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utils.py
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from __future__ import annotations
import os
import warnings
from collections import defaultdict
from collections.abc import Iterable, Mapping, Sequence
from copy import copy
from functools import partial
from pathlib import Path
from types import MappingProxyType
from typing import Any, Literal, Union
import matplotlib
import matplotlib.patches as mpatches
import matplotlib.patches as mplp
import matplotlib.path as mpath
import matplotlib.pyplot as plt
import multiscale_spatial_image as msi
import numpy as np
import pandas as pd
import shapely
import spatial_image
import spatialdata as sd
import xarray as xr
from anndata import AnnData
from cycler import Cycler, cycler
from geopandas import GeoDataFrame
from matplotlib import colors, patheffects, rcParams
from matplotlib.axes import Axes
from matplotlib.collections import PatchCollection
from matplotlib.colors import (
ColorConverter,
Colormap,
LinearSegmentedColormap,
ListedColormap,
Normalize,
TwoSlopeNorm,
to_rgba,
)
from matplotlib.figure import Figure
from matplotlib.gridspec import GridSpec
from matplotlib_scalebar.scalebar import ScaleBar
from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
from numpy.random import default_rng
from pandas.api.types import CategoricalDtype
from scanpy import settings
from scanpy.plotting._tools.scatterplots import _add_categorical_legend
from scanpy.plotting.palettes import default_20, default_28, default_102
from shapely.geometry import LineString, Polygon
from skimage.color import label2rgb
from skimage.morphology import erosion, square
from skimage.segmentation import find_boundaries
from skimage.util import map_array
from spatial_image import SpatialImage
from spatialdata import SpatialData
from spatialdata._core.operations.rasterize import rasterize
from spatialdata._core.query.relational_query import _get_element_annotators, _locate_value, _ValueOrigin, get_values
from spatialdata._types import ArrayLike
from spatialdata.models import Image2DModel, Labels2DModel, PointsModel, SpatialElement, TableModel, get_model
from spatialdata.transformations.operations import get_transformation
from spatialdata_plot._logging import logger
from spatialdata_plot.pl.render_params import (
CmapParams,
FigParams,
ImageRenderParams,
LabelsRenderParams,
OutlineParams,
PointsRenderParams,
ScalebarParams,
ShapesRenderParams,
_FontSize,
_FontWeight,
)
from spatialdata_plot.pp.utils import _get_coordinate_system_mapping
to_hex = partial(colors.to_hex, keep_alpha=True)
ColorLike = Union[tuple[float, ...], str]
def _prepare_params_plot(
# this param is inferred when `pl.show`` is called
num_panels: int,
# this args are passed at `pl.show``
figsize: tuple[float, float] | None = None,
dpi: int | None = None,
fig: Figure | None = None,
ax: Axes | Sequence[Axes] | None = None,
wspace: float | None = None,
hspace: float = 0.25,
ncols: int = 4,
frameon: bool | None = None,
# this args will be inferred from coordinate system
scalebar_dx: float | Sequence[float] | None = None,
scalebar_units: str | Sequence[str] | None = None,
) -> tuple[FigParams, ScalebarParams]:
# handle axes and size
wspace = 0.75 / rcParams["figure.figsize"][0] + 0.02 if wspace is None else wspace
figsize = rcParams["figure.figsize"] if figsize is None else figsize
dpi = rcParams["figure.dpi"] if dpi is None else dpi
if num_panels > 1 and ax is None:
fig, grid = _panel_grid(
num_panels=num_panels, hspace=hspace, wspace=wspace, ncols=ncols, dpi=dpi, figsize=figsize
)
axs: None | Sequence[Axes] = [plt.subplot(grid[c]) for c in range(num_panels)]
elif num_panels > 1:
if not isinstance(ax, Sequence):
raise TypeError(f"Expected `ax` to be a `Sequence`, but got {type(ax).__name__}")
if ax is not None and len(ax) != num_panels:
raise ValueError(f"Len of `ax`: {len(ax)} is not equal to number of panels: {num_panels}.")
if fig is None:
raise ValueError(
f"Invalid value of `fig`: {fig}. If a list of `Axes` is passed, a `Figure` must also be specified."
