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from __future__ import annotations
import sys
import warnings
from collections import OrderedDict
from copy import deepcopy
from pathlib import Path
from typing import Any
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 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 spatialdata import get_extent
from spatialdata._utils import _deprecation_alias
from xarray import DataArray, DataTree
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 (
_get_cs_contents,
_get_elements_to_be_rendered,
_get_valid_cs,
_get_wanted_render_elements,
_maybe_set_colors,
_mpl_ax_contains_elements,
_prepare_cmap_norm,
_prepare_params_plot,
_set_outline,
_validate_image_render_params,
_validate_label_render_params,
_validate_points_render_params,
_validate_shape_render_params,
_validate_show_parameters,
_verify_plotting_tree,
save_fig,
)
# replace with
# from spatialdata._types import ColorLike
# once https://github.com/scverse/spatialdata/pull/689/ is in a release
ColorLike = 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, DataArray | DataTree] | None = None,
labels: dict[str, DataArray | DataTree] | None = None,
points: dict[str, DaskDataFrame] | None = None,
shapes: dict[str, GeoDataFrame] | None = None,
tables: dict[str, AnnData] | None = None,
) -> sd.SpatialData:
"""Copy the current `SpatialData` object, optionally modifying some of its attributes.
Parameters
----------
images : dict[str, DataArray | DataTree] | 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, DataArray | DataTree] | 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,
tables=self._sdata.tables if tables is None else tables,
)
sdata.plotting_tree = self._sdata.plotting_tree if hasattr(self._sdata, "plotting_tree") else OrderedDict()
return sdata
@_deprecation_alias(elements="element", version="0.3.0")
def render_shapes(
self,
element: str | None = None,
color: str | None = None,
fill_alpha: float | int = 1.0,
groups: list[str] | str | None = None,
palette: list[str] | str | None = None,
na_color: ColorLike | None = "default",
outline_width: float | int = 1.5,
outline_color: str | list[float] = "#000000ff",
outline_alpha: float | int = 0.0,
cmap: Colormap | str | None = None,
norm: Normalize | None = None,
scale: float | int = 1.0,
method: str | None = None,
table_name: str | None = None,
**kwargs: Any,
) -> sd.SpatialData:
"""
Render shapes elements in SpatialData.
In case of no elements specified, "broadcasting" of parameters is applied. This means that for any particular
SpatialElement, we validate whether a given parameter is valid. If not valid for a particular SpatialElement the
specific parameter for that particular SpatialElement will be ignored. If you want to set specific parameters
for specific elements please chain the render functions: `pl.render_points(...).pl.render_points(...).pl.show()`
.
Parameters
----------
element : str | None, optional
The name of the shapes element to render. If `None`, all shapes elements in the `SpatialData` object will be
used.
color : str | None
Can either be string representing a color-like or key in :attr:`sdata.table.obs`. The latter can be used to
color by categorical or continuous variables. If `element` is `None`, if possible the color will be
broadcasted to all elements. For this, the table in which the color key is found must annotate the
respective element (region must be set to the specific element). If the color column is found in multiple
locations, please provide the table_name to be used for the elements.
fill_alpha : float | int, default 1.0
Alpha value for the fill of shapes. If the alpha channel is present in a cmap passed by the user, this value
will multiply the value present in the cmap.
groups : list[str] | str | None
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. If elment is None, broadcasting behaviour is attempted (use the same
values for all elements).
palette : list[str] | str | None
Palette for discrete annotations. List of valid color names that should be used for the categories. Must
match the number of groups. If element is None, broadcasting behaviour is attempted (use the same values for
all elements). If groups is provided but not palette, palette is set to default "lightgray".
na_color : ColorLike | None, default "default" (gets set to "lightgray")
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_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 0.0
Alpha value for the outline of shapes. Invisible by default.
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.
method : str | None, optional
Whether to use 'matplotlib' and 'datashader'. When None, the method is
chosen based on the size of the data.
table_name: str | None
Name of the table 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 you want to use different tables for particular
elements, as specified under element.
**kwargs : Any
Additional arguments for customization. This can include:
datashader_reduction : Literal[
"sum", "mean", "any", "count", "std", "var", "max", "min"
], default: "sum"
Reduction method for datashader when coloring by continuous values. Defaults to 'sum'.
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.
