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from __future__ import annotations
import contextlib
import sys
from collections import OrderedDict
from copy import deepcopy
from pathlib import Path
from typing import Any, Literal, cast
import matplotlib
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.backend_bases import RendererBase
from matplotlib.colors import Colormap, Normalize
from matplotlib.figure import Figure
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
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._logging import _log_context, logger
from spatialdata_plot.pl.render import (
_render_images,
_render_labels,
_render_points,
_render_shapes,
_split_colorbar_params,
)
from spatialdata_plot.pl.render_params import (
CBAR_DEFAULT_FRACTION,
CBAR_DEFAULT_LOCATION,
CBAR_DEFAULT_PAD,
CmapParams,
ColorbarSpec,
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, ...] | list[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: ColorLike | None = None,
*,
fill_alpha: float | int | None = None,
groups: list[str] | str | None = None,
palette: list[str] | str | None = None,
na_color: ColorLike | None = "default",
outline_width: float | int | tuple[float | int, float | int] | None = None,
outline_color: ColorLike | tuple[ColorLike] | None = None,
outline_alpha: float | int | tuple[float | int, float | int] | None = None,
cmap: Colormap | str | None = None,
norm: Normalize | None = None,
scale: float | int = 1.0,
method: str | None = None,
table_name: str | None = None,
table_layer: str | None = None,
shape: Literal["circle", "hex", "visium_hex", "square"] | None = None,
colorbar: bool | str | None = "auto",
colorbar_params: dict[str, object] | 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 : ColorLike | None, optional
Can either be color-like (name of a color as string, e.g. "red", hex representation, e.g. "#000000" or
"#000000ff", or an RGB(A) array as a tuple or list containing 3-4 floats within [0, 1]. If an alpha value is
indicated, the value of `fill_alpha` takes precedence if given) or a string representing a 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 | None, optional
Alpha value for the fill of shapes. By default, it is set to 1.0 or, if a color is given that implies an
alpha, that value is used for `fill_alpha`. If an alpha channel is present in a cmap passed by the user,
`fill_alpha` will overwrite 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. By default, non-matching elements are hidden. To show non-matching elements, set ``na_color``
explicitly.
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 NA 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 | tuple[float | int, float | int], optional
Width of the border. If 2 values are given (tuple), 2 borders are shown with these widths (outer & inner).
If `outline_color` and/or `outline_alpha` are used to indicate that one/two outlines should be drawn, the
default outline widths 1.5 and 0.5 are used for outer/only and inner outline respectively.
outline_color : ColorLike | tuple[ColorLike], optional
Color of the border. Can either be a named color ("red"), a hex representation ("#000000") or a list of
floats that represent RGB/RGBA values (1.0, 0.0, 0.0, 1.0). If the hex representation includes alpha, e.g.
"#000000ff", and `outline_alpha` is not given, this value controls the opacity of the outline. If 2 values
are given (tuple), 2 borders are shown with these colors (outer & inner). If `outline_width` and/or
`outline_alpha` are used to indicate that one/two outlines should be drawn, the default outline colors
"#000000" and "#ffffff are used for outer/only and inner outline respectively.
outline_alpha : float | int | tuple[float | int, float | int] | None, optional
Alpha value for the outline of shapes. Invisible by default, meaning outline_alpha=0.0 if both outline_color
and outline_width are not specified. Else, outlines are rendered with the alpha implied by outline_color, or
with outline_alpha=1.0 if outline_color does not imply an alpha. For two outlines, alpha values can be
passed in a tuple of length 2.
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.
colorbar :
Whether to request a colorbar for continuous colors. Use "auto" (default) for automatic selection.
colorbar_params :
Parameters forwarded to Matplotlib's colorbar alongside layout hints such as ``loc``, ``width``, ``pad``,
and ``label``.
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.
table_layer: str | None
Layer of the table to use for coloring if `color` is in :attr:`sdata.table.var_names`. If None, the data in
:attr:`sdata.table.X` is used for coloring.
shape: Literal["circle", "hex", "visium_hex", "square"] | None
If None (default), the shapes are rendered as they are. Else, if either of "circle", "hex" or "square" is
specified, the shapes are converted to a circle/hexagon/square before rendering. If "visium_hex" is
specified, the shapes are assumed to be Visium spots and the size of the hexagons is adjusted to be adjacent
to each other.