)
assert ax is None or isinstance(ax, Sequence), f"Invalid type of `ax`: {type(ax)}, expected `Sequence`."
axs = ax
else:
axs = None
if ax is None:
fig, ax = plt.subplots(figsize=figsize, dpi=dpi, constrained_layout=True)
elif isinstance(ax, Axes):
# needed for rasterization if user provides Axes object
fig = ax.get_figure()
fig.set_dpi(dpi)
# set scalebar
if scalebar_dx is not None:
scalebar_dx, scalebar_units = _get_scalebar(scalebar_dx, scalebar_units, num_panels)
fig_params = FigParams(
fig=fig,
ax=ax,
axs=axs,
num_panels=num_panels,
frameon=frameon,
)
scalebar_params = ScalebarParams(scalebar_dx=scalebar_dx, scalebar_units=scalebar_units)
return fig_params, scalebar_params
def _get_cs_contents(sdata: sd.SpatialData) -> pd.DataFrame:
"""Check which coordinate systems contain which elements and return that info."""
cs_mapping = _get_coordinate_system_mapping(sdata)
content_flags = ["has_images", "has_labels", "has_points", "has_shapes"]
cs_contents = pd.DataFrame(columns=["cs"] + content_flags)
for cs_name, element_ids in cs_mapping.items():
# determine if coordinate system has the respective elements
cs_has_images = any(e in sdata.images for e in element_ids)
cs_has_labels = any(e in sdata.labels for e in element_ids)
cs_has_points = any(e in sdata.points for e in element_ids)
cs_has_shapes = any(e in sdata.shapes for e in element_ids)
cs_contents = pd.concat(
[
cs_contents,
pd.DataFrame(
{
"cs": cs_name,
"has_images": [cs_has_images],
"has_labels": [cs_has_labels],
"has_points": [cs_has_points],
"has_shapes": [cs_has_shapes],
}
),
]
)
cs_contents["has_images"] = cs_contents["has_images"].astype("bool")
cs_contents["has_labels"] = cs_contents["has_labels"].astype("bool")
cs_contents["has_points"] = cs_contents["has_points"].astype("bool")
cs_contents["has_shapes"] = cs_contents["has_shapes"].astype("bool")
return cs_contents
def _get_collection_shape(
shapes: list[GeoDataFrame],
c: Any,
s: float,
norm: Any,
render_params: ShapesRenderParams,
fill_alpha: None | float = None,
outline_alpha: None | float = None,
**kwargs: Any,
) -> PatchCollection:
"""
Get a PatchCollection for rendering given geometries with specified colors and outlines.
Args:
- shapes (list[GeoDataFrame]): List of geometrical shapes.
- c: Color parameter.
- s (float): Scale of the shape.
- norm: Normalization for the color map.
- fill_alpha (float, optional): Opacity for the fill color.
- outline_alpha (float, optional): Opacity for the outline.
- **kwargs: Additional keyword arguments.
Returns
-------
- PatchCollection: Collection of patches for rendering.
"""
cmap = kwargs["cmap"]
try:
# fails when numeric
if len(c.shape) == 1 and c.shape[0] in [3, 4] and c.shape[0] == len(shapes) and c.dtype == float:
if norm is None:
c = cmap(c)
else:
try:
norm = colors.Normalize(vmin=min(c), vmax=max(c))
except ValueError as e:
raise ValueError(
"Could not convert values in the `color` column to float, if `color` column represents"
" categories, set the column to categorical dtype."
) from e
c = cmap(norm(c))
else:
fill_c = ColorConverter().to_rgba_array(c)
except ValueError:
if norm is None:
c = cmap(c)
else:
try:
norm = colors.Normalize(vmin=min(c), vmax=max(c))
except ValueError as e:
raise ValueError(
"Could not convert values in the `color` column to float, if `color` column represents"
" categories, set the column to categorical dtype."
