is rasterized to fit the canvas and dpi).
3) "full": render the full image without rasterization. In the case of a multiscale image, the scale
with the highest resolution is selected. This can lead to long computing times for large images!
4) List that is matched to the list of elements (can contain `None`, scale names or "full").
kwargs
Additional arguments to be passed to cmap and norm.
Returns
-------
None
"""
params_dict = _validate_render_params(
"labels",
self._sdata,
elements=elements,
cmap=cmap,
color=color,
contour_px=contour_px,
fill_alpha=fill_alpha,
groups=groups,
na_color=na_color,
norm=norm,
outline=outline,
outline_alpha=outline_alpha,
palette=palette,
scale=scale,
)
sdata = self._copy()
sdata = _verify_plotting_tree(sdata)
n_steps = len(sdata.plotting_tree.keys())
cmap_params = _prepare_cmap_norm(
cmap=cmap,
norm=norm,
na_color=na_color, # type: ignore[arg-type]
**kwargs,
)
sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
elements=params_dict["elements"],
color=color,
groups=params_dict["groups"],
contour_px=contour_px,
outline=outline,
cmap_params=cmap_params,
palette=params_dict["palette"],
outline_alpha=outline_alpha,
fill_alpha=fill_alpha,
transfunc=kwargs.get("transfunc", None),
scale=params_dict["scale"],
element_table_mapping=table_name,
)
return sdata
def show(
self,
coordinate_systems: list[str] | str | None = None,
legend_fontsize: int | float | _FontSize | None = None,
legend_fontweight: int | _FontWeight = "bold",
legend_loc: str | None = "right margin",
legend_fontoutline: int | None = None,
na_in_legend: bool = True,
colorbar: bool = True,
wspace: float | None = None,
hspace: float = 0.25,
ncols: int = 4,
frameon: bool | None = None,
figsize: tuple[float, float] | None = None,
dpi: int | None = None,
fig: Figure | None = None,
title: list[str] | str | None = None,
share_extent: bool = True,
pad_extent: int | float = 0,
ax: list[Axes] | Axes | None = None,
return_ax: bool = False,
save: str | Path | None = None,
) -> sd.SpatialData:
"""
Plot the images in the SpatialData object.
Parameters
----------
coordinate_systems :
Name(s) of the coordinate system(s) to be plotted. If None, all coordinate systems are plotted.
If a coordinate system doesn't contain any relevant elements (as specified in the render_* calls),
it is automatically not plotted.
figsize :
Size of the figure (width, height) in inches. The size of the actual canvas may deviate from this,
depending on the dpi! In matplotlib, the actual figure size (in pixels) is dpi * figsize.
If None, the default of matlotlib is used (6.4, 4.8)
dpi :
Resolution of the plot in dots per inch (as in matplotlib).
If None, the default of matplotlib is used (100.0).
ax :
Matplotlib axes object to plot on. If None, a new figure is created.
Works only if there is one image in the SpatialData object.
ncols :
Number of columns in the figure. Default is 4.
Returns
-------
sd.SpatialData
A SpatialData object.
"""
# copy the SpatialData object so we don't modify the original
try:
plotting_tree = self._sdata.plotting_tree
except AttributeError as e:
raise TypeError(
"Please specify what to plot using the 'render_*' functions before calling 'show()`."
) from e
_validate_show_parameters(
coordinate_systems,
legend_fontsize,
legend_fontweight,
legend_loc,
legend_fontoutline,
na_in_legend,
colorbar,
wspace,
hspace,
ncols,
frameon,
figsize,
dpi,
fig,
title,
share_extent,
pad_extent,
ax,
return_ax,
save,
)
sdata = self._copy()
# Evaluate execution tree for plotting
valid_commands = [
"get_elements",
"get_bb",
"render_images",
"render_shapes",
"render_labels",
"render_points",
]
# prepare rendering params
render_cmds = []
for cmd, params in plotting_tree.items():
# strip prefix from cmd and verify it's valid
cmd = "_".join(cmd.split("_")[1:])
if cmd not in valid_commands:
raise ValueError(f"Command {cmd} is not valid.")
if "render" in cmd:
# verify that rendering commands have been called before
render_cmds.append((cmd, params))
if not render_cmds:
raise TypeError("Please specify what to plot using the 'render_*' functions before calling 'imshow()'.")
if title is not None:
if isinstance(title, str):
title = [title]
if not all(isinstance(t, str) for t in title):
raise TypeError("All titles must be strings.")