**kwargs : Any
Additional arguments for customization. This can include:
datashader_reduction : Literal[
"sum", "mean", "any", "count", "std", "var", "max", "min"
], default: "max"
Reduction method for datashader when coloring by continuous values. Defaults to 'max'.
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:
logger.warning("`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.")
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,
table_layer=table_layer,
shape=shape,
method=method,
ds_reduction=kwargs.get("datashader_reduction"),
colorbar=colorbar,
colorbar_params=colorbar_params,
)
sdata = self._copy()
sdata = _verify_plotting_tree(sdata)
n_steps = len(sdata.plotting_tree.keys())
for element, param_values in params_dict.items():
final_outline_alpha, outline_params = _set_outline(
params_dict[element]["outline_alpha"],
params_dict[element]["outline_width"],
params_dict[element]["outline_color"],
)
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=final_outline_alpha,
fill_alpha=param_values["fill_alpha"],
transfunc=kwargs.get("transfunc"),
table_name=param_values["table_name"],
table_layer=param_values["table_layer"],
shape=param_values["shape"],
zorder=n_steps,
method=param_values["method"],
ds_reduction=param_values["ds_reduction"],
colorbar=param_values["colorbar"],
colorbar_params=param_values["colorbar_params"],
)
n_steps += 1
return sdata
@_deprecation_alias(elements="element", version="0.3.0")
def render_points(
self,
element: str | None = None,
color: ColorLike | None = None,
*,
alpha: float | int | None = None,
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,
table_layer: str | None = None,
colorbar: bool | str | None = "auto",
colorbar_params: dict[str, object] | 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, optional
Can either be color-like (name of a color as string, e.g. "red", hex representation, e.g. "#000000" or
"#000000ff", or an RGB(A) array as a tuple or list containing 3-4 floats within [0, 1]. If an alpha value is
indicated, the value of `fill_alpha` takes precedence if given) or a string representing a 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 | None, optional
Alpha value for the points. By default, it is set to 1.0 or, if a color is given that implies an alpha, that
value is used instead.
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. By default, non-matching points are filtered out entirely. To show non-matching points, set
``na_color`` explicitly.
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 NA 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.
colorbar :
Whether to request a colorbar for continuous colors. Use "auto" (default) for automatic selection.
colorbar_params :
Parameters forwarded to Matplotlib's colorbar alongside layout hints such as ``loc``, ``width``, ``pad``,
and ``label``.
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.
table_layer: str | None
Layer of the table to use for coloring if `color` is in :attr:`sdata.table.var_names`. If None, the data in
:attr:`sdata.table.X` is used for coloring.
**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:
logger.warning("`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.")
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,
table_layer=table_layer,
ds_reduction=kwargs.get("datashader_reduction"),
colorbar=colorbar,
colorbar_params=colorbar_params,
)
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"),
size=param_values["size"],
table_name=param_values["table_name"],
table_layer=param_values["table_layer"],
zorder=n_steps,
method=method,
ds_reduction=param_values["ds_reduction"],
colorbar=param_values["colorbar"],
colorbar_params=param_values["colorbar_params"],
)
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,
colorbar: bool | str | None = "auto",
colorbar_params: dict[str, object] | 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.
colorbar :
Whether to request a colorbar for continuous colors. Use "auto" (default) for automatic selection.
colorbar_params :
Parameters forwarded to Matplotlib's colorbar alongside layout hints such as ``loc``, ``width``, ``pad``,
and ``label``.
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:
logger.warning("`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.")