) from e
c = cmap(norm(c))
fill_c = ColorConverter().to_rgba_array(c)
fill_c[..., -1] *= render_params.fill_alpha
if render_params.outline_params.outline:
outline_c = ColorConverter().to_rgba_array(render_params.outline_params.outline_color)
outline_c[..., -1] = render_params.outline_alpha
outline_c = outline_c.tolist()
else:
outline_c = [None]
outline_c = outline_c * fill_c.shape[0]
shapes_df = pd.DataFrame(shapes, copy=True)
# remove empty points/polygons
shapes_df = shapes_df[shapes_df["geometry"].apply(lambda geom: not geom.is_empty)]
# reset index of shapes_df for case of spatial query
shapes_df = shapes_df.reset_index(drop=True)
rows = []
def assign_fill_and_outline_to_row(
shapes: list[GeoDataFrame], fill_c: list[Any], outline_c: list[Any], row: pd.Series, idx: int
) -> None:
if len(shapes) > 1 and len(fill_c) == 1:
row["fill_c"] = fill_c
row["outline_c"] = outline_c
else:
row["fill_c"] = fill_c[idx]
row["outline_c"] = outline_c[idx]
# Match colors to the geometry, potentially expanding the row in case of
# multipolygons
for idx, row in shapes_df.iterrows():
geom = row["geometry"]
if geom.geom_type == "Polygon":
row = row.to_dict()
coords = np.array(geom.exterior.coords)
centroid = np.mean(coords, axis=0)
scaled_coords = [(centroid + (np.array(coord) - centroid) * s).tolist() for coord in geom.exterior.coords]
row["geometry"] = mplp.Polygon(scaled_coords, closed=True)
assign_fill_and_outline_to_row(shapes, fill_c, outline_c, row, idx)
rows.append(row)
elif geom.geom_type == "MultiPolygon":
# mp = _make_patch_from_multipolygon(geom)
for polygon in geom.geoms:
mp_copy = row.to_dict()
coords = np.array(polygon.exterior.coords)
centroid = np.mean(coords, axis=0)
scaled_coords = [(centroid + (coord - centroid) * s).tolist() for coord in coords]
mp_copy["geometry"] = mplp.Polygon(scaled_coords, closed=True)
assign_fill_and_outline_to_row(shapes, fill_c, outline_c, mp_copy, idx)
rows.append(mp_copy)
elif geom.geom_type == "Point":
row = row.to_dict()
scaled_radius = row["radius"] * s
row["geometry"] = mplp.Circle(
(geom.x, geom.y), radius=scaled_radius
) # Circle is always scaled from its center
assign_fill_and_outline_to_row(shapes, fill_c, outline_c, row, idx)
rows.append(row)
patches = pd.DataFrame(rows)
return PatchCollection(
patches["geometry"].values.tolist(),
snap=False,
lw=render_params.outline_params.linewidth,
facecolor=patches["fill_c"],
edgecolor=None if all(outline is None for outline in outline_c) else outline_c,
**kwargs,
)
def _panel_grid(
num_panels: int,
hspace: float,
wspace: float,
ncols: int,
figsize: tuple[float, float],
dpi: int | None = None,
) -> tuple[Figure, GridSpec]:
n_panels_x = min(ncols, num_panels)
n_panels_y = np.ceil(num_panels / n_panels_x).astype(int)
fig = plt.figure(
figsize=(figsize[0] * n_panels_x * (1 + wspace), figsize[1] * n_panels_y),
dpi=dpi,
)
left = 0.2 / n_panels_x
bottom = 0.13 / n_panels_y
gs = GridSpec(
nrows=n_panels_y,
ncols=n_panels_x,
left=left,
right=1 - (n_panels_x - 1) * left - 0.01 / n_panels_x,
bottom=bottom,
top=1 - (n_panels_y - 1) * bottom - 0.1 / n_panels_y,
hspace=hspace,
wspace=wspace,
)
return fig, gs
def _get_scalebar(
scalebar_dx: float | Sequence[float] | None = None,
scalebar_units: str | Sequence[str] | None = None,
len_lib: int | None = None,
) -> tuple[Sequence[float] | None, Sequence[str] | None]:
if scalebar_dx is not None:
_scalebar_dx = _get_list(scalebar_dx, _type=float, ref_len=len_lib, name="scalebar_dx")
scalebar_units = "um" if scalebar_units is None else scalebar_units
_scalebar_units = _get_list(scalebar_units, _type=str, ref_len=len_lib, name="scalebar_units")