"""
# TODO add Normalize object in tutorial notebook and point to that notebook here
if "vmin" in kwargs or "vmax" in kwargs:
warnings.warn(
"`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
DeprecationWarning,
stacklevel=2,
)
params_dict = _validate_shape_render_params(
self._sdata,
element=element,
fill_alpha=fill_alpha,
groups=groups,
palette=palette,
color=color,
na_color=na_color,
outline_alpha=outline_alpha,
outline_color=outline_color,
outline_width=outline_width,
cmap=cmap,
norm=norm,
scale=scale,
table_name=table_name,
method=method,
ds_reduction=kwargs.get("datashader_reduction", None),
)
sdata = self._copy()
sdata = _verify_plotting_tree(sdata)
n_steps = len(sdata.plotting_tree.keys())
outline_params = _set_outline(outline_alpha > 0, outline_width, outline_color)
for element, param_values in params_dict.items():
cmap_params = _prepare_cmap_norm(
cmap=cmap,
norm=norm,
na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
)
sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
element=element,
color=param_values["color"],
col_for_color=param_values["col_for_color"],
groups=param_values["groups"],
scale=param_values["scale"],
outline_params=outline_params,
cmap_params=cmap_params,
palette=param_values["palette"],
outline_alpha=param_values["outline_alpha"],
fill_alpha=param_values["fill_alpha"],
transfunc=kwargs.get("transfunc", None),
table_name=param_values["table_name"],
zorder=n_steps,
method=param_values["method"],
ds_reduction=param_values["ds_reduction"],
)
n_steps += 1
return sdata
@_deprecation_alias(elements="element", version="0.3.0")
def render_points(
self,
element: str | None = None,
color: str | None = None,
alpha: float | int = 1.0,
groups: list[str] | str | None = None,
palette: list[str] | str | None = None,
na_color: ColorLike | None = "default",
cmap: Colormap | str | None = None,
norm: Normalize | None = None,
size: float | int = 1.0,
method: str | None = None,
table_name: str | None = None,
**kwargs: Any,
) -> sd.SpatialData:
"""
Render points elements in SpatialData.
In case of no elements specified, "broadcasting" of parameters is applied. This means that for any particular
SpatialElement, we validate whether a given parameter is valid. If not valid for a particular SpatialElement the
specific parameter for that particular SpatialElement will be ignored. If you want to set specific parameters
for specific elements please chain the render functions: `pl.render_points(...).pl.render_points(...).pl.show()`
.
Parameters
----------
element : str | None, optional
The name of the points element to render. If `None`, all points elements in the `SpatialData` object will be
used.
color : str | None
Can either be string representing a color-like or key in :attr:`sdata.table.obs`. The latter can be used to
color by categorical or continuous variables. If `element` is `None`, if possible the color will be
broadcasted to all elements. For this, the table in which the color key is found must annotate the
respective element (region must be set to the specific element). If the color column is found in multiple
locations, please provide the table_name to be used for the elements.
alpha : float | int, default 1.0
Alpha value for the points.
groups : list[str] | str | None
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. If `element` is `None`, broadcasting behaviour is attempted (use the same
values for all elements).
palette : list[str] | str | None
Palette for discrete annotations. List of valid color names that should be used for the categories. Must
match the number of groups. If `element` is `None`, broadcasting behaviour is attempted (use the same values
for all elements). If groups is provided but not palette, palette is set to default "lightgray".
na_color : ColorLike | None, default "default" (gets set to "lightgray")
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
method : str | None, optional
Whether to use 'matplotlib' and 'datashader'. When None, the method is
chosen based on the size of the data.
table_name: str | None
Name of the table 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 you want to use different tables for particular
elements, as specified under element.
**kwargs : Any
Additional arguments for customization. This can include:
datashader_reduction : Literal[
"sum", "mean", "any", "count", "std", "var", "max", "min"
], default: "sum"
Reduction method for datashader when coloring by continuous values. Defaults to 'sum'.
Returns
-------
sd.SpatialData
The modified SpatialData object with the rendered shapes.