# get original axis extent for later comparison
ax_x_min, ax_x_max = (np.inf, -np.inf)
ax_y_min, ax_y_max = (np.inf, -np.inf)
if isinstance(ax, Axes) and _mpl_ax_contains_elements(ax):
ax_x_min, ax_x_max = ax.get_xlim()
ax_y_max, ax_y_min = ax.get_ylim() # (0, 0) is top-left
# handle coordinate system
coordinate_systems = sdata.coordinate_systems if coordinate_systems is None else coordinate_systems
if isinstance(coordinate_systems, str):
coordinate_systems = [coordinate_systems]
for cs in coordinate_systems:
if cs not in sdata.coordinate_systems:
raise ValueError(f"Unknown coordinate system '{cs}', valid choices are: {sdata.coordinate_systems}")
# Check if user specified only certain elements to be plotted
cs_contents = _get_cs_contents(sdata)
elements_to_be_rendered = _get_elements_to_be_rendered(render_cmds, cs_contents, cs)
# filter out cs without relevant elements
cmds = [cmd for cmd, _ in render_cmds]
coordinate_systems = _get_valid_cs(
sdata=sdata,
coordinate_systems=coordinate_systems,
render_images="render_images" in cmds,
render_labels="render_labels" in cmds,
render_points="render_points" in cmds,
render_shapes="render_shapes" in cmds,
elements=elements_to_be_rendered,
)
# catch error in ruff-friendly way
if ax is not None: # we'll generate matching number then
n_ax = 1 if isinstance(ax, Axes) else len(ax)
if len(coordinate_systems) != n_ax:
raise ValueError(
f"Mismatch between number of matplotlib axes objects ({n_ax}) "
f"and number of coordinate systems ({len(coordinate_systems)})."
)
# set up canvas
fig_params, scalebar_params = _prepare_params_plot(
num_panels=len(coordinate_systems),
figsize=figsize,
dpi=dpi,
fig=fig,
ax=ax,
wspace=wspace,
hspace=hspace,
ncols=ncols,
frameon=frameon,
)
legend_params = LegendParams(
legend_fontsize=legend_fontsize,
legend_fontweight=legend_fontweight,
legend_loc=legend_loc,
legend_fontoutline=legend_fontoutline,
na_in_legend=na_in_legend,
colorbar=colorbar,
)
cs_contents = _get_cs_contents(sdata)
# go through tree
for i, cs in enumerate(coordinate_systems):
sdata = self._copy()
_, has_images, has_labels, has_points, has_shapes = (
cs_contents.query(f"cs == '{cs}'").iloc[0, :].values.tolist()
)
ax = fig_params.ax if fig_params.axs is None else fig_params.axs[i]
wants_images = False
wants_labels = False
wants_points = False
wants_shapes = False
wanted_elements = []
for cmd, params in render_cmds:
# We create a copy here as the wanted elements can change from one cs to another.
params_copy = deepcopy(params)
if cmd == "render_images" and has_images:
wants_images = True
wanted_images = (
params_copy.elements if params_copy.elements is not None else list(sdata.images.keys())
)
wanted_images_on_this_cs = [
image
for image in wanted_images
if cs in set(get_transformation(sdata.images[image], get_all=True).keys())
]
params_copy = _match_length_elements_groups_palette(
params_copy, wanted_images_on_this_cs, image=True
)
wanted_elements.extend(wanted_images_on_this_cs)
if wanted_images_on_this_cs:
rasterize = (params_copy.scale is None) or (
isinstance(params_copy.scale, str)
and params_copy.scale != "full"
and (dpi is not None or figsize is not None)
)
_render_images(
sdata=sdata,
render_params=params_copy,
coordinate_system=cs,
ax=ax,
fig_params=fig_params,
scalebar_params=scalebar_params,
legend_params=legend_params,
rasterize=rasterize,
)
elif cmd == "render_shapes" and has_shapes:
wants_shapes = True
wanted_shapes = (
params_copy.elements if params_copy.elements is not None else list(sdata.shapes.keys())
)
wanted_shapes_on_this_cs = [
shape
for shape in wanted_shapes
if cs in set(get_transformation(sdata.shapes[shape], get_all=True).keys())
]
if wanted_shapes_on_this_cs:
params_copy = _create_initial_element_table_mapping(
sdata, params_copy, wanted_shapes_on_this_cs
)
params_copy = _validate_colors_element_table_mapping_points_shapes(
sdata, params_copy, wanted_shapes_on_this_cs
)
params_copy = _match_length_elements_groups_palette(params_copy, wanted_shapes_on_this_cs)
wanted_elements.extend(wanted_shapes_on_this_cs)
if wanted_shapes_on_this_cs:
_render_shapes(
sdata=sdata,
render_params=params_copy,
coordinate_system=cs,
ax=ax,
fig_params=fig_params,
scalebar_params=scalebar_params,
legend_params=legend_params,
)
elif cmd == "render_points" and has_points:
wants_points = True
wanted_points = (
params_copy.elements if params_copy.elements is not None else list(sdata.points.keys())
)
wanted_points_on_this_cs = [
point
for point in wanted_points
if cs in set(get_transformation(sdata.points[point], get_all=True).keys())
]
if wanted_points_on_this_cs:
params_copy = _create_initial_element_table_mapping(
sdata, params_copy, wanted_points_on_this_cs
)
params_copy = _validate_colors_element_table_mapping_points_shapes(
sdata, params_copy, wanted_points_on_this_cs
)
params_copy = _match_length_elements_groups_palette(params_copy, wanted_points_on_this_cs)
wanted_elements.extend(wanted_points_on_this_cs)
if wanted_points_on_this_cs:
_render_points(
sdata=sdata,
render_params=params_copy,
coordinate_system=cs,
ax=ax,
fig_params=fig_params,
scalebar_params=scalebar_params,
legend_params=legend_params,
)
elif cmd == "render_labels" and has_labels:
wants_labels = True
wanted_labels = (
params_copy.elements if params_copy.elements is not None else list(sdata.labels.keys())
)
wanted_labels_on_this_cs = [
label
for label in wanted_labels
if cs in set(get_transformation(sdata.labels[label], get_all=True).keys())
]
wanted_elements.extend(wanted_labels_on_this_cs)
if wanted_labels_on_this_cs:
# Create element to table mapping and check whether specified color columns are in tables.
params_copy = _create_initial_element_table_mapping(
sdata, params_copy, wanted_labels_on_this_cs
)
params_copy = _update_element_table_mapping_label_colors(
sdata, params_copy, wanted_labels_on_this_cs
)
if isinstance(params_copy.color, list):
element_table_mapping = params_copy.element_table_mapping
params_copy.color = (
[
params_copy.color[0] if value is not None else None
for value in element_table_mapping.values()
]
if len(params_copy.color) == 1
else params_copy.color
)
for index, table in enumerate(params_copy.element_table_mapping.values()):
if table is None:
continue
colors = sc.get.obs_df(sdata[table], params_copy.color[index])
if isinstance(colors.dtype, pd.CategoricalDtype):
_maybe_set_colors(
source=sdata[table],
target=sdata[table],
key=params_copy.color[index],
palette=params_copy.palette,
)
params_copy = _match_length_elements_groups_palette(params_copy, wanted_labels_on_this_cs)
rasterize = (params_copy.scale is None) or (
isinstance(params_copy.scale, str)
and params_copy.scale != "full"
and (dpi is not None or figsize is not None)
)
_render_labels(
sdata=sdata,
render_params=params_copy,
coordinate_system=cs,
ax=ax,
fig_params=fig_params,
scalebar_params=scalebar_params,
legend_params=legend_params,
rasterize=rasterize,
)
if title is None:
t = cs
elif len(title) == 1:
t = title[0]
else:
try:
t = title[i]
except IndexError as e:
raise IndexError("The number of titles must match the number of coordinate systems.") from e
ax.set_title(t)
ax.set_aspect("equal")
extent = get_extent(
sdata,
coordinate_system=cs,
has_images=has_images and wants_images,
has_labels=has_labels and wants_labels,
has_points=has_points and wants_points,
has_shapes=has_shapes and wants_shapes,
elements=wanted_elements,
)
cs_x_min, cs_x_max = extent["x"]
cs_y_min, cs_y_max = extent["y"]
if any([has_images, has_labels, has_points, has_shapes]):
# If the axis already has limits, only expand them but not overwrite
x_min = min(ax_x_min, cs_x_min) - pad_extent
x_max = max(ax_x_max, cs_x_max) + pad_extent
y_min = min(ax_y_min, cs_y_min) - pad_extent
y_max = max(ax_y_max, cs_y_max) + pad_extent
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_max, y_min) # (0, 0) is top-left
if fig_params.fig is not None and save is not None:
save_fig(fig_params.fig, path=save)
# Manually show plot if we're not in interactive mode
# https://stackoverflow.com/a/64523765
if not hasattr(sys, "ps1"):
plt.show()
return (fig_params.ax if fig_params.axs is None else fig_params.axs) if return_ax else None # shuts up ruff