else:
_scalebar_dx = None
_scalebar_units = None
return _scalebar_dx, _scalebar_units
def _prepare_cmap_norm(
cmap: Colormap | str | None = None,
norm: Normalize | bool = False,
na_color: str | tuple[float, ...] = (0.0, 0.0, 0.0, 0.0),
vmin: float | None = None,
vmax: float | None = None,
vcenter: float | None = None,
**kwargs: Any,
) -> CmapParams:
is_default = cmap is None
if cmap is None:
cmap = rcParams["image.cmap"]
if isinstance(cmap, str):
cmap = matplotlib.colormaps[cmap]
cmap = copy(cmap)
cmap.set_bad("lightgray" if na_color is None else na_color)
if norm is None:
norm = Normalize(vmin=vmin, vmax=vmax)
elif isinstance(norm, Normalize) or not norm:
pass # TODO
elif vcenter is None:
norm = Normalize(vmin=vmin, vmax=vmax)
else:
norm = TwoSlopeNorm(vmin=vmin, vmax=vmax, vcenter=vcenter)
return CmapParams(cmap, norm, na_color, is_default)
def _set_outline(
outline: bool = False,
outline_width: float = 1.5,
outline_color: str | list[float] = "#0000000ff", # black, white
**kwargs: Any,
) -> OutlineParams:
# Type checks for outline_width
if isinstance(outline_width, int):
outline_width = outline_width
if not isinstance(outline_width, float):
raise TypeError(f"Invalid type of `outline_width`: {type(outline_width)}, expected `float`.")
if outline_width == 0.0:
outline = False
if outline_width < 0.0:
logger.warning(f"Negative line widths are not allowed, changing {outline_width} to {(-1) * outline_width}")
outline_width *= -1
# the default black and white colors can be changed using the contour_config parameter
if len(outline_color) in {3, 4} and all(isinstance(c, float) for c in outline_color):
outline_color = matplotlib.colors.to_hex(outline_color)
if outline:
kwargs.pop("edgecolor", None) # remove edge from kwargs if present
kwargs.pop("alpha", None) # remove alpha from kwargs if present
return OutlineParams(outline, outline_color, outline_width)
def _get_subplots(num_images: int, ncols: int = 4, width: int = 4, height: int = 3) -> plt.Figure | plt.Axes:
"""Set up the axs objects.
Parameters
----------
num_images
Number of images to plot. Must be greater than 1.
ncols
Number of columns in the subplot grid, by default 4
width
Width of each subplot, by default 4
Returns
-------
Union[plt.Figure, plt.Axes]
Matplotlib figure and axes object.
"""
# if num_images <= 1:
# raise ValueError("Number of images must be greater than 1.")
if num_images < ncols:
nrows = 1
ncols = num_images
else:
nrows, reminder = divmod(num_images, ncols)
if nrows == 0:
nrows = 1
if reminder > 0:
nrows += 1
fig, axes = plt.subplots(nrows, ncols, figsize=(width * ncols, height * nrows))
if not isinstance(axes, Iterable):
axes = np.array([axes])
# get rid of the empty axes
_ = [ax.axis("off") for ax in axes.flatten()[num_images:]]
return fig, axes
def _get_random_hex_colors(num_colors: int, seed: int | None = None) -> set[str]:
"""Return a list of random hex-color.
Parameters
----------
num_colors
Number of colors to generate.
Returns
-------
list
List of random colors.
"""
rng = default_rng(seed)
colors: set[str] = set()
while len(colors) < num_colors:
r, g, b = rng.integers(0, 255), rng.integers(0, 255), rng.integers(0, 255)
color = f"#{r:02x}{g:02x}{b:02x}"
colors.add(color)
return colors
def _get_hex_colors_for_continous_values(values: pd.Series, cmap_name: str = "viridis") -> list[str]:
"""Convert a series of continuous numerical values to hex color values using a colormap.
Parameters
----------
values
The values to be converted to colors.
cmap_name
The name of the colormap to be used, by default 'viridis'.
Returns
-------
pd.Series
The converted color values as hex strings.