"""
# TODO add Normalize object in tutorial notebook and point to that notebook here
if "vmin" in kwargs or "vmax" in kwargs:
warnings.warn(
"`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
DeprecationWarning,
stacklevel=2,
)
params_dict = _validate_points_render_params(
self._sdata,
element=element,
alpha=alpha,
color=color,
groups=groups,
palette=palette,
na_color=na_color,
cmap=cmap,
norm=norm,
size=size,
table_name=table_name,
ds_reduction=kwargs.get("datashader_reduction", None),
)
if method is not None:
if not isinstance(method, str):
raise TypeError("Parameter 'method' must be a string.")
if method not in ["matplotlib", "datashader"]:
raise ValueError("Parameter 'method' must be either 'matplotlib' or 'datashader'.")
sdata = self._copy()
sdata = _verify_plotting_tree(sdata)
n_steps = len(sdata.plotting_tree.keys())
for element, param_values in params_dict.items():
cmap_params = _prepare_cmap_norm(
cmap=cmap,
norm=norm,
na_color=param_values["na_color"], # type: ignore[arg-type]
)
sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
element=element,
color=param_values["color"],
col_for_color=param_values["col_for_color"],
groups=param_values["groups"],
cmap_params=cmap_params,
palette=param_values["palette"],
alpha=param_values["alpha"],
transfunc=kwargs.get("transfunc", None),
size=param_values["size"],
table_name=param_values["table_name"],
zorder=n_steps,
method=method,
ds_reduction=param_values["ds_reduction"],
)
n_steps += 1
return sdata
@_deprecation_alias(elements="element", quantiles_for_norm="percentiles_for_norm", version="version 0.3.0")
def render_images(
self,
element: str | None = None,
channel: list[str] | list[int] | str | int | None = None,
cmap: list[Colormap | str] | Colormap | str | None = None,
norm: Normalize | None = None,
na_color: ColorLike | None = "default",
palette: list[str] | str | None = None,
alpha: float | int = 1.0,
scale: str | None = None,
**kwargs: Any,
) -> sd.SpatialData:
"""
Render image elements in SpatialData.
In case of no elements specified, "broadcasting" of parameters is applied. This means that for any particular
SpatialElement, we validate whether a given parameter is valid. If not valid for a particular SpatialElement the
specific parameter for that particular SpatialElement will be ignored. If you want to set specific parameters
for specific elements please chain the render functions: `pl.render_images(...).pl.render_images(...).pl.show()`
.
Parameters
----------
element : str | None
The name of the image element to render. If `None`, all image
elements in the `SpatialData` object will be used and all parameters will be broadcasted if possible.
channel : list[str] | list[int] | str | int | None
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 | str] | Colormap | str | None
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 "default" (gets set to "lightgray")
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.
palette : list[str] | str | None
Palette to color images. The number of palettes should be equal to the number of channels.
alpha : float | int, default 1.0
Alpha value for the images. Must be a numeric between 0 and 1.
scale : str | None
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 ( of a multiscale image) 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.
kwargs
Additional arguments to be passed to cmap, norm, and other rendering functions.
Returns
-------
sd.SpatialData
The SpatialData object with the rendered images.
"""
# TODO add Normalize object in tutorial notebook and point to that notebook here
if "vmin" in kwargs or "vmax" in kwargs:
warnings.warn(
"`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
DeprecationWarning,
stacklevel=2,
)
params_dict = _validate_image_render_params(
self._sdata,
element=element,
channel=channel,
alpha=alpha,
palette=palette,
na_color=na_color,
cmap=cmap,
norm=norm,
scale=scale,
)
sdata = self._copy()
sdata = _verify_plotting_tree(sdata)
n_steps = len(sdata.plotting_tree.keys())
for element, param_values in params_dict.items():
cmap_params: list[CmapParams] | CmapParams
if isinstance(cmap, list):
cmap_params = [
_prepare_cmap_norm(
cmap=c,
norm=norm,
na_color=param_values["na_color"],
)
for c in cmap
]
else:
cmap_params = _prepare_cmap_norm(
cmap=cmap,
norm=norm,
na_color=param_values["na_color"],
**kwargs,
)
sdata.plotting_tree[f"{n_steps+1}_render_images"] = ImageRenderParams(
element=element,
channel=param_values["channel"],
cmap_params=cmap_params,
palette=param_values["palette"],
alpha=param_values["alpha"],
scale=param_values["scale"],
zorder=n_steps,
)
n_steps += 1
return sdata
@_deprecation_alias(elements="element", version="0.3.0")
def render_labels(
self,
element: str | None = None,
color: str | None = None,
groups: list[str] | str | None = None,
contour_px: int | None = 3,
palette: list[str] | str | None = None,
cmap: Colormap | str | None = None,
norm: Normalize | None = None,
na_color: ColorLike | None = "default",
outline_alpha: float | int = 0.0,
fill_alpha: float | int = 0.4,
scale: str | None = None,
table_name: str | None = None,
**kwargs: Any,
) -> sd.SpatialData:
"""
Render labels elements in SpatialData.
