Refactor of colorbar and norm logic

CHANGELOG.md

@@ -18,11 +18,15 @@ and this project adheres to [Semantic Versioning][].
 
 - Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
 - When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+- When rendering a single image channel, a colorbar is now shown (#346)
+- Removed `percentiles_for_norm` parameter (#346)
+- Changed `norm` to no longer accept bools, only `mpl.colors.Normalise` or `None` (#346)
 
 ### Fixed
 
 - Filtering with `groups` now preserves original cmap (#344)
 - Non-selected `groups` are now not shown in `na_color` (#344)
+- Several issues associated with `norm` and `colorbar` (#346)
 
 ## [0.2.5] - 2024-08-23
 

src/spatialdata_plot/pl/basic.py

@@ -166,7 +166,7 @@ def render_shapes(
  outline_color: str | list[float] = "#000000ff",
  outline_alpha: float | int = 0.0,
  cmap: Colormap | str | None = None,
- norm: bool | Normalize = False,
+ norm: Normalize | None = None,
  scale: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -301,7 +301,7 @@ def render_points(
  palette: list[str] | str | None = None,
  na_color: ColorLike | None = "default",
  cmap: Colormap | str | None = None,
- norm: None | Normalize = None,
+ norm: Normalize | None = None,
  size: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -422,7 +422,6 @@ def render_images(
  na_color: ColorLike | None = "default",
  palette: list[str] | str | None = None,
  alpha: float | int = 1.0,
- percentiles_for_norm: tuple[float, float] | None = None,
  scale: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
@@ -457,8 +456,6 @@ def render_images(
  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.
- percentiles_for_norm : tuple[float, float] | None
- Optional pair of floats (pmin < pmax, 0-100) which will be used for quantile normalization.
  scale : str | None
  Influences the resolution of the rendering. Possibilities include:
  1) `None` (default): The image is rasterized to fit the canvas size. For
@@ -486,20 +483,14 @@ def render_images(
  cmap=cmap,
  norm=norm,
  scale=scale,
- percentiles_for_norm=percentiles_for_norm,
  )
 
  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=params_dict[element]["cmap"],
- # norm=norm,
- # na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
- # **kwargs,
- # )
+
  cmap_params: list[CmapParams] | CmapParams
  if isinstance(cmap, list):
  cmap_params = [
@@ -525,7 +516,6 @@ def render_images(
  cmap_params=cmap_params,
  palette=param_values["palette"],
  alpha=param_values["alpha"],
- percentiles_for_norm=param_values["percentiles_for_norm"],
  scale=param_values["scale"],
  zorder=n_steps,
  )

src/spatialdata_plot/pl/render.py

@@ -9,6 +9,7 @@
 import datashader as ds
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import matplotlib.transforms as mtransforms
 import numpy as np
 import pandas as pd
@@ -47,7 +48,6 @@
  _maybe_set_colors,
  _mpl_ax_contains_elements,
  _multiscale_to_spatial_image,
- _normalize,
  _rasterize_if_necessary,
  _set_color_source_vec,
  to_hex,
@@ -128,6 +128,7 @@ def _render_shapes(
  shapes = shapes.reset_index()
  color_source_vector = color_source_vector[mask]
  color_vector = color_vector[mask]
+
  shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
 
  # Using dict.fromkeys here since set returns in arbitrary order
@@ -255,9 +256,13 @@ def _render_shapes(
  for path in _cax.get_paths():
  path.vertices = trans.transform(path.vertices)
 
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
+ if not values_are_categorical:
+ # If the user passed a Normalize object with vmin/vmax we'll use those,
+ # # if not we'll use the min/max of the color_vector
+ _cax.set_clim(
+ vmin=render_params.cmap_params.norm.vmin or min(color_vector),
+ vmax=render_params.cmap_params.norm.vmax or max(color_vector),
+ )
 
  if len(set(color_vector)) != 1 or list(set(color_vector))[0] != to_hex(render_params.cmap_params.na_color):
  # necessary in case different shapes elements are annotated with one table
@@ -603,11 +608,6 @@ def _render_images(
  if n_channels == 1 and not isinstance(render_params.cmap_params, list):
  layer = img.sel(c=channels[0]).squeeze() if isinstance(channels[0], str) else img.isel(c=channels[0]).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layer = _normalize(
- layer, pmin=render_params.percentiles_for_norm[0], pmax=render_params.percentiles_for_norm[1], clip=True
- )
-
  if render_params.cmap_params.norm: # type: ignore[attr-defined]
  layer = render_params.cmap_params.norm(layer) # type: ignore[attr-defined]
 
@@ -623,20 +623,16 @@ def _render_images(
 
  _ax_show_and_transform(layer, trans_data, ax, cmap=cmap, zorder=render_params.zorder)
 
+ if legend_params.colorbar:
+ sm = plt.cm.ScalarMappable(cmap=cmap, norm=render_params.cmap_params.norm)
+ fig_params.fig.colorbar(sm, ax=ax)
+
  # 2) Image has any number of channels but 1
  else:
  layers = {}
  for ch_index, c in enumerate(channels):
  layers[c] = img.sel(c=c).copy(deep=True).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layers[c] = _normalize(
- layers[c],
- pmin=render_params.percentiles_for_norm[0],
- pmax=render_params.percentiles_for_norm[1],
- clip=True,
- )
-
  if not isinstance(render_params.cmap_params, list):
  if render_params.cmap_params.norm is not None:
  layers[c] = render_params.cmap_params.norm(layers[c])

src/spatialdata_plot/pl/utils.py

@@ -489,7 +489,7 @@ def _get_scalebar(
 
 def _prepare_cmap_norm(
  cmap: Colormap | str | None = None,
- norm: Normalize | bool = False,
+ norm: Normalize | None = None,
  na_color: ColorLike | None = None,
  vmin: float | None = None,
  vmax: float | None = None,
@@ -1623,29 +1623,6 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
- if (percentiles_for_norm := param_dict.get("percentiles_for_norm")) is None:
- percentiles_for_norm = (None, None)
- elif not (isinstance(percentiles_for_norm, (list, tuple)) or len(percentiles_for_norm) != 2):
- raise TypeError("Parameter 'percentiles_for_norm' must be a list or tuple of exactly two floats or None.")
- elif not all(
- isinstance(p, (float, int, type(None)))
- and isinstance(p, type(percentiles_for_norm[0]))
- and (p is None or 0 <= p <= 100)
- for p in percentiles_for_norm
- ):
- raise TypeError(
- "Each item in 'percentiles_for_norm' must be of the same dtype and must be a float or int within [0, 100], "
- "or None"
- )
- elif (
- percentiles_for_norm[0] is not None
- and percentiles_for_norm[1] is not None
- and percentiles_for_norm[0] > percentiles_for_norm[1]
- ):
- raise ValueError("The first number in 'percentiles_for_norm' must not be smaller than the second.")
- if "percentiles_for_norm" in param_dict:
- param_dict["percentiles_for_norm"] = percentiles_for_norm
-
  if size := param_dict.get("size"):
  if not isinstance(size, (float, int)):
  raise TypeError("Parameter 'size' must be numeric.")
@@ -1886,7 +1863,6 @@ def _validate_image_render_params(
  cmap: list[Colormap | str] | Colormap | str | None,
  norm: Normalize | None,
  scale: str | None,
- percentiles_for_norm: tuple[float | None, float | None] | None,
 ) -> dict[str, dict[str, Any]]:
  param_dict: dict[str, Any] = {
  "sdata": sdata,
@@ -1898,7 +1874,6 @@ def _validate_image_render_params(
  "cmap": cmap,
  "norm": norm,
  "scale": scale,
- "percentiles_for_norm": percentiles_for_norm,
  }
  param_dict = _type_check_params(param_dict, "images")
 
@@ -1945,8 +1920,6 @@ def _validate_image_render_params(
  else:
  element_params[el]["scale"] = scale
 
- element_params[el]["percentiles_for_norm"] = param_dict["percentiles_for_norm"]
-
  return element_params
 
 

tests/pl/test_render.py

@@ -23,7 +23,9 @@ def test_render_images_can_plot_one_cyx_image(request):
 def test_render_images_can_plot_multiple_cyx_images(share_coordinate_system: str, request):
  fun = request.getfixturevalue("get_sdata_with_multiple_images")
  sdata = fun(share_coordinate_system)
- sdata.pl.render_images().pl.show()
+ sdata.pl.render_images().pl.show(
+ colorbar=False, # otherwise we'll get one cbar per image in the same cs
+ )
  axs = plt.gcf().get_axes()
 
  if share_coordinate_system == "all":

tests/pl/test_render_images.py

@@ -2,7 +2,6 @@
 import matplotlib
 import numpy as np
 import scanpy as sc
-from matplotlib import pyplot as plt
 from matplotlib.colors import Normalize
 from spatial_image import to_spatial_image
 from spatialdata import SpatialData
@@ -49,9 +48,6 @@ def test_plot_can_render_a_single_channel_from_image(self, sdata_blobs: SpatialD
  def test_plot_can_render_a_single_channel_from_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_multiscale_image", channel=0).pl.show()
 
- def test_plot_can_render_a_single_channel_from_image_no_el(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(channel=0).pl.show()
-
  def test_plot_can_render_a_single_channel_str_from_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_image", channel="c1").pl.show()
 
@@ -70,16 +66,13 @@ def test_plot_can_render_two_channels_str_from_image(self, sdata_blobs_str: Spat
  def test_plot_can_render_two_channels_str_from_multiscale_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_multiscale_image", channel=["c1", "c2"]).pl.show()
 
- def test_plot_can_pass_vmin_vmax(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, vmin=0, vmax=0.4).pl.show(ax=axs[1])
-
- def test_plot_can_pass_normalize(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
+ def test_plot_can_pass_normalize_clip_True(self, sdata_blobs: SpatialData):
  norm = Normalize(vmin=0, vmax=0.4, clip=True)
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, norm=norm).pl.show(ax=axs[1])
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
+
+ def test_plot_can_pass_normalize_clip_False(self, sdata_blobs: SpatialData):
+ norm = Normalize(vmin=0, vmax=0.4, clip=False)
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
 
  def test_plot_can_pass_color_to_single_channel(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_image", channel=1, palette="red").pl.show()
@@ -97,9 +90,6 @@ def test_plot_can_pass_cmap_to_each_channel(self, sdata_blobs: SpatialData):
  element="blobs_image", channel=[0, 1, 2], cmap=["Reds", "Greens", "Blues"]
  ).pl.show()
 
- def test_plot_can_normalize_image(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(element="blobs_image", percentiles_for_norm=(5, 90)).pl.show()
-
  def test_plot_can_render_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images("blobs_multiscale_image").pl.show()
 

tests/pl/test_render_shapes.py

@@ -6,6 +6,7 @@
 import pandas as pd
 import scanpy as sc
 from anndata import AnnData
+from matplotlib.colors import Normalize
 from shapely.geometry import MultiPolygon, Point, Polygon
 from spatialdata import SpatialData, deepcopy
 from spatialdata.models import ShapesModel, TableModel
@@ -146,7 +147,8 @@ def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
  sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=True).pl.show()
+ norm = Normalize(vmin=0, vmax=5, clip=True)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=norm).pl.show()
 
  def test_plot_can_plot_shapes_after_spatial_query(self, sdata_blobs: SpatialData):
  # subset to only shapes, should be unnecessary after rasterizeation of multiscale images is included

CHANGELOG.md

@@ -18,11 +18,15 @@ and this project adheres to [Semantic Versioning][].
 
 - Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
 - When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+- When rendering a single image channel, a colorbar is now shown (#346)
+- Removed `percentiles_for_norm` parameter (#346)
+- Changed `norm` to no longer accept bools, only `mpl.colors.Normalise` or `None` (#346)
 
 ### Fixed
 
 - Filtering with `groups` now preserves original cmap (#344)
 - Non-selected `groups` are now not shown in `na_color` (#344)
+- Several issues associated with `norm` and `colorbar` (#346)
 
 ## [0.2.5] - 2024-08-23
 

src/spatialdata_plot/pl/basic.py

@@ -166,7 +166,7 @@ def render_shapes(
  outline_color: str | list[float] = "#000000ff",
  outline_alpha: float | int = 0.0,
  cmap: Colormap | str | None = None,
- norm: bool | Normalize = False,
+ norm: Normalize | None = None,
  scale: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -301,7 +301,7 @@ def render_points(
  palette: list[str] | str | None = None,
  na_color: ColorLike | None = "default",
  cmap: Colormap | str | None = None,
- norm: None | Normalize = None,
+ norm: Normalize | None = None,
  size: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -422,7 +422,6 @@ def render_images(
  na_color: ColorLike | None = "default",
  palette: list[str] | str | None = None,
  alpha: float | int = 1.0,
- percentiles_for_norm: tuple[float, float] | None = None,
  scale: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
@@ -457,8 +456,6 @@ def render_images(
  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.
- percentiles_for_norm : tuple[float, float] | None
- Optional pair of floats (pmin < pmax, 0-100) which will be used for quantile normalization.
  scale : str | None
  Influences the resolution of the rendering. Possibilities include:
  1) `None` (default): The image is rasterized to fit the canvas size. For
@@ -486,20 +483,14 @@ def render_images(
  cmap=cmap,
  norm=norm,
  scale=scale,
- percentiles_for_norm=percentiles_for_norm,
  )
 
  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=params_dict[element]["cmap"],
- # norm=norm,
- # na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
- # **kwargs,
- # )
+
  cmap_params: list[CmapParams] | CmapParams
  if isinstance(cmap, list):
  cmap_params = [
@@ -525,7 +516,6 @@ def render_images(
  cmap_params=cmap_params,
  palette=param_values["palette"],
  alpha=param_values["alpha"],
- percentiles_for_norm=param_values["percentiles_for_norm"],
  scale=param_values["scale"],
  zorder=n_steps,
  )

src/spatialdata_plot/pl/render.py

@@ -9,6 +9,7 @@
 import datashader as ds
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import matplotlib.transforms as mtransforms
 import numpy as np
 import pandas as pd
@@ -47,7 +48,6 @@
  _maybe_set_colors,
  _mpl_ax_contains_elements,
  _multiscale_to_spatial_image,
- _normalize,
  _rasterize_if_necessary,
  _set_color_source_vec,
  to_hex,
@@ -128,6 +128,7 @@ def _render_shapes(
  shapes = shapes.reset_index()
  color_source_vector = color_source_vector[mask]
  color_vector = color_vector[mask]
+
  shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
 
  # Using dict.fromkeys here since set returns in arbitrary order
@@ -255,9 +256,13 @@ def _render_shapes(
  for path in _cax.get_paths():
  path.vertices = trans.transform(path.vertices)
 
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
+ if not values_are_categorical:
+ # If the user passed a Normalize object with vmin/vmax we'll use those,
+ # # if not we'll use the min/max of the color_vector
+ _cax.set_clim(
+ vmin=render_params.cmap_params.norm.vmin or min(color_vector),
+ vmax=render_params.cmap_params.norm.vmax or max(color_vector),
+ )
 
  if len(set(color_vector)) != 1 or list(set(color_vector))[0] != to_hex(render_params.cmap_params.na_color):
  # necessary in case different shapes elements are annotated with one table
@@ -603,11 +608,6 @@ def _render_images(
  if n_channels == 1 and not isinstance(render_params.cmap_params, list):
  layer = img.sel(c=channels[0]).squeeze() if isinstance(channels[0], str) else img.isel(c=channels[0]).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layer = _normalize(
- layer, pmin=render_params.percentiles_for_norm[0], pmax=render_params.percentiles_for_norm[1], clip=True
- )
-
  if render_params.cmap_params.norm: # type: ignore[attr-defined]
  layer = render_params.cmap_params.norm(layer) # type: ignore[attr-defined]
 
@@ -623,20 +623,16 @@ def _render_images(
 
  _ax_show_and_transform(layer, trans_data, ax, cmap=cmap, zorder=render_params.zorder)
 
+ if legend_params.colorbar:
+ sm = plt.cm.ScalarMappable(cmap=cmap, norm=render_params.cmap_params.norm)
+ fig_params.fig.colorbar(sm, ax=ax)
+
  # 2) Image has any number of channels but 1
  else:
  layers = {}
  for ch_index, c in enumerate(channels):
  layers[c] = img.sel(c=c).copy(deep=True).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layers[c] = _normalize(
- layers[c],
- pmin=render_params.percentiles_for_norm[0],
- pmax=render_params.percentiles_for_norm[1],
- clip=True,
- )
-
  if not isinstance(render_params.cmap_params, list):
  if render_params.cmap_params.norm is not None:
  layers[c] = render_params.cmap_params.norm(layers[c])

src/spatialdata_plot/pl/utils.py

@@ -489,7 +489,7 @@ def _get_scalebar(
 
 def _prepare_cmap_norm(
  cmap: Colormap | str | None = None,
- norm: Normalize | bool = False,
+ norm: Normalize | None = None,
  na_color: ColorLike | None = None,
  vmin: float | None = None,
  vmax: float | None = None,
@@ -1623,29 +1623,6 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
- if (percentiles_for_norm := param_dict.get("percentiles_for_norm")) is None:
- percentiles_for_norm = (None, None)
- elif not (isinstance(percentiles_for_norm, (list, tuple)) or len(percentiles_for_norm) != 2):
- raise TypeError("Parameter 'percentiles_for_norm' must be a list or tuple of exactly two floats or None.")
- elif not all(
- isinstance(p, (float, int, type(None)))
- and isinstance(p, type(percentiles_for_norm[0]))
- and (p is None or 0 <= p <= 100)
- for p in percentiles_for_norm
- ):
- raise TypeError(
- "Each item in 'percentiles_for_norm' must be of the same dtype and must be a float or int within [0, 100], "
- "or None"
- )
- elif (
- percentiles_for_norm[0] is not None
- and percentiles_for_norm[1] is not None
- and percentiles_for_norm[0] > percentiles_for_norm[1]
- ):
- raise ValueError("The first number in 'percentiles_for_norm' must not be smaller than the second.")
- if "percentiles_for_norm" in param_dict:
- param_dict["percentiles_for_norm"] = percentiles_for_norm
-
  if size := param_dict.get("size"):
  if not isinstance(size, (float, int)):
  raise TypeError("Parameter 'size' must be numeric.")
@@ -1886,7 +1863,6 @@ def _validate_image_render_params(
  cmap: list[Colormap | str] | Colormap | str | None,
  norm: Normalize | None,
  scale: str | None,
- percentiles_for_norm: tuple[float | None, float | None] | None,
 ) -> dict[str, dict[str, Any]]:
  param_dict: dict[str, Any] = {
  "sdata": sdata,
@@ -1898,7 +1874,6 @@ def _validate_image_render_params(
  "cmap": cmap,
  "norm": norm,
  "scale": scale,
- "percentiles_for_norm": percentiles_for_norm,
  }
  param_dict = _type_check_params(param_dict, "images")
 
@@ -1945,8 +1920,6 @@ def _validate_image_render_params(
  else:
  element_params[el]["scale"] = scale
 
- element_params[el]["percentiles_for_norm"] = param_dict["percentiles_for_norm"]
-
  return element_params
 
 

tests/pl/test_render.py

@@ -23,7 +23,9 @@ def test_render_images_can_plot_one_cyx_image(request):
 def test_render_images_can_plot_multiple_cyx_images(share_coordinate_system: str, request):
  fun = request.getfixturevalue("get_sdata_with_multiple_images")
  sdata = fun(share_coordinate_system)
- sdata.pl.render_images().pl.show()
+ sdata.pl.render_images().pl.show(
+ colorbar=False, # otherwise we'll get one cbar per image in the same cs
+ )
  axs = plt.gcf().get_axes()
 
  if share_coordinate_system == "all":

tests/pl/test_render_images.py

@@ -2,7 +2,6 @@
 import matplotlib
 import numpy as np
 import scanpy as sc
-from matplotlib import pyplot as plt
 from matplotlib.colors import Normalize
 from spatial_image import to_spatial_image
 from spatialdata import SpatialData
@@ -49,9 +48,6 @@ def test_plot_can_render_a_single_channel_from_image(self, sdata_blobs: SpatialD
  def test_plot_can_render_a_single_channel_from_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_multiscale_image", channel=0).pl.show()
 
- def test_plot_can_render_a_single_channel_from_image_no_el(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(channel=0).pl.show()
-
  def test_plot_can_render_a_single_channel_str_from_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_image", channel="c1").pl.show()
 
@@ -70,16 +66,13 @@ def test_plot_can_render_two_channels_str_from_image(self, sdata_blobs_str: Spat
  def test_plot_can_render_two_channels_str_from_multiscale_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_multiscale_image", channel=["c1", "c2"]).pl.show()
 
- def test_plot_can_pass_vmin_vmax(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, vmin=0, vmax=0.4).pl.show(ax=axs[1])
-
- def test_plot_can_pass_normalize(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
+ def test_plot_can_pass_normalize_clip_True(self, sdata_blobs: SpatialData):
  norm = Normalize(vmin=0, vmax=0.4, clip=True)
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, norm=norm).pl.show(ax=axs[1])
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
+
+ def test_plot_can_pass_normalize_clip_False(self, sdata_blobs: SpatialData):
+ norm = Normalize(vmin=0, vmax=0.4, clip=False)
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
 
  def test_plot_can_pass_color_to_single_channel(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_image", channel=1, palette="red").pl.show()
@@ -97,9 +90,6 @@ def test_plot_can_pass_cmap_to_each_channel(self, sdata_blobs: SpatialData):
  element="blobs_image", channel=[0, 1, 2], cmap=["Reds", "Greens", "Blues"]
  ).pl.show()
 
- def test_plot_can_normalize_image(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(element="blobs_image", percentiles_for_norm=(5, 90)).pl.show()
-
  def test_plot_can_render_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images("blobs_multiscale_image").pl.show()
 

tests/pl/test_render_shapes.py

@@ -6,6 +6,7 @@
 import pandas as pd
 import scanpy as sc
 from anndata import AnnData
+from matplotlib.colors import Normalize
 from shapely.geometry import MultiPolygon, Point, Polygon
 from spatialdata import SpatialData, deepcopy
 from spatialdata.models import ShapesModel, TableModel
@@ -146,7 +147,8 @@ def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
  sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=True).pl.show()
+ norm = Normalize(vmin=0, vmax=5, clip=True)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=norm).pl.show()
 
  def test_plot_can_plot_shapes_after_spatial_query(self, sdata_blobs: SpatialData):
  # subset to only shapes, should be unnecessary after rasterizeation of multiscale images is included

CHANGELOG.md

@@ -18,11 +18,15 @@ and this project adheres to [Semantic Versioning][].
 
 - Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
 - When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+- When rendering a single image channel, a colorbar is now shown (#346)
+- Removed `percentiles_for_norm` parameter (#346)
+- Changed `norm` to no longer accept bools, only `mpl.colors.Normalise` or `None` (#346)
 
 ### Fixed
 
 - Filtering with `groups` now preserves original cmap (#344)
 - Non-selected `groups` are now not shown in `na_color` (#344)
+- Several issues associated with `norm` and `colorbar` (#346)
 
 ## [0.2.5] - 2024-08-23
 

src/spatialdata_plot/pl/basic.py

@@ -166,7 +166,7 @@ def render_shapes(
  outline_color: str | list[float] = "#000000ff",
  outline_alpha: float | int = 0.0,
  cmap: Colormap | str | None = None,
- norm: bool | Normalize = False,
+ norm: Normalize | None = None,
  scale: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -301,7 +301,7 @@ def render_points(
  palette: list[str] | str | None = None,
  na_color: ColorLike | None = "default",
  cmap: Colormap | str | None = None,
- norm: None | Normalize = None,
+ norm: Normalize | None = None,
  size: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -422,7 +422,6 @@ def render_images(
  na_color: ColorLike | None = "default",
  palette: list[str] | str | None = None,
  alpha: float | int = 1.0,
- percentiles_for_norm: tuple[float, float] | None = None,
  scale: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
@@ -457,8 +456,6 @@ def render_images(
  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.
- percentiles_for_norm : tuple[float, float] | None
- Optional pair of floats (pmin < pmax, 0-100) which will be used for quantile normalization.
  scale : str | None
  Influences the resolution of the rendering. Possibilities include:
  1) `None` (default): The image is rasterized to fit the canvas size. For
@@ -486,20 +483,14 @@ def render_images(
  cmap=cmap,
  norm=norm,
  scale=scale,
- percentiles_for_norm=percentiles_for_norm,
  )
 
  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=params_dict[element]["cmap"],
- # norm=norm,
- # na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
- # **kwargs,
- # )
+
  cmap_params: list[CmapParams] | CmapParams
  if isinstance(cmap, list):
  cmap_params = [
@@ -525,7 +516,6 @@ def render_images(
  cmap_params=cmap_params,
  palette=param_values["palette"],
  alpha=param_values["alpha"],
- percentiles_for_norm=param_values["percentiles_for_norm"],
  scale=param_values["scale"],
  zorder=n_steps,
  )

src/spatialdata_plot/pl/render.py

@@ -9,6 +9,7 @@
 import datashader as ds
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import matplotlib.transforms as mtransforms
 import numpy as np
 import pandas as pd
@@ -47,7 +48,6 @@
  _maybe_set_colors,
  _mpl_ax_contains_elements,
  _multiscale_to_spatial_image,
- _normalize,
  _rasterize_if_necessary,
  _set_color_source_vec,
  to_hex,
@@ -128,6 +128,7 @@ def _render_shapes(
  shapes = shapes.reset_index()
  color_source_vector = color_source_vector[mask]
  color_vector = color_vector[mask]
+
  shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
 
  # Using dict.fromkeys here since set returns in arbitrary order
@@ -255,9 +256,13 @@ def _render_shapes(
  for path in _cax.get_paths():
  path.vertices = trans.transform(path.vertices)
 
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
+ if not values_are_categorical:
+ # If the user passed a Normalize object with vmin/vmax we'll use those,
+ # # if not we'll use the min/max of the color_vector
+ _cax.set_clim(
+ vmin=render_params.cmap_params.norm.vmin or min(color_vector),
+ vmax=render_params.cmap_params.norm.vmax or max(color_vector),
+ )
 
  if len(set(color_vector)) != 1 or list(set(color_vector))[0] != to_hex(render_params.cmap_params.na_color):
  # necessary in case different shapes elements are annotated with one table
@@ -603,11 +608,6 @@ def _render_images(
  if n_channels == 1 and not isinstance(render_params.cmap_params, list):
  layer = img.sel(c=channels[0]).squeeze() if isinstance(channels[0], str) else img.isel(c=channels[0]).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layer = _normalize(
- layer, pmin=render_params.percentiles_for_norm[0], pmax=render_params.percentiles_for_norm[1], clip=True
- )
-
  if render_params.cmap_params.norm: # type: ignore[attr-defined]
  layer = render_params.cmap_params.norm(layer) # type: ignore[attr-defined]
 
@@ -623,20 +623,16 @@ def _render_images(
 
  _ax_show_and_transform(layer, trans_data, ax, cmap=cmap, zorder=render_params.zorder)
 
+ if legend_params.colorbar:
+ sm = plt.cm.ScalarMappable(cmap=cmap, norm=render_params.cmap_params.norm)
+ fig_params.fig.colorbar(sm, ax=ax)
+
  # 2) Image has any number of channels but 1
  else:
  layers = {}
  for ch_index, c in enumerate(channels):
  layers[c] = img.sel(c=c).copy(deep=True).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layers[c] = _normalize(
- layers[c],
- pmin=render_params.percentiles_for_norm[0],
- pmax=render_params.percentiles_for_norm[1],
- clip=True,
- )
-
  if not isinstance(render_params.cmap_params, list):
  if render_params.cmap_params.norm is not None:
  layers[c] = render_params.cmap_params.norm(layers[c])

src/spatialdata_plot/pl/utils.py

@@ -489,7 +489,7 @@ def _get_scalebar(
 
 def _prepare_cmap_norm(
  cmap: Colormap | str | None = None,
- norm: Normalize | bool = False,
+ norm: Normalize | None = None,
  na_color: ColorLike | None = None,
  vmin: float | None = None,
  vmax: float | None = None,
@@ -1623,29 +1623,6 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
- if (percentiles_for_norm := param_dict.get("percentiles_for_norm")) is None:
- percentiles_for_norm = (None, None)
- elif not (isinstance(percentiles_for_norm, (list, tuple)) or len(percentiles_for_norm) != 2):
- raise TypeError("Parameter 'percentiles_for_norm' must be a list or tuple of exactly two floats or None.")
- elif not all(
- isinstance(p, (float, int, type(None)))
- and isinstance(p, type(percentiles_for_norm[0]))
- and (p is None or 0 <= p <= 100)
- for p in percentiles_for_norm
- ):
- raise TypeError(
- "Each item in 'percentiles_for_norm' must be of the same dtype and must be a float or int within [0, 100], "
- "or None"
- )
- elif (
- percentiles_for_norm[0] is not None
- and percentiles_for_norm[1] is not None
- and percentiles_for_norm[0] > percentiles_for_norm[1]
- ):
- raise ValueError("The first number in 'percentiles_for_norm' must not be smaller than the second.")
- if "percentiles_for_norm" in param_dict:
- param_dict["percentiles_for_norm"] = percentiles_for_norm
-
  if size := param_dict.get("size"):
  if not isinstance(size, (float, int)):
  raise TypeError("Parameter 'size' must be numeric.")
@@ -1886,7 +1863,6 @@ def _validate_image_render_params(
  cmap: list[Colormap | str] | Colormap | str | None,
  norm: Normalize | None,
  scale: str | None,
- percentiles_for_norm: tuple[float | None, float | None] | None,
 ) -> dict[str, dict[str, Any]]:
  param_dict: dict[str, Any] = {
  "sdata": sdata,
@@ -1898,7 +1874,6 @@ def _validate_image_render_params(
  "cmap": cmap,
  "norm": norm,
  "scale": scale,
- "percentiles_for_norm": percentiles_for_norm,
  }
  param_dict = _type_check_params(param_dict, "images")
 
@@ -1945,8 +1920,6 @@ def _validate_image_render_params(
  else:
  element_params[el]["scale"] = scale
 
- element_params[el]["percentiles_for_norm"] = param_dict["percentiles_for_norm"]
-
  return element_params
 
 

tests/pl/test_render.py

@@ -23,7 +23,9 @@ def test_render_images_can_plot_one_cyx_image(request):
 def test_render_images_can_plot_multiple_cyx_images(share_coordinate_system: str, request):
  fun = request.getfixturevalue("get_sdata_with_multiple_images")
  sdata = fun(share_coordinate_system)
- sdata.pl.render_images().pl.show()
+ sdata.pl.render_images().pl.show(
+ colorbar=False, # otherwise we'll get one cbar per image in the same cs
+ )
  axs = plt.gcf().get_axes()
 
  if share_coordinate_system == "all":

tests/pl/test_render_images.py

@@ -2,7 +2,6 @@
 import matplotlib
 import numpy as np
 import scanpy as sc
-from matplotlib import pyplot as plt
 from matplotlib.colors import Normalize
 from spatial_image import to_spatial_image
 from spatialdata import SpatialData
@@ -49,9 +48,6 @@ def test_plot_can_render_a_single_channel_from_image(self, sdata_blobs: SpatialD
  def test_plot_can_render_a_single_channel_from_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_multiscale_image", channel=0).pl.show()
 
- def test_plot_can_render_a_single_channel_from_image_no_el(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(channel=0).pl.show()
-
  def test_plot_can_render_a_single_channel_str_from_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_image", channel="c1").pl.show()
 
@@ -70,16 +66,13 @@ def test_plot_can_render_two_channels_str_from_image(self, sdata_blobs_str: Spat
  def test_plot_can_render_two_channels_str_from_multiscale_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_multiscale_image", channel=["c1", "c2"]).pl.show()
 
- def test_plot_can_pass_vmin_vmax(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, vmin=0, vmax=0.4).pl.show(ax=axs[1])
-
- def test_plot_can_pass_normalize(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
+ def test_plot_can_pass_normalize_clip_True(self, sdata_blobs: SpatialData):
  norm = Normalize(vmin=0, vmax=0.4, clip=True)
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, norm=norm).pl.show(ax=axs[1])
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
+
+ def test_plot_can_pass_normalize_clip_False(self, sdata_blobs: SpatialData):
+ norm = Normalize(vmin=0, vmax=0.4, clip=False)
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
 
  def test_plot_can_pass_color_to_single_channel(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_image", channel=1, palette="red").pl.show()
@@ -97,9 +90,6 @@ def test_plot_can_pass_cmap_to_each_channel(self, sdata_blobs: SpatialData):
  element="blobs_image", channel=[0, 1, 2], cmap=["Reds", "Greens", "Blues"]
  ).pl.show()
 
- def test_plot_can_normalize_image(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(element="blobs_image", percentiles_for_norm=(5, 90)).pl.show()
-
  def test_plot_can_render_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images("blobs_multiscale_image").pl.show()
 

tests/pl/test_render_shapes.py

@@ -6,6 +6,7 @@
 import pandas as pd
 import scanpy as sc
 from anndata import AnnData
+from matplotlib.colors import Normalize
 from shapely.geometry import MultiPolygon, Point, Polygon
 from spatialdata import SpatialData, deepcopy
 from spatialdata.models import ShapesModel, TableModel
@@ -146,7 +147,8 @@ def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
  sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=True).pl.show()
+ norm = Normalize(vmin=0, vmax=5, clip=True)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=norm).pl.show()
 
  def test_plot_can_plot_shapes_after_spatial_query(self, sdata_blobs: SpatialData):
  # subset to only shapes, should be unnecessary after rasterizeation of multiscale images is included

CHANGELOG.md

@@ -18,11 +18,15 @@ and this project adheres to [Semantic Versioning][].
 
 - Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
 - When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+- When rendering a single image channel, a colorbar is now shown (#346)
+- Removed `percentiles_for_norm` parameter (#346)
+- Changed `norm` to no longer accept bools, only `mpl.colors.Normalise` or `None` (#346)
 
 ### Fixed
 
 - Filtering with `groups` now preserves original cmap (#344)
 - Non-selected `groups` are now not shown in `na_color` (#344)
+- Several issues associated with `norm` and `colorbar` (#346)
 
 ## [0.2.5] - 2024-08-23
 

src/spatialdata_plot/pl/basic.py

@@ -166,7 +166,7 @@ def render_shapes(
  outline_color: str | list[float] = "#000000ff",
  outline_alpha: float | int = 0.0,
  cmap: Colormap | str | None = None,
- norm: bool | Normalize = False,
+ norm: Normalize | None = None,
  scale: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -301,7 +301,7 @@ def render_points(
  palette: list[str] | str | None = None,
  na_color: ColorLike | None = "default",
  cmap: Colormap | str | None = None,
- norm: None | Normalize = None,
+ norm: Normalize | None = None,
  size: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -422,7 +422,6 @@ def render_images(
  na_color: ColorLike | None = "default",
  palette: list[str] | str | None = None,
  alpha: float | int = 1.0,
- percentiles_for_norm: tuple[float, float] | None = None,
  scale: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
@@ -457,8 +456,6 @@ def render_images(
  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.
- percentiles_for_norm : tuple[float, float] | None
- Optional pair of floats (pmin < pmax, 0-100) which will be used for quantile normalization.
  scale : str | None
  Influences the resolution of the rendering. Possibilities include:
  1) `None` (default): The image is rasterized to fit the canvas size. For
@@ -486,20 +483,14 @@ def render_images(
  cmap=cmap,
  norm=norm,
  scale=scale,
- percentiles_for_norm=percentiles_for_norm,
  )
 
  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=params_dict[element]["cmap"],
- # norm=norm,
- # na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
- # **kwargs,
- # )
+
  cmap_params: list[CmapParams] | CmapParams
  if isinstance(cmap, list):
  cmap_params = [
@@ -525,7 +516,6 @@ def render_images(
  cmap_params=cmap_params,
  palette=param_values["palette"],
  alpha=param_values["alpha"],
- percentiles_for_norm=param_values["percentiles_for_norm"],
  scale=param_values["scale"],
  zorder=n_steps,
  )

src/spatialdata_plot/pl/render.py

@@ -9,6 +9,7 @@
 import datashader as ds
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import matplotlib.transforms as mtransforms
 import numpy as np
 import pandas as pd
@@ -47,7 +48,6 @@
  _maybe_set_colors,
  _mpl_ax_contains_elements,
  _multiscale_to_spatial_image,
- _normalize,
  _rasterize_if_necessary,
  _set_color_source_vec,
  to_hex,
@@ -128,6 +128,7 @@ def _render_shapes(
  shapes = shapes.reset_index()
  color_source_vector = color_source_vector[mask]
  color_vector = color_vector[mask]
+
  shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
 
  # Using dict.fromkeys here since set returns in arbitrary order
@@ -255,9 +256,13 @@ def _render_shapes(
  for path in _cax.get_paths():
  path.vertices = trans.transform(path.vertices)
 
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
+ if not values_are_categorical:
+ # If the user passed a Normalize object with vmin/vmax we'll use those,
+ # # if not we'll use the min/max of the color_vector
+ _cax.set_clim(
+ vmin=render_params.cmap_params.norm.vmin or min(color_vector),
+ vmax=render_params.cmap_params.norm.vmax or max(color_vector),
+ )
 
  if len(set(color_vector)) != 1 or list(set(color_vector))[0] != to_hex(render_params.cmap_params.na_color):
  # necessary in case different shapes elements are annotated with one table
@@ -603,11 +608,6 @@ def _render_images(
  if n_channels == 1 and not isinstance(render_params.cmap_params, list):
  layer = img.sel(c=channels[0]).squeeze() if isinstance(channels[0], str) else img.isel(c=channels[0]).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layer = _normalize(
- layer, pmin=render_params.percentiles_for_norm[0], pmax=render_params.percentiles_for_norm[1], clip=True
- )
-
  if render_params.cmap_params.norm: # type: ignore[attr-defined]
  layer = render_params.cmap_params.norm(layer) # type: ignore[attr-defined]
 
@@ -623,20 +623,16 @@ def _render_images(
 
  _ax_show_and_transform(layer, trans_data, ax, cmap=cmap, zorder=render_params.zorder)
 
+ if legend_params.colorbar:
+ sm = plt.cm.ScalarMappable(cmap=cmap, norm=render_params.cmap_params.norm)
+ fig_params.fig.colorbar(sm, ax=ax)
+
  # 2) Image has any number of channels but 1
  else:
  layers = {}
  for ch_index, c in enumerate(channels):
  layers[c] = img.sel(c=c).copy(deep=True).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layers[c] = _normalize(
- layers[c],
- pmin=render_params.percentiles_for_norm[0],
- pmax=render_params.percentiles_for_norm[1],
- clip=True,
- )
-
  if not isinstance(render_params.cmap_params, list):
  if render_params.cmap_params.norm is not None:
  layers[c] = render_params.cmap_params.norm(layers[c])

src/spatialdata_plot/pl/utils.py

@@ -489,7 +489,7 @@ def _get_scalebar(
 
 def _prepare_cmap_norm(
  cmap: Colormap | str | None = None,
- norm: Normalize | bool = False,
+ norm: Normalize | None = None,
  na_color: ColorLike | None = None,
  vmin: float | None = None,
  vmax: float | None = None,
@@ -1623,29 +1623,6 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
- if (percentiles_for_norm := param_dict.get("percentiles_for_norm")) is None:
- percentiles_for_norm = (None, None)
- elif not (isinstance(percentiles_for_norm, (list, tuple)) or len(percentiles_for_norm) != 2):
- raise TypeError("Parameter 'percentiles_for_norm' must be a list or tuple of exactly two floats or None.")
- elif not all(
- isinstance(p, (float, int, type(None)))
- and isinstance(p, type(percentiles_for_norm[0]))
- and (p is None or 0 <= p <= 100)
- for p in percentiles_for_norm
- ):
- raise TypeError(
- "Each item in 'percentiles_for_norm' must be of the same dtype and must be a float or int within [0, 100], "
- "or None"
- )
- elif (
- percentiles_for_norm[0] is not None
- and percentiles_for_norm[1] is not None
- and percentiles_for_norm[0] > percentiles_for_norm[1]
- ):
- raise ValueError("The first number in 'percentiles_for_norm' must not be smaller than the second.")
- if "percentiles_for_norm" in param_dict:
- param_dict["percentiles_for_norm"] = percentiles_for_norm
-
  if size := param_dict.get("size"):
  if not isinstance(size, (float, int)):
  raise TypeError("Parameter 'size' must be numeric.")
@@ -1886,7 +1863,6 @@ def _validate_image_render_params(
  cmap: list[Colormap | str] | Colormap | str | None,
  norm: Normalize | None,
  scale: str | None,
- percentiles_for_norm: tuple[float | None, float | None] | None,
 ) -> dict[str, dict[str, Any]]:
  param_dict: dict[str, Any] = {
  "sdata": sdata,
@@ -1898,7 +1874,6 @@ def _validate_image_render_params(
  "cmap": cmap,
  "norm": norm,
  "scale": scale,
- "percentiles_for_norm": percentiles_for_norm,
  }
  param_dict = _type_check_params(param_dict, "images")
 
@@ -1945,8 +1920,6 @@ def _validate_image_render_params(
  else:
  element_params[el]["scale"] = scale
 
- element_params[el]["percentiles_for_norm"] = param_dict["percentiles_for_norm"]
-
  return element_params
 
 

tests/pl/test_render.py

@@ -23,7 +23,9 @@ def test_render_images_can_plot_one_cyx_image(request):
 def test_render_images_can_plot_multiple_cyx_images(share_coordinate_system: str, request):
  fun = request.getfixturevalue("get_sdata_with_multiple_images")
  sdata = fun(share_coordinate_system)
- sdata.pl.render_images().pl.show()
+ sdata.pl.render_images().pl.show(
+ colorbar=False, # otherwise we'll get one cbar per image in the same cs
+ )
  axs = plt.gcf().get_axes()
 
  if share_coordinate_system == "all":

tests/pl/test_render_images.py

@@ -2,7 +2,6 @@
 import matplotlib
 import numpy as np
 import scanpy as sc
-from matplotlib import pyplot as plt
 from matplotlib.colors import Normalize
 from spatial_image import to_spatial_image
 from spatialdata import SpatialData
@@ -49,9 +48,6 @@ def test_plot_can_render_a_single_channel_from_image(self, sdata_blobs: SpatialD
  def test_plot_can_render_a_single_channel_from_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_multiscale_image", channel=0).pl.show()
 
- def test_plot_can_render_a_single_channel_from_image_no_el(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(channel=0).pl.show()
-
  def test_plot_can_render_a_single_channel_str_from_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_image", channel="c1").pl.show()
 
@@ -70,16 +66,13 @@ def test_plot_can_render_two_channels_str_from_image(self, sdata_blobs_str: Spat
  def test_plot_can_render_two_channels_str_from_multiscale_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_multiscale_image", channel=["c1", "c2"]).pl.show()
 
- def test_plot_can_pass_vmin_vmax(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, vmin=0, vmax=0.4).pl.show(ax=axs[1])
-
- def test_plot_can_pass_normalize(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
+ def test_plot_can_pass_normalize_clip_True(self, sdata_blobs: SpatialData):
  norm = Normalize(vmin=0, vmax=0.4, clip=True)
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, norm=norm).pl.show(ax=axs[1])
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
+
+ def test_plot_can_pass_normalize_clip_False(self, sdata_blobs: SpatialData):
+ norm = Normalize(vmin=0, vmax=0.4, clip=False)
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
 
  def test_plot_can_pass_color_to_single_channel(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_image", channel=1, palette="red").pl.show()
@@ -97,9 +90,6 @@ def test_plot_can_pass_cmap_to_each_channel(self, sdata_blobs: SpatialData):
  element="blobs_image", channel=[0, 1, 2], cmap=["Reds", "Greens", "Blues"]
  ).pl.show()
 
- def test_plot_can_normalize_image(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(element="blobs_image", percentiles_for_norm=(5, 90)).pl.show()
-
  def test_plot_can_render_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images("blobs_multiscale_image").pl.show()
 

tests/pl/test_render_shapes.py

@@ -6,6 +6,7 @@
 import pandas as pd
 import scanpy as sc
 from anndata import AnnData
+from matplotlib.colors import Normalize
 from shapely.geometry import MultiPolygon, Point, Polygon
 from spatialdata import SpatialData, deepcopy
 from spatialdata.models import ShapesModel, TableModel
@@ -146,7 +147,8 @@ def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
  sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=True).pl.show()
+ norm = Normalize(vmin=0, vmax=5, clip=True)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=norm).pl.show()
 
  def test_plot_can_plot_shapes_after_spatial_query(self, sdata_blobs: SpatialData):
  # subset to only shapes, should be unnecessary after rasterizeation of multiscale images is included

CHANGELOG.md

@@ -18,11 +18,15 @@ and this project adheres to [Semantic Versioning][].
 
 - Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
 - When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+- When rendering a single image channel, a colorbar is now shown (#346)
+- Removed `percentiles_for_norm` parameter (#346)
+- Changed `norm` to no longer accept bools, only `mpl.colors.Normalise` or `None` (#346)
 
 ### Fixed
 
 - Filtering with `groups` now preserves original cmap (#344)
 - Non-selected `groups` are now not shown in `na_color` (#344)
+- Several issues associated with `norm` and `colorbar` (#346)
 
 ## [0.2.5] - 2024-08-23
 

src/spatialdata_plot/pl/basic.py

@@ -166,7 +166,7 @@ def render_shapes(
  outline_color: str | list[float] = "#000000ff",
  outline_alpha: float | int = 0.0,
  cmap: Colormap | str | None = None,
- norm: bool | Normalize = False,
+ norm: Normalize | None = None,
  scale: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -301,7 +301,7 @@ def render_points(
  palette: list[str] | str | None = None,
  na_color: ColorLike | None = "default",
  cmap: Colormap | str | None = None,
- norm: None | Normalize = None,
+ norm: Normalize | None = None,
  size: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -422,7 +422,6 @@ def render_images(
  na_color: ColorLike | None = "default",
  palette: list[str] | str | None = None,
  alpha: float | int = 1.0,
- percentiles_for_norm: tuple[float, float] | None = None,
  scale: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
@@ -457,8 +456,6 @@ def render_images(
  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.
- percentiles_for_norm : tuple[float, float] | None
- Optional pair of floats (pmin < pmax, 0-100) which will be used for quantile normalization.
  scale : str | None
  Influences the resolution of the rendering. Possibilities include:
  1) `None` (default): The image is rasterized to fit the canvas size. For
@@ -486,20 +483,14 @@ def render_images(
  cmap=cmap,
  norm=norm,
  scale=scale,
- percentiles_for_norm=percentiles_for_norm,
  )
 
  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=params_dict[element]["cmap"],
- # norm=norm,
- # na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
- # **kwargs,
- # )
+
  cmap_params: list[CmapParams] | CmapParams
  if isinstance(cmap, list):
  cmap_params = [
@@ -525,7 +516,6 @@ def render_images(
  cmap_params=cmap_params,
  palette=param_values["palette"],
  alpha=param_values["alpha"],
- percentiles_for_norm=param_values["percentiles_for_norm"],
  scale=param_values["scale"],
  zorder=n_steps,
  )

src/spatialdata_plot/pl/render.py

@@ -9,6 +9,7 @@
 import datashader as ds
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import matplotlib.transforms as mtransforms
 import numpy as np
 import pandas as pd
@@ -47,7 +48,6 @@
  _maybe_set_colors,
  _mpl_ax_contains_elements,
  _multiscale_to_spatial_image,
- _normalize,
  _rasterize_if_necessary,
  _set_color_source_vec,
  to_hex,
@@ -128,6 +128,7 @@ def _render_shapes(
  shapes = shapes.reset_index()
  color_source_vector = color_source_vector[mask]
  color_vector = color_vector[mask]
+
  shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
 
  # Using dict.fromkeys here since set returns in arbitrary order
@@ -255,9 +256,13 @@ def _render_shapes(
  for path in _cax.get_paths():
  path.vertices = trans.transform(path.vertices)
 
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
+ if not values_are_categorical:
+ # If the user passed a Normalize object with vmin/vmax we'll use those,
+ # # if not we'll use the min/max of the color_vector
+ _cax.set_clim(
+ vmin=render_params.cmap_params.norm.vmin or min(color_vector),
+ vmax=render_params.cmap_params.norm.vmax or max(color_vector),
+ )
 
  if len(set(color_vector)) != 1 or list(set(color_vector))[0] != to_hex(render_params.cmap_params.na_color):
  # necessary in case different shapes elements are annotated with one table
@@ -603,11 +608,6 @@ def _render_images(
  if n_channels == 1 and not isinstance(render_params.cmap_params, list):
  layer = img.sel(c=channels[0]).squeeze() if isinstance(channels[0], str) else img.isel(c=channels[0]).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layer = _normalize(
- layer, pmin=render_params.percentiles_for_norm[0], pmax=render_params.percentiles_for_norm[1], clip=True
- )
-
  if render_params.cmap_params.norm: # type: ignore[attr-defined]
  layer = render_params.cmap_params.norm(layer) # type: ignore[attr-defined]
 
@@ -623,20 +623,16 @@ def _render_images(
 
  _ax_show_and_transform(layer, trans_data, ax, cmap=cmap, zorder=render_params.zorder)
 
+ if legend_params.colorbar:
+ sm = plt.cm.ScalarMappable(cmap=cmap, norm=render_params.cmap_params.norm)
+ fig_params.fig.colorbar(sm, ax=ax)
+
  # 2) Image has any number of channels but 1
  else:
  layers = {}
  for ch_index, c in enumerate(channels):
  layers[c] = img.sel(c=c).copy(deep=True).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layers[c] = _normalize(
- layers[c],
- pmin=render_params.percentiles_for_norm[0],
- pmax=render_params.percentiles_for_norm[1],
- clip=True,
- )
-
  if not isinstance(render_params.cmap_params, list):
  if render_params.cmap_params.norm is not None:
  layers[c] = render_params.cmap_params.norm(layers[c])

src/spatialdata_plot/pl/utils.py

@@ -489,7 +489,7 @@ def _get_scalebar(
 
 def _prepare_cmap_norm(
  cmap: Colormap | str | None = None,
- norm: Normalize | bool = False,
+ norm: Normalize | None = None,
  na_color: ColorLike | None = None,
  vmin: float | None = None,
  vmax: float | None = None,
@@ -1623,29 +1623,6 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
- if (percentiles_for_norm := param_dict.get("percentiles_for_norm")) is None:
- percentiles_for_norm = (None, None)
- elif not (isinstance(percentiles_for_norm, (list, tuple)) or len(percentiles_for_norm) != 2):
- raise TypeError("Parameter 'percentiles_for_norm' must be a list or tuple of exactly two floats or None.")
- elif not all(
- isinstance(p, (float, int, type(None)))
- and isinstance(p, type(percentiles_for_norm[0]))
- and (p is None or 0 <= p <= 100)
- for p in percentiles_for_norm
- ):
- raise TypeError(
- "Each item in 'percentiles_for_norm' must be of the same dtype and must be a float or int within [0, 100], "
- "or None"
- )
- elif (
- percentiles_for_norm[0] is not None
- and percentiles_for_norm[1] is not None
- and percentiles_for_norm[0] > percentiles_for_norm[1]
- ):
- raise ValueError("The first number in 'percentiles_for_norm' must not be smaller than the second.")
- if "percentiles_for_norm" in param_dict:
- param_dict["percentiles_for_norm"] = percentiles_for_norm
-
  if size := param_dict.get("size"):
  if not isinstance(size, (float, int)):
  raise TypeError("Parameter 'size' must be numeric.")
@@ -1886,7 +1863,6 @@ def _validate_image_render_params(
  cmap: list[Colormap | str] | Colormap | str | None,
  norm: Normalize | None,
  scale: str | None,
- percentiles_for_norm: tuple[float | None, float | None] | None,
 ) -> dict[str, dict[str, Any]]:
  param_dict: dict[str, Any] = {
  "sdata": sdata,
@@ -1898,7 +1874,6 @@ def _validate_image_render_params(
  "cmap": cmap,
  "norm": norm,
  "scale": scale,
- "percentiles_for_norm": percentiles_for_norm,
  }
  param_dict = _type_check_params(param_dict, "images")
 
@@ -1945,8 +1920,6 @@ def _validate_image_render_params(
  else:
  element_params[el]["scale"] = scale
 
- element_params[el]["percentiles_for_norm"] = param_dict["percentiles_for_norm"]
-
  return element_params
 
 

tests/pl/test_render.py

@@ -23,7 +23,9 @@ def test_render_images_can_plot_one_cyx_image(request):
 def test_render_images_can_plot_multiple_cyx_images(share_coordinate_system: str, request):
  fun = request.getfixturevalue("get_sdata_with_multiple_images")
  sdata = fun(share_coordinate_system)
- sdata.pl.render_images().pl.show()
+ sdata.pl.render_images().pl.show(
+ colorbar=False, # otherwise we'll get one cbar per image in the same cs
+ )
  axs = plt.gcf().get_axes()
 
  if share_coordinate_system == "all":

tests/pl/test_render_images.py

@@ -2,7 +2,6 @@
 import matplotlib
 import numpy as np
 import scanpy as sc
-from matplotlib import pyplot as plt
 from matplotlib.colors import Normalize
 from spatial_image import to_spatial_image
 from spatialdata import SpatialData
@@ -49,9 +48,6 @@ def test_plot_can_render_a_single_channel_from_image(self, sdata_blobs: SpatialD
  def test_plot_can_render_a_single_channel_from_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_multiscale_image", channel=0).pl.show()
 
- def test_plot_can_render_a_single_channel_from_image_no_el(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(channel=0).pl.show()
-
  def test_plot_can_render_a_single_channel_str_from_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_image", channel="c1").pl.show()
 
@@ -70,16 +66,13 @@ def test_plot_can_render_two_channels_str_from_image(self, sdata_blobs_str: Spat
  def test_plot_can_render_two_channels_str_from_multiscale_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_multiscale_image", channel=["c1", "c2"]).pl.show()
 
- def test_plot_can_pass_vmin_vmax(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, vmin=0, vmax=0.4).pl.show(ax=axs[1])
-
- def test_plot_can_pass_normalize(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
+ def test_plot_can_pass_normalize_clip_True(self, sdata_blobs: SpatialData):
  norm = Normalize(vmin=0, vmax=0.4, clip=True)
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, norm=norm).pl.show(ax=axs[1])
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
+
+ def test_plot_can_pass_normalize_clip_False(self, sdata_blobs: SpatialData):
+ norm = Normalize(vmin=0, vmax=0.4, clip=False)
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
 
  def test_plot_can_pass_color_to_single_channel(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_image", channel=1, palette="red").pl.show()
@@ -97,9 +90,6 @@ def test_plot_can_pass_cmap_to_each_channel(self, sdata_blobs: SpatialData):
  element="blobs_image", channel=[0, 1, 2], cmap=["Reds", "Greens", "Blues"]
  ).pl.show()
 
- def test_plot_can_normalize_image(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(element="blobs_image", percentiles_for_norm=(5, 90)).pl.show()
-
  def test_plot_can_render_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images("blobs_multiscale_image").pl.show()
 

tests/pl/test_render_shapes.py

@@ -6,6 +6,7 @@
 import pandas as pd
 import scanpy as sc
 from anndata import AnnData
+from matplotlib.colors import Normalize
 from shapely.geometry import MultiPolygon, Point, Polygon
 from spatialdata import SpatialData, deepcopy
 from spatialdata.models import ShapesModel, TableModel
@@ -146,7 +147,8 @@ def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
  sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=True).pl.show()
+ norm = Normalize(vmin=0, vmax=5, clip=True)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=norm).pl.show()
 
  def test_plot_can_plot_shapes_after_spatial_query(self, sdata_blobs: SpatialData):
  # subset to only shapes, should be unnecessary after rasterizeation of multiscale images is included

CHANGELOG.md

@@ -18,11 +18,15 @@ and this project adheres to [Semantic Versioning][].
 
 - Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
 - When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+- When rendering a single image channel, a colorbar is now shown (#346)
+- Removed `percentiles_for_norm` parameter (#346)
+- Changed `norm` to no longer accept bools, only `mpl.colors.Normalise` or `None` (#346)
 
 ### Fixed
 
 - Filtering with `groups` now preserves original cmap (#344)
 - Non-selected `groups` are now not shown in `na_color` (#344)
+- Several issues associated with `norm` and `colorbar` (#346)
 
 ## [0.2.5] - 2024-08-23
 

src/spatialdata_plot/pl/basic.py

@@ -166,7 +166,7 @@ def render_shapes(
  outline_color: str | list[float] = "#000000ff",
  outline_alpha: float | int = 0.0,
  cmap: Colormap | str | None = None,
- norm: bool | Normalize = False,
+ norm: Normalize | None = None,
  scale: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -301,7 +301,7 @@ def render_points(
  palette: list[str] | str | None = None,
  na_color: ColorLike | None = "default",
  cmap: Colormap | str | None = None,
- norm: None | Normalize = None,
+ norm: Normalize | None = None,
  size: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -422,7 +422,6 @@ def render_images(
  na_color: ColorLike | None = "default",
  palette: list[str] | str | None = None,
  alpha: float | int = 1.0,
- percentiles_for_norm: tuple[float, float] | None = None,
  scale: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
@@ -457,8 +456,6 @@ def render_images(
  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.
- percentiles_for_norm : tuple[float, float] | None
- Optional pair of floats (pmin < pmax, 0-100) which will be used for quantile normalization.
  scale : str | None
  Influences the resolution of the rendering. Possibilities include:
  1) `None` (default): The image is rasterized to fit the canvas size. For
@@ -486,20 +483,14 @@ def render_images(
  cmap=cmap,
  norm=norm,
  scale=scale,
- percentiles_for_norm=percentiles_for_norm,
  )
 
  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=params_dict[element]["cmap"],
- # norm=norm,
- # na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
- # **kwargs,
- # )
+
  cmap_params: list[CmapParams] | CmapParams
  if isinstance(cmap, list):
  cmap_params = [
@@ -525,7 +516,6 @@ def render_images(
  cmap_params=cmap_params,
  palette=param_values["palette"],
  alpha=param_values["alpha"],
- percentiles_for_norm=param_values["percentiles_for_norm"],
  scale=param_values["scale"],
  zorder=n_steps,
  )

src/spatialdata_plot/pl/render.py

@@ -9,6 +9,7 @@
 import datashader as ds
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import matplotlib.transforms as mtransforms
 import numpy as np
 import pandas as pd
@@ -47,7 +48,6 @@
  _maybe_set_colors,
  _mpl_ax_contains_elements,
  _multiscale_to_spatial_image,
- _normalize,
  _rasterize_if_necessary,
  _set_color_source_vec,
  to_hex,
@@ -128,6 +128,7 @@ def _render_shapes(
  shapes = shapes.reset_index()
  color_source_vector = color_source_vector[mask]
  color_vector = color_vector[mask]
+
  shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
 
  # Using dict.fromkeys here since set returns in arbitrary order
@@ -255,9 +256,13 @@ def _render_shapes(
  for path in _cax.get_paths():
  path.vertices = trans.transform(path.vertices)
 
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
+ if not values_are_categorical:
+ # If the user passed a Normalize object with vmin/vmax we'll use those,
+ # # if not we'll use the min/max of the color_vector
+ _cax.set_clim(
+ vmin=render_params.cmap_params.norm.vmin or min(color_vector),
+ vmax=render_params.cmap_params.norm.vmax or max(color_vector),
+ )
 
  if len(set(color_vector)) != 1 or list(set(color_vector))[0] != to_hex(render_params.cmap_params.na_color):
  # necessary in case different shapes elements are annotated with one table
@@ -603,11 +608,6 @@ def _render_images(
  if n_channels == 1 and not isinstance(render_params.cmap_params, list):
  layer = img.sel(c=channels[0]).squeeze() if isinstance(channels[0], str) else img.isel(c=channels[0]).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layer = _normalize(
- layer, pmin=render_params.percentiles_for_norm[0], pmax=render_params.percentiles_for_norm[1], clip=True
- )
-
  if render_params.cmap_params.norm: # type: ignore[attr-defined]
  layer = render_params.cmap_params.norm(layer) # type: ignore[attr-defined]
 
@@ -623,20 +623,16 @@ def _render_images(
 
  _ax_show_and_transform(layer, trans_data, ax, cmap=cmap, zorder=render_params.zorder)
 
+ if legend_params.colorbar:
+ sm = plt.cm.ScalarMappable(cmap=cmap, norm=render_params.cmap_params.norm)
+ fig_params.fig.colorbar(sm, ax=ax)
+
  # 2) Image has any number of channels but 1
  else:
  layers = {}
  for ch_index, c in enumerate(channels):
  layers[c] = img.sel(c=c).copy(deep=True).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layers[c] = _normalize(
- layers[c],
- pmin=render_params.percentiles_for_norm[0],
- pmax=render_params.percentiles_for_norm[1],
- clip=True,
- )
-
  if not isinstance(render_params.cmap_params, list):
  if render_params.cmap_params.norm is not None:
  layers[c] = render_params.cmap_params.norm(layers[c])

src/spatialdata_plot/pl/utils.py

@@ -489,7 +489,7 @@ def _get_scalebar(
 
 def _prepare_cmap_norm(
  cmap: Colormap | str | None = None,
- norm: Normalize | bool = False,
+ norm: Normalize | None = None,
  na_color: ColorLike | None = None,
  vmin: float | None = None,
  vmax: float | None = None,
@@ -1623,29 +1623,6 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
- if (percentiles_for_norm := param_dict.get("percentiles_for_norm")) is None:
- percentiles_for_norm = (None, None)
- elif not (isinstance(percentiles_for_norm, (list, tuple)) or len(percentiles_for_norm) != 2):
- raise TypeError("Parameter 'percentiles_for_norm' must be a list or tuple of exactly two floats or None.")
- elif not all(
- isinstance(p, (float, int, type(None)))
- and isinstance(p, type(percentiles_for_norm[0]))
- and (p is None or 0 <= p <= 100)
- for p in percentiles_for_norm
- ):
- raise TypeError(
- "Each item in 'percentiles_for_norm' must be of the same dtype and must be a float or int within [0, 100], "
- "or None"
- )
- elif (
- percentiles_for_norm[0] is not None
- and percentiles_for_norm[1] is not None
- and percentiles_for_norm[0] > percentiles_for_norm[1]
- ):
- raise ValueError("The first number in 'percentiles_for_norm' must not be smaller than the second.")
- if "percentiles_for_norm" in param_dict:
- param_dict["percentiles_for_norm"] = percentiles_for_norm
-
  if size := param_dict.get("size"):
  if not isinstance(size, (float, int)):
  raise TypeError("Parameter 'size' must be numeric.")
@@ -1886,7 +1863,6 @@ def _validate_image_render_params(
  cmap: list[Colormap | str] | Colormap | str | None,
  norm: Normalize | None,
  scale: str | None,
- percentiles_for_norm: tuple[float | None, float | None] | None,
 ) -> dict[str, dict[str, Any]]:
  param_dict: dict[str, Any] = {
  "sdata": sdata,
@@ -1898,7 +1874,6 @@ def _validate_image_render_params(
  "cmap": cmap,
  "norm": norm,
  "scale": scale,
- "percentiles_for_norm": percentiles_for_norm,
  }
  param_dict = _type_check_params(param_dict, "images")
 
@@ -1945,8 +1920,6 @@ def _validate_image_render_params(
  else:
  element_params[el]["scale"] = scale
 
- element_params[el]["percentiles_for_norm"] = param_dict["percentiles_for_norm"]
-
  return element_params
 
 

tests/pl/test_render.py

@@ -23,7 +23,9 @@ def test_render_images_can_plot_one_cyx_image(request):
 def test_render_images_can_plot_multiple_cyx_images(share_coordinate_system: str, request):
  fun = request.getfixturevalue("get_sdata_with_multiple_images")
  sdata = fun(share_coordinate_system)
- sdata.pl.render_images().pl.show()
+ sdata.pl.render_images().pl.show(
+ colorbar=False, # otherwise we'll get one cbar per image in the same cs
+ )
  axs = plt.gcf().get_axes()
 
  if share_coordinate_system == "all":

tests/pl/test_render_images.py

@@ -2,7 +2,6 @@
 import matplotlib
 import numpy as np
 import scanpy as sc
-from matplotlib import pyplot as plt
 from matplotlib.colors import Normalize
 from spatial_image import to_spatial_image
 from spatialdata import SpatialData
@@ -49,9 +48,6 @@ def test_plot_can_render_a_single_channel_from_image(self, sdata_blobs: SpatialD
  def test_plot_can_render_a_single_channel_from_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_multiscale_image", channel=0).pl.show()
 
- def test_plot_can_render_a_single_channel_from_image_no_el(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(channel=0).pl.show()
-
  def test_plot_can_render_a_single_channel_str_from_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_image", channel="c1").pl.show()
 
@@ -70,16 +66,13 @@ def test_plot_can_render_two_channels_str_from_image(self, sdata_blobs_str: Spat
  def test_plot_can_render_two_channels_str_from_multiscale_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_multiscale_image", channel=["c1", "c2"]).pl.show()
 
- def test_plot_can_pass_vmin_vmax(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, vmin=0, vmax=0.4).pl.show(ax=axs[1])
-
- def test_plot_can_pass_normalize(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
+ def test_plot_can_pass_normalize_clip_True(self, sdata_blobs: SpatialData):
  norm = Normalize(vmin=0, vmax=0.4, clip=True)
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, norm=norm).pl.show(ax=axs[1])
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
+
+ def test_plot_can_pass_normalize_clip_False(self, sdata_blobs: SpatialData):
+ norm = Normalize(vmin=0, vmax=0.4, clip=False)
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
 
  def test_plot_can_pass_color_to_single_channel(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_image", channel=1, palette="red").pl.show()
@@ -97,9 +90,6 @@ def test_plot_can_pass_cmap_to_each_channel(self, sdata_blobs: SpatialData):
  element="blobs_image", channel=[0, 1, 2], cmap=["Reds", "Greens", "Blues"]
  ).pl.show()
 
- def test_plot_can_normalize_image(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(element="blobs_image", percentiles_for_norm=(5, 90)).pl.show()
-
  def test_plot_can_render_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images("blobs_multiscale_image").pl.show()
 

tests/pl/test_render_shapes.py

@@ -6,6 +6,7 @@
 import pandas as pd
 import scanpy as sc
 from anndata import AnnData
+from matplotlib.colors import Normalize
 from shapely.geometry import MultiPolygon, Point, Polygon
 from spatialdata import SpatialData, deepcopy
 from spatialdata.models import ShapesModel, TableModel
@@ -146,7 +147,8 @@ def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
  sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=True).pl.show()
+ norm = Normalize(vmin=0, vmax=5, clip=True)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=norm).pl.show()
 
  def test_plot_can_plot_shapes_after_spatial_query(self, sdata_blobs: SpatialData):
  # subset to only shapes, should be unnecessary after rasterizeation of multiscale images is included

CHANGELOG.md

@@ -18,11 +18,15 @@ and this project adheres to [Semantic Versioning][].
 
 - Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
 - When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+- When rendering a single image channel, a colorbar is now shown (#346)
+- Removed `percentiles_for_norm` parameter (#346)
+- Changed `norm` to no longer accept bools, only `mpl.colors.Normalise` or `None` (#346)
 
 ### Fixed
 
 - Filtering with `groups` now preserves original cmap (#344)
 - Non-selected `groups` are now not shown in `na_color` (#344)
+- Several issues associated with `norm` and `colorbar` (#346)
 
 ## [0.2.5] - 2024-08-23
 

src/spatialdata_plot/pl/basic.py

@@ -166,7 +166,7 @@ def render_shapes(
  outline_color: str | list[float] = "#000000ff",
  outline_alpha: float | int = 0.0,
  cmap: Colormap | str | None = None,
- norm: bool | Normalize = False,
+ norm: Normalize | None = None,
  scale: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -301,7 +301,7 @@ def render_points(
  palette: list[str] | str | None = None,
  na_color: ColorLike | None = "default",
  cmap: Colormap | str | None = None,
- norm: None | Normalize = None,
+ norm: Normalize | None = None,
  size: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -422,7 +422,6 @@ def render_images(
  na_color: ColorLike | None = "default",
  palette: list[str] | str | None = None,
  alpha: float | int = 1.0,
- percentiles_for_norm: tuple[float, float] | None = None,
  scale: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
@@ -457,8 +456,6 @@ def render_images(
  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.
- percentiles_for_norm : tuple[float, float] | None
- Optional pair of floats (pmin < pmax, 0-100) which will be used for quantile normalization.
  scale : str | None
  Influences the resolution of the rendering. Possibilities include:
  1) `None` (default): The image is rasterized to fit the canvas size. For
@@ -486,20 +483,14 @@ def render_images(
  cmap=cmap,
  norm=norm,
  scale=scale,
- percentiles_for_norm=percentiles_for_norm,
  )
 
  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=params_dict[element]["cmap"],
- # norm=norm,
- # na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
- # **kwargs,
- # )
+
  cmap_params: list[CmapParams] | CmapParams
  if isinstance(cmap, list):
  cmap_params = [
@@ -525,7 +516,6 @@ def render_images(
  cmap_params=cmap_params,
  palette=param_values["palette"],
  alpha=param_values["alpha"],
- percentiles_for_norm=param_values["percentiles_for_norm"],
  scale=param_values["scale"],
  zorder=n_steps,
  )

src/spatialdata_plot/pl/render.py

@@ -9,6 +9,7 @@
 import datashader as ds
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import matplotlib.transforms as mtransforms
 import numpy as np
 import pandas as pd
@@ -47,7 +48,6 @@
  _maybe_set_colors,
  _mpl_ax_contains_elements,
  _multiscale_to_spatial_image,
- _normalize,
  _rasterize_if_necessary,
  _set_color_source_vec,
  to_hex,
@@ -128,6 +128,7 @@ def _render_shapes(
  shapes = shapes.reset_index()
  color_source_vector = color_source_vector[mask]
  color_vector = color_vector[mask]
+
  shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
 
  # Using dict.fromkeys here since set returns in arbitrary order
@@ -255,9 +256,13 @@ def _render_shapes(
  for path in _cax.get_paths():
  path.vertices = trans.transform(path.vertices)
 
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
+ if not values_are_categorical:
+ # If the user passed a Normalize object with vmin/vmax we'll use those,
+ # # if not we'll use the min/max of the color_vector
+ _cax.set_clim(
+ vmin=render_params.cmap_params.norm.vmin or min(color_vector),
+ vmax=render_params.cmap_params.norm.vmax or max(color_vector),
+ )
 
  if len(set(color_vector)) != 1 or list(set(color_vector))[0] != to_hex(render_params.cmap_params.na_color):
  # necessary in case different shapes elements are annotated with one table
@@ -603,11 +608,6 @@ def _render_images(
  if n_channels == 1 and not isinstance(render_params.cmap_params, list):
  layer = img.sel(c=channels[0]).squeeze() if isinstance(channels[0], str) else img.isel(c=channels[0]).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layer = _normalize(
- layer, pmin=render_params.percentiles_for_norm[0], pmax=render_params.percentiles_for_norm[1], clip=True
- )
-
  if render_params.cmap_params.norm: # type: ignore[attr-defined]
  layer = render_params.cmap_params.norm(layer) # type: ignore[attr-defined]
 
@@ -623,20 +623,16 @@ def _render_images(
 
  _ax_show_and_transform(layer, trans_data, ax, cmap=cmap, zorder=render_params.zorder)
 
+ if legend_params.colorbar:
+ sm = plt.cm.ScalarMappable(cmap=cmap, norm=render_params.cmap_params.norm)
+ fig_params.fig.colorbar(sm, ax=ax)
+
  # 2) Image has any number of channels but 1
  else:
  layers = {}
  for ch_index, c in enumerate(channels):
  layers[c] = img.sel(c=c).copy(deep=True).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layers[c] = _normalize(
- layers[c],
- pmin=render_params.percentiles_for_norm[0],
- pmax=render_params.percentiles_for_norm[1],
- clip=True,
- )
-
  if not isinstance(render_params.cmap_params, list):
  if render_params.cmap_params.norm is not None:
  layers[c] = render_params.cmap_params.norm(layers[c])

src/spatialdata_plot/pl/utils.py

@@ -489,7 +489,7 @@ def _get_scalebar(
 
 def _prepare_cmap_norm(
  cmap: Colormap | str | None = None,
- norm: Normalize | bool = False,
+ norm: Normalize | None = None,
  na_color: ColorLike | None = None,
  vmin: float | None = None,
  vmax: float | None = None,
@@ -1623,29 +1623,6 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
- if (percentiles_for_norm := param_dict.get("percentiles_for_norm")) is None:
- percentiles_for_norm = (None, None)
- elif not (isinstance(percentiles_for_norm, (list, tuple)) or len(percentiles_for_norm) != 2):
- raise TypeError("Parameter 'percentiles_for_norm' must be a list or tuple of exactly two floats or None.")
- elif not all(
- isinstance(p, (float, int, type(None)))
- and isinstance(p, type(percentiles_for_norm[0]))
- and (p is None or 0 <= p <= 100)
- for p in percentiles_for_norm
- ):
- raise TypeError(
- "Each item in 'percentiles_for_norm' must be of the same dtype and must be a float or int within [0, 100], "
- "or None"
- )
- elif (
- percentiles_for_norm[0] is not None
- and percentiles_for_norm[1] is not None
- and percentiles_for_norm[0] > percentiles_for_norm[1]
- ):
- raise ValueError("The first number in 'percentiles_for_norm' must not be smaller than the second.")
- if "percentiles_for_norm" in param_dict:
- param_dict["percentiles_for_norm"] = percentiles_for_norm
-
  if size := param_dict.get("size"):
  if not isinstance(size, (float, int)):
  raise TypeError("Parameter 'size' must be numeric.")
@@ -1886,7 +1863,6 @@ def _validate_image_render_params(
  cmap: list[Colormap | str] | Colormap | str | None,
  norm: Normalize | None,
  scale: str | None,
- percentiles_for_norm: tuple[float | None, float | None] | None,
 ) -> dict[str, dict[str, Any]]:
  param_dict: dict[str, Any] = {
  "sdata": sdata,
@@ -1898,7 +1874,6 @@ def _validate_image_render_params(
  "cmap": cmap,
  "norm": norm,
  "scale": scale,
- "percentiles_for_norm": percentiles_for_norm,
  }
  param_dict = _type_check_params(param_dict, "images")
 
@@ -1945,8 +1920,6 @@ def _validate_image_render_params(
  else:
  element_params[el]["scale"] = scale
 
- element_params[el]["percentiles_for_norm"] = param_dict["percentiles_for_norm"]
-
  return element_params
 
 

tests/pl/test_render.py

@@ -23,7 +23,9 @@ def test_render_images_can_plot_one_cyx_image(request):
 def test_render_images_can_plot_multiple_cyx_images(share_coordinate_system: str, request):
  fun = request.getfixturevalue("get_sdata_with_multiple_images")
  sdata = fun(share_coordinate_system)
- sdata.pl.render_images().pl.show()
+ sdata.pl.render_images().pl.show(
+ colorbar=False, # otherwise we'll get one cbar per image in the same cs
+ )
  axs = plt.gcf().get_axes()
 
  if share_coordinate_system == "all":

tests/pl/test_render_images.py

@@ -2,7 +2,6 @@
 import matplotlib
 import numpy as np
 import scanpy as sc
-from matplotlib import pyplot as plt
 from matplotlib.colors import Normalize
 from spatial_image import to_spatial_image
 from spatialdata import SpatialData
@@ -49,9 +48,6 @@ def test_plot_can_render_a_single_channel_from_image(self, sdata_blobs: SpatialD
  def test_plot_can_render_a_single_channel_from_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_multiscale_image", channel=0).pl.show()
 
- def test_plot_can_render_a_single_channel_from_image_no_el(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(channel=0).pl.show()
-
  def test_plot_can_render_a_single_channel_str_from_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_image", channel="c1").pl.show()
 
@@ -70,16 +66,13 @@ def test_plot_can_render_two_channels_str_from_image(self, sdata_blobs_str: Spat
  def test_plot_can_render_two_channels_str_from_multiscale_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_multiscale_image", channel=["c1", "c2"]).pl.show()
 
- def test_plot_can_pass_vmin_vmax(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, vmin=0, vmax=0.4).pl.show(ax=axs[1])
-
- def test_plot_can_pass_normalize(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
+ def test_plot_can_pass_normalize_clip_True(self, sdata_blobs: SpatialData):
  norm = Normalize(vmin=0, vmax=0.4, clip=True)
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, norm=norm).pl.show(ax=axs[1])
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
+
+ def test_plot_can_pass_normalize_clip_False(self, sdata_blobs: SpatialData):
+ norm = Normalize(vmin=0, vmax=0.4, clip=False)
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
 
  def test_plot_can_pass_color_to_single_channel(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_image", channel=1, palette="red").pl.show()
@@ -97,9 +90,6 @@ def test_plot_can_pass_cmap_to_each_channel(self, sdata_blobs: SpatialData):
  element="blobs_image", channel=[0, 1, 2], cmap=["Reds", "Greens", "Blues"]
  ).pl.show()
 
- def test_plot_can_normalize_image(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(element="blobs_image", percentiles_for_norm=(5, 90)).pl.show()
-
  def test_plot_can_render_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images("blobs_multiscale_image").pl.show()
 

tests/pl/test_render_shapes.py

@@ -6,6 +6,7 @@
 import pandas as pd
 import scanpy as sc
 from anndata import AnnData
+from matplotlib.colors import Normalize
 from shapely.geometry import MultiPolygon, Point, Polygon
 from spatialdata import SpatialData, deepcopy
 from spatialdata.models import ShapesModel, TableModel
@@ -146,7 +147,8 @@ def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
  sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=True).pl.show()
+ norm = Normalize(vmin=0, vmax=5, clip=True)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=norm).pl.show()
 
  def test_plot_can_plot_shapes_after_spatial_query(self, sdata_blobs: SpatialData):
  # subset to only shapes, should be unnecessary after rasterizeation of multiscale images is included

CHANGELOG.md

@@ -18,11 +18,15 @@ and this project adheres to [Semantic Versioning][].
 
 - Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
 - When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+- When rendering a single image channel, a colorbar is now shown (#346)
+- Removed `percentiles_for_norm` parameter (#346)
+- Changed `norm` to no longer accept bools, only `mpl.colors.Normalise` or `None` (#346)
 
 ### Fixed
 
 - Filtering with `groups` now preserves original cmap (#344)
 - Non-selected `groups` are now not shown in `na_color` (#344)
+- Several issues associated with `norm` and `colorbar` (#346)
 
 ## [0.2.5] - 2024-08-23
 

src/spatialdata_plot/pl/basic.py

@@ -166,7 +166,7 @@ def render_shapes(
  outline_color: str | list[float] = "#000000ff",
  outline_alpha: float | int = 0.0,
  cmap: Colormap | str | None = None,
- norm: bool | Normalize = False,
+ norm: Normalize | None = None,
  scale: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -301,7 +301,7 @@ def render_points(
  palette: list[str] | str | None = None,
  na_color: ColorLike | None = "default",
  cmap: Colormap | str | None = None,
- norm: None | Normalize = None,
+ norm: Normalize | None = None,
  size: float | int = 1.0,
  method: str | None = None,
  table_name: str | None = None,
@@ -422,7 +422,6 @@ def render_images(
  na_color: ColorLike | None = "default",
  palette: list[str] | str | None = None,
  alpha: float | int = 1.0,
- percentiles_for_norm: tuple[float, float] | None = None,
  scale: str | None = None,
  **kwargs: Any,
  ) -> sd.SpatialData:
@@ -457,8 +456,6 @@ def render_images(
  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.
- percentiles_for_norm : tuple[float, float] | None
- Optional pair of floats (pmin < pmax, 0-100) which will be used for quantile normalization.
  scale : str | None
  Influences the resolution of the rendering. Possibilities include:
  1) `None` (default): The image is rasterized to fit the canvas size. For
@@ -486,20 +483,14 @@ def render_images(
  cmap=cmap,
  norm=norm,
  scale=scale,
- percentiles_for_norm=percentiles_for_norm,
  )
 
  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=params_dict[element]["cmap"],
- # norm=norm,
- # na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
- # **kwargs,
- # )
+
  cmap_params: list[CmapParams] | CmapParams
  if isinstance(cmap, list):
  cmap_params = [
@@ -525,7 +516,6 @@ def render_images(
  cmap_params=cmap_params,
  palette=param_values["palette"],
  alpha=param_values["alpha"],
- percentiles_for_norm=param_values["percentiles_for_norm"],
  scale=param_values["scale"],
  zorder=n_steps,
  )

src/spatialdata_plot/pl/render.py

@@ -9,6 +9,7 @@
 import datashader as ds
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import matplotlib.transforms as mtransforms
 import numpy as np
 import pandas as pd
@@ -47,7 +48,6 @@
  _maybe_set_colors,
  _mpl_ax_contains_elements,
  _multiscale_to_spatial_image,
- _normalize,
  _rasterize_if_necessary,
  _set_color_source_vec,
  to_hex,
@@ -128,6 +128,7 @@ def _render_shapes(
  shapes = shapes.reset_index()
  color_source_vector = color_source_vector[mask]
  color_vector = color_vector[mask]
+
  shapes = gpd.GeoDataFrame(shapes, geometry="geometry")
 
  # Using dict.fromkeys here since set returns in arbitrary order
@@ -255,9 +256,13 @@ def _render_shapes(
  for path in _cax.get_paths():
  path.vertices = trans.transform(path.vertices)
 
- # Sets the limits of the colorbar to the values instead of [0, 1]
- if not norm and not values_are_categorical:
- _cax.set_clim(min(color_vector), max(color_vector))
+ if not values_are_categorical:
+ # If the user passed a Normalize object with vmin/vmax we'll use those,
+ # # if not we'll use the min/max of the color_vector
+ _cax.set_clim(
+ vmin=render_params.cmap_params.norm.vmin or min(color_vector),
+ vmax=render_params.cmap_params.norm.vmax or max(color_vector),
+ )
 
  if len(set(color_vector)) != 1 or list(set(color_vector))[0] != to_hex(render_params.cmap_params.na_color):
  # necessary in case different shapes elements are annotated with one table
@@ -603,11 +608,6 @@ def _render_images(
  if n_channels == 1 and not isinstance(render_params.cmap_params, list):
  layer = img.sel(c=channels[0]).squeeze() if isinstance(channels[0], str) else img.isel(c=channels[0]).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layer = _normalize(
- layer, pmin=render_params.percentiles_for_norm[0], pmax=render_params.percentiles_for_norm[1], clip=True
- )
-
  if render_params.cmap_params.norm: # type: ignore[attr-defined]
  layer = render_params.cmap_params.norm(layer) # type: ignore[attr-defined]
 
@@ -623,20 +623,16 @@ def _render_images(
 
  _ax_show_and_transform(layer, trans_data, ax, cmap=cmap, zorder=render_params.zorder)
 
+ if legend_params.colorbar:
+ sm = plt.cm.ScalarMappable(cmap=cmap, norm=render_params.cmap_params.norm)
+ fig_params.fig.colorbar(sm, ax=ax)
+
  # 2) Image has any number of channels but 1
  else:
  layers = {}
  for ch_index, c in enumerate(channels):
  layers[c] = img.sel(c=c).copy(deep=True).squeeze()
 
- if render_params.percentiles_for_norm != (None, None):
- layers[c] = _normalize(
- layers[c],
- pmin=render_params.percentiles_for_norm[0],
- pmax=render_params.percentiles_for_norm[1],
- clip=True,
- )
-
  if not isinstance(render_params.cmap_params, list):
  if render_params.cmap_params.norm is not None:
  layers[c] = render_params.cmap_params.norm(layers[c])

src/spatialdata_plot/pl/utils.py

@@ -489,7 +489,7 @@ def _get_scalebar(
 
 def _prepare_cmap_norm(
  cmap: Colormap | str | None = None,
- norm: Normalize | bool = False,
+ norm: Normalize | None = None,
  na_color: ColorLike | None = None,
  vmin: float | None = None,
  vmax: float | None = None,
@@ -1623,29 +1623,6 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
  if scale < 0:
  raise ValueError("Parameter 'scale' must be a positive number.")
 
- if (percentiles_for_norm := param_dict.get("percentiles_for_norm")) is None:
- percentiles_for_norm = (None, None)
- elif not (isinstance(percentiles_for_norm, (list, tuple)) or len(percentiles_for_norm) != 2):
- raise TypeError("Parameter 'percentiles_for_norm' must be a list or tuple of exactly two floats or None.")
- elif not all(
- isinstance(p, (float, int, type(None)))
- and isinstance(p, type(percentiles_for_norm[0]))
- and (p is None or 0 <= p <= 100)
- for p in percentiles_for_norm
- ):
- raise TypeError(
- "Each item in 'percentiles_for_norm' must be of the same dtype and must be a float or int within [0, 100], "
- "or None"
- )
- elif (
- percentiles_for_norm[0] is not None
- and percentiles_for_norm[1] is not None
- and percentiles_for_norm[0] > percentiles_for_norm[1]
- ):
- raise ValueError("The first number in 'percentiles_for_norm' must not be smaller than the second.")
- if "percentiles_for_norm" in param_dict:
- param_dict["percentiles_for_norm"] = percentiles_for_norm
-
  if size := param_dict.get("size"):
  if not isinstance(size, (float, int)):
  raise TypeError("Parameter 'size' must be numeric.")
@@ -1886,7 +1863,6 @@ def _validate_image_render_params(
  cmap: list[Colormap | str] | Colormap | str | None,
  norm: Normalize | None,
  scale: str | None,
- percentiles_for_norm: tuple[float | None, float | None] | None,
 ) -> dict[str, dict[str, Any]]:
  param_dict: dict[str, Any] = {
  "sdata": sdata,
@@ -1898,7 +1874,6 @@ def _validate_image_render_params(
  "cmap": cmap,
  "norm": norm,
  "scale": scale,
- "percentiles_for_norm": percentiles_for_norm,
  }
  param_dict = _type_check_params(param_dict, "images")
 
@@ -1945,8 +1920,6 @@ def _validate_image_render_params(
  else:
  element_params[el]["scale"] = scale
 
- element_params[el]["percentiles_for_norm"] = param_dict["percentiles_for_norm"]
-
  return element_params
 
 

tests/pl/test_render.py

@@ -23,7 +23,9 @@ def test_render_images_can_plot_one_cyx_image(request):
 def test_render_images_can_plot_multiple_cyx_images(share_coordinate_system: str, request):
  fun = request.getfixturevalue("get_sdata_with_multiple_images")
  sdata = fun(share_coordinate_system)
- sdata.pl.render_images().pl.show()
+ sdata.pl.render_images().pl.show(
+ colorbar=False, # otherwise we'll get one cbar per image in the same cs
+ )
  axs = plt.gcf().get_axes()
 
  if share_coordinate_system == "all":

tests/pl/test_render_images.py

@@ -2,7 +2,6 @@
 import matplotlib
 import numpy as np
 import scanpy as sc
-from matplotlib import pyplot as plt
 from matplotlib.colors import Normalize
 from spatial_image import to_spatial_image
 from spatialdata import SpatialData
@@ -49,9 +48,6 @@ def test_plot_can_render_a_single_channel_from_image(self, sdata_blobs: SpatialD
  def test_plot_can_render_a_single_channel_from_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_multiscale_image", channel=0).pl.show()
 
- def test_plot_can_render_a_single_channel_from_image_no_el(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(channel=0).pl.show()
-
  def test_plot_can_render_a_single_channel_str_from_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_image", channel="c1").pl.show()
 
@@ -70,16 +66,13 @@ def test_plot_can_render_two_channels_str_from_image(self, sdata_blobs_str: Spat
  def test_plot_can_render_two_channels_str_from_multiscale_image(self, sdata_blobs_str: SpatialData):
  sdata_blobs_str.pl.render_images(element="blobs_multiscale_image", channel=["c1", "c2"]).pl.show()
 
- def test_plot_can_pass_vmin_vmax(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, vmin=0, vmax=0.4).pl.show(ax=axs[1])
-
- def test_plot_can_pass_normalize(self, sdata_blobs: SpatialData):
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
+ def test_plot_can_pass_normalize_clip_True(self, sdata_blobs: SpatialData):
  norm = Normalize(vmin=0, vmax=0.4, clip=True)
- sdata_blobs.pl.render_images(element="blobs_image", channel=1).pl.show(ax=axs[0])
- sdata_blobs.pl.render_images(element="blobs_image", channel=1, norm=norm).pl.show(ax=axs[1])
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
+
+ def test_plot_can_pass_normalize_clip_False(self, sdata_blobs: SpatialData):
+ norm = Normalize(vmin=0, vmax=0.4, clip=False)
+ sdata_blobs.pl.render_images(element="blobs_image", channel=0, norm=norm).pl.show()
 
  def test_plot_can_pass_color_to_single_channel(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images(element="blobs_image", channel=1, palette="red").pl.show()
@@ -97,9 +90,6 @@ def test_plot_can_pass_cmap_to_each_channel(self, sdata_blobs: SpatialData):
  element="blobs_image", channel=[0, 1, 2], cmap=["Reds", "Greens", "Blues"]
  ).pl.show()
 
- def test_plot_can_normalize_image(self, sdata_blobs: SpatialData):
- sdata_blobs.pl.render_images(element="blobs_image", percentiles_for_norm=(5, 90)).pl.show()
-
  def test_plot_can_render_multiscale_image(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_images("blobs_multiscale_image").pl.show()
 

tests/pl/test_render_shapes.py

@@ -6,6 +6,7 @@
 import pandas as pd
 import scanpy as sc
 from anndata import AnnData
+from matplotlib.colors import Normalize
 from shapely.geometry import MultiPolygon, Point, Polygon
 from spatialdata import SpatialData, deepcopy
 from spatialdata.models import ShapesModel, TableModel
@@ -146,7 +147,8 @@ def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
  sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=True).pl.show()
+ norm = Normalize(vmin=0, vmax=5, clip=True)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"], norm=norm).pl.show()
 
  def test_plot_can_plot_shapes_after_spatial_query(self, sdata_blobs: SpatialData):
  # subset to only shapes, should be unnecessary after rasterizeation of multiscale images is included