"""
cmap = plt.get_cmap(cmap_name)
norm = plt.Normalize(vmin=values.min(), vmax=values.max())
colors = cmap(norm(values))
return [colors.to_hex(color) for color in colors]
def _normalize(
img: xr.DataArray,
pmin: float | None = None,
pmax: float | None = None,
eps: float = 1e-20,
clip: bool = False,
name: str = "normed",
) -> xr.DataArray:
"""Perform a min max normalisation on the xr.DataArray.
This function was adapted from the csbdeep package.
Parameters
----------
dataarray
A xarray DataArray with an image field.
pmin
Lower quantile (min value) used to perform quantile normalization.
pmax
Upper quantile (max value) used to perform quantile normalization.
eps
Epsilon float added to prevent 0 division.
clip
Ensures that normed image array contains no values greater than 1.
Returns
-------
xr.DataArray
A min-max normalized image.
"""
pmin = pmin or 0.0
pmax = pmax or 100.0
perc = np.percentile(img, [pmin, pmax])
norm = (img - perc[0]) / (perc[1] - perc[0] + eps)
if clip:
norm = np.clip(norm, 0, 1)
return norm
def _get_colors_for_categorical_obs(
categories: Sequence[str | int],
palette: ListedColormap | str | list[str] | None = None,
alpha: float = 1.0,
cmap_params: CmapParams | None = None,
) -> list[str]:
"""
Return a list of colors for a categorical observation.
Parameters
----------
adata
AnnData object
value_to_plot
Name of a valid categorical observation
categories
categories of the categorical observation.
Returns
-------
None
"""
len_cat = len(categories)
# check if default matplotlib palette has enough colors
if palette is None:
if cmap_params is not None and not cmap_params.is_default:
palette = cmap_params.cmap
elif len(rcParams["axes.prop_cycle"].by_key()["color"]) >= len_cat:
cc = rcParams["axes.prop_cycle"]()
palette = [next(cc)["color"] for _ in range(len_cat)]
elif len_cat <= 20:
palette = default_20
elif len_cat <= 28:
palette = default_28
elif len_cat <= len(default_102): # 103 colors
palette = default_102
else:
palette = ["grey" for _ in range(len_cat)]
logger.info("input has more than 103 categories. Uniform " "'grey' color will be used for all categories.")
else:
# raise error when user didn't provide the right number of colors in palette
if isinstance(palette, list) and len(palette) != len(categories):
raise ValueError(
f"The number of provided values in the palette ({len(palette)}) doesn't agree with the number of "
f"categories that should be colored ({categories})."
)
# otherwise, single channels turn out grey
color_idx = np.linspace(0, 1, len_cat) if len_cat > 1 else [0.7]
if isinstance(palette, str):
palette = [to_hex(palette)]
elif isinstance(palette, list):
palette = [to_hex(x) for x in palette]
elif isinstance(palette, ListedColormap):
palette = [to_hex(x) for x in palette(color_idx, alpha=alpha)]
elif isinstance(palette, LinearSegmentedColormap):
palette = [to_hex(palette(x, alpha=alpha)) for x in color_idx] # type: ignore[attr-defined]
else:
raise TypeError(f"Palette is {type(palette)} but should be string or list.")
return palette[:len_cat] # type: ignore[return-value]
def _locate_points_value_in_table(value_key: str, sdata: SpatialData, table_name: str) -> _ValueOrigin:
table = sdata[table_name]
if value_key in table.obs.columns:
value = table.obs[value_key]
is_categorical = isinstance(value.dtype, CategoricalDtype)
return _ValueOrigin(origin="obs", is_categorical=is_categorical, value_key=value_key)
is_categorical = False
return _ValueOrigin(origin="var", is_categorical=is_categorical, value_key=value_key)
# TODO consider move to relational query in spatialdata
def get_values_point_table(sdata: SpatialData, origin: _ValueOrigin, table_name: str) -> pd.Series:
"""Get a particular column stored in _ValueOrigin from the table in the spatialdata object."""