In case of no elements specified, "broadcasting" of parameters is applied. This means that for any particular
SpatialElement, we validate whether a given parameter is valid. If not valid for a particular SpatialElement the
specific parameter for that particular SpatialElement will be ignored. If you want to set specific parameters
for specific elements please chain the render functions: `pl.render_images(...).pl.render_images(...).pl.show()`
.
Parameters
----------
element : str | None
The name of the labels element to render. If `None`, all label
elements in the `SpatialData` object will be used and all parameters will be broadcasted if possible.
color : list[str] | str | None
Can either be string representing a color-like or key in :attr:`sdata.table.obs`. The latter can be used to
color by categorical or continuous variables. If the color column is found in multiple locations, please
provide the table_name to be used for the element if you would like a specific table to be used. By default
one table will automatically be choosen.
groups : list[str] | str | None
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. The list can contain multiple discrete labels to be visualized.
palette : list[str] | str | None
Palette for discrete annotations. List of valid color names that should be used for the categories. Must
match the number of groups. The list can contain multiple palettes (one per group) to be visualized. If
groups is provided but not palette, palette is set to default "lightgray".
contour_px : int, default 3
Draw contour of specified width for each segment. If `None`, fills entire segment, see:
func:`skimage.morphology.erosion`.
cmap : Colormap | str | None
Colormap for continuous annotations, see :class:`matplotlib.colors.Colormap`.
norm : Normalize | None
Colormap normalization for continuous annotations, see :class:`matplotlib.colors.Normalize`.
na_color : ColorLike | None, default "default" (gets set to "lightgray")
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_alpha : float | int, default 0.0
Alpha value for the outline of the labels. Invisible by default.
fill_alpha : float | int, default 0.3
Alpha value for the fill of the labels.
scale : str | None
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!
table_name: str | None
Name of the table containing the color columns.
kwargs
Additional arguments to be passed to cmap and norm.
Returns
-------
None
"""
# TODO add Normalize object in tutorial notebook and point to that notebook here
if "vmin" in kwargs or "vmax" in kwargs:
warnings.warn(
"`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
DeprecationWarning,
stacklevel=2,
)
params_dict = _validate_label_render_params(
self._sdata,
element=element,
cmap=cmap,
color=color,
contour_px=contour_px,
fill_alpha=fill_alpha,
groups=groups,
na_color=na_color,
norm=norm,
outline_alpha=outline_alpha,
palette=palette,
scale=scale,
table_name=table_name,
)
sdata = self._copy()
sdata = _verify_plotting_tree(sdata)
n_steps = len(sdata.plotting_tree.keys())
for element, param_values in params_dict.items():
cmap_params = _prepare_cmap_norm(
cmap=cmap,
norm=norm,
na_color=param_values["na_color"], # type: ignore[arg-type]
)
sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
element=element,
color=param_values["color"],
groups=param_values["groups"],
contour_px=param_values["contour_px"],
cmap_params=cmap_params,
palette=param_values["palette"],
outline_alpha=param_values["outline_alpha"],
fill_alpha=param_values["fill_alpha"],
transfunc=kwargs.get("transfunc", None),
scale=param_values["scale"],
table_name=param_values["table_name"],
zorder=n_steps,
)
n_steps += 1
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.
return_ax :
Whether to return the axes object created. False by default.
colorbar :
Whether to plot the colorbar. True by default.
title :
The title of the plot. If not provided the plot will have the name of the coordinate system as title.
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 = [
"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):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
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: list[str] = []
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:
wanted_elements, wanted_images_on_this_cs, wants_images = _get_wanted_render_elements(
sdata, wanted_elements, params_copy, cs, "images"
)
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:
wanted_elements, wanted_shapes_on_this_cs, wants_shapes = _get_wanted_render_elements(
sdata, wanted_elements, params_copy, cs, "shapes"
)
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:
wanted_elements, wanted_points_on_this_cs, wants_points = _get_wanted_render_elements(
sdata, wanted_elements, params_copy, cs, "points"
)
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:
wanted_elements, wanted_labels_on_this_cs, wants_labels = _get_wanted_render_elements(
sdata, wanted_elements, params_copy, cs, "labels"
)
if wanted_labels_on_this_cs:
if (table := params_copy.table_name) is not None:
colors = sc.get.obs_df(sdata[table], params_copy.color)
if isinstance(colors[params_copy.color].dtype, pd.CategoricalDtype):
_maybe_set_colors(
source=sdata[table],
target=sdata[table],
key=params_copy.color,
palette=params_copy.palette,
)
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]):