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,
colorbar=colorbar,
colorbar_params=colorbar_params,
)
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,
colorbar=param_values["colorbar"],
colorbar_params=param_values["colorbar_params"],
)
n_steps += 1
return sdata
@_deprecation_alias(elements="element", version="0.3.0")
def render_labels(
self,
element: str | None = None,
color: ColorLike | 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 | None = None,
outline_color: ColorLike | None = None,
scale: str | None = None,
colorbar: bool | str | None = "auto",
colorbar_params: dict[str, object] | None = None,
table_name: str | None = None,
table_layer: 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 : ColorLike | None
Can either be color-like (name of a color as string, e.g. "red", hex representation, e.g. "#000000" or
"#000000ff", or an RGB(A) array as a tuple or list containing 3-4 floats within [0, 1]. If an alpha value
is indicated, the value of `fill_alpha` takes precedence if given) or a string representing a key in
:attr:`sdata.table.obs` or in the index of :attr:`sdata.table.var`. 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.
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. By default, non-matching labels are hidden. To show non-matching labels, set ``na_color`` explicitly.
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 | None, optional
Alpha value for the fill of the labels. By default, it is set to 0.4 or, if a color is given that implies
an alpha, that value is used for `fill_alpha`.
outline_color : ColorLike | None
Color of the outline of the labels. Can either be a named color ("red"), a hex representation
("#000000") or a list of floats that represent RGB/RGBA values (1.0, 0.0, 0.0, 1.0). If ``None``,
the outline inherits from the ``color`` parameter when it is a literal color, or uses data-driven
per-label colors when ``color`` refers to a column.
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!
colorbar :
Whether to request a colorbar for continuous colors. Use "auto" (default) for automatic selection.
colorbar_params :
Parameters forwarded to Matplotlib's colorbar alongside layout hints such as ``loc``, ``width``, ``pad``,
and ``label``.
table_name: str | None
Name of the table containing the color columns.
table_layer: str | None
Layer of the AnnData table to use for coloring if `color` is in :attr:`sdata.table.var_names`. If None,
:attr:`sdata.table.X` of the default table is used for coloring.
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:
logger.warning("`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.")
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,
outline_color=outline_color,
palette=palette,
scale=scale,
colorbar=colorbar,
colorbar_params=colorbar_params,
table_name=table_name,
table_layer=table_layer,
)
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"],
col_for_color=param_values["col_for_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"],
outline_color=param_values["outline_color"],
fill_alpha=param_values["fill_alpha"],
transfunc=kwargs.get("transfunc"),
scale=param_values["scale"],
table_name=param_values["table_name"],
table_layer=param_values["table_layer"],
zorder=n_steps,
colorbar=param_values["colorbar"],
colorbar_params=param_values["colorbar_params"],
)
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,
colorbar_params: dict[str, object] | None = None,
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,
show: bool | 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.
show :
Whether to call ``plt.show()`` at the end. If ``None`` (default), the plot is shown
automatically when running in non-interactive mode (scripts) and suppressed in
interactive sessions (e.g. Jupyter). Set to ``False`` to prevent ``plt.show()``
from being called, which is useful when you want to save or further modify the
figure after calling this method.
colorbar :
Global switch to enable/disable all colorbars. Per-layer settings are ignored when this is False.
colorbar_params :
Global overrides passed to colorbars for all axes. Accepts the same keys as per-layer ``colorbar_params``
(e.g., ``loc``, ``width``, ``pad``, ``label``).
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.
"""
_log_context.set("show")
# 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,
colorbar_params,
wspace,
hspace,
ncols,
frameon,
figsize,
dpi,
fig,
title,
share_extent,
pad_extent,
ax,
return_ax,
save,
show,
)
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
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)
pending_colorbars: list[tuple[Axes, list[ColorbarSpec]]] = []
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_colorbar = colorbar
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=legend_colorbar,
)
def _draw_colorbar(
spec: ColorbarSpec,
fig: Figure,
renderer: RendererBase,
base_offsets_axes: dict[str, float],
trackers_axes: dict[str, float],
) -> None:
base_layout = {
"location": CBAR_DEFAULT_LOCATION,
"fraction": CBAR_DEFAULT_FRACTION,
"pad": CBAR_DEFAULT_PAD,
}
layer_layout, layer_kwargs, layer_label_override = _split_colorbar_params(spec.params)
global_layout, global_kwargs, global_label_override = _split_colorbar_params(colorbar_params)
layout = {**base_layout, **layer_layout, **global_layout}
cbar_kwargs = {**layer_kwargs, **global_kwargs}
location = cast(str, layout.get("location", base_layout["location"]))