table = sdata[table_name]
if origin.origin == "obs":
return table.obs[origin.value_key]
if origin.origin == "var":
return table[:, table.var_names.isin([origin.value_key])].X.copy()
raise ValueError(f"Color column `{origin.value_key}` not found in table {table_name}")
def _set_color_source_vec(
sdata: sd.SpatialData,
element: SpatialElement | None,
element_index: int,
value_to_plot: str | None,
element_name: list[str] | str | None = None,
groups: Sequence[str | None] | str | None = None,
palette: list[str | None] | None = None,
na_color: str | tuple[float, ...] | None = None,
cmap_params: CmapParams | None = None,
table_name: str | None = None,
) -> tuple[ArrayLike | pd.Series | None, ArrayLike, bool]:
if value_to_plot is None:
color = np.full(len(element), to_hex(na_color)) # type: ignore[arg-type]
return color, color, False
model = get_model(sdata[element_name])
# Figure out where to get the color from
origins = _locate_value(value_key=value_to_plot, sdata=sdata, element_name=element_name, table_name=table_name)
if model == PointsModel and table_name is not None:
origin = _locate_points_value_in_table(value_key=value_to_plot, sdata=sdata, table_name=table_name)
if origin is not None:
origins.append(origin)
if len(origins) > 1:
raise ValueError(
f"Color key '{value_to_plot}' for element '{element_name}' been found in multiple locations: {origins}."
)
if len(origins) == 1:
if model == PointsModel and table_name is not None:
color_source_vector = get_values_point_table(sdata=sdata, origin=origin, table_name=table_name)
else:
vals = get_values(value_key=value_to_plot, sdata=sdata, element_name=element_name, table_name=table_name)
color_source_vector = vals[value_to_plot]
# numerical case, return early
if color_source_vector is not None and not isinstance(color_source_vector.dtype, pd.CategoricalDtype):
if isinstance(palette, list) and palette[0] is not None:
logger.warning(
"Ignoring categorical palette which is given for a continuous variable. "
"Consider using `cmap` to pass a ColorMap."
)
return None, color_source_vector, False
color_source_vector = pd.Categorical(color_source_vector) # convert, e.g., `pd.Series`
categories = color_source_vector.categories
if groups is not None and groups[0] is not None:
color_source_vector = color_source_vector.remove_categories(categories.difference(groups))
categories = groups
palette_input: list[str] | str | None
if groups is not None and groups[0] is not None:
if isinstance(palette, list):
palette_input = (
palette[0]
if palette[0] is None
else [color_palette for color_palette in palette if isinstance(color_palette, str)]
)
elif palette is not None and isinstance(palette, list):
palette_input = palette[0]
else:
palette_input = palette
color_map = dict(
zip(categories, _get_colors_for_categorical_obs(categories, palette_input, cmap_params=cmap_params))
)
if color_map is None:
raise ValueError("Unable to create color palette.")
# do not rename categories, as colors need not be unique
color_vector = color_source_vector.map(color_map)
if color_vector.isna().any():
if na_cat_color := to_hex(na_color) not in color_vector.categories:
color_vector = color_vector.add_categories([na_cat_color])
color_vector = color_vector.fillna(to_hex(na_color))
return color_source_vector, color_vector, True
logger.warning(f"Color key '{value_to_plot}' for element '{element_name}' not been found, using default colors.")
color = np.full(sdata[table_name].n_obs, to_hex(na_color))
return color, color, False
def _map_color_seg(
seg: ArrayLike,
cell_id: ArrayLike,
color_vector: ArrayLike | pd.Series[CategoricalDtype],
color_source_vector: pd.Series[CategoricalDtype],
cmap_params: CmapParams,
seg_erosionpx: int | None = None,
seg_boundaries: bool = False,
na_color: str | tuple[float, ...] = (0, 0, 0, 0),
) -> ArrayLike:
cell_id = np.array(cell_id)
if color_vector is not None and isinstance(color_vector.dtype, pd.CategoricalDtype):
if isinstance(na_color, tuple) and len(na_color) == 4 and np.any(color_source_vector.isna()):
cell_id[color_source_vector.isna()] = 0
val_im: ArrayLike = map_array(seg, cell_id, color_vector.codes + 1)
cols = colors.to_rgba_array(color_vector.categories)
else:
val_im = map_array(seg, cell_id, cell_id) # replace with same seg id to remove missing segs
try:
cols = cmap_params.cmap(cmap_params.norm(color_vector))
except TypeError:
assert all(colors.is_color_like(c) for c in color_vector), "Not all values are color-like."
cols = colors.to_rgba_array(color_vector)
if seg_erosionpx is not None:
val_im[val_im == erosion(val_im, square(seg_erosionpx))] = 0
# check if no color is assigned, compute random colors
unique_cols = np.unique(cols)
if len(unique_cols) == 1 and unique_cols == 0:
RNG = default_rng(42)
cols = RNG.random((len(cols), 3))
seg_im: ArrayLike = label2rgb(
label=val_im,
colors=cols,
bg_label=0,
bg_color=(1, 1, 1), # transparency doesn't really work
)
if seg_boundaries:
seg_bound: ArrayLike = np.clip(seg_im - find_boundaries(seg)[:, :, None], 0, 1)
return np.dstack((seg_bound, np.where(val_im > 0, 1, 0))) # add transparency here
return np.dstack((seg_im, np.where(val_im > 0, 1, 0)))
def _get_palette(
categories: Sequence[Any],
adata: AnnData | None = None,
cluster_key: None | str = None,
palette: ListedColormap | str | list[str] | None = None,
alpha: float = 1.0,
) -> Mapping[str, str] | None:
palette = None if isinstance(palette, list) and palette[0] is None else palette
if adata is not None and palette is None:
try:
palette = adata.uns[f"{cluster_key}_colors"] # type: ignore[arg-type]
if len(palette) != len(categories):
raise ValueError(
f"Expected palette to be of length `{len(categories)}`, found `{len(palette)}`. "
+ f"Removing the colors in `adata.uns` with `adata.uns.pop('{cluster_key}_colors')` may help."
)
return {cat: to_hex(to_rgba(col)[:3]) for cat, col in zip(categories, palette)}
except KeyError as e:
logger.warning(e)
return None
len_cat = len(categories)
if palette is None:
if len_cat <= 20:
palette = default_20
elif len_cat <= 28:
palette = default_28
elif len_cat <= len(default_102): # 103 colors
palette = default_102
else:
palette = ["grey" for _ in range(len_cat)]
logger.info("input has more than 103 categories. Uniform " "'grey' color will be used for all categories.")
return {cat: to_hex(to_rgba(col)[:3]) for cat, col in zip(categories, palette[:len_cat])}
if isinstance(palette, str):
cmap = ListedColormap([palette])
elif isinstance(palette, list):
cmap = ListedColormap(palette)
elif isinstance(palette, ListedColormap):
cmap = palette
else:
raise TypeError(f"Palette is {type(palette)} but should be string or list.")
palette = [to_hex(np.round(x, 5)) for x in cmap(np.linspace(0, 1, len_cat), alpha=alpha)]
return dict(zip(categories, palette))
def _maybe_set_colors(
source: AnnData, target: AnnData, key: str, palette: str | ListedColormap | Cycler | Sequence[Any] | None = None
) -> None:
from scanpy.plotting._utils import add_colors_for_categorical_sample_annotation
color_key = f"{key}_colors"
try:
if palette is not None:
raise KeyError("Unable to copy the palette when there was other explicitly specified.")
target.uns[color_key] = source.uns[color_key]
except KeyError:
if isinstance(palette, str):
palette = ListedColormap([palette])
if isinstance(palette, ListedColormap): # `scanpy` requires it
palette = cycler(color=palette.colors)
palette = None
add_colors_for_categorical_sample_annotation(target, key=key, force_update_colors=True, palette=palette)
def _decorate_axs(
ax: Axes,
cax: PatchCollection,
fig_params: FigParams,
value_to_plot: str | None,
color_source_vector: pd.Series[CategoricalDtype],
adata: AnnData | None = None,
palette: ListedColormap | str | list[str] | None = None,
alpha: float = 1.0,
na_color: str | tuple[float, ...] = (0.0, 0.0, 0.0, 0.0),
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,
scalebar_dx: Sequence[float] | None = None,
scalebar_units: Sequence[str] | None = None,
scalebar_kwargs: Mapping[str, Any] = MappingProxyType({}),
) -> Axes:
if value_to_plot is not None:
# if only dots were plotted without an associated value
# there is not need to plot a legend or a colorbar
if legend_fontoutline is not None:
path_effect = [patheffects.withStroke(linewidth=legend_fontoutline, foreground="w")]
else:
path_effect = []
# Adding legends
if color_source_vector is not None and isinstance(color_source_vector.dtype, pd.CategoricalDtype):
# order of clusters should agree to palette order
clusters = color_source_vector.unique()
clusters = clusters[~clusters.isnull()]
palette = None if isinstance(palette, list) and palette[0] else palette
palette = _get_palette(
adata=adata, cluster_key=value_to_plot, categories=clusters, palette=palette, alpha=alpha
)
_add_categorical_legend(
ax,
color_source_vector,
palette=palette,
legend_loc=legend_loc,
legend_fontweight=legend_fontweight,
legend_fontsize=legend_fontsize,
legend_fontoutline=path_effect,
na_color=[na_color],
na_in_legend=na_in_legend,
multi_panel=fig_params.axs is not None,
)
elif colorbar:
# TODO: na_in_legend should have some effect here
plt.colorbar(cax, ax=ax, pad=0.01, fraction=0.08, aspect=30)
if isinstance(scalebar_dx, list) and isinstance(scalebar_units, list):
scalebar = ScaleBar(scalebar_dx, units=scalebar_units, **scalebar_kwargs)
ax.add_artist(scalebar)
return ax
def _get_list(
var: Any,
_type: type[Any] | tuple[type[Any], ...],
ref_len: int | None = None,
name: str | None = None,
) -> list[Any]:
"""
Get a list from a variable.
Parameters
----------
var
Variable to convert to a list.
_type
Type of the elements in the list.
ref_len
Reference length of the list.
name
Name of the variable.
Returns
-------
List
"""
if isinstance(var, _type):
return [var] if ref_len is None else ([var] * ref_len)
if isinstance(var, list):
if ref_len is not None and ref_len != len(var):
raise ValueError(
f"Variable: `{name}` has length: {len(var)}, which is not equal to reference length: {ref_len}."
)
for v in var:
if not isinstance(v, _type):
raise ValueError(f"Variable: `{name}` has invalid type: {type(v)}, expected: {_type}.")
return var
raise ValueError(f"Can't make a list from variable: `{var}`")
def save_fig(fig: Figure, path: str | Path, make_dir: bool = True, ext: str = "png", **kwargs: Any) -> None:
"""
Save a figure.
Parameters
----------
fig
Figure to save.
path
Path where to save the figure. If path is relative, save it under :attr:`scanpy.settings.figdir`.
make_dir
Whether to try making the directory if it does not exist.
ext
Extension to use if none is provided.
kwargs
Keyword arguments for :func:`matplotlib.figure.Figure.savefig`.
Returns
-------
None
Just saves the plot.
"""
if os.path.splitext(path)[1] == "":
path = f"{path}.{ext}"
path = Path(path)
if not path.is_absolute():
path = Path(settings.figdir) / path
if make_dir:
try:
path.parent.mkdir(parents=True, exist_ok=True)
except OSError as e:
logger.debug(f"Unable to create directory `{path.parent}`. Reason: `{e}`")
logger.debug(f"Saving figure to `{path!r}`")
kwargs.setdefault("bbox_inches", "tight")
kwargs.setdefault("transparent", True)
fig.savefig(path, **kwargs)
def _get_cs_element_map(
element: str | Sequence[str] | None,
element_map: Mapping[str, Any],
) -> Mapping[str, str]:
"""Get the mapping between the coordinate system and the class."""
# from spatialdata.models import Image2DModel, Image3DModel, Labels2DModel, Labels3DModel, PointsModel, ShapesModel
element = list(element_map.keys())[0] if element is None else element
element = [element] if isinstance(element, str) else element
d = {}
for e in element:
cs = list(element_map[e].attrs["transform"].keys())[0]
d[cs] = e
# model = get_model(element_map["blobs_labels"])
# if model in [Image2DModel, Image3DModel, Labels2DModel, Labels3DModel]
return d
def _multiscale_to_image(sdata: sd.SpatialData) -> sd.SpatialData: